Search Results (150)

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO₂ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. Aiming at overcoming the bottleneck issues of canopy gap identification in mountainous forest regions, we constructed a multi-task deep learning model (ES-Net) integrating an edge–semantic collaborative perception mechanism. First, a refined sample library containing multi-scale interference features was constructed, which included 2808 annotated UAV images. Based on this, a dual-branch feature interaction architecture was designed. A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. Results from selected areas in Yunnan Province (China) demonstrate that ES-Net outperforms U-Net, boosting the Intersection over Union (IoU) by 0.86% (95.41% vs. 94.55%), improving the edge coverage rate by 3.14% (85.32% vs. 82.18%), and reducing the Hausdorff Distance by 38.6% (28.26 pixels vs. 46.02 pixels). Ablation studies further verify that the synergy between SSM and EDM yields a 13.0% IoU gain over the baseline, highlighting the effectiveness of joint semantic–edge optimization. This study provides a terrain-adaptive intelligent interpretation method for forest disturbance monitoring and holds significant practical value for advancing smart forestry construction and ecosystem sustainable management. 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

This paper defines time-series numerical association rule mining in smart-agriculture applications from an explainable-AI perspective. Two novel explainable methods are presented, along with a newly developed algorithm for time-series numerical association rule mining. Unlike previous approaches, such as fixed interval time-series numerical association, the proposed methods offer enhanced interpretability and an improved data science pipeline by incorporating explainability directly into the software library. The newly developed xNiaARMTS methods are then evaluated through a series of experiments, using real datasets produced from sensors in a smart-agriculture domain. The results obtained using explainable methods within numerical association rule mining in smart-agriculture applications are very positive. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The accurate and continuous monitoring of these symptoms is essential for optimizing treatment strategies and improving patient outcomes. Traditionally, clinical assessments have relied on scales and methods that often lack the ability for continuous, real-time monitoring and can be subject to interpretation bias. Recent advancements in wearable technologies, such as smartphones, smartwatches, and activity trackers (ATs), present a promising alternative for more consistent and objective monitoring. This review aims to evaluate the use of smartphones and smart wrist devices, like smartwatches and activity trackers, in the management of PD, assessing their effectiveness in symptom evaluation and monitoring and physical performance improvement. Studies were identified by searching in PubMed, Scopus, Web of Science, and Cochrane Library. Only 13 studies of 1027 were included in our review. Smartphones, smartwatches, and activity trackers showed a growing potential in the assessment, monitoring, and improvement of motor symptoms in people with PD, compared to clinical scales and research-grade sensors. Their relatively low cost, accessibility, and usability support their integration into real-world clinical practice and exhibit validity to support PD management. Full article

Against the backdrop of increasing urban population and continuous expansion of urban scales, achieving “people-centered” urban sustainable development, namely building livable and sustainable cities, faces formidable challenges. Under the shared global vision of achieving the 2030 Sustainable Development Goals (SDGs), existing research has rarely explored the alignment between the construction of livable cities and the SDGs. This study constructs a scientific and universally applicable evaluation system for urban livability to clarify that building livable cities is a crucial pathway to promoting urban sustainable development. This study integrates the core principles of the three pillars of the United Nations’ sustainable development, the five-dimensional classification logic of the Global Urban Monitoring Framework, and the performance evaluation key points of ISO/TC 268 standards for SC1 “smart community infrastructure” to construct a six-dimensional livable city evaluation system covering society, economy, culture, environment, governance, and infrastructure. Starting from theoretical research on the connotation of livable cities and their alignment with the SDGs, and based on the research team’s evaluation experience and assessment paradigm of SDGs progress at the urban level, this study uses the “Indicator Library for Cities’ Sustainable Development (ILCSD)” as a technical tool to explore the technical methods for establishing an evaluation index system for livable cities. Meanwhile, combining qualitative research and statistical analysis with China’s development strategic needs, it selects 24 sample cities to analyze the level differences among different types of cities under the proposed index system and to identify the key factors and mechanisms influencing the sustainable development of livable cities. Through theoretical research and empirical analysis, this study has derived a set of evaluation indicators for livable cities oriented towards the SDGs, offering urban management stakeholders a reasonable and comprehensive universal evaluation technical tool to enhance urban livability and promote the implementation of the SDGs. Full article

The greenhouse effect, responsible for trapping heat in Earth’s atmosphere, has a parallel thermal phenomenon at the indoor scale known as the Room-Level Greenhouse Effect (RGHE), where solar radiation elevates room temperatures and increases energy consumption. The RGHE contributes to indoor temperature increases of 4–10 °C and elevates energy demands by 15–30% in high solar exposure zones, the effect being even worse in tropical zones. To address this problem, an innovative analog microarchitecture is proposed for real-time RGHE detection by sensing the sunlight intensity radiation factor (SIR). A compact analog system is introduced, comprising three stages: a Sensing Circuit Stage (SCS) that isolates the dynamic sunlight signal f (r) from static room condition factors (RCFs), an Amplification Stage (AS) that shifts and boosts the signal, and a Stabilized Peak Detection Stage (SPDS) that captures the peak solar intensity. The microsystem was tested across fixed f (m) levels of 0.75 V, 1.0 V, and 1.5 V, and varying f (r) values of 3 mV, 4 mV, and 5 mV. It successfully detects peak voltages ranging from 1.69 V to 1.92 V, with stabilization achieved within 60 µs, enabling accurate detection of the f (r) signal. The proposed microarchitecture offers a scalable approach to localized thermal monitoring in smart building environments using fully analog circuitry, designed and simulated in Cadence Virtuoso using the TSMC 180 nm technology library. Full article

Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key challenges such as the multi-scale characteristics of surface damage precursors, interference from background noise, and the scarcity of high-quality training samples severely constrain the intelligent transformation of AM quality monitoring systems. This study proposes an innovative microscopic polarization YOLOv11-LSF intelligent inspection framework, which establishes an automated non-destructive testing methodology for AM device micro-nano damage precursors through triple technological innovations, effectively breaking through existing technical bottlenecks. Firstly, a multi-scale perception module is constructed based on the Large Separable Kernel Attention mechanism, significantly enhancing the network’s feature detection capability in complex industrial scenarios. Secondly, the cross-level local network VoV-GSCSP module is designed utilizing GSConv and a one-time aggregation method, resulting in a Slim-neck architecture that significantly reduces model complexity without compromising accuracy. Thirdly, an innovative simulation strategy incorporating physical features for damage precursors is proposed, constructing a virtual and real integrated training sample library and breaking away from traditional deep learning reliance on large-scale labeled data. Experimental results demonstrate that compared to the baseline model, the accuracy (P) of the YOLOv11-LSF model is increased by 1.6%, recall (R) by 1.6%, mAP50 by 1.5%, and mAP50-95 by 2.8%. The model hits an impressive detection accuracy of 99% for porosity-related micro-nano damage precursors and remains at 94% for cracks. Its unique small sample adaptation capability and robustness under complex conditions provide a reliable technical solution for industrial-grade AM quality monitoring. This research advances smart manufacturing quality innovation and enables cross-scale micro-nano damage inspection in advanced manufacturing. Full article

The electrification of energy systems is essential for carbon reduction and sustainable energy goals. However, current network asset ratings and the poor thermal efficiency of older buildings pose significant challenges. This study evaluates the impact of heat pump and electric vehicle (EV) penetration on a UK residential distribution network, considering the highest coincident electricity demand and worst weather conditions recorded over the past decade. The power flow calculation, based on Python, is performed using the pandapower library, leveraging the actual distribution network structure of the Hillingdon area by incorporating recent smart meter data from a distribution system operator alongside historical weather data from the past decade. Based on the outcome of power flow calculation, the transformer loadings and voltage levels were assessed for existing and projected heat pump and EV adoption rates, in line with national policy targets. Findings highlight that varied consumer density and diverse usage patterns significantly influence upgrade requirements. Full article

University libraries are essential academic spaces, yet existing smart systems often overlook user perception in environmental optimization. A key challenge is the lack of adaptive frameworks balancing objective sensor data with subjective user experience. This study introduces an Internet of Things (IoT)-powered framework integrating real-time sensor data, image-based occupancy tracking, and user feedback to enhance study conditions via machine learning (ML). Unlike prior works, our system fuses objective measurements and subjective input for personalized assessment. Environmental factors—including air quality, sound, temperature, humidity, and lighting—were monitored using microcontrollers and image processing. User feedback was collected via surveys and incorporated into models trained using Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Extreme Gradient Boosting (XGBoost), and Naive Bayes. KNNs achieved the highest F1 score (99.04%), validating the hybrid approach. A user interface analyzes environmental factors, identifying primary contributors to suboptimal conditions. A scalable fog–cloud architecture distributes computation between edge devices (fog) and cloud servers, optimizing resource management. Beyond libraries, the framework extends to other smart workspaces. By integrating the IoT, ML, and user-driven optimization, this study presents an adaptive decision support system, transforming libraries into intelligent, user-responsive environments. Full article

This study investigates the synthesis and properties of innovative poly(oligo(alkylene glycol)) methacrylate hydrogels synthesized via gamma radiation-induced copolymerization and the crosslinking of oligo(ethylene glycol) methacrylate (OEGMA) and oligo(propylene glycol) methacrylate (OPGMA) at varying mole fractions. Our primary objective is to investigate the impact of copolymerization on the swelling properties of P(OEGMA/OPGMA) hydrogels compared to their homopolymeric counterparts, namely, POEGMA and POPGMA, which exhibit distinct volume phase transition temperatures (VPTTs) of around 70 and 13 °C, respectively, under physiological conditions. To this end, a comprehensive library of smart methacrylate-based hydrogel biomaterials was developed, featuring detailed data on their swelling behavior across different copolymer molar ratios and physiological temperature ranges. To achieve these objectives, we conducted swelling behavior analysis across a wide range of temperatures, assessed the pH sensitivity of hydrogels, utilized scanning electron microscopy for morphological characterization, performed in vitro biocompatibility assessment through cell viability and hemolysis assays, and employed diclofenac sodium as a model drug to control drug delivery testing. Our findings demonstrate that the newly synthesized P(OEGMA₄₀/OPGMA₆₀) copolymeric hydrogel exhibits desirable characteristics, with VPTT close to the physiological temperatures required for controlled drug delivery applications. Full article

Objective: This study aims to discover the most current trends, impacts, and challenges of using IoT devices and smart technologies in oral health. Method: A modified systematic mapping method was used to generate and answer five research questions. Twelve databases were queried to identify published literature from 2017 to 2023. Abstract screening and full-text review were conducted to identify studies meeting inclusion criteria. The Pandas library in Python Version 3.9.19 and a Fibonacci series were used to identify keyword trends in abstracts. Full-text analysis was conducted to synthesize findings relevant to the impacts and challenges of IoDT. Results: A total of 958 unduplicated articles were identified from the literature databases. After review, 33 articles were included. Publications related to IoDT are rapidly increasing over the last 7 years and keywords relating to toothbrushing were the most common. The most common research strategy was design and creation, followed by experimental methods. Design and creation of smart technologies in oral health are in a phase of measurement optimization using IoT which is being used for prevention, early detection, monitoring, and treatment of dental disease as well as silent communication devices. Challenges in IoDT continue to include measurement accuracy and user acceptability. Conclusions: Research in IoDT is predicted to continue to advance rapidly. Dental providers and public health agencies can look to this research to develop best practices. However, more research on how IoDT can facilitate desirable outcomes in a cost-effective and user-friendly way is needed. Full article

Smart city operation assumes dynamic infrastructure in various aspects. However, organization and process modelling require domain expertise and significant efforts from modelers. As a result, such processes are still not well supported by IT systems and still mostly remain manual tasks. Today, machine learning technologies are capable of performing various tasks including those that have normally been associated with people; for example, tasks that require creativeness and expertise. Generative adversarial networks (GANs) are a good example of this phenomenon. This paper proposes an approach to generating organizational and process models using a GAN. The proposed GAN architecture takes into account both tacit expert knowledge encoded in the training set sample models and the symbolic knowledge (rules and algebraic constraints) that is an essential part of such models. It also pays separate attention to differentiable functional constraints, since learning those just from samples is not efficient. The approach is illustrated via examples of logistic system modelling and smart tourist trip booking process modelling. The developed framework is implemented in a publicly available open-source library that can potentially be used by developers of modelling software. Full article

Electric wheelchairs are the primary means of transportation that enable individuals with disabilities to move independently to their desired locations. This paper introduces a novel, low-cost smart wheelchair system designed to enhance the mobility of individuals with severe disabilities through hand gesture recognition. Additionally, the system aims to support low-income individuals who previously lacked access to smart wheelchairs. Unlike existing methods that rely on expensive hardware or complex systems, the proposed system utilizes an affordable webcam and an Nvidia Jetson Nano embedded computer to process and recognize six distinct hand gestures—“Forward 1”, “Forward 2”, “Backward”, “Left”, “Right”, and “Stop”—to assist with wheelchair navigation. The system employs the “You Only Look Once version 8n” (YOLOv8n) model, which is well suited for low-spec embedded computers, trained on a self-collected hand gesture dataset containing 12,000 images. The pre-processing phase utilizes the MediaPipe library to generate landmark hand images, remove the background, and then extract the region of interest (ROI) of the hand gestures, significantly improving gesture recognition accuracy compared to previous methods that relied solely on hand images. Experimental results demonstrate impressive performance, achieving 99.3% gesture recognition accuracy and 93.8% overall movement accuracy in diverse indoor and outdoor environments. Furthermore, this paper presents a control circuit system that can be easily installed on any existing electric wheelchair. This approach offers a cost-effective, real-time solution that enhances the autonomy of individuals with severe disabilities in daily activities, laying the foundation for the development of affordable smart wheelchairs. Full article

This systematic review examines the integration of directed acyclic graph (DAG)-based blockchain technology in smart mobility ecosystems, focusing on electric vehicles (EVs), robotic systems, and drone swarms. Adhering to PRISMA guidelines, we conducted a comprehensive literature search across Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, screening 1248 records to identify 47 eligible studies. Our analysis demonstrates that DAG-based blockchain addresses critical limitations of traditional blockchains by enabling parallel transaction processing, achieving high throughput (>1000 TPS), and reducing latency (<1 s), which are essential for real-time applications like autonomous vehicle coordination and microtransactions in EV charging. Key technical challenges include consensus mechanism complexity, probabilistic finality, and vulnerabilities to attacks such as double-spending and Sybil attacks. This study identifies five research priorities: (1) standardized performance benchmarks, (2) formal security proofs for DAG protocols, (3) hybrid consensus models combining DAG with Byzantine fault tolerance, (4) privacy-preserving cryptographic techniques, and (5) optimization of feeless microtransactions. These advancements are critical for deploying robust, scalable DAG-based solutions in smart mobility, and fostering secure and efficient urban transportation networks. Full article