Search Results (7,754)

The objective of this retrospective study was to compare outcomes after desmoplasty with fasciotomy (DF) against deep branch of the lateral plantar neurectomy with fasciotomy (NF) for lameness due to proximal suspensory desmopathy in the hindlimb. Medical records from 141 horses with proximal suspensory desmopathy treated by either desmoplasty with fasciotomy or deep branch of the lateral plantar neurectomy with fasciotomy were reviewed. Follow-up after surgery to determine resolution of the lameness and return to use was obtained from medical records, as well as referring veterinarians, owners and trainers by telephone conversations and/or email. Long-term outcomes were obtained for 109 horses. There was no significant difference in resolution of lameness between the two techniques (79% DF vs. 88% NF), or in the percentage of horses returning to work 1–3 years post-operatively (83% DF vs. 93% NF). Horses treated with DF had higher ultrasonography scores compared with horses treated with neurectomy/fasciotomy. Horses treated with DF took longer to return to work compared to NF and received more non-surgical treatments in the recovery period. Post-operative complications were minimal with either technique. Use of either surgical technique for hindlimb proximal suspensory desmopathy can result resolution of lameness and return to athletic use. Full article

Indoor environmental quality is essential for pupils‘ health, comfort, and academic performance. However, recent studies indicate that indoor air quality (IAQ) in classrooms is often inadequate. This observational study examines the impact of three ventilation concepts on IAQ and thermal comfort under real-life school conditions: manual window airing combined with CO₂ traffic lights, decentralized mechanical ventilation, and centralized mechanical ventilation. Eight classrooms in three elementary schools were monitored from October 2023 to April 2024. Continuous long-term measurements covered CO₂, PM_2.5, VOCs, indoor air temperature, relative humidity and window opening states in the classrooms, and ambient data including PM_2.5 at each school. Significant differences were found in all five indoor parameters across the three ventilation concepts. The decentralized ventilation group achieved the lowest CO₂ concentrations (18–22% lower), while the window airing group showed the highest PM_2.5 levels (mean of 6 µg/m³) and the lowest temperatures (21% of the time below 20 °C). Relative humidity tended to be too low for all concepts, particularly with mechanical ventilation (medians below 40%). Windows in the window airing group were opened approximately twice as long. The findings highlight the benefits of well-operated mechanical ventilation systems and underscore the importance of user awareness and system management. Full article

Background: Splenic arterial embolization (SAE) is a well-established technique in the non-operative management of splenic trauma and aneurysms. While its short-term safety and efficacy have been widely documented, medium- and long-term impacts on splenic volume and function remain under-investigated. This study aimed to evaluate volumetric changes and hematological parameters following SAE, with emphasis on its role in preserving splenic integrity and potential integration with AI-enhanced imaging technologies. Methods: We retrospectively analyzed 17 patients treated with SAE between January 2014 and December 2023. Volumetric measurements were performed using computed tomography (CT) with 3D reconstructions before and after SAE. Patients were divided into two groups based on indication: polytrauma (n = 8) and splenic artery aneurysm (n = 9). Hematological parameters including white blood cells (WBCs), red blood cells (RBCs), and hemoglobin (Hb) were evaluated in correlation with clinical outcomes. Statistical significance was assessed using Student’s t-test, and power analysis was conducted. Results: Among the trauma group, mean splenic volume decreased from 190.5 ± 51.2 cm³ to 147.8 ± 77.8 cm³ (p = 0.2158), while in the aneurysm group, volume decreased from 195.4 ± 78.9 cm³ to 143.7 ± 81.4 cm³ (p = 0.184). Though not statistically significant, these changes suggest post-procedural splenic remodeling. The technical success of SAE was 100%, with no cases of late follow-up infarction, abscess, immunological impairment, or secondary splenectomy required. Hematologic parameters remained within normal limits in follow-up assessments. Conclusions: SAE represents a safe and effective intervention for spleen preservation in both traumatic and aneurysmal conditions. Although a reduction in splenic volume has been observed, white blood cell counts, a reliable indicator of splenic function, have remained stable over time. This finding supports the preservation of splenic function following SAE. Full article

In the 21st century, the concept of the Innovation Hubinnovation hub (IH) has become increasingly significant with the emergence of collaborative spaces, entrepreneurial ecosystems, and the pursuit of creative, sustainable solutions to contemporary challenges. While the literature presents various typologies of IHs, a critical knowledge gap remains due to the limited availability of empirical data on their core drivers, functions, and sustainability practices. Addressing this gap through a comprehensive primary and secondary data collection will enhance the global understanding of IH dynamics, supporting evidence-based decision-making; strategic development; and long-term accountability for hub managers, entrepreneurs, and policymakers. This study aims to identify and classify the predominant characteristics of IHs, examining their key drivers, core activities, and sustainability dimensions through an in-depth analysis of three leading innovation hub networks: the European Creative Hubs Network (ECHN), Impact Hub, and Talent Garden (TAG). By exploring how these hubs foster innovation and integrate sustainability into their operational models, this research offers actionable insights for stakeholders seeking to align innovation with inclusive, resilient, and environmentally conscious economic development. Full article

Segmentary lineage theory fell out of favor in cultural anthropology during the 1980s. However, the core ideas of segmentary lineage have continued to shape political mobilization as well as political analysis in Africa long after the theory’s supposed death. This article analyzes how and why the framework of segmentary lineage has endured as a potent means of describing and experiencing politics in the Somali-inhabited Horn of Africa. It theorizes Somali clanship, a classic example of a “pure” segmentary lineage structure, as a framework for managing the near-term future rather than as an objective description of existing social structures. We show how segmentary lineage has been politicized during the colonial and postcolonial eras as a tool for pre-emptive action by governments. We link this broader dynamic of politicization to the functions of clanship in everyday life as a mode of anticipating other people’s likely behavior based on clan-framed narratives about the past. Based on archival research, ethnographic fieldwork, and analysis of media and social media, we argue that Somali clanship operates in politics less as a network of shared interests or mobilization based on anticipated collective gains, and more as a framework for anticipating and attempting to pre-empt other people’s likely behavior. Full article

Background/Objectives: Minimally invasive cardiac surgical (MICS) approaches to the implantation of left ventricular assist devices (LVADs) have gained increasing interest as alternatives to full median sternotomy (FS), particularly in patients with prior cardiac surgeries or elevated surgical risk. However, evidence regarding their safety, feasibility, and clinical outcomes remains fragmented. This systematic review aimed to evaluate the effectiveness and safety of minimally invasive techniques for LVAD implantation in comparison to standard sternotomy, with a focus on mortality, perioperative complications, intensive care unit (ICU) stay, and infection rates. Methods: A comprehensive literature search was conducted in PubMed, Web of Science, Science Direct, Cochrane Library, and Google Scholar up to 1 January 2025. Studies were included if they reported on adult patients undergoing LVAD implantation via minimally invasive thoracotomy or sternotomy-sparing approaches, with or without comparator groups. Data were extracted and synthesized qualitatively; the Newcastle–Ottawa Scale (NOS) was applied to assess the methodological quality of the included cohort and retrospective comparative studies. Results: A total of 12 studies involving 1448 patients were included (584 received MICS and 862 received FS). MICS techniques have demonstrated comparable short and mid-term survival outcomes, with trends toward reduced ICU stay, fewer reoperations for bleeding, and lower incidence of driveline infections. Some studies reported longer operative and cardiopulmonary bypass times in the MICS group. Among high-risk cohorts, such as patients with prior sternotomies or significant comorbidities, MICS was associated with lower morbidity and acceptable safety profiles. However, heterogeneity in patient selection, surgical protocols, and outcome definitions limited quantitative synthesis. Conclusions: Minimally invasive LVAD implantation is a viable alternative to conventional sternotomy in selected patient populations. While current data suggest favorable perioperative outcomes and equivalent survival, high-quality prospective studies are needed to confirm long-term benefits and to guide patient selection. MICS approaches should be considered within multidisciplinary teams experienced in advanced heart failure surgery. Full article

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

The demand for robust safety monitoring in underground coal mines is increasing, yet traditional methods face limitations in long-term stability due to inadequate energy supply and high maintenance requirements. To address the critical challenges of high computational demand and energy constraints in this resource-limited environment, this paper proposes MineVisual, a battery-free visual sensing scheme specifically designed for underground coal mines. The core of MineVisual is an optimized lightweight deep neural network employing depthwise separable convolution modules to enhance computational efficiency and reduce energy consumption. Crucially, we introduce an energy-aware dynamic pruning network (EADP-Net) ensuring a sustained inference accuracy and energy efficiency across fluctuating power conditions. The system integrates supercapacitor buffering and voltage regulation for stable operation under wind intermittency. Experimental validation demonstrates that MineVisual achieves high accuracy (e.g., 91.5% Top-1 on mine-specific tasks under high power) while significantly enhancing the energy efficiency (reducing inference energy to 6.89 mJ under low power) and robustness under varying wind speeds. This work provides an effective technical pathway for intelligent safety monitoring in complex underground environments and conclusively proves the feasibility of battery-free deep learning inference in extreme settings like coal mines. Full article

This study investigates the electromagnetic and NVH characteristics of an outer-rotor surface-mounted permanent magnet synchronous generator (SPMSG) for wave energy applications, focusing on the effect of rotor eccentricity. To reflect potential fault due to manufacturing or assembly defects, a 0.5 mm rotor eccentricity was introduced in finite element method (FEM) simulations. The torque ripple waveform was analyzed using fast Fourier transform (FFT) to identify dominant harmonic components that generate unbalanced electromagnetic forces and induce structural vibration. These harmonic components were further examined under variable marine operating conditions to evaluate their impact on acoustic radiation and vibration responses. Based on the simulation and analysis results, a design-stage methodology is proposed for predicting vibration and noise by targeting critical harmonic excitations, providing practical insights for marine generator design and improving long-term operational reliability in wave energy systems. Full article

This study investigates the operation and management of an advanced Italian liquid waste treatment platform, focusing on its dual-line configuration and the challenges posed by increasingly heterogeneous waste streams. The main objectives are to (i) characterize the technological and operational features of the system, (ii) evaluate strategies for dealing with variable waste compositions and non-compliant inputs, and (iii) propose governance measures to strengthen cooperation between producers and operators. The methodology integrates the analysis of operational data from 2022 to 2024 (waste volumes, European Waste Catalogue Codes, reagent consumption, sludge production, and energy use) with a critical assessment of acceptance procedures and monitoring protocols. Results show a 10% increase in liquid waste treated over the study period, a growing predominance of complex EWC codes, higher oxygen demand in the thermophilic reactor, and seasonal fluctuations in sludge production. At the same time, the plant achieved stable or improved performance indicators, with specific energy consumption decreasing to 2.08 kWh/kg COD removed in 2024. The study concludes that modular, flexible treatment systems, supported by rigorous waste characterization and real-time decision-making, are essential to ensuring efficiency, regulatory compliance, and long-term environmental sustainability in liquid waste management. Full article

The number of patients with end-stage renal disease continues to grow worldwide, placing increasing demands on dialysis technologies. Conventional hemodialysis remains the dominant modality but is often limited by frequent intradialytic hypotension and the insufficient removal of medium-sized toxins. Intermittent infusion hemodiafiltration (I-HDF) is an emerging, hybrid dialysis technique that combines standard hemodialysis with the cyclic backfiltration of ultrapure dialysate. This approach enables dynamic blood volume control and periodic backflushing of the dialyzer membrane. Recent clinical studies demonstrate that I-HDF can reduce intradialytic hypotension incidence, improve systemic and microcirculatory perfusion, and enhance the clearance of middle molecules such as β₂-microglobulin, while minimizing albumin loss. These benefits are particularly relevant to toxin clearance and hemodynamic stabilization, key priorities in optimizing dialysis outcomes. Large-scale cohort data suggest that I-HDF may be linked to improved long-term survival in dialysis patients. Given its physiological advantages and operational flexibility, I-HDF may also offer a practical solution in healthcare systems with limited access to high-volume online hemodiafiltration or kidney transplantation. Further research is warranted to develop individualized infusion protocols and validate its broader applicability. Full article

This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency. It uses the Remaining Useful Life (RUL) prediction technology to establish a sensing platform for ship propellers to capture vibration signals during ship operations. The Diagnosis and RUL Prediction Model is designed to assess bearing aging status and the RUL of the propeller. The synchronized signal reshaping technology is employed in the Diagnosis and RUL Prediction Model to process the original vibration signals as input to the model. The vibration signals obtained are used to analyze the temporal and spectral energy of propeller bearings. Exponential functions are used to generate the health index as model outputs. Model inputs and outputs are simultaneously input into a Long Short-Term Memory (LSTM) model for training, culminating as the Diagnosis and RUL Prediction Model. Compared to Recurrent Neural Network and Support Vector Regression models used in previous studies, the Diagnosis and RUL Prediction Model developed in this study achieves a Mean Squared Error (MSE) of 0.018 and a Mean Absolute Error (MAE) of 0.039, demonstrating outstanding performance in prediction results and computational efficiency. This study integrates the Diagnosis and RUL Prediction Model, bearing aging experimental data, and real-world vibration measurements to develop the diagnosis and RUL prediction system for ship propellers. Experiments with ship propellers show that when the bearing of the propeller enters the worn stage, this diagnostic system for ship propellers can accurately determine the current status of the bearing and its remaining useful life. This study offers a practical solution to insufficient ship maintenance capacity and contributes to improving the operational efficiency of ships. Full article

The long-term safety and operational reliability of zoned earth dams depend on the structural integrity of their internal components, including core, filters, and shell zones. This is particularly relevant for old dams which have been operational for a long period of time. Such existing infrastructure systems are exposed to various loading types over time, including environmental, seepage-related, extreme event, and climate change effects. As a result, even when they look intact externally, changes might affect their internal structure, composition, and possibly functionality. Thus, it is important to delineate a comprehensive and cost-effective strategy to identify potential issues and derive the health status of existing earth dams. This paper outlines a systematic approach for conducting a comprehensive health check of these structures through the implementation of a multi-epoch geotechnical approach based on a variety of standard measured and monitored quantities. The goal is to compare current properties with baseline data obtained during pre-, during-, and post-construction site investigation and laboratory tests. Guidance is provided on how to judge such multi-epoch comparisons, identifying potential outcomes and scenarios. The proposed approach is tested on a well-documented case study in Southern Italy, an area prone to climate change and subjected to very high seismic hazard. The case study demonstrates how the integration of historical and contemporary geotechnical data allows for the identification of critical zones requiring attention, the validation of numerical models, and the proactive formulation of targeted maintenance and rehabilitation strategies. This comprehensive, multi-epoch-based approach provides a robust and reliable assessment of dams’ health, enabling better-informed decision-making workflows and processes for asset management and risk mitigation strategies. Full article

In this work, we propose the use of a reinforcement learning (RL) agent for the control of a cascade hydropower plant system. Generally, this job is handled by power plant dispatchers who manually adjust power plant electricity production to meet the changing demand set by energy traders. This work explores the more fundamental problem with the cascade hydropower plant operation of flow control for power production in a highly nonlinear setting on a data-based digital twin. Using deep deterministic policy gradient (DDPG), twin delayed DDPG (TD3), soft actor-critic (SAC), and proximal policy optimization (PPO) algorithms, we can generalize the characteristics of the system and determine the human dispatcher level of control of the entire system of eight hydropower plants on the river Drava in Slovenia. The creation of an RL agent that makes decisions similar to a human dispatcher is not only interesting in terms of control but also in terms of long-term decision-making analysis in an ever-changing energy portfolio. The specific novelty of this work is in training an RL agent on an accurate testing environment of eight real-world cascade hydropower plants on the river Drava in Slovenia and comparing the agent’s performance to human dispatchers. The results show that the RL agent’s absolute mean error of 7.64 MW is comparable to the general human dispatcher’s absolute mean error of 5.8 MW at a peak installed power of 591.95 MW. Full article

Phenology prediction is critical for optimizing the timing of rice crop management operations such as fertilization and irrigation, particularly in the face of increasing climate variability. This study aimed to estimate three key developmental stages in the temperate irrigated rice systems of Australia: panicle initiation (PI), flowering, and harvest maturity. Extensive and diverse field observations ($n\approx 302$) were collected over four consecutive seasons (2022–2025) from the rice-growing regions of the Murrumbidgee and Murray Valleys in southern New South Wales, encompassing six varieties and three sowing methods. The extent of data available allowed a number of traditional and emerging machine learning (ML) models to be directly compared to determine the most robust strategies to predict Australian rice crop phenology. Among all models, Tabular Prior-data Fitted Network (TabPFN), a pre-trained transformer model trained on large synthetic datasets, achieved the highest precision for PI and flowering predictions, with root mean square errors (RMSEs) of 4.9 and 6.5 days, respectively. Meanwhile, long short-term memory (LSTM) excelled in predicting harvest maturity with an RMSE of 5.9 days. Notably, TabPFN achieved strong results without the need for hyperparameter tuning, consistently outperforming other ML approaches. Across all stages, models that integrated remote sensing (RS) and weather variables consistently outperformed those relying on single-source input. These findings underscore the value of hybrid data fusion and modern time series modeling techniques for accurate and scalable phenology prediction, ultimately enabling more informed and adaptive agronomic decision-making. Full article