Search Results (211)

Understanding the intricate dynamics of precipitation patterns is essential for effective water resource management and climate adaptation in Missouri. Existing analyses of Missouri’s climate variability lack the spatial granularity needed to capture nuanced variations across climate divisions. The Missouri historical agricultural weather database, an open-source tool that contains key weather measurements gathered at Mesonet stations across the state, is beginning to fill in the data sparsity gaps. The aim of this study is to identify core patterns associated with ENSO in the global wavelet output. Using a continuous wavelet transform analysis on data from 32 stations (2000–2024), we identified significant precipitation cycles. Where previous studies used just four Automated Surface Observing Systems (ASOSs) located at airports across Missouri to characterize climate variability, this study uses an additional 28 from the Missouri Mesonet. The use of a global wavelet power spectrum analysis reveals that precipitation patterns, with the exception of southeast Missouri, have a distinct annual cycle. Furthermore, separating the stations based on the significance of their ENSO (El Niño–Southern Oscillation) signal results in the identification of three precipitation zones: an annual, ENSO, and residual zone. This spatial data analysis reveals that the Missouri climate division boundaries broadly capture the three precipitation zones found in this study. Additionally, the results suggest a corridor in central Missouri where precipitation is particularly sensitive to an ENSO signal. These findings provide critical insights for improved water resource management and climate adaptation strategies. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

This paper presents preliminary findings on the effectiveness of a citizen science initiative that engages local communities in rural Kalimantan in collecting wildlife observations within their village forests. By leveraging the power of community participation, the initiative aims to build on local knowledge, promote sustainable management practices, and collect valuable data on species distribution. Through a combination of focus group discussions, training workshops, field surveys, and mobile app-based data collection from 2023 to 2025, the initiative successfully mobilized community members, particularly those with limited technological experience, to actively participate in biodiversity monitoring. We recently introduced a small ‘payment for wildlife observations’ system that significantly boosted observations. The initial results highlight the potential for citizen science to generate valuable species trend data and foster a sense of pride, ownership, and stewardship among community members. While the current manuscript does not provide statistical analyses of the wildlife data, we describe how we plan to overcome data biases that are inherent to opportunistic, unstructured survey efforts. The project continues, but the lessons learned thus far can inform future citizen science initiatives and contribute to the development of sustainable, long-term, low-cost and effective community-based conservation strategies in the region. Full article

Urbanization and mixed-land-use development significantly impact water quality dynamics in watersheds, necessitating continuous monitoring and advanced analytical techniques for sustainable water management. This study employs continuous wavelet analysis to investigate the temporal variability and correlations of real-time water quality parameters in the Credit River watershed, Ontario, Canada. The Integrated Watershed Monitoring Program (IWMP), initiated by the Credit Valley Conservation (CVC) Authority, has facilitated long-term real-time water quality monitoring since 2010. Fundamental and exploratory statistical analyses were conducted to identify patterns, trends, and anomalies in key water quality parameters, including pH, specific conductivity, turbidity, dissolved oxygen (DO), chloride, water temperature ($T_{H_{2}O}^{°}$), air temperature ($T_{air}^{°}$), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, ($T_{H_{2}O}^{°}$), and ($T_{air}^{°}$) showing high power at a 95% confidence level against red noise, particularly from late spring to early fall, rather than throughout the entire year. These findings underscore the seasonal influence on water quality and highlight the need for adaptive watershed management strategies. The study demonstrates the potential of wavelet analysis in detecting temporal patterns and informing decision-making for sustainable water resource management in rapidly urbanizing mixed-land-use watersheds. Full article

As electric scooters (e-scooters) continue to populate city streets and gain popularity as a key mode of micro-mobility, issues such as their energy consumption and demand from the power grid, as well as optimizing their electrical systems, become increasingly important. Improving performance requires a deep understanding of their electrical behavior and the design of smart control strategies. This paper presents a detailed analysis of the entire electrical system of commercial electric scooters, with a particular focus on the performance of key components such as the permanent magnet brushless direct current motor and the lithium-ion battery system. The study involves modeling and simulation of motor control, battery management, and DC-link voltage stabilization using MATLAB/Simulink. The simulations are complemented by laboratory measurements of the motor performance in an SXT Scooters MAX unit under various operating conditions. Additionally, a complete battery charging cycle is analyzed to evaluate charging characteristics and usable energy storage capacity. This paper presents a first step for researchers interested in studying the electrical systems of e-scooters. Additionally, it can serve as educational material for electrical engineers in the field of e-scooters. Full article

Thermal discharge from power plants causes significant concerns in aquatic environments. The purpose of this study is to evaluate how river mouth morphodynamics, particularly spit development and removal, influence the dispersion of thermal plumes. To achieve this, a case study was carried out at a coastal power plant in southwest Türkiye, where thermal effluent is conveyed to the sea through a low-flow river. Field measurements combined with numerical modeling were used to analyze plume dynamics under varying spit configurations. Results revealed that the evolution of a spit on one side of the river mouth influences plume dispersion and redirects the mixing zone toward the opposite shoreline. Numerical simulations demonstrated that spit development reduces dispersion efficiency (by over 75%), while the physical removal of the spit significantly improves it, reducing temperature excess from 4–5 °C to 0–1 °C within the mixing zone, meeting safe environmental standards. The findings highlight the pivotal role of morphological changes in governing thermal discharge behavior and emphasize the importance of continuous monitoring and management strategies, such as periodic dredging, to ensure compliance with environmental regulations. Full article

During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions. Full article

Power transformers are critical components in electrical power systems, where failures can cause significant outages and economic losses. Traditional maintenance strategies, typically based on offline inspections, are increasingly insufficient to meet the reliability requirements of modern digital substations. This work presents an integrated multi-sensor monitoring framework that combines online frequency response analysis (OnFRA^® 4.0), capacitive tap-based monitoring (FRACTIVE^® 4.0), dissolved gas analysis, and temperature measurements. All data streams are synchronized and managed within a SCADA system that supports real-time visualization and historical traceability. To enable automated fault diagnosis, a Random Forest classifier was trained using simulated datasets derived from laboratory experiments that emulate typical transformer and bushing degradation scenarios. Principal Component Analysis was employed for dimensionality reduction, improving model interpretability and computational efficiency. The proposed model achieved perfect classification metrics on the simulated data, demonstrating the feasibility of combining high-fidelity monitoring hardware with machine learning techniques for anomaly detection. Although no in-service failures have been recorded to date, the monitoring infrastructure is already tested and validated through laboratory conditions, enabling continuous data acquisition. Full article

Significant time, money, and energy are invested in Continuing Professional Development (CPD) across Further Education (FE) colleges in England, with the aim of enhancing teaching strategies, sharing “best” practices, and improving educational quality. Despite these intentions, practitioner perceptions of CPD’s value remain mixed, highlighting concerns about the effectiveness of current approaches. CPD managers often face competing financial and operational demands, alongside pressure to comply with external requirements, resulting in CPD that is frequently instrumental, mandatory, and delivered through one-off events. These practices reflect a data-driven, prescriptive management culture that prioritizes measurable outcomes over meaningful educational experiences. Consequently, teachers are compelled to demonstrate compliance within a system where accountability is unevenly distributed. This medium-scale, multi-method practitioner research study investigates how such compliance-driven CPD practices divert attention and resources from genuine educational improvement. This study explores an alternative model of CPD rooted in teacher agency and enriched through engagement with the arts and aesthetic experiences. Drawing on surveys, semi-structured interviews, critical incidents, and narrative accounts, the findings suggest that this approach fosters more democratic, creative, and impactful professional development. In promoting teacher agency and challenging dominant power structures, this study offers a vision of CPD that supports meaningful educational transformation, with practical examples and recommendations for broader implementation. Full article

The power generation sector is a major contributor to global greenhouse gas (GHG) emissions and a significant consumer of freshwater, due to the extensive water use in cooling processes of thermoelectric power plants. While net-zero strategies increasingly focus on eliminating emissions to mitigate climate change, the critical role of water as a key sustainability resource remains underexplored and often underrepresented in mitigation scenarios, strategies, and policy frameworks. This study examines the impact of power sector decarbonization on global and regional electricity-related water demand under two climate ambition scenarios: continuation of current climate policies (CP) and a net-zero emission (NZ) scenario where countries implement their net-zero pledges by 2050 or later. Using the PROMETHEUS energy system model, we quantify how different climate ambitions could affect global and regional water demand, considering different levels of cooling technology evolution. Results show that water demand is not only driven by how much energy is produced but by the technology mix used to generate electricity. The findings highlight the significant co-benefits of power sector decarbonization for reducing water needs and ensuring freshwater resource sustainability, underscoring the importance of integrating water management into climate policy frameworks. This integrated perspective is critical for policymakers, energy system planners, and water resource managers aiming to balance ambitious climate goals with sustainable water use amid growing climate and resource challenges. Full article

Energy management for hybrid electric commercial vehicles, involving continuous power output and discrete gear shifting, constitutes a typical mixed-integer programming (MIP) problem, presenting significant challenges for real-time performance and computational efficiency. To address this, this paper proposes a physics-informed neural network-optimized model predictive control (PINN-MPC) strategy. On one hand, this strategy simultaneously optimizes continuous and discrete states within the MPC framework to achieve the integrated objectives of minimizing fuel consumption, tracking speed, and managing battery state-of-charge (SOC). On the other hand, to overcome the prohibitively long solving time of the MIP-MPC, a physics-informed neural network (PINN) optimizer is designed. This optimizer employs the soft-argmax function to handle discrete gear variables and embeds system dynamics constraints using an augmented Lagrangian approach. Validated via hardware-in-the-loop (HIL) testing under two distinct real-world driving cycles, the results demonstrate that, compared to the open-source solver BONMIN, PINN-MPC significantly reduces computation time—dramatically decreasing the average solving time from approximately 10 s to about 5 ms—without sacrificing the combined vehicle dynamic and economic performance. Full article

As agricultural land continues to expand, the conversion of forests to farmland has intensified, significantly altering the structure and function of agroecosystems. However, the dynamic ecological responses and their interactions with economic outcomes remain insufficiently modeled. This study proposes an integrated framework that combines a dynamic food web model with the Eco-Economic Benefit and Sustainability (EEBS) model, utilizing empirical data from Brazil and Ghana. A system of ordinary differential equations solved using the fourth-order Runge–Kutta method was employed to simulate species interactions and energy flows under various land management strategies. Reintroducing key species (e.g., the seven-spot ladybird and ragweed) improved ecosystem stability to over 90%, with soil fertility recovery reaching 95%. In herbicide-free scenarios, introducing natural predators such as bats and birds mitigated disturbances and promoted ecological balance. Using XGBoost (Extreme Gradient Boosting) to analyze 200-day community dynamics, pest control, resource allocation, and chemical disturbance were identified as dominant drivers. EEBS-based multi-scenario optimization revealed that organic farming achieves the highest alignment between ecological restoration and economic benefits. The model demonstrated strong predictive power ($R^2$ = 0.9619, RMSE = 0.0330), offering a quantitative basis for green agricultural transitions and sustainable agroecosystem management. Full article

As the penetration of intermittent renewable energy sources continues to increase, ensuring reliable power system and frequency stability is of importance. Battery energy storage systems (BESSs) have emerged as an important solution to mitigate these challenges by providing essential grid support services. In this context, a state-of-charge (SOC)-frequency control strategy for grid-forming BESSs is proposed to enhance their role in stabilizing grid frequency and improving overall system performance. In the system, the DC-link capacitor is regulated to maintain the angular frequency through a matching control scheme, emulating the characteristics of the rotor dynamics of a synchronous generator (SG). Thereby, the active power control is implemented in the control of the DC/DC converter to further regulate the grid frequency. More specifically, the relationship between the active power and the frequency is established through the SOC of the battery. In addition, owing to the inevitable presence of differential operators in the control loop, a high-gain observer (HGO) is employed, and the corresponding parameter design of the proposed method is elaborated. The proposed strategy simultaneously achieves frequency regulation and implicit energy management by autonomously balancing power output with available battery capacity, demonstrating a novel dual benefit for sustainable grid operation. To verify the effectiveness of the proposed control strategy, a 0.5-Hz frequency change and a 10% power change are carried out through simulations and also on a hardware-in-the-loop (HIL) platform. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

Introduction: Haglund’s deformity, characterized by bony enlargement at the back of the heel, often coincides with Achilles tendon pathology due to impingement on the retrocalcaneal bursa and tendon insertion. Surgical management of Haglund’s deformity with a preexisting Achilles tendon rupture is complex, and understanding the outcomes of this subset of patients is essential for optimizing treatment strategies. Methods: This retrospective study reviewed patients undergoing open surgical management for Haglund’s syndrome between January 2015 and December 2023. Patients with chronic degenerative changes secondary to Haglund’s deformity and a preoperative Achilles tendon rupture were compared to those without. Data on demographics, surgical techniques, weightbearing protocols, and complications were collected. Univariate analysis was performed using χ² or Fisher’s exact test for categorical variables, and the T-test or Wilcoxon rank-sum test for continuous and ordinal variables, with normality assessed via the Shapiro–Wilk test. Results: Four hundred and three patients were included, with 13 having a preoperative Achilles tendon rupture. There was a higher incidence of preoperative ruptures among males. Surgical repair techniques and postoperative weightbearing protocols varied, though were not randomized. Complications included persistent pain, wound breakdown, infection, plantar flexion weakness, and revision surgery. While patients with Haglund’s deformity and a preoperative Achilles tendon rupture demonstrated a trend toward higher complication rates, including postoperative rupture and wound breakdown, these differences were not statistically significant in our analysis. Conclusions: A cautious approach is warranted in managing these patients, with careful consideration of surgical planning and postoperative rehabilitation. While our findings provide valuable insights into managing patients with Haglund’s deformity and preoperative Achilles tendon rupture, the retrospective design, limited sample size of the rupture group, and short duration of follow-up restrict generalizability and the strength of the conclusions by limiting the power of the analysis and underestimating the incidence of long-term complications. Therefore, the results of this study should be interpreted with caution. Further studies with larger patient cohorts, validated functional outcome measures, and comparable follow-up durations between groups are needed to confirm these results and optimize treatment approaches. Full article