Search Results (2,351)

The calculation of Total Transfer Capability (TTC) for transmission corridors serves as the foundation for security region determination and electricity market transactions. However, existing TTC methods often neglect corridor correlations, leading to overly optimistic results. TTC computation involves complex stability verification and requires enumerating numerous renewable energy operation scenarios to establish security boundaries, exhibiting high non-convexity and nonlinearity that challenge gradient-based iterative algorithms in approaching global optima. Furthermore, practical power systems feature coupled corridor effects, transforming multi-corridor TTC into a complex Pareto frontier search problem. This paper proposes a MOEA/D-FRRMAB (Fitness–Rate–Reward Multi-Armed Bandit)-based method featuring: (1) a TTC model incorporating transient angle stability constraints, steady-state operational limits, and inter-corridor power interactions and (2) a decomposition strategy converting the multi-objective problem into subproblems, enhanced by MOEA/D-FRRMAB for improved Pareto front convergence and diversity. IEEE 39-bus tests demonstrate superior solution accuracy and diversity, providing dispatch centers with more reliable multi-corridor TTC strategies. Full article

Background and Objectives: Dyslipidemia and hyperuricemia frequently co-exist in uncontrolled type 2 diabetes mellitus (T2DM), amplifying renal and cardiovascular risk. This study aimed to develop and evaluate an optimized Renal–Metabolic Risk Score (RMRS) integrating renal and lipid parameters to identify patients with both conditions. Materials and Methods: We conducted a retrospective observational study including 304 patients with uncontrolled T2DM hospitalized at the Emergency County Hospital Oradea, Romania (2022–2023). Hyperuricemia was defined as uric acid > 6 mg/dL in females and >7 mg/dL in males; dyslipidemia was diagnosed according to standard lipid thresholds. RMRS was calculated from standardized values of urea, TG/HDL ratio, and eGFR, with variable weights derived from logistic regression coefficients. The score was normalized to a 0–100 scale. Receiver operating characteristic (ROC) analysis assessed discriminative performance; quartile analysis explored stratification ability. Results: The prevalence of dyslipidemia and hyperuricemia co-occurrence was 81.6%. RMRS was significantly higher in the co-occurrence group compared to others (median 16.9 vs. 10.0; p < 0.001). ROC analysis showed an AUC of 0.78, indicating good discrimination. Quartile analysis demonstrated a monotonic gradient in co-occurrence prevalence from 64.5% in Q1 to 96.1% in Q4. Conclusions: The Renal–metabolic Risk Score (RMRS) demonstrated moderate discriminative performance in identifying patients with uncontrolled T2DM at risk for combined hyperuricemia and dyslipidemia. Because it relies on inexpensive, routine laboratory parameters, RMRS may be particularly useful in resource-limited settings to support early risk stratification, dietary counseling, and timely referral. Further validation in larger and more diverse cohorts is required before its clinical adoption. Full article

Adversarial examples pose severe challenges to information security, as their impacts directly extend to steganography and steganalysis technologies. This scenario, in turn, has further spurred the research and application of adversarial steganography. In response, we propose a novel adversarial embedding scheme rooted in a hybrid, partially data-driven approach. The proposed scheme first leverages an adversarial neural network (UT-GAN, Universal Transform Generative Adversarial Network) to generate stego images as a preprocessing step. Subsequently, it dynamically adjusts the cost function with the aid of a DCTR (Discrete Cosine Transform Residual)-based gradient calculator to optimize the images, ensuring that the final adversarial images can resist detection by steganalysis tools. The encoder in this scheme adopts a unique architecture, where its internal parameters are determined by a partially data-driven mechanism. This design not only enhances the capability of traditional steganography schemes to counter advanced steganalysis technologies but also effectively reduces the computational overhead during stego image generation. Full article

This study investigates premature fatigue failure in rocker arm gears of large-mining-height shearers operating at alternating ±45° working angles, where insufficient lubrication generates non-uniform thermal -stress fields. In this study, an integrated multiphysics framework combining transient thermal–fluid–structure coupling simulations with fatigue life prediction is proposed. Transient thermo-mechanical coupling analysis simulated dry friction conditions, capturing temperature and stress fields under varying speeds. Fluid–thermal–solid coupling analysis modeled wet lubrication scenarios, incorporating multiphase flow to track oil distribution, and calculated convective heat transfer coefficients at different immersion depths (25%, 50%, 75%). These coupled simulations provided the critical time-varying temperature and thermal stress distributions acting on the gears (Z6 and Z7). Subsequently, these simulated thermo-mechanical loads were directly imported into ANSYS 2024R1 nCode DesignLife to perform fatigue life prediction. Simulations demonstrate that dry friction induces extreme operating conditions, with Z6 gear temperatures reaching over 800 °C and thermal stresses peaking at 803.86 MPa under 900 rpm, both escalating linearly with rotational speed. Lubrication depth critically regulates heat dissipation, where 50% oil immersion optimizes convective heat transfer at 8880 W/m²·K for Z6 and 11,300 W/m²·K for Z7, while 25% immersion exacerbates thermal gradients. Fatigue life exhibits an inverse relationship with speed but improves significantly with cooling. Z6 sustains a lower lifespan, exemplified by 25+ days at 900 rpm without cooling versus 50+ days for Z7, attributable to higher stress concentrations. Based on the multiphysics analysis results, two physics-informed engineering optimizations are proposed to reduce thermal stress and extend gear fatigue life: a staged cooling system using spiral copper tubes and an intelligent lubrication strategy with gear-pump-driven dynamic oil supply and thermal feedback control. These strategies collectively enhance gear longevity, validated via multiphysics-driven topology optimization. Full article

The present study examines the possibilities for developing the use of small hydropower plants (SHP) in Slovakia, focusing on the principles of sustainability and compliance with European and national legislation. At present, there is a tendency for the construction of hydroelectric power plants to intervene in the river environment, with the potential to exert a substantial impact on the flow of the river and disrupt the surrounding ecosystem. A potential strategy for minimizing environmental impact would be the construction of SHPs, which require less construction work. The Hornád river sub-basin, located in eastern Slovakia, was selected as the study area. The spatial and hydrological data were processed using Geographic Information System (GIS) tools. The hydrological characteristics of the area were determined through the utilization of a digital terrain model (DMR 5.0). The results of the hydrological analyses were then combined with environmental constraints to identify suitable locations for small hydropower plants. The theoretical and technical potential and gradient were calculated for individual sections of watercourses. It is estimated that approximately 61% of watercourse sections have a gradient greater than or equal to 10 m, which represents suitable conditions for the development of small hydropower plants. The presence of a stable flow regime engenders optimal conditions for the utilization of hydropower in the designated location. The study emphasizes the importance of environmental protection of the area, the resolution of property rights issues, and the streamlining of permitting processes. The results of the study contribute to energy planning at the regional level and confirm the effectiveness of using GIS in determining locations for small hydropower plants. Concurrently, emphasis is placed on the necessity to incorporate environmental and legislative imperatives within the overarching strategy for water energy development. Full article

The purpose of this paper is to determine the local dimension of the critical locus of a generic singularity. We use combinatorial methods to calculate this dimension in terms of a convex object associated with the singularity, called the Newton polyhedron. In this article, we prove that the local dimension of the critical locus of a generic singularity $f:({\mathbb{C}}^n,0)⟶(\mathbb{C},0),$ $n\le 4,$ is equal to the combinatorial dimension of the Newton polyhedron of the gradient mapping $\nabla f.$ Therefore, there is some symmetry between combinatorial properties of the Newton polyhedron of a generic singularity and geometric properties of its critical locus. Full article

The stability of rubble mound breakwaters is highly affected by extreme wave loading. While extensive research has been devoted to wave-induced scour and liquefaction around breakwaters, comprehensive stability evaluations of the rubble mound breakwater core remain limited. This study develops a numerical framework to investigate the stability of rubble mound breakwaters subjected to solitary wave loading. Wave motion is modeled using the Navier–Stokes equations, wave-induced pore pressure is computed based on Darcy’s law, and soil behavior is represented through the Mohr–Coulomb constitutive model. The numerical model is validated against experimental data. To assess structural stability, the strength reduction method is employed to calculate the Factor of Safety (FOS) during wave propagation, with the minimum FOS serving as the stability criterion. Furthermore, the influence of key parameters, including wave height, soil shear strength, wave–current interaction, berm dimensions, and slope gradient, on breakwater stability is systematically analyzed. Full article

The role of wall roughness in heat and mass transfer for fully developed viscous flows in square microchannels is investigated here. Since the roughness, which is the key geometrical feature to be investigated, introduces high velocity gradients at the wall, the effect of the viscous dissipation is considered. A fully developed flow in the forced convection regime is assumed. This assumption allows the two-dimensional treatment of the problem; thus, the velocity and temperature fields are simulated on the microchannel cross-section. The boundary roughness is modeled by randomly throwing points around the nominal square cross-section perimeter and by connecting those points to generate a simple polygon. This modification of the nominal square shape of the cross-section influences the velocity and temperature fields, which are computed by employing a finite element method solver. The heat and mass transfer is studied by calculating the Nusselt and the Poiseuille numbers as a function of roughness amplitude at the boundary. Each Nusselt and Poiseuille number is obtained by employing an averaging procedure over a sample of a thousand cases. Full article

The sustainability of Arctic ecosystems that are extremely vulnerable is contingent upon the state of cryosoils. Understanding the principles of ecosystem stability in permafrost conditions, particularly under external natural or human-induced influences, necessitates an examination of the thermal and moisture regimes of the seasonally thawed soil layer. The study concentrated on the variability in the soil’s thermophysical properties in Central Siberia’s permafrost zone (the northern part of Krasnoyarsk Region, Taimyr, Russia). In the industrially affected area of interest, we evaluated and contrasted the differences in the thermophysical properties of soils between two opposing types of landscapes. On the one hand, these are soils that are characteristic of the natural landscape of flat shrub tundra, with a well-developed moss–lichen cover. An alternative is the soils in the landscape, which have exhibited significant degradation in the vegetation cover due to both natural and human-induced factors. The heat-insulating properties of background areas are controlled by the layer of moss and shrubs, while its disturbance determines the excessive heating of the soil at depth. In comparison to the background soil characteristics, degradation of on-ground vegetation causes the active layer depth of the soils to double and the temperature gradient to decrease. With respect to depth, we examine the changes in soil temperature and heat flow dynamics (q, W/m²). The ranges of thermal conductivity (λ, W/(m∙K)) were assessed using field-measured temperature profiles and heat flux values in the soil layers. The background soil was discovered to have lower thermal conductivity values, which are typical of organic matter, in comparison to the soil of the transformed landscape. Thermal diffusivity coefficients for soil layers were calculated using long-term temperature monitoring data. It is shown that it is possible to use an adjusted model of the thermal conductivity coefficient to reconstruct the dynamics of moisture content from temperature dynamics data. A satisfactory agreement is shown when the estimated ($W_{calc}$, %) and observed ($W_{exp}$, %) moisture content values in the soil layer are compared. The findings will be employed to regulate the effects on landscapes in order to implement sustainable nature management in the region, thereby preventing the significant degradation of ecosystems and the concomitant risks to human well-being. Full article

In this article, we present density functional theory (DFT) calculations for $Z{n}_{(1-x)}{M}_{x}O$, where M represents one of the following substitutional metallic impurities: Ga, Cd, Cu, Pd, Ag, In, or Sn. Our study is based on the wurtzite structure of pristine ZnO. We employ the Quantum Espresso package, using a fully unconstrained implementation of the generalized gradient approximation (GGA) with an additional U correction for exchange and correlation effects. We analyze the density of states, energy gaps, and absorption spectra for these doped systems, considering the limitations of a finite-size cell approximation. Rather than focusing on precise numerical values, we highlight the following two key aspects: the location of impurity-induced electronic states and the overall trends in optical properties across the eight systems, including pristine ZnO. Our results indicate that certain dopants introduce electronic levels within the band gap, which enhance optical absorption in the visible, near-infrared, and near-ultraviolet regions. For instance, Sn-doped ZnO shows a pronounced absorption peak at ∼2.5 eV, which is in the middle of the visible spectrum. In the case of Ag and Pd impurities, they lead to increased electromagnetic radiation absorption at the near ultra-violet spectrum. This represents a promising performance for efficient solar radiation absorption, both at the Earth’s surface and in outer space. Furthermore, Ga- and In-doped ZnO present bandgaps of ∼0.9 eV, promising an interesting performance in the near infrared region. These findings suggest potential applications in solar energy harvesting and selective sensors. Full article

Purpose: Timely removal of ureteral stents is critical to prevent complications such as infection, discomfort and stent encrustation or fragmentation, as well as stone formation associated with neglected stents. Current decisions, however, rely heavily on subjective interpretation of postoperative imaging. This study introduces a semi-automated image-processing algorithm that quantitatively evaluates stent morphology, aiming to support objective and reproducible decision-making in minimally invasive urological care. Methods: Two computational approaches were developed to analyze morphological changes in ureteral stents following surgery. The first method employed a vector-based analysis, using the FitLine function to derive unit vectors for each stent segment and calculating inter-vector angles. The second method applied a slope-based analysis, computing gradients between coordinate points to evaluate global straightening of the ureter over time. Results: The vector-angle method did not demonstrate significant temporal changes (p = 0.844). In contrast, the slope-based method identified significant ureteral straightening (p < 0.05), consistent with clinical observations. These results confirm that slope-based quantitative analysis provides reliable insight into postoperative morphological changes. Conclusions: This study presents an algorithm-based and reproducible imaging analysis method that enhances objectivity in postoperative assessment of ureteral stents. By aligning quantitative image processing with clinical decision support, the approach contributes to precision medicine and addresses the absence of standardized criteria for stent removal. Full article

Background/Objectives: Invasive pulmonary aspergillosis (IPA) represents a critical respiratory condition mainly caused by Aspergillus fumigatus and other ubiquitous species such as Aspergillus flavus, Aspergillus niger, and Aspergillus terreus. IPA clinical management has been complicated by diagnostic challenges and therapeutic difficulties due to antifungal intrinsic or secondary resistance episodes. Despite this assumption, few scientific data have been reported about possible antifungal drug combinations. Herein, we propose an experimental evaluation using isavuconazole/amphotericin B and isavuconazole/caspofungin in vitro synergy assays to investigate their combined activity on Aspergillus spp. IPA clinical isolates. Methods: We globally analyzed 55 Aspergillus spp. isolates, practicing the gradient test methods with single and combined antifungal drugs through the MIC Strip test (Liofilchem, Roseto degli Abruzzi, Italy). The collected MIC values were interpreted according to the EUCAST guidelines and classified as synergy, additivity, indifference, and antagonism cases through a FIC index calculation. A statistical analysis on species’ correlation with particular synergy testing results was finally provided. Results: Despite an interesting activity against A. fumigatus, isavuconazole/amphotericin B did not report statistical significance, obtaining a consistent antagonism percentage (43.6%). On the other hand, isavuconazole/caspofungin showed a promising in vitro synergic activity, except for A. flavus isolates. Conclusions: Synergy testing demonstrated a significant species-specific trend. Future studies should be focused on Aspergillus spp. isolates and antifungal in vitro synergy testing, aiming to discourage or recommend any specific antifungal combinations, depending on the isolated species. Full article

The Rif Mountains in northern Morocco are highly exposed to geohazards, particularly earthquakes and mass movements. In this context, the Zoumi region is most affected, showing various mass movement types involving both unconsolidated and solid materials. This study evaluates the region’s susceptibility to mass movements using logistic regression (LR), applied for the first time in this area. The model incorporates eight key predisposing factors known to influence mass movement: slope gradient, slope aspect, land use, drainage density, elevation, lithology, fracturing density, and earthquake isodepths. Historical mass movements were mapped using remote sensing and field surveys, and statistical analysis calculation was conducted to analyze their spatial correlation with these environmental conditioning factors. A mass movement susceptibility (MMS) map was produced, classifying the region into four susceptibility levels, ranging from low to very high. Landslides were the most frequent movement type (36%). The LR model showed strong predictive performance, with an AUC of 88%, confirming its robustness. The final map reveals that 42% of the Zoumi area falls within the high to very high susceptibility zones. These results highlight the importance of using advanced modeling approaches to support risk mitigation and land use planning in environmentally sensitive mountain regions. Full article

We propose a speed-adaptive robot accurate path-tracking framework based on the soft actor–critic (SAC) and Stanley methods (STANLY_ASAC). First, the Lidar–Inertial Odometry Simultaneous Localization and Mapping (LIO-SLAM) method is used to map the environment and the LIO-localization framework is adopted to achieve real-time positioning and output the robot pose at 100 Hz. Next, the Rapidly exploring Random Tree (RRT) algorithm is employed for global path planning. On this basis, we integrate an improved A* algorithm for local obstacle avoidance and apply a gradient descent smoothing algorithm to generate a reference path that satisfies the robot’s kinematic constraints. Secondly, a network classification model based on U-Net is used to classify common road surfaces and generate classification results that significantly compensate for tracking accuracy errors caused by incorrect road surface coefficients. Next, we leverage the powerful learning capability of adaptive SAC (ASAC) to adaptively adjust the vehicle’s acceleration and lateral deviation gain according to the road and vehicle states. Vehicle acceleration is used to generate the real-time tracking speed, and the lateral deviation gain is used to calculate the front wheel angle via the Stanley tracking algorithm. Finally, we deploy the algorithm on a mobile robot and test its path-tracking performance in different scenarios. The results show that the proposed path-tracking algorithm can accurately follow the generated path. Full article

This study aimed to evaluate short-term clinical outcomes in COVID-19 pneumonia patients using parameters derived from a commercial deep learning-based automatic detection algorithm (DLAD) applied to serial chest radiographs (CXRs). We analyzed 391 patients with COVID-19 who underwent serial CXRs during isolation at a residential treatment center (median interval: 3.57 days; range: 1.73–5.56 days). Patients were categorized into two groups: the improved group (n = 309), who completed the standard 7-day quarantine, and the deteriorated group (n = 82), who showed worsening symptoms, vital signs, or CXR findings. Using DLAD’s consolidation probability scores and gradient-weighted class activation mapping (Grad-CAM)-based localization maps, we quantified the consolidation area through heatmap segmentation. The weighted area was calculated as the sum of the consolidation regions’ areas, with each area weighted by its corresponding probability score. Change rates (Δ) were defined as per-day differences between consecutive measurements. Prediction models were developed using Cox proportional hazards regression and evaluated daily from day 1 to day 7 after the subsequent CXR acquisition. Among the imaging factors, baseline probability and ΔProbability, ΔArea, and ΔWeighted area were identified as prognostic indicators. The multivariate Cox model incorporating baseline probability and ΔWeighted area demonstrated optimal performance (C-index: 0.75, 95% Confidence Interval: 0.68–0.81; integrated calibration index: 0.03), with time-dependent AUROC (Area Under Receiver Operating Curve) values ranging from 0.74 to 0.78 across daily predictions. These findings suggest that the Δparameters of DLAD can aid in predicting short-term clinical outcomes in patients with COVID-19. Full article