Search Results (38,748)

The discharge of synthetic dyes from industries poses severe environmental challenges, necessitating eco-friendly remediation strategies. This study investigated the biosorption of triarylmethane dyes Crystal Violet (CV), and Brilliant Green (BG) using self-immobilized and sponge-immobilized biosorbents of Trametes versicolor (strain CB8, CB8/S2) and Pleurotus ostreatus (strain BWPH, BWPH/S2). Tests were conducted with live and autoclaved biomass under varying conditions of dye concentration (100–400 mg/L), temperature (15–55 °C), and pH (2–10). Sponge-immobilized live biomass (CB8/S2 and BWPH/S2) showed superior performance, removing up to 90.3% and 81.7% of BG and 43.9% and 39.3% of CV, respectively, within 6 h, demonstrating 3–5 times higher efficiency than self-immobilized biomass for both dyes. Maximum sorption of 379.4 mg/g of BG and 48.9 mg/g of CV was achieved by CB8/S2 at 400 mg/L. Principal Component Analysis biplot confirmed immobilization efficacy, where Dim1 (85.9–91.8% variance) dominated dye concentration and contact time. The optimized conditions for BG removal by CB8/S2 was 20.85–32.17 °C and pH 3.4–6, and for CV, at pH 6.5–7.5 and 30 °C. The percentage of dye sorption data fitted well with the quadratic model (p < 0.05). Fourier transform infrared spectroscopy (FT-IR) analysis indicated that hydrogen bonding and electrostatic interactions facilitated dye binding onto fungal mycelium. Notably, sponge-immobilized biosorbents were reusable without additional treatment. The findings support fungal biomass immobilization as a viable strategy to augment the bioremediation potential in treating dye-laden wastewater. Full article

The symbiotic microbiome constitutes a consortium that has been persistently domesticated by a specific algal species, fostering a close and enduring association with the host. The majority of microbial taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter (MDM)”, have important ecological contributions. Given the challenges in discerning symbiotic microbes in natural environments, herein, ecological characteristics of MDM and known taxa within symbiotic communities were investigated in a simulated bloom process using Alexandrium pacificum without antibiotic treatment. Specifically, increased diversification was observed in MDM along the bloom process. Higher trophic interaction and less vulnerability of the molecular network were found in MDM taxa. The “bridge” role of MDM species was better than that of known taxa, as shown by higher betweenness centralization. Deterministic processes dominated in MDM taxa, which promote phylogenic diversity of such groups to some extent. The findings highlight that MDM taxa play an important role in sustaining community stability and functioning. This study broadens our understanding of the ecological contribution of MDM under disturbances from dinoflagellate blooms, providing essential theoretical insights and empirical data to inform the management of coastal toxic blooms. Full article

As the complexity and functional integration of mechanism systems continue to increase in modern practical engineering, the challenges of changing environmental conditions and extreme working conditions are becoming increasingly severe. Traditional uncertainty-based design optimization (UBDO) has exposed problems of low efficiency and slow convergence when dealing with nonlinear, high-dimensional, and strongly coupled problems. In response to these issues, this paper proposes an UBDO framework that integrates an efficient intelligent optimization algorithm with an excellent surrogate model. By fusing butterfly search with Levy flight optimization, an improved chicken swarm algorithm is introduced, aiming to address the imbalance between global exploitation and local exploration capabilities in the original algorithm. Additionally, Bayesian optimization is employed to fit the limit-state evaluation function using a BP neural network, with the objective of reducing the high computational costs associated with uncertainty analysis through repeated limit-state evaluations in uncertainty-based optimization. Finally, a decoupled optimization framework is adopted to integrate uncertainty analysis with design optimization, enhancing global optimization capabilities under uncertainty and addressing challenges associated with results that lack sufficient accuracy or reliability to meet design requirements. Based on the results from engineering case studies, the proposed UBDO framework demonstrates notable effectiveness and superiority. Full article

This present review aims to provide a clear overview of the environmental impact of non-biodegradable materials, and the use of biodegradable materials as their replacements. Non-biodegradable polymers have been used in the past, and now they are considered a threat to the environment. Recently, it has become a necessity to manufacture products with biodegradable polymers to alleviate waste pollution because they can degrade in a short period of time in the environment. Biodegradable polymers can be used in various applications like cosmetics, coatings, wound dressings, gene delivery, and tissue engineering scaffolds. Blending biodegradable polymers could provide an excellent opportunity to produce innovative green biocomposites suitable for any desired applications. This paper reviews all the recent related works on biodegradable PLA and PCL materials and the introduction of fillers for the development of green biocomposites. The properties and characterisation of PLA/PCL blends and PLA-PCL-filler green biocomposites on morphology, thermal, mechanical, thermomechanical, and barrier properties are thoroughly reviewed. The applications, limitations, and future prospects of these green biocomposites is also highlighted. Full article

Curved roofs constructed using hyperbolic paraboloid (HP) modules are gaining popularity in structural engineering due to their unique aesthetic and structural advantages. Consequently, these studies have investigated steel bar modules based on HP geometry, focusing on how variations in geometric configuration and bar topology affect internal force distribution and overall structural performance. Each module was designed on a 4 × 4 m square plan, incorporating external bars that formed the spatial frame and internal grid bars that filled the frame’s interior. Parametric modeling was conducted using Dynamo, while structural analysis and design were performed in Autodesk Robot Structural Analysis Professional (ARSAP). Key variables included the vertical displacement of frame corners (0–1.0 m at 0.25 m intervals), the orientation and spacing of internal bar divisions, and the overall mesh topology. A total of 126 structural models were analyzed, representing four distinct bar topology variants, including both planar and non-planar mesh configurations. The results demonstrate that structural efficiency is significantly influenced by the geometry and topology of the internal bar system, with notable differences observed across the various structural types. Computational analysis revealed that asymmetric configurations of non-planar quadrilateral subdivisions yielded the highest efficiency, while symmetric arrangements proved optimal for planar panel applications. These findings, along with observed design trends, offer valuable guidance for the development and optimization of steel bar structures based on HP geometry, applicable to both single-module and multi-module configurations. Full article

This study examines how the combined design of the window-to-wall ratio (WWR) and glazing type affects thermal comfort and energy use in Qingdao, China, which has a temperate monsoon climate. A prototypical four-story office was modeled using TRNSYS 18, and three representative weeks—January, July, and October—were simulated to capture seasonal responses. Results show marked inter-floor and seasonal differences. In terms of thermal comfort, the combination of 30% WWR with double-glazed windows achieved the best performance in July, with 51.14% of daytime hours maintaining |PMV| ≤ 0.5. While a higher WWR can enhance daytime comfort during winter, it may lead to discomfort in transitional seasons. Regarding energy performance, double glazing consistently reduced energy consumption across all three seasons, with a reduction of 366–500 kWh in total building load during January compared to single glazing. In July and October, energy demand decreased as WWR decreased. However, when WWR varied drastically across floors, the building’s overall energy efficiency deteriorated significantly. In conclusion, adopting a moderate WWR (30%) in combination with high-performance double glazing is an effective strategy to improve year-round thermal comfort and energy efficiency, while minimizing abrupt vertical variations in WWR. The findings are most applicable to mid-rise office buildings in temperate monsoon climates such as Qingdao. Full article

Background: Dental caries arise from a multifactorial interplay between microbial dysbiosis, host immune responses, and enamel degradation visible on radiographs. Deep learning excels in image-based caries detection; however, integrative analyses that combine radiographic, microbiome, and transcriptomic data remain rare because public cohorts are seldom aligned. Objective: To determine whether three independent deep-learning pipelines—radiographic segmentation, microbiome regression, and transcriptome regression—can be reproducible implemented on non-aligned datasets, and to demonstrate the feasibility of estimating microbiome heritability in a matched twin cohort. Methods: (i) A U-Net with ResNet-18 encoder was trained on 100 annotated panoramic radiographs to generate a continuous caries-severity score from a predicted lesion area. (ii) Feed-forward neural networks (FNNs) were trained on supragingival 16S rRNA profiles (81 samples, 750 taxa) and gingival transcriptomes (247 samples, 54,675 probes) using randomly permuted severity scores as synthetic targets to stress-test preprocessing, training, and SHAP-based interpretability. (iii) In 49 monozygotic and 50 dizygotic twin pairs (n = 198), Bray–Curtis dissimilarity quantified microbial heritability, and an FNN was trained to predict recorded TotalCaries counts. Results: The U-Net achieved IoU = 0.564 (95% CI 0.535–0.594), precision = 0.624 (95% CI 0.583–0.667), recall = 0.877 (95% CI 0.827–0.918), and correlated with manual severity scores (r = 0.62, p < 0.01). The synthetic-target FNNs converged consistently but—as intended—showed no predictive power (R² ≈ −0.15 microbiome; −0.18 transcriptome). Twin analysis revealed greater microbiome similarity in monozygotic versus dizygotic pairs (0.475 ± 0.107 vs. 0.557 ± 0.117; p = 0.0005) and a modest correlation between salivary features and caries burden (r = 0.25). Conclusions: Modular deep-learning pipelines remain computationally robust and interpretable on non-aligned datasets; radiographic severity provides a transferable quantitative anchor. Twin-cohort findings confirm heritable patterns in the oral microbiome and outline a pathway toward future clinical translation once patient-matched multi-omics are available. This framework establishes a scalable, reproducible foundation for integrative caries research. Full article

The transportation sector is witnessing a paradigm shift toward more sustainable and efficient propulsion systems, with a particular focus on public transportation vehicles such as buses. In this context, hybrid powertrains combining internal combustion engines with electric propulsion systems have emerged as prominent contenders due to their ability to offer significant fuel savings and CO₂ emission reductions compared to conventional diesel powertrains. In this study, the simulation of a complete hybrid bus vehicle is carried out to evaluate the impact of two different hybrid powertrain architectures compared to the diesel reference one. The selected vehicle is a 12 m city bus that performs typical urban driving routes represented by real measured driving cycles. First, the vehicle model was developed using a state-of-the-art diesel powertrain (internal combustion engine) and validated against literature data. This model facilitates a comprehensive evaluation of system efficiency, fuel consumption, and CO₂ emissions while incorporating the effects of driving cycle variability. Subsequently, two different hybrid configurations (parallel P1 and series) are implemented in the model and compared to predict the relative energy consumption and environmental impact, highlighting advantages and challenges. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1>0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an $R^2$ of 0.901 and RMSE of 19.94 on the training set and an $R^2$ of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. Full article

The accurate prediction of path loss is essential for planning and optimizing communication networks, as it directly impacts the user experience. In 5G signal propagation, the mix of varied terrain and dense high-rise buildings poses significant challenges. For example, signals are more prone to multipath effects and occlusion and shadowing occur often, leading to high nonlinearities and uncertainties in the signal path. Traditional and shallow models often fail to accurately depict 5G signal characteristics in complex terrains, limiting the accuracy of path loss modeling. To address this issue, our research introduces innovative feature engineering and prediction models for 5G signals. By utilizing smartphones as signal receivers and creating a multimodal system that captures 3D structures and obstructions in the N1 and N78 bands in China, the study aimed to overcome the shortcomings of traditional linear models, especially in mountainous areas. It employed the XGBoost algorithm with Optuna for hyperparameter tuning, improving model performance. After training on real 5G data, the model achieved a breakthrough in 5G signal path loss prediction, with an R² of 0.76 and an RMSE of 3.81 dBm. Additionally, SHAP values were employed to interpret the results, revealing the relative impact of various environmental features on 5G signal path loss. This research enhances the accuracy and stability of predictions and offers a technical framework and theoretical foundation for planning and optimizing wireless communication networks in complex environments and terrains. Full article

As a typical enclosed engineering microenvironment, tunnel construction sites exert a profound influence on workers’ unsafe behaviors. This impact is particularly significant in cold regions, where extreme environmental conditions are more likely to trigger unsafe behavior among construction workers. This study utilized two exemplary tunnels in cold regions of China as case studies. During the construction period, microenvironmental data were systematically collected, encompassing temperature, humidity, noise, and dust concentration. In parallel, data on workers’ unsafe behaviors were integrated to construct a nonlinear relationship model, and the importance of each microenvironmental variable was assessed using the random forest algorithm. The results indicate that various microenvironmental factors exhibit significant nonlinear effects on unsafe behavior. Among them, dust concentration had the strongest impact (22.56%), followed by noise (17.40%), humidity (15.02%), and temperature (9.21%). Specifically, the maintenance of temperature control close to 0 °C, humidity levels maintained at 60% to 65%, noise levels not exceeding 82 dB, and dust concentrations below 12 mg/m³ contributed to a significant reduction in unsafe behavior scores. The present study investigates the mechanism of the microenvironment of cold-region tunnel construction on personnel behavioral risk. The study’s findings provide a threshold reference and strategy support for safety optimization and engineering site management of cold-region tunnel construction environments. Full article

This study presents a highly sensitive non-enzymatic electrochemical sensor for detecting Sunset Yellow, a common food additive in beverages, based on palladium-cerium oxide composite decorated carbon black (CB). The sensing material was prepared by depositing palladium nanoparticles onto cerium oxide nanocubes, followed by the uniform dispersion of CB through sonication in a water bath. The strong metal–support interaction between palladium and cerium oxide significantly enhances catalytic activity, while the CB ensures excellent conductivity and structural support for the catalyst. Under optimized conditions, the sensor exhibits a linear response to Sunset Yellow concentrations in the range from 1 to 100 nM, with a limit of detection (LOD) of 0.056 nM. Additionally, the sensor demonstrates remarkable selectivity and stability. Practical application in real orange juice samples yielded recoveries from 99.11% and 101.34%, confirming its reliability for real-world beverage analysis. Full article

Excavations in Iran’s Lorestan province uncovered a 200-year-old water system consisting of four earthenware jars connected by clay pipes, each jar built from six or seven pottery sections. Due to local conditions, the dimensions and spacing of the jars in this water supply system design deviate from the established standards in historical water science literature (a diameter-to-length ratio of less than 1:4). This deviation prompted detailed archaeo-hydraulic investigations, including fieldwork analyses and hydraulic calculations of the discovered water supply system. The system was designed to serve both public and governmental purposes. Structural modifications (diameter-to-length ratio < 1:4) improved durability and strength for regional conditions. The jars divided, ventilated, and filtered water from mud and sand. Comparative analyses suggest the water supply system dates to the late Zand and Qajar periods (18th–19th centuries). Full article

Water pollution by organic dyes poses serious environmental and health challenges, demanding efficient and selective remediation methods. In this study, we engineered tailored organo-clay nanocomposites by modifying montmorillonite with hexadecyltrimethylammonium bromide (HTAB) and intercalating polyethylene glycol (PEG) chains of two distinct molecular weights (PEG200 and PEG4000). Comprehensive characterization techniques (XRD, FTIR, SEM, zeta potential, and TGA) confirmed the successful modification of the composites. Notably, PEG4000 promoted significant interlayer expansion, as evidenced by the shift of the (00l) reflection corresponding to the basal spacing d, indicating an increase in basal spacing. This expansion contributed to the formation of a well-ordered porous framework with uniformly distributed pores. In contrast, PEG200 produced smaller pores with a more uniform distribution but induced less pronounced interlayer expansion. Adsorption tests demonstrated rapid kinetics, achieving equilibrium in under 15 min, and impressive capacities: 420 mg/g of methylene blue (MB) adsorbed on PEG200/MMT@HTAB, and 385 mg/g of Congo red (CR) on PEG4000/MMT@HTAB. The crucial role of PEG chain length in adsorption selectivity was assessed, showing that shorter PEG chains favored methylene blue adsorption by producing narrower pores and faster kinetics, while longer PEG chains enhanced CR uptake via a stable, interconnected pore network that facilitates diffusion of larger dye molecules. Thermodynamic and Dubinin–Radushkevich analyses confirmed that the adsorption was spontaneous, exothermic, and predominantly driven by physical adsorption mechanisms involving weak van der Waals and dipole interactions. These findings highlight the potential of PEG-modified montmorillonite nanocomposites as cost-effective, efficient, and tunable adsorbents for rapid and selective removal of organic dyes in wastewater treatment. Full article