Search Results (495)

Medicinal hemp (Cannabis sativa L.) has gained global attention due to its high-value phytocannabinoids, particularly cannabidiol (CBD) and tetrahydrocannabinol (THC), which exhibit significant therapeutic potential. Protected cultivation offers a climate-resilient and sustainable strategy to optimize hemp production by controlling environmental factors, ensuring year-round supply, and improving quality attributes. This paper outlines the key principles and technologies for protected hemp cultivation, including light spectrum management, temperature and humidity control, CO₂ enrichment, nutrient and water management, pest and disease suppression, and post-harvest handling techniques. Advanced greenhouse and indoor production systems enable precise regulation of microclimate, reduce crop losses, and enhance cannabinoid and terpene profiles compared to open-field production. The integration of smart technologies and autonomous control systems can further enhance operational efficiency and consistency. The paper concludes that protected cultivation systems represent an effective approach to overcome climatic uncertainties and meeting the increasing demand for medicinal hemp production on sustainable grounds. Full article

The chicken farm is a specific type of agricultural site with high electricity and heat consumption, which makes it ideal for the implementation of green energy. The specificity of the farm (need for continuous ventilation, lighting, and heating) allows achieving energy independence and reducing costs. Small farms can meet their own electricity needs using clean energy through the application of photovoltaics and converting waste biomass to usable energy. These two ways of power production could also reduce carbon footprints. In this study, the feasibility of using renewable energy for energy management in a poultry farm by consecutively involving solar and biomass energy was revealed. A biotechnological process for the production of biogas from chicken litter in a continuously stirred system of tank bioreactors was performed. It was supplied by electricity from a photovoltaic system. To obtain the maximum amount of solar energy, a photovoltaic system consisting of four panels, invertor and a battery with smart control was designed to collect, store, and bring energy to the reactor system collector and connected to the laboratory bioreactor, conveying the biogas production process. Several hydraulic retention times (HRT) were tested for optimizing biogas (biomethane) production, reaching a maximum of 575.49 NmL CH₄/dm³ at an HRT of 13.3 days for the first bioreactor and 278.7 NmL CH₄/g VS_add at an HRT of 120 days for the whole system. The energy balance made, reporting meteorological data, showed the economic feasibility for small farms to meet their own electricity needs. Involving renewable energy technologies could solve the problem of fossil fuel dependency and waste management for environmental protection and profit increase. It would permit a transition toward sustainable energy practices in agriculture and food production. Full article

Hydrogels are endowed with exceptional hydrophilicity and biocompatibility by their network structure, while also exhibiting soft physical properties similar to living tissues, which renders them ideal biomaterials. Responsive hydrogels—particularly those constructed from multicomponent systems including proteins, polysaccharides, peptides, and polyphenols—have emerged as a frontier research focus owing to their tunable responsiveness and controllable functional properties. In this review, hydrogel response mechanisms were categorized according to pH, ionic strength, temperature, light, enzymes, and multi-stimuli interactions. Key preparation strategies, encompassing chemical, physical, and enzymatic crosslinking, were systematically introduced. The preparation of hydrogels from various food-grade matrices, such as polysaccharide-based, protein-based, peptide-based, and polyphenol-based systems, was also summarized, with emphasis placed on how their tailored structures govern functional performance. Furthermore, innovative applications of responsive hydrogels were highlighted, including targeted delivery of nutrients and bioactive substances (e.g., probiotics, anthocyanins, vitamins) in functional foods, smart packaging and sensing for real-time freshness monitoring of meat and fruits, food quality detection through colorimetric and photothermal sensors, and 4D food printing for personalized nutrition and dysphagia-friendly foods. Full article

Due to climate change, seedling damage caused by drought stress is expected to increase in both afforestation sites and nurseries. Therefore, to ensure stable seedling production under high-temperature conditions and to cultivate seedlings with enhanced drought tolerance through hardening treatments, the development of an effective irrigation system is required. Conventional physiological methods for non-destructive drought detection, such as chlorophyll fluorescence and leaf temperature measurements, require expensive and manual operation, thereby limiting their real-time applicability in forest nurseries. This study evaluated the applicability of using image-based leaf angle measurements for drought stress detection in Quercus acutissima Carruth. seedlings. One-year-old seedlings were grown under two water regimes—well-watered (CT: control) and unwatered (DT: drought)—through Day 8. Statistical analyses (RMANOVA) revealed that changes in the leaf angle parameter PMD–MD (the difference between the previous and current measurement days) showed treatment effects similar to those of the physiological responses ΦNO (quantum yield of non-regulated energy dissipation) and qL (fraction of open PSII reaction centers) to drought on Day 6. Leaf angle reflected drought stress but did not precede physiological changes, indicating its role as a complementary rather than an early indicator. Multiple regression models identified AT (air temperature), SM (soil moisture), Fm′ (maximum fluorescence in the light-adapted state), and VPD (vapor pressure deficit) as the main factors influencing leaf angle variation. Although leaf angle was affected by combined environmental stresses such as high temperature, it was less sensitive to heat stress than physiological responses based on RMANOVA results. These results indicate the potential of image-based leaf angle measurements for drought stress detection. To establish plant-based smart irrigation systems, future studies should validate and refine this approach using larger datasets. Full article

Essential oils, particularly lemon essential oil (LEO), have attracted increasing interest as natural antimicrobial and antioxidant agents for food preservation. However, their direct incorporation into food systems is limited by high volatility, poor water solubility, oxidative instability, and potential sensory impacts. Encapsulation has emerged as an effective technological strategy to overcome these constraints by improving the stability and controlled release of LEO, especially in lipid-based food matrices such as margarine. This review critically summarizes recent advances (2020–2024) in the extraction, physicochemical characterization, and encapsulation of LEO, with particular emphasis on food-grade delivery systems, including biopolymers and inorganic carriers such as silica. Encapsulation efficiency, protection mechanisms, and release behavior are discussed in relation to oxidative stability and functional performance in real food applications. Special attention is devoted to light margarine as a model lipid system, highlighting the advantages and limitations of different encapsulation strategies in delaying lipid oxidation while preserving sensory quality. Finally, emerging challenges related to scalability, regulatory acceptance, and safety, together with future perspectives on smart food packaging and sustainable encapsulation technologies, are outlined to support the effective translation of LEO-based systems into industrial food applications. Full article

Micro-defects, such as ink spots, scratches, and sintering formed during the screen printing process of photovoltaic cells, significantly impair module performance. Traditional machine vision methods exhibit limited detection efficiency and high false-positive and missed-detection rates, while existing deep learning algorithms struggle to achieve accurate and adaptive detection of small-target defects and background similar defects in complex industrial environments. This study proposes an enhanced defect detection methodology based on an improved YOLOv8 algorithm. A multi-focus image acquisition platform using primary and auxiliary CCDs was independently developed, integrating a high-frame-rate industrial camera and a high-resolution electron microscope, with an LED ring light employed to suppress reflections, thereby establishing a high-quality dataset covering three defect categories. The algorithm was optimized through multiple dimensions: the RepNCSPELAN4 module was incorporated into the backbone network to improve multi-scale feature fusion, and a novel wavelet transform-based WaveConv module was designed to replace traditional downsampling, thereby better preserving defect edges and texture details. The neck network integrates a lightweight shuffle attention mechanism and a new detail enhancement module to strengthen critical features while controlling model complexity. Additionally, a dedicated auxiliary detection head was added for spotting tiny ink dots. Experimental results demonstrate a marked improvement in performance: on the custom dataset, the improved model achieves a stable mean average precision of approximately 92%. Specifically, ink spot detection reached a precision of 84.9% and recall of 77.7%, effectively reducing missed small-target defects; sintering defect detection attained 98.9% precision and 100% recall, addressing previous misclassifications due to background similarity; and scratch detection precision improved to 92.2%. Visual comparisons confirm that the enhanced model effectively overcomes the limitations of the original approach. By constructing a specialized dataset and implementing targeted, coordinated optimizations to the YOLOv8 architecture, this study significantly enhances the accuracy and robustness of screen-printing defect detection in photovoltaic cells, providing an effective solution for real-time online quality inspection in smart manufacturing lines. Full article

Rheumatoid arthritis (RA) is a chronic autoimmune disease that imposes substantial physical, emotional, and socioeconomic burdens on patients. Conventional therapeutic approaches are often limited by systemic toxicity, inadequate joint targeting, and variable patient responses, highlighting the urgent need for advanced drug delivery systems. Smart hydrogels have emerged as a promising platform for RA treatment due to their unique three-dimensional hydrophilic networks, excellent biocompatibility, and tunable physicochemical properties. This review systematically summarizes the preparation strategies and design principles of smart hydrogels, with an emphasis on chemically and physically crosslinked networks as well as composite systems. It further outlines the major stimulus-responsive release mechanisms—including temperature, pH, reactive oxygen species (ROS), light, and enzyme triggers—that enable targeted and controlled drug delivery within the inflamed joint microenvironment. Among the various types discussed, temperature-responsive and multi-responsive hydrogels are most frequently investigated for their potential to achieve localized, on-demand therapy. Despite considerable preclinical progress, the clinical translation of smart hydrogels faces critical challenges, including insufficient long-term biocompatibility data, lack of standardized evaluation protocols, and difficulties in scalable manufacturing. This review aims to provide a conceptual framework for the rational design of smart hydrogels and to stimulate interdisciplinary efforts toward overcoming existing translational barriers in RA treatment. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this paper, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with a passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. When the system detects an unfamiliar individual, it sends out alert notifications and emails to the control room with the captured picture using IoT. A web interface has also been set up to control the robot from a distance through Wi-Fi connection. The proposed face recognition method is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrate the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results indicate that the algorithm can identify human faces with an accuracy of 98.63%. Full article

The growing consumer demand for minimally processed, “clean-label” foods is increasing interest in innovative technologies that maintain quality while ensuring microbial safety. This study sheds light on how the protein:lipid ratio in meat-like model matrices modulates the effectiveness of combined high-intensity ultrasound (20 kHz) and carvacrol treatments applied against Escherichia coli ATCC 25922. Three emulsified systems with geometrically spaced protein:lipid ratios (0.33, 1.0, 3.0) were subjected to combinations of ultrasound and carvacrol (0–1200 ppm) at $30\pm 2$ °C. To address the rheological non-linearity, the matrix index was log-transformed, and the process was modeled using both Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). While both models achieved high predictive accuracy ($R^2>0.96$), lack-of-fit analysis revealed that the reduced polynomial RSM model provided a more robust and statistically valid representation of the process compared to the ANN, which exhibited significant overfitting to experimental noise ($p<{10}^{-9}$). The results highlighted a distinct matrix dependency: ultrasound alone provided the fastest inactivation in the high-lipid matrix, while the high-protein matrix exhibited much slower kinetics due to viscous damping. Consequently, the explicit mathematical relationships derived from the RSM model are proposed as the preferred, transparent kernel for future digital twins and autonomous process-control systems in smart food-processing lines. Full article

Remote photoplethysmography (rPPG) enables contactless estimation of cardiovascular signals from video, but most existing studies assume a fixed, stationary camera. This study investigates the feasibility of performing real-time moving-average rPPG (MA-rPPG) using a drone-mounted camera, where platform motion, vibration, and viewing distance introduce additional challenges. Building on our previously validated real-time MA-rPPG smart mirror platform, we reuse the smart mirror interface as a unified frontend for visualization, synchronization, and logging while adapting the MA-rPPG pipeline to operate on live video streamed from an off-the-shelf DJI Tello micro-drone. Feasibility experiments were conducted with 10 participants under controlled indoor lighting and constrained flight conditions, where the drone maintained a stable hover in front of a standing subject and facial video was processed in real time to estimate heart rate from a forehead region of interest. To avoid cross-modality bias and clarify the effect of the aerial imaging platform, drone-derived MA-rPPG outputs were compared against a fixed desktop-camera MA-rPPG reference using the same trained model, enabling a controlled, like-for-like evaluation. The results indicate that continuous heart-rate estimation from a drone camera is feasible in our controlled hover-only setup, while agreement tended to vary with hover stability and effective facial resolution. This work is presented strictly as a feasibility-stage investigation and does not claim clinical validity. The findings provide an experimental baseline and operating-envelope insight for future motion-robust rPPG on mobile and aerial health-sensing platforms. Full article

People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor environments. In this paper, we present a 60 GHz millimeter-wave (mmWave) radar-based occupancy monitoring system that enables accurate and privacy-preserving people counting. The proposed system leverages echo signals processed through Doppler and range spectrogram and analyzed by an enhanced ResNet-50 deep learning model to classify motion states and count individuals. Experimental results collected in a typical indoor environment demonstrate that the system achieves 95.45% accuracy across 6 classes of movements and 98.86% accuracy for people counting (0–3 persons). The method also shows strong adaptability under limited data and robustness to Gaussian blur interference, providing an efficient and reliable solution for intelligent indoor occupancy monitoring. Full article

Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining algorithms, including classical methods (KNN, SVM, decision trees) and advanced ensemble techniques (XGBoost, LightGBM, CatBoost) optimized for unbalanced datasets. Gradient boosting models with explicit handling of class imbalance—where the absence of pests exceeds 77% of observations—showed exceptional performance, achieving a Macro-F1 score above 0.92 and high precision in identifying the minority high-risk class. Explainability analysis using SHAP consistently revealed that elevated CO₂ concentrations are the primary predictor of infestation, enabling early identification of critical zones. This study demonstrates that carbon dioxide (CO₂) acts as the most robust bioindicator for predicting severe infestations of Periplaneta americana, significantly outperforming conventional environmental variables such as temperature and humidity. The implementation of the model in a real-time monitoring platform generates interpretable heat maps that support proactive and localized interventions, optimizing resource use and reducing dependence on biocides. This study presents a scalable, operationally viable predictive system designed for direct integration into municipal asset management workflows, offering a concrete, industry-ready solution to transform pest control from a reactive, labor-intensive process into a data-driven, proactive operational paradigm. This approach not only transforms pest management from reactive to predictive but also aligns with the Sustainable Development Goals, offering a scalable, interpretable, and operationally viable system for smart cities. Full article

Renewable energy sources have been widely utilized in many applications worldwide in recent years, particularly to support sustainable and energy-efficient systems. One of the most vital applications of these sources is the photovoltaic (PV) traffic light system (TF-LS), which represents a sustainable alternative to conventional grid-powered traffic infrastructure. This paper presents the design and experimental implementation of a stand-alone PV TF-LS, consisting of a PV power system and an integrated TF-LS that operate autonomously while ensuring reliable and efficient energy utilization. The proposed control of the PV power system accomplishes two main functions: maximum power point tracking (MPPT) of the PV module and battery charging management. The MPPT system is implemented using a perturb and observe (P&O)-based PI algorithm with reduced step size and is experimentally validated using a dSPACE 1104 real-time control platform. In addition, this paper proposes and experimentally implements a novel intelligent control strategy for the TF-Ls that relies on vehicle counting and real-time comparison of traffic densities at road intersections instead of the traditional fixed-time scheduling approach, using LabVIEW software and an Arduino microcontroller. Experimental results demonstrate the effectiveness of the proposed control techniques for MPPT, battery charging, and traffic signal operation. Moreover, the proposed TF-LS control demonstrates fast and efficient operation under real-time traffic conditions, providing a simple and practical solution for mitigating traffic congestion. Full article

This paper introduces SmartHouseOperator, a multi-agent intelligent control framework for adaptive and energy-efficient smart-home management. Modern smart homes integrate heterogeneous devices and sensors, yet most existing solutions rely on static rules or manual coordination, limiting their ability to adapt to dynamic environmental conditions and evolving user preferences. SmartHouseOperator addresses these limitations through an agentic architecture that coordinates device-specific agents for air conditioning, lighting, refrigeration, and shutters under a central orchestrator. The system combines contextual inputs (e.g., weather, occupancy, power load), persistent knowledge, reinforcement-learning-based preference modeling, and LLM-powered reasoning to enable coordinated and personalized control decisions. Experimental results show that the framework achieves consistent reasoning performance across multiple agent orchestration engines and reduces air-conditioning power consumption by up to 16% under critical load conditions. These findings demonstrate the potential of multi-agent, learning-enabled control systems to deliver intelligent, energy-aware, and user-centric smart-home operation. Full article