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Recently, much attention has been given to the development of various energy harvesting technologies to power remote electronic sensors, data loggers, and communicators that can be installed on smart grid systems. Magnetic energy harvesting is, perhaps, the most straightforward way to capture a significant amount of power from a current-carrying overhead line. Since the harvester is expected to have a small size, the high currents of the distribution system easily saturate its magnetic core. As a result, the operation of the magnetic harvester is highly nonlinear and makes precise analytical modeling difficult. The operation of an overhead line magnetic energy harvester (OLMEH) generating significant DC power output into a constant voltage load was investigated in this paper. The analysis method was based on the Froelich equation to analytically model the nonlinearity of the core’s BH characteristic. The main findings of this piecewise nonlinear analysis include a closed-form solution that accounts for both the core and rectifiers’ nonlinearities and provides an accurate prediction of OLMEH transfer window length, output current, and harvested power. Continuous and discontinuous operational modes are identified and the mode transition boundary is obtained quantitatively. The theoretical investigation was concluded by comparison with a computer simulation and also verified by the experimental results of a laboratory prototype harvester. A good agreement was found. Full article

Sustainable forest management faces challenges from various biotic and abiotic stresses. The Asian longhorned beetle (ALB) and drought stress both induce water shortages in poplar trees, but require different management strategies. In northwestern China, ALB and drought stress caused massive mortality in poplar shelterbelts, which seriously affected the ecological functions of poplars. Developing a large-scale detection method for discriminating them is crucial for applying targeted management. This study integrated UAV-hyperspectral and LiDAR data to distinguish between ALB and drought stress in poplars of China’s Three-North Shelterbelt. These data were analyzed using a Partial Least Squares-Support Vector Machine (PLS-SVM). The results showed that the LiDAR metric (elev_sqrt_mean_sq) was key in detecting drought, while the hyperspectral band (R970) was key in ALB detection, underscoring the necessity of integrating both sensors. Detection of ALB in poplars improved when the poplars were well watered. The classification accuracy was 94.85% for distinguishing well-watered from water-deficient trees, and 80.81% for detecting ALB damage. Overall classification accuracy was 78.79% when classifying four stress types: healthy, only ALB affected, only drought affected, and combined stress of ALB and drought. The results demonstrate the effectiveness of UAV-hyperspectral and LiDAR data in distinguishing ALB and drought stress in poplar forests, which contribute to apply targeted treatments based on the specific stress in poplars in northwest China. Full article

A Lamb wave is a guided wave that propagates within plate-like structures, with its vibration mode resulting from the coupling of a longitudinal wave and a shear vertical wave, which can be applied in sensors, filters, and frequency control devices. The working principle of Lamb wave sensors relies on the excitation and propagation of this guided wave within piezoelectric material. Lamb wave sensors exhibit significant advantages in various sensing applications due to their unique wave characteristics and design flexibility. Compared to traditional surface acoustic wave (SAW) and bulk acoustic wave (BAW) sensors, Lamb wave sensors can not only achieve higher frequencies and quality factors in smaller dimensions but also exhibit superior integration and multifunctionality. In this paper, we briefly introduce Lamb wave sensors, summarizing methods for enhancing their sensitivity through optimizing electrode configurations and adjusting piezoelectric thin plate structures. Furthermore, this paper systematically explores the development of Lamb wave sensors in various sensing applications and provides new insights into their future development. Full article

The design and development of fluorescent materials for detecting cancer-related enzymes are crucial for cancer diagnosis and treatment. Herein, we present a substituted rhodamine derivative for the chromogenic and fluorogenic detection of the cancer-relevant enzyme γ-glutamyltranspeptidase (GGT). Initially, the probe is non-chromic and non-emissive due to its spirolactam form, which hinders extensive electronic delocalization over broader pathway. However, selective enzymatic cleavage of the side-coupled group triggers spirolactam ring opening, resulting in electronic flow across the rhodamine skeleton, and reduces the band gap for low-energy electronic transitions. This transformation turns the reaction mixture from colorless to intense pink, with prominent UV and fluorescence bands. The sensor’s selectivity was tested against various human enzymes, including urease, alkaline phosphatase, acetylcholinesterase, tyrosinase, and cyclooxygenase, and showed no response. Absorption and fluorescence titration analyses of the probe upon incremental addition of GGT into the probe solution revealed a consistent increase in both absorption and emission spectra, along with intensified pink coloration. The cellular toxicity of the receptor was evaluated using the MTT assay, and bioimaging analysis was performed on BHK-21 cells, which produced bright red fluorescence, demonstrating the probe’s excellent cell penetration and digestion capabilities for intracellular analytical detection. Molecular docking results supported the fact that probe-4 made stable interactions with the GGT active site residues. Full article

This article presents a systematic literature review of technologies and solutions for cattle tracking and monitoring based on a comprehensive analysis of scientific articles published since 2017. The main objective of this review is to identify the current state of the art and the trends in this field, as well as to provide a guide for selecting the most suitable solution according to the user’s needs and preferences. This review covers various aspects of cattle tracking, such as the devices, sensors, power supply, wireless communication protocols, and software used to collect, process, and visualize the data. The review also compares the advantages and disadvantages of different solutions, such as collars, cameras, and drones, in terms of cost, scalability, precision, and invasiveness. The results show that there is a growing interest and innovation in livestock localization and tracking, with a focus on integrating and adapting various technologies for effective and reliable monitoring in real-world environments. Full article

Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments. Full article

Poly(ADP-ribose) polymerases (PARPs) are crucial nuclear proteins that play important roles in various cellular processes, including DNA repair, gene transcription, and cell death. Among the 17 identified PARP family members, PARP1 is the most abundant enzyme, with approximately 1–2 million molecules per cell, acting primarily as a DNA damage sensor. It has become a promising biological target for anticancer drug studies. Enhanced PARP expression is present in several types of tumors, such as melanomas, lung cancers, and breast tumors, correlating with low survival outcomes and resistance to treatment. PARP inhibitors, especially newly developed third-generation inhibitors currently undergoing Phase II clinical trials, have shown efficacy as anticancer agents both as single drugs and as sensitizers for chemo- and radiotherapy. This review explores the properties, characteristics, and challenges of PARP inhibitors, discussing their development from first-generation to third-generation compounds, more sustainable synthesis methods for discovery of new anti-cancer agents, their mechanisms of therapeutic action, and their potential for targeting additional biological targets beyond the catalytic active site of PARP proteins. Perspectives on green chemistry methods in the synthesis of new anticancer agents are also discussed. Full article

The focal length in a typical optical system changes with the angle of view, according to the size of the sensor. This study proposed an optical terahertz (THz) system application where the focal length changed while the angle of view was fixed; thus, the image height was variable and responded to various sensor sizes. Therefore, it is possible to respond to various sensors with one optical system when the inspection distance is fixed. The fundamental optical system was designed by arranging the refractive power, which was determined according to the sensor size using the Gaussian bracketing method. A zoom optical system that changed the image height by fixing the angle of view and changed the focal length by moving the internal lens group was designed. THz waves exhibit minimal change in the refractive index depending on the wavelength. Moreover, their long-wavelength characteristics facilitate the development of millimeter-level pixel sizes. Therefore, the root mean square size of the maximum spot was 0.329 mm, which corrected the aberration to less than 1 mm (smaller than the pixel size). Further, a lighting analysis at 3 and 6 m locations confirmed the expansion of the lighting area by the magnification of the sensor size. After turning off certain light sources, we checked the contrast ratio via lighting analysis and confirmed that the size of one pixel was clearly distinguishable. Consequently, this newly designed optical system performed appropriately as an optical inspection system for THz system applications. Full article

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency. Full article

Nanomaterials are substances with unique properties due to the irintrinsic confinement effect and high surface area that have allowed their use in biology and medicine for sensor application. The key feature of nanomaterials in such applications is their ability to providesensitivity enhancement for sensors. On the other hand, nanomaterials possess the ability to change the biological function in cells or tissues; therefore, it is from this point of view that nanomaterials can be considered as functional. As far as biosensor application is concerned, it is important to optimize the determination of target molecules in spatial and temporal modes. The purpose of the presented work was to study the effect of functional nanomaterials on the growth (the temporal component) and morphology (the spatial component) of cell culture. The aim was to provide a culture condition where an increase in both the spatial and temporal components of configuration could be achieved in order to optimize sensor needs. Since microalgae have a wide range of possibilities for practical use in medicine, pharmacology and various industries, the study of the effect of nanomaterials on their growth and development is very important. It was found that ZnO nanomaterial, which was obtained by volumetric electrospark dispersion, revealed the concentration-dependent effect on both the grown rate and the color intensity interior of Chlamydomonas monadina microalgae culture. Therefore, ZnO functional nanomaterial achieved the optimization of target molecule formation for biosensor application. Full article

The birth process in animals, much like in humans, can encounter complications that pose significant risks to both offspring and mothers. Monitoring these events can provide essential nursing support, but human monitoring is expensive. Although there are commercial monitoring systems for large ruminants, there are no effective solutions for small ruminants, despite various attempts documented in the literature. Inertial sensors are very convenient given their low cost, low impact on animal life, and their flexibility for monitoring animal behavior. This study offers a systematic review of the literature on detecting parturition in small ruminants using inertial sensors. The review analyzed the specifics of published research, including data management and monitoring processes, behaviors indicative of parturition, processing techniques, detection algorithms, and the main results achieved in each study. The results indicated that some methods for detecting birth concentrate on classifying unique animal behaviors, employing diverse processing techniques, and developing detection algorithms. Furthermore, this study emphasized that employing techniques that include analyzing animal activity peaks, specifically recurrent lying down and getting up occurrences, could result in improved detection precision. Although none of the studies provided a completely valid detection algorithm, most results were promising, showing significant behavioral changes in the hours preceding delivery. Full article

High-resolution remote sensing of turbidity in the coastal environment with unmanned aerial vehicles (UAVs) can be adversely affected by the presence of obstructions of vessels and marine objects in images, which can introduce significant errors in turbidity modeling and predictions. This study evaluates the use of two deep-learning-based inpainting methods, namely, Decoupled Spatial–Temporal Transformer (DSTT) and Deep Image Prior (DIP), to recover the obstructed information. Aerial images of turbidity plumes in the coastal environment were first acquired using a UAV system with a multispectral sensor that included obstructions on the water surface at various obstruction percentages. The performance of the two inpainting models was then assessed through both qualitative and quantitative analyses of the inpainted data, focusing on the accuracy of turbidity retrieval. The results show that the DIP model performs well across a wide range of obstruction percentages from 10 to 70%. In comparison, the DSTT model produces good accuracy only with low percentages of less than 20% and performs poorly when the obstruction percentage increases. Full article

The era of digitization and IoT devices is marked by the constant storage of massive amounts of data. The growing adoption of smart home environments, which use sensors and devices to monitor and control various aspects of daily life, underscores the need for effective privacy and security measures. HE is a technology that enables computations on encrypted data, preserving confidentiality. As a result, researchers have developed methodologies to protect user information, and HE is one of the technologies that make it possible to perform computations directly on encrypted data and produce results using this encrypted information. Thus, this research study compares the performance of three ML models, XGBoost, Random Forest, and Decision Classifier, on a real-world smart home dataset using both with and without FHE. Practical results demonstrate that the Decision Classifier showed remarkable results, maintaining high accuracy with FHE and even surpassing its plaintext performance, suggesting that encryption can enhance model accuracy under certain conditions. Additionally, Random Forest showed efficiency in terms of execution time and low prediction errors with FHE, making it a strong candidate for encrypted data processing in smart homes. These findings highlight the potential of FHE to set new privacy standards, advancing secure and privacy-preserving technologies in smart environments. Full article

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future. Full article

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data. Full article