Search Results (69,925)

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression techniques, yielding high coefficients of determination (R² > 0.95) across key water quality parameters. The optimization process targeted maximal reductions in turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) through strategic manipulation of pH and coagulant dosage. The single-objective GWO achieved significant outcomes, including a 96.68% turbidity reduction at pH 5 and 50 mg/L dosage. The MOGWO algorithm identified Pareto-optimal solutions, such as a 94.2% turbidity reduction at pH 5 and 72 mg/L dosage, and a balanced BOD reduction of 52.7% at pH 7. The predictive models indicated that optimal treatment conditions could reduce chemical usage by up to 90% compared to conventional coagulants, resulting in potential cost savings of up to 30%. Moreover, the algorithms demonstrated rapid convergence, averaging 200 iterations, highlighting their computational efficiency and robustness. These findings illustrate that integrating bio-based coagulants with advanced optimization techniques can achieve high treatment efficiency while reducing chemical inputs, thus directly supporting environmental sustainability by minimizing sludge and secondary pollution. In this situation, the wastewater treatment plant will focus on resource-recovery systems with less or no waste at the end of the treatment process. This approach aligns with circular economy principles by promoting eco-friendly, cost-effective wastewater treatment solutions suitable for resource-limited settings. The study offers a forward-looking pathway for environmentally responsible wastewater management practices that significantly reduce chemical dependency and contribute to pollution mitigation efforts. Full article

The aim of this study was to develop an effective and fast tool to support the automatic segmentation of skin lesions, with particular emphasis on the precise differentiation between malignant and benign lesions. In response to the problem of high false positive rates in existing CAD systems, modern neural network architectures from the YOLO family (YOLOv8, YOLOv9, YOLOv11, YOLOv12, and YOLOv26) were used in this research. The models were trained and evaluated on a new, balanced dataset (7000 images) based on the ISIC archive, where the key innovation was the introduction of a dedicated background class representing healthy skin. Through a multi-stage, rigorous optimization process, it was demonstrated that the yolov11s-seg model is highly effective for this task. It achieved a strong balance between effectiveness and processing speed, obtaining an mAP@50 score of 0.840 and an overall precision of 0.852. From a clinical perspective, the model’s high sensitivity (85.9%) in detecting the most aggressive lesion, invasive melanoma (MI), is particularly noteworthy. Thanks to its extremely short inference time (only 4.8 ms), the proposed yolov11s-seg variant overcomes the limitations of heavy hybrid architecture, providing a stable and highly efficient solution showing significant potential for deployment in real-time medical mobile applications. Full article

Large-sized, room-temperature ultrafast scintillator single crystals are highly demanded for fast timing applications such as time of flight–positron emission tomography, high-speed medical imaging, and pulse heavy-ray detection. Sub-nanosecond scintillation was discovered in 16 mm sized CsPbCl₃Br_x single crystals in our previous research. In this work, the crystal size of CsPbCl₃Br_0.03 was enlarged to 2 inches (50.8 mm). Meanwhile, by precisely optimizing the vertical Bridgman growth process, we further increased the concentration of Br dopant to realize even faster scintillation decay. In this study, we conducted a series of tests on the grown crystals, including temperature-dependent photoluminescence tests, alpha particle excitation tests, X-ray imaging tests, etc. Via the strategy of the incorporation of Br₂, Br dopant introduces highly efficient fast recombination centers in perovskite CsPbCl₃Br_0.03 crystals, resulting in an unprecedently fast scintillation decay time of 303 ps under ²⁴¹Am α-particle excitation, which is significantly shorter than that of the pure CsPbCl₃ and all other perovskites by at least two orders of magnitude. Benefiting from the excellent optical transparency and high crystalline quality of the CsPbCl₃Br_0.03 crystal, an X-ray spatial resolution of up to 20 lp/mm is achieved. These results further demonstrate the great potential of large-sized CsPbCl₃Br_x single crystals for fast timing applications. Full article

This study assesses carbon footprint (CF) and explores mitigation potentials through improved resource efficiency for fire-resistant wood doors (WFDs) and fire-resistant bamboo doors (BFDs). Both WFDs and BFDs are certified to the Chinese national fire resistance standard GB 12955-2024, ensuring the same core fire resistance performance and functional equivalence. Results show that WFDs have a slightly lower CF (806.04 kg CO₂ e/m³) than BFDs (830.54 kg CO₂ e/m³), where the raw material phase acts as the main contributor (58.57–64.32%). Crucially, significant mitigation potentials are identified by enhancing resource efficiency across the product life cycle through reducing processing loss, and extending service lifespan, and sustainable recycling. Approximately 35.2 billion kg CO₂ will remain after reducing carbon loss by 5% in the Chinese wood/bamboo industrial sector. Recycling approaches (wood/bamboo panels, bio-based pellet fuel, and biochar) can be utilized with fewer emissions to economize bio-resources. The use of biochar provides greater carbon storage benefits and will help to limit the effects of climate change. Full article

Despite their vital physiological roles, oxidative imbalance caused by reactive oxygen, nitrogen, sulphur, and chlorine species damages essential body macromolecules such as proteins, lipids, and nucleic acids through oxidative stress. This stress is strongly associated with cancer, inflammation, neurological and cardiovascular disorders, and other chronic human diseases. Therefore, antioxidants, natural or synthetic, that counteract oxidative damage are important, with increasing interest in their use within the pharmaceutical, food, and cosmetic industries. However, due to toxicity concerns with the synthetic variants, natural antioxidants are increasingly preferred. Extremophile-derived antioxidants, such as superoxide dismutases, catalases, peroxidases, carotenoids, and melanin, are of renewed interest due to their remarkable stability, robustness, and potency under extreme conditions of temperature, pH, and salinity. These make them better than many mesophile-derived antioxidants and excellent candidates for cost-effective biotechnological, research, and industrial processes that require high operational efficiency. This review summarises key classes of selected enzymatic and pigment antioxidants, their mechanisms of action, and their industrial relevance, with a focus on extremophilic microalgae, bacteria, and fungi. The benefits of extremophilic antioxidants are discussed alongside their current applications and existing challenges, including the need to develop efficient delivery systems, scalability issues, and limited characterisation. Full article

Zr alloy-shaped charge liners (SCLs) offer broad application prospects due to their multiple post-penetration damage effects. However, research on these liners is still in its early stages. The mechanisms of jet formation and penetration for Zr alloys SCL remain unclear, and the specific contribution of different liner regions to the penetration process is not yet understood. This gap in knowledge has limited their structural design to a black-box correlation between global structural parameters and macroscopic penetration efficiency. To address this gap, a region-tracing Smoothed Particle Hydrodynamics (SPH) simulation was employed. Following a strategy of “wall thickness layering + axial segmentation,” the Zr alloy liner was partitioned into ten characteristic regions. This methodology facilitated the tracking of material transport from each region during jet formation and penetration into an AISI 1045 steel target. The contribution of each region to the penetration depth was then quantitatively assessed via post-processing. For the first time, the “critical region” contributing most to penetration depth was identified, and the influence of the liner’s cone angle and wall thickness on the contribution of each region was revealed. This study enhances the theoretical framework for understanding the damage effects of Zr alloy shaped charge liners. It not only advances the fundamental understanding of jet penetration mechanisms but also provides a theoretical basis for the refined design and performance optimization of these liners. Full article

Electrospinning is the most widely used technique for creating nanofibers. However, the low production rate and the usage of a high-voltage setup have become obstacles to its widespread application. One effective method for creating nanofibers from a variety of materials is centrifugal spinning. This review discusses centrifugal spinning (CS) as an effective and scalable nanofiber manufacturing technology, particularly in filtration systems, and presents it as a promising alternative to existing methods, such as electrospinning. The review highlights the advantages of CS, including its high production rate, cost efficiency, and the ability to process various materials to produce nano- and microfibers. Despite its high potential, the issues associated with CS technology include the unpredictability of fiber quality, the inability to control diameters, and the need for more robust mathematical models to predict fiber characteristics. To eliminate these shortcomings and further enhance the industrial utility of centrifugally spun nanofibers in filtration, future studies should focus on improving process control, exploring a broader range of polymers, optimizing melt electrospinning, and designing more advanced nozzle profiles. Full article

In municipal wastewater treatment plants (WWTPs), anaerobic digestion of municipal mixed sludge (MMS) often yields low energy recovery and operational instability due to imbalances between primary and secondary sludges. Anaerobic co-digestion (AcoD) with readily biodegradable wastes, such as fruit and vegetable waste (FVW), can enhance process stability and biogas production. Life cycle assessment (LCA) methodology is used in this study to evaluate the environmental performance of implementing AcoD of MMS and FVW in a municipal WWTP, compared with a business-as-usual scenario combining mono-digestion of MMS and incineration of FVW. The LCA was modelled in openLCA 2.5 using the ecoinvent 3.9.1 database (cut-off allocation approach), and impacts were assessed with the ReCiPe 2016 Midpoint (H) method, focusing on climate change, terrestrial acidification, fossil fuel depletion, and marine eutrophication. Results indicate that AcoD reduces impacts across all environmental categories, mainly due to higher biogas yields that increase on-site electricity generation and decrease reliance on grid electricity. Improved total solids removal also lowers digestate production and composting-related burdens. Electricity consumption remains the main hotspot in both scenarios, highlighting the importance of energy efficiency and electricity mix. Sensitivity analysis on methane content (61–65% v/v) confirms the robustness of AcoD’s environmental benefits. Full article

Accurate segmentation of structural cracks is a core prerequisite for quantifying crack parameters, assessing damage severity, and providing early warning of structural safety. However, different types of structures exhibit significant individual variations in features such as color, texture, and brightness. Consequently, commonly used image segmentation algorithms struggle to establish a universal mathematical model, making it challenging to robustly identify and precisely segment crack targets amidst multi-feature disparities. To address the issue, this paper proposes a crack-segmentation algorithm based on bimodal image fusion and brightness piecewise linear enhancement (CSA-BB), and further enables parameter extraction and crack monitoring. The algorithm utilizes the complementary properties of visible-light and pseudo-color images for bimodal image fusion, thereby enhancing the detailed features of cracks. Furthermore, a brightness piecewise linear function has been devised that automatically selects appropriate parameters for image enhancement of structural cracks across varying background brightness. Subsequently, the crack region is effectively segmented using the bottom-hat transform and the OTSU algorithm. Ultimately, the crack’s safety level is determined from the acquired crack parameters, thereby enabling effective monitoring and assessment of the crack development process. In this paper, the proposed method achieves the best segmentation performance with a Dice coefficient of 0.4511 and a Jaccard index of 0.2981. Compared to the second-best algorithm, it yields significant improvements of 26.9% and 34.5%, respectively, demonstrating higher consistency with the ground truth. Moreover, superior computational efficiency and robustness are achieved, fulfilling the operational demands of real-world engineering environments. Full article

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively. Full article

The accuracy of Digital Elevation Models (DEMs) plays a crucial role in determining their reliability for geoscientific and engineering applications. Next-generation distributed interferometric synthetic aperture radar (SAR) constellations, such as the PIESAT-1 wheel constellation with its “one primary, three secondary” setup, provide a novel method for efficiently acquiring high-precision DEMs. However, a comprehensive and systematic performance evaluation of DEMs derived from such an innovative constellation is lacking, particularly in the context of comparative studies under complex terrain conditions. This study uses PIESAT-1 SAR imagery to generate a 10 m resolution DEM through multi-baseline interferometric processing. The ICESat-2 ATL08 dataset serves as the reference baseline, and mainstream products, including ZY-3, GLO-30, TanDEM-X DEM, and AW3D30, are incorporated for a multidimensional vertical accuracy evaluation, considering land cover, slope, aspect, and topographic profiles. The results indicate that, in three representative mountainous regions, the PIESAT-1 DEM achieves optimal overall accuracy (RMSE = 3.25 m). Furthermore, in regions with significant radar geometric distortions, such as south-facing slopes, vegetation-covered areas, and regions with noticeable anthropogenic topographic changes, the PIESAT-1 DEM demonstrates superior stability and information capture capabilities relative to conventional single- or dual-baseline SAR systems. This study validates the technological potential of the PIESAT-1 wheel constellation in enhancing DEM accuracy and terrain adaptability, and provides insights for the scientific selection of high-resolution topographic data and the design of future spaceborne interferometric missions. Full article

Traditional VANET authentication schemes often face challenges such as centralization bottlenecks and the updating of vehicle keys or pseudonyms. This paper proposes a layered approach that divides VANET into regions, utilizing dual-blockchain to enable anonymous message authentication between vehicles and RSUs, as well as between vehicles within the VANET. Compared to traditional blockchain authentication methods, this paper introduces an approach that enhances authentication efficiency and ensures information security by establishing secure connections between private and consortium chains through a trusted authority (TA). By leveraging third-party public parameter updates, the automatic updating of private and public keys for VANET nodes is achieved without the need for certificate issuance and updates. This approach facilitates long-term anonymous authentication and communication between VANET nodes, reduces the frequency of authentication interactions, simplifies authentication processes, and lowers computational and communication costs. The proposed scheme is well-suited for practical VANET environments that require low authentication latency and robust large-scale privacy protection. Full article

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

Fractured tight sandstone reservoirs are promising targets for underground energy storage, but their heterogeneous nature and often-incomplete historical test data pose significant challenges for accurate deliverability prediction and reservoir evaluation. To address this, a novel hybrid methodology is proposed. For wells with complete historical data, deliverability is calculated using a binomial inflow performance relationship (IPR) model. For wells with incomplete data, a weighted fusion model integrating a Random Forest algorithm and least squares regression is developed to predict natural blowout capacity, a key proxy for energy storage injectivity/productivity. The fusion model achieved superior performance with a mean absolute error (MAE) of 7.19 × 10⁴ m³/day and a Mean Relative Error (MRE) of 8.5%, outperforming standalone methods. Based on the predicted deliverability, reservoirs in the Bozi–North block (Kuche Depression, Tarim Basin) were classified into three potential grades (I, II, III). The study provides a data-adaptive framework for deliverability prediction and offers tailored reformation process recommendations (e.g., sand fracturing for Grade I reservoirs), thereby providing a more reliable and practical decision support tool for the efficient development of tight sandstone energy storage reservoirs. Full article

Digital transformation is reshaping societies by promoting the adoption of advanced technologies. Moreover, the digitization of public services has become an important focus for governments. In this paper, digital government performance indicators are analyzed to improve the efficiency of digitizing public services. Based on this awareness, the seven main criteria and twenty-one sub-criteria are determined. Then, a fuzzy decision-making framework is proposed to evaluate digital government performance across 165 countries as alternatives. To the best of our knowledge, limited studies have investigated an integrated clustering-based fuzzy decision-making framework for evaluating digital government performance. The intuitionistic trapezoidal fuzzy number-based analytical hierarchy process (ITFNAHP), a part of the introduced framework, is developed to find the weights of the main criteria and sub-criteria. Digital technologies, innovation, and the economy are the most significant criteria for digital government operations. The k-means clustering method is then employed to group the alternatives. The four clusters are obtained from the clustering technique. Next, the technique of order preference similarity to ideal solution (TOPSIS) is introduced to rank the digital governments of each cluster. Switzerland, Rwanda, North Macedonia, and Eswatini are the top choices among others in each cluster, respectively. Additionally, a sensitivity analysis is conducted considering the ten different situations. In addition, the managerial and policy implications are discussed, including the achievement of Sustainable Development Goals (SDGs). Full article