Multiple Categories Research Articles

Understanding the manufacturing reshoring decision-making content through the lens of the Eclectic Paradigm: a systematic literature review

Purpose This study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective. Design/methodology/approach Data were collected through a six-step systematic literature review on factors influencing manufacturing reshoring decision-making. The review is based on 100 peer-reviewed journal papers discussing reshoring decision-making contents published from 2009 to 2022. Findings In total, 80 decision factors were extracted and then categorized into resource-seeking (8%), market-seeking (11%), efficiency-seeking (41%) and strategic asset-seeking (16%) advantages. Additionally, 24% of these were identified as hybrid, which means that they were classified into multiple categories. Some decision factors were further identified as reshoring influencing factors (i.e. drivers, enablers and barriers). Research limitations/implications Scholars need to consider what other theories can be used or developed to identify and evaluate the decision factors (determinants) of manufacturing reshoring as well as how currently adopted theory can be further advanced to create clearer and comprehensive theoretical frameworks. Practical implications This research underscores the importance of developing clearer and more comprehensive theoretical frameworks. For practitioners, understanding the multifaceted nature of decision factors could enhance strategic decision-making regarding reshoring initiatives. Originality/value To the best of the authors’ knowledge, this is the first study to investigate the value and practicality of the Eclectic Paradigm in categorizing factors in manufacturing reshoring decision-making content and presents in-depth theoretical classifications. In addition, it bridges the gap between decision factors and influencing factors in the decision-making content research realm.

Telehealth Evaluation in the United States: Protocol for a Scoping Review.

The rapid expansion of telehealth services, driven by the COVID-19 pandemic, necessitates systematic evaluation to guarantee the quality, effectiveness, and cost-effectiveness of telehealth services and programs in the United States. While numerous evaluation frameworks have emerged, crafted by various stakeholders, their comprehensiveness is limited, and the overall state of telehealth evaluation remains unclear. The overarching goal of this scoping review is to create a comprehensive overview of telehealth evaluation, incorporating perspectives from multiple stakeholder categories. Specifically, we aim to (1) map the existing landscape of telehealth evaluation, (2) identify key concepts for evaluation, (3) synthesize existing evaluation frameworks, and (4) identify measurements and assessments considered in the United States. We will conduct this scoping review in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews and in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). This scoping review will consider documents, including reviews, reports, and white papers, published since January 1, 2019. It will focus on evaluation frameworks and associated measurements of telehealth services and programs in the US health care system, developed by telehealth stakeholders, professional organizations, and authoritative sources, excluding those developed by individual researchers, to collect data that reflect the collective expertise and consensus of experts within the respective professional group. The data extracted from selected documents will be synthesized using tools such as tables and figures. Visual aids like Venn diagrams will be used to illustrate the relationships between the evaluation frameworks from various sources. A narrative summary will be crafted to further describe how the results align with the review objectives, facilitating a comprehensive overview of the findings. This scoping review is expected to conclude by August 2024. By addressing critical gaps in telehealth evaluation, this scoping review protocol lays the foundation for a comprehensive and multistakeholder assessment of telehealth services and programs. Its findings will inform policy makers, health care providers, researchers, and other stakeholders in advancing the quality, effectiveness, and cost-effectiveness of telehealth in the US health care system. OSF Registries osf.io/aytus; https://osf.io/aytus. DERR1-10.2196/55209.

Transcriptomic dysregulation and autistic-like behaviors in Kmt2c haploinsufficient mice rescued by an LSD1 inhibitor.

Recent studies have consistently demonstrated that the regulation of chromatin and gene transcription plays a pivotal role in the pathogenesis of neurodevelopmental disorders. Among many genes involved in these pathways, KMT2C, encoding one of the six known histone H3 lysine 4 (H3K4) methyltransferases in humans and rodents, was identified as a gene whose heterozygous loss-of-function variants are causally associated with autism spectrum disorder (ASD) and the Kleefstra syndrome phenotypic spectrum. However, little is known about how KMT2C haploinsufficiency causes neurodevelopmental deficits and how these conditions can be treated. To address this, we developed and analyzed genetically engineered mice with a heterozygous frameshift mutation of Kmt2c (Kmt2c+/fs mice) as a disease model with high etiological validity. In a series of behavioral analyses, the mutant mice exhibit autistic-like behaviors such as impairments in sociality, flexibility, and working memory, demonstrating their face validity as an ASD model. To investigate the molecular basis of the observed abnormalities, we performed a transcriptomic analysis of their bulk adult brains and found that ASD risk genes were specifically enriched in the upregulated differentially expressed genes (DEGs), whereas KMT2C peaks detected by ChIP-seq were significantly co-localized with the downregulated genes, suggesting an important role of putative indirect effects of Kmt2c haploinsufficiency. We further performed single-cell RNA sequencing of newborn mouse brains to obtain cell type-resolved insights at an earlier stage. By integrating findings from ASD exome sequencing, genome-wide association, and postmortem brain studies to characterize DEGs in each cell cluster, we found strong ASD-associated transcriptomic changes in radial glia and immature neurons with no obvious bias toward upregulated or downregulated DEGs. On the other hand, there was no significant gross change in the cellular composition. Lastly, we explored potential therapeutic agents and demonstrate that vafidemstat, a lysine-specific histone demethylase 1 (LSD1) inhibitor that was effective in other models of neuropsychiatric/neurodevelopmental disorders, ameliorates impairments in sociality but not working memory in adult Kmt2c+/fs mice. Intriguingly, the administration of vafidemstat was shown to alter the vast majority of DEGs in the direction to normalize the transcriptomic abnormalities in the mutant mice (94.3 and 82.5% of the significant upregulated and downregulated DEGs, respectively, P < 2.2 × 10-16, binomial test), which could be the molecular mechanism underlying the behavioral rescuing. In summary, our study expands the repertoire of ASD models with high etiological and face validity, elucidates the cell-type resolved molecular alterations due to Kmt2c haploinsufficiency, and demonstrates the efficacy of an LSD1 inhibitor that might be generalizable to multiple categories of psychiatric disorders along with a better understanding of its presumed mechanisms of action.

An improved focal loss for imbalanced network traffic classification

With the continued progress of the Internet, network security has become an increasingly significant issue that requires constant attention and research. Network traffic classification is a key technology used to detect and prevent malicious network activity, and it has accordingly received increasing attention and research. However, datasets related to malicious network traffic classification often have imbalanced characteristics. In conventional traffic classification problems with multiple categories, the sample size characteristics of small categories are often overlooked. To address this issue, the focal loss function was proposed, which focuses on small samples by modulating the trade-off between the positive and negative samples through two hyperparameters and . This article uses convolutional neural networks (CNN) to tackle traffic classification problem and explore the optimal values of parameters in this application scenario. Additionally, this work proposed a novel weight allocation formula to replace , which allowed small class traffic to obtain higher accuracy.

Computads for generalised signatures

We introduce a notion of signature whose sorts form a direct category, and study computads for such signatures. Algebras for such a signature are presheaves with an interpretation of every function symbol of the signature, and we describe how computads give rise to signatures. Motivated by work of Batanin, we show that computads with certain generator-preserving morphisms form a presheaf category, and describe a forgetful functor from algebras to computads. Algebras free on a computad turn out to be the cofibrant objects for a certain cofibrantly generated weak factorisation system, and the adjunction above induces the universal cofibrant replacement, in the sense of Garner, for this weak factorisation system. Finally, we conclude by explaining how many-sorted structures, weak ω-categories, and algebraic semi-simplicial Kan complexes are algebras of such signatures, and we propose a notion of weak multiple category.

Remaining lifespan prediction on multiple types of hard disks under conditions of data imbalance

Most of the previous regression models for predicting the remaining useful life (RUL) of hard disks primarily focused on failure data obtained through SMART monitoring. However, models that only considered failure data were not accurate enough. In order to address the issue of imbalanced sample quantities in different categories within the dataset, this paper proposes a method for predicting the RUL of hard disks under imbalanced conditions. It takes into account the imbalance issues in both SMART failure data and healthy data, as well as the imbalance in monitoring data attributes between hard disk drives and solid-state drives. The paper improves upon the traditional Transformer time series model and constructs a comprehensive and unified model for the RUL of hard disks across multiple categories. The proposed model achieves an RMSE score of 3.325, and has 66 % in MAE improved to the conventional models. The proposed model has promising prospects for future applications in predicting the remaining life of hard disks.

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.

The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.

Characteristics of Pathogenic Escherichia coli Associated with Diarrhea in Children under Five Years in Northwestern Ethiopia.

Diarrheagenic Escherichia coli (DEC) are the leading cause of infectious diarrhea and pose a significant global, regional, and national burden of disease. This study aimed to investigate the prevalence of six DEC pathotypes in children with diarrhea and determine their antibiotic resistance patterns. Samples from 107 diarrheagenic children were collected and processed for Escherichia coli (E. coli). Single-plex PCR was used to detect target virulence genes as well as characterize and categorize DEC pathotypes. Antibiotic resistance patterns were determined by the Kirby-Bauer disk diffusion method. E. coli was detected in 79 diarrheal stool samples, accounting for 73.8% of the samples collected. Additionally, 49.4% (39 out of 79) of the isolates harbored various typical virulence factors. Results revealed six pathotypes of virulence: enterotoxigenic E. coli (ETEC) (53.8%), enteropathogenic E. coli (EPEC) (12.8%), enteroaggregative E. coli (EAEC) (10.3%), Heteropathotypes (7.8%), Shiga toxin-producing E. coli (STEC), and enterohemorrhagic E. coli (EHEC) (7.7% each). The isolates exhibited high antibiotic resistance against trimethoprim/sulfamethoxazole (82.1%), amoxicillin (79.5%), ampicillin (74.4%), gentamicin (69.2%), and streptomycin (64.1%). An overall occurrence of 84.6% of multiple-drug resistance was observed in the isolates, with resistance ranging from three to four antibiotic classes. Our findings revealed a high level of pathogenic E. coli that were highly resistant to multiple categories of antibiotics among children in the Awi zone. These findings highlight the potential role of pathogenic E. coli in childhood diarrhea in tropical low-resource settings and underscore the need for continued research on the characteristics of pathogenic and antibiotic-resistant strains.

Labour segmentation in NCR Delhi’s automobile sector: a political response of capital to labour struggles

This paper aims to understand the changing dynamics of internal labour market segmentation intertwined with the history of working-class struggle in an industrial belt in India’s National Capital Region (NCR). Nuances of workers’ class struggle in the automobile sector in Delhi-NCR over the last 15 years have shown that unity between permanent and contract workers was key to several successful struggles against worsening work conditions and management control within the global value chains of automobile production. Our fieldwork findings, along with direct and indirect political engagement in the region, reveal that increasing salaries and allotment of supervisory roles to small sections of permanent workers, coupled with the simultaneous fragmentation a lower strata of workers into multiple categories with very low salaries, benefits and heavy workloads, have been significant attempts by capital to break class solidarity among different sections of workers. Relatively pro-labour legislation and trade unions tend to concentrate in high-wage zones, while low-wage and precarious employment remains unprotected by such institutional mechanisms. Labour segmentation is inherent to capitalist exploitation, guarded by institutional mechanisms. Advanced mechanisation reduces distinction of skill requirements between highly paid permanent and lowly paid contract workers. In this context, labour segmentation is a political response to trade unionism from the side of capitalist management.

Mrs. Dalloway and the Shecession: The Interconnectedness and Intersectionalities of Care Ethics and Social Time During the Pandemic

Business ethics researchers and practitioners are interested in understanding the temporal mechanisms of various managerial activities, processes, and policies. In this direction, I borrow notions of time from Virginia Woolf’s Mrs. Dalloway to examine how social time intersperses with the paid and (unpaid) care work of female employees during the pandemic. I explore how discussions of social time in connection to care work appear in newspaper discourses of “shecession”, i.e. the large-scale job/income losses experienced by women during the COVID-19 pandemic. Since shecession is a byproduct of the pandemic times, exploring the role of time in it is crucial. In fact, my findings show that the macro social time of the pandemic affects working women with care responsibilities who are situated at the intersection of multiple vulnerable social categories by simultaneously being a penalizer and a lost opportunity. Similarly, I also find that the micro social times of working women embed, stratify, and synchronize differently during the pandemic when compared to normal times. Working women with care responsibilities are thus adversely affected by both micro and macro social time changes. My findings, therefore, could be instrumental in developing and implementing inclusive policies and processes in business organizations and labor markets. In so doing, my study also indicates how a consideration of social time enriches the application of care ethics in work contexts. Ultimately, this article is also about developing caring organizations, societies, and families which care for the caregivers (working women), since receiving care is a precondition of giving care.

What's in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT.

Artificial intelligence chatbots such as ChatGPT (OpenAI) have garnered excitement about their potential for delegating writing tasks ordinarily performed by humans. Many of these tasks (eg, writing recommendation letters) have social and professional ramifications, making the potential social biases in ChatGPT's underlying language model a serious concern. Three preregistered studies used the text analysis program Linguistic Inquiry and Word Count to investigate gender bias in recommendation letters written by ChatGPT in human-use sessions (N=1400 total letters). We conducted analyses using 22 existing Linguistic Inquiry and Word Count dictionaries, as well as 6 newly created dictionaries based on systematic reviews of gender bias in recommendation letters, to compare recommendation letters generated for the 200 most historically popular "male" and "female" names in the United States. Study 1 used 3 different letter-writing prompts intended to accentuate professional accomplishments associated with male stereotypes, female stereotypes, or neither. Study 2 examined whether lengthening each of the 3 prompts while holding the between-prompt word count constant modified the extent of bias. Study 3 examined the variability within letters generated for the same name and prompts. We hypothesized that when prompted with gender-stereotyped professional accomplishments, ChatGPT would evidence gender-based language differences replicating those found in systematic reviews of human-written recommendation letters (eg, more affiliative, social, and communal language for female names; more agentic and skill-based language for male names). Significant differences in language between letters generated for female versus male names were observed across all prompts, including the prompt hypothesized to be neutral, and across nearly all language categories tested. Historically female names received significantly more social referents (5/6, 83% of prompts), communal or doubt-raising language (4/6, 67% of prompts), personal pronouns (4/6, 67% of prompts), and clout language (5/6, 83% of prompts). Contradicting the study hypotheses, some gender differences (eg, achievement language and agentic language) were significant in both the hypothesized and nonhypothesized directions, depending on the prompt. Heteroscedasticity between male and female names was observed in multiple linguistic categories, with greater variance for historically female names than for historically male names. ChatGPT reproduces many gender-based language biases that have been reliably identified in investigations of human-written reference letters, although these differences vary across prompts and language categories. Caution should be taken when using ChatGPT for tasks that have social consequences, such as reference letter writing. The methods developed in this study may be useful for ongoing bias testing among progressive generations of chatbots across a range of real-world scenarios. OSF Registries osf.io/ztv96; https://osf.io/ztv96.

Hierarchical categories in colored searching

In colored range counting (CRC), the input is a set of points where each point is assigned a “color” (or a “category”) and the goal is to store them in a data structure such that the number of distinct categories inside a given query range can be counted efficiently. CRC has strong motivations as it allows data structure to deal with categorical data.However, colors (i.e., the categories) in the CRC problem do not have any internal structure, whereas this is not the case for many datasets in practice where hierarchical categories exist or where a single input belongs to multiple categories. Motivated by these, we consider variants of the problem where such structures can be represented. We define two variants of the problem called hierarchical range counting (HCC) and sub-category colored range counting (SCRC) and consider hierarchical structures that can either be a DAG or a tree. We show that the two problems on some special trees are in fact equivalent to other well-known problems in the literature. Based on these, we also give efficient data structures when the underlying hierarchy can be represented as a tree. We show a conditional lower bound for the general case when the existing hierarchy can be any DAG, through a reduction from the orthogonal vectors problem.

Individual differences in working memory predict the efficacy of experimenter-manipulated gestures in first-grade children.

Why is instructional gesture ineffective in some contexts? And what is it about learners that predicts whether they will learn from gestures? This between-subjects linear measurement training study compares gesture instruction to two controls-operant action and transient action-in a diverse sample of first-grade students (N = 174, Mage = 7.01 years; Nfemale = 84; Nmale = 90, 10% Latinx-identified; 70% White; 6% Black; 6% Asian; 18% multiple racial categories, Mincome = $59,750, SDincome ≈ $25,000; data collected 03/16-03/19). Results show that instructor-manipulated gestures may be less effective than demonstrative actions in part because they are iterative and do not leave a lasting trace. Verbal working memory, but not spatial, positively predicted an ability to learn from gesture and transient action in children with the lowest context knowledge.

Empirical approach to understanding natural language models

In this paper, we try to understand natural language models by treating them as black boxes. We want to learn about these models without going into their technical details pertaining to network architecture, tuning parameters, training datasets, and schedules. We instead take an empirical approach, where we classify the datasets into various categories. For scalability and avoiding subjective bias, we use Latent Dirichlet Allocation (LDA) to categorize language text. We fine-tune and evaluate natural language models for our tasks. We compare the performance of the same model across multiple categories and for the same category across multiple models. This can help not only in choosing models for the desired categories but is also useful in understanding the model attributes that can explain performance variation. We report here the observations from this empirical study and our hypotheses. We find that models do not perform uniformly across all the categories, which could be because of uneven representation of these categories in their training datasets. Models that specialized/fine-tuned for specific tasks had higher variance in performance across categories than the generic models. Some categories have high performance consistently across all models, while others have high variance. The code for this research paper is available here: https://github.com/bhuvishi/llm_understanding

Southern Horse Mackerel (Trachurus trachurus) Spatio-Temporal Distribution Patterns Based on Fine-Scale Resolution Data

In this study, the distribution patterns of southern horse mackerel are examined using commercial fine-scale resolution data. Using landings by size category and VMS data from the Portuguese commercial bottom-trawl fishery, which consistently targets horse mackerel, this study provides a comprehensive analysis of horse mackerel age distributions spanning a decade (2010–2020). Importantly, this study addresses potential biases in commercial effort data and establishes the usefulness of commercial bottom-trawl gear as a suitable method for sampling and evaluating southern horse mackerel stock dynamics. Ordered regression models were applied to allow for the modelling of the distribution of multiple age categories and investigate spatio-temporal migrations off the Portuguese coast. Southern horse mackerel show a widespread age distribution range and stable abundance with indications of seasonal and spatial patterns in the distribution of specific age groups. The insights derived from this research contribute valuable knowledge for understanding the dynamics and distribution patterns of fish populations.

Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence.

The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.

Making Oneself "Fit": Community Perspectives on Disaggregated Race and Ethnicity Data Collection and Reporting in Healthcare Settings.

Patient race/ethnicity data collection in most U.S. health systems abide by federal standards, determined by the federal Office of Management and Budget. Yet, decades of research show that reliance on these categories alone limits understanding of within-group health disparities, systematically erasing key groups from health data. Because granular race/ethnicity data is complex and patients may be hesitant to disclose this personal information, it is important for health leaders to consider community perspectives when making decisions about race/ethnicity data procedures. As such, this study uses community focus groups to understand: (1) how individuals representing different racial/ethnic identities perceive the collection of race/ethnicity in healthcare settings; (2) differences in opinions between disaggregated race/ethnicity data collection instruments and those using federal standards; and (3) recommended practices for collecting race/ethnicity from patients. Participants self-selected into 13 focus groups and one key informant interview based on the race/ethnicity with which they most closely identified. Audio recordings from these groups were transcribed and evaluated using thematic content analysis. Among the 83 total participants in this study, there was a strong preference for more flexible and specific options for self-identifying race/ethnicity in healthcare settings. Participants also felt comfortable disclosing granular race/ethnicity to health providers but expressed discomfort with disclosing this information for other purposes. Recommendations for healthcare leaders include ensuring patients receive detailed communication about race/ethnicity data use and purpose, allowing multiple category selection, keeping the list of disaggregated response options short so as to not overwhelm patients, and providing a free text option to ensure inclusivity.

Ritual networks and the structure of moral communities in Classic Maya society

Ritual plays an important integrative function in the creation, maintenance, and transformation of human society. The shared experience of ritual establishes strong bonds between individuals that defines their membership in certain social groups. However, rituals are not timeless traditions, nor do they simply restore social equilibrium. Rather, rituals are active and ongoing social processes that unite and divide across multiple social categories. This paper applies archaeological network methods to analyze the multiscalar structure of ritual traditions across Classic Maya (ca. 300–900 CE) society using hieroglyphic inscriptions from dated and provenienced monuments cataloged in the Maya Hieroglyphic Database (Looper and Macri 1991–2024). For the Classic Maya, public ritual and performance were highly charged political events where meaning and power could be negotiated, creating opportunities for identity formation and community integration. Such contexts helped establish strong moral bonds in Classic Maya society. However, we know relatively little about the specific forms and content of these ritual practices. In this study we construct ritual similarity networks from hieroglyphic inscriptions to analyze the structure and organization of these moral communities as well as the ritual relations that bound them together.

Randomized controlled trial investigating web-based, therapist delivered eye movement desensitization and reprocessing for adults with suicidal ideation.

Promising preliminary evidence suggests that EMDR may reduce suicidal ideation (SI) when used to treat Major Depressive Disorder, Posttraumatic Stress Disorder, and trauma symptoms in the context of acute mental health crises. EMDR has never been tested specifically for treating SI, and there is a lack of data regarding the safety and effectiveness of web-based, therapist-delivered EMDR in populations with known SI. The primary objective of this study was to investigate the impact of web-based, therapist-delivered EMDR, targeting experiences associated with suicidal thinking. Secondary objectives included examining the effect of EMDR treatment on symptoms of depression, anxiety, posttraumatic stress, emotional dysregulation, and dissociation, as well as safety and attrition. This randomized control trial (ClinicalTrials.gov ID number: NCT04181047) assigned adult outpatients reporting SI to either a web-based EMDR intervention or a treatment as usual (TAU) group. TAU included primary and mental health services available within the Canadian public health system. Participants in the EMDR group received up to 12 web-based EMDR desensitization sessions, delivered twice weekly during the COVID-19 pandemic (2021-2023). The Health Research Ethics Board at the University of Alberta approved the protocol prior to initiation of data collection for this study (protocol ID number: Pro00090989). Forty-two adult outpatients received either EMDR (n=20) or TAU (n=22). Participants reported a high prevalence of early onset and chronic SI, and there was a high rate of psychiatric comorbidity. In the EMDR group, median SI, depression, anxiety, and posttraumatic symptom scale scores decreased from baseline to the four month follow-up. In the TAU group, only the median SI and posttraumatic symptom scale scores decreased from baseline to four month follow up. Although sample size precludes direct comparison, there were numerically fewer adverse events and fewer dropouts in the EMDR group relative to the TAU group. Study results provide promising preliminary evidence that web-based EMDR may be a viable delivery approach to address SI. In this complex population, a short treatment course was associated with reductions of SI and other symptoms across multiple diagnostic categories. Further investigation is warranted to verify and extend these results. https://clinicaltrials.gov/study/NCT04181047?id=NCT04181047&rank=1, identifier NCT04181047.

A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8

Aiming at the characteristics of remote sensing images such as a complex background, a large number of small targets, and various target scales, this paper presents a remote sensing image target detection algorithm based on improved YOLOv8. First, in order to extract more information about small targets in images, we add an extra detection layer for small targets in the backbone network; second, we propose a C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) to enhance the network’s ability to detect targets of different sizes; and lastly, Wise-IoU is used to replace the CIoU loss function in the original algorithm to improve the robustness of the model. Using our improved algorithm for the detection of multiple target categories in the DOTAv1.0 dataset, the mAP@0.5 value is 82.7%, which is 1.3% higher than that of the original YOLOv8 algorithm. It is proven that the algorithm proposed in this paper can effectively improve target detection accuracy in remote sensing images.