Robust System Research Articles

Purpose-Oriented Networks and System Robustness: Core Redundancy and Diversity

Purpose-Oriented Networks and System Robustness: Core Redundancy and Diversity

Purpose-Oriented Networks and System Robustness: Core Redundancy and Diversity

Purpose-Oriented Networks and System Robustness: Core Redundancy and Diversity

Emotion recognition from Burmese speech based on fused features and deep learning method

Burmese language is challenging for speech emotion classification. Moreover, it is lack of resource and few research was made in this topic. To solve the challenging problem, novel feature extraction for Burmese language is proposed. For lack of resource, Burmese speech emotion corpus called BMISEC is built. To support the challenging problem, the advantages of feature extractions are fused to create a robust feature. Four features are fused. Novel text-tone feature, local binary pattern, mel-frequency cepstral coefficient and discrete wavelet transform are fused. To progress the performance, deep learning method called DenseNet-Emotion is used for classification. Support vector machine is used in DenseNet’s classifier layer. To show the robustness of the proposed system, three types of experiments are made on Tensorflow framework. They are ablation study, experiments with three publicly available datasets and experiments with the previous research methods and they are compared with the proposed method. It is found that feature fusion is superior to only one feature in emotion recognition. BMISEC gets better performance than other datasets. Moreover, the proposed method gets the superior result than previous research methods. The proposed method gets the accuracy of 88.388% for 50 epochs.

Extracorporeal Cardiopulmonary Resuscitation: CME Review.

Extracorporeal cardiopulmonary resuscitation (eCPR) is increasingly being used for refractory cardiac arrest for both in-hospital and out-of-hospital cardiac arrests. The term eCPR refers to cannulating a patient to an extracorporeal membrane oxygenation (ECMO) circuit to provide perfusion after cardiac arrest refractory to standard cardiopulmonary resuscitation. Extracorporeal cardiopulmonary resuscitation has been shown to offer increased survival benefit among a select group of adult and pediatric patients experiencing refractory cardiac arrests, both in hospital and out of hospital. Extracorporeal cardiopulmonary resuscitation should be considered when (1) the cardiac arrest is witnessed, (2) the patient receives high-quality cardiopulmonary resuscitation, (3) the patient is at or in close proximity to an ECMO center, (4) there is a reversible cause for the cardiac arrest where the perfusion from the ECMO circuit serves as a bridge to recovery, and (5) the treating facility has a robust multidisciplinary system in place to facilitate rapidly moving patients from site of arrest to site of cannulation to intensive care unit. To develop an eCPR system of care, a multidisciplinary team consisting of prehospital, emergency medicine, in-hospital, proceduralist, perfusionist, and intensive care medical professionals must be established who support the use of eCPR for refractory cardiac arrest. The future of eCPR is the development of systems of care that use eCPR for a narrow subset of pediatric out-of-hospital cardiac arrests.

Development of a Quality Deterioration Index for Sustainable Quality Management in High-Tech Electronics Manufacturing

In high-tech electronics manufacturing, non-quality costs significantly impact organizational profitability and competitiveness. This case study introduces a novel Quality Deterioration Index (QDI) to systematically identify and prioritize root causes of non-quality costs within a leading electronics manufacturer. The primary objective is to integrate sustainable quality management practices that align with green sustainability objectives, such as reducing electronic waste, improving energy efficiency, and minimizing hazardous materials usage. Our comprehensive methodology encompasses a literature review, interviews, document analysis, and statistical analysis of survey data to uncover the influence of procedural, cultural, and environmental factors on quality deviations. The key findings reveal critical areas for improvement, particularly in supply chain inefficiencies, workforce challenges, and procedural gaps. By employing the QDI, we provide a structured framework that enhances both operational efficiency and environmental performance. The novelty of this research lies in its dual approach to simultaneously address economic and environmental performance, offering actionable insights for manufacturers aiming to integrate robust quality management systems with sustainability objectives. This study contributes to the ongoing dialogue on sustainable manufacturing strategies, underscoring the pivotal role of quality management in achieving both economic viability and environmental stewardship. Future research should expand this approach across various industries and global contexts to validate and refine the integration of quality management and sustainability.

Comprehensive Metatranscriptomic Analysis of Plant Viruses in Imported Frozen Cherries and Blueberries

The possibility of new viruses emerging in various regions worldwide has increased due to a combination of factors, including climate change and the expansion of international trading. Plant viruses spread through various transmission routes, encompassing well-known avenues such as pollen, seeds, and insects. However, research on potential transmission routes beyond these known mechanisms has remained limited. To address this gap, this study employed metatranscriptomic analysis to ascertain the presence of plant viruses in imported frozen fruits, specifically cherries and blueberries. This analysis aimed to identify pathways through which plant viruses may be introduced into countries. Virome analysis revealed the presence of six species of plant viruses in frozen cherries and blueberries: cherry virus A (CVA), prunus necrotic ringspot virus (PNRSV), prune dwarf virus (PDV), prunus virus F (PrVF), blueberry shock virus (BlShV), and blueberry latent virus (BlLV). Identifying these potential transmission routes is crucial for effectively managing and preventing the spread of plant viruses and crop protection. This study highlights the importance of robust quality control measures and monitoring systems for frozen fruits, emphasizing the need for proactive measures to mitigate the risk associated with the potential spread of plant viruses.

Data access control for named data of health things

The internet of health things (IoHT) represents an innovative network concept that significantly improving healthcare. However, security and privacy are the main concerns of IoHT because the transmitted health data is often sensitive data about patients’ health status, which needs to be secured and protected from unauthorized users and any leakage. Named data networking (NDN) is considered the most promising architecture for the future internet that perfectly fits with the requirements of IoHT systems, especially regarding security and privacy. In this paper, we exploit the fundamental features of NDN to design a robust system for IoHT to ensure secure communication and access to health data. This system presents a content access control model, which prevents attackers and unauthorized users from accessing health data, allows only authorized users to access these data, and prevents users from accessing “corrupted” or “fake” content. The simulation results show that the proposed mechanism slightly delays the secure retrieval of health data. However, this delay is tolerable since the mechanism protects the health data from unauthorized persons and those who try to inject untrusted data into the network.

Folk art classification using support vector machine

<p dir="ltr">Tremendous amounts of effort have been carried out every year by the governments of all the countries to preserve art and culture. Art in the form of paintings, artifacts, music, dance, and cuisines of every country has the utmost importance. The study of Tribal arts provides deep insight into our history and acts as a milestone in the roadmap of our future. This paper focuses on three popular folk arts namely: Gond, Manjusha, and Warli. 300 images of each artwork have been collected from various online repositories. To generate a robust system, data augmentation is applied which results in 7510 images. A feature vector based on a generalized co-occurrence matrix, local binary pattern, HSV histogram, and canny edge detector is constructed and classification is performed using a linear support vector machine. 10- fold cross-validation produces 99.8% accuracy.</p>

A Robust Optimization Model for Emergency Location Considering the Uncertainty and Correlation of Transportation Network Capacity

Emergencies often lead to the impairment of infrastructure systems, including transportation systems. It is necessary to analyze the uncertainty and correlation of transportation network capacity caused by emergencies, aiming at the problems of emergency facilities’ location and matching in emergency contexts. This study introduces novel concepts, such as flow distribution betweenness centrality (FD-BC) and the transport capacity effect coefficient (TC-EC). Furthermore, we introduce the ellipsoidal uncertainty set to characterize uncertainties in transport capacity. We construct a multi-criteria decision-making (MCDM) model and a multi-strength elitist genetic algorithm (multi-SEGA) to ensure the lower limit of transport capacity between demand and emergency points while minimizing decision-making costs. By designing an uncertain scenario example, we analyze the effect of the perturbation ratio and the uncertainty level on the robust location model. The following results were drawn: (1) Indicators FD-BC and TC-EC effectively indicated the importance of each section in the emergency transportation network. (2) The optimal value of the model’s objective function changed more significantly as the perturbation ratio and uncertainty level increased. (3) After reaching a certain uncertainty level, the robust model with an ellipsoidal uncertainty set became more conservative than the robust model with a box uncertainty set, which lacked practical significance. The research results guarantee the robustness of the emergency support system in uncertain conditions.

High-throughput detection of potential bacteriocin producers in a large strain library using live fluorescent biosensors

The global increase in antibiotic resistances demands for additional efforts to identify novel antimicrobials such as bacteriocins. These antimicrobial peptides of bacterial origin are already used widely in food preservation and promising alternatives for antibiotics in animal feed and some clinical setting. Identification of novel antimicrobials is facilitated by appropriate high throughput screening (HTS) methods. Previously, we have described a rapid, simple and cost-efficient assay based on live biosensor bacteria for detection of antimicrobial compounds that act on membrane integrity using the ratiometric pH-dependent fluorescent protein pHluorin2 (pHin2). Here, we use these biosensors to develop an integrated pipeline for high-throughput identification of bacteriocin producers and their biosynthetic gene clusters. We extend the existing portfolio of biosensors by generating pHin2 expressing strains of Escherichia coli, Bacillus cereus, Staphylococcus epidermidis, and methicillin-resistant Staphylococcus aureus. These strains were characterized, and control experiments were performed to assess heterogeneity of these biosensors in response to known bacteriocins and develop a robust HTS system. To allow detection of compounds that inhibit target bacteria by inhibiting growth without disturbing membrane integrity, the HTS system was extended with a growth-dependent readout. Using this HTS system, we screened supernatants of a total of 395 strains of a collection of lactic acid bacteria. After two rounds of screening 19 strains of the collection were identified that produced antimicrobial activity against Listeria innocua and Listeria monocytogenes. Genomes of confirmed hits were sequenced and annotated. In silico analysis revealed that the identified strains encode between one and six biosynthetic gene clusters (BGCs) for bacteriocins. Our results suggest that pHin2 biosensors provides a flexible, cheap, fast, robust and easy to handle HTS system for identification of potential bacteriocins and their BGCs in large strain collections.

Walrus optimizer-based optimal fractional order PID control for performance enhancement of offshore wind farms

Offshore wind farms (OWFs) play a crucial role in producing renewable energy in modern electrical power systems. However, to ensure that these facilities operate smoothly, they require robust control systems. As a result, this paper employed the newly developed Walrus Optimization algorithm (WaOA) to optimize the design parameters of fractional-order proportional-integral-derivative (FOPID) controllers in the power electronic interface circuits of the studied wind energy conversion system (WECS). In contrast to conventional optimization techniques like GA and PSO, the suggested approach proves more effective. The paper validates the WaOA application in optimizing FOPID controllers within a WECS comprising two, onshore and offshore, VSC stations at the two ends of an HVDC transmission system connecting OWFs to the mainland. The study shows that the WaOA outperforms GA and PSO, improving system stability and enabling quick recovery after disturbances. The study carried out using MATLAB/Simulink highlights the significance of newly recently introduced optimization techniques to ensure efficient and reliable operation of offshore wind energy systems, thereby expediting the transition to sustainable energy sources.

The impact of Zika emergence in remote communities in northwestern Ecuador.

The Zika virus (ZIKV) epidemic in Latin America (2015-2016) has primarily been studied in urban centers, with less understanding of its impact on smaller rural communities. To address this gap, we analyzed ZIKV sero-epidemiology in six rural Ecuadorian communities (2018-2019) with varying access to a commercial hub. Seroprevalence ranged from 19% to 54% measured by NS1 blockade of binding ELISA. We observed a decline in ZIKV seroprevalence between 2018 and 2019 that was greater among younger populations, suggesting that the attack rates in the 2015-16 epidemic were significantly higher than our 2018 observations. These data indicate that the 2015-16 epidemic included significant transmission in rural and more remote settings. Our observations of high seroprevalence in our area of study highlights the importance of surveillance and research in rural areas lacking robust health systems to manage future Zika outbreaks and vaccine initiatives.

The Importance of Surveyors and GPS Technology in Systematic Land Registration in Georgia: A Study of the Challenges and Opportunities

The purpose of this work is to explore the historical and contemporary importance of land registration, focusing on the crucial role played by Georgia’s surveyors. The paper concentrates on the combination of GIS-based state systems with GPS PlateTalk map registrations to resolve conflicting cadastral information as well as for accurate ownership record keeping. The Georgian absence of a unified legal and administrative framework significantly complicates land boundary delineation leading to disputes among surveyors. To standardize surveying practices and improve accuracy, an all-encompassing legal framework must be put in place. The study adopts historical, comparative legal, and hermeneutic research methods using examples from Germany and Holland that demonstrate how such advanced technologies as drones, LiDAR, and blockchain can enhance the precision, speed, and security of survey practice. Furthermore, professional education investment must be increased in both teachers and attorneys who are also informed about Geoinformation Systems (GIS), thus enabling them to perform their functions properly when it comes to law matters concerning land registers. Conclusions urge policy-makers to invest in modern technological gadgets as well as engage in legislative reforms that would make their cadastral systems stronger. That would result in the creation of robust cadastral systems by investing in modern technology as well as carrying out legal reforms that would modernize their existing cadastral systems.

A survey of reinforcement learning theories in autonomous vehicles

This survey explores the core theories, techniques, and core applications of reinforcement learning (RL) in the domain of autonomous vehicles. RL has emerged as a significant contributor to decision-making techniques in this field, enabling agents to make informed decisions based on actions and rewards derived from the environment. The ultimate objective of RL in autonomous driving is to maximize cumulative rewards over time. However, RL faces challenges related to interpretability and sample efficiency, particularly in complex driving scenarios. This survey extensively investigates the utilization of RL in decision-making and control, encompassing various scenarios and addressing the challenges encountered within RL-based autonomous vehicles. By emphasizing the design of effective reward functions, enhancing sample efficiency, and improving model interpretability, future advancements in reinforcement learning for autonomous vehicles can foster the development of more robust, efficient, and trustworthy autonomous systems. Moreover, this survey provides valuable insights into the limitations of RL techniques in autonomous driving decision-making, highlighting areas that require further research and development.

ANALYSIS EXPLORING THE INFLUENCE OF TEACHER-PERCEIVED SUSTAINABLE LEADERSHIP AND ENHANCEMENT STRATEGIES ON CONTINUOUS PROFESSIONAL DEVELOPMENT OF TEACHERS IN CHINESE VOCATIONAL COLLEGES: A GROUNDED THEORY APPROACH

The purpose of this study, which is set against the backdrop of teachers' perceived sustainable leadership, is to investigate the key factors that determine the professional development and potential improvement strategies of Chinese vocational college teachers. ten vocational college teachers in Shandong Province, China, were selected for semi-structured interviews using a purposive sampling method, and the interview texts were coded and analysed using the grounded theory. The findings indicated that the continuous professional development of vocational college teachers is significantly influenced by multiple factors, which include teachers’ perception of their professional development, schools’ policies and systems, the fairness of allocating resources and the organisation’s cultural environment. As a result, the professional growth and autonomy of vocational college teachers can be substantially enhanced by establishing a robust institutional guarantee system, fostering a supportive and inclusive organisational culture, and advocating for the implementation of personalised development plans.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0110/a.php" alt="Hit counter" /></p>

An empirical model for skillful rogue wave forecasts

Abstract One of the leading goals of rogue wave research is to develop a robust rogue wave warning system to mitigate the danger they pose. One such system has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), called the freak wave warning system (FWWS), based on nonlinear wave effects. The FWWS predicts maximum expected wave envelope height as a risk parameter for forecasts. Recently, a data-driven alternative has been proposed by Häfner et al. (2023), which was distilled from a neural network using wave buoy observations. However, it has yet to be evaluated by a spectral wave model for application to operational wave forecasting. The data-driven, learned model emphasizes bandwidth-controlled linear superposition as the predominant mechanism in crest-to-trough rogue wave generation, while nonlinear effects are a secondary term. The present work evaluates the performance of the empirical model using output from an ECMWF global wave hindcast. We find that the prediction models based on bandwidth effects have the highest log likelihood scores, with the empirical model outperforming all other tested models. In contrast, the expected maximum envelope wave height from the FWWS does not predict the occurrence of rogue waves. These results indicate that the empirical model with wave model input is a skillful predictor and should be considered for operational implementation to improve rogue wave forecasting.

Improved Online Result Transcript Generation System

This study addresses the inefficiencies inherent in manual management of students’ academic records by proposing a robust online system for result processing and transcript generation. The aim of this study is to develop and implement an efficient online system for processing results and generating transcripts, to streamline the process and improve accessibility and accuracy. An in-depth analysis of the current manual processes reveals significant challenges and inefficiencies, which guide the design and development of a user-friendly, web-based application. The system employs the Object-Oriented Analysis and Design Methodology (OOADM) and integrates HTML5, CSS3, JavaScript, PHP, and MySQLi technologies to ensure scalability and effective handling of extensive student data. This research offers a substantial contribution to the modernization of academic administrative processes, demonstrating how an automated system can significantly improve result processing and transcript.

Research on Multi-Source Data Fusion and Satellite Selection Algorithm Optimization in Tightly Coupled GNSS/INS Navigation Systems

With the increase in the number of Global Navigation Satellite System (GNSS) satellites and their operating frequencies, richer observation data are provided for the tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS). In this paper, we propose an efficient and robust combined navigation scheme to address the key issues of system accuracy, robustness, and computational efficiency. The tightly combined system fuses multi-source data such as the pseudo-range, the pseudo-range rate, and dual-antenna observations from the GNSS and the horizontal attitude angle from the vertical gyro (VG) in order to realize robust navigation in a sparse satellite observation environment. In addition, to cope with the high computational load faced by the system when the satellite observation conditions are good, we propose a weighted quasi-optimal satellite selection algorithm that reduces the computational burden of the navigation system by screening the observable satellites while ensuring the accuracy of the observation data. Finally, we comprehensively evaluate the proposed system through simulation experiments. The results show that, compared with the loosely coupled navigation system, our system has a significant improvement in state estimation accuracy and still provides reliable attitude estimation in regions with poor satellite observation conditions. In addition, in comparison experiments with the optimal satellite selection algorithm, our proposed satellite selection algorithm demonstrates greater advantages in terms of computational efficiency and engineering practicability.

Research on Linear Active Disturbance Rejection Control Based on Grid-Forming Distributed Photovoltaics

The “double carbon” policy promotes an increasing ratio of new energy installation and generation capacity annually, reducing system inertia and rendering the grid-forming (GFM) power grid increasingly susceptible to frequency fluctuations and other issues. This paper designs a power–frequency controller for grid-forming distributed photovoltaic systems by integrating LADRC and VSG control, aiming to enhance system robustness and stability under disturbances or faults. Furthermore, this study introduces the particle swarm optimization (PSO) algorithm based on LADRC principles to optimize LADRC, thereby improving the robustness and stability of grid-forming distributed photovoltaic systems to a certain extent. The feasibility and effectiveness of these two proposed control strategies are analyzed and validated separately using the MATLAB/Simulink R2022a simulation platform.

466.4-Gbit/s PCS-64QAM wireless transmission enhanced by dual-polarized SISO link and vector-quantized variational autoencoders

We successfully demonstrate photonics-assisted single-carrier 466.4 Gbit/s wireless transmission over 20 km SSMF and 6 m single-input single-output (SISO) wireless delivery at 92.5 GHz. This is achieved by employing a likelihood-aware vector-quantized variational autoencoder (VQ-VAE) equalizer for polarization demultiplexing and an advanced multiple-input multiple-output (MIMO) Volterra nonlinear equalizer (MIMO-VNE) further to compensate linear and nonlinear crosstalk between two polarizations. The capacity and robustness of the fiber-wireless system are extensively investigated, verifying that our proposed VQ-VAE with shared-layer autoencoder (SLAE) model outperforms state-of-the-art blind equalizers such as those based on constant modulus algorithm (CMA), VAE, and VQ-VAE algorithms. Additionally, the dual-polarized 40 Gbaud PCS-64QAM signal achieves a 2 dB power margin while meeting the 0.83 NGMI threshold, and a 0.05 gain in NGMI compared to traditional methods. This low-complexity training framework paves the way for enhancing wireless air interface capacities to approach those of fiber-optic transmission within fiber-wireless integration systems.