Search Results (975)

The impacts of land use on stormwater runoff quality and Best Management Practices to mitigate these impacts have been investigated since the 1970s, yet challenges remain in providing a modeling approach that concomitantly considers contributions from different land use types. In densely developed urban areas, a buildup/washoff approach is often applied, while in rural areas, some type of erosion modeling is employed, as the processes of detachment, entrainment, and transport are fundamentally different. This study presents a coupled modeling approach within PCSWMM, integrating exponential buildup/washoff for impervious surfaces with the Modified Universal Soil Loss Equation (MUSLE) for pervious areas, including construction sites, to characterize water quality in the large mixed urban–rural Sparrovale catchment in Geelong, Australia. The watershed includes an innovative cascading system of 12 online NbS wetlands along one of the main tributaries, Armstrong Creek, to manage runoff quantity and quality, as well as 16 offline NbS wetlands that are tributary to the online system. A total of 78 samples for Total Suspended Solids (TSS), Total Phosphorus (TP), and Total Nitrogen (TN) were collected from six monitoring sites along Armstrong Creek during wet- and dry-weather events between May and July 2024 for model validation. The data were supplemented with six other catchment stormwater quality datasets collected during earlier studies, which provided an understanding of water quality status for the broader Geelong region. Results showed that average nutrient concentrations across all the sites ranged from 0.44 to 2.66 mg/L for TP and 0.69 to 5.7 mg/L for TN, spanning from within to above the ecological threshold ranges for eutrophication risk (TP: 0.042 to 1 mg/L, TN: 0.3 to 1.5 mg/L). In the study catchment, upstream wetlands reduced pollutant levels; however, downstream wetlands that received runoff from agriculture, residential areas, and, importantly, construction sites, showed a substantial increase in sediment and nutrient concentration. Water quality modeling revealed washoff parameters primarily influenced concentrations from established urban neighborhoods, whereas erosion parameters substantially impacted total pollutant loads for the larger system, demonstrating the importance of integrated modeling for capturing pollutant dynamics in heterogeneous, urbanizing catchments. The study results emphasize the need for spatially targeted management strategies to improve stormwater runoff quality and also show the potential for cascading wetlands to be an important element of the Nature-based Solution (NbS) runoff management system. Full article

In modern e-learning environments, ensuring both student identity verification and concentration monitoring during online examinations has become increasingly important. This paper introduces a robust sequential framework that integrates Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs) and fuzzy logic to achieve reliable face recognition and interpretable attentiveness assessment. Images from the Extended Yale B (cropped) dataset are preprocessed through grayscale normalization and resizing, while GANs generate synthetic variations in pose, illumination, and occlusion to enrich the training set and improve generalization. The CNN extracts discriminative facial features for identity recognition, and a fuzzy inference system transforms the CNN’s confidence scores into human-interpretable concentration levels. To stabilize learning and prevent overfitting, the model incorporates dropout regularization, batch normalization, and extensive data augmentation. Comprehensive evaluations using confusion matrices, ROC–AUC, and precision–recall analyses demonstrate an accuracy of 98.42%. The proposed framework offers a scalable and interpretable solution for secure and reliable online exam proctoring. Full article

VOCs are significant precursors for the formation of O₃ and SOA, directly impacting human health. This study employs multiple approaches to analyzing atmospheric VOCs by focusing on OVOCs including aldehydes, ketones, and phenols, with a case study in Beijing, China. We analyzed the concentration levels and compositions of VOCs and their atmospheric activities, offering a new perspective on VOCs. This analysis was conducted through offline measurements of volatile phenols and carbonyl compounds, complemented by online VOC observations during the summer period of high O₃ levels. The total atmospheric VOCs concentration was found to be 51.29 ± 10.01 ppbv, with phenols contributing the most (38.87 ± 11.57%), followed by carbonyls (34.91 ± 6.85%), and aromatics (2.70 ± 1.03%, each compound is assigned to only one category based on its primary functional group, with no double counting). Carbonyls were the largest contributors to the OFP at 59.03 ± 14.69%, followed by phenols (19.94 ± 4.27%). The contribution of phenols to the SOAFP (43.37 ± 9.53%) and the L_OH (67.74 ± 16.72%) is dominant. Among all quantified VOC species, phenol and formaldehyde exhibited the highest species-level contributions to atmospheric reactivity metrics, including L_OH, OFP and SOAFP, owing to their combination of elevated concentrations and large kinetic or MIR coefficients. Using the PMF model for source analysis, six main sources of volatile organic compounds were identified. Solvent use and organic chemicals production were found to be the primary contributors, accounting for 31.76% of the total VOCs emissions, followed by diesel vehicle exhaust (17.80%) and biogenic sources (15.51%). This study introduces important OVOCs such as phenols, re-evaluates the importance of OVOCs and their role in atmospheric chemical processes, and provides new insights into atmospheric VOCs. These findings are crucial for developing effective air pollution control strategies and improving air quality. This study emphasizes the importance of OVOCs, especially aldehydes and phenols, in the mechanism of summer O₃ generation. Full article

The growing global environmental crisis calls for fundamental transformations in production and consumption systems, but the understanding of how circular economy strategies translate into quantifiable environmental benefits remains fragmented across sectors and geographies. The objective of this study is to synthesize current scientific knowledge on the circular economy as an environmental mitigation strategy, identifying conceptual convergences, methodological patterns, geographic distributions, and critical knowledge gaps. A systematic review combined with a bibliometric analysis of 62 peer-reviewed articles published between 2018 and 2024, retrieved from Scopus, Web of Science, ScienceDirect, Springer Link and Wiley Online Library, was conducted following the PRISMA 2020 guidelines. The results reveal a marked methodological convergence around life cycle assessment, with Europe dominating the scientific output (58% of the corpus). Four complementary conceptual frameworks emerged, emphasizing closed-loop material flows, environmental performance, integration of economic sustainability and business model innovation. The thematic analysis identified bioenergy and waste valorization as the most mature implementation pathways, constituting 23% of the research emphasis. However, critical gaps remain: geographic concentration limits the transferability of knowledge to diverse socioeconomic contexts; social, cultural and behavioral dimensions remain underexplored (12% of publications); and environmental justice considerations receive negligible attention. Crucially, the evidence reveals nonlinear relationships between circularity metrics and environmental outcomes, calling into question automatic benefits assumptions. This review contributes to an integrative synthesis that advances theoretical understanding of circularity-environment relationships while providing evidence-based guidance for researchers, practitioners, and policy makers involved in transitions to the circular economy. Full article

The rapid digitalization of financial services in the European Union (EU) has not only enhanced convenience and inclusion but also increased exposure to sophisticated online financial fraud. Digital financial literacy (DFL) is widely promoted as a key tool for empowering consumers and reducing fraud victimization. However, the empirical and conceptual landscape linking DFL to fraud reduction within the specific sociolegal context of the EU remains fragmented. This study uses bibliometric analysis to map the research area, define major themes within the field, and determine the role of DFL in reducing online financial fraud in the EU. Peer-reviewed journal articles were targeted to ensure academic rigor, with a publication window of 2010–2025 reflecting key fintech and regulatory developments. After adhering to PRISMA principles, 87 peer-reviewed publications were chosen out of a total of 568 records identified through OpenAlex and Web of Science, coauthorship, keyword co-occurrence, citation, temporal, and density representations were analyzed using VOSviewer. Findings indicate an increasingly diffuse research field with new clusters concentrating on macroeconomic policy, business technology, social psychology, and interdisciplinary foundations. Results demonstrate that successful implementation of DFL interventions combines behavioral insights, technological protection, and non-discriminatory policy considerations. The study concludes by identifying major gaps in research and providing a path forward for future evidence-based policy efforts toward enhancing consumer protection in the EU digital financial market. Full article

The evolution towards 6G is set to transform Vehicle-to-Everything (V2X) networks by introducing advanced technologies such as Six-Dimensional Movable Antenna (6DMA). This technology endows Roadside Units (RSUs) with dynamic beam-steering capabilities, enabling adaptive coverage. However, traditional RSU deployment strategies, optimized for static coverage, are fundamentally mismatched with these new dynamic capabilities, leading to a critical deployment–optimization mismatch. This paper addresses this challenge by proposing DyDO, a novel Dynamic Deployment and Optimization framework for the utilization of 6DMA-RSUs. Our framework strategically decouples the problem into two modules operating on distinct timescales. On a slow timescale, an offline deployment module analyzes long-term historical traffic data to identify optimal RSU locations. This is guided by a newly proposed metric, the Dynamic Potential Score (DPS), which quantifies a location’s intrinsic value for dynamic adaptation by integrating spatial concentration, temporal volatility, and traffic magnitude. On a fast timescale, an online control module employs an efficient Sequential Angular Search (SAS) algorithm to perform real-time, adaptive beam steering based on immediate traffic patterns. Extensive experiments on a large-scale, real-world trajectory dataset demonstrate that DyDO outperforms conventional static deployment methodologies. This work highlights the necessity of dynamic-aware deployment to fully unlock the potential of 6DMA in future V2X systems. Full article

Feeding time is a critical but understudied factor influencing nutrient dynamics and overall productivity in aquaponic systems. This study examined the effects of two feeding schedules on growth performance of common carp (Cyprinus carpio L.), hydrochemical parameters, and the growth of lettuce (Lactuca sativa) cultivated in an integrated aquaponic system. Two 60-day trials were conducted over consecutive years under identical greenhouse conditions. Carp were fed either in the morning and early afternoon (T1: 08:00, 11:00, 14:00) or later in the day (T2: 11:00, 14:00, 17:00). Hydrochemical indicators, including dissolved oxygen, turbidity, ammonium ions (NH₄⁺), and nitrates (NO₃⁻), were continuously monitored through online measurement. Carp reared under T2 displayed significantly higher specific growth rate, final body mass, and improved feed conversion ratio (p < 0.05). The T2 variant also showed higher dissolved oxygen levels and lower turbidity compared to T1, indicating enhanced system stability. Although NH₄⁺ concentrations were higher and NO₃⁻ levels lower in T2, these differences did not compromise water quality due to efficient plant nutrient uptake. Lettuce grown under T2 exhibited greater stem and root development and higher biomass accumulation, suggesting improved nitrogen utilization linked to the NH₄⁺/NO₃⁻ ratio and enhanced root oxygenation. Overall, aligning feeding time with fish circadian rhythms improved fish performance, plant growth, and nutrient cycling efficiency. These findings demonstrate that feeding schedule is a key management factor capable of enhancing sustainability and productivity in aquaponic systems. Full article

Chemical oxygen demand (COD) serves as a critical indicator for assessing the extent of water pollution caused by organic matter. This study proposes an integrated COD detection methodology that combines laser absorption spectroscopy with laser-induced fluorescence spectroscopy, enabling accurate measurement of COD parameters across a wide concentration range. For high-concentration COD, conventional ultraviolet absorption spectrophotometry based on the Lambert–Beer law is employed. However, since laser absorption spectrophotometry exhibits substantial errors in detecting low-concentration COD, laser-induced fluorescence spectroscopy is adopted for the precise quantification of trace-level COD. By integrating these two laser-based approaches, a spectroscopic COD detection system has been developed that simultaneously records absorbance after the laser passes through the sample and quantifies fluorescence intensity perpendicular to the beam with an image sensor, thereby achieving comprehensive COD analysis. Laboratory validation using COD standard solutions demonstrated relative errors below 11% across the concentration range of 2–220 mg/L. Further application to natural water samples confirmed that the integrated laser absorption–fluorescence spectroscopy approach achieves wide-range COD measurement with high sensitivity, a compact configuration, and rapid response, demonstrating strong potential for real-time online water quality monitoring. Full article

Sustainable language education necessitates scalable, accessible learning environments that foster long-term learner autonomy and reduce educational inequality. While online courses have democratized access to language learning globally, persistent deficiencies in instructor-student interaction and learner engagement compromise their sustainability. The “face effect,” denoting the influence of instructor facial appearance on learning outcomes, remains underexplored as a resource-efficient mechanism for enhancing engagement in digital environments. Furthermore, effective measures linking psychological engagement to sustained learning experiences are notably absent. This study addresses three research questions within a sustainable education framework: (1) How does instructor identity, particularly facial appearance, affect second language learners’ outcomes and interactivity in scalable online environments? (2) How can digital human technology dynamically personalize instructor appearance to support diverse learner populations in resource-efficient ways? (3) How does instructor identity influence learners’ flow state, a critical indicator of intrinsic motivation and self-directed learning capacity? Two controlled experiments with Japanese language learners examined three instructor identity conditions: real teacher identity, learner self-identity, and idol-inspired identity. Results demonstrated that the self-identity condition significantly enhanced oral performance and flow state dimensions, particularly concentration and weakened self-awareness. These findings indicate that identity-adaptive digital human instructors cultivate intrinsic motivation and learner autonomy, which are essential competencies for lifelong learning. This research advances Sustainable Development Goal 4 (Quality Education) by demonstrating that adaptive educational technology can simultaneously improve learning outcomes and psychological engagement in scalable, cost-effective online environments. The personalization capabilities of digital human instructors provide a sustainable pathway to reduce educational disparities while maintaining high-quality, engaging instruction accessible to diverse global populations. Full article

To address the issue of low fertilizer proportioning accuracy in irrigation and fertilization systems due to neglecting the influence of target ions in raw water, this study designed a high-precision online automatic water–fertilizer mixing device that can directly mix raw water (without water purification treatment) with fertilizer stock solution. This device is capable of preparing mixed fertilizer solutions containing N, K, and Ca elements. It employs ion-selective electrodes and flow meters for online detection and feedback of target ion concentrations in the fertilizer solution and flow rate information, and adopts an online fertilizer mixing control strategy that uses a constant raw water flow rate and a fuzzy PID control method to dynamically adjust the pulse frequency of metering pumps, thereby changing the injection volume of nutrient solution. Simulation and experimental analyses show that the piping system of the device is reasonably designed, ensuring stable and smooth fertilizer injection. The temperature-compensated concentration detection models for the three target ions in the fertilizer solution, constructed using a stepwise fitting method, achieve average relative detection errors of 1.94%, 1.18%, and 2.87% for K⁺, NO₃⁻, and Ca²⁺, respectively. When preparing single-element or mixed fertilizer solutions, the device achieves an average steady-state error of no more than 4% and an average steady-state time of approximately 40 s. Compared with deionized water, the average relative errors for potassium ions, nitrate ions, and calcium ions when preparing fertilizer solutions with raw water are 1.33%, 1.12%, and 1.19%, respectively. Compared with the theoretical errors of fertilizer preparation with raw water, the fertilizer proportioning errors of this device for potassium ions, nitrate ions, and calcium ions can be reduced by a maximum of 10.55%, 66.84%, and 62.71%, respectively, which is superior to the performance requirements for water–fertilizer integration equipment specified in the national industry standard DG/T 274-2024. Additionally, the device achieves accurate and stable fertilizer proportioning with safe and reliable operation during 6 h of continuous operation. This device significantly reduces the impact of raw water on fertilizer proportioning accuracy, improves the adaptability of the device to irrigation water sources, and provides theoretical basis and technical support for water-fertilizer integration systems in cost-sensitive agriculture. Full article

Spectroscopic technology offers the advantage of rapid online monitoring and has attracted significant attention in molecular detection. However, the complex optical spectroscopic structure results in a relatively complex structure for spectral detection systems, limiting their widespread application. In water spectral detection, in addition to ensuring the stability of the optical system, waterproofing is also crucial. Therefore, developing miniaturized spectral detection modules in water spectral detection can improve system stability and reduce the complexity of developing and maintaining underwater hardware. This work develops a compact multispectral detection system centered on a miniature multispectral sensor. The system, controlled by a microcontroller, detects eight spectral channels within the 400–700 nm range and transmits data via the I²C bus. The sensitivity and stability of the detection are sufficient for water reflectance spectral detection. Based on the reflectance spectrum obtained by the above module, this work develops a regression algorithm to estimate the chlorophyll concentration in water. By comparing with standard chlorophyll concentration detection instruments, the results demonstrate the effectiveness of the proposed system in accurately estimating chlorophyll concentration. Full article

Inter-turn short-circuit (ITSC) faults in motor drives can induce substantial circulating currents and localized thermal stress, ultimately degrading winding insulation and compromising torque stability. To enhance the operational reliability of open-winding (OW) five-phase fault-tolerant fractional-slot concentrated-winding interior permanent-magnet (FTFSCW-IPM) motor drive systems, this paper proposes a real-time fault-tolerant control strategy that provides current suppression and torque stabilization under ITSC conditions. Upon fault detection, the affected phase is actively isolated and connected to an external dissipative resistor, thereby limiting the fault-phase current and inhibiting further propagation of insulation damage. This reconfiguration allows the drive system to uniformly accommodate both open-circuit (OC) and ITSC scenarios without modification of the underlying control architecture. For OC operation, an equal-amplitude modulation scheme based on carrier-based pulse-width modulation (CPWM) is formulated to preserve the required magnetomotive-force distribution. Under ITSC conditions, a feedforward compensation mechanism is introduced to counteract the disturbance generated by the short-circuit loop. A principal contribution of this work is the derivation of a compensation term that can be estimated online using zero-sequence voltage (ZSV) together with measured phase currents, enabling accurate adaptation across varying ITSC severities. Simulation and experimental results demonstrate that the proposed method effectively suppresses fault-phase current, maintains near-sinusoidal current waveforms in the remaining healthy phases, and stabilizes torque production over a wide range of fault and load conditions. Full article

Objectives: The crime rate in Trinidad and Tobago has increased over the last few years. It is important to understand the impact of rising societal crime on university workplace productivity in order to make meaningful recommendations to mitigate the negative effects of crime. Methods: We conducted semi-structured interviews online via Zoom and face-to-face with both academic and non-academic staff from a university located in Trinidad and Tobago in April 2025. We employed purposive sampling and topics explored included participants’ views on crime, the effect of crime on workplace productivity, the effect of crime on workplace concentration, the effect of crime on participants’ mental health, concerns about safety at the workplace, and desired changes or suggestions to ensure improved safety at the workplace. Data were manually analyzed, and we employed thematic analysis to understand the participants’ data. Results: Analysis included data from 10 participants. Participants represented both academic and non-academic staff, with varied ethnic backgrounds, age range, and were both from Mount Hope and the main campus. Seven of the participants believed that their work productivity had been negatively affected by the crime situation. All the participants agreed that the crime situation was out of control; two of the participants claimed to have been victims of crime. Five of the participants believed they had experienced depressive symptoms, while six participants claimed to have experienced poor concentration on the job. Five participants expressed genuine concerns that something terrible could happen to them within their workplace premises. In order to improve security at the workplace, seven of the participants suggested the employment of more security personnel, while six participants highlighted the need for more surveillance and closed-circuit television (CCTV) cameras. Participants identified four major categories or themes: views on crime and its effects on individuals; effects of crime on workplace productivity; effects of crime on mental well-being; and suggestions and opportunities to improve security at the workplace. Conclusions: From this study, it can be inferred that the majority of the participants were negatively affected by the climate of crime in the country. A comprehensive risk assessment would identify potential risks and vulnerabilities faced by staff, while enhanced surveillance measures and the promotion of the Employee Assistance Program (EAP) can support those impacted. Staff should also be trained to respond effectively to potential threats. Full article

To address the challenges of strong heterogeneity and poor crude oil mobility in tight conglomerate reservoirs of the Mahu Oilfield, this study systematically evaluated the effects of different surfactants on wettability alteration, spontaneous imbibition, and relative permeability through high-temperature/high-pressure spontaneous imbibition experiments, online Nuclear Magnetic Resonance (NMR) monitoring, and relative permeability measurements. Core samples from the Jinlong and Madong areas (porosity: 5.98–17.55%; permeability: 0.005–0.148 mD) were characterized alongside X-Ray Diffraction (XRD) data (clay mineral content: 22–35.7%) to compare the performance of anionic, cationic, nonionic, and biosurfactants. The results indicated that the nonionic surfactant AEO-2 (Fatty Alcohol Polyoxyethylene Ether) (0.2% concentration) at 80 °C exhibited optimal performance, achieving the following results: 1. a reduction in wettability contact angles by 80–90° (transitioning from oil-wet to water-wet); 2. a decrease in interfacial tension to 0.64 mN/m; 3. an imbibition recovery rate of 40.14%—5 to 10 percentage points higher than conventional fracturing fluids. NMR data revealed that nanopores (<50 nm) contributed 75.36% of the total recovery, serving as the primary channels for oil mobilization. Relative permeability tests confirmed that AEO-2 reduced residual oil saturation by 6.21–6.38%, significantly improving fluid flow in highly heterogeneous reservoirs. Mechanistic analysis highlighted that the synergy between wettability reversal and interfacial tension reduction was the key driver of recovery enhancement. This study provides a theoretical foundation and practical solutions for the efficient development of tight conglomerate reservoirs. Full article

To overcome the limitations of lengthy laboratory testing cycles and insufficient on-site responsiveness, this study developed an online rapid monitoring device for volatile organic compounds (VOCs) in soil–water–air systems based on photoionization detection (PID) technology. The device integrates modular sensor units, incorporates an electromagnetic valve-controlled multi-medium adaptive switching system, and employs an internal heating module to enhance the volatilization efficiency of VOCs in water and soil samples. An integrated system was developed featuring “front-end intelligent data acquisition–network collaborative transmission–cloud-based warning and analysis”. The effects of different temperatures on the monitoring performance were investigated to verify the reliability of the designed system. A polynomial fitting model between concentration and voltage was established, showing a strong correlation (R² > 0.97), demonstrating its applicability for VOC detection in environmental samples. Field application results indicate that the equipment has operated stably for nearly three years in a mining area of Shandong Province and an industrial park in Anhui Province, accumulating over 600,000 valid data points. These results demonstrate excellent measurement consistency, long-term operational stability, and reliable data acquisition under complex outdoor conditions. The research provides a distributed, low-power, real-time monitoring solution for VOC pollution control in mining and industrial environments. It also offers significant demonstration value for standardizing on-site emergency monitoring technologies in multi-media environments and promoting the development of green mining practices. Full article