Search Results (46,433)

The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting land use patterns. Such dynamics strain the region’s scarce water resources, negatively affecting the environment, tourism, recreation, agriculture, and economic prospects. Nadap, a hilly settlement within the watershed, experiences frequent flooding and poor water retention, yet it also boasts the highest solar panel capacity per property in Hungary. This research addresses these interconnected challenges by designing a solar-hydrodynamic network comprising four multi-purpose water reservoirs. By leveraging the settlement’s solar capacity and geographical features, the reservoirs provide numerous benefits to local stakeholders and extend their impact far beyond their borders. These include stormwater management with flash flood mitigation, seasonal green energy storage, water security for agriculture and irrigation, wildlife conservation, recreational opportunities, carbon-smart winery developments, and the creation of sustainable blue-green settlements. Reservoir locations and dimensions were determined by analyzing geographical characteristics, stormwater volume, energy demand, solar panel performance, and rainfall data. The hydrodynamic system, modeled in Matlab, was optimized to ensure efficient water usage for irrigation, animal hydration, and other needs while minimizing evaporation losses and carbon emissions. This research presents a design framework for low-carbon and cost-effective solutions that address water management and energy storage, promoting environmental, social, and economic sustainability. The multi-purpose use of retained rainwater solves various existing problems/challenges, strengthens a community’s self-sustainability, and fosters regional growth. This integrated approach can serve as a model for other municipalities and for developing cost-effective inter-settlement and cross-catchment solutions, with a short payback period, facing similar challenges. Full article

While methods like cyclic triaxial testing and p-y model updating theory exist in geotechnical and offshore wind engineering, they have not been systematically applied to solve the specific deformation problems of offshore PV piles. This study investigates a specific offshore photovoltaic (PV) project in Qinhuangdao City, Hebei Province. Initially, field tests of horizontal static load on steel pipe pile foundations were conducted. A finite element model (FEM) of single piles was subsequently developed and validated. Further analysis examined the failure modes, initial stiffness, and ultimate resistance of offshore PV single piles in sandy soil foundations under varying pile diameters and embedment depths. The hyperbolic p-y curve model was modified by incorporating pile diameter size effects and embedment depth considerations. Key findings reveal the following: (1) The predominant failure mechanism of fixed offshore PV monopiles manifests as wedge-shaped failure in shallow soil layers. (2) Conventional API specifications and standard hyperbolic models demonstrate significant deviations in predicting p-y (horizontal soil resistance-pile displacement) curves, whereas the modified hyperbolic model shows good agreement with field measurements and numerical simulations. This research provides critical data support and methodological references for calculating the horizontal bearing capacity of offshore PV steel pipe pile foundations. Full article

This paper innovatively achieves finite-time modified function projection synchronization (MFPS) for different fractional-order chaotic systems. By leveraging the advantages of radial basis function (RBF) neural networks in nonlinear approximation, this paper proposes a novel fractional-order sliding-mode controller. It is designed to address the issues of system model uncertainty and external disturbances. Based on Lyapunov stability theory, it has been demonstrated that the error trajectory can converge to the equilibrium point along the sliding surface within a finite time. Subsequently, the finite-time MFPS of the fractional-order hyperchaotic Chen system and fractional-order chaotic entanglement system are realized under conditions of periodic and noise disturbances, respectively. The effects of the neural network parameters on the performance of the MFPS are then analyzed in depth. Finally, a color image encryption scheme is presented integrating the above MFPS method and exclusive-or operation, and its effectiveness and security are illustrated through numerical simulation and statistical analysis. In the future, we will further explore the application of fractional-order chaotic system MFPS in other fields, providing new theoretical support for interdisciplinary research. Full article

In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution process is complex, and traditional monitoring methods struggle to achieve large-scale, long-term real-time observation. Although numerical simulations can reproduce TN transport processes, they are computationally expensive and have low prediction efficiency. To address this, this study develops a deep learning hybrid model that integrates a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network, referred to as the TCN-LSTM-Hybrid Model, to predict the spatiotemporal distribution of TN concentration fields in Shenzhen Bay. Comparative experiments show that this model outperforms traditional models such as TCN, LSTM, GRU, and MLP in terms of prediction accuracy and spatial generalization, offering higher computational efficiency and breaking through the limitations of “point-based prediction” by achieving “field-based prediction,” thereby providing a new path for pollutant simulation in complex ocean environments, supporting more informed decision making in ocean and coastal management. Full article

Numerous industries have begun using cloud computing. Among other things, this presents a plethora of novel security and dependability concerns. Thoroughly verifying cloud solutions to guarantee their correctness is beneficial, just like with any other computer system that is security- and correctness-sensitive. While there has been much research on distributed system validation and verification, nobody has looked at whether verification methods used for distributed systems can be directly applied to cloud computing. To prove that cloud computing necessitates a unique verification model/architecture, this research compares and contrasts the verification needs of distributed and cloud computing. Distinct commercial, architectural, programming, and security models necessitate distinct approaches to verification in cloud and distributed systems. The importance of cloud-based Service Level Agreements (SLAs) in testing is growing. In order to ensure service integrity, users must upload their selected services and registered services to the cloud. Not only does the user fail to update the data when they should, but external issues, such as the cloud service provider’s data becoming corrupted, lost, or destroyed, also contribute to the data not becoming updated quickly enough. The data saved by the user on the cloud server must be complete and undamaged for integrity checking to be effective. Damaged data can be recovered if incomplete data is discovered after verification. A shared resource pool with network access and elastic extension is realized by optimizing resource allocation, which provides computer resources to consumers as services. The development and implementation of the cloud platform would be greatly facilitated by a verification mechanism that checks the data integrity in the cloud. This mechanism should be independent of storage services and compatible with the current basic service architecture. The user can easily see any discrepancies in the necessary data. While cloud storage does make data outsourcing easier, the security and integrity of the outsourced data are often at risk when using an untrusted cloud server. Consequently, there is a critical need to develop security measures that enable users to verify data integrity while maintaining reasonable computational and transmission overheads. A cryptography-based public data integrity verification technique is proposed in this research. In addition to protecting users’ data from harmful attacks like replay, replacement, and forgery, this approach enables third-party authorities to stand in for users while checking the integrity of outsourced data. This research proposes a Cloud Integrity Verification and Validation Model using the Double Token Key Distribution (CIVV-DTKD) model for enhancing cloud quality of service levels. The proposed model, when compared with the traditional methods, performs better in verification and validation accuracy levels. Full article

Computer viruses continue to threaten the security of digital networks, and their complex propagation dynamics require refined modelling tools. Most existing models rely on integer-order dynamics or assume uniform memory effects, which limit their ability to capture heterogeneous behaviours observed in practice. To address this gap, we propose a discrete incommensurate fractional-order virus model based on Caputo-like delta differences, where each compartment is assigned a distinct fractional order to represent mismatched time scales. The model’s dynamics are analysed in terms of stability, bifurcation, and chaos. Numerical results reveal the emergence of rich chaotic attractors, emphasizing the impact of fractional memory on system evolution. To quantify complexity, we employ Approximate Entropy and Spectral Entropy and relate these indicators to the maximum Lyapunov exponent, confirming the system’s sensitivity and unpredictability. All numerical simulations and visualizations were performed using MATLAB (R2015a). The findings highlight the importance of heterogeneous memory in computer-virus modeling and offer new insights for developing theoretical foundations of robust cybersecurity strategies. Full article

A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials. Full article

The mathematical modeling and experimental validation of a non-conventional vertical-axis wind turbine (VAWT) with a variable-pitch angle are presented, employing the Double-Multiple Streamtube (DMST) method to simulate aerodynamic performance. The aerodynamic coefficients required by the model are obtained through a data-driven approach using a multi-input, two-output multilayer perceptron artificial neural network (MLP–ANN). The model is validated through numerical simulations under two distinct wind input profiles. An experimental evaluation with a prototype replicates the step input. It shows strong agreement with the simulations, particularly in the angular velocity response, which fluctuates between 35 and 55 RPM, with an average value in the range of 40–45 RPM. This hybrid methodology enhances the modeling fidelity of VAWTs and provides a scalable framework for real-time aerodynamic analysis and control. Full article

While agriculture is experiencing localized crises, its indispensable role as the foundation of humanity’s food supply requires its uninterrupted functioning. This conventional system is therefore in a state of competition with alternative models, particularly agroecology, which offers a different paradigm for food production. Given this situation and the need to gather reliable information on regenerative agriculture (RA), this article provides a literature review on its principles, objectives, and edaphic benefits. Additionally, it presents a case study that offers practical knowledge of the techniques and actions implemented by an agroforestry farm in central Spain. With this goal, this article addresses key aspects of RA, such as the use of cover crops, and the integration of livestock, emphasizing its role in improving soil quality and increasing biodiversity, among other benefits. After reviewing numerous scientific articles, and despite widespread interest in RA, there is no commonly accepted definition, so there is a wide range of ways to define RA. Until a generalized definition is accepted, we advocate making proposals and implementing methods with extreme caution and based on the regional or local context in which it is defined. In this sense, based on the implementation of RA at the Kerbest Foundation farm, we propose regenerative agriculture as a set of agroecological actions and processes that fundamentally provide functional soil quality, food quality, ecosystem services, and, especially, healthy and economically profitable livestock farming. Based on all of the above, we can argue that RA is no longer merely a commitment made by farmers but, rather, an environmentally, economically, and socially sustainable solution grounded in scientific knowledge and technical experience. Full article

Background: The tumor microenvironment offers a new perspective in gynecologic oncology. In ovarian cancer, numerous preclinical studies, especially organoid models, have highlighted cellular, immune, and biochemical mechanisms. Beyond these sophisticated findings, more practical aspects require attention, such as the role of vaginal microbiota, which represents an interplay between external agents and internal genitalia, and its potential profiling role in early detection beyond the promise of microbiota-targeted therapies. Objectives: This review aims to assess whether such a correlation is speculative or scientifically grounded. Methods: A focused literature search was conducted on vaginal microbiota and its correlation with ovarian cancer to define the current state of knowledge. Results: Mixed outcomes have been reported, yet there is a rational and scientific basis supporting further investigation. Clinical approaches increasingly consider vaginal microbiota as relevant. However, we have to say that most available evidence is still preliminary and largely preclinical to set realistic expectations for readers. Although additional studies are needed, emerging insights highlight its importance and practical implications. We present a diagnostic–therapeutic management flowchart summarizing current evidence). Discussion: Most links between the vaginal microbiota and ovarian cancer are correlational rather than causal. The idea that microbes ascend from the vagina to the ovaries is proposed but still definitely not demonstrated. Confounding factors like age, hormones, and BRCA status complicate interpretation, and ovarian cancer itself could secondarily alter the microbiota. Mechanistic studies and longitudinal data are still needed to clarify whether dysbiosis contributes to carcinogenesis or is merely a consequence. As gynecologists, we summarize key aspects and emphasize to colleagues the importance of incorporating these findings into daily clinical practice. Vaginal dysbiosis should be considered not only a local imbalance but also a potential strategy for primary cancer prevention. Conclusions: Future research on the tumor microenvironment and vaginal microbiota will expand scientific knowledge and guide innovative preventive and therapeutic strategies. Full article

An important goal in cardiology and other fields is to identify and control dynamic spiral wave patterns in reaction–diffusion partial differential equations. This research focuses on the Barkley model. The spiral wave motion is controlled and suppressed within the Euclidean group rather than through Euclidean symmetry by applying a controller equation. The eigenfunctions associated with the left eigenspace of the adjoint linear equation can be used to characterize the drift or movement of the spiral wave tip trajectory when the system is perturbed. These eigenfunctions provide details regarding how the spiral wave reacts to disruptions. Perturbations to the Barkley system are examined by applying control functions and calculating the principle eigenvalue numerically. The left eigenfunctions of the Barkley equation are determined by solving the left problem associated with the $2\mathrm{D}$ Barkley equation and a $1\mathrm{D}$ dynamical controller. In addition, the control function can be used to suppress the periodic and meandering regimes of the system. In this work, the focus is on the periodic regime. Full article

Costa Rica has experienced 45 tsunamis at both its Pacific and Caribbean coasts, with none to moderated impact. However, the coastal population has increased exponentially in the past few decades, which might lead to higher impact in future tsunamis. In 2018 and 2019, IOC/UNESCO organized Experts Meetings of Tsunami Sources, Hazards, Risks and Uncertainties associated with the Tonga–Kermadec and Colombia–Ecuador subduction zones, where experts defined maximum credible scenarios. Here we modeled the propagation of those tsunami scenarios to Costa Rica and their inundation for selected sites. We found that the Tonga–Kermadec scenarios provoked more inundation than previous modeled sources from that region. However, the large travel time for those scenarios, about 14 h, would allow for a timely evacuation. In the Colombia–Ecuador scenarios, they provoked less inundation than previously modeled sources from that region, a good outcome as their arrival time is between 75 and 150 min. These new results required the update of tsunami evacuation maps and/or plans for many communities but provided more favorable conditions for tsunami preparedness. Yet, the short arrival times of the Colombia–Ecuador scenarios still require a prompt response from the population and authorities. For this, additional to updated tsunami evacuation maps and plans, it is recommended to have tsunami exercises on a regular basis. Full article

Random walks are considered in a one-dimensional monotonously decreasing energy landscape. To reach the minimum within a region ${\Omega }_{ϵ}$, a number of downhill steps have to be performed. A stochastic model is proposed which captures this random downhill walk and to make a prediction for the average number of steps, which are needed to hit the target. Explicit expressions in terms of a recurrence relation are derived for the density distribution of a downhill random walk as well as probability distribution functions to hit a target region ${\Omega }_{ϵ}$ within a given number of steps. For the case of stochastic optimisation, the number of rejected steps between two successive downhill steps is also derived, providing a measure for the average total number of trial steps. Analytical results are obtained for generalised random processes with underlying polynomial distribution functions. Finally the more general case of non-monotonously decreasing energy landscapes is considered for which results of the monotonous case are transferred by applying the technique of decreasing rearrangement. It is shown that the global stochastic optimisation can be fully described analytically, which is verified by numerical experiments for a number of different distribution and objective functions. Finally we discuss the transition to higher dimensional objective functions and discuss the change in computational complexity for the stochastic process. Full article

Aiming at unclear imbibition replacement mechanisms and flowback/production strategies in unconventional reservoirs of the Erlian Block, this study proposes a systematic approach integrating “imbibition-flowback-productivity synergy” to optimize post-fracturing shut-in and production regimes. By developing numerical models incorporating geological and engineering factors, we analyzed fluid dynamics during both the shut-in and production phases. Concurrently, crude oil displacement-fracturing fluid imbibition replacement experiments were conducted to guide parameter optimization. The results indicate that optimized shut-in time and production rates substantially increase recovery efficiency while mitigating reservoir damage and proppant flowback. The well shut-in time of the Erlian Block can achieve the optimal shut-in replacement effect in about 20–25 days. The optimized flowback rate of the unconventional reservoir in the Erlian Block is 25–30 m³/d. The findings offer theoretical insights and practical recommendations for the efficient development of unconventional resources. Full article

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition dynamics and support proactive TSF management. This study applies deep learning (DL) to predict surface elevation changes in tailings storage facilities (TSFs) from high-resolution digital elevation models (DEMs) generated from UAV photogrammetry. Three DL architectures, including multilayer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet), were evaluated across spatial patch sizes of 64 × 64, 128 × 128, and 256 × 256 pixels. The results show that incorporating broader spatial contexts improves predictive accuracy, with ResNet achieving an $R2$ of 0.886 at the 256 × 256 scale, explaining nearly 89% of the variance in observed deposition patterns. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that spatial coordinates and curvature exert the strongest influence, linking deposition patterns to discharge distance and microtopographic variability. By prioritizing predictive performance while providing mechanistic insight, this framework offers a practical and quantitative tool for reliable TSF monitoring and management. Full article