Search Results (25)

The penetration of distributed energy resources (DERs), such as photovoltaic (PV) generation and electric vehicles (EVs), in distribution systems has been increasing rapidly. At the same time, load demand is rising due to the proliferation of data centers and the growing use of artificial intelligence. These trends have introduced new operational challenges: reverse power flow from PV generation during the day and low-voltage conditions during periods of peak load or when PV output is unavailable. To address these issues, this paper proposes a two-stage adaptive rolling horizon (ARH)-based model predictive control (MPC) framework for coordinated voltage and power factor (PF) control in distribution systems. The proposed framework, designed from the perspective of a distributed energy resource management system (DERMS), integrates EV charging and discharging scheduling with PV- and EV-connected inverter control. In the first stage, the ARH method optimizes EV charging and discharging schedules to regulate voltage levels. In the second stage, optimal power flow analysis is employed to adjust the voltage of distribution lines and the power factor at the substation through reactive power compensation, using PV- and EV-connected inverters. The proposed algorithm aims to maintain stable operation of the distribution system while minimizing PV curtailment by computing optimal control commands based on predicted PV generation, load forecasts, and EV data provided by vehicle owners. Simulation results on the IEEE 37-bus test feeder demonstrate that, under predicted PV and load profiles, the system voltage can be maintained within the normal range of 0.95–1.05 per unit (p.u.), the power factor is improved, and the state-of-charge (SOC) requirements of EV owners are satisfied. These results confirm that the proposed framework enables stable and cooperative operation of the distribution system without the need for additional infrastructure expansion. Full article

This paper tackles a core challenge in reverse engineering: high-fidelity reconstruction of continuous B-spline surfaces from discrete point clouds, where optimal knot placement remains pivotal yet not fully resolved. We propose a new fitting method based on the Hungry Predation Algorithm (HPA) to improve efficiency, accuracy, and robustness. This method introduces a hybrid knot-guidance strategy that combines geometry-aware preselection with a complexity-driven probabilistic distribution to address knot placement. On the optimization side, HPA simulates starvation-driven predator–prey dynamics to enhance global search capability, maintain population diversity, and accelerate convergence. We also develop an adaptive parameter adjustment framework that automatically tunes key settings according to surface complexity and accuracy thresholds. Comparative experiments against classical approaches, six state-of-the-art optimizers, and the commercial CAD system CATIA demonstrate HPA’s superiority in control-point reduction, fitting accuracy, and computational efficiency. This method shows high applicability to engineering-scale tasks (e.g., ship hull design), where the point-to-surface RMSE (e.g., <10⁻³ L_max) achieved satisfies stringent requirements for downstream hydrodynamic performance analysis and manufacturing. Full article

Gantry cranes play a key role in modern industrial logistics. However, the traditional dynamic model based on the assumption of cable rigidity faces difficulty in accurately describing the complex swing characteristics of flexible cables, resulting in low load positioning accuracy and limited operation efficiency. To address this problem, this paper proposes a cable modeling method that considers the flexible deformation and nonlinear dynamic characteristics of the cable. Based on the theory of continuum mechanics, a flexible cable dynamic model that can accurately describe the flexible deformation and distributed mass characteristics of the cable is established. In order to solve the transportation time optimization and full-state constraint problems, a velocity trajectory optimization algorithm based on a discretization framework is proposed. Through inverse kinematics analysis and numerical integration technology, a reverse angle enumeration reasoning (RAER) method is proposed to suppress the swing of the load. Under the same constraints of distance, velocity, acceleration, cable swing angle, and residual swing angle, RAER requires a longer transportation time but achieves smaller peak swing and residual swing, making it the only algorithm that satisfies full-state constraints. Under the energy criterion, the proposed algorithm also requires the least amount of energy. Comprehensive comparisons through simulations and experiments show that the predicted swing angles of the flexible cable are highly consistent with the experimental results. Full article

Background: The triazole ring is an attractive structural unit in medicinal chemistry, and chemical compounds containing this type of system in their structure exhibit a wide spectrum of biological activity. They are used in the development of new pharmaceuticals. One of the basic parameters considered in the initial phase of designing potential drugs is lipophilicity, which affects the bioavailability and pharmacokinetics of drugs. Methods: The study aimed to assess the lipophilicity of fifteen new triazole derivatives of aniline using reversed phase thin layer chromatography (RP-TLC) and free web servers. Based on in silico methods, the drug similarity and pharmacokinetic profile (ADMET) of synthesized molecules were assessed. Results: A relationship was observed between the structure of the title compound, including the position of substitution in the aniline ring, and the experimental values of lipophilicity parameters (logP_TLC). Most of the algorithms used to determine theoretical logP values showed less sensitivity to structural differences of the tested molecules. All obtained derivatives satisfy the drug similarity rules formulated by Lipinski, Ghose and Veber. Moreover, in silico analysis of the ADME profile showed favorable values of parameters related to absorption. Full article

Background: Glaucoma is a neurodegenerative ocular disease that is accompanied by cerebral damage extending beyond the visual system. Recent studies based on diffusion tensor tractography have suggested an association between glaucoma and brain structural connectivity but have not clarified causality. Methods: To explore the causal associations between glaucoma and brain structural connectivity, a bidirectional Mendelian randomization (MR) study was conducted involving glaucoma and 206 diffusion tensor tractography traits. Highly associated genetic variations were applied as instrumental variables and statistical data were sourced from the database of FinnGen and UK Biobank. The inverse-variance weighted method was applied to assess causal relationships. Additional sensitivity analyses were also performed. Results: Glaucoma was potentially causally associated with alterations in three brain structural connectivities (from the SN to the thalamus, from the DAN to the putamen, and within the LN network) in the forward MR analysis, whereas the inverse MR results identified thirteen brain structural connectivity traits with a potential causal relationship to the risk of glaucoma. Both forward and reverse MR analyses satisfied the sensitivity test with no significant horizontal pleiotropy or heterogeneity. Conclusions: This study offered suggestive evidence for the potential causality between the risk of glaucoma and brain structural connectivity. Our findings also provided novel insights into the neurodegenerative mechanism of glaucoma. Full article

Restoration of degraded land is a significant concern in the 21st century in order to combat the impacts of climate change. For this reason, the provisional government of Khyber Pakhtunkhwa (KPK), Pakistan, initialized a Billion Tree Tsunami Project (BTTP) in 2013 and finished it in 2017. Although a few researchers have investigated the land use transitions under BTTP in the short term by merging all the vegetation types into one, analysis of the long-term benefits of the project and future persistence were missing. Furthermore, the previous studies have not discussed whether the prime objective of the BTTP was achieved. Considering the existing gaps, this research mainly involves analyzing (i) fluctuations in the green fraction by employing a land change modeler (LCM), along with the spatial location of gain-loss and exchange analysis using a high-resolution dataset (GLC30); (ii) forest cover changes under the influence of the BTTP; (iii) impacts of green fraction changes towards land surface temperature (LST) by utilizing the less-explored technique of curve fit linear regression modeling (CFLR); and finally, (iv) assessing the persistence of the NDVI and LST trends by employing the Hurst exponent. Research findings indicate that as an output of BTTP, despite the government’s claim of increasing the forest cover by 2%, a significant gain of grassland (3904.87 km²) was observed at the cost of bare land. In comparison, the overall increase in forest cover was only 0.39%, which does not satisfy the main objective of this project. On the other hand, the CFLRM-based actual contributions of land cover change (LCC) transition to LST indicate a significant decline in LST in the areas with gains in green fraction for both grassland and forest. At the same time, an increase was observed with reverse transitions. Although the results appear positive for climatic impacts in the short term, the HURST model-based persistence analysis revealed that the spatial locations of increasing vegetation and decreasing LST trends fall under the weakly persistent category, therefore these trends may not continue in the near future. Despite some positive impact on LST attributed to the green fraction increase, this project cannot be regarded as a complete success due to its failure to achieve its prime objective. Full article

In recent years, the explosion of big data has presented unparalleled opportunities for the advancement of machine learning (ML). However, the vast size and sensitive nature of these datasets present significant challenges in terms of privacy and security. Federated Learning has emerged as a promising solution that enables a group of participants to train ML models without compromising the confidentiality of their raw data. Despite its potential, traditional federated learning faces challenges such as the absence of participant incentives and audit mechanisms. Furthermore, these challenges become more significant when dealing with the scale and diversity of big data, making efficient and reliable federated learning a complex task. These limitations may compromise model quality due to potential malicious nodes. To address the above issues, this paper proposes a BlockChain-based Decentralized Federated Learning (BCD-FL) model. In BCD-FL, we design a smart contract approach based on the reverse auction-based incentive mechanism and a reputation mechanism to promote the participation of reliable and high-quality data owners. Theoretical analysis shows that the BCD-FL model satisfies several desirable properties, such as individual rationality, computational efficiency, budget balance, and truthfulness. In addition, experimental results also show that the proposed model enables more efficient federated learning and provides some level of protection against malicious nodes. Therefore, the BCD-FL model presents a potential solution to the challenges in federated learning and opens up new possibilities for achieving efficient large-scale machine learning. Full article

Understanding the spatiotemporal distribution of extreme rainfall and meteorological drought on a watershed scale could be beneficial for local management of any water resources system that supports dam operation and river conservation. This study considered the watershed of Angat as a case, given its economic importance in the Philippines. A series of homogeneity tests were initially conducted on each rainfall dataset from monitoring stations in and near the watershed, followed by trend analysis to determine the rate and direction of change in the annual and seasonal rainfall extreme indices in terms of intensity, duration, and frequency. Three indices, using the rainfall deviation method (%DEV), percent of normal rainfall index (PNRI), and Standardized Precipitation Index (SPI), were also used to identify meteorological drought events. Generally, rainfall in the watershed has an increasing annual PCPTOT (4–32 mm/year), with increasing frequency and intensity in heavy rainfall and wet days. A significant increasing trend (α = 5%) in the seasonal PCPTOT (7–65 mm/year) and R10mm (1.7–10.0 days/decade) was particularly observed in all stations during the Amihan Monsoon Season (Dec–Feb). The observed increasing rainfall intensity and frequency, if it continues in the future, could have an implication both for the water resources operation to satisfy the multiple objectives of Angat Reservoir and for the flood operation that prevents damage in the downstream areas. The effect of each ENSO (El Niño- Southern Oscillation) phase on the rainfall is unique in magnitude, intensity, and duration. The seasonal reversal of the ENSO in the extreme rainfall and meteorological drought signals in Angat Watershed was also evident. The identified meteorological drought events in the watershed based on SPI-12 persisted up to 12–33 months, could reduce more than 60% (PNRI < 40%) of the normal rainfall. Insights from the study have implications for the hydrology of the watershed that should be considered for the water resources management of the Angat Reservoir. Full article

Based on flexible polyurethane foam (FPUF), which is reversible after compression, and expanded polystyrene foam (EPS), which has a high cushioning energy absorption capacity, the parallel and series combinations of FPUF and EPS are provided. According to experimental data of FPUF and EPS uniaxial compression large deformation, the mechanical properties and cushioning effectiveness of the FPUF-EPS combination materials with different structural scale parameters were investigated by theory analysis and finite element simulation. The mechanical response and the cushioning effectiveness influencing factors of FPUF-EPS parallel (FE-P) and FPUF-EPS series (FE-S) combination materials under single compressive load, single-impact load, and multiple compressive loads were obtained. The differences in mechanical properties and cushioning effectiveness of FE-P, FE-S, FPUF, and EPS are analyzed. The influence law of structural scale parameters and load strength on the mechanical properties and cushioning effectiveness of FE-P and FE-S is provided. It indicates that the cushion properties of combination materials should be adjusted to satisfy product protection requirements. It is beneficial for the design optimization of cushioning and packaging protection. Full article

The cyclic loading performance of bamboo double-jointed components of different column base connection types was investigated through reversed cyclic loading tests and finite element analysis. Test results indicated that the types of column base connections played an important role in the failure modes of the engineered bamboo double-jointed columns: for an encased steel plate column base connection, the main failure mode was tensile fracture failure of the bamboo scrimber section at the bottom of the cladding plate; for a slotted-in steel plate column base connection, the main failure mode was splitting failure of the bamboo scrimber cross-grain at the bolt connection line at the bottom of the sheathing plate. The initial stiffness of the encased steel plate column base connection specimen was 41.8% higher than that of the slotted-in steel plate column base connection specimen, with the two specimens having similar average bearing capacities. The ductility ratio of the two specimens was below 3.0 due to the brittle failure nature of the engineered bamboo connections. The finite element model accurately predicted the ultimate bearing capacity of the double-jointed bamboo column members. The modeling error was within 12%, which was sufficient to satisfy the accuracy requirements for engineering purposes. Full article

Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs. Full article

National Undergraduate Engineering Training Integration Ability Competition is essential in Chinese engineering colleges. Among these items, the carbon-free car competition is popular among college students because of its flexible track, challenging design tasks, and fair judging criteria. In this study, to design a reasonable carbon-free car structure according to the track given by the competition, a reverse analysis method was proposed for the double 8-shaped carbon-free car trajectory based on the space cam mechanism. First, the carbon-free car’s structure was designed, and its stress state was analyzed, with the results indicating that the driving force should be reduced as much as possible under the premise of satisfying the starting requirements to increase the travel distance of the carbon-free car with no slipping. Then, the function between the car track and the front wheel swing angle was established, and the displacement law of the push rod of the cam mechanism was calculated through the swing angle of the front wheel. Finally, the cam profile was got based on the operating law of the push rod. Research has shown that compared with the traditional forward design, this method was more accurate with strong feasibility and operability, which provided good technical support for designing the subsequent carbon-free car competition. Full article

Analysis of the optical properties of a gradient (Ti,Cu)Ox thin film is presented in this paper. The thin film was prepared using reactive co-sputtering of Ti and Cu targets. The desired elemental concentration profiles of Cu and Ti versus the thin film thickness were obtained by changing the power delivered to the magnetron equipped with Cu, while the powering of the magnetron equipped with the Ti target was maintained at a constant level during the film deposition. Optical properties were analysed using the reverse engineering method, based on simultaneously measured optical transmittance and reflectance. Detailed microstructure analysis performed using transmission electron microscopy investigations revealed that the thin film consisted of at least four areas with different structural properties. Finding a satisfying fit of theoretical to experimental data required taking into account the heterogeneity in the material composition and microstructure in relation to the depth in the prepared gradient thin film. On the basis of the built equivalent layer stack model, the composition profile and porosity at the cross-section of the prepared gradient film were evaluated, which agreed well with the obtained elemental and microscopy studies. Full article

The vanadium redox flow battery (VRFB) has been regarded as one of the best potential stationary electrochemical storage systems for its design flexibility, long cycle life, high efficiency, and high safety; it is usually utilized to resolve the fluctuations and intermittent nature of renewable energy sources. As one of the critical components of VRFBs to provide the reaction sites for redox couples, an ideal electrode should possess excellent chemical and electrochemical stability, conductivity, and a low price, as well as good reaction kinetics, hydrophilicity, and electrochemical activity, in order to satisfy the requirements for high-performance VRFBs. However, the most commonly used electrode material, a carbonous felt electrode, such as graphite felt (GF) or carbon felt (CF), suffers from relatively inferior kinetic reversibility and poor catalytic activity toward the V²⁺/V³⁺ and VO²⁺/VO₂⁺ redox couples, limiting the operation of VRFBs at low current density. Therefore, modified carbon substrates have been extensively investigated to improve vanadium redox reactions. Here, we give a brief review of recent progress in the modification methods of carbonous felt electrodes, such as surface treatment, the deposition of low-cost metal oxides, the doping of nonmetal elements, and complexation with nanostructured carbon materials. Thus, we give new insights into the relationships between the structure and the electrochemical performance, and provide some perspectives for the future development of VRFBs. Through a comprehensive analysis, it is found that the increase in the surface area and active sites are two decisive factors that enhance the performance of carbonous felt electrodes. Based on the varied structural and electrochemical characterizations, the relationship between the surface nature and electrochemical activity, as well as the mechanism of the modified carbon felt electrodes, is also discussed. Full article

Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics. Full article