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Global Work Arrangements and Outsourcing in the Age of AI
No full textThe rise of AI has reshaped outsourcing and work arrangements in global businesses, transforming how businesses operate and allocate tasks across borders. The use of AI in automation and intelligent workflow management, which enables companies to streamline operations, reduces costs and enhances productivity. While outsourcing has long been a strategy for optimizing labor costs and accessing specialized talent, AI further revolutionizes this landscape by automating routine tasks and augmenting human capabilities. Further exploration may reveal new applications of intelligent technology in the global workforce.Global Work Arrangements and Outsourcing in the Age of AI explores the transformations of global business and workplace environments. It delves into the roles of technology, environmental considerations, mental health, regulatory frameworks, and corporate social responsibility in shaping the future of work, providing an understanding on how work models can adapt to meet development goals. This book covers topics such as resource AI, global development, and sustainability, and is a useful resource for academics, policymakers, business owners, and environmental scientists
A dynamic integrative view of signaling strategies during the franchisee recruitment process
Get PDFDuring the franchisee recruitment process, franchisors and franchisee candidates must both engage and screen each other to determine whether they are a suitable match. To facilitate this, they usually exchange a number of signals that reduce information asymmetry. Whilst several authors have attempted to examine the role of signals in the decision-making process, these studies have overlooked the evolution of signals over the recruitment process and the active role of both parties in reaching an informed decision. Building on a dynamic integrative view of signaling theory, our research aims to provide a more thorough understanding of the signaling strategies franchisors and franchisee candidates adopt during this process. We rely on data stemming from in-depth interviews with 29 franchisors and 34 franchisee candidates in the French market. Our findings challenge the literature on franchisee recruitment by showing how franchisors and candidates alike customize their signals and screen each other over the recruitment process in order to decide whether to contract based on their assessment of person-organization fit. Our research thus confirms the relevance of frameworks combining the latest developments in signaling theory and the person-organization fit perspective to examine contexts of information asymmetry, thereby opening new avenues for research in B2B settings. Additionally, our paper offers managerial implications for franchisors and franchisee candidates by helping them effectively send and interpret mutual signals to avoid adverse selection
Challenges and Feasibility of Multimodal LLMs in ER Diagram Evaluation
Get PDFThis study investigates the capabilities of four multimodal large language models (MMLLMs), namely GPT-4o, Claude-3 Sonnet, Grok-4, and Gemini-2.5-pro, in assessing Entity-Relationship (ER) diagrams created by students. These diagrams are widely used in database design education, yet their evaluation is time-consuming and often subjective. Automated assessment may reduce instructor workload and provide timely, formative feedback. We developed a structured rubric to evaluate entities, attributes, relationships, and cardinalities, and conducted a controlled experiment with forty student diagrams created in Chen notation and Crow’s Foot notation Models were evaluated under four conditions that combined two types of input context, case description based and solution comparison, with two prompting strategies, with or without Chain of Thought(CoT) reasoning. The results of the structural analysis showed that the models extracted the main components of the diagrams reasonably well, although cardinalities were the most difficult to interpret. For rubric-based scoring, alignment with human grading improved when CoT reasoning was used with Chen notation, although its effect in Crow’s Foot notation was mixed. These findings indicate that notation style, input context, and reasoning play important roles in automated evaluation, and that multimodal models can offer scalable support for formative assessment in database education
Weakly Supervised SAR Ship Oriented-Detection Algorithm Based on Pseudo-Label Generation Optimization and Guidance
Get PDFIn recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using HBB labels, effectively addressing scarce OBB annotation data and advancing SAR ship OBB detection. However, current methods for oriented SAR ship detection still suffer from issues such as insufficient quantity and quality of pseudo-labels, low inference efficiency, large model parameters, and limited global information capture, making it difficult to balance detection performance and efficiency. To tackle these, we propose the weakly supervised oriented SAR ship detection algorithm based on optimized pseudo-label generation and guidance. The method introduces pseudo-labels into a single-stage detector via a two-stage training process: the first stage coarsely learns target angles and scales using horizontal bounding box weak supervision and angle self-supervision, while the second stage refines angle and scale learning guided by pseudo-labels, improving performance and reducing missed detections. To generate high-quality pseudo-labels in large quantities, we propose three optimization strategies: Adaptive Kernel Growth Pseudo-Label Generation Strategy (AKG-PLGS), Pseudo-Label Selection Strategy based on PCA angle estimation and horizontal bounding box constraints (PCA-HBB-PLSS), and Long-Edge Scanning Refinement Strategy (LES-RS). Additionally, we designed a backbone and neck network incorporating window attention and adaptive feature fusion, effectively enhancing global information capture and multiscale feature integration while reducing model parameters. Experiments on SSDD and HRSID show that our algorithm achieves an mAP50 of 85.389% and 82.508%, respectively, with significantly reduced model parameters and computational consumption
Compact Wideband Active Integrated Antenna Array Performance Enhancement Under High Mutual Coupling for 5G Applications
Get PDFThis paper presents a compact solution for the design and analysis of the Active Integrated Antenna Array (AIAA) to improve its performance under high mutual coupling and mismatch losses. A simple compensation network (CN) is introduced to eliminate the mismatches in AIAA introduced by the antenna elements’ mutual coupling, which negatively impacts the power amplifier (PA) performance, rather than the previously proposed complex methods. The AIAA consists of a wideband GaN power amplifier (PA) transistor operating from 3 to 5 GHz, a wideband 4-element antenna array with λ g/2 spacing between elements at 3.8 GHz operating from 3.6 to 4 GHz for 5G applications, and a compensation network designed to mitigate mismatch effects. To verify the proposed approach, two AIAA prototypes are measured, one with the CNs connected between each PA and the corresponding antenna element, and the other with the antenna elements directly connected to the PAs. Experimental results show that the minimum realized gain of the proposed AIAA is more than 19.3 dBi over the operating bandwidth, with an enhancement of more than 2.5 dBi when compared to the conventional AIAA, where no CNs are used. Simulations and experimental results are presented along with detailed design and measurement procedures
Digital Dementia: Smart Technologies, mHealth Applications and IoT Devices, for Dementia-Friendly Environments
Get PDFThe global increase in dementia cases, which is predicted to exceed 152 million by 2050, poses substantial challenges to healthcare systems and caregiving structures. Concurrently, the expansion of mobile health (mHealth) technologies offers scalable, cost-effective opportunities for dementia care. This study systematically reviews 100 publicly available dementia-related mobile applications on the Apple App Store (iOS) and the Google Play Store (Android), categorised using the Mobile App Rating Scale (MARS), as well as the targeted end-users, Internet of Things (IoT) integration, data protection, and cost burden. Applications were evaluated for their utility in cognitive training, memory support, carer education, clinical decision-making, and emotional well-being. Findings indicate a predominance of carer resources and support tools, while clinically integrated platforms, cognitive assessments, and adaptive memory aids remain underrepresented. Most apps lack empirical validation, inclusive design, and integration with electronic health records, raising ethical concerns around data privacy, transparency, and informed consent. In parallel, the study identifies promising pathways for energy-optimised IoT systems, Artificial Intelligence (AI), and Ambient Assisted Living (AAL) technologies in fostering dementia-friendly, sustainable environments. Key gaps include limited use of low-power wearables, energy-efficient sensors, and smart infrastructure tailored to therapeutic needs. Application domains such as cognitive training (19 apps) and carer resources (28 apps) show early potential, while emerging innovations in neuroadaptive architecture and emotional computing remain underexplored. The findings emphasize the need for co-designed, evidence-based digital solutions that align with the evolving needs of people with dementia, carers, and clinicians. Future innovations must integrate sustainability principles, promote interoperability, and support global aging populations through ecologically responsible, person-centred dementia care ecosystems
Worldwide Soundscapes: A Synthesis of Passive Acoustic Monitoring Across Realms
Get PDFAimThe urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean).LocationWorldwide, 12,343 sites, all ecosystem types.Time Period1991 to present.Major Taxa StudiedAll soniferous taxa.MethodsWe synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms.ResultsTerrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm2) than aquatic sampling (0.3 and 1.8 sites/Mkm2 in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds.Main ConclusionsPAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio-temporal replication in freshwater habitats. Overall, this worldwide PAM network holds promise to support cross-realm biodiversity research and monitoring efforts
Building the future of exercise oncology: current status of international workforce development and integration into standard cancer care
No full textThe complex requirements of people with cancer can impact the provision of safe, effective, evidence-based exercise prescription. Consequently, a range of essential competencies are required from the exercise oncology workforce. There is a global need for a standardized approach to the development of this workforce. By defining, standardizing, and training the workforce in essential competencies, this will enable various professionals to safely and effectively screen, access, design, and deliver appropriate exercise programs. Therefore, this is also a call for a global collaboration on the development of the exercise oncology workforce with special attention to assisting low- or middle-income countries with their increasing cancer burden and unique challenges, which may require unique context-specific strategies. The building of an appropriate internationally standardized workforce is essential in the provision of physical activity and exercise options as part of standard cancer care
Basking sharks of the Arctic Circle: year-long, high-resolution tracking data reveal wide thermal range and prey-driven vertical movements across habitats
Get PDFUnderstanding the movement ecology of marine megaplanktivores is essential for conserving these ecologically significant species and managing their responses to environmental change. While telemetry has advanced our knowledge of filter-feeding mammal migrations, the annual movement patterns of large filter-feeding sharks, such as basking sharks (Cetorhinus maximus), remain poorly understood. This is particularly the case near their high latitude range limits where climate impacts are intensifying. In this study, we deployed pop-up satellite archival tags (PSATs) on C. maximus in northern Norway to investigate individual movement patterns and possible environmental drivers over an entire annual cycle
Learn@Lunch: Developing a continuing professional development programme to raise knowledge and awareness of drinking alcohol as an occupation in later life
Get PDFIntroduction:: Occupational therapists in acute practice are increasingly likely to work routinely with people in later life who drink alcohol. Therefore, this knowledge translation study aimed to evaluate the development of a continuing professional development Learn@Lunch programme, designed to enhance awareness and understanding of occupational therapists who work in an acute hospital setting, of drinking alcohol as an occupation in later life. Method:: Guided by the Promoting Action of Research Implementation in Health Sciences framework, the continuing professional development programme was developed, and a qualitative evaluation undertaken. This included pre- and post-focus groups with occupational therapists (n = 8) in an acute setting, where the programme was delivered, pre- and post-programme delivery interviews with the research champion recruited from the site, and one interview with the Allied Health Professions Service Lead. Findings:: Findings indicate the Learn@Lunch continuing professional development programme enhanced therapist knowledge and understanding of the changing patterns of drinking alcohol in later life. However, practical barriers exist impacting how participants perceived the value and delivery of the programme. Conclusion:: Learn@Lunch was an effective continuing professional development programme and supported enhanced knowledge and awareness of drinking alcohol as an occupation in later life, leading to changes in local acute occupational therapy practice
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