Search Results (10,561)

Urban development and population growth present new challenges for developing urban public spaces (UPS) in cities. The significance of cities as centers of integration of different socio-cultural groups is growing. Taking into account the diversity of needs and expectations of sensory-sensitive groups (women) as residents and users of urban public spaces is a key task facing modern city managers. Women’s public participation is relevant and important, because recognizing their needs and taking them into account in urban spatial policy contributes to creating cities tailored to the needs and expectations of residents and users, according to the principle of “everyone-important”. This article has goals for the identification of factors that reduce the safety and comfort of women’s activities and the most important architectural features UPS. The results of the research indicate that the determinants that reduce the friendliness of safety and comfort in UPS are a lack of lighting, lack of esthetics of space development, lack of benches and toilets, and street noise. A space tailored to women’s needs and expectations should be well-lit with a level walking and sidewalk surface and include architectural infrastructure elements such as adequate municipal sanitation (toilets), benches and urban furniture, with landscaped green space (squares, flowerbeds, trees), which is in line with the principles of universal design. This study’s results found that such factors can help create safe, egalitarian, and inclusive cities. They complete the research gap in the field of sustainable urban development and are key to developing gender-equitable urban planning and urban development policy practices. Full article

Underwater object tracking holds considerable significance in the field of ocean engineering. Additionally, it serves as a crucial component in the operations of autonomous underwater vehicles (AUVs), particularly during tasks associated with capturing marine organisms. However, the attenuation and scattering of light result in shortcomings such as poor contrast in underwater images. Additionally, the motion deformation of marine organisms poses a significant challenge. Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. To overcome this challenge, we propose a novel tracking architecture for the marine organism capturing of AUVs called ULOTrack. ULOTrack is based on a performance discrimination and re-detection framework and constitutes three modules: (1) an object tracker, which can extract multi-feature information of the underwater target; (2) a multi-layer tracking performance discriminator, which serves the purpose of evaluating the stability of the current tracking state, thereby reducing potential model drift; and (3) lightweight detection, which can predict the candidate boxes to relocate the lost tracked underwater object. We conduct comprehensive experiments to validate the efficacy of the designed modules. Finally, the results of the experimentation demonstrate that ULOTrack significantly outperforms existing approaches. In the future, we aim to carefully scrutinize and select more suitable features to enhance tracking accuracy and speed. Full article

This research investigates the adaptation of conventional injection-molding techniques for producing thick-walled polycarbonate optical components, specifically targeting their application in automotive light guides. With the automotive industry’s growing demand for reliable yet cost-efficient optical products, the study examines how traditional injection-molding processes can be refined to enhance dimensional accuracy and reduce defects. Simulations and experimental trials were conducted to evaluate the impact of critical process parameters, such as melt temperature, mold temperature, injection pressure, and gate design, on the overall quality of the final components. The results show that by carefully optimizing these parameters, it is possible to significantly reduce common defects like warpage, surface imperfections, and dimensional instability. This research highlights the potential of existing molding techniques to meet high industry standards while maintaining cost-effectiveness, offering valuable guidance for manufacturers aiming to produce high-quality optical components for demanding applications like automotive lighting. Full article

This paper presents an advanced AI-based optimization framework for Electric Vehicle (EV) smart charging systems, focusing on efficient energy distribution to meet dynamic user demand. The study leverages machine learning models such as Random Forest, Support Vector Regression (SVR), Gradient Boosting Regressor, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) to forecast user demand and optimize energy allocation. Among the models, XGBoost demonstrated superior predictive performance, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), making it the most effective for real-time user demand prediction in smart charging scenarios. The framework introduces proportional and priority-based allocation strategies to distribute available energy effectively, with a focus on minimizing energy shortfalls and balancing supply with user demand. Results from the XGBoost model reduced prediction error by 15% compared to other models, significantly improving the station’s ability to meet user demand efficiently. The proposed AI framework enhances charging station operations, supports grid stability, and promotes sustainability in the context of increasing EV adoption. Full article

This paper presents a novel driving torque control strategy for the front and rear independently driven electric vehicle (FRIDEV) to reduce energy consumption and enhance vehicle stability. The strategy is built on a comprehensive vehicle model that integrates vertical load transfer, tire slip dynamics, and an electric system model that accounts for losses in induction motors (IMs), permanent magnet synchronous motors (PMSMs), inverters, and batteries. The torque control problem is framed with a nonlinear model predictive control (MPC) method, utilizing state-space equations as representations of vehicle dynamics. The optimization targets adjust in real-time based on road traction conditions, with the slip rate of front and rear wheels determining the torque control strategy. Active slip control is applied when slip rates exceed critical thresholds, while under normal conditions, torque distribution is optimized to minimize energy losses. To enable online real-time implementation, an improved sparrow search algorithm (SSA) is designed. Simulations in MATLAB/Simulink confirm that the proposed online strategy reduces energy consumption by 2.3% under the China light-duty vehicle test cycle-passenger cars (CLTC-P) compared to a rule-based strategy. Under low-adhesion conditions, the proposed online strategy effectively manages slip ratios, ensuring stability and performance. Improved SSA also enhances computational efficiency by approximately 44%–52%, making the online strategy viable for real-time applications. Full article

The genus Ephedra Tourn. ex L. (Ephedraceae) is an important source with pharmacological and environmental potential. Conversely, Ephedra spp. still exhibit taxonomic complexity, especially for the specimens lacking reproductive cones. This complexity is attributed to its xeromorphic features, notably the reduced leaves and analogous assimilating branches, which make the species identification a real challenge. The current study provides a pioneering approach to distinguish fragments of Ephedra species at the gender level. This study was based on the stem anatomy and stem epidermal features using a light microscope for five species (E. alata Decne., E. aphylla Forssk., E. ciliata Fisch. & C.A.Mey., E. foeminea Forssk. and E. Ephedra pachyclada Boiss.) represented by ten genders collected from S. Sinai, Egypt. Anomocytic and brachyparacytic stomata, tanniniferous idioblasts, annual rings, a terete and furrowed outline, the number and width of tracheids, patches of cortical fibers, unicellular trichomes, druses, solitary crystals, and the activity of interfascicular cambium were among the distinguishing features that were found. Different statistical analyses were applied to explore the diversity at interspecific and intra-generic levels. This study revealed that the stem anatomy was not only an efficient tool for identifying the investigated five Ephedra species at the species level but also presented a differential key to distinguish between genders and species. In addition, our results indicated that the epidermal features played a critical role in differentiating the studied Ephedra species at the gender level. This study confirms the efficacy of stem anatomy as an identification approach for the Ephedra species at the gender level and recommends this approach to identify the fragmented Ephedra for taxonomical, pharmaceutical, and medical applications. Full article

In this study, we report the synthesis and characterization of a novel photocatalyst composite composed of functionalized carbon nanotubes (f-CNT) and phenyl-modified graphitic carbon nitride (PhCN). The incorporation of the phenyl group extends the absorption range into the visible spectrum compared to pure g-C₃N₄. Additionally, the formation of the heterostructure in the f-CNT/PhCN composite exhibits improved charge transfer efficiency, facilitating the separation and transfer of photogenerated electron-hole pairs and reducing recombination rates. The photocatalytic performance of this composite was evaluated by the degradation of Rhodamine B (RhB) under visible light irradiation. The f-CNT/PhCN composite exhibits remarkable efficiency in degrading RhB, achieving 60% degradation after 4 h, and 100% after 24 h under low-power white LED excitation. This represents a substantial improvement over the non-functionalized CNT/PhCN composite, which shows much lower performance. In contrast, pure PhCN demonstrates very little activity. Structural and optical properties were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), Raman spectroscopy, and UV–Vis spectroscopy. Time-resolved photoluminescence measurements were used to study the behavior of photoexcited carriers, confirming that the composite improves charge transfer efficiency for photogenerated carriers by approximately 30%. The results indicate that the functionalization of CNTs significantly enhances the photocatalytic properties of the composite, making f-CNT/PhCN a promising candidate for environmental remediation applications, particularly in the degradation of organic pollutants in wastewater. Full article

The lack of in-depth analysis on the reservoir characteristics and the paleoenvironmental conditions of the Niutitang Formation in the study area has led to an unclear understanding of its geological background. In this study, core samples from well SZY1 were selected, and X-ray diffraction (XRD), scanning electron microscopy (SEM), and quantitative elemental analysis were employed to systematically investigate the reservoir properties and paleoenvironment of the shales. The results indicate that the Niutitang Formation shales form a low-porosity, low-permeability reservoir. By utilizing indicators such as the chemical index of alteration (CIA) and elemental ratios, the study delves into the paleoclimate and paleoproductivity of the region. The (La/Yb)_n ratio is approximately 1, indicating a rapid deposition rate that is beneficial for the accumulation and preservation of organic matter. The chondrite-normalized and North American Shale Composite (NASC)-normalized rare earth element (REE) distribution patterns of the shales show consistent trends with minimal variation, reflecting the presence of mixed sources for the sediments in the study area. Analysis reveals that the Niutitang Formation shales are enriched in light rare-earth elements (LREEs) with a negative europium anomaly, and the primary source rocks are sedimentary and granitic, located far from areas of seafloor hydrothermal activity. The Ni_EF and Cu_EF values suggest high paleoproductivity, and the shales were deposited in an anoxic-reducing environment. The depositional environments of the Marcellus and Utica shales in the United States, the Wufeng-Longmaxi black shales in the Changning area of the Sichuan Basin, and the shales in the study area are similar, characterized by anoxic reducing conditions and well-developed fractures. The thermal evolution degree of the study area is relatively moderate, currently in the peak gas generation stage, with the reservoir quality rated as medium to high, indicating good potential for hydrocarbon accumulation and promising exploration prospects. Full article

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMSs) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMSs often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMSs can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and a real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMSs significantly boosts energy efficiency and safety in smart buildings. Simulation results reveal an hourly average power savings of 36.8 kw with the integrated failsafe model for all scenarios. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management. Full article

Microalgae are known for their efficient removal of hexavalent chromium (Cr(VI)) through biosorption and bioaccumulation, yet the subsequent release of Cr(VI) upon cell death remains a challenge. The reduction of Cr(VI) to the less toxic trivalent chromium [Cr(III)] is another critical remediation strategy that mitigates the risk of Cr(VI) re-release, but research on microalgal reduction of Cr(VI) is scarce. In this study, a microalgal strain designated as MQ-1 was isolated from chromium-contaminated mine effluent, demonstrating the capability to tolerate and remove Cr(VI). Phylogenetic analysis revealed that MQ-1 is closely related to the genus Chlorella; hence, it is classified as Chlorella sp. MQ-1. This strain exhibited robust growth at Cr(VI) concentrations below 2 mg/L, achieving a removal rate higher than 82% for initial Cr(VI) concentrations between 0.5 and 1 mg/L after a 5-day incubation period. Mechanistic studies revealed that MQ-1 promoted the removal of Cr(VI) mainly through intracellular bioreduction and bioaccumulation processes, in which more than 60% of Cr(VI) was reduced to the less toxic Cr(III) and stocked in the cells. A two-stage cultivation strategy, involving initial biomass accumulation followed by Cr(VI) treatment, significantly enhanced the removal efficiency, which was further accelerated under illuminated conditions. Notably, MQ-1 cultures with initial OD₆₈₀ values of 4 and 6 accomplished 84.28% and 91.31% Cr(VI) removal from 2 mg/L solutions, respectively, within 30 hours under light exposure. These findings highlight the potential of MQ-1 to utilize renewable solar energy to reduce Cr(VI) and to mitigate the risk of its re-release into the environment. This characteristic positions MQ-1 as a potentially sustainable and cost-effective solution for Cr(VI) remediation and suggests its significant potential for large-scale implementation in bioremediation strategies aimed at Cr(VI)-contaminated waters. Full article

Dual fuel combustion has gained attention as a cost-effective solution for reducing the pollutant emissions of internal combustion engines. The typical approach is combining a conventional high-reactivity fossil fuel (diesel fuel) with a sustainable low-reactivity fuel, such as bio-methane, ethanol, or green hydrogen. The last one is particularly interesting, as in theory it produces only water and NO_x when it burns. However, integrating hydrogen into stock diesel engines is far from trivial due to a number of theoretical and practical challenges, mainly related to the control of combustion at different loads and speeds. The use of 3D-CFD simulation, supported by experimental data, appears to be the most effective way to address these issues. This study investigates the hydrogen-diesel dual fuel concept implemented with minimum modifications in a light-duty diesel engine (2.8 L, 4-cylinder, direct injection with common rail), considering two operating points representing typical partial and full load conditions for a light commercial vehicle or an industrial engine. The numerical analysis explores the effects of progressively replacing diesel fuel with hydrogen, up to 80% of the total energy input. The goal is to assess how this substitution affects engine performance and combustion characteristics. The results show that a moderate hydrogen substitution improves brake thermal efficiency, while higher substitution rates present quite a severe challenge. To address these issues, the diesel fuel injection strategy is optimized under dual fuel operation. The research findings are promising, but they also indicate that further investigations are needed at high hydrogen substitution rates in order to exploit the potential of the concept. Full article

Identifying and reducing customer churn have become a priority for financial institutions seeking to retain clients. Our research focuses on customer churn rate analysis using advanced machine learning (ML) techniques, leveraging a synthetic dataset sourced from the Kaggle platform. The dataset undergoes a preprocessing phase to select variables directly impacting customer churn behavior. SMOTETomek, a hybrid technique that combines oversampling of the minority class (churn) with SMOTE and the removal of noisy or borderline instances through Tomek links, is applied to balance the dataset and improve class separability. Two cutting-edge ML models are applied—random forest (RF) and the Light Gradient-Boosting Machine (LGBM) Classifier. To evaluate the effectiveness of these models, several key performance metrics are utilized, including precision, sensitivity, F1 score, accuracy, and Brier score, which helps assess the calibration of the predicted probabilities. A particular contribution of our research is on calibrating classification probabilities, as many ML models tend to produce uncalibrated probabilities due to the complexity of their internal mechanisms. Probability calibration techniques are employed to adjust the predicted probabilities, enhancing their reliability and interpretability. Furthermore, the Shapley Additive Explanations (SHAP) method, an explainable artificial intelligence (XAI) technique, is further implemented to increase the transparency and credibility of the model’s decision-making process. SHAP provides insights into the importance of individual features in predicting churn, providing knowledge to banking institutions for the development of personalized customer retention strategies. Full article

The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring. Full article

For the first time, N-modified analogues of VV-hemorphin-5 (Valorphin) were synthesised and conjugated with three different 4-substitured-1,8-naphthalimides (H-NVal without substituent, Cl-NVal with chloro-substituent, and NO₂-NVal with nitro-substituent). Cotton fabric was modified with these peptides by soaking it in their ethanol solution, and the colourimetric properties of the obtained fabric were measured. The fluorescent analysis shows that peptide immobilisation on a solid matrix as fabric decreases the molecule flexibility and spectrum maxima shift bathocromically with the appearance of a vibrational structure. The peptides’ contact antimicrobial activity, and the resulting fabrics, have been investigated against model Gram-positive B. cereus and Gram-negative P. aeruginos bacteria. For the first time, the influence of light on bacterial inactivation was investigated by antibacterial photodynamic therapy of similar peptides. Slightly more pronounced activity in liquid media and after deposition on the cotton fabric was obtained for the peptide containing 4-nitro-1,8-naphthalimide compared to the other two peptides. Immobilisation of a peptide on the surface of fibres reduces their antimicrobial activity since their mobility is essential for good contact with bacteria. Cotton fabrics can be used in medical practice to produce antibacterial dressings and materials. Full article

This study presents a rapid and comprehensive method for screening mushroom extracts for the putative discovery of bioactive molecules, including those exhibiting antimicrobial activity. This approach utilizes a panel of bioluminescent bacteria, whose light production is a sensitive indicator of various cellular effects triggered by the extracts, including disruption of bacterial communication (quorum sensing), protein and DNA damage, fatty acid metabolism alterations, and oxidative stress induction. The bioassay’s strength is its ability to efficiently analyze a large number of extracts simultaneously while also assessing several different mechanisms of toxicity, significantly reducing screening time. All samples analyzed exhibited more than one cellular effect, as indicated by the reporter bacteria. Four samples (C. cornucopioides, F. fomentarius, I. obliquus, and M. giganteus) displayed the highest number (six) of possible mechanisms of antibacterial activity. Additionally, combining extraction and purification protocols with a bioluminescent bacterial panel enables simultaneous improvement of the desired antimicrobial properties of the extracts. The presented approach offers a valuable tool for uncovering the diverse antimicrobial mechanisms of mushroom extracts. Full article