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Although food waste is an urgent issue with widespread economic, societal, and environmental impacts, it remains understudied in the marketing discipline. This is surprising, since most food waste occurs at the retail and consumption stages of the food life cycle. This research fills this gap by examining how resource mindset and self-construal jointly shape consumer food waste. Specifically, inducing a scarcity mindset signals that there is no resource to waste, mitigating consumer food waste regardless of self-construal. In contrast, under an abundance mindset, where there is resource to waste, activating an interdependent (vs. independent) self-construal can effectively reduce consumer food waste. The authors identify sharing obligation, the tendency to share valuable resources with in-groups, as a key mechanism behind the effect. In support of this mechanism, enhancing sharing obligation (e.g., highlighting the sharing concept, highlighting others' food needs) or diminishing it (e.g., highlighting family resource abundance) attenuates the effect of self-construal on consumer food waste under an abundance mindset. The results from one large-scale field study, four controlled experiments, and a country-level secondary data analysis provide convergent support for the proposed framework. This research not only contributes to the related literature but also provides actionable strategies for mitigating consumer food waste. [ABSTRACT FROM AUTHOR]

This paper investigates the role of short-term economic dynamism in responding to crisis induced supply shortages. We focus on the domestic manufacturing ramp-up of surgical masks, respirators, and their intermediary products in response to the COVID-19 pandemic. We develop a novel method for timely identification and validation of the evolving state of domestic manufacturing. To unpack the activities of domestic manufacturers and related institutions, we triangulate across 56 qualitative interviews, certifications, Thomasnet.com®, industry associations, and other public data. We find that while large manufacturers could rapidly scale up, onshore, or diversify production to enter into domestic production of critical medical supplies, these large manufacturers alone were insufficient to meet the spike in demand. In face of this shortage, small and medium enterprises (SME), who entered into mask and respirator production as de novo firms, spin-offs, and by diversifying, were important in increasing overall domestic capacity and serving markets unmet by large hospital distributors. These firms often had fewer competencies and resources compared to larger firms, and received less effective government support. Despite these disadvantages, a number of SMEs succeeded in entering into domestic production, and our interviews suggest this capacity could have been better integrated into the national response. We propose new theory for how and when federal and state governments should support short-term economic dynamism (firm entry into target products and/or markets) during crises to address supply shortages, and the types of market and network failures federal or state governments may be most effective at addressing. [ABSTRACT FROM AUTHOR]

Over the last half-century, consumer research has often depicted scarcity as a dominant factor increasing price. But should we assume that scarcity's upward pressure on price remains intact, in a world where novel forms of digital products proliferate? In this article, we propose that blockchain-encrypted digital goods, in particular, non-fungible tokens (NFTs), offer good reason to revisit this assumption. In this context, we argue and find that social value can outweigh intrinsic value as a determinant of willingness-to-pay. As a result, when scarcity threatens access to high levels of social value, its effect on price can be negative rather than positive—an inversion of a pattern typically observed for offline collectibles. Secondary data taken from the NFT platform Opensea and a set of experimental studies support this social value-based lens. Given these findings, we propose a research agenda to ground future work in this area. We also suggest that NFTs offer a laboratory in which past theories related to social value, scarcity, and price can be reconsidered and future theories developed, hopefully allowing consumer researchers to lead knowledge development in these domains over the next 50 years. [ABSTRACT FROM AUTHOR]

Through a randomized field experiment, this study compares the economic effects of two categories of nudges—self-assurance- and pressure-based interventions—on consumers' purchase and return behaviors. In contrast to pressure-oriented nudges, such as quantity scarcity, time scarcity, and social persuasion, self-assurance nudges are intended to facilitate the validation of product choice and style/size characteristics as well as the self-assurance-grounded justification of the purchase. The findings reveal that self-assurance nudges designed to help consumers make better choices have both short-term (high sales) and long-term (few product returns) benefits. Although pressure-driven nudges offer slightly higher short-term benefits (high sales), they eventually engender unfavorable long-term outcomes (high product returns) for consumers and online retailers. Finally, using return-adjusted net sales as performance measures, the authors find that self-assurance-based nudges are as effective in stimulating purchase as those that capitalize on scarcity and social pressure. [ABSTRACT FROM AUTHOR]

Scarcity appeals in marketing have long captured the attention of scholars and practitioners, yet we know little about their effectiveness across different cultures. Drawing on cultural differences (i.e., self-concept, need for uniqueness, and susceptibility to normative influence), the authors investigate the impact of culture on the effectiveness of (demand- vs. supply-based) scarcity appeals. The authors also study the impact of product visibility while considering the moderating effect of culture on the effectiveness of scarcity appeals (demand- vs. supply-based). To do so, the authors conducted experimental research with participants from Pakistan and France. The authors find that (1) demand-based scarcity appeals were more effective than supply-based scarcity appeals in Eastern cultures, whereas the reverse was found in Western cultures; (2) such moderating role of culture was stronger for high-visibility products than for low-visibility products; and (3) the respective prevalence of interdependent (vs. independent) self-construal and its subsequent impact on susceptibility to normative influence and need for uniqueness mediated the moderating role of culture. The authors conclude by discussing the key theoretical contributions and managerial implications of these findings and suggesting future research directions. [ABSTRACT FROM AUTHOR]

The H-1B visa program allows companies to hire skilled foreign workers. Before 2014, the vast majority of these visas were allocated on a first-come-first-served basis. Since then, the program has been severely oversubscribed and all cap-subject visas have been allocated through lotteries. The authors merged Compustat data with administrative firm-level data on the universe of approved petitions for H-1B visas. Using difference-in-differences and matching estimators, this article finds that the switch in the visa allocation system negatively affected the growth of companies that used the H-1B program. Results indicate that these effects are quantitatively large and that their magnitudes grow over time. [ABSTRACT FROM AUTHOR]

A primary challenge in pattern recognition is imbalanced datasets, resulting in skewed and biased predictions. This problem is exacerbated by limited data availability, increasing the reliance on expensive expert data labeling. The study introduces a novel method called contrastive dissimilarity, which combines dissimilarity-based representation with contrastive learning to improve classification performance in imbalance and data scarcity scenarios. Based on pairwise sample differences, dissimilarity representation excels in situations with numerous overlapping classes and limited samples per class. Unlike traditional methods that use fixed distance functions like Euclidean or cosine, our proposal employs metric learning with contrastive loss to estimate a custom dissimilarity function. We conducted extensive evaluations in 13 databases across multiple training–test splits. The results showed that this approach outperforms traditional models like SVM, random forest, and Naive Bayes, particularly in settings with limited training data. [ABSTRACT FROM AUTHOR]

This article proposes a holistic overview of value cocreation and value codestruction among stakeholders at transformative events. Through a thematic research design rooted in value cocreation and codestruction, 31 semistructured in-depth interviews with transformative event stakeholders including transformative experience facilitators, cofacilitators, event designers, and participants, were analyzed. The study's findings conclude that value cocreation and value codestruction occur between various stakeholders and further refine resources and activities that contribute to or obstruct the formation of value within the events industry. Given the scarcity of research in transformative event design in general, and the cocreative or codestructive role of stakeholders beyond the consumer perspective in particular, the study is novel in contributing to value formation in transformative experiences from a multistakeholder perspective. For event management practice, this research provides an overview of activity categories and resources for transformative event stakeholders to intentionally increase cocreated value outcomes and avoid value codestruction. [ABSTRACT FROM AUTHOR]

The scale of white foreign fibers in bobbin yarn is small, resulting in multiple types of data imbalance in the dataset. These imbalances include a severe imbalance of foreign fiber pixels compared to background pixels and an imbalance in the size target scale. Consequently, conventional semantic segmentation networks struggle to segment these fibers effectively. First, in tackling the scarcity of white foreign fiber instances within bobbin yarn samples, this research utilizes original foreign fiber images to train the DCGAN and generate adequate training samples. Secondly, a multiscale residual U-Net is constructed to extract foreign fiber features from different scales. The network is encouraged to learn semantic features at each scale and each layer of the decoding stage. This overcomes the problem of scale imbalance in the foreign fiber dataset and enhances the model's capability to extract weak semantic information from small targets. Thirdly, a weighted binary cross-entropy loss function is integrated into the network's training phase to rectify the class imbalance and refine segmentation performance. This function adjusts the weighting of foreign fiber pixel data, thereby addressing the disproportionate distribution between foreign fibers and background pixels within the dataset. Finally, the proposed method is experimentally validated using a dataset of white foreign fibers. The experimental results show that the proposed method achieves better results in the critical evaluation metrics, as evidenced by the accuracy of 97.52 %, the MIoU of 95.26 %, the DICE coefficient of 81.29 %, and the F1 Score of 84.92 %. These statistics demonstrate the method's efficacy in achieving high-precision segmentation of white foreign fibers. [ABSTRACT FROM AUTHOR]

While misinformation has been around for centuries, the effects of online brand-related misinformation on consumers' engagement remain tenuous. Addressing this gap, we develop the concept of online brand-related misinformation engagement, a consumer's (e.g. cognitive/emotional) resource investment in their interactions with misinformation about brands. Recognizing the persuasive nature of misinformation, we draw on Cialdini's Weapons of Persuasive Influence to develop a typology comprising three positively valenced online brand-related misinformation engagement sub-types (i.e. reciprocal, social proof-, and consistency-based misinformation engagement), and three negatively valenced online brand-related misinformation engagement sub-types (i.e. repudiating, thwarting, and oppositional misinformation engagement). We then develop a Weapons of Influence-informed model that outlines the effect of online brand-related misinformation authority and scarcity on consumers' brand-related misinformation evaluation (liking), and its subsequent effect on their positive or negative online brand-related misinformation engagement, respectively. While consumers' positive online brand-related misinformation engagement is predicted to yield online misinformation continuation or -intensification, its negative counterpart will generate online misinformation adjustment or -correction. [ABSTRACT FROM AUTHOR]

Using a lab-in-the-field experiment with Ugandan fishers, we study if and how the use of a common pool resource changes when the resource is either scarce or abundant and when the number of users increases over time. Both resource scarcity and a growing group require users to be more constrained, that is, more cooperative, in order to maintain the resource. However, the results show that fishers do not curtail their harvesting behavior under increased pressure, leading to rapid overexploitation when scarce resources are used by a growing group. This implies a particular need for sustainable management when scarce resources are exposed to in-migration. [ABSTRACT FROM AUTHOR]

Seismic fragility analysis and resilience assessment of large‐span cable‐stayed bridge structures are critical for evaluating their seismic performance. However, there is a scarcity of research on the effects of multi‐support ground motions and their non‐Gaussian characteristics on seismic fragility and resilience. This paper aims to addresses this issue. Initially, random ground motions with spatial variability and non‐Gaussian characteristics are simulated using the Spectral Representation Method (SRM) and the Unified Hermite Polynomial Model (UHPM). Subsequently, the Fractional Exponential Moments‐based Maximum Entropy Method (FEM‐MEM) and the Adaptive Gaussian Mixture Model (AGMM) are employed for seismic reliability‐based fragility analysis, overcoming the shortcomings of conventional lognormal assumption. Component‐ and system‐level fragility analyses are conducted sequentially, followed by seismic resilience assessment of bridge structures based on the results of system‐level fragility analysis. A numerical example is presented to validate the proposed method. Computational results indicate that: (1) The proposed method offers higher accuracy and broader applicability for seismic fragility analysis of large‐span cable‐stayed bridge structures compared to traditional assumptions. (2) The non‐Gaussian characteristics of ground motions may significantly impact the seismic fragility analysis and resilience assessment of large‐span bridge structures. [ABSTRACT FROM AUTHOR]

This study explores how the scarcity of virtual products in virtual gaming spaces influences brand evaluation in the real world. While previous studies suggest that scarcity in virtual spaces can enhance brand value, the mechanisms and conditions under which this effect occurs (e.g., the characteristics of virtual spaces, consumer traits, and stimulus). Drawing on insights from scarcity research on physical products, we propose that the effects of scarcity may stem from the expectation of impression management. We developed a model that incorporates insights from physical product scarcity research with the unique aspects of virtual spaces and consumer characteristics. Through four studies, we discovered that the effect of supply scarcity in enhancing brand evaluation is pronounced when the space is online and when consumers have a high level of social presence. Furthermore, in cases where social presence is not high, encounters with others wearing scarce products were found to enhance the scarcity effect. Companies can enhance brand evaluation by strategically using virtual product scarcity in online gaming, particularly when targeting consumers with high social presence or creating opportunities for players to interact with others wearing scarce items. [ABSTRACT FROM AUTHOR]

Purpose: In Cameroon's blood banks, the shortage of blood bags is difficult to overcome because of the lack of donor loyalty and the complexity of recruiting new donors. This study explores the possibility of retaining donors and turning them into prospectors by analysing their satisfaction. Design/methodology/approach: A Servqual questionnaire was administered to a sample of 109 donors obtained by voluntary sampling. We used customer satisfaction to determine the satisfaction score and the tetraclass model to analyse the contribution of the elements to satisfaction. Findings: Our results show that the blood donor satisfaction score is low (60.33%) compared with the norm (80%). The post-donation follow-up stage is the factor with the greatest influence on improving blood donor satisfaction. The elements that require particular attention in the supervision of blood donors are the snacking and blood-taking stages, because they have a strong capacity to worsen donor satisfaction when they are badly perceived by donors. Originality/value: The study focuses on aspects specific to the Cameroonian situation and sheds a unique light on blood donor satisfaction in this context, while offering a general understanding of this issue on an international scale. [ABSTRACT FROM AUTHOR]

Emerging professionals are crucial in advancing ecological restoration by connecting newcomers with established leaders and blending new ideas with traditional practices. This paper highlights the vital role emerging scientists play in restoration ecology and the Society for Ecological Restoration (SER). A survey conducted during the SER2023 conference workshop, organized by the Students and Emerging Professionals committee, showed that emerging professionals contribute significantly to every stage of scientific work, from planning to publication. However, participants emphasized the scarcity of funding opportunities for non‐senior scientists, which limits academic growth. The workshop underscored the importance of nurturing emerging professionals to develop future leaders for SER and ensure continuity. Fostering non‐hierarchical and inclusive networking is key to engaging and empowering these professionals, ultimately contributing to the long‐term success of ecological restoration efforts. [ABSTRACT FROM AUTHOR]

Carbazole derivatives are vital in medicinal and materials chemistry, yet accessing diverse substitutions remains challenging. Here, we introduce a regioselective approach for C1 and C8 alkynylation of carbazoles via palladium‐catalyzed C−H activation. This method provides a direct route to mono‐ and di‐alkynylated derivatives, addressing the scarcity of alkynylated carbazole‐based materials. Our study broadens the synthetic toolbox for carbazole functionalization, offering potential applications in optoelectronics, bio‐imaging, and beyond, while contributing to developing sustainable C−H activation methodologies. [ABSTRACT FROM AUTHOR]

Introduction: Motion blur, primarily caused by rapid camera movements, significantly challenges the robustness of feature point tracking in visual odometry (VO). Methods: This paper introduces a robust and efficient approach for motion blur detection and recovery in blur-prone environments (e.g., with rapid movements and uneven terrains). Notably, the Inertial Measurement Unit (IMU) is utilized for motion blur detection, followed by a blur selection and restoration strategy within the motion frame sequence. It marks a substantial improvement over traditional visual methods (typically slow and less effective, falling short in meeting VO's realtime performance demands). To address the scarcity of datasets catering to the image blurring challenge in VO, we also present the BlurVO dataset. This publicly available dataset is richly annotated and encompasses diverse blurred scenes, providing an ideal environment for motion blur evaluation. [ABSTRACT FROM AUTHOR]

Growing evidence suggests that news media significantly contribute to disseminating, endorsing, or merely increasing the visibility of populist rhetoric. While a large amount of literature has focused on the type and volume of news coverage provided to populist actors and parties (populism through the media), there is more scarcity of knowledge regarding the media's orientation towards populism (populism by the media), partly due to a lack of systematic comparative evidence. To address this shortfall, we introduce a novel, large-scale comparative dataset, the Media Populism (M-POP) expert survey (including a website and a ShinyApp). This survey provides metrics for media populism across 38 national and subnational contexts globally, thereby offering an empirically grounded evaluation of the primary theoretical interpretations of media populism. In the article, we detail the dataset, evaluate the advantages and drawbacks of using an expert survey method, and conduct a systematic analysis of the determinants of media populism, particularly focusing on its right-wing manifestation, to validate our metrics. We then explore two empirical applications of the M-POP dataset in a comprehensive comparative manner, tackling key questions in political communication literature: the interplay between media populism and tabloid journalism, and the correlation between individual populist sentiments and media populism. [ABSTRACT FROM AUTHOR]

Short text classification has been widely used in many fields. Due to the scarcity of labelled data, implementing short text classification under semi‐supervised learning setting has become increasingly popular. Semi‐supervised short text classification methods based on graph neural networks can achieve state‐of‐the‐art classification performance by utilizing the expressive power of graph neural networks. However, these methods usually fail to mine the hidden patterns of a large amount of short text node data in the graph to optimize the short text node embeddings, which limits the semantic representation power of the short texts, thus leading to suboptimal classification performance. To overcome the limitation, this paper proposes a novel semi‐supervised short text classification method called the Heterogeneous Graph Contrastive Learning with Adaptive Data Augmentation (HGCLADA). In the knowledge bases guided soft prompt‐based data augmentation component, the related words of the tag words are used to optimize the soft prompts for generating diverse augmented samples. In the heterogeneous graph contrastive learning framework component, a heterogeneous graph that is constructed using short texts and keywords and an effective edge augmentation scheme based on a short text clustering algorithm are proposed. The optimized short text embeddings can be obtained to achieve the effective semi‐supervised short text classification. Extensive experiments on six benchmark datasets show that our HGCLADA method outperforms four classes of state‐of‐the‐art methods in terms of classification accuracy, especially with significant performance improvements of 8.74% on the TagMyNews dataset when each class only contains 20 labelled data. [ABSTRACT FROM AUTHOR]

Competition today has become a central policy imperative in higher education. Connected to resource efficiency and scarcity, it remains closely attached to the idea of the market but reaches beyond when related to positional or status orders. In the higher education literature such varieties of competition – as distinct social processes – are still under-investigated. In drawing on common references to competition in the literature and empirical examples the paper identifies and outlines two separable forms: market and field competition. The distinction shows that there are fundamental differences in the way competition in higher education plays out which should not be viewed merely as variations of market settings. To outline the differences among the two forms the paper follows a two-step iterative process. The paper first highlights a time-bound analytical problem in common market-references. Drawing on empirical work by Michel Callon it specifies market competition as the construction, singularisation and detachment of a good. As empirical examples from the German higher education case show, such singularisation and detachment are rarely complete, competition rather operates through the attachment of universities to status-consecrating intermediaries, as analysed in the status competition literature. In a second step, the paper outlines the main premises of status competition but also its lack of a comprehensive frame that could qualify it as a distinct form of competition. The 'field' fills this void by providing a selective relational space on which universities can act and observe themselves competing, reinforced by multiple intermediaries. [ABSTRACT FROM AUTHOR]

This review surveys recent evidence on environmental security, bringing diverse approaches to the subject and evidence relating to different environmental issues into conversation with one another. We focus on the five environmental issues most commonly viewed as having conflict or security effects: climate change, water, forests and deforestation, biodiversity and conservation, and mining and industrial pollution. For each issue, we consider evidence along three dimensions: the impacts of environmental variables on violent conflict, the conflict impacts of policy and development interventions vis-à-vis these environmental issues, and their global policy framing and institutionalization. Through this, we draw particular attention to the poverty and/or inconsistency of the evidence relating to environmental variations, which stands in stark contrast to the extensive evidence on policy and development interventions; noting that policymakers have been much more concerned with the former theme than the latter, we call for this imbalance to be addressed. [ABSTRACT FROM AUTHOR]

Precisely identifying interior decoration styles holds substantial significance in directing interior decoration practices. Nevertheless, constructing accurate models for the automatic classification of interior decoration styles remains challenging due to the scarcity of expert annotations. To address this problem, we propose a novel pseudo-label-guided contrastive mutual learning framework (PCML) for semi-supervised interior decoration style classification by harnessing large amounts of unlabeled data. Specifically, PCML introduces two distinct subnetworks and selectively utilizes the diversified pseudo-labels generated by each for mutual supervision, thereby mitigating the issue of confirmation bias. For labeled images, the inconsistent pseudo-labels generated by the two subnetworks are employed to identify images that are prone to misclassification. We then devise an inconsistency-aware relearning (ICR) regularization model to perform a review training process. For unlabeled images, we introduce a class-aware contrastive learning (CCL) regularization to learn their discriminative feature representations using the corresponding pseudo-labels. Since the use of distinct subnetworks reduces the risk of both models producing identical erroneous pseudo-labels, CCL can reduce the possibility of noise data sampling to enhance the effectiveness of contrastive learning. The performance of PCML is evaluated on five interior decoration style image datasets. For the average AUC, accuracy, sensitivity, specificity, precision, and F1 scores, PCML obtains improvements of 1.67%, 1.72%, 3.65%, 1.0%, 4.61%, and 4.66% in comparison with the state-of-the-art method, demonstrating the superiority of our method. [ABSTRACT FROM AUTHOR]

The emergence of few-shot object detection provides a new approach to address the challenge of poor generalization ability due to data scarcity. Currently, extensive research has been conducted on few-shot object detection in natural scene datasets, and notable progress has been made. However, in the realm of remote sensing, this technology is still lagging behind. Furthermore, many established methods rely on two-stage detectors, prioritizing accuracy over speed, which hinders real-time applications. Considering both detection accuracy and speed, in this paper, we propose a simple few-shot object detection method based on the one-stage detector YOLOv5 with transfer learning. First, we propose a Segmentation Assistance (SA) module to guide the network's attention toward foreground targets. This module assists in training and enhances detection accuracy without increasing inference time. Second, we design a novel detection head called the Triplet Head (Tri-Head), which employs a dual distillation mechanism to mitigate the issue of forgetting base-class knowledge. Finally, we optimize the classification loss function to emphasize challenging samples. Evaluations on the NWPUv2 and DIOR datasets showcase the method's superiority. [ABSTRACT FROM AUTHOR]

Autonomous technologies have developed incrementally, especially in the car industry. Countries worldwide are giving impetus to developing automated driving (AD) and autonomous vehicles (AVs). China is used as an example of an innovative development model for autonomous driving that has significant practical implications for AD's development in the global context. Germany also keeps its role as a global hub for the AVs industry, and it has released the most advanced AD laws. This research conducted a thorough comparison of related policies, regulations, and industries in China and Germany in an effort to support the development of AD in China. Further, AD's advantages, disadvantages, opportunities, and threats in China and Germany are discussed. It sought to identify any shortages of AD in China and find an efficient way to enhance AD cooperation between China and Germany. Based on the dissimilarity of the current status and the disparity in policies, a series of countermeasures and suggestions for the development of the Chinese AD are presented in this study. [ABSTRACT FROM AUTHOR]

The influential rare biosphere concept in microbial ecology focuses on taxonomic scarcity and treats functional rarity implicitly. Microbes can possess functions distinct from other taxa in a community and can be present at high or low abundances. We define functionally rare microbes as possessing distinct functions and being numerically scarce. Functionally rare microbes can contribute disproportionately to ecosystem multifunctionality. The functional rarity concept helps to mechanistically connect microbial functional diversity, ecosystem functioning, and evolutionary processes. It also highlights the need for conservation of rare functions in microbial communities. The influential concept of the rare biosphere in microbial ecology has underscored the importance of taxa occurring at low abundances yet potentially playing key roles in communities and ecosystems. Here, we refocus the concept of rare biosphere through a functional trait-based lens and provide a framework to characterize microbial functional rarity, a combination of numerical scarcity across space or time and trait distinctiveness. We demonstrate how this novel interpretation of the rare biosphere, rooted in microbial functions, can enhance our mechanistic understanding of microbial community structure. It also sheds light on functionally distinct microbes, directing conservation efforts towards taxa harboring rare yet ecologically crucial functions. [ABSTRACT FROM AUTHOR]

The dead donor rule (DDR) has facilitated the saving of hundreds of thousands of lives. Recent advances in heart donation, however, have exposed how DDR has limited donation of all organs. We propose advancing the moment in the dying process at which death can be determined to increase substantially the supply of organs for transplantation. We justify this approach by identifying certain flaws in the Uniform Determination of Death Act and proposing a modification of that law that permits earlier procurement of healthier organs in greater numbers. [ABSTRACT FROM AUTHOR]

Subjective experiences of resource scarcity can make individuals short‐term oriented, capture attention, and trigger feelings of unfairness. However, the impact of scarcity on information processing and ethical decision‐making remains poorly understood. This eye‐tracking study explored how acute financial scarcity affects selective information search and ethical decision‐making in an economic task with competing incentives (N = 60, 12,000 observations). Results revealed that participants experiencing financial scarcity displayed a strong attentional bias towards financially tempting information, although they ultimately did not behave more unethically. These findings might reveal a "moral boundary" dictating when attentional biases translate into decision‐making. Our results contribute to understanding how individuals in scarcity contexts process and prioritize information in ethical decision‐making, helping organizations and policymakers combat stereotypes surrounding resource‐deprived individuals, and design evidence‐based policy interventions promoting ethical behavior in financially scarce situations. [ABSTRACT FROM AUTHOR]

Node classification is a crucial task for efficiently analyzing graph-structured data. Related semi-supervised methods have been extensively studied to address the scarcity of labeled data in emerging classes. However, two fundamental weaknesses hinder the performance: lacking the ability to mine latent semantic information between nodes, or ignoring to simultaneously capture local and global coupling dependencies between different nodes. To solve these limitations, we propose a novel semantic-enhanced graph neural networks with global context representation for semi-supervised node classification. Specifically, we first use graph convolution network to learn short-range local dependencies, which not only considers the spatial topological structure relationship between nodes, but also takes into account the semantic correlation between nodes to enhance the representation ability of nodes. Second, an improved Transformer model is introduced to reasoning the long-range global pairwise relationships, which has linear computational complexity and is particularly important for large datasets. Finally, the proposed model shows strong performance on various open datasets, demonstrating the superiority of our solutions. [ABSTRACT FROM AUTHOR]

Daqing in China is well-known as a, if not the, "Chinese oil city". However, the reasons behind its importance for the People's Republic of China and the origins of its current spatial organization remain unclear. "How was such growth achieved against the backdrop of the so-called de-urbanization processes? What was occurring at the local level? What happened to the people of my parents' generation?" Hou Li, a leading Chinese urban scholar, addresses these questions in her latest book, Building for Oil, explaining how the lowering of urban construction standards to the level of rural ones, the focus of investments on production, and the reliance on individual commitment led to China's unprecedented growth. Despite the absence (or the concealment) of a critical opinion on the part of the author on the events and decisions described, this contribution is of particular importance to understand the path leading to contemporary China. [ABSTRACT FROM AUTHOR]

In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity. [ABSTRACT FROM AUTHOR]

Unlike previous generations of artificial intelligence (AI), AI assistants today can autonomously perform actions without human input or intervention. Several studies have proposed but not tested the notion that increased levels of AI autonomy may ultimately conflict with consumers' fundamental need for autonomy themselves. Across five experiments (N = 1981), including representative samples and pre‐registered hypotheses, we investigate consumer responses to high (vs. low) AI autonomy in the context of online shopping. The results show a pronounced negative effect of high AI autonomy on consumers' adoption intentions – an effect mediated by consumers' relative state of powerlessness in the presence of high AI autonomy. However, when consumers face situations characterized by scarcity, such as when preferred options are being sold out rapidly (e.g. Black Friday), the aversive aspects of high (vs. low) AI autonomy are attenuated. Together, these findings offer novel insights regarding whether, when and why consumers are willing to adopt high (vs. low)‐autonomy AI assistants in online shopping settings. [ABSTRACT FROM AUTHOR]

Semi-supervised learning leverages both labeled and unlabeled images for model training, addressing the scarcity of labeled data. However, challenges persist, including the determination of appropriate thresholds for pseudo-labeling, the effective utilization of uncertain unlabeled images, the absence of consistency regularization, and the oversight of inter-image relationships among images in low-density areas. This study introduces a novel approach named the Individual-Relational Consistency for Bad Semi-supervised Generative Adversarial Networks (IRC-BSGAN) to tackle these issues. IRC-BSGAN integrates bad adversarial training, consistency regularization, and pseudo-labeling to reduce error rates and enhance classifier performance. It includes various components, such as a bad generator network, a discriminator network, a classifier, and consistency regularization modules. IRC-BSGAN introduces new individual and relational consistency regularization losses on bad fake images in low-density areas, thereby generating informative images that precisely estimate the classifier's decision boundary. The proposed method ensures diversity and consistent labeling of bad fake images by integrating consistency mechanisms. It particularly focuses on low-density areas and extracts extra semantic details from these images by promoting local consistency and coherence among them. The effectiveness of IRC-BSGAN is realized by improving the pseudo-labeling of unlabeled images, especially for low-confidence unlabeled images. For the SVHN dataset with 1000 labeled training images and the CIFAR-10 dataset with 4000 labeled training images, the error rate reduced from 3.89 to 3.67 and from 7.29 to 6.17, respectively. Similarly, on the CINIC-10 dataset with 1000 labeled training images per class, IRC-BSGAN achieved a reduction in error rate from 19.38 to 15.45. On the COVID-19 dataset with 30 labeled training images, the error rate decreased from 7.41 to 5.55. [ABSTRACT FROM AUTHOR]

Fabric defect detection has always been a key issue, and it positively correlated its efficiency with productivity. From manual visual methods to machine vision and deep learning-based techniques, a variety of methods have been studied to improve production efficiency and product quality. Although deep learning-based methods have proven to be powerful tools for segmentation, there are still many pressing issues that need to be addressed in practical applications. First, the scarcity of defective samples compared to normal samples can cause data imbalance and thus affect accuracy. Second, high real-time performance is also required in the actual detection process. To overcome these problems, we propose a high real-time convolutional neural network, named Mobile-Deeplab, to implement end-to-end defect segmentation. In addition, we proposed a loss function to consider the fabric image sample imbalance problem. We evaluated the performance of the model with two public structured datasets and three self-constructed structured datasets. The experimental results show that the segmentation method has better segmentation accuracy than other segmentation models, which verifies the segmentation effect of the method. In addition, 87.11 frames per second on a 256 × 256 size image meet industrial real-time requirements. [ABSTRACT FROM AUTHOR]

Transfer printing is a key technology in the fabrication of flexible electronics. Transfer printing method based on curvature provides a simple and effective way to transfer films onto weakly adhesive or even adhesiveless surfaces, overcoming the shortage of traditional transfer printing methods that it is difficult to print functional materials from strong interface to weak interface and can only be applicable to surfaces with certain interfacial strength. So far, the theoretical principle of the curvature‐based transfer printing method has not yet been developed. In this article, a dual‐interface competitive fracture model is established to analyze the mechanism of printing and picking up processes and quantitatively provide the critical transfer printing radius in terms of material and geometric properties of the transfer printing system. This model is verified to be both correct and widely applicable by rich experimental results, providing a new and reliable theory for the fabrication of inorganic flexible electronics. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)-assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour. [ABSTRACT FROM AUTHOR]

ABSTRACT The efficacy of conveying information through maps heavily depends on the quality of map generalization. However, automating map generalization poses a complex decision‐making challenge, requiring a profound understanding of the process—specifically, knowledge about the generalization procedure. Currently, there is a scarcity of research on the sequence of generalization operations, particularly for cartographic generalization involving symbolization and labeling. On the contrary, customary maps generated in practical applications consistently adhere to the specified generalization and symbolization protocol, which makes it feasible and credible to construct this overall process based on expert knowledge. To reconcile this incongruity, this paper presents a knowledge‐guided automated cartographic generalization process construction. Firstly, an exhaustive examination of the sequential procedures involved in manual generalization and a well‐applied automated generalization system are delineated, drawing upon map analysis methodologies, observations, and expert interviews. Then, elaborate guidelines governing each phase within this process, particularly concerning the symbolization and labeling of map features, are explored. Ultimately, details of the expert interview are described and a map generalized by the well‐applied system is analyzed. The results show that the automated generalization system follows the knowledge‐guided process in this paper can significantly improve production efficiency in practice, this study serves as a connection between cartographers and developers and may help achieve a higher level of automated cartographic generalization. [ABSTRACT FROM AUTHOR]

Detection of plant individuals is imperfect. Not accounting for this issue can result in biased estimates of demographic parameters as important as population growth rates. In mobile organisms, a common practice is to explicitly account for detection probability during the estimation of most demographic parameters, but no study in plant populations has examined the consequences of ignoring imperfect detectability on the estimation of population growth rates. The lack of accounting for detection probability occurs because plant demographers have frequently assumed that detection is perfect, and because there is a scarcity of studies that formally compare the performance of estimation methods that incorporate detection probabilities with respect to methods that ignore detectabilities. Based on field data of five plant species and data simulations, we compared the performance of three methods that estimate population growth rates, two that do not estimate detection probabilities (direct counts of individuals and the minimum‐number‐alive method) and the other that explicitly accounts for detection probabilities (temporal symmetry models). Our aims were 1) to estimate detection probabilities, and 2) to evaluate the performance of these three methods by calculating bias, accuracy, and precision in their estimates of population growth rates. Our five plant species had imperfect detection. Estimates of population growth rates that explicitly incorporate detectabilities had better performance (less biased estimates, with higher accuracy and precision) than those obtained with the two methods that do not calculate detection probabilities. In these latter methods, bias increases as detection probability decreases. Our findings highlight the importance of using robust analytical methods that account for detection probability of plants during the estimation of critical demographic parameters such as population growth rates. In this way, estimates of plant population parameters will reliably indicate their actual status and quantitative trends. [ABSTRACT FROM AUTHOR]

Though previous studies have showed that social class is positively associated with subjective well-being (SWB), in fact, subjective social class has a stronger impact on SWB compare to objective social class. Thus, it is more important to explore the relationship between subjective social class and SWB. In the current study, we suggested that perceived scarcity and sense of control would play the mediating role between subjective social class and SWB. Study 1 used two big samples from China (CGSS2017) and over the world (WVS7) to test the hypothesis, the results confirmed that the effect of individual subjective social class on SWB was mediated by perceived scarcity and sense of control. More importance, we also find a sequential mediation effect of perceived scarcity and sense of control between individual subjective social class and SWB. Similar results were also both found in Study 2 using an empirical approach and Study 3 using a measure approach in China. These findings extend previous work by examining the psychological mechanisms between subjective social class and SWB from the resource scarcity theory. [ABSTRACT FROM AUTHOR]

Detection and Dissection of Anomalous Tissue Domains (DDATD) from multi-sample spatial transcriptomics (ST) data provides unprecedented opportunities to characterize anomalous tissue domains (ATDs), revealing both population-level and individual-specific pathogenic factors for understanding pathogenic heterogeneities behind diseases. However, no current methods can perform de novo DDATD from ST data, especially in the multi-sample context. Here, we introduce STANDS, an innovative framework based on Generative Adversarial Networks which integrates three core tasks in multi-sample DDATD: detecting, aligning, and subtyping ATDs. STANDS incorporates multimodal-learning, transfer-learning, and style-transfer techniques to effectively address major challenges in multi-sample DDATD, including complications caused by unalignable ATDs, under-utilization of multimodal information, and scarcity of normal ST datasets necessary for comparative analysis. Extensive benchmarks from diverse datasets demonstrate STAND's superiority in identifying both common and individual-specific ATDs and further dissecting them into biologically distinct subdomains. STANDS also provides clues to developing ATDs visually indistinguishable from surrounding normal tissues. The authors introduce STANDS, a GAN-based framework that integrates three core tasks for the multi-sample detection and dissection of anomalous tissue domains from spatial transcriptomics data, revealing pathogenic heterogeneity behind diseases. [ABSTRACT FROM AUTHOR]

Antibiotic shortages have become a global issue, affecting countries worldwide. These shortages often lead to the overuse of specialized or emergency-reserve antibiotics, which impacts the treatment and increases the risk of drug resistance, making infections harder to treat over time. The COVID-19 pandemic highlighted the severity of these shortages, showing they are neither rare nor isolated but rather common and widespread. This short commentary explores two main questions: firstly, how has antibiotic shortage developed into a serious concern for countries like the United States and Europe, once leaders in antibiotic production; and secondly, how has antibiotic shortage grown from localized and isolated issues into the global concern, affecting countries around the world? By employing historical analysis, this commentary aims to underscore the growing but overlooked issue, pointing to a broad and deeply rooted problem of the pharmaceutical production landscape. [ABSTRACT FROM AUTHOR]

The limited availability of thermal infrared (TIR) training samples leads to suboptimal target representation by convolutional feature extraction networks, which adversely impacts the accuracy of TIR target tracking methods. To address this issue, we propose an unsupervised cross-domain model (UCDT) for TIR tracking. Our approach leverages labeled training samples from the RGB domain (source domain) to train a general feature extraction network. We then employ a cross-domain model to adapt this network for effective target feature extraction in the TIR domain (target domain). This cross-domain strategy addresses the challenge of limited TIR training samples effectively. Additionally, we utilize an unsupervised learning technique to generate pseudo-labels for unlabeled training samples in the source domain, which helps overcome the limitations imposed by the scarcity of annotated training data. Extensive experiments demonstrate that our UCDT tracking method outperforms existing tracking approaches on the PTB-TIR and LSOTB-TIR benchmarks. [ABSTRACT FROM AUTHOR]

To address the challenges of change detection tasks, including the scarcity and dispersion of labeled samples, the difficulty in efficiently extracting features from unstructured image objects, and the underutilization of high-order correlation information, we propose a novel architecture based on hypergraph convolutional neural networks. By characterizing superpixel vertices and their high-order correlations, the method implicitly expands the number of labels while assigning adaptive weight parameters to adjacent objects. It not only describes changes in vertex features but also uncovers local and consistent changes within hyperedges. Specifically, a vertex aggregation mechanism based on superpixel segmentation is established, which segments the difference map into superpixels of diverse shapes and boundaries, and extracts their significant statistical features. Subsequently, a dynamic hypergraph structure is constructed, with each superpixel serving as a vertex. Based on the multi-head self-attention mechanism, the connection probability between vertices and hyperedges is calculated through learnable parameters, and the hyperedges are generated through threshold filtering. Moreover, a framework based on hypergraph convolutional neural networks is customized, which models the high-order correlations within the data through the learning optimization of the hypergraph, achieving change detection in remote sensing images. The experimental results demonstrate that the method obtains impressive qualitative and quantitative analysis results on the three remote sensing datasets, thereby verifying its effectiveness in enhancing the robustness and accuracy of change detection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Research in Developmental Social Work is the property of Beni Suef University 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.)

Due to the lack of annotations for nighttime low-light images, object detection in low-light images has always been a challenging problem. Achieving high-precision results at night is also an issue. Additionally, we aim to use a single nighttime dataset to complete the knowledge distillation task while improving the detection accuracy of object detection models under nighttime low-light conditions and reducing the computational cost of the model, especially for small targets and objects contaminated by special nighttime lighting. This paper proposes a Nighttime Unsupervised Domain Adaptation Network (NUDN) based on knowledge distillation to address these issues. To improve the detection accuracy of nighttime images, high-confidence bounding box predictions from the teacher and region proposals from the student are first fused, allowing the teacher to perform better in subsequent training, thus generating a combination of high-confidence and low-confidence pseudo-labels. This combination of feature information is used to guide model training, enabling the model to extract feature information similar to that of source images in nighttime low-light images. Nighttime images and pseudo-labels undergo random size transformations before being used as input for the student, enhancing the model's generalization across different scales. To address the scarcity of nighttime datasets, we propose a nighttime-specific augmentation pipeline called LightImg. This pipeline enhances nighttime features, transforming them into daytime features and reducing issues such as backlighting, uneven illumination, and dim nighttime light, enabling cross-domain research using existing nighttime datasets. Our experimental results show that NUDN can significantly improve nighttime low-light object detection accuracy on the SHIFT and ExDark datasets. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University 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.)

Many biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We develop a deep learning framework—Meta Model Agnostic Pseudo Label Learning (MMAPLE)—to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when conventional transfer learning fails. The uniqueness of MMAPLE is to integrate the concept of meta-learning, transfer learning and semi-supervised learning into a unified framework. The power of MMAPLE is demonstrated in three applications in an OOD setting where chemicals or proteins in unseen data are dramatically different from those in training data: predicting drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions. MMAPLE achieves 11% to 242% improvement in the prediction-recall on multiple OOD benchmarks over various base models. Using MMAPLE, we reveal novel interspecies metabolite-protein interactions that are validated by activity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques. MMAPLE, a semi-supervised meta-learning framework, effectively addresses data scarcity and out-of-distribution challenges in deep learning. It uncovers novel drug-target, metabolite-enzyme, and microbiome-human interactions. [ABSTRACT FROM AUTHOR]

The environment, population and conflict thesis remains major to existing environment and security arguments. Throughout the 1990s, a huge increase in scholarship and policy focus was committed to unraveling the linkages amongst the three parameters. While it can very easily be asserted that both the analysis and policy communities have made substantial improvements, the academic results and policy instructions continue to be the subject matter of intense discussion. This article explores an assumptive method for the environment–conflict connection that views a collection of local parameters. The variable of resource management regimes is explored in more detail, illustrated by a case study from an Iraqi Internally Displaced Persons (IDPs) camp. The article finds theoretical and empirical evidence to support the view that participatory and inclusive resource management regimes may enable communities to construct resource-use conflicts in ways that help to prevent unproductive conflict. Such forms of governance can potentially be initiated in places where the state is failing to mitigate conflict through its own institutional resources. Thus, there may be an opportunity to respond to the 'ingenuity gap' that Homer-Dixon identifies as a key linkage between scarcity and conflict. [ABSTRACT FROM AUTHOR]

Introduction: Between 2017 and 2022, it was carried out research on the management of cultural heritage organizations in the Colombian Caribbean. Caught our attention the quantity and quality of cultural organizations built in Valledupar, Colombia, in comparison to larger cities, and with greater economic resources. Objective: In this study we attempt to identify the economic support ecosystem in the cultural heritage organizations of the city of Valledupar (Cesar, Colombia) and, based on in-depth interviews, formulate postulates about the forces that have facilitated the development of a rich and complex cultural ecosystem, despite the scarcity of resources. Methodology: Social network analysis ARS was used, based on the methodology of Organizational life stories. Results: the relationship dynamics of seven cultural heritage organizations are shown. The network of support that organizations use to survive were represented, which allow them to improve management and strengthen their relationship systems for financing, learning and access to new services and resources. The SNA methodology allowed us to identify the greatest supports, the type of networks, and the development possibilities of the organizations analyzed. [ABSTRACT FROM AUTHOR]