Research Domain Research Articles

Landscape of geriatric oncology in Switzerland: Results from a nationwide survey.

e13855 Background: Geriatric oncology (GO) addresses the unique needs of older cancer patients, an increasingly critical population as the demographic shift towards aging continues. However, evidence gaps and a lack of standardized guidelines hinder the integration of GO into daily oncology practice. We conducted a comprehensive survey among physicians evaluate the current status of geriatric oncology in Switzerland . identify positive aspects as well as barriers. Methods: A structured questionnaire was disseminated to approximately 300 oncology professionals including hematologists, pathologists, gynecologic oncologists via email through the Swiss Group for Clinical Cancer Research (SAKK) between July and September 2024. Anonymous responses were collected using MS Forms with follow-up at the SAKK semi-annual meeting. The survey assessed baseline characteristics, utilization of GO guidelines, screening practices, and perceptions regarding the benefits and barriers to implementing GO in clinical settings. Results: A total of 100 responses were analyzed (response rate: > 30%). Key findings include: Participant Profile: 55% female; 45% male. Predominantly professionals managing solid tumors, with 50% utilizing GO guidelines. The majority of participants were senior specialists ( > 10 years of clinical experience). Screening Practices: The G8 tool was the most frequently used (45/68), primarily for patients aged > 65 with geriatric syndromes or > 75 years. Benefits of GO Assessments: Improved treatment personalization (69 mentions), quality of life (61 mentions), and decision-making accuracy (58 mentions) were cited as key advantages. Barriers: Personnel and expertise shortages were noted by 48% of respondents, alongside limited interest in GO workshops and insufficient healthcare policy support. Clinical Impact: Treatment modifications following GO assessments were reported occasionally (85%) or often (8%), with frequent exclusion of chemotherapy when inappropriate. Research Interests: High interest was observed in research domains like gynecological oncology, hematology, and surgery. Conclusions: While Switzerland has a robust network of experienced oncology professionals and supportive services, the integration of GO remains inconsistent and sparse. Addressing barriers such as personnel training, policy frameworks, and fostering research collaboration is essential. Initiatives to promote early education in GO, advocate for patient-centered care, and establish national consensus standards are critical next steps. These efforts will position Switzerland as a leader in geriatric oncology amidst a rapidly aging European population. Future Directions: The Swiss Geriatric Oncology Group (SGOG) aims to expand its efforts in education, research, and policy advocacy to bridge gaps in the implementation of GO, ensuring optimal care for older cancer patients.

Barriers and facilitators to implementing the clinical trial navigator program: A qualitative analysis.

e23198 Background: The Clinical Trials Navigator (CTN) Program was launched in 2019 to increase enrollment in cancer clinical trials. It is free for all Canadians, and over 550 people have now used the Program. This study assesses the perspectives of people with cancer regarding the implementation of clinical trial navigation in Canada. Methods: People with cancer and their caregivers (n = 21) were recruited from across Canada to participate in a 30-60 minute semi-structured interview. Based on the Consolidated Framework for Implementation Research (CFIR) domains, we assessed the benefits, needs, facilitators, and access to clinical trial navigation in Canada. Thematic analyses were performed by two independent researchers in duplicate using inductive and deductive coding. Results: 10 Interviewees had contacted the CTN Program (Pre-CTN) but had not yet received navigation for clinical trials, 10 had never contacted the CTN Program (Non-CTN) and 1 completed the CTN Program (Post-CTN). The participants valued early and direct access to information about clinical trials and perceived the CTN Program as a unique and trustworthy resource because it was free and created by Canadians. CTN 9: “I know you're Canadian. So that matters to me because I'm also Canadian” CTN 5: “Yeah, it's ethical. It's transparent and free. Free is a good thing. Mostly what I'm looking for where I can get a straight answer for a straight question.” The CTN Program provided a sense of relief and control for people with cancer and their caregivers during their cancer journey. CTN 5: “It’s really important for people who are going through this kind of psychological and emotional whirlwind to know that there is that avenue available in Canada Participants valued finding a clinical trial when needed. Post-CTN 1 “Ohhhhh yes, because I'm going to know that I have options when I need them ...Good to know that I have something to fall back on...it certainly made a difference.” There were differences among oncologists in the support of identifying clinical trials, and the CTN Program was perceived as an important solution to reducing this variability. CTN 9 “And my oncologist is very well informed. So I don't know, my experience has been that. [Clinical trials] were mentioned to me. I don't know if everyone has that experience.” CTN 2 “And I've been asking my previous oncologist on how I can get on it, and I wasn't really helped, so I went out because ...my treatment options are limited and or there are none no more.” Conclusions: Our results illustrate the gaps in the Canadian clinical trial ecosystem and emphasize the value of the CTN Program to Canadians with cancer. The CTN Program addresses issues such as the timeliness and legitimacy of clinical trials information, increases patient sense of control and alleviates the burden of unexplored options.

Assessment of thermal characteristics in diverse lithium-ion battery enclosures and their influence on battery performance

Battery technology is an emerging research domain within the automotive sector, with a focus on various battery chemistries such as Li-ion, LiFePO4, NMC, and NaCl, as well as specialized cells like LiSOCl2. These chemistries are crucial in advancing electric vehicle (EV) battery technology. Batteries are available in different packaging formats, including prismatic, cylindrical, and pouch designs, each tailored to diverse operational environments. This study investigates the impact of these various battery packages on overall battery performance. Additionally, it assesses the influence of temperature on battery efficiency, aiming to identify the optimal temperature range for maximum performance. A significant part of the research focuses on the development of efficient battery thermal management (BTM) systems, which are designed to control and maintain battery temperature within the desired range, thereby enhancing efficiency. The outcomes of this study provide valuable insights for improving the reliability and efficiency of EV batteries. These findings are crucial for ensuring optimal battery performance and safety across different field conditions. Automotive manufacturers and battery suppliers can leverage these insights to refine their product designs, ensuring the dependability and safety of EV batteries. By enhancing battery performance through improved packaging and effective thermal management, this research contributes significantly to the advancement of EV technology, making electric vehicles more reliable and efficient for consumers.

On a new content indeterminacy problem in neuroscience

ABSTRACT Whether neurons represent or play a mere causal role is a foundational issue in philosophy of neuroscience. Evidence that neurons perform a representational role is weakened by the possibility of explaining experimental results by appeal to brute causal processes alone. Despite this, neuroscientists ascribe representational content to patterns of neural activity to explain experimental results. An important problem with this practice is determining which content to ascribe to the neural representation. One view is that researchers are only warranted in ascribing the content determined by particular experimental results. An alternative view is that researchers are warranted in appealing to the broader research domain to determine the content of a putative neural representation. In this paper, I argue that both are warranted; either alone is insufficient. Using optogenetics research on memory engrams as a case study, I show how researchers ascribe content to neural representations and justify their approach. Whether a particular content ascription is warranted depends on particular experimental results, the broader research domain that is appealed to, and how results from various animal models, probes, and experimental paradigms are generalized.

Machine learning-based optimisation algorithms for sentiment analysis: an analysis of the state-of-the-art and upcoming challenges

ABSTRACT The rapid growth of Internet-based applications, exemplified by the widespread adoption of social media platforms and weblogs, has started an era where comments and evaluations of everyday activities have become pervasive. In this context, sentiment analysis uses the capabilities of natural language processing, text analysis, and computational techniques to automate the extraction and classification of emotions and sentiments expressed in textual reviews. The insights derived from public opinions hold substantial value for businesses, governmental bodies, and individuals, serving as a critical source of information to support informed decision-making. Consequently, sentiment analysis has become a focal point within the field of decision support systems. Moreover, sentiment analysis based on machine learning (ML) has emerged as a cross-disciplinary approach to exploring opinion mining, particularly within the domains of media and communication research. These ML-based models have demonstrated great promise in achieving the goals of social media analysis due to their ability to capture both the structural subtleties and semantic complexities of text, without the need for the extensive feature engineering that characterised earlier approaches. While marketing researchers have employed various methods to analyse textual reviews, there remains a lack of a comprehensive performance evaluation framework, which complicates the selection of appropriate methods for future applications. This situation raises a fundamental question: What aspects are most frequently examined by researchers using ML-based sentiment analysis methods? To address this question, we propose a systematic literature review focused on sentiment analysis using ML techniques. This comprehensive review examines recent research efforts, highlighting the contributions of various scholars and focusing on ML techniques categorised into four primary clusters. The findings demonstrate the effectiveness of ML methodologies in conducting sentiment analysis with greater efficiency and accuracy. Notably, these ML models outperform their simpler counterparts, leading to a strong endorsement of their superiority.

Mapping the Green Frontier: Bibliometric Analysis of Sustainable Marketing & Consumer Behavior in Hospitality

Environmental sustainability and green marketing have emerged as critical priorities within the hospitality industry. Nonetheless, a comprehensive understanding of how these concepts intersect—especially regarding consumer behavior, corporate strategies, and emerging technological enablers—remains underexplored. This study bridges that gap by employing an extended, cross-database bibliometric approach that integrates advanced co-occurrence, co-citation, bibliographic coupling, and co-authorship analyses. Drawing on a robust dataset of 67,059 articles extracted from Scopus and ABS-indexed journals—and triangulated with 229 core studies filtered from the Web of Science (WoS)—we map the evolution of primary thematic clusters and subtopics over time. These include green supply chain management, pro-environmental purchase intention, AI-driven eco-innovation, transformative tourism, and crisis management. Our methodology involves refining and applying carefully curated keyword sets (e.g., “green marketing,” “pro-environmental behavior,” “hospitality,” “consumer behavior”), extracting large-scale records from Scopus and ABS-indexed journals, and incorporating WoS core studies to ensure comprehensive coverage and cross-validation of emerging trends, and utilizing specialized clustering techniques—such as fractional counting, LinLog normalization, and modularity-based clustering—to achieve high-resolution thematic groupings. The advanced analyses reveal two dominant yet interlinked research domains: (a) corporate sustainability and marketing strategies, and (b) attitudinal and psychological factors driving pro-environmental consumer behavior. Key results indicate that green marketing approaches—underpinned by robust brand image, CSR initiatives, and circular economy practices—significantly influence purchase intentions and consumer loyalty, particularly in hotels and tourism settings. Moreover, digital transformation, evidenced by the integration of AI-based personalization and big data analytics, is reshaping consumer engagement by enabling real-time, data- driven insights to predict and influence eco-friendly behaviors. These findings highlight the multidisciplinary and rapidly evolving nature of green marketing research, calling for intensified cross- disciplinary collaboration among marketing, environmental psychology, and technological innovation. For practitioners, our results highlight the necessity of implementing authentic, data-driven green strategies to meet the demands of increasingly eco-conscious consumers. Thus, the originality of this study lies in its holistic, state-of-the-art bibliometric methodology, which elucidates not only the historic landscape of green marketing research but also the emergent frontiers that can guide both researchers and industry stakeholders toward more impactful sustainability practices.

Evidence categories in systematic assessment of cancer overdiagnosis

The phenomenon of cancer overdiagnosis, the diagnosis of a malignant tumour that, without detection, would never lead to adverse health effects, has been reported for several cancer types in different populations. There has been an increase in studies focused on overdiagnosis, creating an opportunity to synthesise evidence on specific cancer types. However, studies that systematically assess evidence across different research domains remain scarce, with most of them relying on data from studies that already mentioned overdiagnosis as a potential concern. In this review, we consider several evidence categories that are used to systematically assess the presence and magnitude of overdiagnosis, including (1) data from cancer surveillance, (2) studies exploring the ‘true’ prevalence of cancer in the population, (3) studies that explore the use of diagnostics and its effect on incidence and mortality and (4) studies that explore changes and progress in cancer management and its effect on cancer mortality. This article highlights the strengths and weaknesses of different evidence categories, provides examples of studies on different cancer types and discusses how these categories can help synthesise evidence on cancer overdiagnosis.

Prompting the Machine: Introducing an LLM Data Extraction Method for Social Scientists

This research note addresses a methodological gap in the study of large language models (LLMs) in social sciences: the absence of standardized data extraction procedures. While existing research has examined biases and the reliability of LLM-generated content, the establishment of transparent extraction protocols necessarily precedes substantive analysis. The paper introduces a replicable procedural framework for extracting structured political data from LLMs via API, designed to enhance transparency, accessibility, and reproducibility. Canadian federal and Quebec provincial politicians serve as an illustrative case to demonstrate the extraction methodology, encompassing prompt engineering, output processing, and error handling mechanisms. The procedure facilitates systematic data collection across multiple LLM versions, enabling inter-model comparisons while addressing extraction challenges such as response variability and malformed outputs. The contribution is primarily methodological—providing researchers with a foundational extraction protocol adaptable to diverse research contexts. This standardized approach constitutes an essential preliminary step for subsequent evaluation of LLM-generated content, establishing procedural clarity in this methodologically developing research domain.

Unpacking the connection between funding configurations and research teams’ interdisciplinary practices

Abstract Research funding programs often portray interdisciplinary research as crucial to stimulate scientific creativity, boost innovation, and tackle societal challenges. However, structural conditions in the organization and governance of science are not typically conducive to performance of interdisciplinary research. Although funding programs encourage interdisciplinarity, there is no systematic evidence on the connection between research funding and interdisciplinary practices. In the present study we investigate whether receipt of financial support from diverse funding organizations is more likely to promote interdisciplinary research. We employ funding acknowledgement data to assess different funding configurations (i.e. diversity of funding sources) and use co-authorship networks to examine interdisciplinarity in teamwork dynamics. We focus on three European countries (Norway, Denmark, the Netherlands) and two research domains (Food Science and Renewable Energy) over the 11-year period 2009-2019. Panel data analysis reveals a positive association between a diverse funding configuration and interdisciplinary research at the research team level. This finding holds for different aspects of funding (e.g. funder origin, number of funders, funder type) and different measures of interdisciplinarity (e.g. variety, balance, disparity). We argue that research teams that respond to multiple funder priorities are more likely to engage in interdisciplinary research.

IoT-based Smart Device for Monitoring Cardiovascular Disorder: Detection and Treatment Towards Telemedicine

Background: The IoT has transformed the data collection and analysis of the data, specifically in smart cities, smart farming systems, smart tutoring, and healthcare systems. At the time of the COVID-19 situation, the medical area has provided an enhanced understanding of IoT for distant health monitoring to fulfill patient requirements efficiently. The main features of the technology are to enhance the human health. Flexible electronics play a crucial role in wearables and fulfilling real-time monitoring and telemedicine results. The healthcare scheme utilizes IoT to satisfy the demands of the challenges and its novel applications in COVID-19. The demand for the healthcare system has increased in COVID-19 time to provide aid to patients at their place. The IoT system efficiently examines the patients by the network to ignore the challenges that arise due to the healthcare system. Various data were investigated by Google Scholar, Elsevier, ACM, Research Gate, Scopus, and many more. The manuscript gives the research domain for IoT applications in healthcare systems to provide a non-invasive examination of the system. The integration of AI with IoT enhances computation and is also advantageous for smart systems. In the modern world, flexible electronics have an important role in human health. Nowadays, many wearable smart devices are used in the monitoring of human health. Novel telemedicine is also used in the treatment of humans. Because without humans, the world is nothing; therefore many world organizations like WHO, Welcome Trust, Gates Foundation, etc., are trying to save the life and introduce emerging technologies in the field of human health. We want to introduce some wearable smart devices for the human health monitoring system with treatments using telemedicine. Our systems supported the 2030 sustainable goal of the United Nations. We designed two flexible electronics-based wearable devices, such as a smart kit and smart t-shirt, using telemedicine for the detection of COVID and ECG. Methods: The study is carried out and utilized in flexible T-shirts and wearable, flexible electronics for healthcare applications. The research can be conducted by taking the database from Google Scholar, Elsevier, Research Gate, physionet.org, and GitHub. The database can be used to detect COVID-19 for estimating ECG and all the vital parameters used for detection. There are two devices that we are proposing in the manuscript: one is a smart portable IoT-based healthcare monitoring system and the other one is a smart T-shirt. The AI being integrated into IoT can be used for data analytics and prediction of the disease. Results: The study illustrates the addition of IoT with flexible electronics system and telemedicine, which crucially enhances the healthcare system by making them non-invasive and initial diagnosis of COVID-19 and health problems. Giving real-time data analytics and distant healthcare explanations, reducing hospital visits. Conclusion: IoT-based illustrations, specifically wearables like a smart kit and smart T-shirt, carry the crucial potential for evolving healthcare. These inventions align with worldwide health imports, giving mountable, maintainable and patient customized devices. The future work can be exceeded to improve device abilities for adding the advancing implementations for health domains.

From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques.

The underwater world has gained significant attention in research in recent years, particularly in the context of ocean exploration. Images serve as a valuable data source for underwater tasks, but they face several issues related to light behavior in this environment. Given the complexity of capturing data from the sea and the large variability of environmental components (depth, distance, suspended particles, turbidity, etc.), synthesized underwater scenes can provide relevant data to improve image processing algorithms and computer vision tasks. The main goal of this survey is to summarize techniques to underwater image synthesis, their contributions and correlations, and to highlight further directions and opportunities in this research domain.

Shared service centers (SSCs) and administrative cost reduction: a systematic review and research agenda

PurposeFor decades, the concept of Shared Service Centers (SSCs) has been recognized in management discourse as a strategic approach to restructuring organizational support functions. Scholars have extensively examined this organizational trend, consistently emphasizing cost savings as the primary rationale behind SSC implementation. However, a comprehensive synthesis of the existing research – encompassing both qualitative and quantitative evidence – to substantiate this critical claim is still lacking. The purpose of this study is to fill this gap by systematically analyzing the SSC literature and assessing the empirical support for cost savings as a central implementation motive.Design/methodology/approachA systematic literature review was conducted, involving a screening of scientific databases for SSC-related publications. Following a structured review procedure, 89 articles were identified as containing information on cost savings. These prioritized publications were subjected to a detailed framework-based analysis employing a Theories-Characteristics-Contexts-Methods scheme to extract both qualitative and quantitative data. Ultimately, 306 relevant commentaries were gathered, with 40 publications offering author-generated evidence that underwent an in-depth analysis.FindingsThe structured evaluation of the evidence highlights a significant research gap: the lack of quantitative evidence based on objective financial metrics to substantiate the reduction of administrative costs achieved by SSC organizations within a company-wide profit-and-loss context. Existing research predominantly emphasizes qualitative commentary, often referenced from non-scientific third-party sources. When quantitative evidence is presented, it is frequently derived from single case studies and lacks detailed information on the calculation methodology and the associated baseline.Originality/valueThis paper represents the inaugural publication providing a comprehensive mapping of cost-saving evidence within the research domain. The findings underscore the urgent need for the development of a standardized approach capable of effectively capturing the cost-saving contributions of SSCs. Greater transparency regarding the concrete effects of SSCs in this context would significantly enhance top management’s decision-making processes regarding the implementation and expansion of such concepts. Enhanced quantitative rigor would constitute a pivotal advancement in the scientific field, addressing longstanding debates and establishing a novel approach to validating such contributions.

How might we achieve water security? An interdisciplinary case study in the Netherlands to inspire future research and policy

AbstractClean water that is available, accessible, and utilizable in a stable way is essential for nature and society. Yet, nature and society face water security risks including pollution, scarcity, and flooding. We, as a multidisciplinary scientific team, set out to identify timely interdisciplinary questions at the frontiers of water security research to inspire and guide future research and policy. To do this, we surveyed academics (n = 51), predominantly those working in the Netherlands, across 33 disciplines. Based on the responses we received, we identified 125 questions, linked them to four pillars of water security (availability, accessibility, utilizability, and stability), and evaluated them for their relevance to four broad research domains (law and governance, nature and ecology, societal impact and awareness, and technology and innovation). After this process, we tallied the number of domains associated with each question: the majority of questions (78%; n = 98) were associated with at least two domains, with the rest being associated with one or none. Furthermore, questions (n = 47) most frequently focused on the intersection of availability (pillar) and technology and innovation (domain), while very rarely (n = 8) incorporated both accessibility (pillar) and nature and ecology (domain). We invite others to engage in similar exercises with the overall goals of expanding the list of questions and distinguishing questions that are globally, regionally, or locally relevant. To address these pressing yet complex water‐related questions, we encourage fellow researchers to look beyond their own disciplines and to maintain or initiate inter‐ and transdisciplinary collaborations. Also, we call on funding agencies, scientific associations, and organizing committees of scientific conferences to provide the resources and platforms that are needed to stimulate interdisciplinary research and disseminate results.

Assessing the Research Skills of Bachelor of Elementary Education Students at Apayao State College: A Needs-Based Approach

In today’s academic landscape, research competency is a foundational requirement for student success in higher education and lifelong learning. This study aimed to assess the research skills of Bachelor of Elementary Education (BEEd) students at Apayao State College using a contextualized, researcher-developed self-assessment tool grounded in the Philippine Professional Standards for Teachers (PPST). Specifically, the study evaluated student proficiency in seven core research domains: conceptualizing research titles, formulating the statement of the problem, developing the research paradigm, identifying the research design, analyzing data, interpreting and discussing results, and writing conclusions and recommendations. A descriptive-evaluative research design was employed, involving a total enumeration of 78 first-, second-, and third-year BEEd students. Quantitative data were collected through a 35-item Likert-type instrument, validated by a panel of faculty experts. Statistical analysis was conducted using mean scores to identify skill levels, with descriptors anchored on PPST-based standards. Findings revealed that the respondents’ overall research competence was at the “Developing” level with a grand mean of 2.12, indicating foundational awareness but limited independent application of research processes. The highest-rated skill area was Identifying the Research Design with a mean of 2.57, while the lowest were Developing the Research Paradigm (1.95), Analyzing Data (1.93), and Interpreting Results (1.96). The study identified key skill gaps in higher-order reasoning, synthesis, and data interpretation. Based on these, a needs-based intervention matrix was proposed, including workshops, mentoring, and faculty-led research support strategies. This study highlights the necessity of structured, contextualized interventions to elevate student research capabilities and strengthen institutional research culture in higher education institutions.

Impression Management Tactics in the Chairperson’s Statement: A Systematic Literature Review and Avenues for Future Research

The chairperson’s statement (CS) has evolved into a key component of corporate reporting, offering an authoritative, high-level summary of a company’s activities, initiatives, operations, financial performance, and achievements over the preceding financial year, along with insights into future outlooks. Recognised for its informative value, the CS is consistently ranked by stakeholders as the most read and most influential section of the integrated report. Despite its importance, the CS is also a platform where corporate management often engages in impression management (IM) to portray a biased and overly positive image of the company. This study conducted a systematic literature review to examine the IM tactics employed within the CS. Based on the findings, an integrative conceptual framework was developed. Identified IM tactics include readability, textual characteristics, the influence of culture, legal systems and capital markets, paratext and intertextuality, the tone of language, forward-looking statements, retrospective sense-making, ambiguous language, the use of photographs and graphs, impersonalisation and evaluative language, and self-serving attributions. The results highlight that the study of IM strategies in CSs represents a rich and relevant research domain that warrants deeper exploration. Given its qualitative complexity and underexplored dimensions, this area offers several promising avenues for future investigation.

Incremental Learning with Dynamic Adaptive Elastic Weight Consolidation for Adaptive, Scalable, and Generalizable User-Defined Behavior Recognition and Analysis of Cetacean and Pinniped Species

Traditional animal behavior recognition models require extensive labeled datasets and frequent retraining, limiting their adaptability across species and environments. Additionally, existing systems rely on predefined behavior categories, making it difficult for researchers to customize recognition models to specific behavioral patterns relevant to their studies. Different research fields, such as animal welfare monitoring, conservation, and behavioral ecology, often require distinct behavior classifications, yet current systems lack the flexibility to accommodate these varying needs. This study aims to develop an expandable and user-driven animal behavior recognition system utilizing DeepLabCut for pose estimation and a BiLSTM-based classification model. By integrating Dynamic Adaptive Elastic Weight Consolidation (DA-EWC), the system enables incremental learning, allowing new behaviors to be added with minimal annotation while preserving previously learned behaviors. The proposed system is trained on dolphin behavior datasets using DeepLabCut for keypoint extraction and a BiLSTM model for sequence classification. Additionally, a user-friendly interface is implemented to facilitate behavior annotation and efficient model updates. The proposed system achieves 96.5% accuracy in behavior classification, surpassing conventional models such as Faster R-CNN. Compared to standard EWC, DA-EWC maintains an average of 8.3% higher accuracy when incorporating new behaviors. Furthermore, the system reduces annotation efforts by 9.3%, enabling users to expand behavior categories efficiently. This expandable behavior recognition system significantly enhances adaptability and efficiency in animal behavior monitoring. By supporting user-driven incremental learning, it provides a scalable solution for behavior analysis across different research domains, addressing the need for customizable and evolving behavior classification.

Authenticated general hierarchical multi-secret sharing scheme based on high-dimensional quantum system

Abstract Quantum secret sharing, a pivotal research domain within quantum cryptography, has significant applications across diverse fields such as information security and distributed quantum computing. This paper proposes a d-dimensional hierarchical quantum multi-secret sharing scheme with a general access structure. In the proposed scheme, the distributor constructs a special entangled state by leveraging the secret values at each level and a monotone span program. During the secret reconstruction phase, participants only need to measure the particles in their possession, and authorized participants at each level can recover multiple secrets shared at that level. Additionally, each participant can independently compute and obtain multiple lower-level secrets but cannot access any higher-level secret information. Compared to existing hierarchical quantum secret sharing schemes, the proposed scheme is better suited for specific application scenarios with unique requirements. Furthermore, security analysis demonstrates that the scheme can resist various attacks, such as measurement attacks, intercept-resend attacks, entanglement-measurement attacks, Trojan horse attacks, and collusion attacks.

AI usage among medical students in Palestine: a cross-sectional study and demonstration of AI-assisted research workflows

BackgroundArtificial Intelligence (AI) is transforming medical education globally, offering solutions to challenges such as resource limitations and limited clinical exposure. However, its integration in resource-constrained settings like Palestine remains underexplored. This study evaluates the prevalence, impact, and challenges of AI use among Palestinian medical students, focusing on academic performance, clinical competence, and research productivity.MethodsA cross-sectional study was conducted among 590 medical students from Palestinian universities. Data were collected using a validated electronic questionnaire, covering demographics, AI usage patterns, and perceived impacts across academic, clinical, and research domains. Initial analysis was conducted using AI tools, specifically ChatGPT, to facilitate insights and structure the results effectively. Statistical analyses were performed using IBM SPSS v27 to validate findings. Statistical significance was set at p < 0.05. The draft underwent detailed reviews by the research team to confirm accuracy and validity.ResultsAI adoption was high, with 87% of students frequently using tools like ChatGPT (76%) and virtual simulators (26%). Students reported significant improvements in academic performance (mean score: 4.2, SD = 0.7) and research productivity (mean score: 4.5, SD = 0.6), particularly in literature reviews and data analysis. Clinical competence received moderate ratings (mean score: 3.6, SD = 0.8), reflecting AI's limited role in practical skill development. Time management was highly rated (mean score: 4.6, SD = 0.5), highlighting AI's ability to automate repetitive tasks. Challenges included ethical concerns, data accuracy, and limited AI literacy, with 91% lacking formal AI training.ConclusionAI demonstrates significant potential to enhance medical education in resource-constrained settings by improving academic outcomes and research efficiency. ChatGPT played a critical role in this study, not only as a tool used by participants but also in the research process itself, including data analysis and manuscript drafting. These findings were cross-verified using SPSS to ensure robustness. Despite its promise, limitations in practical clinical applications and technical understanding highlight the need for targeted AI literacy programs and ethical guidelines. This study underscores the importance of integrating AI into medical curricula to address existing gaps and maximize its benefits in similar global contexts.

Machine and deep learning for personality traits detection: a comprehensive survey and open research challenges

Natural language processing (NLP), a prominent research domain of Artificial Intelligence (AI), analyzes users’ generated content on social media for various purposes like sentiment analysis, text summarization, chatbots, fake news detection, etc. Recent advancements in NLP have helped for analysis of human behavior analysis and predicting various human personality traits. Understanding personality traits has long been a fundamental pursuit in psychology and cognitive sciences due to its vast applications for understanding from individuals to social dynamics. Due to online social platforms where people express their views, experiences and comments, NLP is applied for users’ behavior and personality analysis, which is helpful in defining marketing strategies, consumers’ behavior analysis, team building, etc. This research study provides a comprehensive overview of existing methodologies, applications, and challenges in the field of personality traits detection using shallow machine learning, ensemble learning and deep learning. To conduct this study, recent research publications relevant to NLP for this new but emerging research domain are reviewed. The background knowledge of personality models of various nature is discussed for better domain understanding. The study encompasses machine learning and deep learning models with thorough analysis of traditional and innovative techniques including ensemble learning and transformer-based models in chronological order highlighting the trend analysis showing evolution of application of advanced methods. The review also presents and compares the widely used datasets which may guide the researchers for selection of datasets in future studies. Performance evaluation metrics have been discussed which are used in the relevant literature. Furthermore, it explores the application of research of personality traits detection in various domains highlighting its significance. We have also carried out extensive empirical analysis using conventional textual to advanced deep embedding features and applying machine learning, ensemble learning and deep learning algorithms. Finally, before conclusion, the review highlights the open research issues and challenges as potential future directions for the researchers.

Acceptability and feasibility of a task-shifted collaborative care model for depression and anxiety in primary HIV clinics in the Philippines: a qualitative inquiry

BackgroundDepression and anxiety can greatly impact the overall health of a person living with HIV (PLHIV). Management of mental health disorder should be an integral part of HIV care. The Collaborative Care Model (CoCM) is an evidence-based model of care that integrates mental health in primary care. This study aimed to assess the acceptability and feasibility of implementing the CoCM for depression and anxiety in HIV clinics in the Philippines using HIV counsellors as care managers.MethodsWe conducted a descriptive qualitative study by facilitating focus group discussions (n = 7) and key informant interviews (n = 18) with 53 HIV and mental health stakeholders, including PLHIV (n = 20), HIV counsellors (n = 11), physicians (n = 10), clinic heads (n = 4), policy makers (n = 4), and mental health providers (n = 4) from August 2021 to March 2022. Participants were recruited from 17 HIV clinics in the Philippines. We employed a thematic analysis using the Consolidated Framework for Implementation Research (CFIR) domains as themes.ResultsAlmost all PLHIV participants were men (95%), with a mean age of 28 years. The other stakeholders had a mean age of 44 and had worked in their field for an average of 8 years. Overall, 58% were women. Factors that influenced the acceptability of the CoCM included the possibility of increased access to mental health services with a more holistic care team. Perceived barriers included inadequate numbers of psychiatrists, an overburdened and understaffed HIV workforce, low mental health knowledge among HIV providers, and implementation cost. Perceived facilitators were willingness of HIV providers to provide care and knowledge of HIV counselling.ConclusionWe found the CoCM to be acceptable among study participants. Recommendations included capacity building for HIV providers, collaborations within and across clinics to facilitate access to psychiatrists, clear management protocols, and pilot testing. Mental health and HIV care coverage within national policies should be amended to allow for non-mental health specialists to provide low-intensity therapies. Closer partnerships among HIV and mental health policy makers would improve integration implementation.