Search Results (8,151)

Background/Objectives: Adult spinal deformity (ASD) correction is a complex surgery to restore spinal alignment and relieve patients’ symptoms. Modern techniques and technologies allow for aggressive surgical correction in tissue-friendly ways that preserve anatomy and may enable faster recovery. Robotic-assisted posterior spinal stabilization may be used as an adjunct to complex ASD reconstruction to facilitate a minimally invasive approach, reduce perioperative morbidity and physiological insult, and allow for the performance of procedures traditionally reserved for large academic centers to be effectively performed by qualified surgeons in optimized patients at smaller hospitals with fewer resources. The objective of this study is to assess realignment, perioperative complications, and patient-reported outcomes of complex, minimally invasive, robotic-assisted adult spinal deformity correction in a surgical specialty hospital. Methods: Demographic, surgical, and perioperative data were collected from the medical record. The Oswestry Disability Index (ODI) and Numeric Rating Scale (NRS) for pain scores were collected preoperatively and at regular post-op visits. X-rays were captured preoperatively before hospital discharge and at follow-up visits. Results: Fifty consecutive deformity patients were corrected with a two-stage approach (anterior column reconstruction followed by posterior stabilization with robotic-assisted screw placement on the next day) at a 48-bed (eight operating rooms), surgeon-owned, subspecialty hospital. The average patient age was 70 years, and 64% were female. The average estimated blood loss (EBL) values for the first and second stages were 62 mL and 205 mL, respectively. The average operative time was 172 min during the first stage and 210 min for the second stage. Three interbody spacers (first stage) and 16 screws (second stage) were inserted on average in each procedure. The average length of stay (LOS) in the hospital was 5 days, and the average follow-up period was 10.6 months. No patients required a transfer to another facility with intensive care unit (ICU) capabilities, and none required a revision of hardware placement. There was an average reduction in the lumbar coronal scoliotic curve of 14.5° and an increase in lumbar lordosis of 14.8° at the latest follow-up (p < 0.01). The average mismatch between pelvic incidence and lumbar lordosis (PI-LL) preoperatively was 17.6°, which was reduced to 9.6° at the latest postoperative follow-up (p < 0.01). Mean ODI (%) and NRS scores were significantly improved by 33.8% (46.7 ± 13.3 to 30.9 ± 19.8; p < 0.01) and 55% (6.0 ± 2.2 to 2.7 ± 2.6; p < 0.01), respectively, at last follow-up. Conclusions: This study demonstrates the feasibility of performing complex, robotic-assisted ASD corrective surgery in a surgical specialty hospital, achieving significant correction of sagittal and coronal deformities, relieving patients’ symptoms, and offering efficiency and consistency to pedicle screw placement. This study demonstrates that a minimally invasive approach to complex deformity reconstruction reduces perioperative morbidity with decreased operative times, EBL, and LOS when compared to historic controls. This approach allows for the democratization of deformity care in that procedures typically reserved for large academic centers can be successfully accomplished at smaller institutions in optimized patients by qualified surgeons with appropriate perioperative support staff. Full article

Translating large-scale motion datasets into robust, deployable humanoid controllers is a critical challenge in engineering informatics, primarily due to the scarcity of high-quality annotations, the risk of mode collapse in conditional generation, and the strict constraints of onboard computing hardware. This paper presents a deployable two-stage learning system that maps clip-level motion datasets to a single-policy multi-skill controller and its deployable counterpart. We adopt coarse one-hot skill labels that can be assigned automatically at the clip level with negligible manual effort, enabling scalable dataset construction. To prevent conditional discriminators from ignoring skill conditions, we inject mismatched (transition, label) pairs and introduce a condition-aware loss that explicitly penalizes incorrect transition–label associations, improving controllability and mitigating mode collapse. For real-world deployment, we further propose a two-stage training strategy: a privileged teacher policy is first trained in simulation and then distilled into a student policy that relies on stacked historical proprioceptive observations, ensuring robustness against sensing noise and latency without relying on external state estimation. Extensive evaluations in simulation and on real hardware demonstrate improved skill coverage, transition coverage, realism, and training efficiency across heterogeneous embodiments. With the onboard computer of a Unitree G1 robot, the distilled policy runs at 100 Hz with 15–25 ms latency, confirming the system’s engineering feasibility. Full article

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

Background/Objectives: Loss of nuclear BAP1 (BRCA1-associated protein 1) expression is a well-established adverse prognostic marker in uveal melanoma (UM). However, data from Central and Southeastern European populations are limited. This descriptive study aimed to evaluate BAP1 immunohistochemical expression in a Croatian enucleation-based UM cohort, characterize its associations with clinicopathological parameters, and contextualize the findings within the published literature. Methods: Formalin-fixed, paraffin-embedded tumor tissue from 58 consecutive patients with primary choroidal and ciliary body melanoma treated with enucleation at University Hospital Centre Zagreb (2006–2016) was analyzed immunohistochemically for BAP1 nuclear expression. Associations with clinicopathological parameters were assessed using chi-square and Fisher’s exact tests. Survival analysis was performed using Kaplan–Meier estimation, log-rank tests, and Cox proportional hazards regression with a median follow-up of 11.2 years. Results: Loss of nuclear BAP1 expression was observed in 53/58 (91.4%) specimens, resulting in a severely imbalanced distribution (53 versus 5 patients) precluding meaningful comparative survival analysis. Five-year and 10-year overall survival rates were 72.4% and 51.7%, respectively, with a median overall survival of 14.5 years. BAP1 loss was associated with longer disease-free survival (log-rank p = 0.020); however, this finding likely reflects a statistical artifact attributable to the extremely small BAP1-retained group (n = 5) harboring concurrent adverse features and should not be interpreted biologically. The study was underpowered to draw prognostic inferences regarding BAP1 status. Exploratory survival analyses are presented for transparency but should not be interpreted inferentially. Conclusions: The exceptionally high prevalence of BAP1 loss reflects the selection bias inherent in enucleation-based cohorts, which are enriched for large, molecularly high-risk tumors. This study provides the first comprehensive BAP1 immunohistochemical data from Croatia, contributing to the growing evidence that enucleation cohorts represent a distinct, biologically high-risk subgroup in which BAP1 immunohistochemistry offers limited discriminatory value. The extended follow-up of 11.2 years confirms the prolonged natural history of UM. Future multi-center studies incorporating molecular validation and diverse treatment modalities are needed to establish the prognostic utility of BAP1 across the full spectrum of UM disease. Full article

A novel flexible single-parameter polynomial distribution is presented in this study. The forms of hazard rate and density functions are examined. Additionally, exact formulas for a number of numerical characteristics of distributions are obtained. Stochastic ordering, the moment technique, the maximum likelihood, and a Bayesian analysis of this novel distribution based on type II censored data are used to derive the extreme order statistics. We construct Bayes estimators and the associated posterior risks using a variety of loss functions, such as the generalized quadratic, entropy, and Linex functions. Since tractable analytical formulations of these estimators are unattainable, we suggest using a simulation technique based on Markov chain Monte-Carlo (MCMC) to examine their performance. Furthermore, we construct maximum likelihood estimators given initial values for the model’s parameters. Additionally, we use integrated mean square error and Pitman’s proximity criteria to compare their performance with that of the Bayesian estimators. Lastly, we apply the new family to many real-world datasets to show its versatility, and we model cancer survival data using this new distribution to explain our methodology. Full article

Background: Isolated adrenal metastases may occur in several solid malignancies, and surgical resection has been associated with improved local control and potential survival benefit in carefully selected patients. Open adrenalectomy has traditionally been considered the standard approach, while laparoscopic adrenalectomy has progressively gained acceptance as a minimally invasive alternative. More recently, robotic adrenalectomy has been introduced. However, its role in the management of adrenal metastases remains incompletely defined. Methods: This retrospective comparative study analyzed a prospectively maintained database including patients who underwent adrenalectomy for isolated adrenal metastasis between January 2001 and December 2025 at two academic centers. Patients were stratified according to surgical approach into open, laparoscopic, and robotic groups. Perioperative outcomes, postoperative morbidity, resection margin status, and oncological adequacy of the resection were compared among groups. Results: A total of 89 patients underwent adrenalectomy for isolated adrenal metastasis (robotic n = 27, laparoscopic n = 28, open n = 34). Metastasis size was larger in the robotic group compared with the laparoscopic group (51.4 vs. 44.2 mm, p = 0.043). Laparoscopy showed the shortest operative time, with respect to both robotic (p = 0.042) and open surgery (p = 0.045). Estimated blood loss was significantly higher in the open group (263 mL) than in the robotic (117 mL) and laparoscopic groups (106 mL) (p = 0.042 and 0.040, respectively). R0 resection rates were comparable across approaches (96%, 89%, and 94%, respectively). Hospital stay was shorter after both robotic and laparoscopic surgery with respect to open surgery (2.5–3.1 vs. 5.6 days, p = 0.043 and 0.046, respectively). Conclusions: Open, laparoscopic, and robotic adrenalectomy are all feasible surgical options for isolated adrenal metastases with acceptable perioperative outcomes. Robotic adrenalectomy may represent a potential extension of the applicability of minimally invasive surgery. Full article

The aim of this study was to characterise the pedigree and genetic structure of Polish Sport Horses competing in Grand Prix show jumping events and to assess the implications for international sport horse breeding. Pedigrees of 513 horses were analysed, encompassing a total of 18,836 individuals over a maximum of 16 generations. The completeness and depth of the pedigrees allowed for a reliable estimation of inbreeding coefficients and genetic diversity. The mean inbreeding coefficient was low (0.645%), yet 82% of the horses exhibited some degree of inbreeding. The greatest loss of genetic variability was observed in non-founder generations, most likely due to the intensive use of a limited number of high-value stallions with domestic mares—a bottleneck effect. The most significant founders contributing to the population were the Thoroughbred stallions Ladykiller and Rantzau, as well as the Anglo-Arab stallion Ramzes, highlighting the international influence on the contemporary population. These findings emphasise the need for systematic monitoring of genetic diversity and the strategic use of pedigree data to minimise inbreeding and preserve the genetic potential of Polish Sport Horses for international breeding programmes. Full article

Sediment accumulation in reservoirs represents a critical challenge for sustainable water resources management in semi-arid regions. In Jordan, accelerated sedimentation threatens the operational capacity of major dams, including the King Talal Dam (KTD), which serves as a key water resource in the Amman–Zarqa Basin (AZB). This study assesses sediment yield and erosion risk at the catchment scale using the Soil and Water Assessment Tool (SWAT) integrated with the Modified Universal Soil Loss Equation (MUSLE). The AZB was subdivided into 31 sub-basins and 586 Hydrological Response Units (HRUs) based on land use, soil characteristics, topography, and slope. The model was calibrated for the period 1993–2002 and validated for 2003–2012 using hydrological and sediment observations from 17 monitoring stations. Long-term simulations covering more than two decades were conducted to quantify spatial and temporal sediment yield patterns across the basin. Results indicate a mean annual sediment yield of 2.79 t ha⁻¹ yr⁻¹, corresponding to approximately 0.59 MCM yr⁻¹ of sediment inflow to the reservoir. These estimates closely agree with bathymetric survey results reported by the Jordan Valley Authority, which indicate sedimentation rates of 2.59 t ha⁻¹ yr⁻¹ (0.55 MCM yr⁻¹). Overall, the model demonstrates strong agreement between observed and simulated sediment loads, confirming its reliability for sediment dynamics assessment. The findings are relevant to Sustainable Development Goals (SDGs) 6 (clean water and sanitation) and 15 (life on land) by informing sustainable watershed and soil erosion management practices. Full article

To address the issues of corn harvester field operations relying on driver visual guidance for row alignment, high labor intensity, and unstable operation accuracy, this study innovatively proposes a “vision-dominant, gateway-enhanced” dual-mode collaborative row-alignment assistance architecture, and independently develops the R²DC-Mask R-CNN instance segmentation network and MCC-KF robust filtering algorithm to form a deeply coupled hardware–software-assisted driving system. The R²DC-Mask R-CNN network is autonomously designed for corn row-detection scenarios, achieving accurate perception in complex field environments; the MCC-KF algorithm innovatively solves the state estimation divergence problem during transient vision failures through a multi-criteria constraint mechanism, ensuring continuous navigation capability; the intelligent gateway and vision system form a confidence-driven master–slave switching mechanism that adaptively enhances system robustness when vision is restricted. Field experiments demonstrate that within the speed range of 0.5–5.0 km/h, the average lateral deviation in the row alignment assisted by the system is 3.82–5.30 cm, the proportion of deviations less than 10 cm exceeds 96%, and all sample deviations remain within 20 cm; at a speed of 3.5 km/h, the system reduces the average grain loss rate from 3.76% under manual operation to 2.65%, a decrease of 29.5%. This system effectively improves row alignment accuracy and harvest quality, providing a practical human–machine collaborative solution for intelligent harvester operations. Full article

Environmental and financial datasets often display complex distributional characteristics, including heavy tails, high skewness and the presence of extreme observations. Traditional probability models such as the exponential, gamma or log-normal distributions may not adequately capture these behaviours particularly when modelling extreme events such as rainfall, pollution levels, stock returns or loss severities. By integrating the characteristics of Pareto and exponential distributions into an exponentiated framework that can describe datasets arising from environmental and finance fields, this study presents a novel three-parameter exponentiated Pareto exponential distributions using the exponentiated Pareto family of distributions with classical exponential distribution as the baseline model. This novel model extends the classical exponential distribution with the addition of extra shape parameters which simultaneously regulate the centre and tail behaviours of the new model. The statistical and mathematical characteristics of the proposed distribution are determined and studied. The maximum likelihood estimate approach is used in a conducted simulation exercise, and the estimator’s efficiency is evaluated as seen from the results. The practical applicability of the model is illustrated with four real-life datasets utilising model adequacy and goodness-of-fit measurements such as log–likelihood, Akaike information criteria and Bayesian information criteria. The data reveal that the proposed model gives a better fit than the models chosen as comparators, making the EPE distribution useful and robust in environmental and financial fields of study. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Accurate monitoring of water spatiotemporal dynamics is critical for hydrological process analysis and climate impact assessment. While remote sensing enables effective water monitoring, public satellite imagery is limited by mixed-pixel effects that hinder small river detection, and high-resolution commercial data suffers from low temporal frequency and restricted coverage. To address these limitations, this study proposes a deep learning-based super-resolution (SR) framework for multispectral remote sensing imagery. This paper constructs a matched dataset for GF2 and Sentinel-2 imagery and develops an Attention Enhanced Super Resolution Generative Adversarial Network (AESRGAN). By integrating attention mechanisms and a spectral-structural loss design, the network is optimized to adapt to the characteristics of multispectral remote sensing imagery. Experimental results demonstrate that AESRGAN achieves strong reconstruction performance, with a Peak Signal-to-Noise Ratio (PSNR) of 33.83 dB and a Structural Similarity Index Measure (SSIM) of 0.882. Water extraction based on the reconstructed imagery using the U-Net++ model achieved an overall accuracy of 0.97 and a Kappa coefficient of 0.92. In addition, the reconstructed imagery improved the estimation accuracy of river length, width, and area by 0.34%, 3.28%, and 8.51%, respectively. The proposed framework provides an effective solution for multi-source remote sensing data fusion and high-precision surface water monitoring, offering new potential for long-term hydrological observation using medium-resolution satellite imagery. Full article

Background: Recent research has highlighted the potential of postbiotics for addressing obesity and associated metabolic disorders. In this randomized, double-blind clinical trial, the efficacy of a postbiotic product in managing overweight and associated parameters was assessed. Methods: Sixty individuals were randomized into two groups: one group (n = 30) received the Postbiotic (heat-killed L. fermentum strain K8-Lb1) and the other (n = 30) a Placebo control. Body weight, waist circumference, body composition, vital signs, blood biomarkers and questionnaires for quality of life, eating behavior, eating control and gastrointestinal symptoms were assessed. Results: After a 12-week intervention, body fat mass (primary parameter) was significantly (p = 0.016) reduced in the Postbiotic group (98.15 ± 3.32% of baseline) compared to the Placebo group (100.41 ± 3.39%). In line with this, body weight (p = 0.047) and waist circumference (p = 0.034) were significantly reduced and visceral fat tended to be reduced (p = 0.053). Accordingly, the Postbiotic group tended (p = 0.066) to feel more in control of their body weight. Despite weight loss, muscle mass tended (p = 0.062) to increase. ALT, AST and GGT tended to be reduced, which may indicate an improvement in liver steatosis. Estimated average glucose (eAG) differed significantly between the groups in individuals with normal fasting glucose levels. The ability to concentrate significantly (p = 0.014) improved. Conclusions: Under an ad libitum diet, the postbiotic L. fermentum strain K8-Lb1 reduced body fat mass, body weight, and waist circumference, improved the ability to concentrate, and showed a trend towards an increase in muscle mass. The results of this pilot trial need confirmation by a pivotal trial. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Load-redistribution false-data-injection (LR-FDI) attacks can degrade power-system operation by reshaping the perceived nodal demand pattern, thereby inducing congestion-aware redispatch and economic inefficiency while preserving the net system load. Prior LR-FDI studies commonly adopt bilevel/Stackelberg formulations with a continuous attack vector and an embedded operator response; however, these formulations often (i) do not represent explicit compromised-load selection, (ii) become computationally restrictive when combinatorial target sets are considered, and (iii) offer limited transparency for structured, stage-wise attack planning. This paper proposes a sequential two-stage attacker–operator framework for LR-FDI vulnerability assessment that integrates sparse load compromise decisions with screening-regularized attack synthesis and post-attack operational evaluation. In Stage-1, a mixed-integer nonlinear program identifies economically influential load buses via binary selection and determines admissible perturbation magnitudes under total-load conservation and proportional shift bounds. To confine the attacker-side search region and avoid economically exaggerated solutions, a screening-derived conservative operating-cost ceiling is first estimated through a parametric load-sensitivity analysis and then used to regularize the attack-synthesis step. In Stage-2, the system operator’s corrective redispatch is evaluated by solving an active-power-oriented economic dispatch model with nonlinear network-consistent assessment of operational outcomes. Using the IEEE 24-bus RTS, results show that the hourly operating-cost deviation reaches ≈0.2% in the most adverse feasible cases, and the cumulative daily impact approaches ≈5% only under selectively realizable compromised-load patterns, accompanied by a nearly 80% increase in total active-power transmission losses relative to the base case. Overall, the framework yields a practically grounded quantification of conditionally severe economic and network stress under coordinated LR-FDI scenarios and provides actionable insight for prioritizing vulnerable load locations for protection and monitoring. Full article