Search Results (445)

In mineral processing, visual-based online particle size analysis systems depend on high-precision image segmentation to accurately quantify ore particle size distribution, thereby optimizing crushing and sorting operations. However, due to multi-scale variations, severe adhesion, and occlusion within ore particle clusters, existing segmentation models often exhibit undersegmentation and misclassification, leading to blurred boundaries and limited generalization. To address these challenges, this paper proposes a novel semantic segmentation model named VTC-Net. The model employs VGG16 as the backbone encoder, integrates Transformer modules in deeper layers to capture global contextual dependencies, and incorporates a Convolutional Block Attention Module (CBAM) at the fourth stage to enhance focus on critical regions such as adhesion edges. BatchNorm layers are used to stabilize training. Experiments on ore image datasets show that VTC-Net outperforms mainstream models such as UNet and DeepLabV3 in key metrics, including MIoU (89.90%) and pixel accuracy (96.80%). Ablation studies confirm the effectiveness and complementary role of each module. Visual analysis further demonstrates that the model identifies ore contours and adhesion areas more accurately, significantly improving segmentation robustness and precision under complex operational conditions. Full article

Plastic waste is a global issue due to the popularity of the product. Over time, plastic degrades into smaller particles known as microplastics and becomes harder to deal with as it easily disperses and can be missed by physical catches. Conventional degradation involves environmental forces like ultraviolet (UV) light, water, temperature, and physical abrasion. However, there is increasing interest in microbial plastic degradation, which could positively impact the transformation of (micro)plastics in various environmental matrices. Most of the available research has focused on bacterial degradation, but there is mounting evidence on the impact of fungal degradation. This review discusses conventional and bacterial degradation, then discusses the advantages of fungal involvement in the degradation of microplastics. Biodegradation enhanced by fungal enzymes is a valuable tool that could greatly improve the removal of these microplastic pollutants from the environment. Due to some biochemical complexities, fungi are naturally omnipresent in marine and terrestrial environments under all sorts of climates. Fungi could thrive by themselves or in association with other microorganisms, which could also be applied in non-biotic plastic degradation processes as an alternative to other forms of plastic management in the environment. Full article

The Jiahezi Reservoir is a typical plain reservoir constructed on a sediment-laden river in the mountainous region of Xinjiang. After 58 years of operation, it faces critical challenges—particularly insufficiently characterized sediment size distribution and siltation behavior—that continue to undermine its long-term operational performance. This study investigates particle-size distribution and depositional trends through integrated field sampling and two-dimensional (2D) numerical sediment modeling, using the Jiahezi Reservoir as a case study. Results show that the median particle size (d₅₀) is 0.027 mm, with 76.48% of the particles ranging from 0.005 to 0.25 mm, indicating predominantly fine-grained sediment. Particle size gradually decreases both downstream and laterally from the channel. Hydrodynamic sorting produces distinct spatial distribution patterns, with fine particles occupying more extensive areas than coarse ones. The sedimentation patterns within the reservoir area can be broadly categorized into four distinct zones. By analyzing sediment transport mechanisms and depositional characteristics, this study establishes a foundation for developing targeted strategies to enhance the reservoir’s operational longevity. Full article

The progressive depletion of high-grade ore bodies has shifted attention toward the exploitation of lower-grade deposits as viable sources of value. In recent years, there has been growing emphasis on mining and processing methods that incorporate sustainability by addressing both environmental and socio-economic considerations. To maximize resource recovery, integrated strategies that combine exploration, grade control drilling, mine planning, and processing are essential. Within this framework, particle sorting has emerged as an effective coarse separation method that can upgrade low-grade feed prior to the more energy-demanding milling and subsequent processing stages. Incorporating screening before particle sorting not only assists in identifying the distribution of metals but also determines the most suitable particle size ranges for sorting performance. This study reports on the applicability of sensor-based sorting technologies to low-grade gold and nickel ores from Australia, with a focus on grade deportment by particle size. The results demonstrate that substantial upgrading of low-grade ores is possible, achieving 70%–80% metal recovery within approximately 30%–40% of the original mass through the use of induction and XRT sensors. Overall, the findings indicate that both induction and XRT sorting methods are broadly effective across ore types, offering enhanced upgrading capability and improved processing efficiency. Full article

Open-channel irrigation systems often face constraints due to water supply uncertainty and insufficient gate control precision. This study proposes an integrated framework for canal water allocation and gate control that combines interval-based uncertainty analysis with intelligent optimization to address these challenges. First, we predict the inflow process using an Auto-Regressive Integrated Moving Average (ARIMA) model and quantify the range of water supply uncertainty through Maximum Likelihood Estimation (MLE). Based on these results, we formulate a bi-objective optimization model to minimize both main canal flow fluctuations and canal network seepage losses. We solve the model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to obtain Pareto-optimal water allocation schemes under uncertain inflow conditions. This study also designs a Fuzzy Proportional–Integral–Derivative (Fuzzy PID) controller. We adaptively tune its parameters using the Particle Swarm Optimization (PSO) algorithm, which enhances the dynamic response and operational stability of open-channel gate control. We apply this framework to the Chahayang irrigation district. The results show that total canal seepage decreases by 1.21 × 10⁷ m³, accounting for 3.9% of the district’s annual water supply, and the irrigation cycle is shortened from 45 days to 40.54 days, improving efficiency by 9.91%. Compared with conventional PID control, the PSO-optimized Fuzzy PID controller reduces overshoot by 4.84%, and shortens regulation time by 39.51%. These findings indicate that the proposed method can significantly improve irrigation water allocation efficiency and gate control performance under uncertain and variable water supply conditions. Full article

This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia–Ukraine war, during the COVID-19 crisis and Russia–Ukraine war, and after the COVID-19 pandemic and during the Russia–Ukraine war. Metaheuristics, Non-dominated Sorting Genetic Algorithm (NSGAII), Strength Pareto Evolutionary Algorithm (SPEA2), and Particle Swarm Optimization (PSO) are applied to find the best allocation. The results reveal that there a significant preference for the S&P Green Bond during the four periods of study according to three algorithms, thanks to its portfolio diversification abilities. During the COVID-19 pandemic and the geopolitical crisis, the most optimal portfolio was Nikkei 225 because of its quick recovery from the pandemic and poor reliance on the Russia–Ukraine markets, while WTI crude oil and both dirty and clean cryptocurrencies were poor contributors to the investment portfolio because these assets are sensitive to geopolitical problems. After the end of the pandemic and during the ongoing Russia–Ukraine war, the three algorithms obtained remarkably different results: the NSGAII portfolio was invested in various assets, 32% of the SPEA2 portfolio was allocated to the S&P Green Bond, and half of the PSO portfolio was allocated to the S&P Green Bond too. This may be due to changes in investors’ preferences to protect their fortune and to diversify their portfolio during the war. From a risk-averse perspective, NSGAII does not underestimate the risk, while in terms of forecasting accuracy, PSO is an adequate algorithm. In terms of time, NSGAII is the fastest algorithm, while SPEA2 requires more time than the NSGAII and PSO algorithms. Our results have important implications for both investors and risk managers in terms of portfolio and risk management decisions, and they highlight the factors that influence investment choices during health and geopolitical crises. Full article

For the ship pipe routing design (SPRD) problem, previous studies have mainly employed bio-inspired algorithms such as multi-objective ant colony optimization (MOACO), non-dominated sorting genetic algorithm II (NSGA-II), and multi-objective particle swarm optimization (MOPSO). This paper proposes a novel approach based on the multi-objective A* (MOA*) algorithm to solve the SPRD. First, the optimization objectives and constraints of the SPRD problem are defined, and then an MOA*-based routing framework is developed. The time and space complexities of the approach are analyzed, and key components such as the cost functions, the solution dominance relationship, dynamic probability-based pruning, and neighbor node exploration strategy are designed to enhance solution diversity and search efficiency. Additionally, a space cascade expansion method is proposed to improve the computational efficiency of the MOA* in large-scale grid spaces. Comparative studies with MOACO, NSGA-II, GA-A*, and gray wolf optimization (GWO) on simulated cases of varying complexities and practical piping scenarios demonstrate the effectiveness of the MOA*. Furthermore, the applicability of the MOA* is validated against practical piping requirements, including the rapid generation of sub-optimal solutions, non-orthogonal routing, and partitioned pipe layouts. Experimental results, supported by a C++/OpenGL-based prototype software, show that the MOA* requires no extensive parameter tuning, exhibits stable computational efficiency and optimization capability, and demonstrates competitive performance in Pareto-optimal diversity compared with other algorithms. Full article

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints. Full article

Low-quality fine-grained coal cannot be effectively separated in a conventional gas–solid fluidized bed. To enhance the density stratification and separation of low-quality fine-grained coal, this paper introduces a vibration force field to create a vibrating airflow composite force field. By investigating the force characteristics and sorting behavior of particles within this vibrating airflow composite force field, we reveal the mechanical properties of both high-density and low-density particles. An energy dissipation model for the vibrational energy among particles in the bed is established, clarifying how vibration acceleration varies between the front and rear sections of the bed. The experimental results indicate that acceleration at the feeding end is significantly greater than that at the discharging end. This higher acceleration at the feeding end facilitates the stratification and segregation of selected particles, while acceleration at the discharging end provides the necessary energy for the transport of gangue. The acceleration curve for low-density particles exhibits greater fluctuations compared to that for high-density particles; additionally, the forces acting on these particles along the y-axis direction promote density segregation. The forces tend to decrease gradually along the z-axis direction, which aids in particle migration and movement. The particle-sorting effectiveness within this vibrating airflow composite force field initially increases with rising vibration frequencies and gas velocities before subsequently decreasing. Under a frequency of 30 Hz and a gas velocity of 35 cm/s, the ash content and yield of the clean coal product from the bed are 7.1% and 52.6%, respectively, achieving the maximum degree of ash separation. Full article

Cell analysis is vital in clinical diagnostics and cell engineering research. Among the various analytical techniques, dielectrophoresis (DEP) is a particularly promising label-free method for distinguishing biological particles, which eliminates the need for fluorescent dyes or magnetic beads. In this study, we present a high-precision single-cell analysis system based on the evaluation of DEP forces in a controlled microfluidic flow environment. The system integrates a microfluidic chamber equipped with an electrode array to exert DEP forces and flow-induced shear forces to facilitate force balance analysis. We quantitatively characterized the DEP response to successfully discriminate between healthy skin cells and cancer cells using the proposed DEP-based cell-sorting platform. The proposed system successfully distinguished between these cell types even when their dielectrophoretic properties were similar, highlighting its potential for sensitive and selective cell classification in biomedical applications. Full article

Processing complex ore remains a challenge due to energy-intensive grinding and complex beneficiation and pyrometallurgical treatments that consume large amounts of water whilst generating significant waste and polluting the environment. Sensor-based ore sorting, which separates ore particles based on their physical or chemical properties before downstream processing, is emerging as a transformative technology in mineral processing. However, its application to complex and heterogeneous ores remain limited by the constraints of single-sensor systems. In addition, existing hybrid sensor strategies are fragmented and a consolidated framework for implementation is lacking. This review explores these challenges and underscores the potential of multimodal sensor integration for complex ore pre-concentration. A multi-sensor framework integrating machine learning and computer vision is proposed to overcome limitations in handling complex ores and enhance sorting efficiency. This approach can improve recovery rates, reduce energy and water consumption, and optimize process performance, thereby supporting more sustainable mining practices that contribute to the United Nations Sustainable Development Goals (UNSDGs). This work provides a roadmap for advancing efficient, resilient, and next-generation mineral processing operations. Full article

An integrated approach for enhancing microparticle separation efficiency in acoustofluidic lab-on-a-chip systems is presented, combining numerical modeling in COMSOL 6.2 Multiphysics^® with reinforcement learning techniques implemented in Python 3.10.14. The proposed method addresses the limitations of traditional parameter tuning, which is time-consuming and computationally intensive. A simulation framework based on LiveLink™ for COMSOL–Python integration enables the automatic generation, execution, and evaluation of particle separation scenarios. Reinforcement learning algorithms, trained on both successful and failed experiments, are employed to optimize control parameters such as flow velocity and acoustic frequency. Experimental data from over 100 numerical simulations were used to train a neural network, which demonstrated the ability to accurately predict and improve sorting efficiency. The results confirm that incorporating failed outcomes into the reward structure significantly improves learning convergence and model accuracy. This work contributes to the development of intelligent microfluidic systems capable of autonomous adaptation and optimization for biomedical and analytical applications, such as label-free separation of microplastics from biological fluids, selective sorting of soot and ash particles for environmental monitoring, and high-precision manipulation of cells or extracellular vesicles for diagnostic assays. Full article

This study proposes a new approach to identify structural parameters under seismic excitation using classical and quantum-inspired algorithms. Traditional methods often struggle with complex effects, noise, and computing limits. A five-story building model with mass–spring–damper system was tested to find properties during earthquakes. The study used optimization methods including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and five quantum-inspired versions: Quantum Genetic Algorithm (QGA), Quantum Particle Swarm Optimization (QPSO), Quantum Non-Dominated Sorting Genetic Algorithm II (QNSGA-II), Quantum Differential Evolution (QDE), and Quantum Simulated Annealing (QSA). Additionally, statistical analysis used Shapiro–Wilk for normality, Levene and Bartlett for variance, ANOVA with Tukey–Bonferroni comparisons, Bootstrap model ranking, and Borda count. The results show that the quantum-inspired methods perform better than classical ones. QSA reduced mean squared error (MSE) by 15.3% compared to GA, and QNSGA-II reduced MSE by 8.6% and root mean squared error (RMSE) by 3.5%, with less variation and tighter rankings. The framework addresses computing cost and response time; quantum methods need significant computing power and their accuracy suits offline earthquake assessments and model updates. This balance helps monitor building health when real-time speed is less critical but accuracy matters. The method provides a scalable tool for checking civil structures and could enable digital twins. Full article

Electrokinetic phenomena offer promising tools for the manipulation of micro- and nanoparticles in liquid media. However, most existing techniques rely on complex configurations and are often limited to particle separation based on large size differences or distinct material properties. Here, we present a simple and tunable method for spatial control and separation of nanoparticles using interdigitated electrodes under AC electric fields. Our approach exploits subtle differences in the electroosmotic and dielectrophoretic responses of particles with small size variations but identical material compositions. By adjusting the frequency and amplitude of the applied voltage, particles can be selectively directed and accumulated at designated regions of the device, enabling precise control over their positioning and segregation. We demonstrate the effectiveness of our method using micro- and nanoparticles composed of the same material, achieving accurate spatial separation based solely on their electrokinetic behavior. This technique offers a low-cost, easily integrable platform for diverse applications, including cell manipulation, water purification, and targeted drug delivery. Full article

The coupled method of inertial focusing and magnetic separation is effective for detecting and isolating circulating tumor cells (CTCs) from blood, wherein the design of a multi-physics coupled microfluidic device plays a critical role in the sorting efficiency. This paper presents a novel compact microfluidic device that combines inertial and magnetic forces for CTC separation. Using the finite element method, the effects of three major parameters (e.g., fluid velocity, particle properties, and magnetic field distribution) on sorting efficiency were comprehensively investigated and discussed. Simulated and experimental results demonstrate that the designed compact microfluidic device with coupled physical fields achieves high separation purity (>98%) for CTCs larger than 19 μm in diameter over a wide range of parameters, such as a fluid velocity greater than 3.5 × 10⁻⁸ m³/s, a remanent flux density between 1.08 T and 1.28 T, and the position of the magnet ranging from 2.5 mm to 4 mm. Full article