Search Results (1,262)

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article

The rapid increase in the global population of older adults presents a significant challenge, but also a unique opportunity to leverage technological advancements for promoting independent living and well-being. This study introduces the CIREI framework, which is a comprehensive model designed to enhance the integration of smart home and assistive technologies specifically for pre-frail older adults. Developed through a systematic literature review and innovative and comprehensive co-design activities, the CIREI framework captures the nuanced needs, preferences, and challenges faced by older adults, caregivers, and experts. Key findings from the co-design workshop highlight critical factors such as usability, privacy, and personalised learning preferences, which directly influence technology adoption. These insights informed the creation of an intelligent middleware prototype named WISE-WARE, which seamlessly integrates commercial off-the-shelf (COTS) devices to support health management and improve the quality of life for older adults. The CIREI framework’s adaptability ensures it can be extended and refined to meet the ever-changing needs of the ageing population, providing a robust foundation for future research and development in user-centred technology design. All workshop materials, including tools and methodologies, are made available to encourage the further exploration and adaptation of the CIREI framework, ensuring its relevance and effectiveness in the dynamic landscape of ageing and technology. This research contributes significantly to the discourse on ageing in place, digital inclusion, and the role of technology in empowering older adults to maintain independence. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

This paper investigated the issues of unstable data collection links and low efficiency in IoT data collection for smart cities by combining active STAR-RIS with UAVs to enhance channel quality, achieving efficient data collection in complex environments. To this end, we propose an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted UAV-enabled NOMA data collection system that jointly optimizes active STAR-RIS beamforming, SN power allocation, and UAV trajectory to maximize the system energy efficiency (EE) and the data complete collection rate (DCCR). We apply block coordinate ascent (BCA) to decompose the non-convex problem into three alternating subproblems: combined beamforming optimization of phase shift and amplification gain matrices, power allocation, and trajectory optimization, which are iteratively processed through successive convex approximation (SCA) and fractional programming (FP) methods, respectively. Simulation results demonstrate the proposed algorithm’s rapid convergence and significant advantages over conventional NOMA and OMA schemes in both throughput rate and DCCR. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

This paper looks at and describes the potential of using artificial intelligence in smart environments. Various environments such as houses and residential and commercial buildings are becoming smarter through the use of various technologies, i.e., various sensors, smart devices and elements based on artificial intelligence. These technologies are used, for example, to achieve different levels of security in environments, for personalized comfort and control and for ambient assisted living. We investigated the deep learning approach, and, in this paper, describe its use in this context. Accordingly, we developed four deep learning models, which we describe. These are models for hand gesture recognition, emotion recognition, face recognition and gait recognition. These models are intended for use in smart environments for various tasks. In order to present the possible applications of the models, in this paper, a house is used as an example of a smart environment. The models were developed using the TensorFlow platform together with Keras. Four different datasets were used to train and validate the models. The results are promising and are presented in this paper. Full article

AI-powered health assistants offer promising opportunities to enhance health management among older adults. However, real-world uptake remains limited, not only due to individual hesitation, but also because of complex interactions among users, platforms, and public policies. This study investigates the dynamic behavioral mechanisms behind adoption in aging populations using a tripartite evolutionary game model. Based on replicator dynamics, the model simulates the strategic behaviors of older adults, platforms, and government. It identifies evolutionarily stable strategies, examines convergence patterns, and evaluates parameter sensitivity through a Jacobian matrix analysis. Results show that when adoption costs are high, platform trust is low, and government support is limited, the system tends to converge to a low-adoption equilibrium with poor service quality. In contrast, sufficient policy incentives, platform investment, and user trust can shift the system toward a high-adoption state. Trust coefficients and incentive intensity are especially influential in shaping system dynamics. This study proposes a novel framework for understanding the co-evolution of trust, service optimization, and institutional support. It emphasizes the importance of coordinated trust-building strategies and layered policy incentives to promote sustainable engagement with AI health technologies in aging societies. Full article

Planar heaters are designed to deliver uniform heat across broad surfaces and serve as critical components in applications requiring energy efficiency, safety, and mechanical flexibility, such as wearable electronics and smart textiles. However, conventional metal-based heaters are limited by poor adaptability to curved or complex surfaces, low mechanical compliance, and susceptibility to oxidation-induced degradation. To overcome these challenges, we applied a protein-assisted electroless copper (Cu) plating strategy to electrospun poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) nanofiber substrates to fabricate flexible, conductive planar heating membranes. For interfacial functionalization, a protein-based engineering approach using bovine serum albumin (BSA) was employed to facilitate palladium ion coordination and seed formation. The resulting membrane exhibited a dense, continuous Cu coating, low sheet resistance, excellent durability under mechanical deformation, and stable heating performance at low voltages. These results demonstrate that the BSA-assisted strategy can be effectively extended to complex three-dimensional fibrous membranes, supporting its scalability and practical potential for next-generation conformal and wearable planar heaters. Full article

With water scarcity becoming worse, and demand increasing, the urgency for the water industry to hit net-zero carbon is accelerating. Even as a multitude of utilities have pledged to reach net-zero by 2050, advancing beyond the energy–water nexus remains a heavy lift. This paper, using a systematic literature review that complies with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), aims to propose sustainable water resource management (SWRM) strategies that may assist water utilities in decarbonizing their value chains and achieving net-zero carbon. In total, 31 articles were included from SCOPUS, ResearchGate, ScienceDirect, and Springer. The findings show that water utilities are responsible for 3% of global greenhouse gas emissions and could reduce these emissions by more than 45% by employing a few strategies, including the electrification of transport fleets, the use of renewables, advanced oxidation processes (AOPs) and energy-efficient technologies. A broad-based case study from Scottish Water shows a 254,000-ton CO₂ reduction in the period since 2007, indicative of the potential of these measures. The review concludes that net-zero carbon is feasible through a mix of decarbonization, wastewater reuse, smart systems and policy-led innovation, especially if customized to both large and small utilities. To facilitate a wider and a more scalable transition, research needs to focus on development of low-cost and flexible strategies for underserved utilities. Full article

Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge gaps, and establish clear frameworks to guide future developments. This systematic review addresses these needs by analyzing 89 peer-reviewed sources retrieved from the Scopus database, focusing on the application of biomimetic robotics and sensing technologies in healthcare and rehabilitation contexts. The findings indicate a predominant focus on enhancing human mobility and support, with rehabilitative and assistive technologies comprising 61.8% of the reviewed literature. Additionally, 12.36% of the studies incorporate intelligent control systems and Artificial Intelligence (AI), reflecting a growing trend toward adaptive and autonomous solutions. Further technological advancements are demonstrated by research in bioengineering applications (13.48%) and innovations in soft robotics with smart actuation mechanisms (11.24%). The development of medical robots (7.87%) and wearable robotics, including exosuits (10.11%), underscores specific progress in clinical and patient-centered care. Moreover, the emergence of transdisciplinary approaches, present in 6.74% of the studies, highlights the increasing convergence of diverse fields in tackling complex healthcare challenges. By consolidating current research efforts, this review aims to provide a comprehensive overview of the state of the art, serving as a foundation for future investigations aimed at improving healthcare outcomes and enhancing quality of life. Full article

Navigational aids play a vital role in enhancing the mobility and independence of blind and visually impaired (VI) individuals. However, existing solutions often present challenges related to discomfort, complexity, and limited ability to provide detailed environmental awareness. To address these limitations, we introduce SnapStick, an innovative assistive technology designed to improve spatial perception and navigation. SnapStick integrates a Bluetooth-enabled smart cane, bone-conduction headphones, and a smartphone application powered by the Florence-2 Vision Language Model (VLM) to deliver real-time object recognition, text reading, bus route detection, and detailed scene descriptions. To assess the system’s effectiveness and user experience, eleven blind participants evaluated SnapStick, and usability was measured using the System Usability Scale (SUS). In addition to the 94% accuracy, the device received an SUS score of 84.7%, indicating high user satisfaction, ease of use, and comfort. Participants reported that SnapStick significantly improved their ability to navigate, recognize objects, identify text, and detect landmarks with greater confidence. The system’s ability to provide accurate and accessible auditory feedback proved essential for real-world applications, making it a practical and user-friendly solution. These findings highlight SnapStick’s potential to serve as an effective assistive device for blind individuals, enhancing autonomy, safety, and navigation capabilities in daily life. Future work will explore further refinements to optimize user experience and adaptability across different environments. Full article

Pelvic organ prolapse (POP) is a common pelvic floor disorder with substantial impact on women’s quality of life, necessitating accurate and reproducible diagnostic methods. This study investigates the use of three-dimensional (3D) transperineal ultrasound, integrated with artificial intelligence (AI), to evaluate pelvic floor biomechanics and identify correlations between biometric parameters and prolapse severity. Thirty-seven female patients diagnosed with genital prolapse (mean age: 65.3 ± 10.6 years; mean BMI: 29.5 ± 3.8) were enrolled. All participants underwent standardized 3D transperineal ultrasound using the Mindray Smart Pelvic system, an AI-assisted imaging platform. Key biometric parameters—anteroposterior diameter, laterolateral diameter, and genital hiatus area—were measured under three functional states: rest, maximal Valsalva maneuver, and voluntary pelvic floor contraction. Additionally, two functional indices were derived: the distensibility index (ratio of Valsalva to rest) and the contractility index (ratio of contraction to rest), reflecting pelvic floor elasticity and muscular function, respectively. Statistical analysis included descriptive statistics and univariate correlation analysis using Pelvic Organ Prolapse Quantification (POP-Q) system scores. Results revealed a significant correlation between laterolateral diameter and prolapse severity across multiple compartments and functional states. In apical prolapse, the laterolateral diameter measured at rest and during both Valsalva and contraction showed positive correlations with POP-Q point C, indicating increasing transverse pelvic dimensions with more advanced prolapse (e.g., r = 0.42 to 0.58; p < 0.05). In anterior compartment prolapse, the same parameter measured during Valsalva and contraction correlated significantly with POP-Q point AA (e.g., r = 0.45 to 0.61; p < 0.05). Anteroposterior diameters and genital hiatus area were also analyzed but showed weaker or inconsistent correlations. AI integration facilitated real-time image segmentation and automated measurement, reducing operator dependency and increasing reproducibility. These findings highlight the laterolateral diameter as a strong, reproducible anatomical marker for POP severity, particularly when assessed dynamically. The combined use of AI-enhanced imaging and functional indices provides a novel, standardized, and objective approach for assessing pelvic floor dysfunction. This methodology supports more accurate diagnosis, individualized management planning, and long-term monitoring of pelvic floor disorders. Full article

Introduction: Total temporomandibular joint replacement (TMJR) is a well-established surgical solution for patients with severe TMJ disorders. It aims to relieve chronic pain, restore jaw mobility, and significantly enhance quality of life. This systematic review evaluates QoL outcomes following TMJR, analyzes complication profiles, compares custom versus stock prostheses, explores pediatric applications, and highlights technological innovations shaping the future of TMJ reconstruction. Methods: A systematic search of PubMed, Embase, and the Cochrane Library was conducted throughout April 2025 in accordance with PRISMA 2020 guidelines. Sixty-four studies were included, comprising 2387 patients. Results: Primary outcomes assessed were QoL improvement, pain reduction, and functional gains such as maximum interincisal opening (MIO). Secondary outcomes included complication rates and technological integration. TMJR consistently led to significant pain reduction (75–87%), average MIO increases of 26–36 mm, and measurable QoL improvements across physical, social, and psychological domains. Custom prostheses were particularly beneficial in anatomically complex or revision cases, while stock devices generally performed well for standard anatomical conditions. Pediatric TMJR demonstrated functional and airway benefits with no clear evidence of growth inhibition over short- to medium-term follow-up. Complications such as heterotopic ossification (~20%, reduced to <5% with fat grafting), infection (3–4.9%), and chronic postoperative pain (~20–30%) were reported but were largely preventable or manageable. Recent advancements, including CAD/CAM planning, 3D-printed prostheses, augmented-reality-assisted surgery, and biofilm-resistant materials, are enhancing personalization, precision, and implant longevity. Conclusions: TMJR is a safe and transformative treatment that consistently improves QoL in patients with end-stage TMJ disease. Future directions include long-term registry tracking, growth-accommodating prosthesis design, and biologically integrated smart implants. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

Facial reconstruction presents complex challenges due to the intricate nature of craniofacial anatomy and the necessity for individualized treatment. Conventional reconstructive methods—such as autologous bone grafts and prefabricated alloplastic implants—pose limitations, including donor site morbidity, implant rejection, and suboptimal aesthetic results. The emergence of 3D printing technology has introduced patient-specific implants (PSIs) that enhance anatomical fit, functional restoration, and biocompatibility. This review outlines the evolution of 3D-printed implants, key materials, computer-assisted design (CAD), and their applications across trauma, oncology, congenital conditions, and aesthetics. It also addresses current challenges and explores future directions, such as bioprinting, smart implants, and drug-eluting coatings. Full article