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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]

In the domain of Space Situational Awareness (SSA), the challenges related to orbit determination and catalog correlation are notably pronounced, exacerbated by data scarcity. This study introduces an initial orbit determination methodology that relies on data obtained from a single surveillance radar, with the need for fast algorithms within an operational context serving as the main design driver. The result is a linearized least-squares fitting procedure incorporating an analytically formulated approximation of the dynamics under the J 2 perturbation, valid for short-term propagation. This algorithm utilizes all available observables, including range-rate, distinguishing it from other similar methods. A significant contribution of this paper is the enhancement of estimation quality by incorporating information about the object's predicted orbital plane into the methodology, a method denoted as OPOD. The proposed methods are evaluated through a series of simulations against a classical range and angles fitting method (GTDS) to examine the effects of track length and measurement density on the quality of full state estimation, including the impact of using arcs that are too short. The OPOD methodology shows promising results throughout a wide range of scenarios. [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]

The study investigates temporal aspects of home care in Sweden, as experienced and interpreted by older care users. Data consists of 34 interviews with 36 care users and 15 participant observations during home visits by care staff. By focusing on care users' recalibration of time and events, the analysis identifies a form of interpreted experience – here termed 'home care time' – that comprises two governing sublogics – care users' understanding of themselves as part of the home care organisation, and care users' empathetic understanding for care staff – where each sublogic evolved from several approaches where care users expressed time empathy, tried to increase time efficiency, or adapted to other care users. Some care users described attempts to take control and influence temporal aspects. Such attempts resulted in adjustments to services at the individual care user's request, but they were also associated with the risk of conflict between care users and care workers. The study addresses the gap in the literature, which mostly studies and theorises care from a care provider perspective. The logic of home care time identified in the study can explain why older people may say they are satisfied with services though poorly performed due to time shortages. This explanation is a corrective to stereotypes that portray older care users as docile and grateful. [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]

This article contributes to the discourse on global justice as it pertains to resources and the just distribution of resources. The focus is on the concept of scarcity. In examining different understandings of scarcity through the use of analogies and examples, a distinction is made between absolute scarcity and relative scarcity. I examine the conceptual understanding of scarcity of resources in the context of distributive justice. Conceptual clarity is necessary to ensure that scarcity is properly referred to in the discourse so that scarcity is not erroneously used when justifications are sought for material deprivation and consequent human suffering. The field of distributive justice arguably requires a full comprehension of scarcity of resources, and this can only be achieved by properly examining the concept of scarcity. [ABSTRACT FROM AUTHOR]

Conducting randomized controlled trials for medications targeting rare diseases presents significant challenges, due to the scarcity of participants and ethical considerations. Under such circumstances, leveraging real-world data (RWD) to generate supporting evidence may be accepted by the regulatory agency. Constructing an external control arm (ECA) from RWD for a single-arm trial has been conducted occasionally. A complication in this design is that patients from RWD may be eligible at multiple time points. Most studies approach this by selecting one time point as the index date for ECA patients. Here, we propose a novel design for externally controlled trials that permits the inclusion of ECA patients at various entry points. Accompanying this design, we make recommendations for statistical methods to account for measured confounders, limited sample size, within-subject correlation, and potential overdispersion inherent in count data. Furthermore, we present an idea for the blinding process for this type of study. We have conducted a series of simulations to assess the performance of the design and statistical methods in terms of bias, type I error, and efficiency, as compared to the approach of selecting only one entry per ECA patient. The study and parameter setup were based on a hypothetical case inspired by a rare disease study. The results indicate that allowing multiple entries for ECA patients can lead to enhanced performance in many aspects. It provides a controlled type I error, robustness against certain model misspecifications, and a moderate power improvement compared with selecting a single entry per ECA patient. [ABSTRACT FROM AUTHOR]

The scarcity of training samples and label information poses a major challenge for deep learning defect detection technology. Using generative models to expand sample data is considered an effective approach to address this issue. However, existing GAN models struggle to generate high-fidelity and type-controllable magnetic tile defect samples. Therefore, this paper proposes a type-controllable magnetic tile generation method (MTGGAN) that generates data with inherent label information. The model enhances fidelity and diversity in conditional generation through dual discriminator, auxiliary classifier, and auxiliary generator mechanisms. Additionally, focal loss is introduced to address the model’s class tendency issue during training. Extensive experiments demonstrate that, compared to advanced GAN methods, samples generated by MTGGAN exhibit superior quality and diversity. Furthermore, the generated data can train superior detection models. Effectively alleviating the problem of data scarcity in defect detection. [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]

Grounded Situation Recognition (GSR) aims to generate structured image descriptions. For a given image, GSR needs to identify the key verb, the nouns corresponding to roles, and their bounding-box groundings. However, current GSR research demands numerous meticulously labeled images, which are labor-intensive and time-consuming, making it costly to expand detection categories. Our study enhances model accuracy in detecting and localizing under data scarcity, reducing dependency on large datasets and paving the way for broader detection capabilities. In this paper, we propose the Grounded Situation Recognition under Data Scarcity (GSRDS) model, which uses the CoFormer model as the baseline and optimizes three subtasks: image feature extraction, verb classification, and bounding-box localization, to better adapt to data-scarce scenarios. Specifically, we replace ResNet50 with EfficientNetV2-M for advanced image feature extraction. Additionally, we introduce the Transformer Combined with CLIP for Verb Classification (TCCV) module, utilizing features extracted by CLIP's image encoder to enhance verb classification accuracy. Furthermore, we design the Multi-source Verb-Role Queries (Multi-VR Queries) and the Dual Parallel Decoders (DPD) modules to improve the accuracy of bounding-box localization. Through extensive comparative experiments and ablation studies, we demonstrate that our method achieves higher accuracy than mainstream approaches in data-scarce scenarios. Our code will be available at https://github.com/Zhou-maker-oss/GSRDS. [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]

In modern industrial production, unsupervised anomaly detection methods have gained significant attention due to their ability to address the challenge posed by the scarcity of labeled anomaly samples. Among them, unsupervised anomaly detection methods based on reverse distillation (RD) have become a mainstream choice, which has attracted extensive research due to their excellent anomaly detection performance. However, there is a problem of "feature leakage" in the RD model, which may lead to non-anomalous regions being incorrectly identified as defects. To solve this problem, we propose a Normal Feature-Enhanced Reverse teacher–student Distillation (NFERD) method. Specifically, we designed and incorporated a normal feature bank (NFB) module into the basic RD network. This module stores normal features extracted by the teacher model, assisting the student model in learning normal features more efficiently, thereby addressing the problem of "feature leakage". In addition, to effectively fuse the feature maps extracted by the student model with the feature maps in NFBs, we designed a Hybrid Attention Fusion Module (HAFM), which ensures the preservation of key information during the feature fusion process by the parallel processing of spatial and channel attention mechanisms. Through experimental verification on two publicly available datasets, i.e., MVTec and KSDD, our method outperformed the existing mainstream methods in both image-level and pixel-level anomaly detection. Specifically, we achieved an average I-AUROC score of 99.32% on MVTec and a 98.75% P-AUROC on the KSDD, showing clearer segmentation results, especially in complex scenarios. Furthermore, our method surpassed the second-best method by over 1.4% PRO on MVTec, demonstrating its effectiveness. [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]

Marine science is widely recognized as one of the least diverse fields within geoscience. Despite substantial investments in diversity initiatives and resources aimed at engaging underserved communities, the representation and recognition of Black individuals in marine science remain limited. This lack of representation highlights a broader issue: a shortage of professionals who are attuned to the pressing issues within Black communities. Black In Marine Science (BIMS) is making waves by tackling systemic and cultural issues that have historically excluded Black talent from marine science, and this article outlines actionable solutions we have developed to drive meaningful change. BIMS has created a blueprint that can help others increase diversity, equity, and inclusion in scientific spaces with the goal of ocean justice for all. Further than what BIMS has done inside the organization, joy-centered partnerships and direct membership feedback have led to the development of this entire journal issue. Dedicated to highlighting the scientific achievements of BIMS scholars, the BIMS Issue is a manifestation of the outcomes achieved once the BIMS Blueprint is successfully implemented. [ABSTRACT FROM AUTHOR]

Star anise, a widely popular spice, benefits from classification that enhances its economic value. In response to the low identification efficiency and accuracy of star anise varieties in the market, as well as the scarcity of related research, this study proposes an efficient identification method based on non-similarity augmentation and a lightweight cascaded neural network. Specifically, this approach utilizes a Siamese enhanced data network and a front-end SRGAN network to address sample imbalance and the challenge of identifying blurred images. The YOLOv8 model is further lightweight to reduce memory usage and increase detection speed, followed by optimization of the weight parameters through an extended training strategy. Additionally, a diversified fusion dataset of star anise, incorporating open data, was constructed to further validate the feasibility and effectiveness of this method. Testing showed that the SA-SRYOLOv8 detection model achieved an average detection precision (mAP) of 96.37%, with a detection speed of 146 FPS. Ablation experiment results showed that compared to the original YOLOv8 and the improved YOLOv8, the cascade model's mAP increased by 0.09 to 0.81 percentage points. Additionally, when compared to mainstream detection models such as SSD, Fast R-CNN, YOLOv3, YOLOv5, YOLOX, and YOLOv7, the cascade model's mAP increased by 1.81 to 19.7 percentage points. Furthermore, the model was significantly lighter, at only about 7.4% of the weight of YOLOv3, and operated at twice the speed of YOLOv7. Visualization results demonstrated that the cascade model accurately detected multiple star anise varieties across different scenarios, achieving high-precision detection targets. The model proposed in this study can provide new theoretical frameworks and ideas for constructing real-time star anise detection systems, offering new technological applications for smart agriculture. [ABSTRACT FROM AUTHOR]

This article describes how energy, and the 'condition' of the runner, achieved through the successful management of energy, is understood as trans-bodily and social by Ethiopian long-distance runners. The way energy flows between people and the environment means it is deeply implicated in how people understand relational ethics. By describing both morally appropriate training sociality as well as instances of rupture, I argue that rather than seeing competitive pressure as driving atomisation, the notion of shared energy deepens athletes' reliance on others. By bringing together literature on energy with debates about ethics I argue that in conditions of scarcity energetic concerns become relational issues in ways that are particularly important to theorise in a moment characterised by the pressures of an economy constrained by ecological and energetic limits. The notion of 'energetic subjectivities' can help us make sense of these challenges from the perspectives of those caught up in them. [ABSTRACT FROM AUTHOR]

Considering the shortcomings of Ruspini partition-based fuzzy clustering in revealing the intrinsic correlation between different classes, a series of harmonic fuzzy local information C-means clustering for noisy image segmentation are proposed. Firstly, aiming at the shortage of Zadeh's fuzzy sets, a new concept of generalized harmonic fuzzy sets is originally introduced and the corresponding harmonic fuzzy partition is further defined. Then, based on the concept of symmetric harmonic partition, a new harmonic fuzzy local information C-means clustering (HLICM) is proposed and the local convergence of the algorithm is rigorously proved using Zangwill's theorem. Finally, inspired by the improved fuzzy local information C-means clustering (IFLICM) and kernel-based weighted fuzzy local information C-means clustering (KWFLICM), two enhanced robust HLICM algorithms are constructed to further improve the ability of the algorithm to suppress noise. Compared with existing state-of-the-art robust fuzzy clustering-related algorithms, it has been confirmed that the two proposed algorithms have significant competitiveness and superiority. [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]

Face sketch-to-photo synthesis is widely used in law enforcement and digital entertainment, which can be achieved by Image-to-Image (I2I) translation. Traditional I2I translation algorithms usually regard the bidirectional translation of two image domains as two symmetric processes, so the two translation networks adopt the same structure. However, due to the scarcity of face sketches and the abundance of face photos, the sketch-to-photo and photo-to-sketch processes are asymmetric. Considering this issue, we propose a few-shot face sketch-to-photo synthesis model based on asymmetric I2I translation, where the sketch-to-photo process uses a feature-embedded generating network, while the photo-to-sketch process uses a style transfer network. On this basis, a three-stage asymmetric training strategy with style transfer as the trigger is proposed to optimize the proposed model by utilizing the advantage that the style transfer network only needs few-shot face sketches for training. Additionally, we discover that stylistic differences between the global and local sketch faces lead to inconsistencies between the global and local sketch-to-photo processes. Thus, a dual branch of the global face and local face is adopted in the sketch-to-photo synthesis model to learn the specific transformation processes for global structure and local details. Finally, the high-quality synthetic face photo can be generated through the global-local face fusion sub-network. Extensive experimental results demonstrate that the proposed Global-Local Asymmetric (GLAS) I2I translation algorithm compared to SOTA methods, at least improves FSIM by 0.0126, and reduces LPIPS (alex), LPIPS (squeeze), and LPIPS (vgg) by 0.0610, 0.0883, and 0.0719, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive literature review, focusing on the emerging intersection of chatbot technology and the generation of process models. As an evolving field of study, the integration of interactive chatbots into process model generation represents a promising approach, blending advancements in artificial intelligence in general, and natural language processing in particular, with process management methods. This review systematically examines the existing literature across multiple disciplines, identifying and analyzing studies that touch upon the individual components of this nascent topic: chatbot technology, process model generation, and their synergistic potential. Despite the scarcity of direct research aimed at using chatbots for process model generation, this review synthesizes relevant findings from related domains, such as natural language processing applications in process modeling, and the broader impact of chatbot interfaces in various domains. Through this analysis, we aim to map the current landscape of research, highlight significant gaps, and suggest potential pathways for future investigations. This paper not only aggregates existing knowledge, but also assesses the applicability and implications of current technologies and theories when generating process models with the assistance of interactive chatbots. The outcome is a foundational compendium for researchers and practitioners interested in exploring this innovative intersection, providing a springboard for future research and development in this promising area. [ABSTRACT FROM AUTHOR]

Single-Pilot Operations (SPO) mode is set to reshape the decision-making process between human-machine and air-ground operations. However, the limited on-board computing resources impose greater demands on the organization of performance parameters and the optimization of process efficiency in SPO mode. To address this challenge, this paper first investigates the flexible requirements of avionics systems arising from changes in SPO operational scenarios, then analyzes the architecture of Reconfigurable Integrated Modular Avionics (RIMA) and its resource allocation framework in the context of scarcity and configurability. A "mission-function-resource" mapping relationship is established between the reconfiguration service elements of SPO mode and avionics resources. Subsequently, the Proximal Policy Optimization (PPO) algorithm is introduced to simulate the resource allocation process of IMA reconfiguration in SPO mode. The objective optimization process is transformed into a sequential decision-making problem by considering constraints and optimization criteria such as load, latency, and power consumption within the feasible domain of avionics system resources. Finally, the resource allocation scheme for avionics system reconfiguration is determined by controlling the probability of action selection during the interaction between the agent and the environment. The experimental results show that the resource allocation scheme based on the PPO algorithm can effectively reduce power consumption and latency, and the DRL model has strong anti-interference and generalization. This enables avionics resources to respond dynamically to the capabilities required in SPO mode and enhances their ability to support the aircraft mission at all stages. [ABSTRACT FROM AUTHOR]

Polymer-bonded magnets are increasingly being used in terms of applications in drive technology and, more specifically, in new concepts based on reluctance motors. The increasing demand for polymer-bonded magnets, especially in the context of electromobility, is leading to a shortage of materials, mainly in terms of the finite resource neodymium–iron–boron (NdFeB). So far, the recycling strategy for polymer-bonded magnets based on thermosets is pyrolysis, which leads to either a massive reduction of the magnetic properties or a high energy requirement. Therefore, the paper investigates an alternative recycling strategy for polymer-bonded magnets based on thermosets based on the reusage of shreds. Several influencing factors such as the form of the carrier material and the temperature level were varied in order to find a suitable recycling method. It was found that the magnetic properties were reduced by at least 15% compared to the pure material. The required energy and the CO2 emission were reduced by 90% compared to the pyrolysis. Thus, the strategy of recycling polymer-bonded magnets based on thermosets by the reusage of shreds leads to improved conditions compared to pyrolysis and is, therefore, a suitable alternative. [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]

Morocco's approach to developing palliative care is notably constrained, with an almost complete scarcity of laws and regulations in this area. Despite some progress, palliative care remains fragmented and underdeveloped, with persistent disparities in its accessibility and provision. Nationally, there is a lack of sufficient and detailed information about its progress. This paper aims to fill this gap by offering an overview of the history, infrastructure, education, and legislation framework surrounding palliative care in the country. Significant strides have been made since its implementation, but challenges persist, including the need for a comprehensive legislation framework, more trained professionals, and expanded services beyond oncology to other chronic diseases. Strengthening infrastructure and policies is essential to meet the growing needs of Morocco's population. [ABSTRACT FROM AUTHOR]

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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]