Search Results (821)

Automatic classification of livestock behaviors, including feeding, rumination, standing, lying, walking, and drinking, using wearable accelerometers has become an important tool in precision livestock farming (PLF). However, the influence of preprocessing strategies on classification performance and computational efficiency remains poorly understood. This study investigated the effects of filtering, normalization, data representation, and machine learning algorithms using five accelerometer datasets comprising 3,533,974 records collected from different livestock species and sampling frequencies. Four filtering strategies, three normalization methods, two data representations, and four machine learning algorithms were evaluated using a standardized pipeline. Model performance was assessed using weighted F1-scores together with statistical and computational analyses. The absence of filtering achieved the highest average performance, reaching a weighted F1-score of 0.773 with Random Forest, whereas high-pass filtering consistently reduced performance (minimum average F1 = 0.605 across sampling frequencies) while increasing computational cost. Z-score standardization improved the performance of scale-sensitive algorithms, increasing SVM performance by up to 8.6% compared with no normalization. Feature-based representations provided greater stability for conventional machine learning models, whereas raw signals generally benefited the 1D-CNN. These findings demonstrate that preprocessing strategies should be selected according to the learning algorithm, signal characteristics, and computational constraints rather than applied as universal procedures, providing methodological guidance for the development of efficient livestock behavior monitoring systems. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

Molybdenum (Mo), owing to its excellent properties, is widely used as a plasma-facing material and is recognized as a typical difficult-to-machine material. Achieving high-quality, low-damage machining is essential for ensuring the service reliability of Mo components. However, studies on the milling of Mo remain limited. Therefore, this study investigates a high-quality, low-damage milling technique for Mo based on analyses of milling force, machined surface roughness, and white layer formation. First, the effects of machining parameters, including radial depth of cut (a_e), spindle speed (n), and feed per tooth (f_z), on the responses, namely milling force (F) and surface roughness (R_a), were investigated. The relationships between milling force, surface roughness, and white layer formation were analyzed. Subsequently, the response surface methodology (RSM) was employed to reveal the influence mechanisms of the machining parameters and their interactions on the response variables. Finally, a Kriging surrogate model integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was adopted to identify the optimal machining parameter combination for high-quality, low-damage milling. The results indicate that the milling force and white-layer thickness exhibit consistent increasing trends with increasing feed per tooth under the investigated conditions, demonstrating that controlling the milling force is an effective approach for achieving high-quality, low-damage milling of Mo. For the simultaneous minimization of milling force and surface roughness, the optimal machining parameters were determined to be a radial depth of cut of 0.2101 mm, a spindle speed of 10,090.7 rpm, and a feed per tooth of 0.01 mm/z. These findings provide valuable process parameter guidance for the precision machining of Mo components. Full article

To achieve the multi-objective collaborative optimization of milling processes, orthogonal experiments are conducted to develop prediction models for vibration acceleration and milling force, and range analysis together with variance analysis are adopted to reveal the sensitivity of each milling parameter to machining performance. Taking low vibration, small milling force and high material removal rate (MRR) as optimization objectives, the Multi-Objective Whale Optimization Algorithm (MOWOA) is employed to tackle this multi-criteria optimization problem, and a set of Pareto non-dominated solutions with balanced trade-offs are acquired. By integrating the weight assignment of the Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), comprehensive decision-making for all candidate schemes is implemented in accordance with practical machining requirements of users, and the optimal milling process parameters are determined. The results indicate an inherent trade-off among machining efficiency, milling load and machine tool vibration. An increase in the material removal rate will inevitably lead to simultaneous rises in milling force and machine tool vibration magnitude. The optimal combination of process parameters screened to meet comprehensive multi-objective requirements is spindle speed n = 12,000.00 r/min, feed rate v_f = 1048.26 mm/min, and axial milling depth a_p = 3.00 mm. Full article

Freeform machining of impellers involves extended cycle times, leading to high energy consumption and costs necessitating efficient process optimization. This study develops a CAD/CAM-integrated hybrid Taguchi-Artificial Neural Network (ANN) model to optimize machining parameters for a freeform impeller. Four controllable factors, namely cutting feed ($C_f$), feed Z ($F_z$), retract feed ($R_f$), and cutter diameter ($C_D$), were investigated at five levels using an L25 orthogonal array, with machining time as the response. Taguchi analysis identified cutting feed as the most dominant factor, while retract feed was insignificant, and a first-order regression model yielded an R² of 95.88%. A two-layer feedforward neural network with six hidden neurons achieved an R² of 0.9999 and a mean absolute error of 0.0976 min. To rigorously validate generalization, leave-one-out cross-validation was employed, identifying three hidden neurons as optimal with a cross-validated R² of 0.9823, RMSE of 0.5350 min, and MAE of 0.3429 min. The final model trained on all samples achieved an R² of 0.9996. Comparison with a quadratic regression model on the same test set confirmed the superior predictive capability of the ANN ($R^2=0.9992$ vs. $0.9983$). Optimal parameters ($C_f=\mathrm{12,000} \mathrm{m}\mathrm{m}/\mathrm{m}\mathrm{i}\mathrm{n}$, $F_z=600 \mathrm{m}\mathrm{m}/\mathrm{m}\mathrm{i}\mathrm{n}$, $R_f=4000 \mathrm{m}\mathrm{m}/\mathrm{m}\mathrm{i}\mathrm{n}$, $C_D=6 \mathrm{m}\mathrm{m}$) were validated through simulation, yielding a machining time of 11.05 min, representing a 52.6% reduction from 23.32 min. The hybrid Taguchi–ANN framework effectively optimizes freeform impeller machining, significantly enhancing productivity while maintaining process reliability. Full article

Rare earth elements (REEs) are critical for advanced manufacturing and clean energy, yet their separation remains extremely challenging due to the nearly identical ionic radii of adjacent lanthanides. Conventional solvent extraction, ion exchange, and precipitation methods are limited by their high reagent consumption, slow kinetics, poor selectivity, and environmental burdens. Nanofiltration (NF) offers a green and efficient alternative—operating in the aqueous phase with low energy demand and continuous high throughput. This review systematically summarizes NF-based REE separation. We first elucidate the fundamental mechanisms (size exclusion, Donnan exclusion, dielectric exclusion, and complexation enhancement), and discuss how lanthanide hydration chemistry underpins these synergistic effects. Membrane materials, from commercial to biomimetic, are critically surveyed, with an emphasis on strategies to overcome the trade-off between permeability and selectivity. The impacts of operating conditions and solution chemistry are analyzed, and NF applications ranging from single REE systems to real leachates are assessed. A comparative evaluation positions NF against conventional technologies. Key challenges remain: poor adjacent REE selectivity, membrane fouling, performance loss at high salinity, chemical instability, and a gap between model and real feeds. Future directions include designing high-selectivity membranes, integrating machine learning optimization, establishing standardized protocols, and realizing closed-loop process integration. Full article

The model predictive current control (MPCC) of an interior permanent magnet synchronous machine (IPMSM) requires an accurate motor parameter model to predict future currents and achieve high control performance. However, the inductance parameters of an IPMSM are easily affected by factors such as magnetic field saturation, leading to large current prediction errors, high current ripple, and poor stability. Therefore, an MPCC strategy for an IPMSM based on parameter adaptive feedback correction is proposed. First, based on the mathematical model of the IPMSM in the synchronous rotary coordinate, the cross-coupling relationship between the dq-axis inductance deviations and the current prediction error is derived to form an explicit prediction error model. Then, the influence of the d-axis and q-axis inductance parameter deviations of the IPMSM on the current prediction error is discussed in detail. Next, based on the established mathematical model of the prediction error, the recursive least squares scheme is adopted to identify the d-axis and q-axis deviations of the inductance parameters online. Finally, unlike conventional open-loop RLS correction, a PI-based closed-loop correction loop is designed that feeds the prediction error back to adjust the inductance deviations, thereby forcing the prediction error toward zero while inherently compensating for inverter dead-time effects. Simulations and experiments were conducted, and the results show that the proposed scheme greatly improves the accuracy of current prediction and inductance parameter estimation, and enhances robustness against parameter mismatch and dead-time disturbances. The key novelty lies in the PI-feedback-driven RLS closed-loop structure that simultaneously achieves error elimination and dead-time compensation. Full article

Pastures rich in water-soluble carbohydrates (WSCs) can decrease milk fat concentration in dairy ruminants, associated with rumen pH reduction. To prevent milk fat reduction, sodium bicarbonate was supplemented as a rumen buffer to sheep fed indoors on tetraploid annual ryegrass (Lolium multiflorum Lam. ssp. westervoldicum) herbage rich in WSCs (28% DM). Ten lactating Sarda ewes were assigned to the control group (CNT) or the sodium bicarbonate group (BIC; 25 g/day per ewe before ryegrass feeding). All ewes were housed indoors, machine-milked at 16:30 and 07:30, fed freshly cut ryegrass ad libitum (12:30 to 07:30 of subsequent day), and fed concentrates during milkings. Individual grass, measured with automatic feeders, and concentrate intake were recorded. Total dry matter intake (DMI) (p = 0.03), ryegrass DMI (p = 0.03), milk fat concentration in afternoon (p < 0.01) and morning (p = 0.03) milkings, and afternoon milk fat yield (p = 0.03) were higher in BIC than CNT. Milk yield was unaffected. In conclusion, sodium bicarbonate supplementation can increase milk fat concentration in ewes fed high-WSC herbage, with rapid effect on the rumen. WSC concentration should be considered when formulating rations for pasture-fed dairy sheep and, more generally, dairy ruminants. Full article

Grinding is the final precision machining step for bearing rings, which induces subsurface gradients in microstructure and mechanical properties. Rolling contact fatigue life and service reliability are directly determined by the gradients. Current research of the grinding metamorphic layer in bearing steels is synthesized in this review. The formation mechanisms, characterization approaches, and the influence of grinding parameters on metamorphic layers is covered. The coupled thermal–mechanical–phase transformation framework encompasses heat-driven phase transformation, high-strain-rate gradient plastic deformation, and their interactions, which collectively govern the formation of the three-layer gradient structure. When the surface temperature exceeds the austenitization threshold, the governing regime shifts from mechanically dominated to thermally dominated, producing an abrupt increase in white layer thickness and concurrent dark layer softening. The capabilities and limitations of characterization techniques for probing the gradient microstructure and residual stress profile are evaluated. The influence of grinding depth, wheel speed, feed rate, wheel characteristics, and cooling conditions on the metamorphic layer is analyzed. The areas requiring deeper investigation are identified. These insights aim to establish correlations between the grinding process and the surface integrity and service performance of bearing components, and to provide directions for future research on the grinding metamorphic layer. Full article

This research work investigates the machinability of WP7V die steel of very high toughness and wear resistance in turning with a TiAlN-coated carbide tool, with the chip reduction coefficient (CRC) serving as a guide for machining performance and energy requirements. The machining parameters, like cutting speed, feed rate, depth of cut, and machining environment, were assessed to find parameter combinations that encourage established cutting and enhanced chip control. The results illustrate that the CRC is strongly influenced by cutting speed, and at a higher cutting speed (210 m/min), the lowest CRC values are obtained. In dry machining, a medium feed rate (0.1 mm/rev) favors chip breaking, and wet machining results in medium-spiral chips. Long, continuous chips with laminar and sheared surfaces are produced at a low cutting speed (70 m/min). The findings suggest that low CRC values are correlated with stable machining behavior and decreased energy utilization. High cutting speed and the suitable selection of feed rates are needed for the efficient machining of WP7V steel. Full article

Ion-imprinted polymers (IIPs) are advanced adsorbents featuring selective recognition cavities for targeted metal ion capture, offering a promising route to high-efficiency separation in extractive metallurgy. In the present work, the evolution, design principles, synthesis strategies, separation mechanisms, and practical applicability of IIPs for metal recovery from complex aqueous matrices are overviewed. Key material components, including functional monomers, crosslinkers, template ions, initiators, solvents, and support materials, are discussed in relation to adsorption capacity, selectivity, kinetics, stability, and recyclability. Major preparation routes, such as surface imprinting, bulk polymerization, in situ polymerization, and sol–gel methods, are critically compared to clarify their advantages and limitations. Recent applications for base metals, precious metals, and rare-earth elements demonstrate that IIPs can achieve high specificity and rapid equilibrium under optimized conditions. However, their translation from simulated solutions to real leachates remains constrained by interfering ions, organic contaminants, mass transfer resistance, incomplete template removal, and matrix complexity. Mitigation strategies, including sample pretreatment, improved polymer architecture, and hybrid supports, are therefore emphasized. Additionally, chemometric modelling, machine learning, or artificial intelligence-assisted design may be implemented to advance the prospects of IIPs in industry. Conclusively, IIPs represent a strong separation platform, yet industrial deployment requires robust validation with real feed streams and scalable regeneration protocols during column operation, as well as under chemically aggressive conditions at scale. Full article

Cutting tool wear significantly influences machining performance, surface quality, and manufacturing cost. Proper minimization of cutting tool wear will result in enhanced life of the cutting tool, surface integrity, precision, and sustainability of the machining process. There are various methods for minimizing cutting tool wear in machining operations. These include the optimization of parameters such as reducing the feed and speed, use of proper coating such as TiN and Al₂O₃, lubrication/cooling, and proper material for the cutting tool like carbide and ceramic materials. The application of chip breakers and high machine rigidity can minimize wear by lowering heat and friction, which are the major causes of wear. Reduction in wear will ensure a better surface finish, enhanced tool life, and economic efficiency of the machining process. The main objective of this research paper is to conduct an extensive study on wear of cutting tools in machining operations. As a result, the study discusses several advanced methods of tool wear detection in cutting tools, including sensor-based methods, machine vision, and AI/ML-assisted predictive maintenance. Additionally, a critical assessment in tool wear minimization is conducted to apply new material to the cutting tool, the coating process, cutting parameter and path optimization, cooling and lubrication systems such as minimum amount lubrication and cryogenic cooling. Moreover, various challenges with intelligent and autonomous manufacturing systems that arise in tool wear prediction with regard to availability of data and reliability of prediction models are discussed in the study. Finally, potential future research directions are provided, with an emphasis on the importance of using digital twin technologies and sustainable manufacturing approaches in tool wear management. Full article

Bio-jet fuels produced from biomass-derived feedstocks represent a promising pathway for reducing the carbon intensity of aviation energy systems. However, designing supply chain networks for bio-jet fuel production requires solving large-scale mixed-integer linear programming (MILP) models that integrate facility location, feedstock allocation, material flows, and routing decisions. These models can become computationally expensive, particularly when evaluating multiple network configurations or large candidate sets of production and processing facilities. This study develops a hybrid machine learning and optimization framework to improve the computational efficiency of bio-jet fuel supply chain network design while preserving high-quality decision outcomes. The proposed iterative procedure uses supervised learning to approximate the relationship between facility location decisions and total supply chain cost. First, an initial set of supply chain configurations is generated by solving the optimization model while using randomly selected facility locations. These solutions are then used to train predictive models, including ridge regression, feed-forward neural networks, and ensemble neural networks, with facility-location configurations as inputs and total supply chain cost as the output. The trained learner is subsequently used to identify promising facility-location candidates through Thompson sampling and small-scale linear programming. These candidate solutions are evaluated by the original mixed-integer model, and the resulting observations are fed back into the learning process until convergence. Numerical experiments show that the proposed hybrid approach obtains near-optimal bio-jet fuel supply chain designs while substantially reducing computational time. For the linear case, the method achieves solutions within 0.23–0.29% of the objective function value while reducing computational time by 70.95–81.95%. For nonlinear learning models, the optimality gap decreases further to 0.13–0.15%, with computational time reductions of 45.37–60.36%. For the Texas case study and the modeling assumptions evaluated, the findings demonstrate that machine-learning-assisted optimization can reduce computational effort while preserving high-quality supply chain solutions. The extent of these benefits may vary with network size, candidate-facility structure, facility-capacity assumptions, demand characteristics, and the amount of information available to train the learning models. Full article

Modern Global Navigation Satellite System (GNSS) architectures incorporate authorized signals like GPS M-code; however, conventional extraction methods suffer from performance degradation under low signal-to-noise-ratio (SNR) conditions and exhibit strong dependence on high-gain antennas and precise synchronization. This paper proposes a machine learning-based end-to-end extraction framework leveraging time-frequency domain feature fusion. This method breaks through the constraint of relying solely on either time-domain or frequency-domain features. It jointly feeds the time-domain waveforms and spectral features of baseband signals into models such as Multi-Layer Perceptron (MLP) and Transformer, enabling automatic learning of the nonlinear time-frequency characteristics of M-code. This approach effectively suppresses interference from P(Y) code sidelobes and fully exploits the information contained in both the main and side lobes of the M code spectrum. Experimental results demonstrate that under the extremely low SNR condition of −10 dB, the extraction accuracy of the proposed method is improved by 13.7% compared with conventional methods. Systematic accuracy–efficiency trade-off analysis shows that the lightweight MLP model achieves comparable accuracy to the complex Transformer model, with only 6.8% of the parameter scale and 6.2 times faster inference speed, making it the most competitive solution for real-time engineering deployment. In the real-world measurement scenario using a 7.5-m antenna, an extraction accuracy of 94.7% is achieved with only 10 ms of small-sample training data. This method significantly enhances the extraction performance of authorized signals under low-SNR non-cooperative reception conditions. Full article

This article presents an Artificial Intelligence and Internet of Things (AI–IoT/AIoT) decision-making framework for smart water management and sustainable aquaponic systems. The framework connects sensors, IoT telemetry, machine learning algorithms, and real-time monitoring of key water quality parameters, with the aim of early detection of deviations, operational decision support, and risk reduction in system management. A special contribution of the paper is that water is viewed simultaneously as a limiting resource, a biological factor and an operational cost. The proposed framework defines the structure of a decision support system, including monitoring of temperature, pH value, dissolved oxygen, ammonia/ammonium, EC/TDS value, water flow, feeding regime, and basic biological indicators. In the methodological sense, the paper presents a conceptual-methodological framework based on publicly available data, scenario estimates, and a clearly defined protocol for future pilot validation of high-frequency operational data. In addition to the technical architecture, the framework includes elements of responsible application of AIoT systems: data quality control, sensor deviation and drift detection, model explainability through XAI/SHAP, data transfer security, and the possibility of human confirmation before risky interventions. The economic part of the paper shows ROI/NPV as a scenario estimate, based on explicit assumptions about costs, resource consumption and possible operational savings, and not as a confirmed financial result. The framework is aligned with the principles of the circular bioeconomy, as it links the monitoring of water quality, the reduction in nutrient losses, the reuse of resources, and better planning of interventions in aquaculture and aquaponics. The results indicate the potential of AIoT approaches to improve monitoring, transparency and operational decision-making, while the actual effects on productivity, water consumption, food consumption, energy, and economic sustainability must be confirmed in a pilot phase. Full article