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Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research., Validerad;2024;Nivå 2;2024-04-02 (hanlid);Funder: DTU Strategic Alliances Fund;Full text license: CC BY 4.0

Doping is defined as the use of prohibited substances or methods by the World Anti-Doping Agency and the aim with doping control analysis is to detect the use of these illicit substances or methods. Substances that are prohibited in human or equine sports have either a positive or negative impact on the performance. Since administered drugs generally are metabolized to a varying degree and thereby not only excreted in their original form, their metabolite profiles are of high interest because drug metabolites may be present in the body for a longer time than the administered drug itself. Thereby detection of metabolites can improve the window of detection. Unfortunately, the metabolite profiles of non-approved drugs that are mainly available on the Internet, such as Selective Androgen Receptor Modulators (SARMs) are often unknown. This thesis consists of four papers that all encompass drug metabolite profiling either in vivo, in vitro or in a combination, utilizing separation with liquid chromatography and detection with high resolution mass spectrometry. In paper I and II, the equine in vivo metabolite profiles of the two SARMs ACP-105 and LGD-3303 were investigated and the results showed that using drug metabolites as analytical targets can prolong the detection time. For ACP-105, the in vivo metabolite profile was compared with different incubation models such as liver microsomes, S9 fractions and the fungus Cunninghamella elegans. The in vivo and in vitro metabolite profiles showed an interesting overlap for several metabolites, demonstrating the importance and usefulness for in vitro methods in doping control, especially since microsome incubates are allowed as reference material. An optimization of microsome incubation conditions utilizing experimental design was presented in paper III and IV, showing that the optimized conditions greatly impacted the yield of drug metabolites, but also that the optimal conditions are substance dependent. In paper III, a mult

Doping is defined as the use of prohibited substances or methods by the World Anti-Doping Agency and the aim with doping control analysis is to detect the use of these illicit substances or methods. Substances that are prohibited in human or equine sports have either a positive or negative impact on the performance. Since administered drugs generally are metabolized to a varying degree and thereby not only excreted in their original form, their metabolite profiles are of high interest because drug metabolites may be present in the body for a longer time than the administered drug itself. Thereby detection of metabolites can improve the window of detection. Unfortunately, the metabolite profiles of non-approved drugs that are mainly available on the Internet, such as Selective Androgen Receptor Modulators (SARMs) are often unknown. This thesis consists of four papers that all encompass drug metabolite profiling either in vivo, in vitro or in a combination, utilizing separation with liquid chromatography and detection with high resolution mass spectrometry. In paper I and II, the equine in vivo metabolite profiles of the two SARMs ACP-105 and LGD-3303 were investigated and the results showed that using drug metabolites as analytical targets can prolong the detection time. For ACP-105, the in vivo metabolite profile was compared with different incubation models such as liver microsomes, S9 fractions and the fungus Cunninghamella elegans. The in vivo and in vitro metabolite profiles showed an interesting overlap for several metabolites, demonstrating the importance and usefulness for in vitro methods in doping control, especially since microsome incubates are allowed as reference material. An optimization of microsome incubation conditions utilizing experimental design was presented in paper III and IV, showing that the optimized conditions greatly impacted the yield of drug metabolites, but also that the optimal conditions are substance dependent. In paper III, a mult

A chemometrically based approach was applied to select the most efficient drug adsorbent among the biochars obtained from the novel feedstock, the leaves of the invasive plant (Ailanthus altissima). The representative target adsorbates (atenolol, paracetamol, ketorolac and tetracycline) were selected on the basis of their physicochemical properties to cover a wide chemical space, which is the usual analytical challenge. Their adsorption was investigated using design of experiments as a comprehensive approach to optimise the performance of the adsorption system, rationalise the procedure and overcome common drawbacks. Among the response surface designs, the central composite design was selected as it allows the identification of important experimental factors (solid-to-liquid ratio, pH, ionic strength) and their interactions, and allows the selection of optimal experimental conditions to maximise adsorption performance. The biochars were prepared by pyrolysis at 500 °C and 800 °C (BC-500 and BC-800) and the ZnCl2-activated biochars were prepared at 650 °C and 800 °C (AcBC-650 and AcBC-800). The FTIR spectra revealed that increasing the pyrolysis temperature without activator decreases the intensity of all bands, while activation preserves functional groups, as evidenced by the spectra of AcBC-650 and AcBC-800. High temperatures during activation promoted the development of an efficient surface area, with the maximum observed for AcBC-800 reaching 347 m2 g−1. AcBC-800 was found to be the most efficient adsorbent with removal efficiencies of 34.1, 51.3, 55.9 and 38.2 % for atenolol, paracetamol, ketorolac and tetracycline, respectively. The models describing the relationship between the removal efficiency of AcBC-800 and the experimental factors studied, showed satisfactory predictive ability (predicted R2 > 0.8) and no significant lack-of-fit was observed. The results obtained, including the mathematical models, the properties of the adsorbates and the adsorbents, cle

This research aims to determine the development of Eucheumaspinosium seaweed cultivation techniques using the long-line method and the quality of seaweed using the long-line method. The samples in this research are seaweed Eucheuma sp. The data collection technique is quantitative data in quantities derived from the shape and cleanliness of seaweed cultivated using the long line method. The data obtained were analyzed using an experimental design (RAL). From the results of the data analysis, it can be seen that cultivating Eucheuma Spinosium seaweed, using node distances of 2cm, 5cm, 10cm, 15cm, and 20 cm, the best node distance to use using the long line method is 10 cm which shows greater growth. fast compared to other distances, but this 10 cm distance still does not affect the quality of the Eucheuma sp seaweed.

Sound exposure studies require replicated sound treatments for the results to be representative for sound classes in general. Additionally, reused treatments in replicated designs need to be accounted for statistically. The lack hereof is referred to as simple and sacrificial pseudoreplication, respectively, and results should be interpreted accordingly. We quantified the occurrence of these issues and subsequent interpretation of results in 104 underwater sound exposure studies (2019–2023). The majority of the studies (85%) did not replicate sound treatments. From the ones that did, most did not statistically acknowledge the hierarchical structure of the data. Unreplicated treatment designs limit the generalizability of the findings. Nevertheless, only small differences were found in how the results of unreplicated and replicated treatment designs were interpreted. This commentary aims to provide guidance in the design, analysis and interpretation of sound exposure studies, which are equally valid for aquatic and terrestrial research.

In an era where scientific experiments can be very costly, multifidelity emulators provide a useful tool for cost-efficient predictive scientific computing. For scientific applications, the experimenter is often limited by a tight computational budget, and thus wishes to (i) maximize predictive power of the multifidelity emulator via a careful design of experiments, and (ii) ensure this model achieves a desired error tolerance with some notion of confidence. Existing design methods, however, do not jointly tackle objectives (i) and (ii). We propose a novel stacking design approach that addresses both goals. A multilevel reproducing kernel Hilbert space (RKHS) interpolator is first introduced to build the emulator, under which our stacking design provides a sequential approach for designing multifidelity runs such that a desired prediction error of epsilon > 0 is met under regularity assumptions. We then prove a novel cost complexity theorem that, under this multilevel interpolator, establishes a bound on the computation cost (for training data simulation) needed to achieve a prediction bound of epsilon . This result provides novel insights on conditions under which the proposed multifidelity approach improves upon a conventional RKHS interpolator which relies on a single fidelity level. Finally, we demonstrate the effectiveness of stacking designs in a suite of simulation experiments and an application to finite element analysis.

Recently, there has been growing interest in healthy foods enriched with prebiotics. Psyllium (Plantago Ovata) is a natural hydrocolloid with prebiotic properties that also enhances sauce viscosity. Thus, its incorporation into mayonnaise-style sauces could be a delicious and convenient way to create sauces with lower oil content. Low-calorie mayonnaise-type sauces were prepared using milk as protein source for contributing to the emulsification process and the influence of oil content (10–40%) and psyllium amount (1.5–3%) on the rheological properties of the resulting emulsions was evaluated. The physical stability was assessed by multiple light scattering techniques. Following preliminary studies to establish ranges for the oil-to-water ratio and psyllium concentration, a circumscribed central composite design was utilised to determine optimal values. This involved defining five levels for these two factors and two iterations of the central point. The experimental data was analysed using MINITAB software and response surface graphics were obtained. Thus, while samples with low-fat content (10–15%) were possible, the study indicates that increasing the amount of Psyllium and oil raised the viscosity of the sauce. Additionally, it was noted that a higher quantity of Psyllium and less oil (2.8% and 15%, respectively) achieved the same consistency as sauces with lower prebiotic content but higher oil (1.7–35%, respectively).

Per- and polyfluoroalkyl substances (PFAS) are organic synthetic compounds used in several industries because of their unique properties and thermal and chemical stability. Perfluorooctanesulfonic acid (PFOS) and perfluorooctaonic acid (PFOA) are two of the most prominent PFAS that are undegradable and accumulate in nature. To study the impact of PFOS and PFOA on the liver in a controlled environment, organoids can be used. A sensitive and selective LC-MS/MS method for individual and simultaneous analysis of PFOS and PFOA in liver organoid media and equipment used in organoid analyses was developed. For detection of low concentrations, ability to analyse complex organoid samples, and limit background contamination, a solid phase extraction (SPE) column, automatic filter (AFFL) and a trap column was included. The AFFL-SPE-LC-MS/MS was optimised efficiently through Design of Experiment (DoE) regarding the loading phase in the LC and six MS parameters for PFOS and PFOA. Validation was controlled against Eurachem’s guideline showing high sensitivity, detecting LOD at 6 pg/mL. The method demonstrated high repeatability with an RSD below 8 % for most samples. Simultaneous analysis of PFOS and PFOA demonstrated high selectivity. Nevertheless, the method showed low intermediate precision and varying reliability, as well as persistent background contamination limiting detection of lower concentrations. The method was fit for purpose and allowed rapid analysis of PFOS and PFOA in organoid media and equipment used in organoid analyses. Result from studies of PFAS in liver organoids through analysis with this method can aid in understanding the connection between PFAS and metabolic diseases., Populärvetenskaplig sammanfattning Per- och polyfluorerade alkylsubstanser (PFAS) är en grupp av människoskapta, syntetiska ämnen med unika egenskaper. Dessa egenskaper gör att de är olja- och vattenavvisande, och har många applikationsområden. De finns i textiler, livsmedelsförpackningar, brandsläckningsskum och andra industriprodukter. De är väldigt termiskt och kemisk stabila, vilket gör att de inte bryts ner och därmed ackumulerar i miljön. Flera studier har också visat koppling mellan PFAS och många kroniska sjukdomar, som hormonstörningar, cancer, immunsuppression och metabolt associerad fettlever (MAFLD, tidigare nonalkoholisk fettlever (NAFLD)). Kopplingen mellan MAFLD och PFAS har fått mycket uppmärksamhet då levern har visats sig vara ett målorgan för PFAS. Eftersom PFAS är ihärdiga, har ett komplicerat spridningsbeteende och ackumulerar i naturen är det svårt att studera kopplingen mellan MAFLD och PFAS i en kontrollerad miljö. För att studera effekten av PFAS kan man använda organoider, laboratorieodlade 3D modeller gjord från stamceller för att imitera ett äkta organ. Någon av de mest omtalade PFAS ämnen är perfluoroktansyra (PFOA) och perfluoroktansulfonat (PFOS), vilket är fokus för detta arbete. Leverorganoiderna kan utsättas för PFOS och PFOA, och mediet de ligger i kan extraheras och studeras med konventionella analytiska metoder för att få en bild av hur PFAS påverkar levern. I detta arbete vill analysen ske via vätskekromatografi med masspektrometri som detektion (LC-MS/MS). Med LC-MS/MS separeras den studerade molekylen, analyten från lösningen baserat på dess kemiska egenskaper. Analyten detekteras baserat på dess massa, mer bestämd massa/laddning-fördelningen (m/z). För att anpassa LC-MS metoden till injektion av komplexa organoidprover inkluderades ett automatiskt filter (AFFL) samt ett extra automatiskt separationssteg med en kolonn med fastfasextraktion (SPE). I övrigt ger SPE möjligheten att små mängder PFAS kan uppkoncentreras och foku, Health Effects of Persistent Organic Pollutants

Doping is defined as the use of prohibited substances or methods by the World Anti-Doping Agency and the aim with doping control analysis is to detect the use of these illicit substances or methods. Substances that are prohibited in human or equine sports have either a positive or negative impact on the performance. Since administered drugs generally are metabolized to a varying degree and thereby not only excreted in their original form, their metabolite profiles are of high interest because drug metabolites may be present in the body for a longer time than the administered drug itself. Thereby detection of metabolites can improve the window of detection. Unfortunately, the metabolite profiles of non-approved drugs that are mainly available on the Internet, such as Selective Androgen Receptor Modulators (SARMs) are often unknown. This thesis consists of four papers that all encompass drug metabolite profiling either in vivo, in vitro or in a combination, utilizing separation with liquid chromatography and detection with high resolution mass spectrometry. In paper I and II, the equine in vivo metabolite profiles of the two SARMs ACP-105 and LGD-3303 were investigated and the results showed that using drug metabolites as analytical targets can prolong the detection time. For ACP-105, the in vivo metabolite profile was compared with different incubation models such as liver microsomes, S9 fractions and the fungus Cunninghamella elegans. The in vivo and in vitro metabolite profiles showed an interesting overlap for several metabolites, demonstrating the importance and usefulness for in vitro methods in doping control, especially since microsome incubates are allowed as reference material. An optimization of microsome incubation conditions utilizing experimental design was presented in paper III and IV, showing that the optimized conditions greatly impacted the yield of drug metabolites, but also that the optimal conditions are substance dependent. In paper III, a mult

A rapid sample preparation fabric phase sorptive extraction (FPSE) methodology followed by gas chromatography coupled to tandem mass spectrometry is proposed for the first time to determine 12 synthetic musks including nitro-, polycyclic and macrocyclic musk in environmental water and wastewater. An asymmetric screening design of experiments was used to evaluate the influence of nine parameters affecting FPSE in only 16 experiments. The factors included the sol–gel sorbent coating, stirring mode (both extraction and desorption), extraction and desorption time, salting-out effect, sample volume, type of solvent, and desorption solvent volume. The selected conditions imply the use of the sol–gel PDMS sorbent coating, 15 min extraction by ultrasound energy employing 10 mL of sample and 0.5 mL of ethanol as desorption solvent during 5 min. Accuracy and precision were assessed in different real water samples. Matrix effects were evaluated performing recovery studies in several aqueous matrices: river, sea, spring, laundry washing place, and wastewater at different concentration levels (0.1, 2, 10 µg L−1) demonstrating method accuracy (results ranged between 82 and 110%) and good precision (relative standard deviation, RSD < 12% in all cases). Limits of detection (LODs) were below 8 ng L−1 for the analyzed synthetic musks. As extraction was quantitative (exhaustive extraction), calibration was carried out using solvent standards without the need of repeating the extraction step, which is required in most microextraction methods, improving sample throughput, and reducing costs. Finally, the analysis of real contaminated samples revealed the presence of 11 out of the 12 target synthetic musks at concentrations up to 30 µg L−1, three of them banned in cosmetic regulation (musk tibetene, ambrette, and moskene). The developed FPSE based methodology uses low volume (0.5 mL) of a Generally Recognized as Safe (GRAS) solvent (ethanol), as well as minimum energy and time consumptio

This study was conducted with the aim of investigating the effect of lesson planning on the performance of elementary school students. The research focused specifically on class 8th students in the subject of English. The chosen research design was experimental, employing pre-test post-test control group design. This design allowed for the measurement and observation of both the groups experimental and control before and after exposure to a treatment. The population of the study included all students enrolled at Government Girls Middle School Koteri Qandeel Bagh, Azad Jammu and Kashmir. A sample of forty students was selected for the study, with twenty students assigned to each of the two groups. The data collection process involved conducting pre and post-tests in the classroom, administered before and after the intervention. Data was analyzed using independent and paired sample t-test. The findings revealed a significant and positive effect of lesson planning on the academic performance of students. These results have important implications for educators, policymakers, and curriculum developers, emphasizing the critical role of effective lesson planning in enhancing students' academic achievements at the elementary level. By implementing well-structured and engaging lesson plans, educators can optimize teaching and learning experiences, leading to improved educational outcomes. The findings highlight the importance of incorporating comprehensive lesson planning strategies in curriculum design and instructional practices. Future research should further explore the long-term effects of lesson planning on academic performance, considering diverse subjects and student populations. By embracing evidence-based practices, educators can continue to enhance teaching methods and foster academic progress among elementary school students. This research study was conducted at Government Girls Middle School Tangyat district Bagh

This study was conducted with the aim of investigating the effect of lesson planning on the performance of elementary school students. The research focused specifically on class 8th students in the subject of English. The chosen research design was experimental, employing pre-test post-test control group design. This design allowed for the measurement and observation of both the groups experimental and control before and after exposure to a treatment. The population of the study included all students enrolled at Government Girls Middle School Koteri Qandeel Bagh, Azad Jammu and Kashmir. A sample of forty students was selected for the study, with twenty students assigned to each of the two groups. The data collection process involved conducting pre and post-tests in the classroom, administered before and after the intervention. Data was analyzed using independent and paired sample t-test. The findings revealed a significant and positive effect of lesson planning on the academic performance of students. These results have important implications for educators, policymakers, and curriculum developers, emphasizing the critical role of effective lesson planning in enhancing students' academic achievements at the elementary level. By implementing well-structured and engaging lesson plans, educators can optimize teaching and learning experiences, leading to improved educational outcomes. The findings highlight the importance of incorporating comprehensive lesson planning strategies in curriculum design and instructional practices. Future research should further explore the long-term effects of lesson planning on academic performance, considering diverse subjects and student populations. By embracing evidence-based practices, educators can continue to enhance teaching methods and foster academic progress among elementary school students. This research study was conducted at Government Girls Middle School Tangyat district Bagh

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research., Validerad;2024;Nivå 2;2024-04-02 (hanlid);Funder: DTU Strategic Alliances Fund;Full text license: CC BY 4.0

Prior research on networks has established that tensions might arise between organizational structures and processes and network goals. This study investigates the role that network managers can play in mitigating these tensions through network orchestration. Based on an experimental design involving students and practitioners from the social domain, the combined effects of network orchestration and organizational formalization on goal orientation were examined. Results show that a negative effect of organizational formalization on network goal orientation is mitigated by network orchestration. Thereby, this study contributes to our understanding of the role of network orchestration in coping with tensions inherent to networks.

Clinical trials are the backbone of medical scientific research. However, this experimental strategy has some drawbacks. We focused on two issues: (a) The internal validity ensured by clinical trial procedures does not necessarily allow for generalization of efficacy results to causal claims about effectiveness in the population. (b) Statistical significance does not imply clinical or practical significance; p-values should be supplemented with effect size (ES) estimators and an interpretation of the magnitude of the effects found. We conducted a systematic review (from 2000 to 2020) on Scopus, PubMed, and four ProQuest databases, including PsycINFO. We searched for experimental studies with significant effects of pharmacological treatments on depressive symptoms, measured with a specific scale for depression. We assessed the claims of effectiveness, and reporting and interpreting of effect sizes in a small, unbiased sample of clinical trials (n = 10). Only 30% of the studies acknowledged that efficacy does not necessarily translate to effectiveness. Only 20% reported ES indices, and only 40% interpreted the magnitude of their findings. We encourage reflection on the applicability of results derived from clinical trials about the efficacy of antidepressant treatments, which often influence daily clinical decision-making. Comparing experimental results of antidepressants with supplementary observational studies can provide clinicians with greater flexibility in prescribing medication based on patient characteristics. Furthermore, the ES of a treatment should be considered, as treatments with a small effect may be worthwhile in certain circumstances, while treatments with a large effect may be justified despite additional costs or complications. Therefore, researchers are encouraged to report and interpret ES and explicitly discuss the suitability of their sample for the clinical population to which the antidepressant treatment Will be applied., Depto. de Psicobiología y Metodología en Ciencias del Comportamiento, Fac. de Psicología, TRUE, pub, Descuento UCM

Per- and polyfluoroalkyl substances (PFAS) are organic synthetic compounds used in several industries because of their unique properties and thermal and chemical stability. Perfluorooctanesulfonic acid (PFOS) and perfluorooctaonic acid (PFOA) are two of the most prominent PFAS that are undegradable and accumulate in nature. To study the impact of PFOS and PFOA on the liver in a controlled environment, organoids can be used. A sensitive and selective LC-MS/MS method for individual and simultaneous analysis of PFOS and PFOA in liver organoid media and equipment used in organoid analyses was developed. For detection of low concentrations, ability to analyse complex organoid samples, and limit background contamination, a solid phase extraction (SPE) column, automatic filter (AFFL) and a trap column was included. The AFFL-SPE-LC-MS/MS was optimised efficiently through Design of Experiment (DoE) regarding the loading phase in the LC and six MS parameters for PFOS and PFOA. Validation was controlled against Eurachem’s guideline showing high sensitivity, detecting LOD at 6 pg/mL. The method demonstrated high repeatability with an RSD below 8 % for most samples. Simultaneous analysis of PFOS and PFOA demonstrated high selectivity. Nevertheless, the method showed low intermediate precision and varying reliability, as well as persistent background contamination limiting detection of lower concentrations. The method was fit for purpose and allowed rapid analysis of PFOS and PFOA in organoid media and equipment used in organoid analyses. Result from studies of PFAS in liver organoids through analysis with this method can aid in understanding the connection between PFAS and metabolic diseases., Populärvetenskaplig sammanfattning Per- och polyfluorerade alkylsubstanser (PFAS) är en grupp av människoskapta, syntetiska ämnen med unika egenskaper. Dessa egenskaper gör att de är olja- och vattenavvisande, och har många applikationsområden. De finns i textiler, livsmedelsförpackningar, brandsläckningsskum och andra industriprodukter. De är väldigt termiskt och kemisk stabila, vilket gör att de inte bryts ner och därmed ackumulerar i miljön. Flera studier har också visat koppling mellan PFAS och många kroniska sjukdomar, som hormonstörningar, cancer, immunsuppression och metabolt associerad fettlever (MAFLD, tidigare nonalkoholisk fettlever (NAFLD)). Kopplingen mellan MAFLD och PFAS har fått mycket uppmärksamhet då levern har visats sig vara ett målorgan för PFAS. Eftersom PFAS är ihärdiga, har ett komplicerat spridningsbeteende och ackumulerar i naturen är det svårt att studera kopplingen mellan MAFLD och PFAS i en kontrollerad miljö. För att studera effekten av PFAS kan man använda organoider, laboratorieodlade 3D modeller gjord från stamceller för att imitera ett äkta organ. Någon av de mest omtalade PFAS ämnen är perfluoroktansyra (PFOA) och perfluoroktansulfonat (PFOS), vilket är fokus för detta arbete. Leverorganoiderna kan utsättas för PFOS och PFOA, och mediet de ligger i kan extraheras och studeras med konventionella analytiska metoder för att få en bild av hur PFAS påverkar levern. I detta arbete vill analysen ske via vätskekromatografi med masspektrometri som detektion (LC-MS/MS). Med LC-MS/MS separeras den studerade molekylen, analyten från lösningen baserat på dess kemiska egenskaper. Analyten detekteras baserat på dess massa, mer bestämd massa/laddning-fördelningen (m/z). För att anpassa LC-MS metoden till injektion av komplexa organoidprover inkluderades ett automatiskt filter (AFFL) samt ett extra automatiskt separationssteg med en kolonn med fastfasextraktion (SPE). I övrigt ger SPE möjligheten att små mängder PFAS kan uppkoncentreras och foku, Health Effects of Persistent Organic Pollutants

Introduction: Chronic non-cancer pain (CNCP) is a lifelong condition with radical consequences, calling for management involving patients’ families. Interventions based on the family systems nursing framework by Wright and Leahey have proved beneficial in other populations but require investigation in a CNCP population. This trial assumed that family nursing conversations (FNCs) based on the family systems nursing framework would increase patients’ and family members’ self-efficacy concerning CNCP management. Objective: To investigate whether an intervention with FNCs as an add-on to the usual multidisciplinary treatment of CNCP would have an effect on patients’ and family members’ self-efficacy. Additionally, to investigate any impact on family function, health-related quality of life, anxiety, and depression. Methods: The trial applied a prospective non-blinded quasi-experimental design with two comparable groups of patients and family members: a historical control group (HCG) and an intervention group (IG). The intervention was executed by nurses employed at a multidisciplinary pain center in the Capital Region of Denmark. HCG data were collected before the nurses’ intervention training. The primary outcome was self-efficacy. Secondary outcomes were family function, health-related quality of life, anxiety, and depression. Results: In total, 58 patients and 85 family members were included. The primary outcome, self-efficacy, detected no statistically significant between-group differences in mean change for patients, p =.990, or family members, p =.765. A statistically significant effect in favor of the IG was found in between-group differences in mean change in patients’ behavioral family function, p =.034, and anxiety, p =.031. No statistically significant between-group differences were detected in family members’ secondary outcomes. Conclusion: The intervention had no effect on patients’ or family members’ self-efficacy but a positive effect on patients’ beha

Introduction: Chronic non-cancer pain (CNCP) is a lifelong condition with radical consequences, calling for management involving patients’ families. Interventions based on the family systems nursing framework by Wright and Leahey have proved beneficial in other populations but require investigation in a CNCP population. This trial assumed that family nursing conversations (FNCs) based on the family systems nursing framework would increase patients’ and family members’ self-efficacy concerning CNCP management. Objective: To investigate whether an intervention with FNCs as an add-on to the usual multidisciplinary treatment of CNCP would have an effect on patients’ and family members’ self-efficacy. Additionally, to investigate any impact on family function, health-related quality of life, anxiety, and depression. Methods: The trial applied a prospective non-blinded quasi-experimental design with two comparable groups of patients and family members: a historical control group (HCG) and an intervention group (IG). The intervention was executed by nurses employed at a multidisciplinary pain center in the Capital Region of Denmark. HCG data were collected before the nurses’ intervention training. The primary outcome was self-efficacy. Secondary outcomes were family function, health-related quality of life, anxiety, and depression. Results: In total, 58 patients and 85 family members were included. The primary outcome, self-efficacy, detected no statistically significant between-group differences in mean change for patients, p =.990, or family members, p =.765. A statistically significant effect in favor of the IG was found in between-group differences in mean change in patients’ behavioral family function, p =.034, and anxiety, p =.031. No statistically significant between-group differences were detected in family members’ secondary outcomes. Conclusion: The intervention had no effect on patients’ or family members’ self-efficacy but a positive effect on patients’ beha

Doping is defined as the use of prohibited substances or methods by the World Anti-Doping Agency and the aim with doping control analysis is to detect the use of these illicit substances or methods. Substances that are prohibited in human or equine sports have either a positive or negative impact on the performance. Since administered drugs generally are metabolized to a varying degree and thereby not only excreted in their original form, their metabolite profiles are of high interest because drug metabolites may be present in the body for a longer time than the administered drug itself. Thereby detection of metabolites can improve the window of detection. Unfortunately, the metabolite profiles of non-approved drugs that are mainly available on the Internet, such as Selective Androgen Receptor Modulators (SARMs) are often unknown. This thesis consists of four papers that all encompass drug metabolite profiling either in vivo, in vitro or in a combination, utilizing separation with liquid chromatography and detection with high resolution mass spectrometry. In paper I and II, the equine in vivo metabolite profiles of the two SARMs ACP-105 and LGD-3303 were investigated and the results showed that using drug metabolites as analytical targets can prolong the detection time. For ACP-105, the in vivo metabolite profile was compared with different incubation models such as liver microsomes, S9 fractions and the fungus Cunninghamella elegans. The in vivo and in vitro metabolite profiles showed an interesting overlap for several metabolites, demonstrating the importance and usefulness for in vitro methods in doping control, especially since microsome incubates are allowed as reference material. An optimization of microsome incubation conditions utilizing experimental design was presented in paper III and IV, showing that the optimized conditions greatly impacted the yield of drug metabolites, but also that the optimal conditions are substance dependent. In paper III, a mult

The combination of UV–vis spectrophotometry with a chemometric calibration tool based on partial least squares (PLS) has allowed us the development of a multivariate analytical method that simultaneously estimates the concentration and size of mixtures of silver nanoparticles (AgNPs) in environmental water samples. The method is based on changes in the surface plasmon resonance band (SPRB) of AgNPs when they form aggregated/assembled structures with L-cysteine (L-cys). Measurementts were performed by employed a fixed-time kinetics method that implies that the final spectra (response) are obtained by subtstracting the solutions spectra at fixed times. Optimization of experimental conditions affecting aggregation such as time, temperature, pH and concentration of aggregating substance was performed by experimental design and response surface methodologies (RSM). A multivariate calibration model using AgNPs of known diameter size ((20 ± 3), (41 ± 3), (59 ± 5) and (79 ± 7) nm) within a concentration range between 0.62 and 2.5 mg L−1 was constructed by using a mixture experimental design and PLS. The method was finally applied to estimate size and concentration of AgNPs in AgNPs-spiked river and tap water samples. Water samples were spiked with individual, binary and ternary mixtures of AgNPs of different sizes and by using two types of AgNPs: citrate-coated AgNPs (cit-AgNPs) and polyvinylpyrrolidone-coated AgNPs (PVP-AgNPs). A good correspondence was obtained between predicted values and the total amount of AgNPs added with recovery values ranged within 80–160% for the individual mixtures, 68–108% for the binary mixtures and 60–64% for the ternary mixtures of AgNPs. Finally, transmission electron microscopy (TEM) measurements were performed for those cases where discrepancies between the expected and the obtained values were observed. TEM micrographs evidenced the presence of agglomerates or aggregates of AgNPs in some of the mixtures or water tested., Spanish Commission of Science and Technology, CommunityofMadrid/ FEDERProgramme, Depto. de Química Analítica, Fac. de Ciencias Químicas, TRUE, pub

Several recent works in human-robot-interaction (HRI) have begun to highlight the importance of the replication crisis and open science practices for our field. Yet, suggestions and recommendations tailored to child-robot-interaction (CRI) research, which poses its own additional set of challenges, remain limited. There is also an increased need within both HRI and CRI for inter and cross-disciplinary collaborations, where input from multiple different domains can contribute to better research outcomes. Consequently, this workshop aims to facilitate discussions between researchers from diverse disciplines within CRI. The workshop will open with a panel discussion between CRI researchers from different disciplines, followed by 3-minute flash talks of the accepted submissions. The second half of the workshop will consist of breakout group discussions, where both senior and junior academics from different disciplines can share their experiences of conducting CRI research. Through this workshop, we hope to create a common ground for addressing shared challenges in CRI, as well as identify a set of possible solutions going forward.

The ubiquitous occurrence of microplastics is raising broad concerns and motivating effect studies. In these studies, however, particle behaviour in the water and aggregation are rarely considered leading to contradictory results reported by different studies. Using an environmentally relevant experimental setup with Daphnia magna as a test organism, we investigated how experimental conditions affect particle aggregation and the aggregate heterogeneity in terms of the particle size distribution. The experimental factors considered were (1) exposure duration (48 h vs 120 h), (2) the total mass of suspended solids (0–10 mg/l) composed of natural mineral particles (kaolin) and microplastics, (3) the proportion of the microplastics in the particle suspension (0–10% by mass), (4) dissolved organic matter (DOM; 0 vs 20 mg agarose/l), and (5) presence of the test organism (0 and 5 daphnids/vial). We found that particle aggregation occurs within the first 48 h of incubation in all treatments, no substantial change in the aggregate heterogeneity is observed afterwards. The median aggregate size was ∼2-fold higher than the nominal average particle size of clay and microplastics in the stock suspensions used to prepare the experimental mixtures. The strongest positive driver of the aggregate size and heterogeneity was DOM, followed by the presence of daphnids and the concentration of the suspended solids in the system. Also, microplastics were found to facilitate aggregation, albeit they were the weakest contributor. Moreover, besides directly increasing the aggregation, DOM relaxed the effects of the total solids and daphnids on the aggregate size. Thus, the particle size distribution was established early during the exposure and shaped by all experimental factors and their interactions. These findings improve our understanding of the processes occurring in the exposure systems when conducting effect studies with microplastics and other particulates and demonstrate the necess

Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity-variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity-estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs-indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis.

This work applied the mixture design with the PDCA cycle for the optimization of the properties of an architectural coating. In the Plan stage, an experimental plan was designed to achieve the properties of interest; three types of fillers; calcium carbonate, talc and kaolin were established as the main components of the mixture. The simplex design with increased centroid was used to execute the experimental runs (Do), 20 experiments were executed in total and were characterized in terms of viscosity, contrast ratio percentage, whiteness percentage, brightness and cost. In the Check stage, the experimental results were evaluated and each property was fitted to a statistical model for the prediction of its values. The viscosity, percent whiteness and gloss were fitted to a quadratic model and the percent contrast ratio was fitted to a special cubic model. Finally, in the Act stage, the properties were optimized according to the established restrictions and at the lowest possible cost. The optimum mixture was composed of 0.2934 calcium carbonate, 0.5450 talc and 0.1616 kaolin. The experimental test was carried out with the optimized mixture to guarantee the accuracy and precision of the results of the statistical model., Este trabajo aplicó el diseño de mezclas con el ciclo PHVA para la optimización de las propiedades de un recubrimiento arquitectónico. En la etapa Planificar se diseñó un plan experimental que permita alcanzar las propiedades de interés, se estableció como componentes principales de la mezcla a tres tipos de cargas; carbonato de calcio, talco y caolín. El diseño simplex con centroide aumentado se utilizó para ejecutar las corridas experimentales (Hacer), se ejecutaron 20 experimentos en total y fueron caracterizados en cuanto a viscosidad, porcentaje de relación de contraste, porcentaje blancura, brillo y costo. En la etapa Verificar, se evaluaron los resultados experimentales y cada propiedad se ajustó a un modelo estadístico para la predicción de sus valores. La viscosidad, porcentaje de blancura y brillo fueron ajustados a un modelo cuadrático y el porcentaje de relación de contraste fue ajustado a un modelo cúbico especial. Finalmente, en la etapa Actuar, se realizó la optimización de las propiedades de acuerdo con las restricciones establecidas y al menor costo posible. La mezcla óptima estuvo compuesta por 0,2934 de carbonato de calcio, 0,5450 de talco y 0,1616 de caolín. La prueba experimental se realizó con la mezcla optimizada para garantizar la exactitud y precisión de los resultados del modelo estadístico.

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

Populations with a migration background have a higher prevalence of mental health problems than their native counterparts. They are also more likely to have unmet medical needs and are less frequently referred to mental health services. One potential explanation for this is that physicians, such as general practitioners (GPs), may unintentionally discriminate against migrant patients, particularly when they lack humanization. To date, no experimental study has investigated this hypothesis. This paper assesses the influence of humanization on GPs’ discriminatory decisions regarding migrant patients with depression. A balanced 2 × 2 factorial experiment was carried out with Belgian GPs (N = 797) who received video-vignettes depicting either a native patient or a migrant patient with depression. Half of the respondents were exposed to a text that humanized the patient by providing more details about the patient’s life story. Decisions related to diagnosis, treatment and referral werecollected, as well as the time spent on each video and text, and were analysed using ANOVA. Migrant patients’ symptoms were judged to be less severe than those of native patients (F = 7.71, p < 0.05). For almost all treatments, the decision was less favourable for the migrant patient. Humanization had little effect on medical decisions. We observed that GPs spent significantly more time on the vignette with the humanization intervention, especially for the migrant patients. The results indicate that ethnic differences in the management of depression persist in primary care. Humanization, however, does not mitigate those differences in medical decisions.

A microgrid is a group of interconnected loads and distributed energy resources that can fill the gap between the dependence on a bulk power grid and the transition to renewable energies. The islanded mode presents itself as the most interesting scenario, when local controllers should maintain the power quality standards based on several parameters. A tool specifically focused on the process of parameter tuning of the secondary consensus-based control for inverter-based islanded microgrids was proposed in this paper. One often-quoted drawback in this process is the great number of parameters that must be tuned, even for a very simple microgrid structure. To manage such a large number of parameters, the design of experiments was used in this study. The main motivation for this work was to present an optimized way to define the correct parameters for the secondary consensus control for inverter-based islanded microgrids. The study shows how experimental design methodology can be an efficient tool to tune microgrid parameters, which are typically multi-objective-based experiments. From the results, it is correct to state that the design of experiments is able to reach the optimal setting with a minimal number of experiments, which would be almost impossible to obtain with the trial-and-error method., This work was supported by the Brazilian agencies of the Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), and Research Support Foundation of the Minas Gerais State (FAPEMIG). This publication is part of the R&D project PID2021-122835OB-C21, financed by MCIN/AEI/10.13039/ 501000011033 and FEDER “Una manera de hacer Europa”., Peer Reviewed, Postprint (published version)

Printed circuit boards (PCBs) are essential components in various electronic devices, playing a vital role in their functionality. Over time, PCBs have evolved significantly, becoming smaller, more complex, and multi-layered to meet the demands of advancing technology and consumer preferences. The quality of solder paste deposits is measured by circularity, volume, positioning, and the number of satellites. Mycronic is a supplier of jet printing machines for PCB manufacturers and needs to investigate and counteract an unwanted variation in volume and positioning within the first dot of the solder paste strip, and its so-called “sea saw” effect for the following three dots. This master’s thesis has two aims. The first aim is to develop and evaluate an experimental method to reduce variation between the dots in the strip. The second aim is to reduce the variation between the 1st dot and the following three dots by finding a combination of pre-feed, first, second, and third waveforms to obtain improved precision, volume, circularity, and reduced volume variation. Using an experimental design a full factorial design was used, examining six experimental factors. The design was further augmented with a central composite design to describe the second-order model. The knowledge from the experiment was used to optimize and improve the quality factors of jet printing, which were verified with an additional experiment. The results of the study provided compelling evidence that only the selected experimental factors had a significant impact on improving the volume metric. It was observed that the presence or absence of satellites during the experiment did not have any effect on the results, and neither did the positioning and circularity factors contribute to any improvement or deterioration. Specifically, the most significant difference in volume between 1st and following three dots for the optimized solution is 0.5 nl, and the currently used settings have a difference of 2.6

The incorporation of drug-loaded mesoporous materials in dosage forms prepared with fused deposition modeling (FDM) has shown the potential to solve challenges relating to additive manufacturing techniques, such as the stability of poorly-soluble drugs in the amorphous state. However, the addition of these non-melting mesoporous materials significantly affects the mechanical properties of the filament used in FDM, which in turn affects the printability of the feedstock material. Therefore, in this study a full-factorial experimental design was utilized to investigate different processing parameters of the hot melt extrusion process, their effect on various mechanical properties and the potential correlation with the filaments' printability. The thermolabile, poorly-soluble drug ibuprofen was utilized as a model drug to assess the potential of two mesoporous materials, Mesoporous Magnesium Carbonate (MMC) and a silica-based material (MCM-41), to thermally protect the loaded drug. Factorial and principal components analysis displayed a correlation between non-printable MCM-41 filaments and their mechanical properties where printable filaments had a maximum stress >7.5 MPa and a Young's modulus >83 MPa. For MMC samples there was no clear correlation, which was in large part attributed to the filaments' inconsistencies and imperfections. Finally, both mesoporous materials displayed a thermal protective feature, as the decomposition due to the thermal degradation of a significant portion of the thermolabile drug was shifted to higher temperatures post-loading. This highlights the potential capability of such a system to be implemented for thermosensitive drugs in FDM applications.

The incorporation of drug-loaded mesoporous materials in dosage forms prepared with fused deposition modeling (FDM) has shown the potential to solve challenges relating to additive manufacturing techniques, such as the stability of poorly-soluble drugs in the amorphous state. However, the addition of these non-melting mesoporous materials significantly affects the mechanical properties of the filament used in FDM, which in turn affects the printability of the feedstock material. Therefore, in this study a full-factorial experimental design was utilized to investigate different processing parameters of the hot melt extrusion process, their effect on various mechanical properties and the potential correlation with the filaments' printability. The thermolabile, poorly-soluble drug ibuprofen was utilized as a model drug to assess the potential of two mesoporous materials, Mesoporous Magnesium Carbonate (MMC) and a silica-based material (MCM-41), to thermally protect the loaded drug. Factorial and principal components analysis displayed a correlation between non-printable MCM-41 filaments and their mechanical properties where printable filaments had a maximum stress >7.5 MPa and a Young's modulus >83 MPa. For MMC samples there was no clear correlation, which was in large part attributed to the filaments' inconsistencies and imperfections. Finally, both mesoporous materials displayed a thermal protective feature, as the decomposition due to the thermal degradation of a significant portion of the thermolabile drug was shifted to higher temperatures post-loading. This highlights the potential capability of such a system to be implemented for thermosensitive drugs in FDM applications.

Printed circuit boards (PCBs) are essential components in various electronic devices, playing a vital role in their functionality. Over time, PCBs have evolved significantly, becoming smaller, more complex, and multi-layered to meet the demands of advancing technology and consumer preferences. The quality of solder paste deposits is measured by circularity, volume, positioning, and the number of satellites. Mycronic is a supplier of jet printing machines for PCB manufacturers and needs to investigate and counteract an unwanted variation in volume and positioning within the first dot of the solder paste strip, and its so-called “sea saw” effect for the following three dots. This master’s thesis has two aims. The first aim is to develop and evaluate an experimental method to reduce variation between the dots in the strip. The second aim is to reduce the variation between the 1st dot and the following three dots by finding a combination of pre-feed, first, second, and third waveforms to obtain improved precision, volume, circularity, and reduced volume variation. Using an experimental design a full factorial design was used, examining six experimental factors. The design was further augmented with a central composite design to describe the second-order model. The knowledge from the experiment was used to optimize and improve the quality factors of jet printing, which were verified with an additional experiment. The results of the study provided compelling evidence that only the selected experimental factors had a significant impact on improving the volume metric. It was observed that the presence or absence of satellites during the experiment did not have any effect on the results, and neither did the positioning and circularity factors contribute to any improvement or deterioration. Specifically, the most significant difference in volume between 1st and following three dots for the optimized solution is 0.5 nl, and the currently used settings have a difference of 2.6

The incorporation of drug-loaded mesoporous materials in dosage forms prepared with fused deposition modeling (FDM) has shown the potential to solve challenges relating to additive manufacturing techniques, such as the stability of poorly-soluble drugs in the amorphous state. However, the addition of these non-melting mesoporous materials significantly affects the mechanical properties of the filament used in FDM, which in turn affects the printability of the feedstock material. Therefore, in this study a full-factorial experimental design was utilized to investigate different processing parameters of the hot melt extrusion process, their effect on various mechanical properties and the potential correlation with the filaments' printability. The thermolabile, poorly-soluble drug ibuprofen was utilized as a model drug to assess the potential of two mesoporous materials, Mesoporous Magnesium Carbonate (MMC) and a silica-based material (MCM-41), to thermally protect the loaded drug. Factorial and principal components analysis displayed a correlation between non-printable MCM-41 filaments and their mechanical properties where printable filaments had a maximum stress >7.5 MPa and a Young's modulus >83 MPa. For MMC samples there was no clear correlation, which was in large part attributed to the filaments' inconsistencies and imperfections. Finally, both mesoporous materials displayed a thermal protective feature, as the decomposition due to the thermal degradation of a significant portion of the thermolabile drug was shifted to higher temperatures post-loading. This highlights the potential capability of such a system to be implemented for thermosensitive drugs in FDM applications.

INTRODUCCIÓ. Els xiquets amb Trastorn de l'Espectre Autista (TEA) presenten dificultats de socialització i comunicació que provoquen problemes d'atenció i de desenvolupament cognitiu. S'han utilitzat diversos tipus d'intervencions que ajuden a millorar les mancances d'aquest col·lectiu, entre les quals destaquen les intervencions mitjançant l'ús de l'art. En aquest treball presentem una intervenció educativa artística grupal en un conjunt de 14 xiquets amb TEA, amb l'objectiu d'analitzar els seus efectes en la millora de competències socials i emocionals. MÈTODE. Utilitzem un disseny experimental, amb un grup de control. A diferència d'altres treballs, en què la intervenció és individual, utilitzem un format grupal. Els canvis de comportament es van detectar mitjançant un qüestionari emplenat abans i després de la intervenció per pares, terapeutes i investigadors, en els quals les preguntes s'agrupen en quatre categories: Reciprocitat social i emocional, Comunicació no verbal, Inflexibilitat a canvis i Estereotipies i reactivitat sensorial. RESULTATS. S'obtenen dos tipus de resultats: d'una banda, una bona adaptació a la dinàmica de les sessions, i per un altre, progressos significatius després de la intervenció en el grup tractat, especialment en Estereotipies i Inflexibilitat a canvis. Les dades s'analitzen mitjançant dos procediments diferents per a garantir la seua robustesa. DISCUSSIÓ. Els resultats suggereixen que les intervencions grupals mitjançant l'art poden ser un instrument eficaç per a la socialització dels xiquets amb TEA, així com ajudar-los a compartir emocions. El disseny i la dinàmica de les sessions s'han adaptat bé a les preferències sensorials dels xiquets, afavorint la interacció. Aquest treball és un estudi preliminar de la potencialitat de les intervencions grupals mitjançant l'art per a xiquets amb TEA., INTRODUCTION. Children with Autism Spectrum Disorder (ASD) show difficulties of socialization and communication giving rise to attention and cognitive development problems. Different types of intervention have been used to help improving those disabilities, among them, art interventions. In this paper we present an art educative intervention in a group of 14 children with ASD, intending to analyze its effects in improving their emotional and social competencies. METHOD. We use an experimental design, with a control group. Instead of individual intervention, we here have a group format. To detect changes in behavior, a questionnaire, filled up by parents, therapists and researchers, before and after the intervention was used. The questions are grouped into four categories: Social and emotional reciprocity; Non-verbal communication; Inflexibility to changes; and Stereotypes and sensorial reactivity. RESULTS. Two different types of results emerge: on the one hand, a good adaptation to the dynamics of the sessions. On the other hand, significant progress after the intervention in the treated group, in particular in Stereotypes and sensorial reactivity and Inflexibility to changes. Two different procedures to analyze the data were used, to add robustness. DISCUSSION. Our results suggest that art interventions in a group format could be an efficient tool to help children with ASD to improve socialization and sharing emotions. The design and dynamics of the sessions adapted to the sensorial preferences of the children, favoring the interaction between the participants. This work is a preliminary study about the potential of group art interventions for children with ASD., INTRODUCCION. Los niños con Trastorno del Espectro Autista (TEA) presentan dificultades de socialización y comunicación que provocan problemas de atención y de desarrollo cognitivo. Se han utilizado diversos tipos de intervenciones que ayudan a mejorar las carencias de este colectivo, entre las que destacan las intervenciones mediante el uso del arte. En este trabajo presentamos una intervención educativa artística grupal en un conjunto de 14 niños con TEA, con el objetivo de analizar sus efectos en la mejora de competencias sociales y emocionales. METODO. Utilizamos un diseño experimental, con un grupo de control. A diferencia de otros trabajos, en que la intervención es individual, utilizamos un formato grupal. Los cambios de comportamiento se detectaron mediante un cuestionario rellenado antes y después de la intervención por padres, terapeutas e investigadores, en los que las preguntas se agrupan en cuatro categorías: Reciprocidad social y emocional, Comunicación no verbal, Inflexibilidad a cambios y Estereotipias y reactividad sensorial. RESULTADOS. Se obtienen dos tipos de resultados: por una parte, una buena adaptación a la dinámica de las sesiones, y por otro, progresos significativos tras la intervención en el grupo tratado, especialmente en Estereotipias e Inflexibilidad a cambios. Los datos se analizan mediante dos procedimientos diferentes para garantizar su robustez. DISCUSION. Los resultados sugieren que las intervenciones grupales mediante el arte pueden ser un instrumento eficaz para la socialización de los niños con TEA, así como ayudarles a compartir emociones. El diseño y la dinámica de las sesiones se han adaptado bien a las preferencias sensoriales de los niños, favoreciendo la interacción. Este trabajo es un estudio preliminar de la potencialidad de las intervenciones grupales mediante el arte para niños con TEA.

Deep learning algorithms are increasingly popular for complex prediction and classification tasks, and hyperparameter configurations play an important role in algorithm performance. However, the best hyperparameter tuning strategy still remain unresolved. While grid search and random search can be used to detect better hyperparameters, they are costly for big deep learning algorithms and may not produce the optimal result. Bayesian Optimization balancing the exploration and exploitation trade-off shows significant improvement over grid search and random search in both efficiency and accuracy, but the algorithm makes computation on the entire domain, which can be still costly especially in higher dimension settings. In this paper, we propose a space adjustment algorithm selecting top percent points at each iteration that can be incorporated in additional to Bayesian Optimization framework to further reduce experimental cost and improve optimization efficiency. We show our algorithm’s adaptable nature to the response surface of hyperparameter configuration space. We demonstrate our algorithm’s outstanding performance compared with Efficient Global Optimization through a variety of test functions and an application to machine learning datasets.

Several recent works in human-robot-interaction (HRI) have begun to highlight the importance of the replication crisis and open science practices for our field. Yet, suggestions and recommendations tailored to child-robot-interaction (CRI) research, which poses its own additional set of challenges, remain limited. There is also an increased need within both HRI and CRI for inter and crossdisciplinary collaborations, where input from multiple different domains can contribute to better research outcomes. Consequently, this workshop aims to facilitate discussions between researchers from diverse disciplines within CRI. The workshop will open with a panel discussion between CRI researchers from different disciplines, followed by 3-minute flash talks of the accepted submissions. The second half of the workshop will consist of breakout group discussions, where both senior and junior academics from different disciplines can share their experiences of conducting CRI research. Through this workshop, we hope to create a common ground for addressing shared challenges in CRI, as well as identify a set of possible solutions going forward., Part of ISBN 9781450399708QC 20230801

A heterojunction is the key junction for charge extraction in many thin film solar cell technologies. However, the structure and band alignment of the heterojunction in the operating device are often difficult to predict from calculations and, due to the complexity and narrow thickness of the interface, are difficult to measure directly. In this study, we demonstrate a technique for direct measurement of the band alignment and interfacial electric field variations of a fully functional lead halide perovskite solar cell structure under operating conditions using hard X-ray photoelectron spectroscopy (HAXPES). We describe the design considerations required in both the solar cell devices and the measurement setup and show results for the perovskite, hole transport, and gold layers at the back contact of the solar cell. For the investigated design, the HAXPES measurements suggest that 70% of the photovoltage was generated at this back contact, distributed rather equally between the hole transport material/gold interface and the perovskite/hole transport material interface. In addition, we were also able to reconstruct the band alignment at the back contact at equilibrium in the dark and at open circuit under illumination., QC 20230412

The incorporation of drug-loaded mesoporous materials in dosage forms prepared with fused deposition modeling (FDM) has shown the potential to solve challenges relating to additive manufacturing techniques, such as the stability of poorly-soluble drugs in the amorphous state. However, the addition of these non-melting mesoporous materials significantly affects the mechanical properties of the filament used in FDM, which in turn affects the printability of the feedstock material. Therefore, in this study a full-factorial experimental design was utilized to investigate different processing parameters of the hot melt extrusion process, their effect on various mechanical properties and the potential correlation with the filaments' printability. The thermolabile, poorly-soluble drug ibuprofen was utilized as a model drug to assess the potential of two mesoporous materials, Mesoporous Magnesium Carbonate (MMC) and a silica-based material (MCM-41), to thermally protect the loaded drug. Factorial and principal components analysis displayed a correlation between non-printable MCM-41 filaments and their mechanical properties where printable filaments had a maximum stress >7.5 MPa and a Young's modulus >83 MPa. For MMC samples there was no clear correlation, which was in large part attributed to the filaments' inconsistencies and imperfections. Finally, both mesoporous materials displayed a thermal protective feature, as the decomposition due to the thermal degradation of a significant portion of the thermolabile drug was shifted to higher temperatures post-loading. This highlights the potential capability of such a system to be implemented for thermosensitive drugs in FDM applications.

The study of movement behavior and home range in animals is fundamental to understanding ecological dynamics, yet designing efficient tracking studies remains a challenge due to the lack of clear guidelines. To address this knowledge gap we created 'movedesign,' a user-friendly application that enables researchers to evaluate the precision of three commonly reported movement and spatial ecology estimates: home range area, speed, and distance traveled. Leveraging the 'ctmm' R package, our application employs autocorrelated kernel density estimators (AKDEs) and continuous-time speed and distance (CTSD) estimators, effectively addressing biases inherent in animal movement datasets. By evaluating the interplay between data resolution and battery life, 'movedesign' guides researchers in making informed decisions to achieve reliable estimates of home range and velocity while considering trade-offs. With broad applications for researchers and decision-makers, this tool empowers efficient deployment strategies, optimizing sampling design for insightful and robust outcomes in movement ecology studies.

Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

This paper explored the effects of different feedback manipulations on letter-string problem solving by analogical transfer. Specifically, the present studies assessed the effects of the type of feedback (first experiment); long exposure to feedback (second experiment) and the number of feedback problems during the practice phase (experiment 3). Participants were college undergraduate students who voluntarily received and answered printed letter string problems. Results showed that correct answer feedback has a significant positive effect on deep structure problem solving; they also showed that problem solution occurs much faster and evolves in a more straightforward manner with this type of feedback. Results also showed a direct relationship between the number of problems with correct answer feedback during the training phase, and the number of correct answers during the testing trial. Results are discussed in terms of the importance of using within-subject experimental designs in both the feedback interventions and analogical transfer literatures., This paper explored the effects of different feedback manipulations on analogous letter-string problem solving. Specifically, the present studies assessed the effects of the type of feedback (first experiment); long exposure to feedback and training (second experiment) and the number of feedback problems during the practice phase (experiment 3). Participants were college undergraduate students who voluntarily received and answered printed letter string problems. Results showed that correct answer feedback had a significant positive effect on deep structure problem solving; they also showed that problem solution occurs much faster and evolves in a more straightforward manner with this type of feedback. Results also suggest a direct relationship between the number of problems with correct answer feedback during the training phase and the number of correct answers during the testing trial. The discussion centers on the importance of experimental design as a tool for better understanding causal relationships in educational and other sciences.

The overall quality of processed food depends on many processing parameters such as temperature, processing time, mixing speed, pressure, pH value, size of food pieces, type of added ingredients, etc. The goal of food products devel- opment is not only to improve the quality of the final product but also to reduce the energy consumption and to improve resources management. Independent evaluation of a single parameter or response is not the best way to set and optimize experiments, because improving one response by controlling one parameter can negatively affect other responses. Experi- mental design is frequently used for experiment planning when multiple variables should be optimized, while the desirability function allows the determination of operating conditions that provide the “most desirable” response values. The desirability function allows the optimization of several responses simultaneously and the determination of the overall/cumulative/global quality. In this way, it is possible to determine the most suitable conditions for achieving the best overall product quality. In the desirability function, all responses are transformed into dimensionless individual desirability functions (di), which take values from 0 to 1. The value 0 indicates an undesirable response of the system, while the value 1 represents the most de-sirable response. All values between 0 and 1 indicate more or less desirable responses. There are three different equations for evaluating individual desirability functions depending on whether it is ideal for the response to be maximal, minimal or to have some target value. After calculating the individual desirability functions, they are combined into one global/overalldesirability function (D) [1,2]. If the value of this function is different from 0, it means that for all responses, the desired responses were achieved at the same time. If the value of the D is equal to 0, it means that for at least one response the desired response has not be

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.

Cheese powder is a multifunctional ingredient that is produced by spray drying a hot cheese emulsion called cheese feed. Feed stability is achieved by manipulating calcium equilibrium using emulsifying salts. However, the increased demand for ‘green’ products created a need for alternative production methods. Therefore, this study investigated the impact of ohmic heating (OH) on Cheddar cheese, mineral balance, and the resulting cheese feed characteristics compared with a conventional method. A full factorial design was implemented to determine the optimal OH parameters for calcium solubilization. Electric field exposure and temperature had a positive correlation with mineral solubilization, where temperature had the greatest impact. Structural differences in pre-treated cheeses (TC) were analyzed using thermorheological and microscopic techniques. Obtained feeds were analyzed for particle size, stability, and viscosity. OH-treatment caused a weaker cheese structure, indicating the potential removal of calcium phosphate complexes. Lower component retention of OH_TC was attributed to the electroporation effect of OH treatment. Microscopic images revealed structural changes, with OH_TC displaying a more porous structure. Depending on the pre-treatment method, component recovery, viscosity, particle size distribution, and colloidal stability of the obtained feeds showed differences. Our findings show the potential of OH in mineral solubilization; however, further improvements are needed for industrial application.

A Shiny R application that aims to assist researchers in designing animal tracking projects related to two main research questions: the estimation of home range and of speed and distance traveled. The application makes use of simulations to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration).

Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

Worldwide, ecosystems are suffering important taxonomic and functional modifications in response to anthropogenic disturbances, operating at multiple spatial and temporal scales. Awareness on biodiversity losses has led to the adoption of conservation policies and the development of programs devoted to the conservation and the restoration of terrestrial, freshwater, and marine ecosystems. The assessment of the ecological health of ecosystems requires measuring and characterizing restoration or degradation dynamics and their consequences on the ecological quality with respect to reference conditions defined pragmatically as conservation targets. Methodological innovations, in terms of data collection, analysis, and visualization, have an important influence on the ability of ecologists to understand biodiversity changes. The assessment of the quality of ecosystems with respect to reference conditions requires to address, notably, three main challenges: the definition of reference conditions, the assessment of the degree of achievement of conservation objectives, and the qualitative and quantitative characterization of recovering and departing patterns. We propose here the ecological quality assessment (EQA) framework as a data‐driven approach to track ecological quality focusing on the distance of the tested stations with respect to a chosen reference envelope using fuzzy logic and trajectory analysis. We take advantage of those analytical tools to propose a general and flexible multivariate framework by quantifying the achievement of reference conditions, measuring restoration and degradation dynamics when temporal series are available, and representing and synthesizing this information. To take into account the natural spatiotemporal variability of sites considered as reference, we gave two variants to our framework: a state‐based variant when no temporal replications are available and a trajectory‐based variant specially devoted to compare whole trajectories to a

The overall quality of processed food depends on many processing parameters such as temperature, processing time, mixing speed, pressure, pH value, size of food pieces, type of added ingredients, etc. The goal of food products devel- opment is not only to improve the quality of the final product but also to reduce the energy consumption and to improve resources management. Independent evaluation of a single parameter or response is not the best way to set and optimize experiments, because improving one response by controlling one parameter can negatively affect other responses. Experi- mental design is frequently used for experiment planning when multiple variables should be optimized, while the desirability function allows the determination of operating conditions that provide the “most desirable” response values. The desirability function allows the optimization of several responses simultaneously and the determination of the overall/cumulative/global quality. In this way, it is possible to determine the most suitable conditions for achieving the best overall product quality. In the desirability function, all responses are transformed into dimensionless individual desirability functions (di), which take values from 0 to 1. The value 0 indicates an undesirable response of the system, while the value 1 represents the most de-sirable response. All values between 0 and 1 indicate more or less desirable responses. There are three different equations for evaluating individual desirability functions depending on whether it is ideal for the response to be maximal, minimal or to have some target value. After calculating the individual desirability functions, they are combined into one global/overalldesirability function (D) [1,2]. If the value of this function is different from 0, it means that for all responses, the desired responses were achieved at the same time. If the value of the D is equal to 0, it means that for at least one response the desired response has not be