Search Results (102)

This study develops and applies a climate-based, user-centred and data-informed framework to assess lighting performance in educational buildings through the integrated use of daylight, high-efficiency LED systems and smart lighting controls. The research was conducted as a case study in university classrooms in Madrid, Spain, using a mixed-methods approach that combined in situ illuminance measurements, climate-based simulations with DIALux Evo 12.1, lighting energy assessment and structured user-perception surveys. The main objective was to quantify the dynamic interaction between daylight availability, electric lighting demand and perceived visual comfort, while assessing the energy-saving potential of daylight-responsive control strategies. Results show that the existing LED systems meet current illuminance requirements, with calculated lighting power density values ranging from 4.38 to 12.47 W/m². However, the analysis also reveals that high daylight availability does not necessarily guarantee better lighting performance, since excessive or uneven daylight can generate spatial imbalance, glare risk, and reduced visual stability. Survey results confirmed a strong student preference for daylight and exterior views but also showed that visual task clarity and glare control remain essential for user-centred lighting design. Overall, the findings demonstrate that effective classroom lighting retrofits should move beyond LED replacement alone towards adaptive, daylight-driven and user-centred control strategies capable of reducing energy use while maintaining visual comfort in educational buildings under Mediterranean climatic conditions. Full article

Neighborhood-scale decarbonization is essential to achieving urban climate neutrality, yet existing methods often rely on complex, technology-intensive models that are difficult to implement in aging urban areas. This study introduces a simplified smart analysis method and decision-support framework to facilitate net-zero energy and emissions transitions at the neighborhood level through impactful, low-disruption interventions. Applied to a mixed-use neighborhood in Izmir, Türkiye, part of the European Union Mission for Climate-Neutral and Smart Cities, the methodology evaluates four intervention strategies: rooftop Photovoltaic systems, air-source heat pumps, solar-powered LED street lighting, and repurposing idle public spaces. The analysis quantifies energy demand, CO₂ emissions, and economic performance based on standardized data and incremental renovation scenarios. Results show that a gradual renovation approach, with a 10% annual replacement rate for heating systems, full rooftop Photovoltaic deployment, and street lighting retrofitting, can achieve a net-zero energy balance in 6–7 years. Redirecting fossil fuel and electricity subsidies to support renewable technologies makes these interventions economically viable within the same period. This framework demonstrates that neighborhood-scale climate neutrality can be attained without extensive structural changes, providing a replicable tool for cities with similar conditions aiming to meet European Union climate targets. Full article

Artificial lighting accounts for roughly 30% of total electricity use in supermarkets and significantly affects product perception, customer experience, and purchasing behavior. Increasing the availability of natural light, combined with appropriate architectural energy retrofitting strategies, offers a major opportunity to reduce electricity demand. This study proposes a data-driven framework for evaluating energy retrofit strategies in commercial buildings, integrating Building Information Modeling (BIM) and Building Energy Modeling (BEM). A parametric methodology is used to evaluate multiple architectural retrofitting scenarios aimed at enhancing daylighting and reducing artificial lighting demand, while improving energy efficiency and environmental performance. The scenarios investigated include variations in skylight geometry and orientation, glazing type, photovoltaic integration, and advanced lighting controls. Three Key Performance Indicators (KPIs)—real energy effectiveness, lighting control performance, and environmental impact—are used to assess how design modifications influence energy use, indoor lighting quality, and environmental performance. The methodology is applied to three real food-retail buildings in Italy. Results show that lighting energy consumption can be reduced by up to 60% in scenarios combining LED technology with smart control systems, while total building electricity savings vary across case studies depending on building characteristics and usage patterns. Environmental impact reductions of approximately 15–20% are achieved, reflecting both operational and life-cycle improvements. The study demonstrates the potential of parametric architectural retrofitting to support multi-criteria decision-making for sustainable refurbishment of food-retail environments. Full article

The rapid proliferation of single-phase non-linear loads, such as LED lighting and IT equipment, in modern Smart Buildings has introduced significant power quality challenges in low-voltage electrical installations. A critical but often underestimated consequence is the severe overloading of the neutral conductor caused by triplen harmonics (particularly the 3rd harmonic), which sum algebraically even in balanced three-phase systems. This paper analyzes the electrical and thermal impact of these distortions using a detailed MATLAB/Simulink model of a 400/230 V (3P + N) network. The simulation results demonstrate that under highly distorted conditions (Scenario S3), the neutral current can reach 180% of the nominal phase current (18 A vs. 10 A). Furthermore, the Joule losses analysis reveals a thermal stress more than three times higher on the neutral conductor (peak ~65 W) compared to the phase conductor (~20 W), challenging the traditional design practice of neutral undersizing. To address these safety issues, this study proposes a novel neutral-to-phase current ratio index (k_N) and a proactive decision matrix for Building Management Systems (BMS). Unlike traditional mitigation strategies that rely on static hardware oversizing, passive filters, or specialized transformers, the proposed approach offers a dynamic, cost-effective, and software-driven solution that can be easily integrated into the existing automation infrastructure of modern Smart Buildings. The model identifies a critical tipping point at a 3rd harmonic content of 35.3%, where k_N ≥ 1. By continuously monitoring the k_N parameter, the proposed algorithm enables a transition from passive protection to active power management, triggering automated responses to prevent insulation degradation and mitigate fire hazards. Full article

The external color of smoked sausages is a critical indicator of quality and uniformity in processing. Commercial colorimeters are unsuitable for high-throughput sorting due to the challenges posed by the sausage’s curved cylindrical surface and the need for an inline application. This study introduces a novel non-contact sensing module (LEDs at 45°, fiber optic collection at 0°) to acquire spectral data (400–700 nm) and derive CIE LAB. First, a handheld prototype validated the accuracy of the sensing module against a benchtop spectrophotometer. It successfully categorized five color grades (‘Over light’, ‘Light’, ‘Standard’, ‘Dark’, and ‘Over dark’) with a clear distribution on the a*-L* diagram. This established acceptable color boundary conditions (44.2 < L* ≤ 61.3, 14.1 < a* < 23.9). Second, three sensing modules were integrated around a conveyor belt at 120° intervals, forming the core of an automated inline sorting system. Blind field tests (n = 150) achieved high sorting accuracies of 95.3–97.3% with an efficient inspection time of less than 2 s per sausage. This work realizes the standardization, digitalization, and automation of food color inspection, demonstrating strong potential for smart manufacturing in the processed meat industry. Full article

This work presents an experimental assessment of radiative cooling (RC) films and compound parabolic concentrator (CPC) optics integrated into systems relevant for smart cities: LED street luminaires and small photovoltaic (PV) and thermoelectric (TE) modules used as energy-harvesting (EH) sources for IoT devices. Using commercial RC film and simple 2D/3D CPC geometries, we conducted outdoor measurements under realistic conditions. For a commercial LED luminaire, several configurations were compared (painted aluminum reference, full RC coverage of the head, partial RC strips above the LED and driver, and RC combined with CPCs), recording surface temperatures during daytime and nighttime operation. In parallel, single-junction PV cells and Peltier-type TE generators were mounted on aluminum plates in three configurations: reference, RC-coated, RC + 3D-CPC. Their surface temperatures and open-circuit (OC) voltages were monitored in daylight. Across all campaigns, RC consistently reduced device or surface temperatures by a few degrees Celsius compared to the reference, with larger reductions under higher irradiance. For PV and TE modules, thermal differences produced small but measurable increases in OC voltage—percent-level for PV, millivolt-level for TE. CPCs generally preserved or slightly enhanced the cooling effect in some configurations, acting as incremental modifiers rather than primary drivers. The experiments are deliberately exploratory and provide initial experimental evidence that RC integration can be beneficial in real devices. They establish an empirical baseline for future work on long-term, multi-season campaigns, electrical characterization, optimized materials/optics, and system-level prototypes in smart-city lighting and IoT EH applications. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

With the advancement of smart management technologies, research on self-powered silicon PIN photodetectors has become increasingly important. In this paper, a triboelectric nanogenerator (TENG)-driven silicon PIN photodetector based on power management circuitry is proposed. Through rectification and filtering, the pulse signal from the TENG is converted into stable DC voltage, providing reverse bias for the photodetector. With a 5 MΩ sampling resistor, the system generates a voltage of 0.4 V in the absence of light, which gradually increases to 7.3 V and saturates as the light intensity increases to 300 Lux, demonstrating good compatibility and near independence from the TENG rotation speed. Additionally, a light communication system is constructed, with the TENG-driven silicon PIN photodetector as the receiver unit and a signal transmission unit consisting of a finger-pressed TENG combined with an LED. This system successfully transmits Morse code signals such as “SOS” and “TENG”. Full article

Mesoporous silica nanoparticles (MSNs) are among the most adaptable nanocarriers in modern pharmaceutics, characterized by a high surface area, tunable pore size, controllable morphology, and excellent biocompatibility. These qualities enable effective encapsulation, protection, and the delivery of drugs in a specific area and, therefore, MSNs are powerful platforms for the targeted and controlled delivery of drugs and theragnostic agents. Over the past ten years and within the 2021–2025 period, the advancement of MSN design has led to the creation of hybrid nanostructures into polymers, lipids, metals, and biomolecules that have yielded multifunctional carriers with enhanced stability, responsiveness, and biological activities. The current review provides a review of the synthesis methods, surface functionalization techniques, and physicochemical characterization techniques that define the next-generation MSN-based delivery systems. The particular focus is put on stimuli-responsive systems, such as redox, pH, enzyme-activated, and light-activated systems, that enable delivering drugs in a controlled and localized manner. We further provide a summary of the biomedical use of MSNs and their hybrids such as in cancer chemotherapy, gene and nucleic acid delivery, antimicrobial and vaccine delivery, and central nervous system targeting, supported by recent in vivo and in vitro studies. Important evaluations of biocompatibility, immunogenicity, degradation, and biodistribution in vivo are also provided with a focus on safety in addition to the regulatory impediments to clinical translation. The review concludes by saying that there are still limitations such as large-scale reproducibility, long-term toxicity, and standardization by the regulators, and that directions are being taken in the future in the fields of smart programmable nanocarriers, green synthesis, and sustainable manufacture. Overall, mesoporous silica and hybrid nanoparticles represent a breakthrough technology in the nanomedicine sector with potentials that are unrivaled in relation to targeted, controlled, and personalized therapeutic interventions. Full article

Visible light positioning (VLP) has emerged as a promising indoor positioning technology, owing to its high accuracy and cost-effectiveness. In practical scenarios, signal attenuation, multiple light reflections, or light-deficient regions, particularly near room corners or furniture, can significantly degrade the light quality. In addition, the non-uniform light distribution by light-emitting diode (LED) luminaires can also introduce errors in VLP estimation. To mitigate these challenges, recent studies have increasingly explored the use of machine learning (ML) techniques to model the complex nonlinear characteristics of indoor optical channels and improve VLP performance. Convolutional neural networks (CNNs) have demonstrated strong potential in reducing positioning errors and improving system robustness under non-ideal lighting conditions. However, the performance of CNN-based systems is highly sensitive to their hyperparameters, including learning rate, dropout rate, batch size, and optimizer selection. Manual tuning of these parameters is not only time-consuming but also often suboptimal, particularly when models are applied to new or dynamic environments. Therefore, there is a growing need for automated optimization techniques that can adaptively determine optimal model configurations for VLP tasks. In this work, we propose and demonstrate a VLP system that integrates received signal strength (RSS) signal pre-processing, a CNN, and particle swarm optimization (PSO) for automated hyperparameter tuning. In the proof-of-concept VLP experiment, three different height layer planes (i.e., 200, 225, and 250 cm) are employed for the comparison of three different ML models, including linear regression (LR), an artificial neural network (ANN), and a CNN. For instance, the mean positioning error of a CNN + pre-processing model at the 200 cm receiver (Rx)-plane reduces from 9.83 cm to 5.72 cm. This represents an improvement of 41.81%. By employing a CNN + pre-processing + PSO, the mean error can be further reduced to 4.93 cm. These findings demonstrate that integrating PSO-based hyperparameter tuning with a CNN and RSS pre-processing significantly enhances positioning accuracy, reliability, and model robustness. This approach offers a scalable and effective solution for real-world indoor positioning applications in smart buildings and Internet of Things (IoT) environments. Full article

Niosomes (NIOs), a class of nanovesicular drug delivery system, have garnered significant attention due to their unique architecture, resulting from the self-assembly of non-ionic surfactants (with or without cholesterol) in aqueous media. This bilayered structure enables the encapsulation of both hydrophilic agents in the aqueous core and lipophilic compounds within the lipid bilayer, offering remarkable versatility in therapeutic applications. This article provides an overview of the key principles underlying niosomal formulations, including their composition, preparation methods, formulation conditions and the critical physicochemical parameters influencing vesicle formation and performance. Special emphasis is placed on recent innovations in surface and content modifications that have led to the development of stimuli-responsive niosomal systems, with precise and controlled drug release. These smart carriers are designed to respond to endogenous stimuli (such as pH variations, redox gradients, enzymatic activity, or local temperature changes in pathological sites), as well as to exogenous triggers (including light, ultrasound, magnetic or electric fields, and externally applied hyperthermia), thereby enhancing therapeutic precision. These surface and content modulation strategies effectively transform conventional NIOs into intelligent, stimuli-responsive platforms, reinforcing their innovative role in drug delivery and highlighting their significant potential in the development of smart nanomedicine. Full article

The transition from traditional power systems to smart grids demands advanced methods for detecting and classifying Power Quality Disturbances (PQDs)—variations in voltage, current, or frequency that disrupt device performance. The rise of renewable energy and nonlinear loads, such as LED lighting, has increased PQD occurrences. While deep learning models can effectively analyze data from grid sensors to detect PQD occurrences, privacy concerns often prevent operators from sharing raw data which is necessary to train the models. To address this, Federated Learning (FL) enables collaborative model training without exposing sensitive information. However, FL’s decentralized design introduces new risks, particularly data poisoning attacks, where malicious clients corrupt model updates to degrade the global model accuracy. Despite these risks, PQD classification under FL and its vulnerability to such attacks remain largely unexplored. In this work, we develop FL-based classifiers for PQD detection and compare their performance to traditionally trained, centralized models. As expected from prior FL research, we observed a slight drop in performance: the model’s accuracy decreased from 97% (centralized) to 96% (FL), while the false alarm rate increased from 0.19% to 4%. We also emulate five poisoning scenarios, including indiscriminate attacks aimed at degrading model accuracy and class-specific attacks intended to hide particular disturbance types. Our experimental results show that the attacks are very successful in reducing the accuracy of the classifier. Furthermore, we implement a detection mechanism designed to identify and isolate corrupted client updates, preventing them from influencing the global model. Experimental results reveal that our defense substantially curtails the performance degradation induced by poisoned updates, thereby preserving the robustness of the global model against adversarial influence. Full article

Cardiovascular disease (CVD) remains the leading cause of global mortality, highlighting the need for continuous vital sign monitoring. Photoplethysmography (PPG) is well suited for wearable devices. Smart rings, benefiting from dense capillary distribution and minimal tissue interference, can capture high-quality PPG signals with comfort, making them a promising next-generation wearable. However, ring rotation relative to the finger alters the optical path, especially for multi-wavelength light, thus reducing accuracy. This paper proposes a rotation-robust PPG sensor for smart rings. Monte Carlo simulations analyze photon transmission under different LED–photodiode (PD) angles, showing that at ±60°, green, red, and infrared light achieve optimal penetration into the microcirculation layer. Considering non-ideal conditions, the green-light angle is adjusted to ±30°, and a symmetrical sensor design is adopted. A prototype smart ring is developed, capable of recording 4-channel PPG, 3-axis acceleration, and 4-channel temperature signals at 100, 25, and 0.2 Hz, respectively. The system achieves reliable PPG acquisition with only 0.59 mA average current consumption. In continuous testing, heart rate estimation reached mean absolute errors of 0.82, 0.79, and 0.78 bpm for green, red, and IR light. The results provide a reference for future smart ring development. Full article

This article proposes an improved Visible Light Communication (VLC) solution that, besides the indoor lighting and data transfer, offers an energy-efficient alternative for modern workspaces. Unlike Light-Fidelity (LiFi), designed for high-speed data communication, VLC primarily targets applications where fast data rates are not essential. The developed prototype ensures reliable communication under variable lighting conditions, addressing low-speed requirements such as test bench monitoring, occupancy detection, remote commands, logging or access control. Although the tested data rate was limited to 100 kb/s with a Bit Error Rate (BER) below 10⁻⁷, the key innovation is the light dimming dynamic adaptation. Therefore, the system self-adjusts the LED duty cycle between 10% and 90%, based on natural or artificial ambient light, to maintain a minimum illuminance of 300 lx at the workspace level. Additionally, this work includes a scalability analysis through simulations conducted in an office scenario with up to six users. The results show that the system can adjust the lighting level and maintain the connectivity according to users’ presence, significantly reducing energy consumption without compromising visual comfort or communication performance. With this light intensity regulation algorithm, the proposed solution demonstrates real potential for implementation in smart indoor environments focused on sustainability and connectivity. Full article

The convergence of carbon nanomaterials and functional polymers has led to the emergence of smart carbon–polymer nanocomposites (CPNCs), which possess exceptional potential for next-generation sensing and actuation systems. These hybrid materials exhibit unique combinations of electrical, thermal, and mechanical properties, along with tunable responsiveness to external stimuli such as strain, pressure, temperature, light, and chemical environments. This review provides a comprehensive overview of recent advances in the design and synthesis of CPNCs, focusing on their application in multifunctional sensors and actuator technologies. Key carbon nanomaterials including graphene, carbon nanotubes (CNTs), and MXenes were examined in the context of their integration into polymer matrices to enhance performance parameters such as sensitivity, flexibility, response time, and durability. The review also highlights novel fabrication techniques, such as 3D printing, self-assembly, and in situ polymerization, that are driving innovation in device architectures. Applications in wearable electronics, soft robotics, biomedical diagnostics, and environmental monitoring are discussed to illustrate the transformative impact of CPNCs. Finally, this review addresses current challenges and outlines future research directions toward scalable manufacturing, environmental stability, and multifunctional integration for the real-world deployment of smart sensing and actuation systems. Full article