Urban stormwater runoff continues to challenge cities worldwide due to increasing impervious surfaces and intensified rainfall from climate change. Swales—vegetated conveyance channels designed to manage runoff volume and quality—offer a nature-based solution that integrates hydrological function, ecological enhancement, and cost-effectiveness. This study investigates the performance and lifecycle economics of swale systems using a case study in South Australia. A MUSICX model simulation was conducted to quantify pollutant removal and flow reduction, and lifecycle costing was performed to evaluate construction and annual maintenance requirements. Results indicate exceptionally high treatment efficiencies, with over 99% removal of total suspended solids, nitrogen, phosphorus, and gross pollutants, and a 99.09% reduction in runoff volume. The total capital cost of the swale network was estimated at $19,726.50, with annual maintenance at $6157.49. Economic benefits from pollutant removal and avoided downstream treatment were valued at $14,874 per year, demonstrating a favorable benefit-cost profile. The findings underscore the potential of well-designed swales to function as cost-effective, modular components of decentralized stormwater management systems. These results contribute evidence supporting the broader integration of swales into urban planning, particularly in water-sensitive design frameworks seeking to achieve sustainability, climate adaptation, and SDG-aligned outcomes.
Offshore floating photovoltaic systems are highly susceptible to salt crystallization on the surfaces of photovoltaic modules, highlighting the need for intelligent inspection and cleaning technologies to improve operational efficiency and overcome the limitations of conventional manual maintenance methods. However, the presence of surface gridlines on the photovoltaic modules introduces significant visual interference, which complicates the accurate identification of salt deposition regions. To address this challenge, a semantic information-guided detection framework is proposed to enable precise segmentation of salt-affected areas. The key innovation lies in the effective classification of gridlines as background features by extracting semantic priors through low-level thresholding, which are then fused with the original red-green-blue image to construct a four-channel input. This fusion enhances the model’s ability to extract and discriminate features related to salt crystallization. Experimental results demonstrate that the proposed method achieves a 4.6% improvement in segmentation accuracy and a 3.7% increase in recognition accuracy compared to conventional models, based on evaluation metrics such as mean average precision and F1-score. The proposed framework offers a robust technical foundation for developing intelligent maintenance systems tailored to offshore floating photovoltaic applications.
Global industrialization and rising living standards have driven widespread adoption of fiber materials. However, the rapid growth of the textile industry has also caused substantial resource depletion and environmental pollution. Each year, over 92 million tons of textile waste are generated worldwide, most of which is landfilled or incinerated, while only a small proportion is recycled. This paper systematically reviews the latest advancements in the recycling and reuse of fiber-based products, focusing on mechanical, chemical, and biological recycling technologies and the reapplication of recycled fibers. Mechanical recycling is a mature and cost-effective process, but it results in reduced fiber quality. Chemical recycling can produce high-purity raw materials, yielding regenerated fibers with properties close to virgin fibers, but the process is complex and energy-intensive. Biological recycling operates under mild conditions with low energy consumption but is limited by low efficiency and long reaction times. This paper also explores the applications of recycled fibers in regenerated apparel, automotive textiles, construction materials, medical supplies, and eco-friendly filtration materials. Fiber recycling technologies should advance toward greener, more innovative, and circular economy-oriented approaches. Technological innovation, industrial collaboration, and policy guidance can significantly enhance the resource utilization of textile waste.
This study investigated the relationship between resilience and disruptive behaviour among in-school adolescents in Lagos State, Nigeria. The objectives were to examine: the association between six resilience dimensions (family support, confidant-friend support, school support, adjustment, sense of struggle, and empathy) and disruptive behaviour; the differences between sex and family type on disruptive behaviour. A cross-sectional design was employed, sampling 897 adolescents (M = 14.8 years; 50.8% male) from selected secondary schools using a multi-stage sampling technique. Data were collected using validated psychological resilience and disruptive behaviour scales. Results revealed a significant negative correlation between disruptive behaviour and four resilience dimensions: family support, school support, sense of struggle, and empathy. Regression analysis showed that these resilience dimensions jointly accounted for 6.6% of the variance in disruptive behaviour, with only family and school support emerging as significant predictors. Male adolescents exhibited significantly higher disruptive behaviour than females, while no significant differences were found based on family type. The findings highlight the crucial role of familial and school support in behavioural regulation and suggest the need for gender-sensitive and context-specific interventions.
Smart factories increasingly rely on real-time data to optimize manufacturing, yet machining operations, particularly in aerospace stack drilling, still face challenges such as low productivity and accelerated tool wear. While advanced CNC machines already capture rich process data, its full potential for real-time decision-making remains underexplored. This work introduces a novel approach that leverages machine learning (ML) to identify material layers and optimize cutting conditions during drilling (helical milling) of aluminum–titanium stacks. Unlike prior methods that require additional sensors or complex instrumentation, our approach uniquely utilizes only spindle power signals from the CNC machine. Data maps consisting of cutting coefficients are used to train ML models to reliably predict material transitions across multiple layers under a range of cutting conditions. The results demonstrate appropriate material identification in comparison to experiments, enabling significant improvements in the hole-making of aerospace stacks. This study contributes a scalable, sensor-free, and non-intrusive framework for smart machining, establishing a practical pathway for process optimization in aerospace manufacturing without disrupting existing shop-floor setups.
The discovery of CRISPR based technologies has transformed genome engineering and synthetic biology. With advancements in the ability to do multiplex genome editing, it is now emerging as an ideal approach for trait stacking to improve crops, functional genomics, and complex metabolic engineering in various biological systems. This review discusses engineering and optimization of the latest CRISPR effectors for scalable and precise multiplex editing, ranging from well-known systems like Cas9 and Cas12 variants, to newer, smaller variants such as CasMINI, Cas12j2, and Cas12k. We highlight how the emergence of base editors and prime editors enabled efficient editing across multiple loci without double strand breaks. We also elaborate on the expression and processing strategies of crRNA arrays, which are central to any multiplexing approach. These include tRNA-based and ribozyme-mediated methods, synthetic modular designs, and AI-optimized guide RNAs tailored to diverse systems. Additionally, we assess next-generation delivery platforms such as lipid nanoparticles, virus-like particles, and metal-organic frameworks that overcome conventional barriers in in vivo applications. This review provides a critical take on technological advances enabling precise, high-throughput, and programmable multiplex genome editing across biological systems, setting the foundation for future innovations in synthetic biology, crop improvement, and therapeutic intervention in multigene diseases.
The quadrotor is an underactuated, nonlinear system that presents significant challenges in both modeling and control design. This work develops a decoupled control framework based on the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor. A Linear Quadratic Gaussian (LQG) regulator is implemented for control, with two extended Kalman filters employed for state estimation in the respective dynamic subsystems. The full design process, from dynamic modeling to flight simulation presented in detail. Key elements include nonlinear simulation, model linearization, state-space representation, feedforward compensation, Linear Quadratic Regulator (LQR) gain tuning, actuator dynamics, sensor noise, LQG design, and extended Kalman filter. The limitations of applying linear control to a nonlinear system are also presented.
Drone simulation refers to the emulation of Unmanned Aerial Vehicles (UAVs) in a virtual environment, replicating real-world conditions to study and test the behavior, performance, and functionalities of drones. This paper explores the simulation of UAVs in the Unreal Engine environment using MAVProxy (Micro Air Vehicle Proxy) and the Python library DroneKit. By leveraging the computational capabilities of computers, this approach enables precise visualization and control of UAV flight dynamics in three dimensions. The use of Blueprints in Unreal Engine facilitates a cost-effective and accessible simulation process, allowing engineers and scientists to refine their UAV designs before real-world deployment. Results show the applicability of this approach vs. different environments, where an alternative approach also emerges as a viable option for visualizing textured buildings. This approach shows the power of open-source collaboration in advancing innovative solutions in the dynamic field of science and technology.
The debate surrounding the legal nature of carbon emission rights arises from the tension between their dual characteristics of public and private law, which challenges traditional property rights theory. This tension has led to conflicts regarding the effectiveness of legal frameworks, fragmented regulations, and a crisis of institutional trust within the carbon market. Carbon emission rights should be redefined as a novel form of usufructuary right, with ecological capacity resources—owned by the state—serving as the object. These rights are realized through digitalization and specificity enabled by blockchain technology. Their powers and functions can be understood as twofold: the power of quota control, which falls under public law constraints, and the power of ecological benefits, which exists within private law autonomy. The former limits the boundaries of private rights by ecological thresholds, while the latter translates ecological value into non-possession benefits. To address these issues, a “two-stage governance” system can be established through a dynamic interpretation of Article 329 of the Civil Code of the People’s Republic of China (2020), creating a registration system and enacting specialized legislation for Carbon Emission Rights Trading. By conceptualizing carbon emission rights as a new type of usufructuary right, the contradictions between public and private law can be reconciled, enabling the transition of the carbon market from a policy-driven to a rights- and law-based operation.
This study uses a qualitative approach with a case study strategy in four villages in South Konawe Regency, Indonesia, to explore environmental management practices based on local wisdom with the Building Village Index (BVI) instrument, which includes social, economic, and environmental resilience dimensions. The study results show that local wisdom, such as traditional planting patterns, customary law, and water and natural resource management through traditional rituals, play a significant role in maintaining the balance of the village ecosystem while strengthening cultural identity. The integration of local wisdom with appropriate technology has been proven to increase ecological awareness, strengthen social solidarity, and support equitable distribution of resources, although improvements in waste and energy governance are still needed. Theoretically, these findings enrich the literature on village resilience based on local wisdom, while practically providing evidence-based policy recommendations to strengthen ecological conservation and sustainable village development.