Search Results (1,334)

This study aimed to evaluate the impact of preoperative anti-VEGF injections on pars plana vitrectomy (PPV) outcomes in patients with proliferative diabetic retinopathy (PDR). Material and methods: We analysed 232 eyes with proliferative diabetic vitreoretinopathy treated with posterior vitrectomy. There were 112 women and 120 men. The patients were divided into two groups of 116 eyes each. In 116 eyes (study group), an anti-VEGF injection was administered 3 to 5 days before vitrectomy. The control eyes were not injected with anti-VEGF due to systemic contraindications to anti-VEGF treatment or lack of patient consent. All participants underwent pars plana vitrectomy with silicone oil injection. The oil was removed within 2–3 months after PPV. Results: At 2 years of observation, after removal of silicone oil, visual acuity (VA) was 0.24 ± 0.27 logMAR in the study and 0.37 ± 0.45 logMAR in the control group (p = 0.003). Intraocular pressure was 16.84 ± 6.25 mmHg in the study group and 17.78 ± 6.22 mmHg in the control group (p = 0.04). The mean duration of surgery was 47.62 ± 9.87 and 50.05 ± 9.41 min in the study and control groups, respectively (p = 0.02). The size of intraoperative haemorrhage was 0.97 ± 0.86 dd in the study group and 1.51 ± 1.22 dd in the control group (p = 0.003). The frequency of surgery-induced retinal breaks was 0.34 ± 0.56 in the study group and 0.56 ± 0.76 in the control group (p = 0.003). The recurrence rate of retinal detachment was 0.05 ± 0.22 in the study group and 0.1 ± 0.31 in the control group (p = 0.15). Conclusions: Preoperative anti-VEGF therapy shortens the duration of surgery, reduces complications, and improves long-term outcomes in terms of visual acuity and maintenance of normal eye function. Full article

The advancement of non-thermal disinfection technologies represents a critical pathway for ensuring food safety, meeting environmental sustainability requirements, and meeting consumer preferences for clean-label products. This study systematically evaluated the combined preservation effect of ultrasonic-assisted slightly acidic electrolyzed water (US+SAEW) on mirror carp fillets during refrigeration. Results demonstrated that US+SAEW exhibited superior antimicrobial efficacy compared to individual US or SAEW, achieving reductions of 0.73, 0.74, and 0.79 log CFU/g in total viable counts (TVC), Aeromonas bacteria, and lactic acid bacteria counts compared to the control, respectively. Furthermore, the combined intervention significantly suppressed microbial proliferation throughout the refrigeration period while simultaneously delaying protein and lipid degradation/oxidation induced by spoilage bacteria, thereby inhibiting the formation of alkaline nitrogenous compounds. Consequently, lower levels of pH, total volatile basic nitrogen (TVB-N), protein carbonyl, and thiobarbituric acid reactive substances (TBARS) were observed in US+SAEW compared to the other treatments. Multimodal characterization through low-field nuclear magnetic resonance (LF-NMR), texture, and color analysis confirmed that US+SAEW effectively preserved quality characteristics, extending the shelf life of mirror carp fillets by four days. This study provides a novel non-thermal preservation strategy that combines microbial safety maintenance with quality retention, offering particular advantages for thermolabile food. Full article

The efficiency decline of Francis turbines, a key component of hydroelectric power generation, presents a multifaceted challenge influenced by interconnected factors such as water quality, incidence angle, erosion, and runner wear. This paper is structured into two main sections to address these issues. The first section applies the Buckingham π theorem to establish a dimensional analysis (DA) framework, providing insights into the relationships among the operational variables and their impact on turbine wear and efficiency loss. Dimensional analysis offers a theoretical basis for understanding the relationships among operational variables and efficiency within the scope of this study. This understanding, in turn, informs the selection and interpretation of features for machine learning (ML) models aimed at the predictive maintenance of the target variable and important features for the next stage. The second section analyzes an extensive dataset collected from a Francis turbine in Colombia, a country that is heavily reliant on hydroelectric power. The dataset consisted of 60,501 samples recorded over 15 days, offering a robust basis for assessing turbine behavior under real-world operating conditions. An exploratory data analysis (EDA) was conducted by integrating linear regression and a time-series analysis to investigate efficiency dynamics. Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. This study seeks to develop a comprehensive understanding of the factors driving Francis turbine efficiency loss and to propose strategies for mitigating wear-induced performance degradation. The synergy lies in DA’s ability to reduce dimensionality and identify meaningful features, which enhances the ML models’ interpretability, while ML leverages these features to model non-linear and time-dependent patterns that DA alone cannot address. This integrated approach results in a linear regression model with a performance (R²-Test = 0.994) and a time series using ARIMA with a performance (R²-Test = 0.999) that allows for the identification of better generalization, demonstrating the power of combining physical principles with advanced data analysis. The preliminary findings provide valuable insights into the dynamic interplay of operational parameters, contributing to the optimization of turbine operation, efficiency enhancement, and lifespan extension. Ultimately, this study supports the sustainability and economic viability of hydroelectric power generation by advancing tools for predictive maintenance and performance optimization. Full article

Traditional 2D optical markers degrade quickly in underwater applications due to sediment accumulation and marine biofouling, becoming undetectable within weeks. This paper presents a Three-Dimensional Binary Marker, a novel passive fiducial marker designed for underwater Long-Term Deployment. The Three-Dimensional Binary Marker addresses the 2D-markers limitation through a 3D design that enhances resilience and maintains contrast for computer vision detection over extended periods. The proposed solution has been validated through simulation, water tank testing, and long-term sea trials for 5 months. In each stage, the marker was compared based on detection per visible frame and the detection distance. In conclusion, the design demonstrated superior performance compared to standard 2D markers. The proposed Three-Dimensional Binary Marker provides compatibility with widely used fiducial markers, such as ArUco and AprilTag, allowing quick adaptation for users. In terms of fabrication, the Three-Dimensional Binary Marker uses additive manufacturing, offering a low-cost and scalable solution for underwater localization tasks. The proposed marker improved the deployment time of fiducial markers from a couple of days to sixty days and with a range up to seven meters, providing robustness and reliability. As the marker survivability and detection range depend on its size, it is still a valuable innovation for Autonomous Underwater Vehicles, as well as for inspection, maintenance, and monitoring tasks in marine robotics and offshore infrastructure applications. Full article

Coatings that are tolerant of poor surface preparation are often used for rapid, real-time maintenance of aging steel surfaces. In this study, a modified epoxy (EP) anti-rust coating was proposed, utilizing methyl gallate (MG) as a rust conversion agent, graphene oxide (GO) as an active functional material, and epoxy resin as the film-forming material. The anti-rust mechanism was investigated using potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), laser scanning confocal microscopy (LSCM), and the scanning vibration electrode technique (SVET). The results demonstrated that over a period of 21 days, the impedance of the coating increases while the corrosion current density decreases with prolonged soaking time. The coating exhibited a maximum impedance of 2259 kΩ, and a lower corrosion current density of 8.316 × 10⁻³ A/m², which demonstrated a three-order magnitude reduction compared to the corrosion current density observed in mild steel without coating. LSCM demonstrated that MG can not only penetrate the tiny gap between the rust particles, but also effectively convert harmful rust into a complex. SVET showed a much more uniform current density distribution in the micro-zones of mild steel with the anti-rust coating compared to uncoated mild steel, indicating that the presence of GO not only enhanced the electrical conductivity of the coating, but also improved the structure of the coating, which contributed to the high performance of the modified epoxy anti-rust coating. This work highlights the potential application of anti-rust coating in the protection of metal structures in coastal engineering. Full article

Objective: This study aimed to identify plasma exosomal microRNAs (miRNAs) associated with weight loss and type 2 diabetes (T2D) remission following low-calorie diet (LCD) intervention. Methods: A 6-month dietary intervention targeting T2D remission was conducted among individuals with T2D. Participants underwent a 3-month intensive weight loss phase consuming LCD (815–835 kcal/day) and a 3-month weight maintenance phase (N = 32). Sixteen participants were randomly selected for characterization of plasma-derived exosomal miRNA profiles at baseline, 3 months, and 6 months using small RNA sequencing. Linear mixed-effects models were used to identify differentially expressed exosomal miRNAs between responders and non-responders. Pathway enrichment analyses were conducted using target mRNAs of differentially expressed miRNAs. Logistic regression models assessed the predictive value of differentially expressed miRNAs for T2D remission. Results: Among the 16 participants, 6 achieved weight loss ≥10% and 12 achieved T2D remission. Eighteen exosomal miRNAs, including miR-92b-3p, miR-495-3p, and miR-452b-5p, were significantly associated with T2D remission and weight loss. Pathway analyses revealed enrichment in PI3K-Akt pathway, FoxO signaling pathway, and insulin receptor binding. The addition of individual miRNAs including miR-15b-3p, miR-26a-5p, and miR-3913-5p to base model improved the area under the curve values by 0.02–0.08 at 3 months and by 0.02–0.06 at 6 months for T2D remission. Conclusions: This study identified exosomal miRNAs associated with T2D remission and weight loss following LCD intervention. Several exosomal miRNAs might serve as valuable predictors of T2D remission in response to LCD intervention. Full article

Currently, numerous conventional airport runways suffer from cracking distresses and cannot meet their structural and functional requirements. To address the urgent demand for rapid and durable maintenance of airport runways, this study investigates the material optimization and curing behavior of cold-mix epoxy asphalt (CEA) for non-disruptive overlays. Eight commercial CEAs were examined through tensile and overlay tests to evaluate their strength, toughness, and reflective cracking resistance. Two high-performing formulations (CEA 1 and CEA 8) were selected for further curing characterization using differential scanning calorimetry (DSC) tests, and the non-isothermal curing kinetics were analyzed with different contents of Component C. The results reveal that CEA 1 and CEA 8 were selected as promising formulations with superior toughness and reflective cracking resistance across a wide temperature range. DSC-based curing kinetic analysis shows that the curing reactions follow an autocatalytic mechanism, and activation energy decreases with conversion, confirming a self-accelerating process of CEA. The addition of Component C effectively modified the curing behavior, and CEA 8 with 30% Component C reduced curing time by 60%, enabling traffic reopening within half a day. The curing times were accurately predicted for each type of CEA using curing kinetic models based on autocatalytic and iso-conversional approaches. These findings will provide theoretical and practical guidance for high-performance airport runway overlays, supporting rapid repair, extended service life, and environmental sustainability. Full article

LLMs are trained using many thousands of GPUs in well-known conventional models. It is necessary to address numerous issues in the training process, such as manual data collection organization, data parallel, model parallel, evaluation, testing, deployment, transferring large data streams, detecting errors, ongoing maintenance, and project management. A team of dozens of engineers is required to handle system problems in the training process. Therefore, it is time-consuming and expensive to build an efficient and fault-tolerant system based on Kubernetes. This paper develops SimpleScale for building LLMs based on FSDP and Slurm, which is a simple and efficient training system that includes the training agent, the efficient parallel strategy, the optimal step of checkpoint, and so on. Using the proposed system enables us to significantly accelerate the process of building the LLM without incurring substantial time spent on various manual issues. The proposed 1024-GPU cluster was tested on TinyLlama, which has 1.1 billion parameters and 300 billion tokens. For example, utilizing a 16-H100 GPU cluster accelerated the traditional TinyLlama training time costs from 89.05 days to 39.05 days. In the future, the project team plans to integrate Flash-Attention3, aiming for an MFU of more than 60% while maintaining the acceleration achieved in the present work. Full article

Background/Objectives: Total knee arthroplasty (TKA) is commonly associated with postoperative muscle atrophy and weakness, while traditional rehabilitation is often limited by pain and patient compliance. Passive blood flow restriction (pBFR) training may offer a safe, low-threshold method to attenuate muscle loss in this early phase. This pilot study examined the feasibility, safety, and early effects of pBFR initiated during hospitalization on muscle mass, swelling, and functional recovery after TKA. Methods: In a prospective, single-blinded trial, 26 patients undergoing primary or aseptic revision TKA were randomized to either a control group (CON: sham BFR at 20 mmHg) or intervention group (INT: pBFR at 80% limb occlusion pressure). Both groups received 50 min daily in-hospital rehabilitation sessions for five consecutive days. Outcomes, including lean muscle mass (DXA), thigh/knee circumference, 6 min walk test (6 MWT), handgrip strength, and patient-reported outcomes, were assessed preoperatively and at discharge, six weeks, and three months postoperatively. Linear mixed models with Bonferroni correction were applied. Results: The INT group showed significant preservation of thigh circumference (p = 0.002), reduced knee swelling (p < 0.001), and maintenance of lean muscle mass (p < 0.01), compared with CON, which exhibited significant declines. Functional performance improved faster in INT (e.g., 6 MWT increase at T3: +23.7%, p < 0.001; CON: −7.2%, n.s.). Quality of life improved in both groups, with greater gains in INT (p < 0.05). No adverse events were reported. Conclusions: Initiating pBFR training on the first postoperative day is feasible, safe, and effective in preserving muscle mass and reducing swelling after TKA. These findings extend prior BFR research by demonstrating its applicability in older, surgical populations. Further research is warranted to evaluate its integration with standard rehabilitation programs and long-term functional benefits. Full article

Pharmacist-led transitions of care (TOC) services decrease preventable hospital readmission. TOC service implementation assessment can inform translation to real-world settings. The purpose of this study was to evaluate the implementation of a TOC service for patients with multiple admissions at a regional hospital using the RE-AIM framework. In this quasi-experimental, non-randomized study, individuals with ≥2 recent hospitalizations received pharmacist-led discharge medication reconciliation and counseling, management of drug-related problems, post-discharge telephonic visits, and social support. The reach, effectiveness, implementation, and maintenance RE-AIM dimensions were assessed using patient and service records. Outcomes included 30-day readmission rates for individuals completing ≥1 outpatient pharmacist visit (intervention) versus those unreachable in the outpatient setting (comparison), completed interventions, implementation features, and service adaptations. Chi-square and Fisher’s exact tests were used for comparison of categorical variables and the t-test was used for continuous variables. From February 2022 to August 2023, 72.7% of the 66 service participants participated in the intervention (reach). Additionally, 30-day readmission was 22.9% (intervention) versus 55.6% (comparison; p = 0.01). In total, 2279 interventions were documented (effectiveness). The service was adapted (implementation) and expanded to include additional populations (maintenance) to enhance sustainability. Based on RE-AIM evaluation, the pharmacist-led TOC intervention appears to be a sustainable solution for addressing readmission in multivisit patients. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

A reliable estimation of protein requirements in lactating dairy cows is necessary for formulating nutritionally adequate diets, improving feed efficiency, and minimizing nitrogen excretion. This study aimed to develop machine learning-based models to predict net protein requirements for maintenance (NPm) and lactation (NPl) using random forest regression (RFR) and support vector regression (SVR). A total of 1779 observations were assembled from 436 peer-reviewed publications and open-access databases. Predictor variables included farm-ready variables such as milk yield, dry matter intake, days in milk, body weight, and dietary crude protein content. NPm was estimated based on the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) equations, while NPl was derived from milk true protein yield. The model adequacy was evaluated using 10-fold cross-validation. The RFR model demonstrated higher predictive performance than SVR for both NPm (R² = 0.82, RMSEP = 22.38 g/d, CCC = 0.89) and NPl (R² = 0.82, RMSEP = 95.17 g/d, CCC = 0.89), reflecting its capacity to model the rule-based nature of the NASEM equations. These findings suggest that RFR may provide a valuable approach for estimating protein requirements with fewer input variables. Further research should focus on validating these models under field conditions and exploring hybrid modeling frameworks that integrate mechanistic and machine learning approaches. Full article

Domestic wastewater discharge is the major source of pollution in Malaysia. Phytoremediation under hydroponic conditions was initiated to treat domestic wastewater and, at the same time, to resolve the space limitation issue by installing a hydroponic system in vertical space at the site. Water hyacinth (WH) was selected in this study to identify its performance of water hyacinth in removing nutrients in raw sewage under batch operation. In the batch experiment, the ratio of COD_initial/plant_initial was identified, and SPSS ANOVA analysis shows that the number of plant size factors was not statistically different in this study. Therefore, four WH, each with an initial weight of 60 ± 20 g, were recommended for this study. Throughout the 10 days of the batch experiment, the average of COD, BOD, TSS, TP, NH4, and color removal was 73%, 73%, 86%, 79%, 77%, and 54%, respectively. The WH biomass weight increased by an average of 78%. The plants have also improved the DO level from 0.24 mg/L to 4.88 mg/L. However, the pH of effluent decreased from pH 7.05 to pH 4.88 below the sewage Standard B discharge limit of pH 9–pH 5.50. Four WH plant groups were recommended for future study, as the COD removal among the other plant groups is not a statistically significant difference (p < 0.05). Furthermore, the lower plant biomass is preferable for the high pollutant removal performance due to the fact that it can reduce the maintenance and operating costs. Full article

This study examines the effects of polyester and denim microfibers (MFs) on aerobic granular sludge (AGS) over a 42-day period. Treatment performance, granulation, and microbial community changes were assessed at 0, 10, 70, 210, and 1500 MFs/L. Reactors with 70 MFs/L achieved rapid granulation and showed improved settling by day 9, while 0 and 10 MFs/L reactors showed delayed granule formation, which was likely due to limited nucleation and weaker shear conditions. Severe clogging and frequent maintenance occurred at 1500 MFs/L. Despite > 98% MF removal in all reactors, treatment performance declined at higher MF loads. Nitrogen removal dropped from 93% to 68%. Phosphate removal slightly increased in reactors with no or low microfiber loads (96–99%), declined in reactors with 70 or 210 MFs/L (92–91%, 89–88%), and dropped significantly in the reactor with1500 MFs/L (86–70%, p < 0.05). COD removal declined with increasing MF load. Paracoccus (denitrifiers) dominated low-MF reactors; Acinetobacter (associated with complex organic degradation) and Nitrospira (nitrite-oxidizing genus) were enriched at 1500 MFs/L. Performance decline likely stemmed from nutrient transport blockage and toxic leachates, highlighting the potential threat of MFs to wastewater treatment and the need for upstream MF control. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article