Search Results (777)

The selection of an appropriate operating system (OS) in educational environments is a critical decision that impacts the functionality, user experience, and overall efficiency. Financial factors, along with the availability and functionality of tools used on these systems, play a crucial role in this selection. Furthermore, the OS affects the user experience, security, and adaptability to learning. Previous research in this area is scarce, and this paper contributes to a better understanding of the OS selection process in education. This paper proposes a novel multi-criteria decision-making approach to evaluate and select the most suitable OS for educational institutions. The methodology integrates the PIPRECIA method for assessing weighted criteria, alongside utility analysis (NWA), enabling a balanced decision-making process between institutional management and IT experts. The evaluation considered the following criteria: performance, cost, security, usability, implementation, support, and documentation. The study compared three popular operating systems: Microsoft Windows 11, GNU Linux Ubuntu 22.04 LTS, and Apple macOS 12 using the proposed approach. The results, based on the integrated evaluation of all criteria, indicate that GNU Linux Ubuntu 22.04 LTS (0.562) ranked highest, followed by Microsoft Windows 11 (0.553) and Apple macOS 12 (0.543). This paper emphasizes the importance of objective, group-based decision-making in selecting an OS for education, providing practical insights and guidelines for effective technology integration in academic settings. Full article

“Whence the strength?” This compelling question, posed by Aaron Antonovsky in 1979, sets the stage for understanding the role of sense of coherence (SOC), a human-focused psychosocial concept, in fostering resilience amidst escalating climate-induced disasters such as hurricanes, floods, and earthquakes. This paper is the first step in a larger research agenda aimed at exploring how the human experience of disasters, guided by Antonovsky’s SOC framework, can be better integrated into disaster recovery planning and design, laying the theoretical foundation for subsequent studies. This paper examines which supports help people stay resilient during disasters, focusing on the role of SOC in recovery. By integrating Antonovsky’s SOC concept with Hobfoll’s Conservation of Resources (COR) theory, it also draws from other published works on stress and disaster recovery to explore how disaster recovery planning and design can be improved. The findings indicate that the post-disaster recovery phase presents a critical window for implementing policies that address vulnerabilities in disaster-prone communities and enhance long-term resilience. Methodologically, this paper advocates for an interdisciplinary approach, suggesting that both quantitative and qualitative insights are vital for capturing human experiences in disaster contexts. Ultimately, this paper presents a framework for integrating human dimensions of resilience into disaster recovery planning. Full article

Pressure transmitters are widely used in the process industry for pressure measurement. The sensing line, a core component of the pressure sensor in the pressure transmitter, significantly impacts the accuracy of the pressure transmitter’s output. The reliability of pressure transmitters is critical in the nuclear power industry. Blockage is recognized as a common failure in pressure sensing lines; therefore, a novel detection method based on Trend Features in Time–Frequency domain characteristics (TFTF) is proposed in this paper. The dataset of pressure transmitters comprises both fault and normal data. This method innovatively integrates multi-scale time series decomposition algorithms with time-domain and frequency-domain feature extraction techniques. Initially, this dataset is decomposed into multi-scale time series to mitigate periodic component interference in diagnosis. Subsequently, via the sliding window algorithm, both the time-domain features and frequency-domain features of the trend components are extracted, and finally, the XGBoost algorithm is used to detect faults. The experimental results demonstrate that the proposed TFTF algorithm achieves superior fault detection accuracy for diagnosing sensing line blockage faults compared with traditional machine learning classification algorithms. Full article

Seizure prediction is a critical challenge in epilepsy management, offering the potential to improve patient outcomes through timely interventions. This study proposes a novel framework combining a convolutional neural network (CNN) based on EfficientNet-B0 and an ensemble of six Support Vector Machines (SVMs) with a voting mechanism for robust seizure prediction. The framework leverages normalized Short-Time Fourier Transform (STFT) and channel correlation features extracted from EEG signals to capture both spectral and spatial information. The methodology was validated on the CHB-MIT dataset across preictal windows of 10, 20, and 30 min, achieving accuracies of 96.12%, 94.89%, and 94.21%, and sensitivities of 95.21%, 93.98%, and 93.55%, respectively. Comparing the results with state-of-the-art methods, we highlight the framework’s robustness and adaptability. The EfficientNet-B0 backbone ensures high accuracy with computational efficiency, while the SVM ensemble enhances prediction reliability by mitigating noise and variability in EEG data. Full article

Due to cyanobacterial toxin (cyanotoxin) contamination issues in 2018, the city of Salem, Oregon, issued a 33-day do-not-drink advisory for vulnerable people among the 200,000 residents. After the incident, the state of Oregon put in place drinking water rules to require the routine testing of raw water, as well as finished water, in cases where the raw water cyanotoxin concentrations exceeded trigger values. The United States Environmental Protection Agency (EPA) total microcystins drinking water health advisory level (HAL) for small children is 0.3 µg/L. This is equivalent to the minimum reporting level (MRL) for EPA Method 546. Consequently, there was no ability to provide early warnings via toxin testing for total microcystins using the EPA method. In this study, we performed a comparison of the precision and accuracy of the enzyme-linked immunosorbent assay (ELISA) described in the EPA method to a more sensitive assay, the Streptavidin-enhanced Sensitivity (SAES) assay. Based on these precision and accuracy studies and quantitation limit determinations and confirmations, the EPA Office of Ground Water and Drinking Water (OGWDW) has concluded the SAES kit meets the requirements of EPA Method 546. With an MRL that is one-third of the original concentration, the new kit provides a small but critical window for identifying early warnings. Challenges remain with providing early warnings due to the variability in bloom dynamics; however, the new MRL allowed Oregon to lower the trigger level for susceptible systems, thereby providing an additional early warning. Full article

Topoisomerase II inhibitors, particularly etoposide, have long been integral to the treatment of lung cancer, especially small cell lung cancer. This review comprehensively examines the mechanisms of action of etoposide, its clinical efficacy, and its role in current lung cancer treatment regimens. Etoposide exerts its anticancer effects by inducing DNA strand breaks through the inhibition of topoisomerase II, leading to cancer cell apoptosis. Despite their widespread use, challenges such as drug resistance, toxicity, and limited efficacy in non-small cell lung cancer have spurred ongoing research on combination therapies and novel drug formulations. Emerging therapeutic strategies include the integration of etoposide with immunotherapy, targeted therapies, and novel drug delivery systems aimed at enhancing the therapeutic window and overcoming drug resistance. This article aims to inform the development of more effective treatment strategies by providing a critical overview of the clinical applications of etoposide and exploring future directions for lung cancer therapy. Full article

Environmental changes, such as applied medication, nutrient depletion, and accumulation of metabolic residues, affect cell culture activity. The combination of these factors reflects on the local temperature distribution and local oxygen concentration towards the cell culture scaffold. However, determining the temporal variation of local temperature, independent of local oxygen concentration changes in biological specimens, remains a significant technological challenge. The process of triplet–triplet annihilation upconversion (TTA-UC), performed in a nanoconfined environment with a continuous aqueous phase, appears to be a possible solution to these severe sensing problems. This process generates two optical signals (delayed emitter fluorescence (dF) and residual sensitizer phosphorescence (rPh)) in response to a single external stimulus (local temperature), allowing the application of the ratiometric-type sensing procedure. The ability to incorporate large amounts of sacrificial singlet oxygen scavenging materials, without altering the temperature sensitivity, allows long-term protection against photo-oxidative damage to the sensing moieties. Translucent agarose/silk fibroin hydrogels embedding non-ionic micellar systems containing energetically optimized annihilation couples simultaneously fulfill two critical functions: first, to serve as mechanical support (for further application as a cell culture scaffold); second, to allow tuning of the material response window to achieve a maximum temperature sensitivity better than 0.5 K for the physiologically important region around 36 °C. Full article

Current global conditions and challenges in industrial manufacturing, marked by dynamism, competition, and the need for responsible resource management, have increased the demand for sustainable manufacturing practices. The integration of Industry 4.0 and the recent development of Industry 5.0 have added dynamism, which has generated profound implications for quality control and process monitoring, focusing mainly on recognising control patterns within the manufacturing environment. This study introduces a novel methodology for evaluating the performance of pattern classification models used in advanced quality control. Our approach incorporates robust performance metrics, early detection, window size, network hyperparameters, and concurrent patterns within a simulated monitoring environment. Unlike previous research, our evaluation methodology addresses the sensitivity of classification models to various factors, emphasising the critical balance between early detection and minimising false alarms. The findings reveal that window size significantly impacts the model’s sensitivity to pattern changes, highlighting that measuring early detection alone is impractical in real-world applications. Furthermore, optimal hyperparameter selection enhances the model’s practical applicability. Full article

The window of implantation (WOI) is a critical phase of the menstrual cycle during which the endometrial lining becomes receptive and facilitates embryo implantation. Drawing on findings from various branches of “omics”, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, metabolomics, and microbiomics, this narrative review aims to (1) discuss mechanistic insights on endometrial receptivity and its implication in infertility; (2) highlight advances in investigations for endometrial receptivity; and (3) discuss novel diagnostic and therapeutic strategies that may improve reproductive outcomes. Full article

As the Industrial Internet of Things (IIoT) increasingly integrates with traditional networks, advanced persistent threats (APTs) pose significant risks to critical infrastructure. Traditional Intrusion Detection Systems (IDSs) and Anomaly Detection Systems (ADSs) are often inadequate in countering sophisticated multi-step APT attacks. This highlights the necessity of studying attacker strategies and developing predictive models to mitigate potential threats. To address these challenges, we propose DeepOP, a hybrid framework for attack sequence prediction that combines deep learning and ontological reasoning. DeepOP leverages the MITRE ATT&CK framework to standardize attacker behavior and predict future attacks with fine-grained precision. Our framework’s core is a novel causal window self-attention mechanism embedded within a transformer-based architecture. This mechanism effectively captures local causal relationships and global dependencies within attack sequences, enabling accurate multi-step attack predictions. In addition, we construct a comprehensive dataset by extracting causally connected attack events from cyber threat intelligence (CTI) reports using ontological reasoning, mapping them to the ATT&CK framework. This approach addresses the challenge of insufficient data for fine-grained attack prediction and enhances the model’s ability to generalize across diverse scenarios. Experimental results demonstrate that the proposed model effectively predicts attacker behavior, achieving competitive performance in multi-step attack prediction tasks. Furthermore, DeepOP bridges the gap between theoretical modeling and practical security applications, providing a robust solution for countering complex APT threats. Full article

Supraventricular arrhythmias (SVAs), including the often-asymptomatic supraventricular extrasystole (SVE), pose significant challenges in early detection and precise diagnosis. These challenges are of paramount importance, as recurrent SVEs may elevate the risk of developing severe SVAs, potentially resulting in cardiac weakening and subsequent heart failure. In the study conducted, an innovative approach was introduced that combined a convolutional neural network (CNN) architecture to enable the early identification and characterization of SVEs within electrocardiogram (ECG) signals. The analysis leveraged a dataset comprising 78 half-hour recordings from the highly regarded MIT-BIH Arrhythmia Database, which included annotation headers serving as labels for each recording. Signals were down-sampled by a factor of 2 and split into windows of 512 samples, with 12,288 observations for training. Following the methodology, classic signal preprocessing techniques (filtering and data normalization) were used. The proposed model was based on the UNET 1D model. A binary cross-entropy loss function, Adam optimizer, and a batch size of 128 were obtained after a hyperparameter tuning. As a training-validation methodology, a 50-fold cross-validation technique was used. The approach demonstrated a Dice coefficient of 79.01%, a precision of 80.96%, and a recall rate of 86.60% in detecting SVE events. These findings were corroborated through meticulous comparison with the annotations provided by the MIT-BIH database. The results underscore the immense potential of CNN and deep learning techniques in the early detection of supraventricular arrhythmias. This approach not only offers a valuable tool for healthcare professionals engaged in telemonitoring and early intervention strategies but also represents a significant contribution to the field of cardiac health monitoring. By facilitating efficient and precise identification of SVEs, our research sets the stage for improved patient outcomes and the prevention of severe SVAs, marking substantial advancements in this critical domain. Full article

With the escalating demand for automation in chemical laboratories, multi-robot systems are assuming an increasingly prominent role in chemical laboratories, particularly in the task of transporting reagents and experimental materials. In this paper, we propose a multi-robot path planning approach based on the combination of the A* algorithm and the dynamic window algorithm (DWA) for optimizing the efficiency of reagent transportation in chemical laboratories. In environments like chemical laboratories, dynamic obstacles (such as people and equipment) and transportation tasks that demand precise control render traditional path planning algorithms challenging. To address these issues, in this paper, we incorporate the cost information from the current point to the goal point into the evaluation function of the traditional A* algorithm to enhance the search efficiency and add the safety distance to extract the critical points of the paths, which are utilized as the temporary goal points of the DWA algorithm. In the DWA algorithm, a stop-and-wait mechanism and a replanning strategy are added, and a direction factor is included in the evaluation function to guarantee that the robots can adjust their paths promptly in the presence of dynamic obstacles or interference from other robots to evade potential conflicts or traps, thereby reaching the goal point smoothly. Additionally, regarding the multi-robot path conflict problem, this paper adopts a dynamic prioritization method, which dynamically adjusts the motion priority among robots in accordance with real-time environmental changes, reducing the occurrence of path conflicts. The experimental results highlight that this approach effectively tackles the path planning challenge in multi-robot collaborative transportation tasks within chemical laboratories, significantly enhancing transportation efficiency and ensuring the safe operation of the robots. Full article

Path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in power line inspection. In complex inspection environments characterized by densely distributed and dynamic obstacles, traditional path-planning algorithms struggle to ensure both efficiency and safety. To address these challenges, this study proposes a dynamic path-planning method that integrates an improved Rapidly exploring Random Tree Star (RRT*) algorithm with the Dynamic Window Approach (DWA). The proposed method includes key components such as sampling-point search, random tree growth, global path-node optimization, and local dynamic obstacle avoidance. In the sampling-point search, a target-biased search strategy is introduced to guide the random tree growth toward the target point, while an attractive function is added to enhance search efficiency. Based on a breadth-first search strategy, the path obtained is optimized to reduce path complexity. To address the RRT* algorithm’s limitation in dynamic obstacle avoidance, a local path-planning method combining the improved DWA algorithm is proposed, improving efficiency in areas with dense obstacles. Simulation results show that, compared to traditional algorithms, the proposed method achieves an 8% to 12% optimization in path length, more than 50% in node optimization, and over 95% in planning time optimization. Furthermore, in dynamic obstacle avoidance across different motion directions, the proposed method ensures effective local dynamic obstacle avoidance while minimizing global path fluctuations. Full article

The present study introduces an innovative blown coextrusion die technology designed to address a critical gap in the production of multilayer films. Unlike conventional systems, this novel die allows for the creation of films with a high number of layers, ensuring layer integrity even in the micro-nano scale. A key advancement of this die is its ability to increase the number of layers without extending the residence time since it does not require an additional multiplier element. The risk of thermal degradation can, thus be, minimized. The die can easily be combined with existing cast coextrusion technologies, making it very versatile. Stability maps were developed to define processability and, in association with rheological analysis, optimal processing windows were determined. This study highlights the potential of enhancing material efficiency by increasing the number of layers while reducing the need for high percentages of EVOH. The produced multilayer films exhibited strong layer adhesion without the use of tie layers, thus improving recyclability and supporting sustainability goals. Full article

Silicon–glass anode bonding is the key technology in the process of wafer-level packaging for MEMS sensors. During the anodic bonding process, the device may experience adhesion failure due to the influence of electric field forces. A common solution is to add a metal shielding layer between the glass substrate and the device. In order to solve the problem of device failure caused by the electrostatic attraction phenomenon, this paper designed a double-ended solidly supported cantilever beam parallel plate capacitor structure, focusing on the study of the critical size of the window opening in the metal layer for the electric field shielding effect. The metal shield consists of 400 Å of Cr and 3400 Å of Au. Based on theoretical calculations, simulation analysis, and experimental testing, it was determined that the critical size for an individual opening in the metal layer is 180 μm × 180 μm, with the movable part positioned 5 μm from the bottom, which does not lead to failure caused by stiction due to electrostatic pull-in of the detection structure. It was proven that the metal shielding layer is effective in avoiding suction problems in secondary anode bonding. Full article