Search Results (2,217)

The primary objective when operating a distribution network is to minimize operating costs while taking technical constraints into account. Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. In this paper, a flexible, dynamic reconfiguration model is developed that enables a distribution network to minimize operating costs on an hourly basis. The model fitness function is to minimize the system costs, including power loss, voltage deviation, purchased power from the upstream network, renewable generation, and switching costs. The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. To evaluate the performance of the presented approach, it is validated on the IEEE 33-bus radial system and TPC 83-bus real system. The simulation results show the impact of dynamic reconfiguration on reducing operation costs. It is found that dynamic reconfiguration is an efficient solution for reducing power losses and total energy drawn from the upstream network by increasing the number of switching operations. Full article

The anthocyanins in apple leaves can indicate their growth status, and the health of apple leaves not only reveals the nutritional supply of the apple tree but also reflects the quality of the fruit. Therefore, real-time monitoring of anthocyanins in apple leaves can monitor apple growth, thereby promoting the development of the apple industry. This study utilizes ground hyperspectral imaging to estimate anthocyanins in Fuji apple leaves in the Loess Plateau through spectral transformation, feature extraction (including band selection and spectral indices construction), and regression algorithm selection, establishing models for three growth stages. The results indicate: (1) The average anthocyanins in apple leaves decrease from the Final Flowering stage to the Fruit Enlargement stage. The original hyperspectral imaging at wavelengths before 720 nm shows a decrease in reflectance as the growth stages progress, while the spectral curves after 720 nm remain largely consistent across stages; (2) Compared to single original spectral variables, multivariate estimation models using original spectra and second-order derivative transformed spectra show improved accuracy for anthocyanins estimation across different growth stages, with the most significant improvement during the Fruit Enlargement stage; (3) Although the computation of the three-band spectral indices is resource-intensive and time-consuming, it can enhance anthocyanins estimation accuracy; (4) Among all models, the CatBoost model based on original spectra and second-order derivative transformed spectra indices for the entire growth period achieved the highest accuracy, with a validation set R² of 0.934 and a RPD of 3.888, and produced effective leaf anthocyanins inversion maps. In summary, this study achieves accurate estimation and visualization of anthocyanins in apple leaves across different growth stages, enabling rapid, accurate, and real-time monitoring of apple growth. It provides theoretical guidance and technical support for apple production and fertilization management. Full article

The study investigates controlled environment agriculture (CEA) in Nigeria focusing on its feasibility, economic benefits, environmental impact, and socio-economic implications. While CEA technologies such as hydroponics, vertical farming, automation, and greenhouse systems offer efficiency and yield improvements, this review highlights the extent to which they can be utilized in solving the food challenges facing the country including food shortages, wasteful use of land, and climatic disturbances in agriculture. However, their adoption faces challenges like high initial costs, technical knowledge gaps, and unstable energy infrastructure. Additionally, there is a lack of localized research on resource utilization, crop profitability, and the scalability of these systems in Nigeria’s urban and rural contexts, which further hinders adoption. Government policy reforms, renewable energy access, and capacity-building programs are crucial to overcoming these barriers. Localized pilot projects and field studies are also necessary to validate the feasibility of CEA systems under Nigeria’s unique socio-economic and climatic conditions. Cross-country comparisons with South Africa and Kenya reveal actionable insights for Nigeria’s CEA implementation such as South Africa’s public-private partnerships and Kenya’s solar-powered vertical farms which can serve as actionable blueprints for Nigeria’s CEA adoption and expansion. Nigeria with its teeming population is food import-dependent, with agricultural imports reaching 3.35 trillion Naira between 2019 and 2023. This is unsustainable and requires alternative measures including targeted CEA interventions to increase its agricultural productivity. Overall, for CEA to contribute meaningfully to the Nigerian agricultural sector, specific changes including targeted subsidies, policy reforms, renewable energy access, stakeholder engagement, capacity-building programs, and infrastructure development must be instituted to achieve sustainable agricultural growth. Furthermore, strategies such as hybridizing traditional and CEA practices and creating “pay-as-you-grow” financial models for CEA infrastructure can make the transition more viable for smallholder farmers, who dominate Nigeria’s agricultural sector. Full article

Striking velocity is a key performance indicator in striking-based combat sports, such as boxing, Karate, and Taekwondo. This study aims to develop a low-cost, accelerometer-based system to measure kick and punch velocities in combat athletes. Utilizing a low-cost mobile phone in conjunction with the PhyPhox app, acceleration data was collected and analyzed using a custom algorithm. This involved strike segmentation and numerical integration to determine velocity. The system demonstrated moderate reliability (intraclass correlation coefficient (ICC) $3,1$ = $0.746$ to $0.786$, standard error of measurement (SEM) = $0.488$ to $0.921$ m/s), comparable to commercially available systems. Biological and technical variations, as well as test standardization issues, were acknowledged as factors influencing reliability. Despite a relatively low sampling frequency, the hardware and software showed potential for reliable measurement. The study highlights the importance of considering within-subject variability, hardware limitations, and the impact of noise in software algorithms. Average strike velocities exhibited higher reliability than peak velocities, making them a practical choice for performance tracking, although they may underestimate true peak performance. Future research should validate the system against gold-standard methods and determine the optimal sampling frequency to enhance measurement accuracy. Full article

Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms. Background/Objectives: Over the past few decades, ML and other AI tools have been explored extensively in sepsis, with models developed for the early detection, diagnosis, prognosis, and even real-time management of treatment strategies. Methods: This review was conducted according to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research Type) framework to define the study methodology. A critical overview of each paper was conducted by three different reviewers, selecting those that provided original and comprehensive data relevant to the specific topic of the review and contributed significantly to the conceptual or practical framework discussed, without dwelling on technical aspects of the models used. Results: A total of 194 articles were found; 28 were selected. Articles were categorized and analyzed based on their focus—early prediction, diagnosis, mortality or improvement in the treatment of sepsis. The scientific literature presents mixed outcomes; while some studies demonstrate improvements in mortality rates and clinical management, others highlight challenges, such as a high incidence of false positives and the lack of external validation. This review is designed for clinicians and healthcare professionals, and aims to provide an overview of the application of AI in sepsis management, reviewing the main studies and methodologies used to assess its effectiveness, limitations, and future potential. Full article

Citrus nurseries significantly increase production costs due to the application of strictly technical and sanitary protocols. The growth media used are generally based on peat, a limited resource that is becoming increasingly scarce and consequently more expensive. Among the alternatives to peat is biochar, which could constitute a valid growing medium component for citrus seedling production. Three growth media were compared, each containing 50% sandy volcanic soil and the remaining 50% being: (i) biochar 50%; (ii) black peat 25% + biochar 25%; and (iii) black peat 25% + lapillus 25% as the control. The impact on the agronomic performance of citrus seedlings was assessed, and the involvement of specific genes in macronutrient uptake was evaluated. Destructive and molecular analyses were performed on leaves and roots during two different periods of the year: February and April. Based on physicochemical parameters and seedling growth, it can be assumed that peat can be partially substituted by conifer wood biochar in a total amount of 25 or 50%. A general comparison of the averages from the sampling and the various analyzed substrates revealed that in February, the evaluated genes involved in the absorption and transport of nutrients were differentially expressed in both leaves and roots, while in April, the expression was not consistent. Additionally, a general comparison between the analyzed tissues showed that, in most cases, expression was higher in the roots than in the leaves. Overall, a comparison among plants grown in different substrates indicated that the medium with 50% biochar displayed the highest expression levels. Full article

Reinforcing concrete beams with adhesive steel plates is a widely adopted method for enhancing structural performance. However, its ability to significantly improve the load-carrying capacity of reinforced concrete (RC) beams is constrained and often leads to “over-reinforced” failure. To overcome these limitations, this study introduces a novel composite reinforcement strategy that integrates steel plates in the tensile zone with Engineered Cementitious Composite (ECC) layers in the compression zone of RC beams. Static and fatigue tests were conducted on the reinforced beams, and a finite element model was developed to perform nonlinear analyses of their structural behavior under cyclic loading. The model incorporates the nonlinear material properties of concrete and rebar, enabling accurate simulation of material degradation under cyclic conditions. The model’s accuracy was validated through comparison with experimental data, demonstrating its effectiveness in analyzing the structural performance of RC beams under cyclic loading. Furthermore, a parametric study demonstrated that increasing the thickness of steel plates and ECC layers substantially improves the beams’ ductility and load-carrying capacity. These findings provide effective reinforcement strategies and offer valuable technical insights for engineering design. Full article

Background/Objectives: Emergency airway management is a critical skill for healthcare professionals, particularly in life-threatening situations like “cannot intubate, cannot oxygenate” (CICO) scenarios. Errors and delays in airway management can lead to adverse outcomes, including hypoxia and death. Cognitive aids, such as checklists and algorithms, have been proposed as tools to improve decision-making, procedural competency, and non-technical skills in these high-stakes environments. This systematic review aims to evaluate the effectiveness of cognitive aids in enhancing emergency airway management skills among health professionals and trainees. Methods: A systematic search of MEDLINE, Embase, CINAHL, Cochrane Library, Scopus, Web of Science, and ClinicalTrials.gov was conducted from February to March 2024. Studies examining the use of cognitive aids, such as the Vortex method, the ASA difficult airway algorithm, and visual airway aids, in emergency airway scenarios were included. Outcomes assessed included decision-making speed, procedural success rates, and non-technical skills. Data were extracted using standardized protocols, and the quality of included studies was appraised. Results: Five studies met inclusion criteria, encompassing randomized controlled trials, controlled studies, and mixed-methods research. Cognitive aids improved decision-making times (reduced by 44.6 s), increased procedural success rates, and enhanced non-technical skills such as teamwork and crisis management. Participants reported reduced anxiety and improved confidence levels (self-efficacy scores increased by 1.9 points). The Vortex method and visual cognitive aids demonstrated particular effectiveness in simulated scenarios. Conclusions: Cognitive aids significantly enhance emergency airway management skills, improving performance, reducing errors, and increasing provider confidence. Integrating cognitive aids into training programs has the potential to improve patient safety and outcomes. Further research is needed to validate these findings in clinical settings and optimize cognitive aid design and implementation. Full article

The gradual growth of oxides inside thermal barrier coatings is a key factor leading to the degradation of thermal barrier coating performance until its failure, and accurate monitoring of the growth stress during this process is crucial to ensure the long-term stable operation of engines. In this study, terahertz time-domain spectroscopy was introduced as a new method to characterize the growth stress in thermal barrier coatings. By combining metallographic analysis and scanning electron microscope (SEM) observation techniques, the real microstructure of the oxide layer was obtained, and an accurate simulation model of the oxide growth was constructed on this basis. The elastic solutions of the thermally grown oxide layer of thermal insulation coatings were obtained by using the controlling equations in the rate-independent theoretical model, and the influence of the thickness of the thermally grown oxide (TGO) layer on the stress distribution was explored. Based on experimental data, multidimensional 3D numerical models of thermal barrier coatings with different TGO thicknesses were constructed, and the terahertz time-domain responses of oxide coatings with different thicknesses were simulated using the time-domain finite difference method to simulate the actual inspection scenarios. During the simulation process, white noise with signal-to-noise ratios of 10 dB to 20 dB was embedded to approximate the actual detection environment. After adding the noise, wavelet transform (WT) was used to reduce the noise in the data. The results showed that the wavelet transform had excellent noise reduction performance. For the problems due to the large data volume and small sample data after noise reduction, local linear embedding (LLE) and kernel-based extreme learning machine (KELM) were used, respectively, and the kernel function was optimized using the gray wolf optimization (GWO) algorithm to improve the model’s immunity to interference. Experimental validation showed that the proposed LLE-GWO-KELM hybrid model performed well in predicting the TGO growth stress of thermal insulation coatings. In this study, a novel, efficient, nondestructive, online, and high-precision measurement method for the growth in TGO stress of thermal barrier coatings was developed, which provides reliable technical support for evaluating the service life of thermal barrier coatings. Full article

Near-net-shaped metal products manufactured by high-pressure diecasting (HPD) encountered more or less critical failure during operation, owing to the development of micro-defects and structural inhomogeneity attributed to the complexity of geometrical die design. Because the associated work primarily relies on technical experience, it is necessary to perform the structural analysis of the HPDed component in comparison with simulation-based findings that forecast flow behavior, hence reducing trial and error for optimization. This study validated the fluidity and solidification behaviors of a commercial-grade Al-Si-Cu-Mg alloy (ALDC12) that is widely used in electric vehicle housing parts using the ProCAST tool. Both experimental and simulation results exhibited that defects at the interface of a compact mold filling were barely detected. However, internal micro-pores were seen in the bolt region, resulting in a 17.27% drop in micro-hardness compared to other parts, for which the average values from distinguished observation areas were 111.24 HV, 92.03 HV, and 103.87 HV. The simulation aligns with structural observations on defect formation due to insufficient fluidity in local geometry. However, it may underestimate the cooling rate under isothermal conditions. Thus, the simulation used in this work provides reliable predictions for optimizing HPD processing of the present alloy. Full article

The anatomy of the pelvis may obscure differences in pelvic tilt, potentially underestimating its correlation with clinical measures. Measuring the total sagittal range of pelvic movement can serve as a reliable indicator of pelvic function. This study assessed the inter- and intra-examiner reliability of the Kinovea^® version 0.9.5 and its agreement with the Qualisys System (3D motion capture) for measuring the total pelvic range of movement (ROM) in the sagittal plane, establishing Kinovea^®’s validity in standing and sitting positions with 45° of hip flexion. A cross-sectional study was conducted with 13 asymptomatic participants. Pelvic kinematics were recorded using both systems. Pelvic posture, anterior and posterior tilt, and total pelvic ROM in the sagittal plane were analyzed. The Intraclass Correlation Coefficient (ICC) was used to evaluate reliability and validity. Additionally, the technical error of measurement (TEM), relative TEM, standard error of measurement, and minimal detectable change (MDC) were calculated to establish Kinovea^®’s accuracy. Kinovea^® demonstrated excellent inter- and intra-examiner reliability for total pelvic ROM in standing and sitting measurements (ICC > 0.90), with relative TEM values below 10% and MDC values between 1.60°and 11.20°. Validity showed good-to-excellent ICC values when comparing Kinovea^® and the Qualisys System. This finding suggests that Kinovea^® is a valid tool for obtaining reproducible measurements of total pelvic ROM in the sagittal plane in standing and sitting positions, demonstrating excellent-to-good inter- and intra-examiner reliability for pelvic kinematics. Full article

Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. Consequently, the current machine learning denoising techniques fall short in delivering precise processing of Wide Field Electromagnetic Method (WFEM) data. To eliminate the noise, this paper presents an electromagnetic data denoising approach based on the improved dung beetle optimized (IDBO) gated recurrent unit (GRU) and its application. Firstly, Spatial Pyramid Matching (SPM) chaotic mapping, variable spiral strategy, Levy flight mechanism, and adaptive T-distribution variation perturbation strategy were utilized to enhance the DBO algorithm. Subsequently, the mean square error is employed as the fitness of the IDBO algorithm to achieve the hyperparameter optimization of the GRU algorithm. Finally, the IDBO-GRU method is applied to the denoising processing of WFEM data. Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. Moreover, the time domain of the processed WFEM data is more in line with periodic signal characteristics, its overall data quality is significantly enhanced, and the electric field curve is more stable. Therefore, the IDBO-GRU is more adept at processing the time domain sequence, and the application results also validate that the proposed method can offer technical support for electromagnetic inversion interpretation. Full article

Traditional methods for evaluating tennis technique, such as visual observation and video analysis, are often subjective and time consuming. On the other hand, a quick and accurate assessment can provide immediate feedback to players and contribute to technical development, particularly in less experienced athletes. This study aims to validate the use of a single inertial measurement system to assess some relevant technical parameters of amateur players. Among other things, we attempt to search for significant correlations between the flexion extension and torsion of the torso and the lateral distance of the ball from the body at the instant of impact. This research involved a group of amateur players who performed a series of standardized gestures (forehands and serves) wearing a sensorized chest strap fitted with a wireless inertial unit. The collected data were processed to extract performance metrics. The percentage coefficient of variation for repeated measurements, Wilcoxon signed-rank test, and Spearman’s correlation were used to determine the system’s reliability. High reliability was found between sets of measurements in all of the investigated parameters. The statistical analysis showed moderate and strong correlations, suggesting possible applications in assessing and optimizing specific aspects of the technique, like the player’s distance to the ball in the forehand or the toss in the serve. The significant variations in technical execution among the subjects emphasized the need for tailored interventions through personalized feedback. Furthermore, the system allows for the highlighting of specific areas where intervention can be achieved in order to improve gesture execution. These results prompt us to consider this system’s effectiveness in developing an on-court mobile application. Full article

During the harvesting process of Cerasus humilis, the fruits are susceptible to compression and impacts from the combing teeth, leading to internal damage to the pulp and rupture of the peel. This compromises the quality of the harvested fruits and subsequent processing, resulting in significant economic losses. To investigate the mechanical behavior of Cerasus humilis fruit, this study measured the geometric parameters as well as the mechanical properties (failure load, elastic modulus, compressive strength, and fracture energy) of the peel, pulp, and core in both the axial and radial directions. A geometric model of Cerasus humilis fruit was constructed using three-dimensional reverse engineering technology. The rupture process of the fruit under compressive loading was simulated and analyzed using Abaqus software (Version 2023). The damage mechanisms were investigated, and the accuracy and reliability of the finite element model were validated through compression experiments. The experimental results indicated that the mechanical properties of the peel of Cerasus humilis fruit exhibited no significant differences between the axial and radial directions, allowing it to be regarded as an isotropic material. In contrast, the mechanical properties of the pulp and core showed significant differences in both directions, demonstrating anisotropic characteristics. Additionally, the axial compressive strength of the Cerasus humilis fruit was higher than its radial compressive strength. The simulation results revealed that during axial compression, when the surface stress of the peel reached 0.08 MPa, the fruit completely fractured. The location and morphology of the cracks in the simulation were consistent with those observed in the experimental results. Furthermore, under different compression directions, the force–displacement curves obtained from actual compression tests closely aligned with those from the finite element simulations. The finite element model established in this study effectively simulates and predicts the cracking and internal damage behavior of Cerasus humilis fruit under compressive loads. This research provides a theoretical foundation and technical guidance for reducing mechanical damage during the harvesting process of Cerasus humilis. Full article

A serious limitation to the deployment of IoT solutions in rural areas may be the lack of available telecommunications infrastructure enabling the continuous collection of measurement data. A nomadic computing system, using a UAV carrying an on-board gateway, can handle this; it leads, however, to a number of technical challenges. One is the intermittent collection of data from ground sensors governed by weather conditions for the UAV measurement missions. Therefore, each sensor should be equipped with software that allows for the cleaning of collected data before transmission to the fly-over nomadic gateway from erroneous, misleading, or otherwise redundant data—to minimize their volume and fit them in the limited transmission window. This task, however, may be a barrier for end devices constrained in several ways, such as limited energy reserve, insufficient computational capability of their MCUs, and short transmission range of their RAT modules. In this paper, a comprehensive approach to these problems is proposed, which enables the implementation of an anomaly detector in time series data with low computational demand. The proposed solution uses the analysis of the physics of the measured signals and is based on a simple anomaly model whose parameters can be optimized using popular AI techniques. It was validated during a full 10-month vegetation period in a real Rural IoT system deployed by Gdańsk Tech. Full article