Search Results (89)

Microbiological contamination of healthcare workers’ gowns represents a critical risk for the transmission of healthcare-associated infections (HAIs). Despite their use as protective equipment, gowns can act as reservoirs of antibiotic-resistant bacteria, favouring the spread of pathogens between healthcare workers and patients. The presence of these resistant bacteria on healthcare workers’ gowns highlights the urgent need to address this risk as part of infection control strategies. The aim of this work was to assess the microbiological risks associated with the contamination of healthcare staff gowns with Gram-negative bacteria, including the ESKAPE group, and their relationship with antimicrobial resistance. An observational, cross-sectional, prospective study was conducted in 321 hospital workers. The imprinting technique was used to quantify the bacterial load on the gowns, followed by bacterial identification by MALDI-TOF mass spectrometry. In addition, antimicrobial resistance profiles were analysed, and tests for carbapenemases and BLEE production were performed. The ERIC-PCR technique was also used for molecular analysis of Pantoea eucrina clones. Several Gram-negative bacteria were identified, including bacteria of the ESKAPE group. The rate of microbiological contamination of the gowns was 61.05% with no association with the sex of the healthcare personnel. It was observed that critical areas of the hospital, such as intensive care units and operating theatres, showed contamination by medically important bacteria. In addition, some strains of P. eucrina showed resistance to carbapenemics and cephalosporins. ERIC-PCR analysis of P. eucrina isolates showed genetic heterogeneity, indicating absence of clonal dissemination. Healthcare personnel gowns are a significant reservoir of pathogenic bacteria, especially in critical areas of Hospital Juárez de México. It is essential to implement infection control strategies that include improving the cleaning and laundering of gowns and ideally eliminating them from clothing to reduce the risk of transmission of nosocomial infections. Full article

This study aimed to comprehensively review surgical interventions for ocular surface diseases (OSDs), including dry eye syndrome (DES), exposure keratopathy, Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and ocular graft versus host disease (oGVHD), and to highlight the indications, contraindications, outcomes, and complications of various oculoplastic procedures used in their management. A narrative review was performed based on expert-guided selection of relevant studies retrieved from PubMed, Scopus, and Web of Science. Relevant keywords included “ocular surface disease”, “dry eye syndrome”, “exposure keratopathy”, “thyroid eye disease (TED)”, “neurotrophic keratopathy (NK)”, “Stevens-Johnson syndrome”, “toxic epidermal necrolysis”, “punctal occlusion”, “tarsorrhaphy”, “botulinum toxin”, “eyelid loading”, “retractor weakening”, “corneal neurotization (CN)”, “amniotic membrane transplantation (AMT)”, “conjunctival flap”, “ocular graft versus host disease”, and “salivary gland transplantation (SGT)”. Studies addressing surgical approaches for OSDs were included. In conclusion, surgical options for OSDs offer significant benefits when non-invasive treatments fail. Surgical techniques such as punctal occlusion, eyelid fissure narrowing, AMT, and conjunctival flap procedures help stabilize the ocular surface and alleviate symptoms. Advanced methods like CN and SGT target the underlying pathology in refractory cases such as oGVHD. The outcomes vary depending on the disease severity and surgical approach. Each procedure carries specific risks and requires individualized patient selection. Therefore, a tailored approach based on clinical condition, anatomical involvement, and patient factors is essential to achieve optimal results. Ongoing innovations in reconstructive surgery and regenerative medicine are expected to further improve outcomes for patients with OSDs. Full article

Cylindrical roller bearings are widely used in industrial machinery, automotive systems, and aerospace applications, where their reliability directly affects the performance and safety of mechanical systems. The fatigue life of cylindrical roller bearings is significantly affected by their elastohydrodynamic lubrication condition, with variations potentially reaching multiple times. However, conventional quasi-static models often neglect lubrication effects. This study establishes a quasi-static analysis model for cylindrical roller bearings that incorporates the effects of elastohydrodynamic lubrication by integrating elastohydrodynamic lubrication theory with the Lundberg–Palmgren life model. The isothermal line contact elastohydrodynamic lubrication equations are solved using the multigrid method, and the contact load distribution is determined through nonlinear iterative techniques to calculate bearing fatigue life. Taking the N324 support bearing on the main shaft of an SFW250-8/850 horizontal hydro-generator as an example, the influences of radial load, inner race speed, and lubricant viscosity on fatigue life are comparatively analyzed. Experimental validation is conducted under both light-load and heavy-load operating conditions. The results demonstrate that elastohydrodynamic lubrication markedly increases contact loads, leading to a reduced predicted fatigue life compared with that of the De Mul model (which ignores lubrication). The proposed lubrication-integrated model achieves an average deviation of 5.3% from the experimental data, representing a 16.1% improvement in prediction accuracy over the De Mul model. Additionally, increased rotational speed and lubricant viscosity accelerate fatigue life degradation. Full article

Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces $MsMA$, a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, $MsMA$ divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques. Full article

Electric power load forecasting is essential for the efficient operation and strategic planning of utilities. Decisions regarding the electric market, power generation, load management, and infrastructure development all rely on accurate load predictions. This work presents a novel methodology for day-ahead load forecasting. The approach employs a long short-term memory neural network (LSTM NN) trained on representative load and meteorological data from the region. Before training, the load dataset is grouped by its statistical seasonality through K-means clustering analysis. Clustering load demand, along with similar-day data management, enables more focused training of the LSTM network on uniform data subsets, enhancing the model’s ability to capture temporal patterns and reducing the complexity associated with high variability in demand data. A case study using hourly load demand time-series data provided by the Centro Nacional de Control de Energía (CENACE) is analyzed, and the mean absolute percentage error (MAPE) is calculated, showing lower MAPE than traditional methods. This hybrid approach demonstrates the potential of integrating clustering techniques with neural networks and representative meteorological data from the region to achieve more reliable and accurate regional day-ahead load forecasting. Full article

The security of a network is based on the foundation of confidentiality, integrity, and availability, often referred to as the CIA triad. The privacy of data over a network, maintained by confidentiality, has long been one of the major issues in network settings. With the decoupling of the data plane and control plane in the software-defined networking (SDN) environment, this challenge is significantly amplified. This paper aims to address the challenges of confidentiality in SDN by introducing a genetic algorithm-based hybrid encryption network policy to secure messages across the network. The proposed approach achieved an average entropy of 0.989, revealing a significant improvement in the strength of the encryption with the hybrid mechanism. However, the method exhibited processing overhead, significantly increasing the transmission time for encrypted messages compared to unencrypted transmission. Compared to standalone AES, DES, and RSA, this approach shows better encryption randomness, but a trade-off between security and network performance is evident in the absence of load-balancing techniques. Full article

Unlike traditional tap changers, which require transformers to be de-energized before making changes, On-Load Tap Changers (OLTCs) can adjust taps while the transformer is in service, ensuring continuous power supply during voltage regulation. OLTCs enhance grid reliability and support load balancing, reducing strain on the network and optimizing power quality. Their importance has grown as the demand for stable voltage and the integration of renewables has increased, making them vital for modern and resilient power systems. While enhanced OLTCs often incorporate stronger materials and improved designs, mechanical components like contacts and diverter switches can still experience wear over time. This can result in longer maintenance intervals. In the era of digitalization, advanced diagnostic techniques capable of detecting early signs of wear or malfunction are essential to enable preventive maintenance for these important components. This contribution introduces a novel method for detecting faults and irregularities in OLTCs, leveraging vibroacoustic signals to enhance OLTC diagnostics. This paper proposes a tolerance-based approach using the envelope of vibroacoustic signals to identify faults. A significant challenge in this field is the limited availability of faulty signal data, which hinders the performance of machine learning algorithms. To address this, this study introduces a nonlinear model utilizing amplitude modulation with a Gaussian carrier to simulate faults by introducing controlled distortions. The dataset used in this study, with data recorded under real operating conditions from 2016 to 2023, is free of anomalies, providing a robust foundation for the analysis. The results demonstrate a marked improvement in the robustness of detecting simulated faults, offering a promising solution for enhancing OLTC diagnostics and preventive maintenance in modern power systems. Full article

In this paper, a novel dual-slot-fed dielectric resonator antenna (DRA) with rectangular and irregular elements, designed for 5G wireless applications, is presented. The DRA achieves wideband capability by combining the resonant modes of the rectangular and irregular DRA elements, which is a less common feature in conventional designs. A frequency ratio adjustment technique, based on the concept of inductive de-loading, is uniquely proposed for the independent frequency adjustment of the irregular DRA. Unlike traditional methods, an equivalent circuit presentation was developed to interpret the impedance characteristics of single-element DRAs, and to provide new insights into the presence of inductive de-loading from a circuit perspective. For verification, a dual-slot-fed prototype was fabricated through digital light processing (DLP)-based 3D printing technology, with the aim of customizable design and low-cost fabrication. The measured and simulated results of reflection coefficients and radiation patterns showed good agreements, with a measured bandwidth of 51.6% (2.96–5.02 GHz), effectively covering the desired 5G n77–n79 (3.3–5.0 GHz) frequency bands. Full article

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. To achieve this goal, this paper builds on DEN2DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. Results indicate that the RF model with Recursive Feature Elimination (RFECV) achieves 94.28% precision and 81.05% recall, surpassing SVM (precision 89.32%, recall 6.95%) and ANN (precision 72.17%, recall 13.49%) in fault detection accuracy and reliability. Full article

This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This study was carried out on the Tower of the Cathedral “Santa Iglesia Catedral de Santa María” in Murcia, Spain. The novel distribution of a continuous nonlinear profile along the outer and inner perimeters of the Tower allowed us to obtain a 3D ERT model of the subsoil, even under its load-bearing walls. This nonlinear configuration of the electrodes allowed us to reach adequate investigation depths in buildings with limited interior and exterior space for data collection without disturbing the historic structure. The ERT results were compared with GPR measurements and with information from archaeological excavations conducted in 1999 and 2009. The geometry and distribution of the cavities in the entire foundation slab of the Tower were determined, verifying the proposed procedure. This methodology allows the acquisition of a detailed understanding of the singularities of the foundations of protected historic towers in urban areas with limited space, reducing time and costs and avoiding the use of destructive techniques, with the aim of implementing a more efficient and effective strategy for the protection of other tower foundations. Full article

Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time. Full article

De-coring is an essential process in the casting process chain, determining the quality and cost of production. In this study, a coupled multibody system (MBS) and finite element modeling (FEM) technique is presented to study the mechanical loads during the de-coring process. The removal of cast-in sand cores from the inner regions of the cast part by de-coring or knocking out is a complex process with dynamic loads. Currently, the process relies upon empirical knowledge and tests. Inorganic sand cores pose additional challenges in the success of the de-coring process. Increasing complexity in geometry and stringent environmental regulations compel a predictive process in the earlier stages of design. Predicting the process’ success is challenged by the dynamic non-linearities of the system. The dynamic characteristics and the interaction between hammer and casting were studied here for the first time using an industrial-based test rig, and a novel modeling approach was formulated. The results of the developed model are in good compliance with the experiments. The methodology presented in this study can be used to include a varying number of hammers and loads. The proposed approach presents the possibility to discretize the process and qualitatively assess the process parameters for optimization. Full article

In this study, ceria nanoparticles (NPs) and deep eutectic solvent (DES) were synthesized, and the ceria-NP’s surfaces were modified by DES to form DES-ceria NP filler to develop mixed matrix membranes (MMMs). For the sake of interface engineering, MMMs of 2%, 4%, 6% and 8% filler loadings were fabricated using solution casting technique. The characterizations of SEM, FTIR and TGA of synthesized membranes were performed. SEM represented the surface and cross-sectional morphology of membranes, which indicated that the filler is uniformly dispersed in the polysulfone. FTIR was used to analyze the interaction between the filler and support, which showed there was no reaction between the polymer and DES-ceria NPs as all the peaks were consistent, and TGA provided the variation in the membrane materials with respect to temperature, which categorized all of the membranes as very stable and showed that the trend of stability increases with respect to DES-ceria NPs filler loading. For the evaluation of efficiency of the MMMs, the gas permeation was tested. The permeability of CO₂ was improved in comparison with the pristine Polysulfone (PSF) membrane and enhanced selectivities of 35.43 ($\mathsf{\alpha }$_CO2/CH4) and 39.3 ($\mathsf{\alpha }$_CO2/N2) were found. Hence, the DES-ceria NP-based MMMs proved useful in mitigating CO₂ from a gaseous mixture. Full article

Contra-rotating fans (CRFs) have garnered significant attention due to their higher power-to-weight ratio compared to traditional fans; however, limited focus has been given to the localization and development of local aerodynamic losses. Furthermore, there is a need for further research on the impact of load distribution along the radius on local entropy production. Therefore, this study aims to investigate a contra-rotating fan as the research subject. An optimal design for load distribution along the radius is achieved by constructing a surrogate model in combination with a genetic algorithm. The effectiveness of this design has been verified through experimentation using a specific test device. In this study, a local entropy production rate (EPR) model adapted to the shear stress transport-detached eddy simulation (SST-DES) technique is constructed to evaluate the loss distribution of the contra-rotating fan. This paper primarily focuses on comparing and analyzing the blade profile and overall performance of the CRFs before and after optimization. The EPR contribution of each interval along the radius is compared to the corresponding blade channel to identify the approximate range of high-EPR regions. Furthermore, an investigation is conducted to examine the distribution of EPR along the streamwise direction in these high-EPR regions. After that, by comparing the development of the flow structure near a stall before and after optimization, combined with the analysis of the EPR contours, the EPR mechanism of this CRF is revealed. Full article

Energy demand is continuously increasing, leading to yearly expansions in low-voltage (LV) distribution systems integrated with PVs to deliver electricity to users with techno-economic considerations. This study proposes and compares different topology planning strategies with and without PVs in a rural area of Cambodia over 30 years of planning. Firstly, the optimal radial topology from a distribution transformer to end-users is provided using the shortest path algorithm. Secondly, two different phase balancing concepts (i.e., pole balancing and load balancing) with different phase connection methods (i.e., power losses and energy losses) are proposed and compared to find the optimal topology. Then, the integration of centralized (CePV) and decentralized PV (DePV) into the optimal topology is investigated for three different scenarios, which are zero-injection (MV and LV levels), no sell-back price, and a sell-back price. Next, the minimum sell-back price from CePV and DePV integration is determined. To optimize phase balancing, including the location and size of PV, an optimization technique using a water cycle algorithm (WCA) is applied. Finally, an economic analysis of each scenario based on the highest net present cost (NPC), including capital expenditure (CAPEX) and operational expenditure (OPEX) over the planning period, is evaluated. In addition, technical indicators, such as autonomous time and energy, and environmental indicator, which is quantified by CO₂ emissions, are taken into account. Simulation results validate the effectiveness of the proposed method. Full article