Search Results (253)

Diabetes is a growing chronic disease with complications that impose a significant burden on healthcare systems worldwide. Pharmacists are readily accessible for diabetes management beyond simply dispensing medications. Consequently, they are involved in disease prevention and detection, therapy management, and patient monitoring. However, with the current escalating impact of diabetes, pharmacists must upgrade their strategies by integrating guidelines from sources like the American Diabetes Association (ADA) 2024 with pharmacy expertise. This perspective serves as a guide for pharmacists, identifying key foundations involved in diabetes management, highlighting five crucial steps for optimal disease control, ranging from prevention strategies to pharmacist-led counseling interventions. We employed PubMed, CDC, WHO guidelines, and key reference texts. Searches were performed using combinations of terms such as “pharmacist”, “type 2 diabetes”, “diabetes prevention”, “pharmacist intervention”, and “diabetes management”, covering publications from January 2010 to March 2025. Studies were included if they focused on pharmacist-led prevention, intervention, or management strategies related to type 2 diabetes (T2D) and were published in English. Studies focusing exclusively on type 1 diabetes were excluded. Generative artificial intelligence was employed to order and structure information as described in the acknowledgments. Conflicting evidence was resolved by giving relevance to recent systematic reviews, randomized trials, and major guidelines. Additional insights were gained through consultations with PharmD professionals experienced in diabetes care. Evidence from selected studies suggests that pharmacist-led care models may enhance and promote the early detection of T2D, improve therapy adherence, enhance glycemic control, and increase overall treatment efficiency. This work suggests that pharmacists must play a key role in diagnosing, preventing, managing, and mitigating the consequences associated with T2D. They must contribute to early treatments with appropriate training and involvement to improve therapeutic outcomes and reduce diabetes-related complications. Full article

Fiber differential protection (FDP) is the primary protection scheme in power systems. However, with the increasing proportion of photovoltaic (PV) grids connected in the power system, the controllability and weak power supply characteristics of photovoltaic power stations change the amplitude and phase angle characteristics of fault currents, which makes the sensitivity of fiber differential protection decline and even increases the risk of failure to operate. In view of this phenomenon, combined with the digital and intelligent development of the new energy power system, this study integrates deep learning with relay protection to propose a protection algorithm based on a two-dimensional spatial current trajectory image and deep learning. In this algorithm, the PV side current and the system side current are, respectively, mapped to the two-dimensional space plane as X- and Y-axes to form the current trajectory image. Under different fault conditions, they have obvious differences. A data-enhanced convolutional neural network (A-CNN) based on cross-overlapping data sets is used to identify trajectory features and locate faults. After performance evaluation, the protection algorithm has the advantages of adapting to new energy access, resisting transition resistance, and robustness to current transformer (CT) saturation, and outliers. Full article

Applications of virtual reality (VR) have grown in significance in medicine, as they are able to recreate real-life scenarios in 3D while posing reduced risks to patients. However, there are several interaction challenges to overcome when moving from 2D screens to 3D VR environments, such as complex controls and slow user adaptation. More intuitive techniques are needed for enhanced user experience. Our research explored the potential of intelligent speech interfaces to enhance user interaction while conducting complex medical tasks. We developed a speech-based assistant within a VR application for maxillofacial implant planning, leveraging natural language processing (NLP) to interpret user intentions and to execute tasks such as obtaining surgical equipment or answering questions related to the VR environment. The objective of the study was to evaluate the usability and cognitive load of the speech-based assistant. We conducted a mixed-methods within-subjects user study with 20 participants and compared the voice-assisted approach to traditional interaction methods, such as button panels on the VR view, across various tasks. Our findings indicate that NLP-driven speech-based assistants can enhance interaction and accessibility in medical VR, especially in areas such as locating controls, easiness of control, user comfort, and intuitive interaction. These findings highlight the potential benefits of augmenting traditional controls with speech interfaces, particularly in complex VR scenarios where conventional methods may limit usability. We identified key areas for future research, including improving the intelligence, accuracy, and user experience of speech-based systems. Addressing these areas could facilitate the development of more robust, user-centric, voice-assisted applications in virtual reality environments. Full article

With the increasing penetration of distributed photovoltaic-energy storage system (PV-ESS) access distribution networks, the safe and stable operation of the system has brought a huge impact, in which the voltage regulation of PV-ESS distribution networks is more prominent. This paper comprehensively reviews the voltage over-run mechanism in the PV-ESS distribution network and combs through the current mainstream voltage regulation strategies, of which two strategies of direct voltage regulation and current optimization are summarized. At the same time, this paper discusses the advantages and limitations of centralized, distributed, multi-timescale, voltage-reactive joint optimization and other regulation methods and focuses on the analysis of heuristic algorithms and algorithms based on deep reinforcement learning in the voltage regulation of the relevant research progress. Finally, this paper points out the main challenges currently facing voltage regulation in PV-ESS distribution networks, including cluster dynamic partitioning technologies, multi-timescale control of hybrid voltage regulation devices, and synergistic problems of demand-side resources, such as electric vehicle participation in voltage regulation, etc., and gives an outlook on future research directions. The aim of this paper is to provide a theoretical basis and practical guidance for voltage regulation of PV-ESS distribution networks and to promote the intelligent construction and sustainable development of power grids. Full article

Background/Objectives: Integrating Artificial Intelligence Clinical Decision Support Systems (AI-CDSSs) into healthcare can improve diagnostic accuracy, optimize clinical workflows, and support evidence-based medical decision-making. However, the adoption of AI-CDSSs remains uneven, influenced by technological, organizational, and perceptual factors. This study, conducted between November 2024 and February 2025, analyzes the determinants of AI-CDSS adoption among healthcare professionals through investigating the impacts of perceived benefits, technological costs, and social and institutional influence, as well as the transparency and control of algorithms, using an adapted Path Dependence-Augmented–Unified Theory of Acceptance and Use of Technology model. Methods: This research was conducted through a cross-sectional study, employing a structured questionnaire administered to a sample of 440 healthcare professionals selected using a stratified sampling methodology. Data were collected via specialized platforms and analyzed using structural equation modeling (PLS-SEM) to examine the relationships between variables and the impacts of key factors on the intention to adopt AI-CDSSs. Results: The findings highlight that the perceived benefits of AI-CDSSs are the strongest predictor of intention to adopt AI-CDSSs, while technology effort cost negatively impacts attitudes toward AI-CDSSs. Additionally, social and institutional influence fosters acceptance, whereas perceived control and transparency over AI enhance trust, reinforcing the necessity for explainable and clinician-supervised AI systems. Conclusions: This study confirms that the intention to adopt AI-CDSSs in healthcare depends on the perception of utility, technological accessibility, and system transparency. The creation of interpretable and adaptive AI architectures, along with training programs dedicated to healthcare professionals, represents measures enhancing the degree of acceptance. Full article

Access control systems protect homes and businesses in the continually evolving security industry. This paper designs and implements a Raspberry Pi-based facial recognition door lock system using artificial intelligence and computer vision for reliability, efficiency, and usability. With the Raspberry Pi as its CPU, the system uses facial recognition for authentication. A camera module for real-time image capturing, a relay module for solenoid lock control, and OpenCV for image processing are essential. The system uses the DeepFace library to detect user emotions and adaptive learning to improve recognition accuracy for approved users. The device also adapts to poor lighting and distances, and it sends real-time remote monitoring messages. Some of the most important things that have been achieved include adaptive facial recognition, ensuring that the system changes as it is used, and integrating real-time notifications and emotion detection without any problems. Face recognition worked well in many settings. Modular architecture facilitated hardware–software integration and scalability for various applications. In conclusion, this study created an intelligent facial recognition door lock system using Raspberry Pi hardware and open-source software libraries. The system addresses traditional access control limits and is practical, scalable, and inexpensive, demonstrating biometric technology’s potential in modern security systems. Full article

Human–Machine Interfaces (HMIs) in traditional automobiles are essential in connecting drivers, passengers, and vehicle systems. In automated vehicles, the HMI has become a critical component. A well-designed HMI facilitates effective human oversight, enhances situational awareness, and mitigates risks associated with system failures or unexpected scenarios. Simultaneously, it serves as a crucial safeguard against cyber threats, preventing unauthorized access and ensuring the integrity of vehicular operations in increasingly connected environments. This narrative review delves into the evolving landscape of automotive HMI design, emphasizing its role in enhancing user experience (UX) and safety. By exploring usability challenges, technological advancements, and the integration of rapidly evolving technologies such as AI (Artificial Intelligence), AR (Augmented Reality), and gesture-based controls, this study highlights how effective HMIs minimize cognitive load while maintaining functionality. Significant attention is given to the new challenges that arise from technological advancements in terms of security and safety. Full article

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age. Full article

With the growing number of patients living with chronic conditions, there is an increasing need for efficient systems that can automatically capture and convert medical device readings into digital data, particularly in home-based care settings. However, most home-based medical devices are closed systems that do not support straightforward automatic data export and often require complex connections to access or transmit patient information. Since most of these devices display clinical information on a screen, this research explores how a standard smartphone camera, combined with artificial intelligence, can be used to automatically extract the displayed data in a simple and non-intrusive way. In particular, this study provides a comparative analysis of several You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD) models to evaluate their effectiveness in detecting and recognizing the readings on medical device displays. In addition to these comparisons, we also explore a hybrid approach that combines the YOLOv8l model for object detection with a Convolutional Neural Network (CNN) for classification. Several iterations of the aforementioned models were tested, using image resolutions of 320 × 320 and 640 × 640. The performance was assessed using metrics such as precision, recall, mean average precision at 0.5 Intersection over Union (mAP@50), and frames per second (FPS). The results show that YOLOv8l (640) achieved the highest mAP@50 of 0.979, but at a lower inference speed (13.20 FPS), while YOLOv8n (320) offered the fastest inference (129.79 FPS) with a reduction in mean average precision (0.786). Combining YOLOv8l with a CNN classifier resulted in a slight reduction in overall accuracy (0.96) when compared to the standalone model (0.98). While the results are promising, the study acknowledges certain limitations, including dataset-specific biases, controlled acquisition settings, and challenges in adapting to real-world scenarios. Nevertheless, the comparative analysis offers valuable insights into the trade-off between inference time and accuracy, helping guide the selection of the most suitable model based on the specific demands of the intended scanning application. Full article

The percentage of historical heritage buildings in Italy is substantial. Many of these buildings are abandoned or not adequately restored for public access due to safety concerns. However, some are managed by city councils and made available to local communities. These heritage buildings, valued for their historical significance, are now frequently used for live events, including musical performances by ensembles and small groups. This paper deals with the acoustics of two rooms provided with barrel-vaulted ceilings: Sisto V Hall in Naples and Aula Magna at the University of Parma. These spaces are structurally very similar, differing mainly in length. Acoustic measurements conducted in both halls reveal reverberation times of approximately 4.5 s at mid frequencies, resulting in poor speech clarity. This is primarily due to the presence of reflective surfaces, as the walls and ceilings are plastered, and the floors are tiled. To optimize their acoustic properties for functions such as celebrations, gatherings, and conferences, an acoustic design intervention was proposed. Digital models of the halls were calibrated and used to correct the acoustics by incorporating absorbing panels on the walls and carpeting on the floors of the central walk path. This treatment successfully balanced the reverberation time to approximately 1.3–1.4 s at mid frequencies, making speech more intelligible. Additionally, an amplified audio system was analyzed to enhance sound distribution, ensuring uniform coverage, even in the last rows of seating. Under amplified conditions, sound pressure levels (SPLs) range between 90 dB and 93 dB, with appropriate gain control applied to the column array speakers. Full article

With the spread of electric vehicles (EVs), the EV load will have a significant impact on the planning and operation of the grid and the operation of the electricity market. Due to the charging and discharging characteristics of EVs, as well as their randomness and dispersion, it is feasible and challenging to introduce EV loads into the grid as a means of frequency regulation and peak shaving of the power system. In this paper, considering multi-subject distribution networks and the interaction of source–grid–load–storage with Intelligent Transportation Systems (ITS), a density peak clustering (DPC) algorithm based on principal component analysis is employed to analyze the spatial and temporal characteristics of EV loads and identify the access status of EV charging stations and EV load status in each region in real time, as well as analyze the adjustable capacity and adjustable range of EV loads. Based on the adjustable capacity of the EV load, the optimization objectives include the maximum regulation of the EV load and the most economical operation cost. An accurate load regulation strategy based on automatic active control (APC) is proposed to reduce the maximum frequency deviation by 25% by integrating the load regulation of electric vehicles into the original AGC frequency regulation. At the same time, the feasibility of electric vehicles in peaking and standby scenarios is studied and verified through simulation cases, which can reduce the peak value of thermal power generation by 15% and 10% in the morning and evening. Full article

The prospect of integrating every object under a unified infrastructure, which provides humans with the possibility to monitor, access, and control objects and systems, has played a significant role in the geometric growth of the Internet of Things (IoT) paradigm, across various applications. However, despite the numerous possibilities that the IoT paradigm offers, security and privacy within and between the different interconnected devices and systems are integral to the long-term growth of IoT networks. Various sophisticated intrusions and attack variants have continued to plague the sustainability of IoT technologies and networks. Thus, effective methodologies for the prompt identification, detection, and mitigation of these menaces are priorities for stakeholders. Recently, data-driven artificial intelligence (AI) models have been considered effective in numerous applications. Hence, in recent literature studies, various single and ensemble AI subset models, such as deep learning and reinforcement learning models, have been proposed, resulting in effective decision-making for the secured operation of IoT networks. Considering the growth trends, this study presents a critical review of recently published articles whereby learning models were proposed for IoT security analysis. The aim is to highlight emerging IoT security issues, current conventional strategies, methodology procedures, achievements, and also, importantly, the limitations and research gaps identified in those specific IoT security analysis studies. By doing so, this study provides a research-based resource for scholars researching IoT and general industrial control systems security. Finally, some research gaps, as well as directions for future studies, are discussed. Full article

The progress in automotive technology, communication protocols, and embedded systems has propelled the development of the Internet of Vehicles (IoV). In this system, each vehicle acts as a sophisticated sensing platform that collects environmental and vehicular data. These data assist drivers and infrastructure engineers in improving navigation safety, pollution control, and traffic management. Digital artefacts stored within vehicles can serve as critical evidence in road crime investigations. Given the interconnected and autonomous nature of intelligent vehicles, the effective identification of road crimes and the secure collection and preservation of evidence from these vehicles are essential for the successful implementation of the IoV ecosystem. Traditional digital forensics has primarily focused on in-vehicle investigations. This paper addresses the challenges of extending artefact identification to an IoV framework and introduces the Collaborative Forensic Platform for Electronic Artefacts (CFPEA). The CFPEA framework implements a collaborative forensic-by-design mechanism that is designed to securely collect, store, and share artefacts from the IoV environment. It enables individuals and groups to manage artefacts collected by their intelligent vehicles and store them in a non-proprietary format. This approach allows crime investigators and law enforcement agencies to gain access to real-time and highly relevant road crime artefacts that have been previously unknown to them or out of their reach, while enabling vehicle owners to monetise the use of their sensed artefacts. The CFPEA framework assists in identifying pertinent roadside units and evaluating their datasets, enabling the autonomous extraction of evidence for ongoing investigations. Leveraging CFPEA for artefact collection in road crime cases offers significant benefits for solving crimes and conducting thorough investigations. Full article

This study conducts a bibliometric and content analysis based on publications indexed in the Web of Science Core Collection, aiming to map the evolution and key themes in horticultural research in the context of technological innovation and sustainability. The results reveal a strong orientation toward digitalization and automation, particularly through the integration of artificial intelligence, mechatronic systems, and sensor-based monitoring in crop management. In the field of biotechnology, keywords such as gene expression, genetic diversity, and micropropagation reflect a sustained research interest in improving crop resilience and disease resistance through genetic and in vitro propagation techniques. Furthermore, concepts such as environmental control, soilless culture, energy efficiency, and co-generation highlight the focus on optimizing growing conditions and integrating renewable energy sources into protected horticultural systems. The geographical distribution of studies highlights increased academic output in countries like India and regions of sub-Saharan Africa, reflecting a global interest in transferring advanced technologies to vulnerable areas. Moreover, collaboration networks are dominated by leading institutions such as Wageningen University, which act as hubs for knowledge diffusion. The findings suggest that future research should prioritize the development of durable, energy-efficient horticultural technologies adapted to various agro-climatic zones. It is recommended that policymakers and stakeholders support interdisciplinary research initiatives, promote knowledge transfer mechanisms, and ensure equitable access to innovation for smallholder farmers and emerging economies. Full article

Cooperative Intelligent Transportation Systems (C-ITSs) are emerging as transformative technologies, paving the way for safe and fully automated driving solutions. As the demand for autonomous vehicles accelerates, the development of advanced Radio Access Technologies capable of delivering reliable, low-latency vehicular communications has become paramount. Standardized approaches for Vehicular-to-Everything (V2X) communication often fall short in addressing the dynamic and diverse requirements of multi-service, multi-priority systems. Conventional vehicular networks employ static parameters such as Access Category (AC) in IEEE 802.11p-based ITS-G5 and Resource Reservation Interval (RRI) in C-V2X PC5 for prioritizing different V2X services. This static parameter assignment performs unsatisfactorily in dynamic and diverse requirements. To bridge this gap, we propose intelligent Multi-Attribute Decision-Making algorithms for adaptive AC selection in ITS-G5 and RRI adjustment in C-V2X PC5, tailored to the varying priorities of active V2X services. These adaptations are integrated with a priority-aware rate-control mechanism to enhance congestion management. Through extensive simulations conducted using NS3, our proposed strategies demonstrate superior performance compared to standardized methods, achieving improvements in one-way end-to-end latency, Packet Reception Ratio (PRR) and overall communication reliability. Full article