Robust Mechanism Research Articles

Comprehensive Proteomic Characterization of Intra-Golgi Trafficking Intermediates.

The Conserved Oligomeric Golgi (COG) complex is critical for efficient intra-Golgi trafficking and glycosylation. Prior research has demonstrated that COG dysfunction or RNAi-induced depletion leads to the accumulation of non-tethered COG complex-dependent (CCD) vesicles. However, the precise connection between COG deficiency, degradation of Golgi enzymes, and its impact on vesicular trafficking has not been fully elucidated. In this study, we conducted a comprehensive proteomic analysis of Golgi-derived vesicles from both wild-type and COG-depleted cells. We specifically analyzed three distinct populations of vesicles immunoisolated with antibodies targeting transmembrane proteins from the cis, medial, and trans-Golgi sub-compartments. Our findings reveal that, while the vesicle content encompasses the entire Golgi proteome, the molecular signatures of vesicles derived from wild- type cells were markedly distinct, underscoring a robust recycling mechanism for Golgi- dependent proteins. Notably, these vesicles retained various vesicular coats, and COG depletion significantly accelerated their uncoating. Furthermore, the increased overlap in molecular signatures upon COG depletion indicates that persistent defects in vesicle tethering severely compromise intra-Golgi sorting mechanisms. Crucially, our analysis highlights that the entire Golgi glycosylation machinery recycles within CCD vesicles in a COG-dependent manner, while secretory proteins and components involved in ER-Golgi and Golgi-endosome trafficking were not enriched. These results strongly support a model of multi-step intra-Golgi vesicular recycling of the glycosylation machinery, orchestrated by the COG complex in concert with a cisternae-specific array of vesicular coats, coiled-coil tethers, Rabs, and SNARE proteins.

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Effect of Credit Risk Management Techniques on Financial Performance of Deposit Taking SACCOs in Kenya

Financial performance is crucial in the realm of finance. The requirement to explain why two organizations operating in the same environment perform differently is a puzzle, and various financial research works have been devoted to solving this puzzle. Co-operative societies in Kenya are recognized as a significant contributor to national development since their presence can be traced in virtually all sectors of the economy. Although significant progress has been made by the deposit taking SACCOs in Kenya, their performance and sustainability has been debatable. The objective of this research was to assess the effect of financial risk management techniques on financial performance of deposit taking SACCOs in Kenya. The specific objective was to determine the effect of credit risk management techniques on financial performance of deposit taking SACCOs in Kenya. The study was based on Credit Risk Theory. A descriptive survey design was adopted. The population of the study was the 175-deposit-taking SACCOs in Kenya as of 31st December 2022. Due to relative population nature, sampling was not conducted and therefore the study was a census. The respondent was the risk manager of each deposit-taking SACCO. The study utilized primary data. The data was collected using a structured questionnaire. Data was analyzed using descriptive statistics such as the mean and standard deviation and inferential statistics which included correlation and regression analysis. Reliability tests were conducted on the variables each meeting the 0.7reliability threshold with validity tests being confirmed by the Bartlett’s test that confirmed validity of the variables being measured. The regression analysis revealed significant insights into the relationship between financial risk management techniques and the financial performance of deposit-taking SACCOs in Kenya. Credit risk management techniques were found to have a positive impact on financial performance with a regression coefficient of β = 0.238 and a significant value of P <0.005. In conclusion, the study provides compelling evidence of the importance of comprehensive risk management frameworks in driving the financial performance of deposit-taking SACCOs in Kenya. By implementing effective risk management practices tailored to address credit risk, SACCOs can enhance their stability, profitability, and resilience in a dynamic operating environment. These findings have significant implications for policymakers, practitioners, and stakeholders in the SACCO sector, highlighting the need to prioritize investments in risk management capabilities to promote sustainable financial growth. The study recommended that SACCOs should focus on enhancing their credit risk management frameworks. This can be achieved by implementing robust credit risk assessment mechanisms, such as comprehensive credit scoring models and regular analysis of customer credit histories.

Caspase-1 in Cx3cr1-expressing cells drives an IL-18-dependent T cell response that promotes parasite control during acute Toxoplasma gondii infection.

Inflammasome activation is a robust innate immune mechanism that promotes inflammatory responses through the release of alarmins and leaderless cytokines, including IL-1α, IL-1β, and IL-18. Various stimuli, including infectious agents and cellular stress, cause inflammasomes to assemble and activate caspase-1. Then, caspase-1 cleaves targets that lead to pore formation and leaderless cytokine activation and release. Toxoplasma gondii has been shown to promote inflammasome formation, but the cell types utilizing caspase-1 and the downstream effects on immunological outcomes during acute in vivo infection have not been explored. Here, using knockout mice, we examine the role of caspase-1 responses during acute T. gondii infection globally and in Cx3cr1-positive populations. We provide in vivo evidence that caspase-1 expression is critical for, IL-18 release, optimal interferon-γ (IFN-γ) production, monocyte and neutrophil recruitment to the site of infection, and parasite control. Specifically, we find that caspase-1 expression in Cx3cr1-positive cells drives IL-18 release, which potentiates CD4+ T cell IFN-γ production and parasite control. Notably, our Cx3cr1-Casp1 knockouts exhibited a selective T cell defect, mirroring the phenotype observed in Il18 knockouts. In further support of this finding, treatment of Cx3cr1-Casp1 knockout mice with recombinant IL-18 restored CD4+ T cell IFN-γ responses and parasite control. Additionally, we show that neutrophil recruitment is dependent on IL-1 receptor accessory protein (IL-1RAP) signaling but is dispensable for parasite control. Overall, these experiments highlight the multifaceted role of caspase-1 in multiple cell populations contributing to specific pathways that collectively contribute to caspase-1 dependent immunity to T. gondii.

Role of students’ academic procrastination in shaping mathematics achievement

ABSTRACT This study explores the role of academic procrastination in shaping mathematics achievement in Nepali schools. Utilising a quantitative research design, a cross-sectional survey was conducted with 474 students selected through multi-stage cluster sampling from eight public secondary schools in Nepal. The data were analysed using t-tests, ANOVA, and multiple linear regressions. The findings reveal significantly higher levels of both academic achievement and procrastination. Moreover, a high level of academic procrastination was found to have a significant negative effect on mathematics achievement. Influential factors in this correlation include students’ ethnicity, study hours at home, family size, and parents’ educational background. Stakeholders, including parents, teachers, and policymakers, should collaborate synergistically to address the underlying causes of procrastination and provide robust support mechanisms to enhance students’ learning outcomes.

EDP-938 Has a High Barrier to Resistance in Healthy Adults Experimentally Infected with Respiratory Syncytial Virus.

EDP-938 is an oral once-daily RSV nucleoprotein (N) inhibitor with potent antiviral activity. In a human RSV challenge trial, EDP-938 significantly reduced viral load and symptom severity. During antiviral development, it is critical to understand the propensity for resistance to develop. In vitro studies of EDP-938 suggest a higher barrier to resistance as compared to RSV fusion inhibitors. We evaluated the development of viral resistance to EDP-938 in a human challenge trial. A subset of the 124 participants with RSV infection were chosen for genetic analysis; 159 nasal wash samples from 48 participants were analyzed using next-generation sequencing of the N gene of RSV. Of the 48 participant sampled, 37 were from EDP-938-treated and 11 were placebo-treated participants, representing 45% and 26% of the participants, respectively. The effects of treatment-emergent mutations on viral load, EDP-938 efficacy, and viral fitness were evaluated. Two of the 37 EDP-938-treated participants with samples sequenced had treatment-emergent mutations: N:L139I and N:E112G. From in vitro analysis, N:L139I reduced sensitivity to EDP-938 by approximately 10-fold, while N:E112G had no effect. However, N:L139I was associated with a reduction in viral fitness, suggesting clinical resistance is associated with fitness costs. Neither of these variants were associated with reduced viral clearance. In human RSV infections treated with EDP-938, emergence of RSV variants with reduced sensitivity to EDP-938 occurred in only 1 participant and was associated with reduced viral fitness. EDP-938's high barrier to resistance highlights its robust mechanism of action. NCT03691623.

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preprocessed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier’s performance, focusing on f1-score rates compared to other machine learning models. Results demonstrate that by optimizing class weights, applying a custom prediction function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted f1-score and 93.31% in the macro f1-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

Accumulation of heavy metals (Cr, Cu, As, Cd, Pb, Zn, Fe, Ni, Co) in the water, soil, and plants collected from Edayar Region, Ernakulam, Kerala, India

The accumulation of heavy metals in the environment is a significant concern due to their potential toxicity and persistence. This study investigates the levels of heavy metal contamination in the water, soil, and plants of the Edayar region in Ernakulam, Kerala, India. The region has experienced industrialization and urbanization, leading to concerns about heavy metal pollution. The study aims to assess the concentrations of chromium (Cr), copper (Cu), arsenic (As), cadmium (Cd), lead (Pb), zinc (Zn), iron (Fe), nickel (Ni), and cobalt (Co) in water, soil, aquatic and terrestrial plants. Samples were collected from various locations within the Edayar region, and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was conducted to quantify heavy metal concentrations. The findings of this study will contribute to the assessment of heavy metal pollution in the Edayar region. Plants with a high diversity index were taken for analysis from both aquatic and terrestrial habitats. Scoparia dulcis L. seems to specialize in metal accumulation, possibly for protective purposes. Synedrella nodiflora Gaertn demonstrates adaptability to metal-rich environments through robust metal uptake and tolerance mechanisms. Alternanthera philoxeroides (Mart.) Griseb, on the other hand, appears to have developed mechanisms to manage heavy metal exposure. The results indicate significant levels of heavy metal contamination across all samples, with the highest concentrations detected in soil, followed by water and plants. Chromium and lead levels in soil exceeded the permissible limits set by international standards, posing potential risks to human health and the ecosystem. The accumulation patterns in plants varied, with higher bioaccumulation factors observed for zinc and copper, suggesting their preferential uptake. This study highlights the urgent need for remediation strategies and continuous monitoring to mitigate the impact of heavy metal pollution in the Edayar region. The results will help in understanding the environmental impact of human activities.

Development of a chemical mechanism for ammonia/hydrogen blends for engines

ABSTRACT Ammonia/hydrogen blend fuels have been extensively used as alternative fuel for engines. The combustion and emissions characteristics of ammonia/hydrogen engines have been investigated utilising a CFD model. Developing a robust and accurate chemical reaction mechanism for ammonia/hydrogen blend fuels is of utmost importance. In this paper, a chemical mechanism has been developed and validated for the combustion characteristics of ammonia/hydrogen, which includes 31 species and 223 reactions. Extensive validation has been conducted using experimental data as the basis. The obtained data indicate that the simulated values for the ignition delay times of the shock tube and the concentration of the main components in the stirred jet reactor align well with the measured values. The simulated laminar flame speed under turbulent flow conditions shows a remarkable agreement with the corresponding experimental data. The study compared simulated flame evolution values with experimental data from a constant volume combustion bomb. The results indicate that the proposed mechanism is able to produce favourable flame evolution and combustion characteristics. The presented detailed mechanism demonstrates credible overall performance in terms of combustion behaviours, indicating its suitability for effectively modelling ammonia/hydrogen combustion in real engines.

Refugee Rights and International Law: Evaluating the Efficacy of Global Humanitarian Responses to Forced Displacement

The increasing scale of forced displacement due to conflict, persecution, and environmental crises has brought refugee rights to the forefront of global humanitarian discourse. This article critically examines the efficacy of international law in protecting refugee rights and assessing global responses to the growing refugee crisis. Through an analysis of key international legal frameworks, such as the 1951 Refugee Convention and the role of the United Nations High Commissioner for Refugees (UNHCR), the study evaluates the adequacy of current policies in addressing the challenges faced by refugees. It highlights gaps in protection, such as unequal burden-sharing among nations, restricted access to asylum, and prolonged displacement in refugee camps. Case studies from various regions reveal how inconsistent application of international law undermines refugees’ access to fundamental rights, including safety, healthcare, and education. The article argues for stronger international cooperation, better policy implementation, and more robust legal mechanisms to ensure the protection of refugee rights. It concludes by offering recommendations for improving global humanitarian responses and creating more equitable and sustainable solutions for forced displacement.

Early Childhood Education And Village Corruption: An Evidence From Indonesia

This study explores the relationship between early childhood education (ECE) exposure and corruptive behavior among village leaders in Indonesia, particularly in light of the decentralization efforts that began in 2014 with the Village Fund Initiative. The objective is to determine whether village leaders who were exposed to ECE in their early years are less prone to engage in corrupt practices when managing public funds. The research utilizes a staggered difference-in-difference approach, comparing corruption cases between leaders exposed to ECE and those who were not. The study collected data from the Indonesian village census (PODES) between 1993 and 2018, focusing on Java Island. The corruption cases were sourced from the Indonesian Supreme Court, specifically analyzing the behavior of village heads elected post-2014. The methodology allowed for a quasi-experimental design by leveraging the timing of village elections as a key variable. The results ECE exposure did not universally reduce corruption across the sample. In fact, corruption surged among early-treated groups who experienced the program in the 1990s, suggesting that ECE alone is not a strong deterrent to corruption. However, for later-treated groups, ECE exposure did lead to a reduction in corrupt behavior, pointing to an adaptation effect as village leaders observed peers being prosecuted for corruption. In conclusion, while early childhood education plays a role in shaping governance outcomes, its impact on reducing corruption is nuanced. The findings suggest that integrating anti-corruption education into the ECE curriculum could strengthen governance at the village level, particularly when combined with robust law enforcement mechanisms

An automated intrusion detection system in IoT system using Attention based Deep Bidirectional Sparse Auto Encoder model

Nowadays, the Internet of Things (IoT) is a smart network connected to the Internet for transmitting gathered data with verified protocols. Attackers frequently use communication protocol defects as the basis for their attacks. Better protection measures are required since attacks affect the reputations of service providers. Both machine learning (ML) and deep learning (DL) methods have been developed in a number of research works to detect network intrusions. However, the system's security is limited by the rising number of new threats. Critical problems in IoT platforms, cyber-physical systems, wireless networks, and fog computing are caused by such attacks. The development of various cyber-security attacks reinforces the need for a strong intrusion detection system (IDS) in the IoT platform. The proposed study introduced a robust deep-feature learning mechanism for automatically detecting network intruders in the IoT platform. Initially, input data are gathered from the given dataset. Pre-processing helps reduce any noise in the data and improves the data quality using cleaning, outlier removal, and min-max normalization. The proposed Attention-based Deep Bidirectional Sparse Auto Encoder (AD-BiSA) model is the most important feature retrieved using the attention-based deep Bi-LSTM model. The different IoT network threats are categorized using a sparse Autoencoder approach. The chaotic Seagull Optimization (CSGO) algorithm decreases the loss and enhances the weight in the proposed DL technique. The UNSW NB15_IDS and NSL-KDD datasets achieve accuracy rates of 99.71% and 98.97%, respectively, for the proposed technique. The proposed method achieves better performance than existing approaches.

Environmental, social and governance (ESG) performance and abnormal positive tone

Purpose The purpose of this study is to explore the relationship between environmental, social and governance (ESG) performance and tone management in the annual report. This is based on the notion that managers, driven by personal interests, may use their ESG accomplishments by using an abnormal positive tone to enhance their reputation or career prospects. Design/methodology/approach Using panel data from Chinese listed companies from 2010 to 2022, this study first investigates the relationship between ESG performance and abnormal tone management. The study then uncovers this relationship is mediated through the mechanisms of equity-based incentive and analyst coverage. The conclusions of this paper hold even after a series of robustness tests, such as propensity score matching, Heckman two-stage method and two-stage least squares with instrumental variables. Findings This study finds a positive correlation between ESG performance and the presence of abnormal positive tone in annual reports. Furthermore, the mechanistic analysis reveals that managers in companies with strong ESG performance are motivated to use an overly positive tone, largely due to their vested interests in equity-based compensation. Moreover, in an effort to alleviate the pressure stemming from heightened financial analyst coverage and enhance the impression conveyed through analysts' reports, managers with superior ESG performance also tend to inflate the tone within their annual reports. Practical implications This study provides significant insights into the ongoing dialogue surrounding ESG-related equity incentives, which incentivize managerial manipulation of stock prices through the use of abnormal positive tone. The findings call upon investors to exercise greater vigilance in examining narrative information in annual reports, as abnormally positive tones may not always faithfully represent performance but rather reflect managerial self-interest. Social implications There is an emphasis on the importance of robust oversight mechanisms within corporate governance bodies to curb the manipulation of tone for managers’ personal gain. Originality/value This study enhances the theoretical foundation of ESG studies, offering a holistic perspective on the intricate interplay among ESG performance, managerial behavior and financial markets, with potential implications for researchers, investors and regulators.

A Novel Point of Common Coupling Direct Power Control Method for Grid Integration of Renewable Energy Sources: Performance Evaluation among Power Quality Phenomena

Robust control mechanisms are needed in microgrids to ensure voltage source inverters (VSIs) effectively integrate renewable energy sources such as solar photovoltaic (PV) systems into the power network. Current control approaches often have limitations regarding velocity, stability, and robustness. The paper details a newly developed method named Point of Common Coupling Direct Power Control (PCC-DPC) for renewable energy systems connected to the grid. PCC-DPC is used to instantly control voltage at the point of common coupling (PCC) inside the microgrid as opposed to other conventional techniques. This leads to a simplified controller design that does not require complex Park transformations and phase-locked loop (PLL) systems, and has a lower computational burden and less power fluctuation in a stable manner. Moreover, this research critically examines power quality phenomena through comparing PCC-DPC with a Vector Current Controller (VCC). Simulations performed on an Opal Re-al-Time simulator showed improved tracking performance and overall system efficiency due to the PCC-DPC approach over others. These results demonstrate that it can effectively be used as one of the most suitable methods for integrating renewable energy into electricity grids, which is reliable in regards to changes in power grid dynamics.

High-Fidelity Sensing Modality for Anomaly Detection in Inkjet Printing

Abstract Inkjet three-dimensional (3D) printing has emerged as a transformative manufacturing technique, finding applications in diverse fields such as biomedical, metal fabrication, and functional materials production. It involves precise deposition of materials onto a moving substrate through a nozzle, achieving submillimeter scale resolution. However, the dynamic nature of droplet deposition introduces uncertainties, challenging consistent quality control. Current process monitoring, relying on image-based techniques, is slow and limited, hindering real-time feedback in erratic droplet ejection. In response to these challenges, we present the zero-dimensional ultrafast sensing (0-DUS) system, a novel, cost-effective, in situ monitoring tool designed to assess the quality of drop-on-demand inkjet printing. The 0-DUS system leverages the sensitivity of the light-beam field interference effect to rapidly and precisely detect and analyze localized droplets. Two core technical advancements drive this innovation: first, the exploration of integral sensing of the computational light-beam field, which allows for efficient extraction of temporal and spatial information about droplet evolution, introducing a novel in situ sensing modality; second, the establishment of a robust mapping mechanism that aligns sensor data with image-based data, facilitating accurate estimation of droplet characteristics. We successfully implemented the 0-DUS system within a commercial inkjet printer and conducted a comparative analysis with ground truth data. Our experimental results demonstrate a detection accuracy exceeding 95%, even at elevated speeds, allowing for an impressive in situ certification throughput of up to 500 Hz. Consequently, our proposed 0-DUS system meets the stringent quality assurance requirements, thereby expanding the potential applications of inkjet printing across a wide spectrum of industrial sectors.

Blockchain Enabled Secure Data Transmission With NB‐IoT Deployment in Smart Agriculture Crop Watch

ABSTRACTThe integration of Narrowband Internet of Things (NB‐IoT) and blockchain technology presents a promising solution to address security and efficiency concerns in data transmission within the Internet of Things (IoT). This research focuses on its application in crop Watch, an agricultural monitoring system. The IoT has revolutionized various industries, including agriculture, by facilitating the collection and transmission of extensive data through sensors and devices. However, ensuring secure and efficient data transmission remains a critical challenge. NB‐IoT boasts advantages such as improved coverage in challenging areas, massive connectivity with up to 100 000 links, low energy usage through prolonged discontinuous receipt (eDRX), and cost‐effectiveness. Despite these benefits, NB‐IoT faces security issues, including authentication, anonymity, and fault tolerance. To enhance data security, public key cryptography is proposed to be incorporated into the NB‐IoT framework. Blockchain technology, with its decentralized and immutable nature, can potentially address these security concerns and enhance data accountability. By creating a trust‐less and transparent infrastructure, blockchain complements NB‐IoT's capabilities and provides a robust mechanism for data verification and integrity. This research paper emphasizes the logical integration of NB‐IoT, public key cryptography, and blockchain technology, offering a secure and efficient data transmission framework for agricultural applications like crop Watch. Bridging the gap between data connectivity and security holds promise for advancing agricultural monitoring and driving IoT innovations.

Enhancing Cybersecurity through AI-Powered Security Mechanisms

In the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.

TOWARDS THE EFFECTIVE PROFESSIONAL UPSKILLING OF LOW-SKILLED EMPLOYEES: KEY INFLUENCING FACTORS

The increasing demand for skilled employees in contemporary economies underscores the importance of effective upskilling of the low-skilled. This study addresses the significant challenge of enhancing the professional development of low-skilled workers, focusing on the factors that either facilitate or hinder their engagement in learning activities. The primary aim was to identify the key characteristics that contribute to the effective professional development of low-skilled employees and the practical application of the skills they acquire. A systematic review was conducted, examining empirical studies published between 2004 and 2024. The search encompassed six databases, resulting in 352 identified articles. After rigorous screening and quality assessment, 22 high and good-quality studies (published in 2011-2023) were included in the final selection. The studies spanned various countries and methodologies, including qualitative, quantitative, and mixed-method designs. Key findings highlight that both personal and contextual factors influence the effectiveness of professional upskilling for low-skilled employees. Personal factors include motivation, self-direction, and reflexivity, while contextual factors encompass the quality of the work environment, learning opportunities, and institutional support. The review emphasises the importance of tailored training programmes, practical and interactive learning methods, continuous support and mentorship, and robust evaluation mechanisms. Effective transfer of training is facilitated by the relevance and applicability of training content, learner engagement and organisational support. The results highlight the need for holistic approaches that address both personal and contextual factors to enhance the professional development and skill utilisation of low-skilled workers; ultimately contributing to their career trajectories and job satisfaction. Keywords: adult education, low-skilled employees, upskilling, systematic review, training transfer

Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment.

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.

Cybersecurity Certification Requirements for Distributed Energy Resources: A Survey of SunSpec Alliance Standards

This survey paper explores the cybersecurity certification requirements defined by the SunSpec Alliance for Distributed Energy Resource (DER) devices, focusing on aspects such as software updates, device communications, authentication mechanisms, device security, logging, and test procedures. The SunSpec cybersecurity standards mandate support for remote and automated software updates, secure communication protocols, stringent authentication practices, and robust logging mechanisms to ensure operational integrity. Furthermore, the paper discusses the implementation of the SAE J3072 standard using the IEEE 2030.5 protocol, emphasizing the secure interactions between electric vehicle supply equipment (EVSE) and plug-in electric vehicles (PEVs) for functionalities like vehicle-to-grid (V2G) capabilities. This research also examines the SunSpec Modbus standard, which enhances the interoperability among DER system components, facilitating compliance with grid interconnection standards. This paper also analyzes the existing SunSpec Device Information Models, which standardize data exchange formats for DER systems across communication interfaces. Finally, this paper concludes with a detailed discussion of the energy storage cybersecurity specification and the blockchain cybersecurity requirements as proposed by SunSpec Alliance.