The escalating demand for versatile wireless devices has fostered the need to reduce the antenna footprint to support the integration of multiple new functionalities. This poses a significant challenge for the Internet of things (IoT) antenna designers tasked with creating antennas capable of supporting multiband operation within physical constraints. This work aims to address this challenge by focusing on the optimization of an antenna booster element to achieve multiband performance, accomplished through the design of a band-reject filter. This proposal entails a printed circuit board (PCB) measuring 142 mm × 60 mm, with a clearance area of 12 mm × 40 mm, incorporating an antenna booster element of 30 mm × 3 mm × 1 mm (0.07 λ). This configuration covers frequencies in the LFR (low-frequency range) from 698 MHz to 960 MHz and the HFR (high-frequency range) from 1710 MHz to 2690 MHz. A theoretical analysis is conducted to optimize bandwidth in both frequency regions. Finally, a prototype validates the analytic results. Full article

Consider a drone that aims to find an unknown number of static targets at unknown positions as quickly as possible. A multi-target particle filter uses imperfect measurements of the target positions to update an intensity function that represents the expected number of targets. We propose a novel receding-horizon planner that selects the next position of the drone by maximizing an objective that combines exploration and target refinement. Confidently localized targets are saved and removed from consideration along with their future measurements. A controller with an obstacle-avoidance component is used to reach the desired waypoints. We demonstrate the performance of our approach through a series of simulations as well as via a real-robot experiment in which a Parrot Mambo drone searches from a constant altitude for targets located on the floor. Target measurements are obtained on-board the drone using segmentation in the camera image, while planning is done off-board. The sensor model is adapted to the application. Both in the simulations and in the experiments, the novel framework works better than the lawnmower and active-search baselines. Full article

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m. Full article

This paper presents a novel method for forecasting the impact of cloud cover on photovoltaic (PV) fields in the nowcasting term, utilizing PV panels as sensors in a combination of physical and persistence models and integrating energy storage system control. The proposed approach entails simulating a power network consisting of a 22 kV renewable energy source and energy storage, enabling the evaluation of network behavior in comparison to the national grid. To optimize computational efficiency, the authors develop an equivalent model of the PV + energy storage module, accurately simulating system behavior while accounting for weather conditions, particularly cloud cover. Moreover, the authors introduce a control system model capable of responding effectively to network dynamics and providing comprehensive control of the energy storage system using PID controllers. Precise power forecasting is essential for maintaining power continuity, managing overall power-system ramp rates, and ensuring grid stability. The adaptability of our method to integrate with solar fencing systems serves as a testament to its innovative nature and its potential to contribute significantly to the renewable energy field. The authors also assess various scenarios against the grid to determine their impact on grid stability. The research findings indicate that the integration of energy storage and the proposed forecasting method, which combines physical and persistence models, offers a promising solution for effectively managing grid stability. Full article

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice. Full article

Gas detection is crucial for detecting environmentally harmful gases. Organic field-effect transistor (OFET)-based gas sensors have attracted attention due to their promising performance and potential for integration into flexible and wearable devices. This review examines the operating mechanisms of OFET-based gas sensors and explores methods for improving sensitivity, with a focus on porous structures. Researchers have achieved significant enhancements in sensor performance by controlling the thickness and free volume of the organic semiconductor layer. Additionally, innovative fabrication techniques like self-assembly and etching have been used to create porous structures, facilitating the diffusion of target gas molecules, and improving sensor response and recovery. These advancements in porous structure fabrication suggest a promising future for OFET-based gas sensors, offering increased sensitivity and selectivity across various applications. Full article

Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train. Full article

Although the rapid expansion of urban rail transit offers convenience to citizens, the issue of subway vibration cannot be overlooked. This study investigates the spatial distribution characteristics of vibration in the Fayuan Temple historic and cultural reserve. It involves using a V001 magnetoelectric acceleration sensor capable of monitoring low amplitudes with a sensitivity of 0.298 V/(m/s²), a measuring range of up to 20 m/s², and a frequency range span from 0.5 to 100 Hz for in situ testing, analyzing the law of vibration propagation in this area, evaluating the impact on buildings, and determining the vibration reduction scheme. The reserve is divided into three zones based on the vertical vibration level measured during the in situ test as follows: severely excessive, generally excessive, and non-excessive vibration. Furthermore, the research develops a dynamic coupling model of vehicle–track–tunnel–stratum–structure to verify the damping effect of the wire spring floating plate track and periodic pile row. It compares the characteristics of three vibration reduction schemes, namely, internal vibration reduction reconstruction, periodic pile row, and anti-vibration reinforcement or reconstruction of buildings, proposing a comprehensive solution. Considering the construction conditions, difficulty, cost, and other factors, a periodic pile row is recommended as the primary treatment measure. If necessary, anti-vibration reinforcement or reconstruction of buildings can serve as supplemental measures. Full article

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds. Full article

This paper presents an in-depth analysis of the oscillation phenomenon occurring in multi-chip parallel automotive-grade power modules under short-circuit conditions and investigates three suppression methods. We tested and analyzed two commercial automotive-grade power modules, one containing two chips and the other containing a single chip, and found that short-circuit gate oscillations were more likely to occur in multi-chip parallel packaged modules than in single-chip packaged modules. Through experimental and simulation analyses, we observed that gate oscillations were mainly caused by the interaction between internal parasitic parameters of the module and the external drive circuit, and we found that high drive resistance and low common emitter inductance between parallel chips could effectively suppress gate voltage oscillations. We also analyzed the two mainstream suppression schemes, increasing the drive gate resistance and placing the drive capacitors in parallel. Unfortunately, we found that these suppression schemes were not ideal solutions because both schemes changed the switching characteristics of the power module. As an alternative, we propose a simple and effective solution that involves adding parallel connections between the parallel chips. Simulation calculations showed that this optimized method reduced the emitter inductance between parallel chips in the upper bridge arm by about 30% and in the lower bridge arm by 35%. Through short-circuit experiments conducted at different DC bus voltages, it has been verified that the new optimized solution effectively resolves gate oscillation issues without affecting the switching characteristics of the power module. Full article

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task’s performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests. Full article

In engineering measurements, metal foil strain gauges suffer from a limited range and low sensitivity, necessitating the development of flexible sensors to fill the gap. This paper presents a flexible, high-performance piezoresistive sensor using a composite consisting of graphene nanoplatelets (GNPs) and polydimethylsiloxane (PDMS). The proposed sensor demonstrated a significantly wider range (97%) and higher gauge factor (GF) (6.3), effectively addressing the shortcomings of traditional strain gauges. The microstructure of the GNPs/PDMS composite was observed using a scanning electron microscope, and the distribution of the conductive network was analyzed. The mechanical behavior of the sensor encapsulation was analyzed, leading to the determination of the mechanisms influencing encapsulation. Experiments based on a standard equal-strength beam were conducted to investigate the influence of the base and coating dimensions of the sensor. The results indicated that reducing the base thickness and increasing the coating length both contributed to the enhancement of the sensor’s performance. These findings provide valuable guidance for future development and design of flexible sensors. Full article

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: “Lightweight Trust Management based on Bayesian and Entropy (LTMBE)”, “Beta-based Trust and Reputation Evaluation System (BTRES)”, and “Lightweight and Dependable Trust System (LDTS)”. To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs. Full article

The qualitative impact of pollutants on water quality is mainly related to their nature and their concentration, but in any case, they determine a strong impact on the involved ecosystems. In particular, refractory organic compounds represent a critical challenge, and several degradation processes have been studied and developed for their removal. Among them, heterogeneous Fenton treatment is a promising technology for wastewater and liquid waste remediation. Here, we have developed mono- and bimetallic formulations based on Co, Cu, Fe, and Mn, which were investigated for the degradation of three model organic dyes (methylene blue, rhodamine B, and malachite green). The treated samples were then analyzed by means of UV-vis spectrophotometry techniques. Bimetallic iron-based materials achieved almost complete degradation of all three model molecules in very short time. The Mn-Fe catalyst resulted in the best formulation with an almost complete degradation of methylene blue and malachite green at pH 5 in 5 min and of rhodamine B at pH 3 in 30 min. The results suggest that these formulations can be applied for the treatment of a broad range of liquid wastes comprising complex and variable organic pollutants. The investigated catalysts are extremely promising when compared to other systems reported in the literature. Full article

The concept of nanomedicine has evolved significantly in recent decades, leveraging the unique phenomenon known as the enhanced permeability and retention (EPR) effect. This has facilitated major advancements in targeted drug delivery, imaging, and individualized therapy through the integration of nanotechnology principles into medicine. Numerous nanomedicines have been developed and applied for disease treatment, with a particular focus on cancer therapy. Recently, nanomedicine has been utilized in various advanced fields, including diagnosis, vaccines, immunotherapy, gene delivery, and tissue engineering. Multifunctional nanomedicines facilitate concurrent medication delivery, therapeutic monitoring, and imaging, allowing for immediate responses and personalized treatment plans. This review concerns the major advancement of nanomaterials and their potential applications in the biological and medical fields. Along with this, we also mention the various clinical translations of nanomedicine and the major challenges that nanomedicine is currently facing to overcome the clinical translation barrier. Full article

Leishmaniasis and Human African trypanosomiasis pose significant public health threats in resource-limited regions, accentuated by the drawbacks of the current antiprotozoal treatments and the lack of approved vaccines. Considering the demand for novel therapeutic drugs, a series of BODIPY derivatives with several functionalizations at the meso, 2 and/or 6 positions of the core were synthesized and characterized. The in vitro activity against Trypanosoma brucei and Leishmania major parasites was carried out alongside a human healthy cell line (MRC-5) to establish selectivity indices (SIs). Notably, the meso-substituted BODIPY, with 1-dimethylaminonaphthalene (1b) and anthracene moiety (1c), were the most active against L. major, displaying IC₅₀ = 4.84 and 5.41 μM, with a 16 and 18-fold selectivity over MRC-5 cells, respectively. In contrast, the mono-formylated analogues 2b and 2c exhibited the highest toxicity (IC₅₀ = 2.84 and 6.17 μM, respectively) and selectivity (SI = 24 and 11, respectively) against T. brucei. Further insights on the activity of these compounds were gathered from molecular docking studies. The results suggest that these BODIPYs act as competitive inhibitors targeting the NADPH/NADP⁺ linkage site of the pteridine reductase (PR) enzyme. Additionally, these findings unveil a range of quasi-degenerate binding complexes formed between the PRs and the investigated BODIPY derivatives. These results suggest a potential correlation between the anti-parasitic activity and the presence of multiple configurations that block the same site of the enzyme. Full article

Anion-driven, nanoscale polar–apolar structural organization is investigated in a solvate ionic liquid (SIL) setting by comparing sulfonate-based anions with long and short perfluorinated alkyl chains. Representative SILs are created from 1,2-bis(2-methoxyethoxy)ethane (“triglyme” or “G3”), lithium nonafluoro-1-butanesulfonate, and lithium trifluoromethanesulfonate. Molecular dynamics simulations, density functional theory computations, and vibrational spectroscopy provide insight into the overall liquid structure, cation–solvent interactions, and cation–anion association. Significant competition between G3 and anions for cation-binding sites characterizes the G3–LiC₄F₉SO₃ mixtures. Only 50% of coordinating G3 molecules form tetradentate complexes with Li⁺ in [(G3)₁Li][C₄F₉SO₃]. Moreover, the SIL is characterized by extensive amounts of ion pairing. Based on these observations, [(G3)₁Li][C₄F₉SO₃] is classified as a “poor” SIL, similar to the analogous [(G3)₁Li][CF₃SO₃] system. Even though the comparable basicity of the CF₃SO₃⁻ and C₄F₉SO₃⁻ anions leads to similar SIL classifications, the hydrophobic fluorobutyl groups support extensive apolar domain formation. These apolar moieties permeate throughout [(G3)₁Li][C₄F₉SO₃] and persist even at relatively low dilution ratios of [(G3)₁₀Li][C₄F₉SO₃]. By way of comparison, the CF₃ group is far too short to sustain polar–apolar segregation. This demonstrates how chemically modifying the anions to include hydrophobic groups can impart unique nanoscale organization to a SIL. Moreover, tuning these nano-segregated fluorinated domains could, in principle, control the presence of dimensionally ordered states in these mixtures without changing the coordination of the lithium ions. Full article

Despite continuous work on new power systems for vehicles, machines, and devices, the combustion engine is still the dominant system. The operation of the combustion engine is initiated during the starting process using starting devices. The most common starting system used is the electric starter. The starting process of an internal combustion engine depends on the following factors: the technical condition of the starting system, technical condition of the engine, battery charge level, lubricating properties, engine standstill time, engine and ambient temperature, type of fuel, etc. This article presents the results of laboratory tests of the electrical parameters of the starting process of a single-cylinder compression–ignition engine with variable fuel injection parameters and ambient temperature conditions. It was confirmed that for the increased fuel dose FD2, higher values of the measured electrical parameters (Imax, Pmax, and Pmed) were obtained compared to the series of tests with the nominal fuel dose. Knowledge of the values of the electrical parameters of the starting process is important not only for the user (vehicle driver, agricultural machinery operator, etc.), but above all for designers of modern starting systems for combustion engines and service personnel. The obtained results of testing the electrical parameters of the combustion engine during start-up may be helpful in designing new drive systems supported by a compression–ignition combustion engine. Full article

This study presents the effects of using a battery-powered chainsaw on work efficiency and ergonomics under real conditions during timber harvesting. The study was conducted during the felling and processing of coniferous and deciduous trees with a diameter at breast height (DBH) of 13 cm to 78 cm using both a petrol-powered and battery-powered chainsaw. The results include comparisons of time composition, work efficiency, psychophysical workload, and noise exposure. Heart rate and noise exposure were measured over ten days as part of a time study using the Husqvarna 543 XP petrol-powered chainsaw and the Husqvarna 540i HP battery-powered chainsaw. The comparison of the time composition between the chainsaws used showed 3%–4% differences in the duration of productive time operations and 16% in service time. The difference in work efficiency during the productive time between the two chainsaws was statistically insignificant, but generally higher when working with the battery-powered chainsaw than with the petrol-powered chainsaw. During the main productive time, the work efficiency was 9.89 min/t for the petrol-powered chainsaw and 9.44 min/t for the battery-powered chainsaw. The psychophysical workload of the feller was lower when using the battery-powered chainsaw than when using the petrol-powered chainsaw as the relative working heart rates during the entire productive time was 32.5% for the battery-powered chainsaw and 35.0% for the petrol-powered chainsaw. The noise exposure of the workers was lower when using a battery-powered chainsaw, namely 6.0 dB(A) and 0.4 dB(C) compared to the use of a petrol-powered chainsaw. The results of this paper indicate that battery-powered chainsaws can compete with petrol chainsaws in harvesting conditions that are currently considered unsuitable due to the large volume of trees. Full article

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation. Full article

This study examined the impact of green mergers and acquisitions (green M&As) on corporate environmental performance. Applying the Differences-in-Differences (DID) model to a sample of Chinese heavy-polluting-industry companies listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2022, our study results show that the adoption of green M&As by the listed Chinese heavy polluters can lower corporate environmental capital expenditure and significantly improve corporate environmental performance. Meanwhile, the positive effects of green M&As on environmental performance are also found to be stronger for state-owned enterprises, young enterprises, and enterprises located in areas with low financial investments in energy efficiency and environmental protection, according to a heterogeneity study conducted for this paper. The analysis of mediating effects shows that the green M&A of heavily polluting firms will have a catalytic effect on the improvement of firms’ environmental performance by promoting their green technological innovation and, in turn, their environmental performance. Furthermore, the moderating effect analysis demonstrates that the quality of the firm’s internal controls and the CEO’s prior environmental experience are both factors that can support the beneficial impact of green M&A on the enhancement of the firm’s environmental performance. This paper enriches the theoretical research system of green M&A and green investment driving mechanisms, and at the same time provides empirical support and strategic reference for the green strategy decision of heavy-polluting enterprises. Full article

Up to 30% of people over the age of 60 are at high risk of falling, which can cause injury, aggravation of pre-existing conditions, or even death, with up to 684,000 fatal falls reported annually. This is due to the difficult task of establishing a preventive system for the care of the elderly, both in the hospital environment and at home. Therefore, this work proposes the development of an intelligent vision system that uses a novel methodology to infer fall risk from the analysis of kinetic and spatiotemporal gait parameters. In general, each patient is assessed using the Tinetti scale. Then, the computer vision system estimates the biomechanics of walking and obtains gait features, such as stride length, cadence, period, and range of motion. Subsequently, this information serves as input to an artificial neural network that diagnoses the risk of falling. Ninety-six participants took part in the study. The system’s performance was 99.1% accuracy, 94.4% precision, 96.9% recall, 99.4% specificity, and 95.5% F1-Score. Thus, the proposed system can evaluate the fall risk assessment, which could benefit clinics, hospitals, and even homes by allowing them to assess in real time whether a person is at high risk of falling to provide timely assistance. Full article