Support Capability Research Articles

Gender Differences in the Adoption, Usage, and Perceived Effectiveness of AI Writing Tools

The purpose of this paper is to investigate gender differences in the adoption, use patterns and perceived effectiveness of AI writing tools by undergraduate students at the University for Development Studies (UDS). In light of the rapid adoption of AI tools into the landscape of higher education, it is important to examine male and female students as they relate to the usage and perceptions of AI in higher education. This study employs the Technology Acceptance Model (TAM) as a theoretical framework to explore how factors such as perceived ease of use, perceived usefulness, and attitudes toward technology influence AI adoption and usage patterns. A cross-sectional survey design was used for data collection using a structured questionnaire from 320 students across three departments. Statistical analyses (including t-tests and chi-square tests) were used to examine differences by gender. More than three-fourths (76.9%) of student respondents reported the use of AI tools, while 31.6% reported daily use. No significant differences were found between male and female students on perceived effectiveness, and both male and female students perceived AI tools as being effective. Results demonstrate the comorbid use of artificial intelligence in the academic writing context and imply that any gender differences in AI output are mitigated by the supportive capabilities of AI tools. The study's implications highlight the need for educators to provide equitable access and training in AI tools to support diverse student needs. Policymakers and developers should focus on designing inclusive AI writing tools that address potential barriers, ensuring all students benefit equally from technological advancements in education.

LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems

This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses.

Fighter optimal selection based on sequential multi-criteria decision-making with uncertainty measurement

Abstract Rapid and comprehensive fighter optimisation is an important part of modern combat decision-making. However, due to the numerous influencing factors, it is difficult for decision-makers to consider comprehensively and specify the optimal decision, and it is highly subjective, which leads to different decision conclusions from person to person. Therefore, to solve the above deficiencies in fighter selection, this paper proposes a sequential decision-making framework that comprehensively considers the effectiveness, maintenance, support capability and health status of the fighter aircraft. Based on the multi-dimensional state, it provides comprehensive and credible auxiliary support for commanders. The sequential decision-making framework (called GRA-VIKOR-IFNs) uses the combination of equation and fuzzy multi-criteria decision-making (FMCDM) to evaluate the effectiveness, support capability and health in turn, to complete the step-by-step selection of fighter models, troops and sorties. The evaluation equation is for the effectiveness evaluation and a hybrid method using the extended grey correlation analysis (GRA) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on intuitionistic fuzzy numbers (IFNs) is for the support capability and health evaluation. The proposed strategy is in line with the logic and demand of actual combat and training decision-making and takes into account the influence of uncertain factors. Finally, a comparison with some classical methods is carried out, such as the full consistency method (FUCOM), the technique for order of preference by similarity to ideal solution (TOPSIS) and so on. The GRA-VIKOR-IFNs method is consistent with the results of other methods and the result sort resolution is 0.0619 and at least 40% higher than other methods, which can lead the commanders to a more reliable and clear decision.

Assessing the clinical support capabilities of ChatGPT 4o and ChatGPT 4o mini in managing lumbar disc herniation

PurposeThis study evaluated and compared the clinical support capabilities of ChatGPT 4o and ChatGPT 4o mini in diagnosing and treating lumbar disc herniation (LDH) with radiculopathy.MethodsTwenty-one questions (across 5 categories) from NASS Clinical Guidelines were input into ChatGPT 4o and ChatGPT 4o mini. Five orthopedic surgeons assessed their responses using a 5-point Likert scale for accuracy and completeness, and a 7-point scale for reliability. Flesch Reading Ease scores were calculated to assess readability. Additionally, ChatGPT 4o analyzed lumbar images from 53 patients, comparing its recognizable agreement with orthopedic surgeons using Kappa values.ResultsBoth models demonstrated strong clinical support capabilities with no significant differences in accuracy or reliability. However, ChatGPT 4o provided more comprehensive and consistent responses. The Flesch Reading Ease scores for both models indicated that their generated content was “very difficult to read,” potentially limiting patient accessibility. In evaluating lumbar disc herniation images, ChatGPT 4o achieved an overall accuracy of 0.81, with LDH recognition precision, recall, and F1 scores exceeding 0.80. The AUC was 0.80, and the Kappa value was 0.61, indicating moderate agreement between the model’s predictions and actual diagnoses, though with room for improvement.ConclusionWhile both models are effective, ChatGPT 4o offers more comprehensive clinical responses, making it more suitable for high-integrity medical tasks. However, the difficulty in reading AI-generated content and occasional use of misleading terms, such as “tumor,” indicate a need for further improvements to reduce patient anxiety.

Impact of new fire support capabilities from a joint functions perspective

Impact of new fire support capabilities from a joint functions perspective

The foundation for a joint fire support capability using the NATO model

The foundation for a joint fire support capability using the NATO model

Enhanced Digital Capabilities for Building Resilient Supply Chains: Exploring Visibility, Collaboration, and Resilience in China’s Electric Vehicle Industry

This study investigates the impact of digital capabilities (DC) on building resilient supply chains in China’s electric vehicle (EV) industry. As the complexity of the EV sector continues to grow, improving supply chain resilience (SCR) is essential for sustaining long-term growth and maintaining competitiveness. This research focuses on how visibility and collaboration, supported by DC, contribute to the development of SCR. Using structural equation modeling (SEM), data from 399 Chinese EV supply chain enterprises were analyzed to examine the moderating effects of DC and their sub-dimensions—digital infrastructure capability, digital analytics capability, and strategic support capability—on the relationships between visibility, collaboration, and resilience. The results reveal that both visibility and collaboration significantly and positively influence resilience, with visibility having the strongest impact. Furthermore, digital analytics capability enhances the positive effect of collaboration on resilience, while overall DC and other dimensions, such as digital infrastructure and strategic support capabilities, show limited impact. The findings also underscore that digital infrastructure capability plays a vital role in amplifying the impact of visibility on resilience. Consequently, EV supply chain enterprises are encouraged to invest continuously in digital infrastructure and analytics capabilities to strengthen their SCR.

ServiceNow: Boosting Productivity and Innovation in Healthcare, Manufacturing, and Research

ServiceNow has emerged as a transformative force in enterprise digital transformation across healthcare, manufacturing, and research sectors. The platform's multi-instance cloud architecture enables robust workflow automation and system integration capabilities, fundamentally changing how organizations operate and deliver services. In healthcare, ServiceNow optimizes patient care through automated scheduling, EHR integration, and clinical pathway management, while ensuring regulatory compliance. The manufacturing sector benefits from enhanced production workflows, quality control automation, and predictive maintenance through IoT integration and real-time analytics. In research institutions, the platform streamlines grant management, resource allocation, and regulatory compliance while enabling secure collaboration across organizations. The implementation of comprehensive security measures, including end-to-end encryption and multi-factor authentication, ensures data protection across all sectors. Through phased implementation approaches and continuous improvement processes, organizations achieve significant operational efficiencies and cost reductions. Future developments in AI/ML integration and advanced connectivity options promise to further enhance the platform's capabilities in process automation and decision support.

Hotel digital intelligence capability: dimension exploration and scale development

Purpose This study aims to explore the dimensional structure of hotel digital intelligence capability and develop a measurement scale. Design/methodology/approach This study adopts qualitative and quantitative approaches to conduct an exploratory inquiry into the structural dimensions of hotel digital intelligence capability with the help of grounded theory. Based on this, several questionnaires were developed to test the measurement scale and verify its validity. Findings The results reveal that hotel digital intelligence capability comprises four dimensions: data collection and processing capability, customer service personalization capability, digital intelligence decision support capability and sustainable development capability. The measurement scale consists of four factors and 13 items, with reliability and validity tests demonstrating ideal levels. Originality/value This study not only provides a new perspective to understand hotel digital intelligence capability but also develops a corresponding measurement scale, laying a solid theoretical basis for hotel managers to scientifically evaluate this capability to achieve sustainable competitive advantage.

Preservation empowered dual-warehousing for perishable product under trade-credit financing scheme incorporating diverse demand

This work addresses a dealer's operational planning problem for handling perishable product's two-level trade through a financially sustainable supply chain set-up under two-level trade-credit and dual-warehousing arrangement equipped with preservation technology. Financial support is provided to client at both levels of trade, under bilaterally agreed situation-based specific terms and conditions alleviating lean cash flow. Pivotal objective of this work is developing a mathematical model that supports this decision-making situation for optimising cost efficiency in supply chain operations. Formulating this mathematical model and its solution methodology are perplexing due to multiple cases divulging through the utilisation of warehousing arrangements combined with financial outcomes of two-level trade-credit settlements. Both are accomplished in this work. Ancillary models are further developed for evaluating strategic benefits of trade-credit financing and additional warehousing space availability. Implementations on real case-based multiple data sets are presented, wherein computational results confirm the capability of decision support developed on provisioning all circumstances of warehousing requirements and financial liabilities towards both levels of trade credit. Comparative analysis performed through computations on ancillary models establishes strategic benefits of addressed contemporary business practices. Furthermore, variational analysis is extensively performed for identifying crucial parameters pertaining to the stability of the developed decision mechanism.

Modulation strategy of a DAB-type isolated DC-AC converter with reactive power support capabilities

Modulation strategy of a DAB-type isolated DC-AC converter with reactive power support capabilities

Construction of digital creation development model of intangible cultural heritage crafts in China

The innovative use of digital technology to create resources for intangible cultural heritage crafts (ICHCs) has emerged as a new focus for preserving and transmitting ICHCs. However, not enough research has been done on the variables impacting how ICHCs behave when they are exploited for digital cultural creativity. In order to synthesized and refined the key factors influencing the digital cultural and creative development of intangible heritage crafts. A theoretical model for the digital development of intangible heritage crafts was constructed, and the structural relationships among the factors within the model were analyzed. This study collects relevant textual data from the official websites of China’s main provinces’ ICH (Intangible Cultural Heritage) and large databases between 2016 and 2022. It also develops a theoretical model using grounded theory. The findings indicate that the five primary categories of innovation and inheritance needs, management and maintenance capacity, creative development type, creative application strategy, and creative development benefits have a significant impact. Among these, the ICHC’s digital innovation and development behaviors are a result of their need for innovation, development, and inheritance. The development of digital innovation is contingent upon possessing management and maintenance capacity, which includes technical expertise, digital platform construction, Education and Training support, Community collaboration support and digital governance capability. There are three development modes for digital innovation and ICHC’s development: primary, secondary, and fusion. It utilizes the Focused Crafts Ontology approach, the Branded IP Benefits strategy, and the Focus on Product Innovation approach. The practice of digital innovation and the growth of ICHCs have led to the creation of new opportunities and the benefits of creative development, which are evident in economic, social, and endogenous development. A precise theoretical foundation for systematically examining the digital creative growth of ICHC can be obtained from the research presented in this paper. Establish a digital cultural creation resource that the global community can utilize to revive Intangible Cultural Heritage (ICH) and preserve traditional crafts.

Inertia Support Capability Evaluation for Wind Turbine Generators Based on Symmetrical Operation

With the increasing integration of new energy into the grid, the level of system inertia has been significantly reduced, posing a severe challenge to frequency stability. Consequently, there is an urgent need for wind turbine generators (WTGs) to actively provide inertia support through virtual inertia control. Assessing the inertia support capability of WTGs reasonably and setting appropriate controller parameters based on this assessment is a topic worthy of discussion. As WTGs’ characteristics are mostly ignored in the evaluation of inertia support capability for WTGs, an evaluation method based on symmetrical operation is proposed. The proposed method considers the impact of real inertia and aerodynamic characteristics, thereby helping to determine reasonable virtual inertia coefficients and de-loading reserve capacity for WTGs. With the proposed method, it can be determined that large WTGs can provide inertia support capabilities close to those of synchronous generators to the grid without exceeding a 0.1% reduction in reserve capacity during de-loading operation.

Multi-Objective Coordinated Optimization Method of Active and Reactive Power Considering Power Characteristics of Renewable Energy Converters

In new power systems with a high proportion of renewable energy, optimization criteria based solely on economic efficiency or system stability may lead to a reduction in the static stability domain of the system or lead to long-term deviations from economic operation, thus reducing the overall applicability of such methods. This paper proposes a multi-objective active–reactive power coordinated optimization model that considers both economic efficiency and static stability indicators. The goal of the model is to minimize operating costs while optimizing static stability margins. It combines the reactive power support capabilities of converters and other reactive power compensation equipment to ensure safe and economical dispatch of the system. The proposed method is verified through a case study, which shows that this method can make full use of the potential reactive power regulation capability of the converter. At the same time, the economics and stability of the system are significantly improved by using this method. The overall improvement is about is 12.3%.

Direct Contact Membrane Distillation of Hydroponic Solutions for Recycling of Phosphate and Potassium

A critical issue facing extraterrestrial expansion has always been long-term life support capabilities. The large energy requirements to move even small amounts of material from Earth necessitate the ability to reuse and recycle as much as possible, particularly waste. The weight of food supplies eventually starts to limit the length of the expedition. Hydroponic growth systems offer the ability to grow plants, and with them, a miniature ecosystem. This offers the ability to repurpose both carbon dioxide and waste salts such as ammonia and other compounds, such as those found in urine. A major issue facing hydroponic systems is the need to provide a stable water-based nutrient stream. Direct contact membrane distillation (DCMD) was tested for viability as a method of re-concentrating and stabilizing the nutrient-rich water stream. Polytetrafluoroethylene (PTFE)- and polyvinylidene (PVDF)-based polymer hydrophobic membranes were used to separate solutes from water. The DCMD method was tested with the feed stream operating at temperatures of 50 °C, 65 °C, and 80 °C. The results were analyzed using UV-Visible spectroscopy to determine concentrations. The benefits and limitations of the PTFE and PVDF membranes in DCMD were compared. The larger-pore PTFE membranes concentrated solutions effectively at 80 °C, while the PVDF membranes removed more water at lower temperatures, but permitted detectable phosphate ion leakage. Adjusting temperature and flow rates can help maintain stable ion and water transfer, benefiting hydroponic systems in achieving reliable nutrient levels.

The Transition towards Artificial Intelligence in Healthcare: A Systematic Review of Cases from Community Pharmacies

This review aims to assess the role of artificial intelligence (AI) in community pharmacy by analyzing the most recent studies and identifying trends, gaps, and future directions for integrating AI technologies in these settings. A systematic literature review was performed on the PubMed, Scopus, and Google Scholar databases, looking at papers from 2019 to 2024. The search was further refined using the terms "AI in pharmacy," "telepharmacy," or "clinical decision support systems." Fourteen studies were included in the review after applying specific inclusion and exclusion criteria for further analysis. AI technologies have potential effects on community pharmacy practice. The most beneficial impact was noted in medication management, where 15% of medication errors were reduced, and patient compliance improved by 10%. In telepharmacy, AI supports encouraging adherence and access to pharmacy services where geographical barriers exist. However, there are concerns such as lack of privacy for system users, implementation costs, and onboarding pharmacists to such systems. AI has significant supportive and transformative capabilities for community pharmacy, but crucial barriers must be overcome first. Addressing the barriers and their ethical aspects is critical for further research.

Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’

The rapid development of specific-purpose Large Language Models (LLMs), such as Med-PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering unprecedented capabilities in clinical decision support, diagnostics, and personalized health monitoring. This paper reviews the advancements in medicine-specific LLMs, the integration of Retrieval-Augmented Generation (RAG) and prompt engineering, and their applications in improving diagnostic accuracy and educational utility. Despite the potential, these technologies present challenges, including bias, hallucinations, and the need for robust safety protocols. The paper also discusses the regulatory and ethical considerations necessary for integrating these models into mainstream healthcare. By examining current studies and developments, this paper aims to provide a comprehensive overview of the state of LLMs in medicine and highlight the future directions for research and application. The study concludes that while LLMs hold immense potential, their safe and effective integration into clinical practice requires rigorous testing, ongoing evaluation, and continuous collaboration among stakeholders.

Control strategy of the novel stator free speed regulating wind turbine generation system.

Building a high-proportion renewable energy power system is a key measure to address the challenges of the energy revolution and climate change. However, current high-proportion renewable energy systems face issues of frequency instability and voltage fluctuations. To address these challenges, this paper proposes a novel topology for a stator free speed regulating wind turbine generation system. The stator free speed regulating machine connects a gearbox and a synchronous generator, forming a flexible drive chain that increases system inertia and enhances frequency stability in high-proportion renewable energy power systems. The synchronous generator at the end of the drive chain can be directly connected to the grid without the converter, thanks to the speed regulation provided by the stator free speed regulating machine, thus avoiding harmonic pollution caused by power electronic devices in traditional wind turbines, enhancing the reactive power support capability, and improving voltage stability in high-proportion renewable energy systems. Given that the proposed stator free speed regulating machine consists solely of a rotating inner and outer rotor without a stator, traditional motor control strategies are not applicable. Therefore, this paper focuses on developing control strategies for the stator free speed regulating machine, employing a dual closed-loop PI control strategy with an outer loop for speed and an inner loop for current, based on flux orientation of the outer rotor. Simulation experiments will validate the feasibility of the proposed stator free speed regulating wind turbine generation system topology and the effectiveness of the control strategy.

Study on the grid supporting effects for GFM energy storage system in distribution networks under grid faults

The grid-forming energy storage system (GFM-ESS) plays a critical role in enhancing the reliability of power-electronic-based power systems by providing voltage support to the grid. In this paper, the grid-supporting function of GFM-ESS on different buses was studied under the different types of short-circuit faults. First, the mathematic models for control strategies of the grid-following (GFL) converter and grid-forming (GFM) converter as well as their duality relationships were studied and illustrated. Secondly, the response of the GFM inner control loop to different short-circuit fault types is analyzed using symmetrical component decomposition. An optimization algorithm is then proposed to improve the voltage-supporting capabilities of the GFM-ESS and to identify the optimal placement of energy storage systems (ESSs) within the distribution network. The relationship between the support capabilities of the GFM-ESS under different short-circuit faults was derived and represented. Finally, the results of case studies were presented, analyzed, and discussed. The optimal location for the GFM ESS in the distribution network can be determined with the proposed mathematic models and the method for the optimal placement.

Perceptions and Needs for a Technology-Based Dyadic Intervention on Symptom Management Among Patients With Colorectal Cancer and Their Caregivers: A Qualitative Study.

Colorectal cancer (CRC) patients and their caregivers often experience multiple co-occurring symptoms (eg, fatigue, depression, anxiety, and sleep disturbance). There is a noticeable gap in research regarding symptom management for patient-caregiver dyads, particularly using technology-based tools. This study aimed to describe the needs and perceptions of patient-caregiver dyads regarding a technology-based tool to manage their multiple symptoms. This study utilized a qualitative descriptive design with purposive sampling. Semistructured interviews were conducted with CRC patients undergoing chemotherapy and their caregivers. Thematic analysis was performed to identify key themes. The study included 11 patients and 8 caregivers (8 patient-caregiver dyads and 3 patients without dyads). Four main themes emerged: (1) high symptom burden experienced by CRC patients and their caregivers; (2) symptom self-management strategies, including information seeking and self-education, family and social support, and coping strategies; (3) input and suggestions for technology use, covering aspects such as content, format, and specific technology features; and (4) concerns about barriers to healthcare access, including technological literacy, geographical distance, and time constraints. Our findings provide novel insights into experiences, needs, and suggestions of CRC patient-caregiver dyads for managing multiple symptoms. Participants emphasized the importance of developing a tailored, technology-based tool that includes personalized, evidence-based content; accessible and intuitive design features; and interactive social support capabilities. Leveraging these findings, we aim to develop a technology-based dyadic intervention tool that addresses the psychosocial needs of CRC patient-caregiver dyads. This approach can also enhance health equity by making care more accessible.