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Abstract Background The use of online food ordering applications is widespread; however, to date, there have been no studies on the effect of the menu calorie labeling in online food ordering applications on the consumers’ dietary habits and food choices in Saudi Arabia. Therefore, this study aimed to evaluate the response of the consumers to the menu energy-labeling on online food ordering applications in Saudi Arabia by exploring: (a) the consumers’ preference and frequency of ordering from online food applications; (b) the consumers' self-reported knowledge, awareness, and interest towards menu calorie information on online food ordering applications; (c) the impact of menu calorie information on online food ordering applications on consumers’ food choices. Methods This is a cross-sectional study conducted between October and November of 2022. A total of 419 participants were recruited using an online questionnaire. Results The findings showed that most participants (59%) preferred to order using online food ordering applications. Saving time and effort are the main reasons for using online food ordering applications (61%). Nearly half of the participants (45%) were interested in viewing calorie information on online food ordering applications menus and (47%) did notice calorie information displayed on the menu when ordering from an online food ordering application. Calorie information in online food ordering applications menus is primarily utilized to monitor intake for weight maintenance (19%). The ability to calculate energy requirements and interest in viewing calorie information on online food ordering applications menus were linked to younger age and a higher level of education (p

Monitoring wind turbine performance is vital for ensuring wind turbines’ safe, efficient, and cost-effective operation over time. Using principal component analysis (PCA), k-means clustering for labeling, and an ensemble classifier for finding outliers, this study suggests a new way to find anomalies in wind turbines. The primary objective is to improve the precision of anomaly detection in wind turbines by leveraging machine-learning techniques. The proposed methodology utilizes the output of the PCA-Kmeans model to label supervisory control and data acquisition (SCADA) data. Furthermore, a stacking ensemble classifier is employed to refine the model’s precision. Our proposed model achieved a classification accuracy of 99%, which is a significant improvement compared to existing approaches. The significance of this study lies in its potential to enable more efficient wind turbine operation by identifying and resolving anomalies that may reduce their performance. This can ultimately contribute to achieving a sustainable and renewable energy future.

Printed mathematical expression recognition (MER) models are usually trained and tested using LaTeX-generated mathematical expressions (MEs) as input and the LaTeX source code as ground truth. As the same ME can be generated by various different LaTeX source codes, this leads to unwanted variations in the ground truth data that bias test performance results and hinder efficient learning. In addition, the use of only one font to generate the MEs heavily limits the generalization of the reported results to realistic scenarios. We propose a data-centric approach to overcome this problem, and present convincing experimental results: Our main contribution is an enhanced LaTeX normalization to map any LaTeX ME to a canonical form. Based on this process, we developed an improved version of the benchmark dataset im2latex-100k, featuring 30 fonts instead of one. Second, we introduce the real-world dataset realFormula, with MEs extracted from papers. Third, we developed a MER model, MathNet, based on a convolutional vision transformer, with superior results on all four test sets (im2latex-100k, im2latexv2, realFormula, and InftyMDB-1), outperforming the previous state of the art by up to 88.3%.

Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence, it is performed manually to a large extent. Even though full automation of such a process is difficult, semi-automation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75 % of the labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.

The detection of wet snow by satellite imaging is currently done in an unsupervised way and lacks quantitative evaluation due to the difficulty of collecting ground truths in extreme environments. In this article, we propose to take into account information associated with a physical model to label satellite data for the purpose of supervised learning of snow properties using synthetic aperture radar (SAR) imagery. This dataset is constructed from Sentinel-1 SAR images, augmented with topographic information obtained from a digital elevation model. The labeling of this data is done at the scale of the Northern Alps using the CROCUS physical snow model. Then, we trained, over 13 combinations of labeled dataset, a wide range of machine learning models to quantitatively identify the most relevant learners for the wet snow detection task. The results demonstrate consistency among the different algorithms, with significant improvement observed when incorporating polarimetric combinations and topographic orientation data in the input of the model. The best algorithmic solution trained on this dataset is evaluated by comparing the obtained wet snow map over a validation area in the French massif of the Grandes Rousses with the existing Copernicus products, fractional snow cover, and SAR wet snow. We also compare the temporal results obtained at one meteorological station located in the test area. The results show a better representation of wet snow during the melting period using the supervised learning approach, as well as a reduction in areas classified as wet during the winter season.

The halopyrimidine 5-bromo-2′-deoxyuridine (BrdU) is an exogenous marker of DNA synthesis. Since the introduction of monoclonal antibodies against BrdU, an increasing number of methodologies have been used for the immunodetection of this synthesized bromine-tagged base analogue into replicating DNA. BrdU labeling is widely used for identifying neuron precursors and following their fate during the embryonic, perinatal, and adult neurogenesis in a variety of vertebrate species including birds, reptiles, and mammals. Due to BrdU toxicity, its incorporation into replicating DNA presents adverse consequences on the generation, survival, and settled patterns of cells. This may lead to false results and misinterpretation in the identification of proliferative neuroblasts. In this review, I will indicate the detrimental effects of this nucleoside during the development of the central nervous system, as well as the reliability of BrdU labeling to detect proliferating neuroblasts. Moreover, it will show factors influencing BrdU immunodetection and the contribution of this nucleoside to the study of prenatal, perinatal, and adult neurogenesis. Human adult neurogenesis will also be discussed. It is my hope that this review serves as a reference for those researchers who focused on detecting cells that are in the synthetic phase of the cell cycle.

AbstractThis paper empirically and theoretically investigates the syntax of split topicalization in Jordanian Arabic, a previously undiscovered phenomenon. The results of a large-scale acceptability judgment task (n = 463) reveal that Jordanian Arabic displays split topicalization of various categorial types. Building on the labeling framework, the remnant movement analysis and the idea that there exists an IP-internal focus position below T and above the vP phase, I propose a unified analysis of all the categorial types of ST in Jordanian Arabic, where ST is reduced to remnant movement of a constituent (NP, VP, AP, etc.), from which a subconstituent has undergone focus movement to an IP-internal focus position. The analysis captures all cases of ST in Jordanian Arabic, such as splitting with ditransitives. It accounts for the fact that ST in JA is only grammatical with bridge-contour intonation: the intonation is read off the syntactic structure in which functional projections such as TopP and FocP are encoded. The analysis provides insights into the structure of the left periphery of the vP phase: it lends support to the view that this area could have a more elaborate structure than previously thought, parallel to that of the left periphery of the CP phase.

In Argentina, a nation committed to prioritizing food quality, there is a pressing necessity to fortify quality control systems and establish stringent standards ensuring food safety. This study aims to scrutinize commercial samples of Origanum vulgare L. in the province of Jujuy, Argentine Republic, with the objective of assessing their authenticity and overall quality. Both macroscopic and microscopic techniques were employed to analyze composition and verify identity. Simultaneously, a thorough examination of product labels was conducted to ensure compliance with food regulations. Ethnographic techniques, including merchant interviews, were utilized to gather comprehensive insights. The findings reveal considerable variability in the composition of "oregano" samples, with instances of foreign elements such as stones and dust detected in some cases. Microscopic identification unequivocally confirmed the presence of contaminating elements. Notably, the majority of industrial products adhered to labeling standards, while a mere 10% of artisanal samples achieved similar compliance. The study also unveiled diverse culinary and medicinal applications for the examined oregano samples. These discoveries underscore the imperative for heightened oversight to guarantee the quality and purity of oregano products. The correlation between microscopic results and previous studies bolsters the reliability of this analytical approach. In conclusion, the study emphasizes the paramount importance of instituting policies and programs that advance food security. The proposed methodology emerges as an effective means to enhance quality control in the marketing of plant products in the province. [ABSTRACT FROM AUTHOR]

Examples of labeling polysaccharides at hydroxyl groups are described in this paper, which are especially in demand for molecules with a blocked reducing end. The protocols presented are suitable for the microscale synthesis of labeled polysaccharides that do not require a chromatography step for isolation. Examples of hydroxyl labeling include (1) direct modification with fluorescein isothiocyanate; (2) reaction with a fluorescein-dichlorotriazine derivative; (3) reaction with biotin-dichlorotriazine; (4) indirect two-step modification (given for glycosphingolipid) with glutaric anhydride followed by amidation with aminospacered BODIPY or SuCy5. The labeling of carboxyl groups of hyaluronic acid with BODIPY is also described. The staining of plant tissue sections with biotinylated polysaccharide versus being fluorescein labeled is compared. [ABSTRACT FROM AUTHOR]

The continuous developments in information technologies have resulted in a significant rise in security concerns, including cybercrimes, unauthorized access, and cyberattacks. Recently, researchers have increasingly turned to social media platforms like X to investigate cyberattacks. Analyzing and collecting news about cyberattacks from tweets can efficiently provide crucial insights into the attacks themselves, including their impacts, occurrence regions, and potential mitigation strategies. However, there is a shortage of labeled datasets related to cyberattacks. This paper describes CybAttT, a dataset of 36,071 English cyberattack-related tweets. These tweets are manually labeled into three classes: high-risk news, normal news, and not news. Our final overall Inner Annotation agreement was 0.99 (Fleiss kappa), which represents high agreement. To ensure dataset reliability and accuracy, we conducted rigorous experiments using different supervised machine learning algorithms and various fine-tuned language models to assess its quality and suitability for its intended purpose. A high F1-score of 87.6% achieved using the CybAttT dataset not only demonstrates the potential of our approach but also validates the high quality and thoroughness of its annotations. We have made our CybAttT dataset accessible to the public for research purposes. [ABSTRACT FROM AUTHOR]

Comparative assessment enables consumers to make an informed choice and use high-quality and safe sunflower oil, which has a positive effect on their health and economic well-being. Vegetable oils, especially sunflower, are a popular source of dietary fat in economically developed countries, promoting heart and brain health. They contain essential fatty acids useful for cell growth and brain development. Sunflower oil is also the most widespread type of vegetable oil in Ukraine. A healthy lifestyle requires quality food. The issue of assessing the quality of sunflower oil is timely and relevant. The purpose of our research was to assess the quality of sunflower oil samples of leading producers in accordance with the requirements of current regulatory documentation and the declared labeling. Based on the survey, it was determined which types of sunflower oil are preferred by consumers. These samples were chosen as research objects. At the first stage of the research, the appearance of the package and the quality of the information provided on the label were evaluated. Then the quality of the samples was assessed according to organoleptic and physicochemical parameters. The assessment of the labeling and quality of sunflower oil showed that the information on the labels meets the requirements for mandatory information about the food product. The organoleptic characteristics of the oil samples meet the requirements of the State Standard of Ukraine 4492 "Sunflower oil. Specifications". Some samples have an increased moisture and volatile content, which can affect the stability and quality of the product during storage. However, the iodine value of all samples is within acceptable limits. Acid and peroxide numbers also do not exceed the established norms. The results of the research can be useful both for consumers when choosing high-quality oil, and for producers in order to improve product quality. [ABSTRACT FROM AUTHOR]

Summarization aims at discovering a small set of typical subsequences (patterns) in the given long time series that represent the whole series. Further, one can implement unsupervised labeling of the given time series by assigning each subsequence a tag that corresponds to its most similar pattern. In the previous research, we developed the PSF (Parallel Snippet-Finder) algorithm for the time series summarization on GPU, where a snippet is the given-length subsequence, which is similar to many other subsequences w.r.t. the bespoke distance measure MPdist. However, PSF is limited by the demand that the snippet length be predefined by a domain expert. In this article, we introduce the novel parallel algorithm PaSTiLa (Parallel Snippet-based Time series Labeling) that discovers snippets and produces the labeling of the given time series on an HPC cluster with GPU nodes. As opposed to its predecessor, PaSTiLa employs the automatic selection of the snippet length from the specified range through our proposed heuristic criterion. In the experiments on labeling quality over time series from the TSSB (Time Series Segmentation Benchmark) dataset, PaSTiLa outperforms state-of-the-art segmentation-based competitors in average score. In the case of long-length time series (typically more than 8–10 K points), PaSTiLa outruns the rivals. Finally, over the million-length time series, our algorithm demonstrates a close-to-linear speedup. [ABSTRACT FROM AUTHOR]

Purpose: To explore how boxed warning (BW) information fits within the context of prescribers' overall treatment decision‐making and communication with patients. Methods: In‐depth interviews (N = 52) were conducted with primary care providers and specialists. Participants were presented with one of two prescribing scenarios: (1) estrogen vaginal inserts to treat vulvovaginal atrophy (VVA) associated with menopause; or (2) direct‐acting antivirals (DAA) to treat chronic hepatitis C virus infection (HCV). The semi‐structured interviews explored participants' treatment decision‐making within the scenario, reactions to current prescribing information for a product within the FDA‐approved drug class, as well as their perceptions of BWs generally. Results: Across scenarios, providers described that the BW is only one of several factors that influence treatment decision‐making. In the VVA scenario, symptom severity, family history, and experience with nonprescription drugs were raised as common factors that influence prescribing considerations; compared to comorbid infections, viral load, and HCV genotype in the HCV scenario. Perceptions of the DAA BW were generally positive or neutral, as many participants found the information important and appropriate. The VVA BW was viewed less favorably, with many participants stating the BW overstates the risk for this drug. Conclusions: Findings suggest that BWs are one of several factors that influence providers' treatment decisions, and BW influence largely depends on context. Providers across scenarios expressed notable differences in their perceptions of the risk information provided in the presented BWs; however, across scenarios participants expressed consideration of how patients may perceive the BW. [ABSTRACT FROM AUTHOR]

(1) Background: The morphology of the pelvic cavity is important for decision-making in obstetrics. This study aimed to estimate the accuracy and reliability of pelvimetry measures obtained when radiologists manually label anatomical landmarks on three-dimensional (3D) pelvic models. A second objective was to design an automatic labeling method. (2) Methods: Three operators segmented 10 computed tomography scans each. Three radiologists then labeled 12 anatomical landmarks on the pelvic models, which allowed for the calculation of 15 pelvimetry measures. Additionally, an automatic labeling method was developed based on a reference pelvic model, including reference anatomical landmarks, matching the individual pelvic models. (3) Results: Heterogeneity among landmarks in radiologists' labeling accuracy was observed, with some landmarks being rarely mislabeled by more than 4 mm and others being frequently mislabeled by 10 mm or more. The propagation to the pelvimetry measures was limited; only one out of the 15 measures reported a median error above 5 mm or 5°, and all measures showed moderate to excellent inter-radiologist reliability. The automatic method outperformed manual labeling. (4) Conclusions: This study confirmed the suitability of pelvimetry measures based on manual labeling of 3D pelvic models. Automatic labeling offers promising perspectives to decrease the demand on radiologists, standardize the labeling, and describe the pelvic cavity in more detail. [ABSTRACT FROM AUTHOR]

This paper examines free object order alternation in ditransitives, focusing on Slovenian. It is shown that neither a scrambling nor a base generation analysis is fully satisfactory. A new analysis based on the Labeling Algorithm is proposed, where it is argued that the introduction of a second object creates an ambiguous labeling scenario ({NP,VP}), which has two equivalent resolutions: (i) movement of the VP with the first object inside, or (ii) movement of the second object. This is shown to derive both free object order in the general case and the restrictions on object order in select contexts, as due to the specifics of the VP-movement analysis it is possible for selectional restrictions to filter out either derivation (i) or (ii). [ABSTRACT FROM AUTHOR]

This paper discusses the concept of weak head in Chomsky's (2015. Problems of projection: Extensions. In Elisa Di Domenico, Cornelia Hamann & Simona Matteini (eds.), Structures, strategies and beyond: Studies in honour of Adriana Belletti, 3–16. Amsterdam: John Benjamins) sense from a typological perspective. This paper first establishes a novel generalization that large-scale pied-piping is available in a language only if the language has indeterminate pronouns in Kuroda's (1965. Generative grammatical studies in the Japanese language. Cambridge, MA: Massachusetts Institute of Technology dissertation) sense and the clause to be pied-piped is head-final. To deduce this generalization, this paper first offers a labeling theoretic account of large-scale pied-piping. It then proposes that weak heads are (i) heads that have unvalued features and (ii) morpho-phonologically weak. It is shown that the generalization is deduced from this conception of weak heads, given Inaba's (2011. The morphosyntax of constituent ordering patterns. The Hiroshima University Studies, Graduate School of Letters 71. 43–72) generalization that head-final complementizers are generally affixal. This paper further argues that the proposed conception of weak heads also allows us to deduce Agree from Minimal Search, which is a third factor principle according to Chomsky (2013. Problems of projection. Lingua 130. 33–49), hence minimizes UG. [ABSTRACT FROM AUTHOR]

This paper proposes a general multi-dimensional derivation model under a free-MERGE system. MERGE indicates the derivational dimensions that participate the computation. Set-MERGE is considered as an operation that keeps the two merge-mates in the same dimension, and Pair-MERGE sends the two merge-mates to two different dimensions. Each dimension has its own Labeling Algorithm (LA); the LA inside one dimension ignores syntactic objects merged from other dimensions. The resulting multi-dimensional structure will then be transferred to the Conceptual-Intentional (C-I) interface. A syntactic object resulted from Pair-MERGE will be interpreted in terms of coordination relation or modification relation. Different possible labelings in a multi-dimensional structure gives rise to different modification possibilities at C-I. C-I can see the competition of different labels, and will exhaust every possibility to decide which labeling gives rise to an appropriate interpretation. [ABSTRACT FROM AUTHOR]

We explore the possible deleterious lifelong impacts for youth who serve stints of incarceration in adult jails or prisons. Our study uses a sample of all youth ages 16 and 17 arrested in New York State in 1987 and follows their criminal careers for 24 years. New York was selected as the state processed, not just some, but all youth of this age as adults, allowing us to overcome issues of selection bias and to use natural variation to create a propensity score matched sample to compare similar youth who either were, or who were not, subject to this punishment. Findings reveal that youth who spent time in an adult jail or prison recidivate more often, more quickly, and commit more total offenses. We also find that being offered youthful offender status, a status that removes the public stigma of a criminal record, reduces recidivism, regardless of the incarceration experience. Our study is situated in theories of deterrence, social learning, and labeling, and we apply our findings to greater societal implications of subjecting youth to punishments traditionally reserved for mature adults. [ABSTRACT FROM AUTHOR]

Let G be a graph with q edges. A graph G is called even vertex odd mean graph if there exist an injective labeling f : V(G) → {0, 2, 4, 6, ..., 2q} with an induced edge labeling f* : E(G) → {1,3,5, ..., 2q-1} such that for each edge uv, f*(uv) = f(u)+f(v)/2 is bijection. In this paper we obtain sufficient conditions for certain uniform theta graphs to be even vertex odd mean graphs. [ABSTRACT FROM AUTHOR]

We examine three constructions across several languages in which a mass noun is embedded in what appears to be a count environment, but the construction as a whole remains mass. We argue that the discussed phenomena—"Q-noun" constructions like lots of water, bare measure constructions like kilos of sugar, and pluralised mass nouns in languages like Greek and Persian—all involve portioning-out of the embedded mass denotation. We provide a structural account of portioning out and propose structures that derive both mass and count portioning out. Adopting an overlap-based approach to the mass/count distinction (e.g. Landman 2011; Rothstein 2011; Khrizman et al. 2015; Landman 2016) we provide a compositional semantics for the proposed structures. The examined phenomena all share an inference of large quantity or abundance that, we argue, cannot be reduced to the lexical meaning of the portioning-out expression, nor to a multiplicity inference contributed by plural morphology. We show that our cases of mass portioning-out involve a total order ≤ on portion size and propose to analyse the abundance inference in terms of an uninformativity-based Quantity implicature, following the analysis of the positive form (Mary is tall) in Rett's (2015) approach to adjectival gradability. [ABSTRACT FROM AUTHOR]

Background: The use of online food ordering applications is widespread; however, to date, there have been no studies on the effect of the menu calorie labeling in online food ordering applications on the consumers' dietary habits and food choices in Saudi Arabia. Therefore, this study aimed to evaluate the response of the consumers to the menu energy-labeling on online food ordering applications in Saudi Arabia by exploring: (a) the consumers' preference and frequency of ordering from online food applications; (b) the consumers' self-reported knowledge, awareness, and interest towards menu calorie information on online food ordering applications; (c) the impact of menu calorie information on online food ordering applications on consumers' food choices. Methods: This is a cross-sectional study conducted between October and November of 2022. A total of 419 participants were recruited using an online questionnaire. Results: The findings showed that most participants (59%) preferred to order using online food ordering applications. Saving time and effort are the main reasons for using online food ordering applications (61%). Nearly half of the participants (45%) were interested in viewing calorie information on online food ordering applications menus and (47%) did notice calorie information displayed on the menu when ordering from an online food ordering application. Calorie information in online food ordering applications menus is primarily utilized to monitor intake for weight maintenance (19%). The ability to calculate energy requirements and interest in viewing calorie information on online food ordering applications menus were linked to younger age and a higher level of education (p < 0.05). Conclusions: Overall, consumers showed limited awareness and interest towards the menu calorie information displayed in the online food ordering applications. These findings highlight the importance of developing public health programs to increase public awareness about calorie labeling on menus to aid in the success and effectiveness of the calorie labeling in online food ordering applications as a tool to promote balanced energy intake. Further research is needed to understand the most effective way to deliver calorie information to consumers on an online food ordering application. [ABSTRACT FROM AUTHOR]

Acetaminosalol labeling reaction with technetium‐99m was optimized, and the radiocomplex was obtained in a high radiochemical yield of 98.9 ± 0.6% and high stability (>30 h). The tracer was characterized, and its binding to the PPARγ receptor was assessed in silico. To reduce radiation exposure to non‐target organs and increase accumulation in the colon, the tracer was formulated as pH‐sensitive microspheres with a mean particle size of 201 ± 2.1 μm, a polydispersity index of 0.18, a 25.3 ± 3.6 zeta potential, and 98.6 ± 0.33% entrapment efficiency. The system suitability was assessed in vivo in normal and ulcerative rats, and the biodistribution profile in the colon showed 56.5 ± 1.4% localization within 4 h. Blocking study suggested the selectivity of the tracer to the target receptor. Overall, the reported data encouraged the potential use of the labeled microspheres to target ulcerative colitis. [ABSTRACT FROM AUTHOR]

This scientific commentary discusses the role of soil nutrients in the drought responses of trees. The article highlights the sensitivity of forest trees to climate change and anthropogenic activities, such as increased nitrogen deposition. It emphasizes the need to understand the mechanisms of carbon and nitrogen cycling in forests and their implications for tree drought responses. The commentary also presents findings from a study on sessile oak and Scots pine seedlings, which showed that prior fertilization can lead to faster tree mortality during drought, particularly in sessile oak. The study suggests that nutrient availability can either intensify or mitigate drought stress responses in trees, depending on factors such as tree species and the amount/type of fertilizer added. The authors call for further research to better understand the complex dynamics of nutrient availability and drought resilience in trees, as well as potential mitigation measures through adapted forest management. [Extracted from the article]

This is a school-based intervention study to promote food label understanding among early adolescents. The researcher assessed the existing nutrition knowledge, attitude, and understanding of food labels. Based on Bandura's social cognitive theory, an educational program was developed. The change was accessed from pre- and post-test intervention scores, using questionnaire technique. The sample comprised of 490 children. The developed program was implemented on an experimental group of 250 students. The control group consisted of 240 students. The test scores increased for nutrition knowledge from 2.62 to 8.24, attitude towards information from 20.41 to 29.92, practice of using information from 20.63 to 28.24, and understanding from 1.24 to 3.75, whereas no improvement in scores was observed in control group. [ABSTRACT FROM AUTHOR]

Background: Nearly two million adults in the US currently live with an Opioid Use Disorder (OUD) diagnosis. Recent efforts have encouraged and facilitated widespread adoption of empirically supported medications for opioid use disorder (MOUD), yet MOUD and OUD behavioral health interventions remain dramatically underutilized. Fear of discrimination and judgment, compounded by systemic and regulatory barriers, hinder individuals' access to specialty treatment.Objectives: The goal of the current study was to (1) reveal how perspectives toward OUD treatment may differ across medication types, program settings, and drug use history; (2) address systemic and regulatory components that potentially foster and propagate positive or negative attributions to OUD; and (3) understand how experiences reduce patients' willingness to pursue and/or maintain long term treatment.Methods: Twenty-four adults engaged in buprenorphine treatment at two outpatient addiction treatment centers participated in in-depth, qualitative interviews between 2019 and 2020 in Providence, Rhode Island.Results: Thematic analysis revealed negative attributions toward OUD across all participants. Three key themes developed from the coding and analysis: (1) differential perceptions of therapeutic medications (2) negative perceptions of treatment programs and (3) perceptions of drugs and people who use drugs.Conclusions: Stigmatizing language remains a major public health issue that needs to be addressed to facilitate treatment for individuals for OUD and other drug use disorders. Incorporating strategies targeting labeling across medication types, program settings, and drug use may improve treatment outcomes by reducing the inaccurate beliefs surrounding OUD and connecting patients to evidence-based support. [ABSTRACT FROM AUTHOR]

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In this article, we analyze five reduced structures in Italian that display morphological agreement between their past participle and their internal argument. Three of the five structures have full illocutionary force despite lacking the middle field and the left periphery. We explain this fact (and the differences with the two remaining participial structures) by extending to object agreement cases Chomsky's (2019) hypothesis that clauses are exocentric but can be labeled by a mechanism of feature sharing. This goes against the hypothesis that all reduced structures interpreted as clauses must be elliptical. [ABSTRACT FROM AUTHOR]

Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes. [ABSTRACT FROM AUTHOR]

Examples of labeling polysaccharides at hydroxyl groups are described in this paper, which are especially in demand for molecules with a blocked reducing end. The protocols presented are suitable for the microscale synthesis of labeled polysaccharides that do not require a chromatography step for isolation. Examples of hydroxyl labeling include (1) direct modification with fluorescein isothiocyanate; (2) reaction with a fluorescein-dichlorotriazine derivative; (3) reaction with biotin-dichlorotriazine; (4) indirect two-step modification (given for glycosphingolipid) with glutaric anhydride followed by amidation with aminospacered BODIPY or SuCy5. The labeling of carboxyl groups of hyaluronic acid with BODIPY is also described. The staining of plant tissue sections with biotinylated polysaccharide versus being fluorescein labeled is compared.

ABSTRACTInnovation in agriculture has been essential in improving productivity of crops and forages to support a growing population, improving living standards while contributing toward maintaining environment integrity, human health, and wellbeing through provision of more nutritious, varied, and abundant food sources. A crucial part of that innovation has involved a range of techniques for both expanding and exploiting the genetic potential of plants. However, some techniques used for generating new variation for plant breeders to exploit are deemed higher risk than others despite end products of both processes at times being for all intents and purposes identical for the benefits they provide. As a result, public concerns often triggered by poor communication from innovators, resulting in mistrust and suspicion has, in turn, caused the development of a range of regulatory systems. The logic and motivations for modes of regulation used are reviewed and how the benefits from use of these technologies can be delivered more efficiently and effectively is discussed.

The increasing reliance on electric vehicle (EV) charging in buildings requires balancing the load from other building systems to support the new demand. This paper uses a study case in a Near-Zero Energy Building (NZEB) educational facility located in the Brazilian Amazon to verify how much the energy efficiency (EE) measures would improve the existing ratings of the building and supply the expansion of EV demand. A comprehensive building energy load and energy performance analysis were conducted in four steps, following the mandatory Brazilian requirements for EE in public buildings, using measured data, computer modeling, and thermoenergetic analyses using OpenStudio version 1.1.0 and EnergyPlus software version 9.4.0. First, the EE retrofit measures were proposed and evaluated, targeting the air conditioning and lighting systems. Subsequently, an equation was elaborated to indicate the maximum level of energy consumption that could be increased without compromising the building’s energy performance and NZEB classification. Finally, Open DSS software version 10.0.0.2 was used to simulate the increased availability of EV charging after the retrofit. With the proposed retrofit, the building improved the EE ratings by three levels, and the percentage of the NZEB rating increased by 33.28%. These measures also increased the EV charging load by 20%, maintaining the maximum EE level and the NZEB classification, although EV maximization reduced self-sufficiency by 9.78% compared to the retrofit-only scenario.

The growth of electric vehicles (EVs) and their integration into existing and future buildings bring new considerations for energy efficiency (EE) and balance when combined with renewable energy. However, for buildings with an energy efficiency label, such as Near Zero Energy Building (NZEB) or Positive Energy Building (PEB), the introduction of EVs may result in the declassification of the EE label due to the additional energy required for the charging infrastructure. This underscores the increasing relevance of demand-side management techniques to effectively manage and utilize energy consumption and generation in buildings. This paper evaluates the influence of electric vehicle (EV) charging on NZEB/PEB-labeled buildings of the Brazilian Building Labeling Program (PBE Edifica). Utilizing on-site surveys, computational modeling, and thermos-energetic analysis with software tools such as OpenStudio v. 1.1.0 and EnergyPlus v. 9.4.0, an energy classification was conducted in a building in the city of Belem, State of Para, Brazil. Subsequently, power flow simulations employing probabilistic models and Monte Carlo approaches were executed in the OpenDSS software v. 10.0.0.2 to examine the impact of EV integration, both with and without the implementation of demand-side management techniques. Analyses using the labeling methodology demonstrated that the building has EE level C and NZEB self-sufficiency classification. The assessment of the impact of EV integration on the building’s total energy consumption in the base (current) scenario was carried out in two scenarios, with (2) and without (1) supply management. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 resulted in a smaller increase in energy consumption of 40.50%, and guaranteed the return of the NZEB class lost in scenario 1, but it was not enough to return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies, as the findings show that the two scenarios present a difference of 41.55% in energy consumption. Nonetheless, these strategies are not enough if other consumption restrictions or energy efficiency measures are not applied to other building systems.

One of the main public health issues that has recently been observed in a greater number of children is being overweight. The cause certainly lies in the decreasing physical activity of children, but mostly in their eating habits. Soft drinks are recognized as the most significant contributor to body overweight due to high sugar content; thus, as a result of numerous campaigns, part of the sugar is replaced by artificial sweeteners (ASs). Despite their advantage due to their low caloric value, WHO recommends that they should not be used to achieve weight control or as prevention for reducing the risk of non-communicable diseases, as there is no evidence of their effectiveness. Apart from beverages, artificial sweetener combinations are also added to a variety of “low fat” and “high protein” food products, which are highly favored especially among the young population. Therefore, it is necessary to take care of the cumulative intake. The conducted study included a survey of 323 parents of children aged 1–14 years, as well as an analysis of the AS content in the products most often consumed by the respondents. The results of the survey show that a large part of children (40%) aged 3–14 often consume soft drinks. Different products (soft drinks, juices/nectars, syrups) were sampled based on the respondents’ responses, and an analysis showed that 54% of them contained one or more ASs. In addition, the survey indicated parents’ lack of information about the presence of AS in products, as 51% of parents declared that they do not read the declarations of the products they buy. It is necessary to persist in consumer education and changes in dietary preferences and habits, especially among children.

Radiolabeled peptides are valuable tools for diagnosis or therapies; they are often radiofluorinated using an indirect approach based on an F-18 prosthetic group. Herein, we are reporting our results on the F-18 radiolabeling of three peptides using two different methods based on click reactions. The first one used the well-known CuAAC reaction, and the second one is based on our recently reported hetero-Diels–Alder (HDA) using a dithioesters (thia-Diels–Alder) reaction. Both methods have been automated, and the 18F-peptides were obtained in similar yields and synthesis time (37–39% decay corrected yields by both methods in 120–140 min). However, to obtain similar yields, the CuAAC needs a large amount of copper along with many additives, while the HDA is a catalyst and metal-free reaction necessitating only an appropriate ratio of water/ethanol. The HDA can therefore be considered as a minimalist method offering easy access to fluorine-18 labeled peptides and making it a valuable additional tool for the indirect and site-specific labeling of peptides or biomolecules.

The amalgamation of the Internet of Things (IoT) and federated learning (FL) is leading the next generation of data usage due to the possibility of deep learning with data privacy preservation. The FL architecture currently assumes labeled data samples from a client for supervised classification, which is unrealistic. Most research works in the literature focus on local training, update receiving, and global model updates. However, by principle, the labeling must be performed on the client side because the data samples cannot leave the source under the FL principle. In the literature, a few works have proposed methods for unlabeled data for FL using "class-prior probabilities" or "pseudo-labeling". However, these methods make either unrealistic or uncommon assumptions, such as knowing class-prior probabilities are impractical or unavailable for each classification task and even more challenging in the IoT ecosystem. Considering these limitations, we explored the possibility of performing federated learning with unlabeled data by providing a clustering-based method of labeling the sample before training or federation. The proposed work will be suitable for every type of classification task. We performed different experiments on the client by varying the labeled data ratio, the number of clusters, and the client participation ratio. We achieved accuracy rates of 87 % and 90 % by using 0.01 and 0.03 of the truth labels, respectively. [ABSTRACT FROM AUTHOR]

Research suggests police stops are associated with delinquency and arrest in adolescence, but limited research has examined the extent to which these associations vary by intersectional identities. The labeling and life-course perspectives argue police stops may increase later delinquency/arrest and that these relationships may vary according to when an individual is first stopped. Critical race feminist perspectives suggest Black girls may be especially likely to experience negative consequences following a police stop. The goal of the current study was to examine the relationships between police stops in childhood and adolescence and delinquency and arrest in adolescence, as well as identify how these relationships vary by gender and race. Results suggest the relationships between police stops and subsequent delinquency and arrest differ across gender and race. Further, Black girls experience the greatest increase in probability of arrest following a stop in adolescence. Findings have important implications for research, policy, and practice. [ABSTRACT FROM AUTHOR]

This study highlights the experiences of Turkish asylum seekers who have been persecuted, victimized, stigmatized, labeled, discriminated against, prosecuted, segregated, and targeted following the Turkish government crackdown after the failed coup attempt in July 2016 and settled in the United States, Canada, and European countries. Data were collected through in-depth interviews, field notes, and documentation, conducted, and subsequently analyzed using the principles of Transcendental Phenomenological Analysis. After examining the most frequently repeated concepts in the interviews, the following three themes emerged: 1) labeling, 2) targeting, and 3) social pressure and psychological status. This study showed the depths of the psychological pressure, discrimination, torture, and humiliation that detained and jailed participants who were excluded from society endured. The paper ends with several recommendations for Turkey to rejoin the community of nations protecting, fulfilling, and respecting human rights. [ABSTRACT FROM AUTHOR]

Numbering is a vital process in museums; it provides a unique identification for each object. Without numbering, objects cannot be tracked or utilized in a museum. Sometimes adding numbers to artifacts can be very challenging due to artifacts diversity in shape and materials. Numbering must be durable as it holds the identity of the artifact as it allows the curator to track it easily and retrieve information quickly. This number is connecting the object with its own other information such as; documentation, description and current condition of the object. A range of new numbering techniques appeared and practiced in many museums beside the traditional ones. This paper will point out and focus on one of new numbering method developed and planning to be applied for the first time on glass mount that hold papyrus in the Grand Egyptian museum through using CO2 marker machine and U.V laser printer. Both methods are tested on glass mount and results compared to select the better method to be applied. It was found that both methods provided sustainable numbering on papyrus mount. Numbers made by these two methods are durable and can't be removed from glass but, one approach was preferred over the other as it will be revealed in this paper. [ABSTRACT FROM AUTHOR]