Search Results (265)

This study evaluates the application of an expert opinion-based fuzzy method for reservoir operation in humid subtropical climate/hot-summer Mediterranean climatic classes (Cfa/Csa in the Köppen–Geiger climate classification system), which are characterized by humid subtropical to Mediterranean conditions with ample rainfall and seasonal water availability challenges. Effective reservoir management in these regions is critical for balancing water storage and downstream release and maintaining ecosystem health under variable hydrological conditions. The performance of the fuzzy method was compared to two meta-heuristic algorithms: gravitational search algorithm (GSA) and shuffled frog leaping algorithm (SFLA). System performance was assessed using key indices such as the reliability index as a measure of meeting water demands. The fuzzy method achieved the highest reliability index of 0.690, outperforming GSA (0.677) and SFLA (0.688), demonstrating its superior ability to ensure consistent water supply downstream. The fuzzy method, leveraging expert knowledge, not only enhanced downstream water supply reliability but also reduced computational time compared to the meta-heuristic approaches. The incorporation of expert opinions provides a practical, robust, and efficient framework for reservoir management in challenging climate conditions such as Cfa/Csa classes. Additionally, the fuzzy solution demonstrated superior adaptability to diverse hydrological conditions, balancing ecological and water supply needs effectively. These findings highlight the potential of using expert opinions to support sustainable reservoir operations by achieving optimal trade-offs between competing objectives and addressing challenges in water resource management under varying climatic conditions. Full article

As a popular tool for gene function characterization and gene therapy, RNA interference (RNAi)-based gene silencing has been increasingly explored for potential applications to control invasive species. At least two major hurdles exist when applying this approach to invasive plants: (1) the design and screening of species- and gene-specific biomacromolecules (i.e., gene-silencing agents or GSAs) made of DNA, RNA, or peptides that can suppress the expression of target genes efficiently, and (2) the delivery vehicle needed to penetrate plant cell walls and other physical barriers (e.g., leaf cuticles). In this study, we investigated the cell-penetrating peptide (CPP)-mediated delivery of multiple types of GSAs (e.g., double-stranded RNA (dsRNA), artificial microRNA (amiRNA), and antisense oligonucleotide (ASO)) to knock down a putative phytoene desaturase (PDS) gene in the invasive common reed (Phragmites australis spp. australis). Both microscopic and quantitative gene expression evidence demonstrated the CPP-mediated internalization of GSA cargos and transient suppression of PDS expression in both treated and systemic leaves up to 7 days post foliar application. Although various GSA combinations and application rates and frequencies were tested, we observed limitations, including low gene-silencing efficiency and a lack of physiological trait alteration, likely owing to low CPP payload capacity and the incomplete characterization of the PDS-coding genes (e.g., the recent discovery of two PDS paralogs) in P. australis. Our work lays a foundation to support further research toward the development of convenient, cost-effective, field-deployable, and environmentally benign gene-silencing technologies for invasive P. australis management. Full article

A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s). It is demonstrated that the FPGA data-processing speed matches that of the radar output, thus eliminating data loss. The radar system achieves a remarkable speed of over 9000 waveforms per second on each channel. The proposed architecture is scalable to accommodate higher sampling rates and various waveform periods. It is also multi-functional since the FPGA controls and synchronizes two transmitters and a dual-channel receiver, performs signal reconstruction on both channels simultaneously, and carries out user-defined averaging, trace windowing, and interference suppression for improving the receiver’s signal-to-noise ratio. We also investigate the throughput rate while offloading radar data onto an external device through an Ethernet link. Since the radar data rate significantly exceeds the Ethernet link capacity, we show how the FPGA-based averaging and windowing functions are leveraged to reduce the amount of offloaded data while fully utilizing the radar output. Full article

As the global population ages, respiratory health among the elderly has become a key public health concern. Although urban green infrastructure (UGI) has the potential to improve air quality and promote health, research on how its layout patterns influence respiratory health among older adults remains limited. This study focuses on elderly residents aged 60 and above in the central urban area of Shenyang, China, to evaluate the relative importance and interactions of different features affecting respiratory health. We utilized the St. George’s Respiratory Questionnaire (SGRQ) to collect data on respiratory health and employed hierarchical regression and random forest (RF) models to analyze the impact of UGI factors across three spatial scales (300 m, 500 m, and 1000 m). The results indicate that UGI within a 300 m radius of participants’ residences contributes most significantly to respiratory health, with diminishing marginal effects as the spatial scale increases. Green space area (GSA) and the NDVI were identified as the most important factors influencing respiratory health, while green landscape pattern metrics had a greater influence at larger spatial extents. Additionally, a significant nonlinear marginal effect was observed between UGI and respiratory health. These findings provide key insights for health-oriented urban planning and green infrastructure design. Full article

This study examines the challenges associated with processing hard coal, with a specific focus on gravitational enrichment methods and the utilization of jigs for coal separation. The research involves the simulation and modeling of physical property distributions and the analysis of both the feed density distribution and the characteristics of the enrichment products. Findings indicate that the resultant density distributions are influenced not only by the gravitational enrichment process but also by the preceding procedures and the inherent properties of the coal, such as particle size, sulfur content, and ash content, all of which significantly affect the quality of the outcomes. In modeling and optimization efforts, the study emphasizes approximating grain density using selected statistical distributions—specifically, the Weibull, logistic, and Gaudin–Schuhmann–Andreyev (GSA) distributions—before and after the enrichment process. Statistical analyses demonstrate that the GSA distribution most accurately fits the grain density distribution in the feed, while the Weibull distribution provides the best approximation for the separation products. The quality of these approximations was assessed using the coefficient of determination (R²) and the Mean Squared Error (MSE). The best quality of approximation for feed was obtained by means of the GSA distribution function, and the MSE was approximately 3.1 for two analyzed values of feed flow rates. In the case of concentrates and tailings, the results are not unequivocal. Full article

Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but is crucial to rank the importance of each factor in terms of its non-linear impact on the soil erosion rate. Hence, the goal of this study was to perform a GSA of each factor of RUSLE for a soil erosion assessment in southern Bahia, Brazil. To meet this goal, three non-linear topographic factor (LS factor) equations alternately implemented in RUSLE, coupled with geographic information system (GIS) software and a variogram analysis of the response surfaces (VARSs), were used. The results showed that the average soil erosion rate in the Pardo River basin was 25.02 t/ha/yr. In addition, the GSA analysis showed that the slope angle which is associated with the LS factor was the most sensitive parameter, followed by the cover management factor (C factor) and the support practices factor (P factor) (CP factors), the specific catchment area (SCA), the sheet erosion (m), the erodibility factor (K factor), the rill (n), and the erosivity factor (R factor). The novelty of this work is that the values of parameters m and n of the LS factor can substantially affect this factor and, thus, the soil loss estimation. Full article

The Trabocchi Coast in the Chieti district of the mid-Adriatic (Italy) is one of the few rocky areas within the General Fisheries Commission GSA 17, alongside Mount Conero (Ancona 43°00′01″ N 13°52′13″ E) and the small San Nicola Rock (Ascoli Piceno; 43°32′0″ N 13°36′0″ E). This coastline is known for its biodiversity-rich bays, inlets, and submerged cliffs. Since 2015, annual biodiversity surveys have been conducted in the area, focusing on marine species richness and the identification of non-native species. In September 2024, a juvenile ornate wrasse (Thalassoma pavo) was documented for the first time in the middle Adriatic during an underwater visual survey at Trabocco Punta Torre, a key site along the Trabocchi Coast near artificial and biogenic reefs. This record extends the known distribution of T. pavo, a thermophilic species previously reported only along the southern Adriatic coast of Puglia. This is the first confirmed sighting on the middle and northern Adriatic coast of Italy. The discovery highlights the importance of ongoing biodiversity monitoring to track changes in marine ecosystems, particularly as the Adriatic Sea faces environmental shifts linked to climate warming. The presence of T. pavo in this area suggests the potential for the species to establish populations in previously uninhabited northern regions. Further research is needed to explore the role of biotic and abiotic factors—such as water temperature, current patterns, and habitat availability—in the survival and potential reproduction of T. pavo in the middle Adriatic. The observation contributes to the broader understanding of the meridionalization process in the Adriatic Sea, where rising water temperatures are facilitating the northward expansion of thermophilic species. Continuous monitoring is recommended to assess the long-term viability of T. pavo populations in the Adriatic Sea and better predict the impacts of ongoing climate change on marine biodiversity. Full article

The advent of 5G technology has facilitated the adoption of private cellular networks in industrial settings. Ensuring reliable coverage while maintaining certain requirements at its boundaries is crucial for successful deployment yet challenging without extensive measurements. In this article, we propose the leveraging of unmanned aerial vehicles (UAVs) and Gaussian processes (GPs) to reduce the complexity of this task. Physics-informed mean functions, including a detailed ray-tracing simulation, are integrated into the GP models to enhance the extrapolation performance of the GP prediction. As a central element of the GP prediction, a quantitative evaluation of different mean functions is conducted. The most promising candidates are then integrated into an informative path-planning algorithm tasked with performing an efficient UAV-based cellular network mapping. The algorithm combines the physics-informed GP models with Bayesian optimization and is developed and tested in a hardware-in-the-loop simulation. The quantitative evaluation of the mean functions and the informative path-planning simulation are based on real-world measurements of the 5G reference signal received power (RSRP) in a cellular 5G-SA campus network at the Port of Lübeck, Germany. These measurements serve as ground truth for both evaluations. The evaluation results demonstrate that using an appropriate mean function can result in an enhanced prediction accuracy of the GP model and provide a suitable basis for informative path planning. The subsequent informative path-planning simulation experiments highlight these findings. For a fixed maximum travel distance, a path is iteratively computed, reducing the flight distance by up to 98% while maintaining an average root-mean-square error of less than 6 dBm when compared to the measurement trials. Full article

This work focused on demonstrating the use of dynamic time warping (DTW) as a metric for the elementary effects computation in Morris-based global sensitivity analysis (GSA) of model parameters in multivariate dynamical systems. One of the challenges of GSA on multivariate time-dependent dynamics is the modeling of parameter perturbation effects propagated to all model outputs while capturing time-dependent patterns. The study establishes and demonstrates the use of DTW as a metric of elementary effects across the time domain and the multivariate output domain, which are all aggregated together via the DTW cost function into a single metric value. Unlike the commonly studied coefficient-based functional approximation and covariance decomposition methods, this new DTW-based Morris GSA algorithm implements curve alignment via dynamic programing for cost computation in every parameter perturbation trajectory, which captures the essence of “elementary effect” in the original Morris formulation. This new algorithm eliminates approximations and assumptions about the model outputs while achieving the objective of capturing perturbations across time and the array of model outputs. The technique was demonstrated using an ordinary differential equation (ODE) system of mixed-order adsorption kinetics, Monod-type microbial kinetics, and the Lorenz attractor for chaotic solutions. DTW as a Morris-based GSA metric enables the modeling of parameter sensitivity effects on the entire array of model output variables evolving in the time domain, resulting in parameter rankings attributed to the entire model dynamics. Full article

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article

The structural integrity of buried pipelines is threatened by the effects of Permanent Ground Deformation (PGD), resulting from seismic-induced landslides and lateral spreading due to liquefaction, requiring accurate analysis of the system performance. Analytical fragility functions allow us to estimate the likelihood of seismic damage along the pipeline, supporting design engineers and network operators in prioritizing resource allocation for mitigative or remedial measures in spatially distributed lifeline systems. To efficiently and accurately evaluate the seismic fragility of a buried operating steel pipeline under longitudinal PGD, this study develops a new analytical model, accounting for the asymmetric pipeline behavior in tension and compression under varying operational loads. This validated model is further implemented within a fragility function calculation framework based on the Monte Carlo Simulation (MCS), allowing us to efficiently assess the probability of the pipeline exceeding the performance limit states, conditioned to the PGD demand. The evaluated fragility surfaces showed that the probability of the pipeline exceeding the performance criteria increases for larger soil displacements and lengths, as well as cover depths, because of the greater mobilized soil reaction counteracting the pipeline deformation. The performed Global Sensitivity Analysis (GSA) highlighted the influence of the PGD and soil–pipeline interaction parameters, as well as the effect of the service loads on structural performance, requiring proper consideration in pipeline system modeling and design. Overall, the proposed analytical fragility function calculation framework provides a useful methodology for effectively assessing the performance of operating pipelines under longitudinal PGD, quantifying the effect of the uncertain parameters impacting system response. Full article

This study presents the complex transfer matrix method (CTMM) as an advanced mathematical model, providing significant advantages over the finite element method (FEM) by yielding rapid solutions for complex optimization problems. In order to design a more efficient structure of a three-stage turbomachine rotor, we integrated this method with various optimization algorithms, including genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), gravitational search algorithm (GSA), black hole (BH), particle swarm optimization (PSO), Harris hawk optimization (HHO), artificial bee colony (ABC), and non-metaheuristic pattern search (PS). Thus, the best rotor geometry can be obtained fast with minimum bearing forces and disk deflections within design limits. In the results, the efficiency of the CTMM for achieving optimized designs is demonstrated. The CTMM outperformed the FEM in both speed and applicability for complex rotordynamic problems. The CTMM was found to deliver results of comparable quality much faster than the FEM, especially with higher element quality. The use of the CTMM in the iterative optimization process is shown to be highly advantageous. Furthermore, it is noted that among the different optimization algorithms, ABC provided the best results for this multi-objective optimization problem. Full article

The focus of this study is to introduce a hybrid predictive framework encompassing different meta-heuristic optimization and machine learning techniques to identify the regions susceptible to snow avalanches. To accomplish this aim, the present research sought to acquire the best-performed model among nine different hybrid scenarios encompassing three different meta-heuristics, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and Cuckoo Search (CS), and three different ML approaches, i.e., support vector classification (SVC), stochastic gradient boosting (SGB), and k-nearest neighbors (KNN), pertaining to different predictive families. According to diligent analysis performed with regard to the blinded testing set, the PSO-SGB illustrated the most satisfactory predictive performance with an accuracy of 0.815, while the precision and recall were found to be 0.824 and 0.821, respectively. The F1-score of the predictions was found to be 0.821, and the area under the receiver operating curve (AUC) was obtained to be 0.9. Despite attaining similar predictive success via the CS-SGB model, the time-efficiency analysis underscored the PSO-SGB, as the corresponding process consumed considerably less computational time compared to its counterpart. The SHapley Additive exPlanations (SHAP) implementation further informed that slope, elevation, and wind speed are the most contributing attributes to detecting snow avalanche susceptibility in the French Alps. Full article

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW. Full article