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Daqu, a crucial fermentation starter in the production of various Chinese fermented foods, plays a pivotal role in shaping complex enzyme profiles and diverse flavour precursors. This review aims to elucidate the microbial communities within Daqu, focusing on their functionalities in the context of flavour development. We delve into the detection methods of microorganisms and flavour substances in Daqu, employing advanced technologies including high-performance liquid chromatography, gas chromatography, pseudo-targeted metabolomics, and headspace solid-phase microextraction-gas chromatography-mass spectrometry. This review explores high throughput sequencing technologies, including pyrosequencing, clonal library sequencing, metaproteomic, and metagenomics, to gain a comprehensive understanding of the intricate microbial dynamics. Additionally, we discuss the metabolic pathways involved in flavour substance production within Daqu. By synthesizing information on Daqu types, microorganisms present, detection methodologies, and flavour substance metabolic pathways, this review contributes to a deeper comprehension of the intricate processes underpinning the Flavors of Chinese fermented foods. [Display omitted] • Crucial role of Daqu in enzyme profiles and flavor precursors in Chinese foods. • Microbial communities in Daqu and their role in flavor development. • High throughput sequencing: pyrosequencing, clonal library sequencing, metaproteomic, metagenomics. • Comprehensive synthesis of Daqu types, microorganisms, and metabolic pathways. [ABSTRACT FROM AUTHOR]

Cirque landforms, which are distinctive features of cold regions, play a pivotal role in understanding environmental changes and climate research. Currently, machine learning-based automatic glacier recognition models have significantly improved recognition speed. However, these models rely solely on elevation data from Digital Elevation Model (DEM), neglecting other topographic factors that influence cirque identification, such as slope and aspect. This study integrates DEM, slope, and aspect data through pseudo-color fusion technology and utilizes the RetinaNet network to achieve automatic cirque recognition. The pseudo-color fusion image-based recognition model outperforms methods solely relying on DEM, achieving mAP (Intersection over Union: IoU @0.5) of 82.3 %. In practical applications, the pseudo-color fusion image-based recognition model demonstrates balanced performance in identifying different categories of cirques, particularly excelling in recognizing intricate terrains and irregularly shaped cirques. [ABSTRACT FROM AUTHOR]

Escherichia coli (E. coli) and Shigella species, being highly similar, present a challenge for differentiation using classical methods such as phenotyping, 16S rRNA sequencing, or protein profiling using matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). The paper proposes a method for identifying E. coli , S. flexneri , S. sonnei , and S. boydii by augmenting the Bruker Biotyper database with reference spectra of lipid profiles obtained using MALDI MS in the positive mode. Lipid extracts were made from cultured E. coli, S. flexneri, S. sonnei and S. boydii using the Bligh & Dyer protocol. MALDI MS spectra in positive ion mode were performed for the extracts. Reference spectra were created from 30 spectra for each bacterium and added to the Bruker Biotyper database. Identification of bacteria based on lipid profiles in the Biotyper database gave correct results with scores above 2.49. Statistical analysis of the results by Partial Least Squares-Discriminant Analysis (PLS-DA) showed that it is possible to correctly differentiate the microorganisms studied using the lipidomic approach. A panel of six m/z values was proposed for which the value of the area under the ROC curve is 1, thus enabling the identification of E. coli and S. flexneri with 100 ​% accuracy. Identification of bacteria from lipid fingerprints obtained by the MALDI MS technique is possible and may become a useful tool in the future, especially for microorganisms that are difficult to distinguish by other methods. [ABSTRACT FROM AUTHOR]

Comprendre les enjeux de « la dysphorie de genre » et déchiffrer les raisons de sa croissance exponentielle chez les mineurs. Tout en se maintenant strictement sur le plan clinique et déontologique, afin de défendre une éthique médicale à distance des considérations idéologiques. L'objectif de cet article est donc de déchiffrer ce phénomène pandémique et de le questionner comme un phénomène peut-être plus social que médical. À partir de l'analyse de la littérature internationale, nous nous sommes focalisées sur les études épidémiologiques internationales, et sur les quelques données chiffrées à notre disposition en France. Nous confrontons ces données avec des témoignages d'adolescents en voie de « transition identitaire » ou de « détransition ». Cet ensemble est mis à l'épreuve des savoirs institués en psychopathologie de l'enfance et de l'adolescence et aux théories sexuelles infantiles. Les résultats nous invitent d'abord à distinguer fondamentalement le phénomène chez l'enfant et chez l'adolescent. Ensuite, à l'adolescence cette revendication identitaire concerne indiscutablement, en une très grande majorité, des filles. Enfin, cette demande, sexuée donc, peut être abordée comme une autre dysmorphophobie pubertaire , en plus d'une comorbidité importante fréquente. Nous constatons que ce phénomène se présente comme une épidémie sociale dans la jeunesse, renforcée par la place actuelle des revendications identitaires et des impacts de propagandes sur les réseaux sociaux en direction de la jeunesse. Un rappel sur la question particulière du corps, nous amènera à interroger le refus du féminin au XXIe siècle. La dimension mystique est argumentée et soulignée. Les pratiques médicales qui se sont spécialisées en « genre » aussi en France apparaissent à contre-temps et en contradiction avec les dernières déclarations des protagonistes de la WPATH. Nous rappelons l'importance d'une prise en charge psychothérapeutique et psychiatrique pour tous ces enfants. Nous recommandons d'agir avec la prudence et l'éthique nécessaires à ces âges de la vie. Any clinician cannot fail to wonder about the notion of "gender dysphoria" as a disease or as a disorder. The review of the few international epidemiological studies on gender transition requests leads to an attempt to "decipher" the reasons for the exponential growth of this new clinical category. Is it a psychopathological or a social phenomenon, or both? We will examine separately the cases of children and adolescents. Children experiencing hesitations about their sexual identity tend to give up requests for transitions; as for teenagers (12 girls for every boy), they seem to be largely influenced by social networks. We will position ourselves strictly on the clinical and deontological level, to defend medical ethics, at a distance from the ideological considerations disseminated by the media. Starting from the recognition of the very strong increase in France of requests to "change sex/gender" in children and adolescents in recent years, we will note that this claim of "gender dysphoria" is presented differently by children before and after puberty. From a clinical point of view, it is necessary to understand that gender dysphoria would be the result of different identification processes for children and adolescents; it also presents as a gendered behavior like other known behavioral disorders in adolescence. The objective of this article is therefore to decipher this pandemic phenomenon and to question it as a social rather than a medical phenomenon. We must then recommend great caution in medical procedures, sometimes irreversible, on these children. Based on an analysis of the international literature, we focused on international epidemiological studies and on the scarce data available to us in France. We compare these data with those from testimonies of adolescents in the process of "identity transition" or "detransition" in France and in other countries, because the complaints and claims are identical. These data were collected and studied until the end of 2021. These data and testimonies will be confronted with our knowledge in the domain of child and adolescent psychiatry and psychoanalysis, the latter being a theoretical construction of childhood sexuality. The results drawn from the analysis of these testimonies and from existing epidemiological studies show, firstly, that a fundamental distinction must be made between the phenomenon of "gender dysphoria" in children and in adolescents. The evolution of the demands is drastically different in terms of volume and environmental causality. Secondly, in adolescence, this demand for a change of identity indisputably concerns a very large majority of girls. Finally, this demand, therefore sexualized, can be seen as a fragile identity, added to other pathologies. It should also be considered with demands regarding identity and the impact of propaganda on social networks towards youth. These new demands for changes in gender identity should be discussed before administering lifelong medical and surgical treatments that have not proven their effectiveness and harmlessness, as is the case in Sweden and the U.K. Our discussion here focuses on some clinical and epidemiological elements that allow us to consider these new demands for recognition of "gender dysphoria" from adolescents as a "social epidemic" phenomenon. We will discuss the notion of gender and identification in children and adolescents, along with the frequent and worrying "associated disorders." A reflection on the particular question of the body will lead us to question the refusal of the feminine in the 21st century. we will also consider the impact of the choice of treatment and support provided, before concluding with a reflection on the requests of adolescent girls. We recall the importance of providing global psychological, psychodynamic, and psychiatric care for all these children. We recommend acting with the necessary prudence and ethics at these stages of life, and we defend the necessity for all children to develop thanks to the normal evolution of their chosen identifications. The diagnosis of "gender dysphoria" should not be understood as a diagnosis that would bind the child or adolescent in a gender (whether desired or not, conversion therapy is now forbidden in France), but, on the contrary, should allow the child or adolescent to be accompanied towards an emancipation in the face of these gender stereotypes in which he or she is most often trapped, or even kidnapped. Because the body is not sick, it is necessary to protect it against any deleterious transformation. But above all, by avoiding any stigmatization of identity, we can allow the child as well as the adolescent to reappropriate the image of her/his body and thus a psychic and anatomical unity. To thus preserve the freedom of choice amongst the multiple adult sexualities with which s/he will have to deal later on. It is obviously for lack of this protective psychological accompaniment that these young people prematurely put an end to any genital sexual life (homo- or heterosexual or other). [ABSTRACT FROM AUTHOR]

• The error model of a 5-DOF PEF is established based on the POE formula to satisfy completeness, continuity, and minimality. • An MPC-based parameter identification approach is proposed to address the challenge in the measurement of pose information. • A novel calibration method containing the POE-based error model and the MPC-based identification approach is formed. • The method makes advances in model accuracy and measurement difficulty, and can cost-effectively finish calibration tasks. Limb deformity is a common complaint in orthopedic surgery. Currently, gradual treatment using parallel external fixator (PEF) has become the preferred option for deformity correction. As the key medical apparatus with the special application properties of temporary assembly and direct utilization, the geometric errors generated during the manufacturing and assembly process of the PEF contribute to the post-correction residual and iatrogenic deformities. However, considering the complexity of clinical scenarios, there is a lack of an applicable solution to reduce the effect of the above error source of PEF. To overcome these problems, taking a 4-U P S/S P R PEF as the research object, this paper proposes a novel kinematic calibration method. First, the error model with the features of completeness, continuity, and minimality is first established based on the product of exponentials (POE) formula. Then, the identifiability analysis is conducted to eliminate the redundant error parameters and ensure the stability and accuracy of the iterative calculation in the process of parameter identification. Furthermore, a multiple plane constraint (MPC)-based parameter identification approach is introduced to the calibration task of the PEF to effectively reduce the difficulty of the pose information measurement in the current cost-effective constraint calibration method. Finally, the simulation and prototype experiments validate that the proposed calibration method (i.e., the combination of the POE-based error model and the MPC-based parameter identification approach) can accomplish the calibration tasks in clinical applications and meet the cost-effectiveness and efficiency requirements. [ABSTRACT FROM AUTHOR]

Large health insurance claims databases can be used to estimate rates of rare safety outcomes. We measured incidence rates of rare outcomes that could be used to contextualize adverse events among people receiving pneumococcal vaccines in clinical trials or clinical practice. However, algorithms used to identify outcomes in administrative databases are subject to error. Using two algorithms for each outcome, we assessed the influence of algorithm choice on the rates of the outcomes. We used closed administrative medical and pharmacy claims in the Healthcare Integrated Research DatabaseSM (HIRD) to construct a broad cohort of individuals less than 100 years old (i.e., the target cohort) and a trial-similar cohort of individuals resembling those potentially eligible for a vaccine clinical trial (e.g., for a pneumococcal vaccine). We stratified by age and sex and used specific and sensitive algorithms to estimate rates of 39 outcomes including cardiac/cerebrovascular, metabolic, allergic/autoimmune, neurological, and hematologic outcomes. Specific algorithms intended to reduce false positive errors, while sensitive algorithms intended to reduce false negative errors, thereby providing lower and upper bounds for the "true" rates. We followed approximately 40 million individuals in the target cohort for an average of 3 years. Of 39 outcomes, 14 (36 %) had a rate from the specific algorithm that was less than half the rate from the sensitive algorithm. Rates of cardiac/cerebrovascular outcomes were most consistent (mean ratio of rates from specific algorithms compared to rates from sensitive algorithms = 0.76), while the rates of neurological and hematologic outcomes were the least consistent (mean ratio of rates = 0.33 and 0.36, respectively). For many cardiac/cerebrovascular outcomes, rates were similar regardless of the algorithm. For other outcomes, rates varied substantially by algorithm. Using multiple algorithms to ascertain outcomes in claims data can be informative about the extent of uncertainty due to outcome misclassification. [ABSTRACT FROM AUTHOR]

The objective of this study is to explore the diagnostic value of machine learning (ML) in borderline ovarian tumors through meta-analysis. Pubmed, Embase, Web of Science, and Cochrane Library databases were comprehensively retrieved from database inception untill February 16, 2023. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was adopted to evaluate the risk of bias in the original studies. Sub-group analyses of ML were conducted according to clinical features and radiomics features. We separately discussed the discriminative value of ML for borderline vs benign and borderline vs malignant tumors. Eighteen studies involving 12,778 subjects were included in our analysis. The modeling variables mainly consisted of radiomics features (n=13) and a small number of clinical features (n=5). When distinguishing between borderline and benign tumors, the ML model based on radiomic features achieved a c-index of 0.782 (95% CI: 0.732–0.831), sensitivity of 0.75 (95% CI: 0.67–0.82), and specificity of 0.75 (95% CI: 0.67–0.81) in the validation set. When distinguishing between borderline and malignant tumors, the ML model based on radiomic features achieved a c-index of 0.916 (95% CI: 0.891–0.940), sensitivity of 0.86 (95% CI: 0.78–0.91), and specificity of 0.88 (95% CI: 0.82–0.92) in the validation set. In addition, we analyzed the discriminatory ability of radiologists and found that their sensitivity was 0.26 (95% CI: 0.12–0.46) and specificity was 0.94 (95% CI: 0.90–0.97). ML has tremendous potential in the preoperative diagnosis and differentiation of borderline ovarian tumors and may be more accurate than radiologists in diagnosing and differentiating borderline ovarian tumors. • We discussed the value of ML in borderline ovarian tumors through meta-analysis. • ML has potential in the preoperative diagnosis of borderline ovarian tumors. • ML may be more accurate than radiologists in diagnosing borderline ovarian tumors. [ABSTRACT FROM AUTHOR]

Machine learning (ML) extends rapidly in many research areas including design of novel processing routines, imaging, and material science. Particularly, ML enables design of new materials with complex structures and shorten the development cycle through model prediction from existing database. Calcium carbonate (CaCO 3) particles are regarded as promising candidates for drug delivery, biomedical, food, and industrial filler applications due to their good physicochemical properties and biocompatibility. However, a prerequisite for these applications is the production of particles with desired morphology, size, and phase compositions. Here, it is shown that the crystal growth and phase transition induced the transformation of spherical particles into spindle-like, square and needle-like morphologies with increasing temperature, and the increase of concentration increased this transition temperature. Furthermore, it is found that the concentration of the reacting salt solutions shifted the phase transition temperatures to higher values. Subsequently, ML is applied to precisely investigate and predict the polymorph formation of CaCO 3 particles based on the experimental data obtained under 85 conditions, which would enable us to track crystallization trends, aiding in the identification of optimal conditions for generating monophase samples, and provide a feasible scheme for learning similar materials. [ABSTRACT FROM AUTHOR]

• An innovative star identification algorithm is proposed for lost-in-space scenarios. • A unique approach is utilized, which involves singular values in the form of Gaussian mixture models to generate distinctive features. • The matching process involving candidate filtering, similarity comparison, and validation is proposed to enhance identification capabilities. This paper presents an innovative star identification algorithm specifically designed for lost-in-space scenarios in satellite missions. The algorithm introduces a unique approach by utilizing singular values to generate Gaussian mixture models as distinctive features for star identification. By employing a matching process involving candidate filtering, similarity comparison, and validation, the algorithm demonstrates remarkable performance in precisely identifying stars while minimizing false identifications even in demanding environmental conditions. Extensive simulations are conducted to evaluate the algorithm's effectiveness under various scenarios with positional error and the presence of false stars. The algorithm exhibits reliable and consistent performance, making it highly suitable for dynamic scenarios and contributing to the reduction of hardware burden in star sensor systems. Furthermore, the proposed algorithm offers prominent performance across varying field of view sizes, thereby enhancing its practicality and usability in a wide range of missions. [ABSTRACT FROM AUTHOR]

Collar use in cats is a controversial topic. Cat owners do have very different reasons for either deciding to use a collar on their cat or rejecting collar use. We conducted an online survey on cat management, use of and attitudes toward collars. Our survey participants were mainly women (88.8%), mostly came from Germany (88.2%), and owned 2 ± 2 cats. Collar use was reported by 32.9%. Overall, participants showed significantly higher agreement to the subscale risk perception than to the subscale benefit perception (Z = −56.997, P < 0.001, N = 4940). Participants (N = 318) who reported first-time collar fitting on their cat in the past 2 years used most often plastic breakaway buckles (rounded edges: 44.2%; round with edges inwards: 17.9%; rectangular: 10.4%), non-breakaway buckles were used by 14.4% (plastic: 9.4%; metal: 4.9%). The most common objects attached to the collar were ID tags/tubes (32.4%) and bells (22.1%) and tracker (19.7%). Based on lifetime experience, collar users were older, less often first-time owners, more often allowed their cats free roaming, and had less often professional experience with cats. Agreement to risks was lower than in non-users but still higher than agreement to benefits. In contrast, the perception of benefits outweighed perception of risks in recent collar users. Collar use in the past was strongly associated to risks from entrapment. This suggests that owners are willing to try collars but do not continue to use them over time as they may perceive more risks than benefits. • Collar use in cats is controversially discussed by cat owners and experts. • We explored owner attitudes most influential in the decision to use a collar. • Statements regarding risks received higher agreement than regarding benefits. • Recent collar users perception of benefits outweighed perception of risks. • In contrast, past collar users agreed strongly to risks, in particular entrapment. [ABSTRACT FROM AUTHOR]

Tattoos serve as a valuable tool for identification. In the forensic context, it is vital to establish a systematic approach for documenting tattoo-related information to facilitate efficient and fast comparisons, especially in postmortem cases. Despite some countries failing to recognize the potential significance of tattoos, this study presents a methodological framework for gathering comprehensive data on this form of body modification. This article presents the results of an investigation made in Mexico during 2019–2022. The proposed methodology introduces a systematic and distinct classification system tailored to the country in which it will be implemented. The proposal is accompanied by applying the methodology in a Forensic Medical Service (SEMEFO) in Mexico for a week to test its effectiveness and speed under high workloads and stressful conditions. The novelty of this article lies in emphasizing the need for established, replicable, and homologous methodologies for tattoo codification. Additionally, it presents a more in-depth codification, where the details of the tattoos to be classified are thoroughly analyzed. [ABSTRACT FROM AUTHOR]

DNA technology is the gold standard with respect to the identification of individuals from biological evidence. The technology offers the convenience of a universally similar approach and methodology for analysis across the globe. However, the technology has not realised its full potential in India due to the lack of a DNA database and lacunae in sample collection and preservation from the scene of crime and victims (especially those of sexual assault). Further, statistical interpretation of DNA results is non-existent in the majority of cases. Though the latest technologies and developments in the field of DNA analysis are being adopted and implemented, very little has been enacted practically to improve optimise sample collection and preservation. This article discusses current casework scenarios that highlight the pitfalls and ambiguous areas in the field of DNA analysis, especially with respect DNA databases, sampling, and statistical approaches to genetic data analysis. Possible solutions and mitigation measures are suggested. [ABSTRACT FROM AUTHOR]

The precise model for Polymer Electrolyte Membrane Fuel Cells (PEMFCs) is vital for simulation, control, and performance analysis of PEMFCs. It is crucial to accurately estimate the model parameters. Over the past decade, the extraction of unknown parameters of PEMFCs model is formulated as optimization problem and several meta-heuristic techniques have emerged to solve this problem. Despite the development of these techniques to tackle the problem, the slow convergence rate and susceptibility to being trapped in local minima are regarded as weaknesses of these methods. Additionally, because the PEMFC model is a nonlinear and complex model, not all optimization algorithms are suitable for solving it. This article introduces a novel approach that utilizes the Autonomous Groups Particle Swarm Optimization (AGPSO) algorithm for extracting precise values for uncertain parameters inherent in PEMFC model of 250Wstack and BCS-500W stack utilized significantly in the literature. The optimization problem's fitness function is formulated as the total squared errors (TSEs) between the voltage datasets measured and estimated. Three different versions of AGPSO algorithm are presented. Statistical analysis is performed on these three versions to assess their robustness, and the most robust version is identified. Furthermore, the performance of the most robust version is extensively tested and analyzed through a complete comparison with recent optimization algorithms findings from the updated state-of-the-art literature such as BO, QOBO, TGA, HHO and ASO. Further, a statistical analysis was carried out that ensured the reliability and robustness of the introduced AGPSO. The results highlight the efficacy and feasibility of AGPSO-based approach across all compared algorithms, demonstrating an enhancement in the accuracy of the PEMFC model. A two-phase sensitivity analysis, conducted through Monte Carlo Simulation (MCS), was employed to assess how variations in optimized parameters affect the objective function (TSEs). Furthermore, the uncertainty in the maximum power reference current is identified. • Applying the AGPSO algorithm to extract the uncertain parameters for the PEMFC model. • The performance of the AGPSO algorithm is compared to other optimization algorithms. • Simulation results validate the superiority of the AGPSO over the other algorithms. • Sensitivity analysis is used to show effect of parameter changes on fitness function. • The uncertainty in the maximum power reference current is identified. [ABSTRACT FROM AUTHOR]

Accuracy in spacecraft attitude determination has been improved continuously in parallel to scientific payloads requiring higher accuracy. Star trackers aim to maintain attitude determination by matching observations to the reference stars. They provide very high accuracy at any point in space at cost of higher computational complexity. This study presents an algorithm of lost-in-space star identification with high accuracy and acceptable computational complexity. This task is achieved by means of a novel dictionary-based star matching method by applying nearest neighbor classification through a binary search of Euclidean distances and regularization sequentially, accompanied by use of unconventional feature vectors representing each observation individually. The proposed method yields an estimated direction of the boresight vector orthogonal to the camera plane with an estimated axial rotation. The Hipparcos catalog is used for database generation, and the sensor parameters are taken from the project SharjahSat-1. The performance is evaluated in consideration of noise including position, brightness and false stars. By means of complexity analysis of database size and average run time, it is shown that the proposed method achieves better accuracy than other methods and maintains very high robustness to position noise and brightness noise. A high level of accuracy is achieved with an amount of database size and average run time that are competitive in the recent literature. It is demonstrated that the levels of pointing accuracy offered by state-of-the-art ADCS models are achieved by the proposed method with the given probabilities. [Display omitted] • A lost-in-space star identification based on pattern recognition and regularization. • Pattern recognition by nearest neighbor classifier with a novel feature extraction. • Regularization by LASSO regression with a dictionary obtained from the classifier. • High accuracy and acceptable database size and average run time in comparison. • High robustness against position and brightness noise but sensitivity to false and missing stars. [ABSTRACT FROM AUTHOR]

[Display omitted] • 189 patients relevant to BP were collected in our study, • the world's first non-traumatic plexopathy dataset was constructed, • 107 quantitative features of BP were extracted, • 54 machine learning models were trained and evaluated, • the identification performance achieves the accuracy of 0.912 ± 0.016. Automatic abnormality identification of brachial plexus (BP) from normal magnetic resonance imaging to localize and identify a neurologic injury in clinical practice (MRI) is still a novel topic in brachial plexopathy. This study developed and evaluated an approach to differentiate abnormal BP with artificial intelligence (AI) over three commonly used MRI sequences, i.e. T1, FLUID sensitive and post-gadolinium sequences. A BP dataset was collected by radiological experts and a semi-supervised artificial intelligence method was used to segment the BP (based on nnU-net). Hereafter, a radiomics method was utilized to extract 107 shape and texture features from these ROIs. From various machine learning methods, we selected six widely recognized classifiers for training our Brachial plexus (BP) models and assessing their efficacy. To optimize these models, we introduced a dynamic feature selection approach aimed at discarding redundant and less informative features. Our experimental findings demonstrated that, in the context of identifying abnormal BP cases, shape features displayed heightened sensitivity compared to texture features. Notably, both the Logistic classifier and Bagging classifier outperformed other methods in our study. These evaluations illuminated the exceptional performance of our model trained on FLUID-sensitive sequences, which notably exceeded the results of both T1 and post-gadolinium sequences. Crucially, our analysis highlighted that both its classification accuracies and AUC score (area under the curve of receiver operating characteristics) over FLUID-sensitive sequence exceeded 90%. This outcome served as a robust experimental validation, affirming the substantial potential and strong feasibility of integrating AI into clinical practice. [ABSTRACT FROM AUTHOR]

• Introduced the Siamese 4D-Alznet, which combines five distinct blocks of CNNs. • We proposed three custom TL models incorporating frozen and replacing layers. • Our methods prove 95.07% binary classification performance in terms of accuracy. • All techniques performed best for NC, EMCI, LMCI, and AD classification. Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%. [ABSTRACT FROM AUTHOR]

• The thermal behaviors of the newly prepared collagen-based materials and the historical leather artifact were investigated. • The historical leather artifact was identified and validated as vegetable-tanned leather tanned with condensed tannin by integrating thermokinetics and generalized master plots. • The novel method might provide a useful tool for the identification and preservation of collagen-based cultural heritage. Collagen-based cultural heritage is important carrier for human civilization, which witnesses the historical development. However, the manufacturing process of the collagen-based cultural heritage, including the tanning agents used has long been a challenging issue in the field of archaeology science. In the present work, a novel method was provided for the identification of collagen-based cultural heritage by integrating thermokinetics and generalized master plots. The overall thermal behavior of the historical leather artifact was consistent with the newly prepared samples. The fitting of the historical leather artifact was closely aligned with the model for quebracho-tanned leather, which matched the An model at α 〈 0.5, and the F1 model at α 〉 0.5. Therefore, it was confirmed that the historical leather artifact unearthed in Turpan, Xinjiang, China, was made from condensed tannin. The reliability and applicability of this methodology in identifying collagen-based cultural heritage were further supported by FTIR and TG-FTIR-MS analyses. This innovative method holds profound significance for the identification and evaluation of the historical leather artifacts, which might help us in the inheritance and preservation of collagen-based cultural heritage. [ABSTRACT FROM AUTHOR]

The genus Mycobacterium includes well-known bacteria such as M. tuberculosis causing tuberculosis and M. leprae causing leprosy. Additionally, various species collectively termed non-tuberculous mycobacteria (NTM) can cause infections in humans and animals, affecting individuals across all age groups and health conditions. However, information on NTM infection prevalence in Panama is limited. This study conducted a retrospective analysis of clinical records from 2017 to 2021, specifically focusing on patients with NTM isolates. Data were categorized by variables like sex, age, HIV status, and sample source. Among the 4430 clinical records analyzed, 698 were linked to patients with NTM isolates. Of these patients, 397 were male, and 301 were female. Most female patients with NTM isolates (n = 190) were aged >45 to 85 years, while most male patients (n = 334) fell in the >25 to 75 years age group. A noteworthy proportion of male patients (n = 65) were aged 25–35 years. A significant age difference between male (median [min-max] = 53 years [3–90]) and female (median [61 years [6–94]) patients was observed (p < 0.001). Regarding HIV status, 77 positive individuals were male, and 19 were female (p < 0.001). Most samples (n = 566) were sputum samples, with additional pulmonary-associated samples such as broncho-alveolar lavage, tracheal secretions, and pleural fluid samples. Among extrapulmonary isolates (n = 48), sources included catheter secretions, intracellular fluids, peritoneal fluid, blood cultures, cerebrospinal fluid, bone marrow samples, and capillary transplant lesions. Specifically, the analysis identified the pathogenic microorganisms responsible for mycobacteriosis in Panama during the specific period 2017–2021, as M. fortuitum (34.4%), M. intracellulare (20.06%), and M. abscessus (13.75%), respectively. This study highlights the growing public health concern of NTM infections in Panama. The research provides valuable insights into the prevalence and distribution of NTM species in the country, offering a foundation for the development and implementation of effective prevention and control strategies for NTM infections in Panama. [ABSTRACT FROM AUTHOR]

[Display omitted] • ATR-FTIR spectroscopy was used to distinguish 3 wild cats from black guard hair. • The PLS-DA model successfully classified three species into distinct groups. • The PLS-DA model successfully differentiated human and non-human hair. • 100 % accuracy was achieved during the blind test. Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way. [ABSTRACT FROM AUTHOR]

Accurate identification of the failure modes of Reinforced Concrete (RC) columns based on the design parameters of the structural members is critical for earthquake-resistant design and safety evaluation of existing structures. Existing identification methods have some problems, such as high cost, incomplete consideration of influencing factors, and low precision or recall in identifying shear or flexural-shear failure. In this paper, the main factors for the failure modes of RC columns are first analyzed and studied. Then, the problem of class imbalance in data samples is investigated. To identify the failure modes of RC columns, oversampling of data (BSB-FMC), model ensembling (RFB-FMC), cost-sensitive learning (CSB-FMC) and a fusion model of three strategies (BSFCB-FMC) are proposed. And finally, the SHapley Additive exPlanations (SHAP) method is used to provide a better interpretation of the designed model. The results show that the developed strategies can improve the accuracy of identifying the failure modes of RC columns compared to the models using a single Artificial Neural Network (ANN), a Support Vector Machine (SVM), a Random Forest (RF), and Adaptive Boosting (AdaBoost). The overall accuracy of the developed BSFCB-FMC model reaches 97%, and the precision and recall for the three failure modes are both above 90%. The designed model provides a solution for fast, accurate and cost-effective identification of the failure modes of RC columns. • We develop the models for identifying the failure modes of RC columns considering class imbalance problem. • The BSFCB-FMC model is developed by considering data oversampling, model ensembling, and cost-sensitive learning. • The designed model provides engineers with an interpretable, fast and stable model for identifying failure modes. [ABSTRACT FROM AUTHOR]

Accurately identifying the stage of the excavator working cycle is the prerequisite to achieve the staged energy-saving control. However, current identification methods often overlook the influence of hydraulic system latency on identification results and depend on a single model, resulting in poor generalization performance of the identification approaches. Moreover, expert calibration system remains a necessary factor for improving identification accuracy. Aiming at these issues, a hybrid multi-scale feature extractor and a decision-level data fusion classifier approach (HMSFE-DFC) is proposed to identify the working cycle stages of excavator. The input signal employs mixed signals from the main pump pressure and the control current of the proportional solenoid valve to reduce the response delay caused by the single main pump pressure signal. A hybrid multi-scale feature extractor is constructed using a convolutional neural network temporal self-attention feature extraction mechanism and one-dimensional ResNet-50 architecture to extract multiscale features. To prevent overfitting, a decision-level data fusion classifier is used to fuse the decisions information of numerous classifiers. The accuracy of stage identification for 10 consecutive working cycles reaches 95.21%, which verifies its effectiveness. • A hybrid multiscale feature extractor and decision level data fusion classifier approach is presented. • The main pump pressure and control current of the proportional solenoid valve are mixed to reduce the response delay. • The proposed approach can achieve an identification accuracy of 95.21% for 10 consecutive working cycles of an excavator. [ABSTRACT FROM AUTHOR]

The field of person re-identification (re-ID) encounters challenges such as inter-person similarity and occlusion. In this paper, we introduce an occluded pedestrian re-ID method based on Multiple Fusion and Semantic feature Mining (MFSM) to mitigate the adverse effects of occlusion on feature representation. Specifically, we devise a Multi-branch Strategic Enhancement Module (SEM) to bolster the robustness of data augmentation in addressing the issues posed by data memorization more consistently. This module emulates various real-world disturbances by implementing distinct and independent augmentation strategies. Furthermore, we propose the Triplet Cross Unit (TCU) to comprehensively exploit and consolidate both regional and relational visual cues, as well as multi-level features. The TCU facilitates transformers in acquiring an early understanding of translation invariance in images by transferring local patterns such as edges, textures, and colors from the lower CNN layers to the shallower transformer layers. Simultaneously, deeper transformer features offer more abstracted semantic visual representations that complement CNN's high-level semantic features. Lastly, we introduce the Global Squeeze-Excitation Fusion (GSEF) module to address the challenge of global feature segregation across different models in final outputs. The GSEF selectively merges features based on attention mechanisms, prioritizing the comprehensive utilization of valid global features. Extensive experiments demonstrate the state-of-the-art performance of our model in handling pedestrian occlusion, thus validating the efficacy of our method. • MFSM framework enables CNN-Transformer interaction, improving person re-identification accuracy, especially under occlusion. • Our strategic enhancement module expands data richness, suppressing noise and improving occlusion handling. • TCU combines CNN and Transformer features, improving re-ID performance without overemphasizing local features. • GSEF module consolidates diverse semantic features, enriching re-ID representation and improving accuracy. [ABSTRACT FROM AUTHOR]

This paper discusses the role of Transfer Learning (TL) and transformers in cancer detection based on image analysis. With the enormous evolution of cancer patients, the identification of cancer cells in a patient's body has emerged as a trend in the field of Artificial Intelligence (AI). This process involves analyzing medical images, such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRIs), to identify abnormal growths that may help in cancer detection. Many techniques and methods have been realized to improve the quality and performance of cancer classification and detection, such as TL, which allows the transfer of knowledge from one task to another with the same task or domain. TL englobes many methods, particularly those used in image analysis, such as transformers and Convolutional Neural Network (CNN) models trained on the ImageNet dataset. This paper analyzes and criticizes each method of TL based on image analysis and compares the results of each method, showing that transformers have achieved the best results with an accuracy of 97.41% for colon cancer detection and 94.71% for Histopathological Lung cancer. Future directions for cancer detection based on image analysis are also discussed. [ABSTRACT FROM AUTHOR]

The early assessment of language and communication abilities is critical to meet the needs and challenges of inclusive societies. We developed a novel e-health approach whereby well-known assessment tools are available through online applications that allow a quick (computer, tablet, smartphone) on-screen assessment, making health monitoring and intervention in this field highly flexible and possible in a rich variety of contexts and users, including practitioners, educators, researchers, and caregivers. The app versions of the MacArthur-Bates Communicative Development Inventory Short forms (CDI) and the Communication and Symbolic Behavior Scales Developmental Profile (CSBS-DPTM) Infant-Toddler Checklist offer automatic scoring and percentile profiles for the individual infant and toddler. The easy, quick, decentralized access to health monitoring in the sensitive areas of cognitive and language development fosters the early identification of risk for language and communication impairments, early intervention and diagnosis, with individual, social and economic benefits. [ABSTRACT FROM AUTHOR]

Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management. [ABSTRACT FROM AUTHOR]

An accurate electromagnetic model is essential for an optimal controller tuning of the high-performance servo system. This paper proposes a fractional-order electromagnetic model of a permanent magnet synchronous motor (PMSM) servo system and an identification methodology of this model. The reason why the investigated electromagnetic model should be a fractional-order one is addressed with a detailed explanation. The influence of voltage source inverter nonlinearity, which may cause system identification error, is analyzed. An improved inverter nonlinearity model and compensation method are proposed to promote the accuracy of the model parameter identification. Compared with the existing typical electromagnetic models of the PMSM servo system, the current open-loop and closed-loop experiments prove that the proposed fractional-order electromagnetic model with time delay is more accurate for the actual physical system. The effectiveness of the proposed nonlinearity modeling and compensation scheme of the inverter is also verified on an experimental PMSM servo system. • A fractional-order electromagnetic model with time delay, considering the complete servo platform with PMSM, servo drive circuit, inverter nonlinearity, and digital delay, is proposed. • An identification methodology for fractional-order electromagnetic model is proposed. An improved inverter model based on a sigmoid function is used for voltage error compensation to improve the accuracy of parameter identification. • The experimental results on the PMSM current control platform demonstrate that the proposed model is more accurate than the traditional integer-order model. [ABSTRACT FROM AUTHOR]

The benefit of digitalisation, including the use of information management systems and electronic databases, in improving the effectiveness of social protection programmes is acknowledged by researchers and practitioners. However, concerns about digital- and financial exclusion stemming from increased reliance on digital technologies have also surfaced in recent studies. This study uses official data from South Africa's General Household Survey and the Global Findex database to explore these concerns. The potential gains of expanding social protection programmes through digitalisation are tested with simulations and distinct levels of income shocks. Additionally, two logistic regressions are employed to analyse relevant demographic variables for digital and financial inclusion. In combination with the quantitative approach, qualitative interviews were conducted with social protection experts from the South African Social Security Agency to gain insights into the impact of the digitalisation of social protection programmes. The results suggest that digitalising identification and delivery mechanisms in social protection improves equality through increased income levels. Digital access is associated with education, income, gender, population group, family size, and participation in the formal or informal sector. Financial access is linked to education level and gender. The study also identified perceived opportunities, including enhanced verification and streamlined application processes, which can improve programme effectiveness. However, integration, coordination, limited digital infrastructure, and digital illiteracy challenges may increase both digital- and financial exclusion. [ABSTRACT FROM AUTHOR]

Gender identification from video is an emerging research field that aims to automatically classify the gender of individuals based on video data. Due to the numerous applications for this task, it has received a lot of attention, including surveillance, human-computer interaction, and targeted marketing. In this study, we propose a gender identification system that utilizes the Pelican optimizer algorithm in combination with a Support Vector Machine (SVM) classifier. The Pelican optimizer is a metaheuristic algorithm inspired by the hunting behaviour of pelicans and has shown promising results in solving optimization problems. Pelican optimizer algorithm (POA) is applied to optimize the SVM parameter selection process such as kernel function. The POA algorithm searches for an optimal subset of parameters that maximizes the classification performance of the SVM model after the application of preprocessing and feature extraction techniques such as Local Binary Pattern (LBP). Finally, the selected optimized parameters otherwise known as POA-SVM classifier learns a decision boundary based on the labeled training data. The POA-SVM model is trained to distinguish between male and female samples and generalize the classification to unseen video data. Experimental evaluations are conducted using a benchmark dataset consisting of video samples with labelled gender information. The effectiveness of the suggested system is contrasted with other cutting-edge gender identification techniques. The results demonstrate the effectiveness of the Pelican Optimization Algorithm-SVM system, showing improved accuracy of 95%, and sensitivity of 94.4% at a faster recognition rate in gender classification from video data. [ABSTRACT FROM AUTHOR]

The dual five-axis machining machine is state-of-the-art equipment designed for producing thin-walled aerospace parts like skins and panels. It comprises two five-axis machine tools with mirrored synchronous motions: one bi-rotary machine for milling and the other machine for follow-up support. The high absolute accuracy of the five-axis machine tools themselves and the high synchronous motion accuracy between the two machines are both crucial to ensure machining accuracy. This paper proposes an efficient method to measure and identify geometric and dynamic errors of dual five-axis machine tools. Using the R-test combined with three measurement trajectories to measure the absolute motion error of two five-axis machine tools and the synchronous motion error between the two machines. The designed trajectories include the main rotary axis linkage conditions and can fully reflect the primary motion forms of the dual five-axis machine tool. The main position-independent geometric errors and rotational axis linkage dynamic errors are identified by converting quasi-static and dynamic measurement data from three different trajectories into a unified reference coordinate system. The entire process requires only one instrument installation, effectively avoiding time-consuming repetitive installations and calibration processes while reducing the impact of installation errors on results. The effectiveness of the proposed method for measuring and identifying the geometric and dynamic errors is analyzed combined with Monte Carlo simulation. Experiments were carried out on a dual five-axis mirror milling system (MMS) with a 5000 mm X-stroke. Both simulations and experiments confirmed that the proposed method is efficient in quickly identifying key error sources of the MMS. [Display omitted] • An efficient method to measure and identify geometric and dynamic errors during synchronous motion of the MMS is proposed. • A geometric and dynamic error model is introduced for identifying key errors using error sensitivity theory. • A 3-step measurement strategy that enables rapid separation of linkage errors of the MMS is introduced. • Both simulation and experiment show the proposed method could obtain key error sources of the MMS with high efficiency. [ABSTRACT FROM AUTHOR]

• X-radiography is an innovative tool for studying daguerreotypes sealed and housed. • X-radiography allows for the accurate and reliable typification of hallmarks. • X-radiography identifies the hallmarks on daguerreian plates without dismantling them. • Hallmarks provide information for dating, production details, and potential attribution. • X-radiography is a non-invasive technique and X-rays don't damage the daguerreotypes. X-radiography is an imaging technique widely used in the examination of works of art and heritage objects; however, no references to its application in the study of daguerreotypes have been found. The results obtained in this study demonstrate, for the first time, the usefulness of X-radiography for locating, identifying, and characterising hallmarks on daguerreotype plates. Hallmarks are of great importance for comprehending these photographic artefacts, as they can provide valuable information for dating, details on the production process, and potential attribution. In this study, four daguerreotypes were X-rayed employing two different working procedures designed with the aim of recording the hallmarks: one by means of radiographic film, and the other using a digital detector. The presented working procedures enabled the discovery of the hallmark locations on all studied daguerreotypes and their positive identification. This was able to be done without extracting the daguerreian package or handling the plate, thereby avoiding exposure to damaging environmental factors. X-radiography is thus revealed as an innovative and easily integrated tool for the study of daguerreotypes, both for the characterisation of hallmarks on the plate and for analysis of other elements and manufacturing procedures. [ABSTRACT FROM AUTHOR]

• FH screening in primary care practices are acceptable, appropriate, and feasible. • An implementation strategy package was co-developed with primary care stakeholders to improve screening for FH within their practices. • Demonstrating successful screening for FH in primary care practice will help increase the number of individuals identified with FH. • Generalization of the primary care implementation strategy to other settings will improve FH identification nationwide. We designed the Collaborative Approach to Reach Everyone with Familial Hypercholesterolemia (CARE-FH) clinical trial to improve FH screening in primary care and facilitate guideline-based care. The goal was to incorporate perspectives from end-users (healthcare system leaders, primary care clinicians, cardiologists, genetic counselors, nurses, and clinic staff) and improve translation of screening guidance into practice. We partnered with end-users to sequentially define the current state of FH screening, assess acceptability, feasibility, and appropriateness of implementing an FH screening program, and select clinically actionable strategies at the patient-, clinician-, and system-level to be deployed as a package in the CARE-FH clinical trial. Methods informed by implementation science and human centered design included: contextual inquiries, surveys, and deliberative engagement sessions. Screening for FH occurred rarely in primary care, and then only after a cardiovascular event or sometimes due to a family history of high cholesterol or early heart attack. Surveys suggested FH screening in primary care was acceptable, appropriate, and feasible. Reported and observed barriers to screening include insufficient time at patient encounters to screen, cost and convenience of testing for patients, and knowledge regarding causes of dyslipidemia. Facilitators included clear guidance on screening criteria and new therapies to treat FH. These results led to the development of multilevel strategies that were presented to end-users, modified, and then pilot tested in one primary care clinic. A refined implementation strategy package for FH screening was created with a goal of improving FH awareness, identification, and initiation of guideline-based care. https://clinicaltrials.gov/study/NCT05284513?id=NCT05284513&rank=1 Unique Identifier: NCT05284513 [Display omitted] [ABSTRACT FROM AUTHOR]

Existing feedforward, or FF, controllers are based on differentially flat models. For double-acting single-rod cylinders, more compact than the double-rod ones, flat models could be constructed either under an incompressible-flow assumption for low-pressure, slow-response applications or for a matched valve-cylinder combination. Contrasting the existing models tackling only a single nonlinear aspect like valve leakage, oil compressibility or friction, a novel FF controller, generalized for different types of valve-cylinder-pump combinations, has been developed here. It is based on the highest-order assumption of incompressible flow that sustains the piston motion. An FFPI controller has been implemented in a laboratory setup for displacement tracking against sinusoidal demands up to 9 Hz frequency. The FF parameters has been estimated by minimizing the deviation of simulation prediction from an offline experimental time-varying response. The PI gains have been selected through a stability analysis by extending Routh criteria for linearized time-variant error dynamics. Comparison against a PI-only controller with identical gains has established the energy-saving potential of the FFPI controller both working with the same variable-displacement pump. In comparison to existing nonlinear adaptive controllers, more precise and smoother responses have been obtained over wider frequency range at lower control expenses, admittedly with occasional marginal penalty in the phase variation. The FFPI controller has exhibited the shortest transient and the highest resilience against measurement noise. • Real-time energy-efficient Feedforward-PI control for electrohydraulic system. • Order-separated feedforward design for any double-acting cylinder, addressing multiple nonlinearities. • Estimated Feedforward parameter through GA-based identification. • Selected PI gains via stability analysis, extending Routh criteria for time-variant error dynamics. • Achieved precise and smooth tracking at lower control expense compared to existing nonlinear adaptive controllers. [ABSTRACT FROM AUTHOR]

The interest in the identification of animal species housed in caves or rock-shelters used as livestock pen and herding management along prehistoric and historic ages, is increasing to understand better the development of pastoral activities. In this manuscript, a method for the quantification of β-sterol/phytosterols, bile acids, hormones and hormones metabolites has been developed to determine the main pastoral activities carried out in Vallone Inferno rock-shelter (Scillato, Sicily, Italy) from Middle Neolithic to Early Middle Age. According to the result obtained, the main animals housed in the rock-shelter went gradually changing from ovicaprids in Middle Neolithic to pigs in Early Middle Age. Additionally, new proxies (progesterone/Ʃbile acids and metabolites of progesterone/Ʃbile acids) were used to detect a high hormonal activity at Early Middle Age samples related with female pig management. [Display omitted] [ABSTRACT FROM AUTHOR]

• Great progress has been made previously in MicroRNAs-guided gene regulation in plant-parasitic cyst nematodes interactions. Little, however, is known about miRNAs regulation of nematode stress response in Cucumis metuliferus, which is a relative of cultivated cucumber or melon and highly resistant to Meloidogyne incognita. • Five cmu-miR396 precursors were identified and characterized from C. metuliferus. A total of 12 genes were then predicted to be as the potential targets of cmu-miR396 and their function involved in transcription factor, metabolism, signal transduction and stress response. Moreover, three cmu-GRFs genes in roots of C. metuliferus were amplified and confirmed by RT-PCR experimently. The analysis was performed for the phylogeny and subcellular localization of cmu-GRFs protein, and the three transcription factors cmu-GRFs were confirmed as the candidate targets of cmu-miR396 through 5′RLM-RACE assays. • These data laid a foundation for further functional analyses to reveal the roles played by miR396 during the interaction between C. metuliferus and M. incognita. MicroRNAs (miRNAs), a class of non-coding small RNAs, play an important role in biotic and abiotic stresses. Previously, great progress has been made in miRNA-guided gene regulation in plant-parasitic cyst nematodes interactions. However, little is known about miRNAs regulation of nematode stress response in Cucumis metuliferus , which is a relative of cultivated cucumber or melon and highly resistant to the root-knot nematode Meloidogyne incognita. On the basis of our investigation recently, five cmu-miR396 precursors were firstly identified and characterized from C. metuliferus in this study. Subquently, a total of 12 genes were predicted to be as the potential targets of cmu-miR396 and their function involved in transcription factor, metabolism, signal transduction and stress response. Moreover, three cmu-GRFs genes with significant differential expression in roots of C. metuliferus were successfully amplified and confirmed by RT-PCR experimently. Additionally, the analysis was performed for the phylogeny and subcellular localization of cmu-GRFs protein, and the three transcription factors cmu-GRFs were confirmed as the candidate targets of cmu-miR396 through 5′RLM-RACE assays. These data laid a foundation for further functional analyses to reveal the roles played by miR396 during the interaction between C. metuliferus and M. incognita , leading to a better understanding of the mechanisms and underlying regulatory networks of miR396-mediated resistance to M. incognita in C. metuliferus. [ABSTRACT FROM AUTHOR]

The mesh reflector suffers inevitably from complicated environmental interferences during long-term on-orbit operation and consequently the reflector may deviate significantly from its initial state. It is thus crucial to identify the deformed reflector on-orbit in order to evaluate timely the mesh reflector accuracy. However, the existing reflector identification methods are difficult to achieve this object as the most of them need to install additional equipment on antenna or satellite. Aiming at realizing on-orbit mesh reflector identification, a novel identification approach based on tie cable tensions sensing (TCTS) is proposed in the paper. The TCTS method leverages real-time measurements of tie cable tensions through embedded force sensors to calculate the remaining cable tensions required to address reflector deformations. For this purpose, an optimization problem is established based on tension balance equation and then solved using a proposed iteration solution algorithm incorporating the genetic algorithm. The actual configuration of mesh reflector is then determined by all cable tensions according to the force density method and the reflector can be identified ultimately. The simulated studies on reflector identification of a mesh reflector with 8 m aperture diameter are conducted under temperature loads and cable stiffness degradations respectively. The results indicate that the TCTS method possesses high reflector identification accuracy and good anti-noise capability under different environmental interferences. Furthermore, a reflector identification method based on partial tie cable tensions sensing (PTCTS) is developed in order to reducing system complexity and cost. The sensitivity analyses of tie cable tensions are performed in order to provide a reference for location placement of force sensors in the PTCTS method. The simulated results indicate that the optimized location placement scheme can enhance significantly the reflector identification accuracy of PTCTS method. Besides, the PTCTS method exhibits equal anti-noise capability with the TCTS method. Finally, the experimental studies are conducted on reflector identification of a mesh reflector test model with 1.5 m diameter and 0.5 m height under cable stiffness degradations. Through comparing with the reflector identification results obtained by the photogrammetry method under different cable stiffness degradation degrees, the accuracy and effectiveness of the TCTS/PTCTS methods are demonstrated experimentally. • The mesh reflector is identified based on tie cable tension sensing (TCTS). • Partial tie cable tensions sensing (PTCTS) can reduce system complexity. • The TCTS/PTCTS method provides a promising way to identify mesh reflector in orbit. • The TCTS/PTCTS approach is demonstrated numerically and experimentally. [ABSTRACT FROM AUTHOR]

After the space robot captures the out-of-control spacecraft, they form a combined spacecraft. For the parameter identification problem of combined spacecraft with complex angular rate measurement noise, deep learning could provide a new solution, due to its ability to extract robust features from noisy data. However, the deep neural network (DNN) has substantial computing resource requirements, which restricts its application in spacecraft systems. To solve the above problems, an inertia matrix identification approach combining a structure optimization strategy of DNN is proposed to identify the inertia matrix in the presence of complex measurement noise using the DNN with optimized structure. The attitude dynamics of combined spacecraft are firstly derived, and the identification problem considering complex measurement noise is described. Based on the dynamics model, the construction process of DNN for accurately identifying the inertia matrix is proposed. A structure optimization strategy of DNN is then designed to optimize the structure of DNN for increasing computing efficiency. This strategy provides a whole set of solutions including the structure selection of the initial DNN, the redundant neurons pruning process, and the end-of-optimization condition. By combining the proposed construction process of DNN, the structure of the DNN is finally optimized by iterating the structured pruning and retraining step. The performance of the proposed DNN with optimized network structure is verified on a testing set with complex measurement noise. The results show that the proposed approach can accurately identify the inertia matrix of combined spacecraft, and meanwhile greatly improve the computing efficiency of DNN. [ABSTRACT FROM AUTHOR]

Solar energy is considered a sustainable energy resource for various electrochemical reactions, but its conversion efficacy is controlled by excitons transference and isolation. To increase the conversion efficacy, it is vital to restrict the recombination rate of photocarriers and boost their redox potentials. For this, S-scheme-based photocatalysts offer an effective charge migration mechanism. Herein, we have highlighted the basics of the electronic and optical properties of CdS as a potential photocatalyst using DFT simulation. Afterward, an overview of the thermodynamic kinetics for PWS has been defined. The high point of the review is the CdS-based S-scheme charge transferal route comprising of a reductive and oxidative photocatalyst having staggered band alignment with a difference in Fermi energy level for H 2 evolution. Also, the factors determining the S-scheme charge transferal route with respective validating techniques, like in/ex -situ XPS analysis, EPR, AFM, and DFT theoretical simulations, have been discussed. These characterization techniques successfully validated the S-scheme mechanism. Lastly, shortcomings of CdS-based S-scheme heterostructures that further need to be examined in this promising research arena have been outlined. [Display omitted] • Recent advances in the electronic and optical properties of CdS are revealed. • Mechanistic insights on the emergence of S-scheme mechanisms have been explored. • Various validation techniques for CdS-based S-scheme charge transferal route are delineated. • A concluding remark and future viewpoints on the CdS-based S-scheme are proposed. [ABSTRACT FROM AUTHOR]

This paper presents a method for workshop-suited identification of thermo-elastic errors of three-axis machine tools (MTs) using on-machine measurement. For this purpose, a new design for a three dimensional artifact is presented. Using the artifact, it is possible to identify all relevant geometric errors of three-axis portal milling machines in table design. The method presented is therefore suitable for the one-time identification of geometric errors caused by manufacturing inaccuracies, assembly-related deviations and wear, and for the identification of thermally induced geometry errors over a long-term period. Since the artifact is scalable in size, it can be easily adapted to different machine sizes. The presented method thus represents a universal and inexpensive possibility to measure geometric and especially thermo-elastic errors. Within the scope of this paper, an error model was established for the investigated machine type and an artifact design was derived on the basis of this model. The suitability of the artifact for identifying thermo-elastic errors has also been investigated experimentally. For this purpose, an artifact made of thermo-invariant material (Invar) was produced. This artifact was measured cyclically on a MT with three linear axes, which was exposed to a controlled thermal load in a temperature chamber. The positions of the geometric features were collected using a 3D touch trigger probe. In addition, the ambient temperatures were recorded. The results of this measurement are discussed in this paper. • A novel method for workshop-suited identification of thermo-elastic errors of three-axis machine tools is presented. • A kinematic error model is derived. • A procedure for errors identification using the artifact and a 3D touch trigger probe is developed. • Measurement uncertainty analysis. • Experimental investigation of the suitability of the method for identifying thermo-elastic errors. [ABSTRACT FROM AUTHOR]

In this paper, an effective control and parameters identification scheme was proposed for high-precision trajectory tracking of space robotic manipulators. Our proposed control method employed a linear-extended-state-observer (LESO) based model predictive control (MPC) scheme. The feedback linearization approach was utilized to develop a model predictive controller. The optimization objective function was simplified to a standard quadratic programming (QP) problem for solution. To minimize the impact of the inaccuracy in the dynamics model, a LESO was designed to estimate the disturbance, and its convergence was demonstrated. In order to improve the accuracy of dynamic model, the physical consistency of inertial parameters was constrained using linear matrix inequality (LMI) techniques. Moreover, the dynamics parameters were estimated through semidefinite programming (SDP) techniques. To validate the effectiveness of the proposed control method, it was compared with existing controllers through simulations and experiments. The results demonstrated the superior performance of the proposed scheme, highlighting its potential for achieving high-precision trajectory tracking control in space robotic manipulators. • High-precision trajectory tracking control for space manipulator is investigated. • Feedback linearization based predictive model for MPC is established. • A LESO is designed and its convergence is proven. • A physical consistency parameters identification method is presented. • Both simulations and experiments are performed for validation. [ABSTRACT FROM AUTHOR]

Cisplatin is widely used for the treatment of various types of cancer. However, cisplatin-induced nephrotoxicity (CIN) is frequently observed in patients receiving cisplatin therapy which poses a challenge in its clinical utility. Currently used clinical biomarkers for CIN are not adequate for early detection of nephrotoxicity, hence there is a need to identify potential early biomarkers in predicting CIN. In the current study, a combination of in vitro toxicodynamic (TD) modeling and untargeted global metabolomics approach was used to identify novel potential metabolite biomarkers for early detection of CIN. In addition, we investigated the protective role of cimetidine (CIM), an inhibitor of the organic cation transporter 2 (OCT2), in suppressing CIN. We first characterized the time-course of nephrotoxic effects of cisplatin (CIS) and the protective effects of CIM in a human pseudo-immortalized renal proximal tubule epithelial cell line (RPTEC), SA7K cell line. Secondly, we used a mathematical cell-level, in vitro TD modeling approach to quantitatively characterize the time-course effects of CIS and CIM as single agents and combination in SA7K cells. Based on the experimental and modeling results, we selected relevant concentrations of CIS and CIM for our metabolomics study. With the help of PCA (Principal Component Analysis) and PLS-DA (Projection to Latent Structure – Discriminate Analysis) analyses, we confirmed global metabolome changes for different groups (CIS, CIM, CIS+CIM vs control) in SA7K cells. Based on the criterion of a p-value ≤ 0.05 and a fold change ≥ 2 or ≤ 0.5, we identified 20 top metabolites that were significantly changed during the early phase i.e. within first 12 h of CIS treatment. Finally, pathway analysis was conducted that revealed the key metabolic pathways that were most impacted in CIN. [ABSTRACT FROM AUTHOR]

The polysialyltransferases ST8SIA2 and ST8SIA4 and their product, polysialic acid (polySia), are known to be related to cancers and mental disorders. ST8SIA2 and ST8SIA4 have conserved amino acid (AA) sequence motifs essential for the synthesis of the polySia structures on the neural cell adhesion molecule. To search for a new motif in the polysialyltransferases, we adopted the in silico Individual Meta Random Forest program that can predict disease-related AA substitutions. The Individual Meta Random Forest program predicted a new eight-amino-acids sequence motif consisting of highly pathogenic AA residues, thus designated as the pathogenic (P) motif. A series of alanine point mutation experiments in the pathogenic motif (P motif) showed that most P motif mutants lost the polysialylation activity without changing the proper enzyme expression levels or localization in the Golgi. In addition, we evaluated the enzyme stability of the P motif mutants using newly established calculations of mutation energy, demonstrating that the subtle change of the conformational energy regulates the activity. In the AlphaFold2 model, we found that the P motif was a buried ß-strand underneath the known surface motifs unique to ST8SIA2 and ST8SIA4. Taken together, the P motif is a novel buried ß-strand that regulates the full activity of polysialyltransferases from the inside of the molecule. [ABSTRACT FROM AUTHOR]

This study presents a novel machine learning approach for predicting the anisotropic parameters of the Yld2000–2d non-quadratic yield function using a hole expansion test. Heterogeneous stress-strain fields during the test substitute for multiple experiments required in the conventional parameter identification approach. An artificial neural network model for the parameter prediction is developed using a virtually generated training dataset composed of strains from hole expansion simulations, performed using randomly selected anisotropic parameters. The developed model predicts the Yld2000–2d parameters for AA6022-T4 based on the measured strain field from a hole expansion experiment, and the parameter results are evaluated by comparing anisotropy in uniaxial tension tests, the yield locus, and thinning variation in hole expansion test. [ABSTRACT FROM AUTHOR]

Nowadays, the growing complexities of manufacturing processes and systems make it difficult to identify the root causes of critical deviations in performance. Conventional methods often fall short in capturing the multifaceted nature of these challenges, despite a wealth of diverse untapped manufacturing data. To harness the full potential of diverse data sets and transform them into a valuable asset to guide root cause exploration, this paper presents an innovative approach that combines multimodal predictive analysis and explainable artificial intelligence (XAI) to uncover insights into system dynamics. This work contributes to a paradigm shift in industrial decision-making regarding manufacturing diagnostics. [ABSTRACT FROM AUTHOR]

This paper conducts comparative analysis of speaker identification techniques, namely Gaussian Mixture Models (GMM), Bidirectional Long Short-Term Memory (BiLSTM) neural networks, and Recurrent Neural Networks (RNN), applied to the Kazakh Speech Corpus. The study's central objective is to explore the influence of speech duration on the efficacy of speaker identification. Our results indicate that all models exhibit enhanced accuracy with increased speech duration. Furthermore, the incorporation of delta and fundamental frequency (F0) as supplementary features bolsters the performance of these models. This research contributes to the understanding of how speech duration and additional features can optimize speaker identification techniques, providing valuable insights for future developments in the field. [ABSTRACT FROM AUTHOR]

Systems, including humans and artificial agents, are involved in a set of coordinated tasks. The technology becomes intertwined with an individual's identity in daily life. The concept explored here encapsulates the extended-self idea within social identity theory. In this comprehensive review, the idea of shared identity of individuals interacting with AI systems is explored, along with methods, applications, and implications. Assuming that the transition to AI-enhanced society happens ubiquitously, in different categories (e.g., data analysis, text comprehension), realizing shared identity with AI systems is an important step in societal development. Because identity consists of personal and social experience, the social experience is reflected in global environment, and it in turn extends to the artificial environment. AI systems act as part of an individual's extended identity, allowing them to navigate digital spaces, share personal information, and interact with various services. The main advantage of shared identity with AI systems is in coordination improvement and task efficiency: people are better at managing tasks when a common identity is highlighted; the same might be assumed for the interaction of a human and AI systems. The application of such ideas is discussed within personal, social and global identities including a social norm, personification, bias identification, recommender system, social network, ethical considerations, cybersecurity, medical use, and developing a sense of global identity and propose ideas for refining AI Global policy. The implication of AI for employment and ethics is explored. [ABSTRACT FROM AUTHOR]

This paper investigates the identification of time-delay Boolean networks (TBNs) and time-delay Boolean control networks (TBCNs) via Cheng product. According to all admissible (input-)output sequences, definition on identifiability of the (TBCN) TBN is given. Two algorithms are designed to select suitable delay parameters of the TBN and TBCN, respectively. Based on these, the original systems are divided into several subsystems. Then by virtue of observability, the criteria for identifiability of the TBN and TBCN are obtained. Moreover, the corresponding constructing processes are presented to establish the internal structures of the TBN and TBCN. Finally, two illustrative examples are given to show the feasibility of the proposed methods. • Based on (input-)output data, definition on identifiability of TBNs(TBCNs) is given. • Two algorithms are designed to select suitable delay parameters of the TBN and TBCN. • By observability, the criteria for identifiability of the TBN and TBCN are obtained. • And the constructing processes are presented to establish the internal structures. [ABSTRACT FROM AUTHOR]

In this paper, a method to analyse load features utilising sample shifting technique (SST) for non-intrusive load identification (NILI) is presented and discussed. Fuzzy rules are used as the foundation for the identification logic. Voltage and current signals for electrical home appliances are acquired in order to develop their respective features. Two features like reactive power and total harmonic distortion for current (THD I), are created with the necessary computations of the samples using SST. A method based on fuzzy rules is created in order to identify different electrical equipment both for their individual as well as simultaneous running. Again, the performance of the proposed system is tested under the noisy environment while the accuracy of the system is found satisfactory. By utilising SST, the burden of computation is reduced in comparison to the other methods which are justified with the experimental results. • Only voltage and current information are used for load identification. • Sample Shifting Technique is used to extract load features. • Reactive power and THD for current are used as load features. • Fuzzy logic-based rules are applied for non-intrusive load identification. • The use of of SST assists in the extraction of load features and reduces the complexity of the hardware by eliminating the need for filters or ZCDs. • Individual load can be identified during practical multiple loads are working parallelly. • System shows robustness under noisy environment. [ABSTRACT FROM AUTHOR]

Many recent experiments studying political protests elicit subjects' beliefs and actions, before and after an information intervention, to assess the causal role of beliefs on actions. We show that unless beliefs are symmetric and unimodal, using a belief elicitation scheme that is mismatched with the research question may affect the magnitude and even reverse the sign of identified effects. We provide a simple characterization of when such a sign reversal occurs. As an example, we revisit Cantoni et al. (2019) 's influential study of whether political protests are strategic complements or substitutes. We show how their belief elicitation method allows, in theory, a novel and alternative interpretation of their results, which could have been avoided with a different method. • Political protest experiments elicit subjects' beliefs about protest participation. • Different belief elicitation schemes may recover modal or mean beliefs. • Canonical theories provide testable implications about mean beliefs only. • A mismatched scheme may fail to identify the sign of the effect of interest. • We provide theoretical results and discussions to the political protest literature. [ABSTRACT FROM AUTHOR]

In recent decades, machine learning (ML) techniques have been effectively applied for industrial process monitoring to assure safety and high-quality yield. Traditional process fault detection, identification, and diagnosis (FDI&D) approaches are insufficiently smart to address the modern complex challenges of real-time industrial chemical processes. The detection and diagnostic resolution of the traditional monitoring approach are less robust, inefficient, and produce the wrong interpretation of actual fault information. These approaches are based on a single-scale fault illustration and cannot effectively address multiple fault depiction roots. This study introduces a novel ML-aided methodological framework for industrial and manufacturing monitoring systems. The proposed ML framework is developed using Principal component analysis (PCA), Shannon information entropy (IE), wavelet transformation (WT), and signed directed graph (SDG). It includes fault detection, identification, and diagnostic propagation root-path interpretation to address the safety challenges of modern, real-time industrial chemical processes. The proposed methodological framework is validated using the Tennessee Eastman process (TEP) benchmark to highlight their performance and efficiency. The results of this study determined that the new proposed approach is more efficient in terms of accuracy, robustness, and actual propagation root cause than traditional techniques. It has a high fault detection rate (FDR), low fault alarm rate (FAR) that identifies and recognizes the actual faulty-correlated variables to establish the SDG model framework for determining the actual diagnostic propagation root path. It initially enables operators to react to unusual incidents, ensuring industrial safety, minimizing economic loss, and avoiding disasters. [Display omitted] [ABSTRACT FROM AUTHOR]

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