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A growing interest has been noted among both industry operatives and consumers in cell-based meat (CBM), as visible in the increasing investment into this technology by major food industry corporations. However, in almost all countries worldwide, there is a lack of clear legislation with regard to the labeling of such products. The aim of the article is to collect and review current legal regulations concerning the international approval and labeling of these types of products. In the manuscript, we review and analyze the legal situation of CBM and its labeling in countries from 4 different continents (EU members, the UK, the USA, Canada, Australia and New Zealand, Japan, Singapore and Israel). Aside from Singapore, no other country has approved CBM for placement on the market. The US has reached an agreement and established regulatory frameworks on CBM matters, where both the USDA and the FDA will be the control institutions. Within the European Union, CBM products will be evaluated under the Novel Food Regulation. The most anticipated process in other countries is the evaluation of CBM under the legislation on novel foods and subsequent amendments. Since local laws are still being developed, special care should be taken by the policymakers to avoid implementing local laws which could cause a negative approach to the technology by the consumers. [ABSTRACT FROM AUTHOR]

There are numerous advantages afforded to physically attractive individuals, including better employment opportunities, higher wages, and being viewed as more intelligent, capable, and nicer than those who are less attractive. From a labeling perspective, there is ample theoretical rationale to believe that people who are less attractive may engage in greater deviant behaviors due to the stigma of feeling "ugly." Using a sample of 1,486 college students, this study explores how both perceptions of attractiveness as well as the stigma about one's looks relate to a variety index of norm-breaking behaviors (e.g. chewing with an open mouth, drunk dialing, failing to bathe). Findings revealed that perceptions of physical attractiveness were not a significant correlate of deviance; however, individuals who reported greater levels of stigma about their looks reported significantly greater levels of general deviance than those with lower levels of stigma. Results further showed that stigma moderated a significant negative effect between perceptions of attractiveness and deviance, particularly for people of "average" looks. Echoing prior studies on the importance of physical attractiveness in society, findings from this study highlight that how people feel about their looks carries significant influence on behaviors, including everyday forms of general social deviance. [ABSTRACT FROM AUTHOR]

Emotional arousal, an essential dimension of game users’ experience, plays a crucial role in determining whether a game is successful. Game users’ emotion arousal assessment (GUEA) is of great importance. However, GUEA often faces challenges, such as selecting emotion-inducing games, labeling emotional arousal, and improving accuracy. In this study, the scheme for verifying the effectiveness of emotion-induced games is proposed so that the selected games can induce the target emotions. In addition, the personalized arousal label generation method is developed to reduce the errors caused by individual differences among subjects. Furthermore, to improve the accuracy of GUEA, the Breath Rate Variability (BRV) signal is used as a GUEA indicator along with commonly used physiological signals. Comparative experiments on GUEA based on multimodal physiological signals are conducted. The experimental result shows that the accuracy of GUEA is improved by adding the BRV signal, up to 92%. [ABSTRACT FROM AUTHOR]

Emotion recognition is a key attribute for artificial intelligence systems that need to naturally interact with humans. However, the task definition is still an open problem due to the inherent ambiguity of emotions. In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes. An additional metric is used to evaluate the performance by detecting test utterances with high labelling uncertainty. This removes a major limitation that emotion classification systems only consider utterances with labels where the majority of annotators agree on the emotion class. Furthermore, a frequentist approach is studied to leverage the continuous-valued “soft” labels obtained by averaging the one-hot labels. We propose a two-branch model structure for emotion classification on a per-utterance basis, which achieves state-of-the-art classification results on the widely used IEMOCAP dataset. Based on this, uncertainty estimation experiments were performed. The best performance in terms of the area under the precision-recall curve when detecting utterances with high uncertainty was achieved by interpolating the Bayesian training loss with the Kullback-Leibler divergence training loss for the soft labels. The generality of the proposed approach was verified using the MSP-Podcast dataset which yielded the same pattern of results. [ABSTRACT FROM AUTHOR]

Micro-expressions (ME) are a special form of facial expressions which may occur when people try to hide their true feelings for some reasons. MEs are important clues to reveal people's true feelings, but are difficult or impossible to be captured by ordinary persons with naked-eyes as they are very short and subtle. It is expected that robust computer vision methods can be developed to automatically analyze MEs which requires lots of ME data. The current ME datasets are insufficient, and mostly contain only one single form of 2D color videos. Researches on 4D data of ordinary facial expressions have prospered, but so far no 4D data is available in ME study. In the current study, we introduce the 4DME dataset: a new spontaneous ME dataset which includes 4D data along with three other video modalities. Both micro- and macro-expression clips are labeled out in 4DME, and 22 AU labels and five categories of emotion labels are annotated. Experiments are carried out using three 2D-based methods and one 4D-based method to provide baseline results. The results indicate that the 4D data can potentially benefit ME recognition. The 4DME dataset could be used for developing 4D-based approaches, or exploring fusion of multiple video sources (e.g., texture and depth) for the task of ME analysis in future. Besides, we also emphasize the importance of forming a clear and unified criteria of ME annotation for future ME data collection studies. Several key questions related with ME annotation are listed and discussed in depth, especially about the relationship between AUs and ME emotion categories. A preliminary AU-Emo mapping table is proposed with justified explanations and supportive experimental results. Several unsolved issues are also summarized for future work. [ABSTRACT FROM AUTHOR]

Objective: This study evaluates the effects of traffic light (TL) nutritional label attributes on children's food choices. Data were collected from a survey of 1179 Ecuadorian students attending public middle and high school in three major cities in the country's southern region (Machala, Loja, and Zamora). The survey instrument included two sets of choice experiments: one with yogurt products and the other with soft drinks (sodas and juices). In the choice scenarios, children were presented with two products that differed in price and the TL label colors for sugar, salt, and fat. Children's product selections in the choice experiments were analyzed using mixed logit models. The results indicate that labels affect food choices. Additionally, children are willing to pay increasing premium levels for products with yellow, green, and "does not contain" labels compared to products with red labels. Overall, the study's findings offer evidence that TL labels are effective in helping children make food choices consistent with their preferences for food products with TL labels representing healthier alternatives. [ABSTRACT FROM AUTHOR]

The digitalization of the energy sector enables a broad range of new digital use cases and business models. For instance, blockchain-technology can be used for the verification of tamper-resistant storage of asset data (asset logging) or manipulation-resistant guarantees of origin for electricity (labeling). Yet, it is associated with high implementation and operating effort. But many of these use cases require similar players, interfaces, data sets and data processing, so that synergies can result from a joint implementation. We thus evaluate these synergies in implementation and operating effort for use cases in the field of asset logging and labeling using a bottom-up evaluation of the components based on a methodology of Dossow (Energies 16:2424, 2022). Additionally, we extend this methodology to analyze the scalability of the use cases by assessing the relative effort reduction for an increasing number of players involved. The analysis already shows substantial synergies for combinations of two use cases. Yet, especially for combinations of three or more use cases a high effort reduction potential is derived. The highest synergies are obtained among the asset logging use cases, while a combination of asset logging and labeling use cases shows lower synergies in comparison. The analysis of the scaling of the use cases demonstrates that for labeling use cases the main effort driver is the number of consumers, while for asset logging use cases the number of asset operators shows to be more relevant. Thus, scaling effects outweigh the effort reduction potential of use case combinations especially for combinations of asset logging and labeling cases. [ABSTRACT FROM AUTHOR]

Intestinal organoids have emerged as powerful model systems for studying the complex structure and function of the intestine. However, there is a lack of widely applicable methods for the collection, labeling, and imaging of intestinal organoids. In this study, we developed a novel method for loading and labeling intestinal organoids, a method that efficiently collects the organoids and facilitates imaging of their three-dimensional (3D) structure. Based on this strainer platform, mouse intestinal organoids were adequately collected and immobilized, facilitating the immunolabeling workflow to target proteins of the organoids. After evaluation, the strainer size of 40 μm was considered to be more conducive to the collection and labeling of mouse intestinal organoids. More extensive research on organoids of multiple types and species origins will contribute to broadening the applicability of the methodology. Overall, our study proposes an innovative workflow for loading and analyzing intestinal organoids. The combination of a strainer-based collection method, fluorescent labeling, and 3D reconstruction provides valuable insights into the organization and complexity of these tissue models, thereby offering new avenues for investigating intestinal development, disease modeling, and drug discovery. [ABSTRACT FROM AUTHOR]

This study examines deviant identity in relation to youth offending by combining items tapping both self-appraisal and reflected appraisal. In particular, using survey data from 3,446 Korean youth across five waves of the Korea Youth Panel Survey (KYPS), findings from group-based trajectory modeling (GBTM) present four distinct offending groups—a high-rate chronic group, stable non-offending group, adolescence-limited group, and declining group. Then, findings from the multinomial logit model reveal that deviant identity is a robust predictor of offending for subgroups of adolescents involved in offending at any level in comparison to stable non-offenders. Accordingly, this study supports the idea that deviant identity should be considered as a prominent predictor of a variety of types of youth offending. [ABSTRACT FROM AUTHOR]

Purpose: The Pediatric Research Equity Act (PREA) gives the US Food and Drug Administration (FDA) authority to require pediatric studies for drug and biologics products under certain circumstances and to waive this requirement in some, or all, pediatric ages. When studies are waived for safety, PREA stipulates the safety issue must be described in labeling. This study assessed the rate of including waiver-related safety information in labeling. Methods: FDA databases were reviewed to determine the number of safety-related pediatric study waivers and issued from December 2003 through August 2020, and corresponding labeling to establish when relevant safety information was included. Descriptive comparisons were conducted across Cohort 1: December 2003–2007, Cohort 2: 2008–2011, Cohort 3: 2012–2015, and Cohort 4: 2016-August 2020. Results: One hundred sixteen safety waivers were issued [Cohort 1 (n = 1); Cohort 2 (n = 38), Cohort 3 (n = 37), and Cohort 4 (n = 40)] for 84 unique drugs or biologics. Most (106 of 116; 91%) waiver-related safety issues were described in labeling [Cohort 1 (1 of 1), Cohort 2 (33 of 38), Cohort 3 (33 of 37), and Cohort 4 (39 of 40)]. Safety waivers were most common in patients ≤ 17 years (n = 40) and least common in patients ≤ 6 months (n = 15). Products for infections (n = 32) were the most common group receiving safety waivers; 17 for non-antiviral anti-infective products including treatments for dermatologic infestations/infections, and 15 for antiviral products. Conclusion: The data confirm that FDA consistently describes waiver-related safety information in drug/biologic product labeling since the inception of PREA in December of 2003. [ABSTRACT FROM AUTHOR]

Malnutrition among women of reproductive age and children remains one of the major issues in developing countries, particularly zinc deficiency. Zinc deficiency hinders cognitive and physical development in children as well as adults. This study utilized a non‐hypothetical laboratory valuation experiment to analyze whether positive information about biofortified rice affects consumers' valuations of biofortified as well as non‐biofortified rice. Specifically, we designed a within‐subject experiment based on Becker‐DeGroot‐Marschak (BDM) auction mechanism to compare consumers' valuations of zinc‐biofortified and popular non‐biofortified rice varieties before and after exposure to information about the health benefits of zinc‐biofortified rice. We conducted our experiments in randomly selected four districts of Bangladesh, and a total of 134 consumers participated in our study. Results reveal that consumers assign a significant premium to zinc‐biofortified rice when they receive information about zinc‐biofortified rice and its health benefits. Intriguingly, positive information about zinc‐biofortified rice negatively affects the valuation of non‐biofortified rice varieties, suggesting negative spillover effects. Moreover, our results also identify that both the information provision and labeling are important to increase consumer demand for zinc‐biofortified rice. [ABSTRACT FROM AUTHOR]

Climate change is expected to pose a global threat to forest health by intensifying extreme events like drought and insect attacks. Carbon allocation is a fundamental process that determines the adaptive responses of long-lived late-maturing organisms like trees to such stresses. However, our mechanistic understanding of how trees coordinate and set allocation priorities among different sinks (e.g., growth and storage) under severe source limitation remains limited. Using flux measurements, isotopic tracing, targeted metabolomics, and transcriptomics, we investigated how limitation of source supply influences sink activity, particularly growth and carbon storage, and their relative regulation in Norway spruce (Picea abies) clones. During photosynthetic deprivation, absolute rates of respiration, growth, and allocation to storage all decline. When trees approach neutral carbon balance, i.e., daytime net carbon gain equals nighttime carbon loss, genes encoding major enzymes of metabolic pathways remain relatively unaffected. However, under negative carbon balance, photosynthesis and growth are down-regulated while sucrose and starch biosynthesis pathways are up-regulated, indicating that trees prioritize carbon allocation to storage over growth. Moreover, trees under negative carbon balance actively increase the turnover rate of starch, lipids, and amino acids, most likely to support respiration and mitigate stress. Our study provides molecular evidence that trees faced with severe photosynthetic limitation strategically regulate storage allocation and consumption at the expense of growth. Understanding such allocation strategies is crucial for predicting how trees may respond to extreme events involving steep declines in photosynthesis, like severe drought, or defoliation by heat waves, late frost, or insect attack.

Food producers and retailers throw away large amounts of perfectly edible produce that fails to meet appearance standards, contributing to the environmental issue of food waste. The authors examine why consumers discard aesthetically unattractive produce, and they test a low-cost, easy-to-implement solution: emphasizing the produce's aesthetic flaw through "ugly" labeling (e.g., labeling cucumbers with cosmetic defects "Ugly Cucumbers" on store displays or advertising). Seven experiments, including two conducted in the field, demonstrate that "ugly" labeling corrects for consumers' biased expectations regarding key attributes of unattractive produce—particularly tastiness—and thus increases purchase likelihood. "Ugly" labeling is most effective when associated with moderate (rather than steep) price discounts. Against managers' intuition, it is also more effective than alternative labeling that does not exclusively point out the aesthetic flaw, such as "imperfect" labeling. This research provides clear managerial recommendations on the labeling and the pricing of unattractive produce while addressing the issue of food waste. [ABSTRACT FROM AUTHOR]

Social Justice Content Marketing (SJCM) presents unique challenges to the marketer beyond the traditional marketing tools used to message the consumer effectively. Consumers demand that corporations take action on social issues, yet it is difficult for consumers to define for corporations or marketers what such actions should entail or whether initiatives are effective from the corporate and societal dimensions. Over the last thirty years, changes in media have divided consumer attention across multiple media channels, and message persistence is decreased due to increased message volumes overall. Positioning of SJCM messages is often unintended. Identity via labeling, the backbone of traditional marketing segmentation, targeting, and positioning, is counterproductive in SJCM communications. We used a netnographic approach to capture responses to the National Football League’s SJCM advertisement Football is for Everyone to determine why consumers rejected the advertisement. We applied Stuart Hall’s reception theory to categorize consumer responses and used social dominance theory, labeling and identity frameworks, and semiotics under Pierce. In addition, we examined the benefit to corporate revenue and societal improvement. The results found were that advertisement rejection was due to oppositional and denotative decoding resulting from polysemic symbol versus iconic usage and encoding non-socially dominant beliefs as dominant beliefs in the advertisement. Iconographic messaging reduces non-preferred and negotiated decoding. Further, some corporations may benefit from SJCM financially, but others may suffer substantial financial loss. The gains are more tied to the alignment of core customer demographics and existing beliefs with the advertisement and losses with a disconnect with customer beliefs.

Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled data, but perform poorly compared to the supervised alternatives. In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision. The weak nature of the supervision arises from the requirement of video-level labels, i.e. person identities who appear in the video, in contrast to the more precise frame-level annotations. Towards this goal, we propose a multiple instance attention learning framework for person re-identification using such video-level labels. Specifically, we first cast the video person re-identification task into a multiple instance learning setting, in which person images in a video are collected into a bag. The relations between videos with similar labels can be utilized to identify persons, on top of that, we introduce a co-person attention mechanism which mines the similarity correlations between videos with person identities in common. The attention weights are obtained based on all person images instead of person tracklets in a video, making our learned model less affected by noisy annotations. Extensive experiments demonstrate the superiority of the proposed method over the related methods on two weakly labeled person re-identification datasets.

Reversible addition-fragmentation chain-transfer (RAFT) polymerization is a commonly used polymerization methodology to generate synthetic polymers. The products of RAFT polymerization, i.e., RAFT polymers, have been widely employed in several biologically relevant areas, including drug delivery, biomedical imaging, and tissue engineering. In this article, we summarize a synthetic methodology to display an azide group at the chain end of a RAFT polymer, thus presenting a reactive site on the polymer terminus. This platform enables a click reaction between azide-terminated polymers and alkyne-containing molecules, providing a broadly applicable scaffold for chemical and bioconjugation reactions on RAFT polymers. We also highlight applications of these azide-terminated RAFT polymers in fluorophore labeling and for promoting organelle targeting capability. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Synthesis of the azide derivatives of chain transfer agent and radical initiator Basic Protocol 2: Installation of an azide group on the α-end of RAFT polymers Alternate Protocol: Installation of an azide group on the ω-end of RAFT polymers Basic Protocol 3: Click reaction between azide-terminated RAFT polymers and alkyne derivatives.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole’s anatomic localization. Here, we introduce a novel tool, the “Magnetoencephalography Atlas Viewer” (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient’s Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles’ coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

This paper analyzes the syntactic derivation of so-called ‘wh-doubling’, a single-constituent question-formation strategy that features the overt occurrence of two wh-phrases (e.g. kuza fa la ku’zε (lit.) ‘what does she do what?’) in North Italo-Romance. We argue that the wh-phrases involved in the construction are best treated as being generated independently by External Merge (EM), rather than being bona fide syntactic copies constructed by Internal Merge (IM). This raises the theoretical issue why IM to scope position cannot take place in wh-doubling, despite IM being more economical than EM. We propose that the wh-element merged in argumental position undergoes partial movement to the edge of the v-phase, where it becomes ‘frozen’ upon entering into a Focus configuration. This makes the derivation of wh-doubling identical to the derivation of wh-in situ up to the v-phase as recently proposed for different wh-in situ languages. The derivation of wh-doubling then proceeds by EM of an additional wh-phrase that gives phonological content to the scope Q position in the left periphery. The single-constituent reading is obtained at Conceptual-Intentional (C-I) via the operation FormCopy (FC), which connects the independently generated wh-elements in the syntactic workspace. We moreover discuss issues pertaining to parametric variation.

With the rapid advancement of cell-cultured meat processing technologies and regulations, commercialization of cell-cultured meat to market shelves requires the implementation of labeling that informs and protects consumers while ensuring economic competitiveness. In November 2022, the United States Food and Drug Administration (FDA) completed its first pre-market consultation of cell-cultured meat and did not question the safety of these products for human consumption. As of June 2023, commercialization of cell-cultured meat products has become a reality in the United States. To derive potential label terms and gain insight into how different stakeholders refer to these novel products, we analyzed 1,151 comments submitted to the 2021 U.S. Department of Agriculture’s Food Safety and Inspection Services (USDA-FSIS) call on the labeling of cell-cultured meat and poultry. Our first aim was to systematically assess the nature of comments with regards to their length, cited references, and supplemental materials. In addition, we aimed to identify the most used terms to refer to these products through text analysis. We also asked how these analyses would vary by affiliation category and economic interest. Using the listed organizations for each comment, we first determined financial ties: 77 (7%) comments came from those with an economic interest, 12 (1%) of the comments did not have an identifiable economic interest, while for the remaining 1,062 (92%) comments economic interest could not be determined. We then grouped comments into affiliation categories. Cell-cultured meat companies and animal welfare non-profits had the highest median word count, whereas comments from the unknown affiliation category had the lowest. We found across all comments the predominantly mentioned potential label terms, in descending order, to be cultured meat, lab-grown meat, cultivated meat, cell-cultured meat, clean meat, and cell-based meat. While all label terms were discussed throughout overall submissions, percentages of comments mentioning each term differed between affiliation categories. Our findings suggest differences in how affiliation categories are discussing cell-cultured meat products for the US market. As a next step, the perception and acceptance of these terms must be evaluated to identify the optimal label term regarding the information and protection provided to consumers while ensuring economic competitiveness.

Peptides can be used as effective molecular tool for covalent modification of proteins and play important roles in ligand directed covalent modification. Tyr-selective protein modifications exert a profound impact on protein functionality. Here, we developed a general strategy that involves nucleophilic addition of alkyne for tyrosine modification. The terminal alkyne of propargyl sulfonium is motivated by the sulfonium center to react with phenolic hydroxyl. This approach provides a straightforward method for tyrosine modification due to its high yield in aqueous solution at physiological temperature. In addition, cyclic peptides could be obtained via adjusting pH to 8.0 from peptides consisting of tyrosine and methionine modified by propargyl bromide, and the resulting cyclic peptides are proved to have better stability, excellent 2-mercaptopyridine resistance and improved cellular uptakes. Furthermore, molecules made from the propargylated sulfonium have the potential to be used as warheads against tyrosine containing biomolecules. Collectively, we develop a direct and uncomplicated technique for modifying tyrosine residues, the strategy concerned can be widely utilized to construct stable peptides and biomolecules imaging.

Drought is projected to occur more frequently and intensely in the coming decades, and the extent to which it will affect forest functioning will depend on species-specific responses to water stress. Aiming to understand the hydraulic traits and water dynamics behind water-saver and water-spender strategies in response to drought and recovery, we conducted a pot experiment with two species with contrasting physiological strategies, Scots pine (Pinus sylvestris L.) and Portuguese oak (Quercus faginea L.). We applied two cycles of soil drying and recovery and irrigated with isotopically different water to track fast changes in soil and stem water pools, while continuously measuring physiological status and xylem water content from twigs. Our results provide evidence for a tight link between the leaf-level response and the water uptake and storage patterns in the stem. The water-saver strategy of pines prevented stem dehydration by rapidly closing stomata which limited their water uptake during the early stages of drought and recovery. Conversely, oaks showed a less conservative strategy, maintaining transpiration and physiological activity under dry soil conditions, and consequently becoming more dehydrated at the stem level. We interpreted this dehydration as the release of water from elastic storage tissues as no major loss of hydraulic conductance occurred for this species. After soil rewetting, pines recovered pre-drought leaf water potential rapidly, but it took longer to replace the water from conductive tissues (slower labeling speed). In contrast, water-spender oaks were able to quickly replace xylem water during recovery (fast labeling speed), but it took longer to refill stem storage tissues, and hence to recover pre-drought leaf water potential. These different patterns in sap flow rates, speed and duration of the labeling reflected a combination of water-use and storage traits, linked to the leaf-level strategies in response to drought and recovery. [ABSTRACT FROM AUTHOR]

Data quality plays an important role during the training stage of machine/deep learning models. The annotation hinges on the experiences of domain experts. To acquire the expert’s knowledge in the context of machine learning, manual data labeling, a tedious and time-consuming task in supervised learning, should be given a top priority. However, the domain experts in the line of plentiful manual annotation may easily get distracted or fatigued after long-time work, causing judgment errors, mislabeling, etc. The pattern recognition of wafer defect map is investigated in this paper, the primary goal of which is to train the convolutional neural network (CNN) model through a very limited number of manually labeled data so that the trained model is capable of performing pseudo labeling. Subsequently, a self-assured adaptive ensemble learner in terms of a series of shallow neural networks is proposed to filter wafer map samples with untrusted pseudo-labels. In the result, the amount of human annotations is significantly reduced by 61% for training a highly accurate classifier. A minimum number of manually labeled data is suggested while the equally high classification performance of wafer defect pattern is maintained. For the evaluation purpose, the proposed self-assured learning is compared with the confidence learning. [ABSTRACT FROM AUTHOR]

Automatic defect classification (ADC) systems automatically classify defects that inevitably occur during semiconductor manufacturing processes. ADC is the beginning of defect management that increases the yield of semiconductor chip production, and prevents accidents in the process. It takes a lot of engineer’s labor to classify defects, but ADC can be the answer to classify all defects at low cost. ADC employs the defect image of a wafer surface, captured using scanning electron microscopy (SEM). SEM images can feature a variety of backgrounds based on the defect position on the wafer and the process steps. The manual classification of SEM images involves significant labor costs related to hiring experienced engineers. Despite recent ADC studies reporting good performance, the lack of labeled images and various backgrounds make it difficult to apply ADC in actual manufacturing processes. To address this issue, automated defect classification with defect localization is proposed herein. To this end, a classification model is specifically designed for reducing the effect of varying backgrounds using defect localization. Defect localization uses an object detection model to provide the region information of defects in SEM images. We aimed to design a classification model and defect detection model using semi-supervised learning to reduce labeling costs. Experimental results indicate that the classification performance, over 15 classes, is improved by 12.56% (9.82%p), as compared with that of supervised models. [ABSTRACT FROM AUTHOR]

Background: Labels are a key element of the marketing strategies of infant formula companies, and often include text or images that idealize their use, undermining efforts to promote breastfeeding. Research Aims: To evaluate the prevalence of marketing cues that idealize infant formula on labels of products commercialized in Uruguay and to assess changes after a periodic monitoring of compliance with the International Code of Marketing of Breast-Milk Substitutes (IC). Method: This study is a descriptive, observational, and longitudinal assessment of the information included on infant formula labels. The first data collection was in 2019, as part of a periodic assessment to monitor the marketing of human-milk substitutes. In 2021, the same products were purchased to evaluate changes in their labels. Thirty-eight products were identified in 2019, of which 33 were still available in 2021. All information available on the labels was analyzed through content analysis. Results: Most products included at least one textual or visual marketing cue idealizing infant formula in both 2019 (n = 30, 91%) and 2021 (n = 29, 88%). This represents a violation of both the IC and national regulations. References to nutritional composition were the most frequent marketing cue, followed by references to child growth and development. No relevant changes were observed after the periodic assessment conducted by the Uruguayan government. Conclusions: Monitoring compliance with the IC per se cannot be expected to trigger changes in the marketing strategies of infant formula companies. More explicit regulations and strong enforcement mechanisms are needed to end the inappropriate marketing practices on infant formula labels. [ABSTRACT FROM AUTHOR]

Graph labeling allows for the representation of additional attributes or properties associated with the vertices, edges, or both of graphs. This can provide a more comprehensive and detailed representation of the system being modeled, allowing for a richer analysis and interpretation of the graph. Graph labeling in ladder graphs has a wide range of applications in engineering, computer science, physics, biology, and other fields. It can be applied to various problem domains, such as image processing, wireless sensor networks, VLSI design, bioinformatics, social network analysis, transportation networks, and many others. The versatility of ladder graphs and the ability to apply graph labeling to them make them a powerful tool for modeling and analyzing diverse systems. If a function Υ is an injective vertex assignment in { 1 , 2 , ... q + 1 } and the inductive edge assignment function Υ * in { 1 , 2 , ... q } is expressed as a graph with q edges, defined as Υ * (u v) = 2 [ Υ (u) 2 + Υ (u) Υ (v) + Υ (v) 2 ] 3 [ Υ (u) + Υ (v) ] , then the function is referred to as F-centroidal mean labeling. This is known as the F-centroidal mean criterion. Here, we have determined the F-centroidal mean criteria of the graph ladder, slanting ladder, triangular ladder, T L n ∘ S m , S L n ∘ S m for m ≤ 2 , double-sided step ladder, D n * , and diamond ladder. [ABSTRACT FROM AUTHOR]

Introduction: Mozzarella cheese originates in Italy, where buffalo milk was used in its manufacture, due to scarcity, cow's milk with a 3% fat content was used. Cow's milk in Brazil has become one of the main products in the manufacture of cheese, especially mozzarella cheese, becoming one of the products that cannot be missing from the Brazilian table. Objective: The purpose of this research is to verify that mozzarella cheese sold in the municipality of Itapetinga follows the current RDC established by government agencies. Materails and Methods: Descriptive observational study. Analyzing the nutritional tables, and the information present on the labels of mozzarella cheeses. Results: In the samples, 37.5% of the cheeses do not inform the ingredients, and 87.5% do not inform the way of conservation, as established in the legislation. Conclusion: It was analyzed that some mozzarella cheeses do not have important information to help the consumer and the health food professional. Not complying with current legislation. [ABSTRACT FROM AUTHOR]

There is growing interest in affective computing for the representation and prediction of emotions along ordinal scales. However, the term ordinal emotion label has been used to refer to both absolute notions such as low or high arousal, as well as relation notions such as arousal is higher at one instance compared to another. In this paper, we introduce the terminology absolute and relative ordinal labels to make this distinction clear and investigate both with a view to integrate them and exploit their complementary nature. We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction. Finally, the proposed framework is validated on two speech corpora commonly used in affective computing, the RECOLA and the IEMOCAP databases, across a range of system configurations. The results consistently indicate that integrating relative ordinal information improves absolute ordinal emotion prediction. [ABSTRACT FROM AUTHOR]

The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area. Dataset:  https://zenodo.org/record/7884270. Dataset License: CC-BY [ABSTRACT FROM AUTHOR]

In this letter, we introduce deep active learning (AL) for multi-label image classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several AL query functions for the MLC of RS images. Unlike the existing AL query functions (which are defined for single-label classification or semantic segmentation problems), each query function in this letter is based on the evaluation of two criteria: 1) multi-label uncertainty and 2) multi-label diversity. The multi-label uncertainty criterion is associated with the confidence of the deep neural networks (DNNs) in correctly assigning multi-labels to each image. To assess this criterion, we investigate three strategies: 1) learning multi-label loss ordering; 2) measuring temporal discrepancy of multi-label predictions; and 3) measuring the magnitude of approximated gradient embeddings. The multi-label diversity criterion is associated with the selection of a set of images that are as diverse as possible to each other which prevents redundancy among them. To assess this criterion, we exploit a clustering-based strategy. We combine each of the abovementioned uncertainty strategies with the clustering-based diversity strategy, resulting in three different query functions. All the considered query functions are introduced for the first time in the framework of MLC problems in RS. Experimental results obtained on two benchmark archives show that these query functions result in the selection of a highly informative set of samples at each iteration of the AL process. [ABSTRACT FROM AUTHOR]

Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a group of candidate labels, among which only one of them is valid; this is also known as partial label learning) during the labeling process. Accordingly, how to learn from such data with ambiguous labels is a problem of great practical importance. In this letter, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification. SLAP is mainly composed of two phases: disambiguating the training labels and acquiring the predictive model. Specifically, in the first phase, we propose a superpixelwise LRA-based model, preparing the affinity graph for the subsequent label propagation process while extracting the discriminative representation to enhance the following classification task of the second phase. Then to disambiguate the training labels, label propagation propagates the labeling information via the affinity graph of training pixels. In the second phase, we take advantage of the resulting disambiguated training labels and the discriminative representations to enhance the classification performance. The extensive experiments validate the advantage of the proposed SLAP method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep learning is being increasingly employed for hyperspectral classification, although such use is often predicated on the availability of a sufficiently large set of labeled samples for training. To improve classification performance under a limited training-set size, a semi-supervised network with end-to-end local–global active learning (AL) based on graph convolutional networks (GCNs) is proposed. The proposed AL extracts both global as well as local graph-based features to gauge the discriminative information in unlabeled samples, while semi-supervised classification expands the training set of a fully supervised classifier by attaching pseudo-labels to high-confidence unlabeled samples. Experimental results demonstrate that the proposed network outperforms not only other approaches to semi-supervised classification but also several existing fully supervised methods. The source code of this method can be found at https://github.com/XtaoS/semi-LG-AGCN. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study attempted to create a valid and reliable instrument to measure how gifted children perceive the gifted label and others' perceptions of giftedness. We recruited a total of 300 gifted Turkish children (59.3% males; 40.7% females) aged 10–13 years (M = 11.4, SD = 0.99) to this validity and reliability study. For validity concerns, we first pooled the items relying on the relevant theoretical frameworks. Next, we resorted to expert opinions and employed the Lawshe technique to explore the content validity of the scale. Then, we performed confirmatory factor analysis to investigate the construct validity of the scale after checking multivariate normality through the Relative Multivariate Kurtosis value. Finally, we calculated Cronbach's alpha coefficients to reveal the internal consistency of the five‐factor model. Content validity ratio values showed that each item on the scale yielded an acceptable level of content validity. In addition, we concluded a significant Content validity index for the scale (0.82). Finally, we calculated Cronbach's alpha coefficients to be higher than.70 for the subscales. Overall, the findings suggested that the perception of gifted label scale (PGLS) has an acceptable psychometric quality regarding its validity and reliability. Gifted children's self‐reports may provide more valuable data to better understand the labeling phenomenon and help design preventive educational interventions against the negative impacts of labeling. Therefore the PGLS may lead the way of future studies on the effects of the gifted label on gifted children. Practitioner points: PGLS has an acceptable psychometric quality regarding its validity and reliability.Although the gifted label has positive aspects, it may bring some psychological, emotional, and social obligations.Gifted children's self‐reports may provide more valuable data to better understand the labeling phenomenon and help design preventive educational interventions against the negative impacts of labeling. [ABSTRACT FROM AUTHOR]

According to secondary school placement system (TEOG) between 2013 and 2017, all incoming high school students in Turkey were placed in a high school based on their TEOG exam scores. This placement system became an example of extensive academic grouping. This qualitative study explores the consequences of TEOG-based placement of students, in which students and therefore schools are ranked according to students' test scores. In-depth interviews were conducted with a total of 149 participants selected among students, teachers, and school administrators through stratified purposive sampling. The findings of this study suggest that overall school climate, perceptions towards the school, motivations of students and teachers, learning opportunities presented in the school, and therefore, the overall quality of the education differs largely based on the achievement levels of schools that are identified by TEOG scores of the incoming students. Furthermore, there are strong evidences that TEOG-based placements group students not only based on their academic success but also on their socio-economic backgrounds. Accordingly, students with low socio-economic backgrounds are overrepresented in schools labeled as low and mid-achieving. [ABSTRACT FROM AUTHOR]

Cracks are fractures or breaks that occur in materials such as concrete, metals, rocks, and other solids. Various methods are used to detect and monitor cracks; among many of them, image-based methodologies allow fast identification of the distress and easy quantification of the percentage of cracks in the scene. Two main categories can be identified: classical and deep learning approaches. In the last decade, the tendency has moved towards the use of the latter. Even though they have proven their outstanding predicting performance, they suffer some drawbacks: a "black-box" nature leaves the user blind and without the possibility of modifying any parameters, a huge amount of labeled data is generally needed, a process that requires expert judgment is always required, and, finally, they tend to be time-consuming. Accordingly, the present study details the methodology for a new algorithm for crack segmentation based on the theory of minimal path selection combined with a region-based approach obtained through the segmentation of texture features extracted using Gabor filters. A pre-processing step is described, enabling the equalization of brightness and shadows, which results in better detection of local minima. These local minimal are constrained by a minimum distance between adjacent points, enabling a better coverage of the cracks. Afterward, a region-based segmentation technique is introduced to determine two areas that are used to determine threshold values used for rejection. This step is critical to generalize the algorithm to images presenting close-up scenes or wide cracks. Finally, a geometrical thresholding step is presented, allowing the exclusion of rounded areas and small isolated cracks. The results showed a very competitive F1-score (0.839), close to state-of-the-art values achieved with deep learning techniques. The main advantage of this approach is the transparency of the workflow, contrary to what happens with deep learning frameworks. In the proposed approach, no prior information is required; however, the statistical parameters may have to be adjusted to the particular case and requirements of the situation. The proposed algorithm results in a useful tool for researchers and practitioners needing to validate their results against some reference or needing labeled data for their models. Moreover, the current study could establish the grounds to standardize the procedure for crack segmentation with a lower human bias and faster results. The direct application of the methodology to images obtained with any low-cost sensor makes the proposed algorithm an operational support tool for authorities needing crack detection systems in order to monitor and evaluate the current state of the infrastructures, such as roads, tunnels, or bridges. [ABSTRACT FROM AUTHOR]