Search Results (5,737)

Green and Sustainable Remediation (GSR) has become a guiding framework for selecting remediation solutions for contaminated sites. However, in practice, there is a lack of quantitative decision support tools that can reflect the multi-dimensional environmental, social, and economic objectives of GSR. To address this, a GSR alternative decision-making model was developed, integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method and the Entropy Weight method for weighting, combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for ranking. A preference coefficient was introduced to simulate four typical decision-making scenarios: balanced-preference, health-sensitive, economy-priority, and low-carbon constraint scenarios. Empirical analysis was conducted using three remediation alternatives for a complex contaminated site in Jiangsu Province, China. The results indicate that the optimal alternative selection is highly dependent on decision preferences: under the balanced scenario and low-carbon constraint scenario, Alternative 1 (Cement Kiln Co-processing, CKC) is optimal; under the health-sensitive scenario and economy-priority scenario, Alternative 3 (Ex situ Solidification/Stabilization + Ex situ Thermal Desorption, ESS + ESTD) is optimal. Furthermore, uncertainty analysis demonstrates the robustness of the proposed model. Full article

This study aimed to prepare and characterize nanoemulsions containing eugenol and 1,8-cineole using the emulsification method and to investigate their anesthetic effects on guppy fish. The optimized formulation comprised a 5–10% mixture of eugenol and 1,8-cineole in a 1:2 ratio, stabilized with 15–20% Tween 80. The selected formulations displayed mean particle sizes below 15 nm, a low polydispersity index (PDI) (<0.5), and a zeta potential that was more negative than −40 millivolts (mV), indicating stable emulsions. Their pH ranged from 6.50 to 6.63, indicating slight acidity. The formulations exhibited non-Newtonian rheology, as well as thinning under shear stress. Three formulations (F2, F6, and F12) remained stable after both accelerated and long-term stability testing. All nanoemulsions were able to induce guppy fish to the third stage of anesthesia. The nanoemulsions with concentrations of 50 mg/L and 100 mg/L eugenol effectively induced sedation and anesthesia in both sexes and reduced the induction and recovery times compared with the ethanol solution. In conclusion, this study highlights nanoemulsions as a promising drug delivery system for alternative anesthetics in aquaculture. Full article

The digital transformation of oil and gas pipeline networks has generated substantial volumes of unstructured maintenance documentation from communication systems, creating an urgent need for automated summarization to improve operational efficiency. However, domain-specific text summarization for pipeline communication maintenance remains challenging due to scarce labeled data and the high computational cost of fine-tuning large pretrained models. Parameter-efficient fine-tuning alleviates this issue, but its effectiveness strongly depends on appropriate hyperparameter selection. This paper proposes a unified framework that combines weight-decomposed low-rank adaptation with an enhanced Artificial Bee Colony algorithm for automated hyperparameter optimization. The enhanced algorithm addresses two specific limitations of the standard Artificial Bee Colony algorithm: uninformed random initialization that ignores promising regions, and premature abandonment of stagnated solutions that discards partially useful search directions. These two components represent principled design choices, each targeting a distinct bottleneck in applying swarm intelligence search to high-dimensional mixed-type hyperparameter spaces. The method introduces a hybrid initialization strategy to exploit prior knowledge and a stagnation-guided local search mechanism to refine stagnated solutions instead of discarding them, achieving a better balance between exploration and exploitation. Experimental results on a public Chinese summarization benchmark and an industrial oil and gas pipeline communication maintenance corpus show that the proposed approach consistently outperforms full fine-tuning, manually tuned parameter-efficient methods, and several evolutionary optimization baselines in terms of ROUGE metrics. The automated search introduces modest additional computational overhead compared to manual tuning while eliminating expert-dependent hyperparameter configuration and achieving consistent performance gains across both datasets. Overall, the proposed framework provides an efficient and robust solution for adapting large language models to specialized summarization tasks in the context of pipeline communication system maintenance. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

The sustainable management of olive wastes represents an important environmental challenge. Biochars and hydrochars derived from biomass are promising adsorbents for removing emerging pollutants from water. In the present work, olive stone wastes were converted into biochar and hydrochar by using pyrolysis (500 °C for 30 min) and hydrothermal carbonization (HTC) processes (220 °C for 10 h). Then, the obtained materials were physically activated by using CO₂ gas (750 °C for 30, 60 and 180 min). Various analytical techniques were applied for the chemical, textural and structural characterization of these carbonaceous materials (i.e., ultimate and proximate analysis, scanning electron microscopy (SEM), BET surface area, Raman spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy). Afterwards, the selected activated biochar and hydrochar were applied for the removal of amoxicillin from aqueous solutions. The experimental results show that the generated hydrochar has many microspheres on its surface and inside, while the produced biochar exhibits a porous structure with irregular forms. CO₂ physical activation has induced an important improvement of the biochar and hydrochar’s structural, textural, and surface chemistry properties. For instance, the activated biochar samples show a highly porous structure, with large specific surface areas that increase with the burn-off, reaching 1349.3 m² g⁻¹ following 3 h of activation. Regarding the activated hydrochar samples, they exhibit a spherical morphological structure with an important specific surface area, which increased to 846.7 m² g⁻¹ after 3 h of activation. Moreover, both activated materials have an amorphous structure with low oxygen surface groups. The selected novel CO₂-activated biochar and hydrochar efficiently remove amoxicillin from aqueous solutions under wide experimental conditions, with adsorption capacities of 386.4 and 215.9 mg g⁻¹, respectively. These efficiencies are higher than those reported for various activated biochars derived from lignocellulosic biomass, from sewage sludge, and from animal manure. Future research works are required to assess these materials’ effectiveness in treating real pharmaceutical effluents, to optimize the regeneration of the amoxicillin-loaded materials, and to design full-scale devices for a real application. Full article

Balancing structural safety and economic efficiency in super-tall building design remains a formidable challenge. To address this issue, this study proposes a genetic-algorithm-based multi-variable, multi-objective optimization method. The design variables include the member sizes and vertical layout positions of outrigger and belt trusses, as well as the cross-sectional dimensions of mega-columns. Total structural weight and maximum inter-story drift ratio are adopted as objective functions, while code-specified constraints, such as shear-weight ratio, stiffness-weight ratio, and axial compression ratio, are incorporated to formulate the fitness evaluation for optimization. Taking a 300 m baseline structure designed for 6-degree seismic intensity and equipped with two outrigger trusses and three belt trusses as an example, single-variable sensitivity analyses are first performed. The results show that optimizing any single parameter can yield certain local improvements, yet it cannot overcome the weight–deformation trade-off induced by strong variable coupling. By selecting representative feasible solutions from the multi-variable solution set that match the “optimal” values identified by single-variable optimization as benchmarks, the multi-variable optimum reduces the total structural weight by approximately 6.5–18.4% relative to these representative designs. Moreover, optimal layout strategies of outrigger and belt trusses are investigated for two typical building heights (200 m and 300 m) and two seismic intensity levels associated with design ground motions having a 10% exceedance probability in 50 years, namely 6-degree (0.05 g) and 8-degree (0.20 g). Finally, the proposed method is validated through a case study of a super-tall financial center in Chongqing, where the total structural weight is reduced by 12.3% after optimization while the inter-story drift ratio still satisfies relevant code requirements. The results demonstrate that the proposed framework can generate competitive feasible solutions and provide a systematic means to achieve a balanced trade-off between structural safety and economic efficiency for outrigger–belt-truss super-tall buildings. Full article

The rapid growth of Digital Financial Services (DFSs), including what is occurring in Indonesia, necessitates evaluation methods that are capable of objectively and systematically handling multiple assessment criteria. Therefore, this study aimed to propose a hybrid FSS–CRITIC–TOPSIS framework for selecting optimal DFSs. Fuzzy soft sets (FSSs) were used to model uncertainty and subjectivity in criterion assessments. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method determined the weights objectively based on the degree of contrast and inter-criteria correlation. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to rank the alternatives based on the closeness to the ideal solution. The incorporation led to a formally defined decision operator, τ, which mapped FSS to complete preference orderings while ensuring provable stability and strong discriminative properties. The framework was applied to five major Indonesian digital wallets, namely ShopeePay, GoPay, OVO, LinkAja, and DANA, as well as being evaluated across five criteria. This framework identified DANA as the optimal alternative, with a score of 0.9282, followed by ShopeePay (0.8354) and GoPay (0.6958). Comparative analysis with other methods showed a near-perfect ranking correlation (ρ = 0.9−1) with a more proportional score distribution and ranking results that reflected actual conditions. Sensitivity analysis also confirmed robustness, with ranking changes remaining logically consistent underweight variations. In conclusion, the FSS-CRITIC-TOPSIS framework provided an effective, mathematically rigorous method for multi-criteria decision-making (MCDM) under uncertainty, which applied to digital wallet selection as well as potential extension to broader evaluation contexts supporting SDGs 8, 9, and 10. Full article

Translating heterogeneous user requirements (URs) into robust engineering specifications for public-access products is a critical challenge, often impeded by information uncertainty and fragmented design processes. To address this, we propose an integrated decision-making framework underpinned by Rough Set Theory (RST) as a unified mathematical language for uncertainty management. The framework systematically guides customer-driven product development by integrating a series of RST-based methods: a Kano model analysis to screen URs, a novel rough-Shapley value model to determine their interdependent weights, a rough-QFD approach to translate them into weighted design requirements (DRs), and the rough-VIKOR method to select the optimal design alternative. A case study on public-access faucets validates the framework’s efficacy. The results demonstrate its capability to identify critical URs, derive robust DRs by systematically resolving technical attribute conflicts, and select a superior design solution that optimally balances hygiene, durability, and user experience. The application of the framework successfully identified Alternative ${\mathrm{A}}_1$ (Push-Activated Spout) as the optimal solution, demonstrating superior performance in proactive hygiene and core functionality. The results prove that maintaining data integrity through a unified RST pipeline effectively resolves early-stage design conflicts. This research contributes a rigorous, data-driven decision support system that enhances objectivity and information fidelity, providing a transparent and auditable methodology for designing human-centered public infrastructure. Full article

Eye-tracking is a key enabling technology for smart eyewear, supporting hands-free interaction, accessibility, and context-aware human–machine interfaces under strict constraints on size, power consumption, and computational complexity. While camera-based solutions provide high accuracy, their integration into lightweight and low-power wearable platforms remains challenging. This paper is a feasibility study for the design, simulation, and experimental evaluation of a photosensor oculography (PSOG) eye-tracking system that is fully integrated into an eyewear frame, based on near-infrared (NIR) emitters and photodiodes. The proposed approach combines simulation-driven optimization of the optical constellation, a multi-frequency modulation and demodulation scheme enabling parallel source discrimination and robust ambient-light rejection, and a resource-efficient signal acquisition pipeline suitable for embedded implementation. Eye rotations in azimuth and elevation are inferred from differential reflectance patterns of ocular regions (sclera, iris, and pupil) using lightweight regression techniques, including shallow neural networks and Gaussian process regression, selected to balance estimation accuracy with computational and power constraints. System performance is evaluated using a controllable artificial-eye platform under defined geometric and illumination conditions, enabling repeatable assessment of gaze-estimation accuracy and algorithmic behavior. Sub-degree errors are achieved in this controlled setting, demonstrating the feasibility and potential effectiveness of the proposed architecture. Practical considerations for translation to real-world smart eyewear, including human-subject validation, anatomical variability, calibration strategies, and embedded deployment, are discussed and identified as directions for future work. By detailing the optical design methodology, modulation strategy, and algorithmic trade-offs, this work clarifies the distinct contributions of the proposed PSOG system relative to existing frame-integrated and camera-free eye-tracking approaches, and provides a foundation for further development toward wearable and augmented-reality applications. Full article

To address traditional beer detection limitations, this study proposes a rapid NIRS-based method for detecting key indicators and verifying authenticity. Designing Single-task (STL) and Multi-task learning (MTL) strategies, it employs Variable Importance in Projection for wavelength selection. Deep spectral features were extracted utilizing a Multi-Head Attention (MHA)-fused Convolutional Neural Network (CNN-MHA), Long Short-Term Memory (LSTM-MHA), and hybrid CNN-LSTM-MHA networks. To further enhance model performance, the Bayesian Optimization Algorithm globally optimized network hyperparameters in STL, alongside hyperparameters and multi-task loss weights in MTL. Partial least squares regression, support vector machine regression, and partial least squares discriminant analysis models were established using these features. Results indicate that the MTL-based CNN-LSTM-MHA network effectively learns shared features across multiple tasks, significantly improving model generalization. Specifically, the coefficients of determination ($R^2$) for alcohol content and original wort concentration in the validation set were 0.996 and 0.997, respectively, with relative root mean square errors (rRMSE) of 2.024% and 2.515%. In the independent test set, the $R^2$ were 0.995 and 0.991, with rRMSE of 2.515% and 2.087%, respectively. Furthermore, 100% classification accuracy was achieved across all datasets. This method provides an efficient technical solution for beer market regulation and real-time detection in production processes. Full article

This study investigates the structural behavior of metamaterial sandwich panels with Bézier-based lattice cores using parametric finite element modeling. Geometric parameters were varied to assess their influence on mass, stress, and energy absorption capabilities. Ligament thickness was found to strongly affect mass, while curvature influences stress and deformability. The optimization results outline a set of optimal design solutions, enabling selection of configurations based on specific performance priorities. The proposed workflow provides a robust strategy for designing mechanically efficient structures suitable for advanced engineering applications. Full article

This study employed near-infrared (NIR) spectroscopy for real-time spectral acquisition of fermentation broth in lab-scale bioreactors, comparing the performance of transflectance and transmission sensors through glucose modeling and prediction while optimizing modeling approaches. The results demonstrated superior adaptability of transflectance sensors in fermentation environments: in conventional fermentation, glucose models exhibited lower errors (RMSEC = 4.087 g/L, RMSEV = 9.829 g/L) compared to transmission sensors (RMSEC = 5.972 g/L, RMSEV = 10.904 g/L), with significantly higher predictive performance (RPD = 3.735 vs. 2.369), indicating enhanced fitting accuracy and stability. In complex natural media containing peptone and yeast extract, transmission sensor performance deteriorated dramatically due to turbidity interference (R²_cal = 0.134), whereas transflectance sensors maintained robust performance (R²_cal = 0.993), confirming their adaptability to complex matrices. Regarding modeling strategies, the 1550–1700 nm spectral region demonstrated optimal feature extraction capability (RMSEC = 3.269 g/L, R²_cal = 0.987). Basic preprocessing methods such as the moving average smoothing method have become the preferred preprocessing methods, as they strike a balance between calibration and prediction performance. Outlier removal analysis revealed that moderate elimination of 12 high-error samples (accounting for 30% of the total 39 samples) reduced RMSEC to 1.441 g/L and improved R²_cv to 0.996, optimizing model performance; however, excessive removal of outlier samples degraded model capability, necessitating judicious sample selection. For fixed total sample sizes, calibration sets comprising 70–80% of samples yielded more reliable predictions. In conclusion, transflectance sensors demonstrate superior compatibility with multicomponent fermentation systems. Combined with wavelength selection, moving average preprocessing, and rational sample removal and partitioning strategies, this approach provides an effective solution for NIR-based online glucose monitoring. Full article

Volatile organic compounds (VOCs, dominated by xylene, toluene, and benzene) and paint mist emissions from ship painting represent a major environmental and health concern, posing a critical bottleneck to the green transformation of the shipbuilding industry. To tackle this challenge, this study presents an integrated recovery system designed specifically for ship automatic-spraying robots. Guided by the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery,” the system features a modular design that ensures full compatibility with the robots’ spraying trajectory without operational interference. Core adsorption materials, namely glass fiber filter cotton and honeycomb activated carbon fiber, were selected to suit the high-humidity and high-pollutant-concentration environment typical of ship painting. An appropriately matched axial flow fan maintains stable negative pressure throughout the system. Furthermore, the design integrates an air curtain isolation subsystem and an automated control subsystem, enabling coordinated operation and real-time adjustment. Using ANSYS Fluent, geometric and flow field simulation models were established to analyze airflow distribution and pollutant adsorption behavior, which led to the optimization of key structural and material parameters. Field experiments conducted in shipyard environments demonstrated the system’s superior performance: it achieved a VOC removal efficiency of 88.4% and a paint mist capture efficiency of 85.7% under optimal working conditions, with a maximum simulated paint mist capture efficiency of 86.2%. The system maintained stable performance under complex vertical and overhead spraying conditions, with an efficiency attenuation of less than 1.5%, and its outlet emissions fully complied with the mandatory limits specified in the Emission Standard of Air Pollutants for the Shipbuilding Industry (GB 30981.2-2025). The relative error between experimental data and simulation results is less than 2%, confirming the reliability and practicality of the proposed system. This research provides an efficient and adaptable pollution control solution for green shipbuilding and offers valuable technical insights for the sustainable upgrading of automated painting processes in heavy industries. Full article

To address rapid tool wear and unstable hole surface quality during roughing and semi-finishing operations using indexable shallow-hole drills, an optimization study on the spatial mounting posture of the insert is conducted, aiming to improve tool life and machined surface quality. Considering that tool life and surface quality are significantly influenced by cutting force and cutting temperature, radial cutting force and cutting temperature are selected as the multi-objective optimization criteria. A mapping model between the insert mounting posture parameters and cutting performance metrics is established. An improved LO-NSGA-II algorithm is employed to perform multi-objective optimization, yielding a Pareto-optimal solution set, and the entropy weighted-TOPSIS method is subsequently applied to determine the optimal insert mounting posture. Experimental results demonstrate that the optimized spatial mounting posture significantly enhances the overall cutting performance of the tool. Compared with the non-optimized tool, the optimized configuration exhibits a significant extension in tool life and a notable improvement in machined hole surface quality. This study provides an effective methodology for the structural optimization design of indexable shallow-hole drills. Full article

Extracorporeal dialysis for uremic toxin removal and fluid regulation relies on specialized dialyzers whose membranes differ markedly in polymer chemistry, pore architecture, adsorption capacity, surface bioactivity, and convective performance. These structural and material distinctions result in wide variation in the clearance of chemically diverse uremic solutes. Despite the expanding range of dialyzer options, membrane selection in clinical practice remains largely non-individualized. In this review, we propose a phenotype-based model for dialyzer membrane selection. We outline how distinct membrane families achieve differential solute clearance and integrate these functional characteristics into a framework that considers residual kidney function, nutritional and inflammatory status, cardiovascular physiology, protein-bound toxin burden, and hemodynamic vulnerability. Because access to advanced membranes varies across regions and dialysis providers, implementation will require adaptation to local formulary constraints. Nevertheless, aligning membrane properties with patient-specific toxin profiles offers a promising strategy to optimize extracorporeal therapy and improve outcomes in chronic dialysis. Full article