Netizens' perceptions on Umrah Fraud in Malaysia

Recent trends in the media have showed that many Umrah pilgrims reported that they have been scammed for Umrah travel packages that they had paid towards the so called “authorized travel agents”. Despite the victims lodging police reports, the cases often remain unresolved, and the cycle of fraud continues and more people continuously being cheated. In response to this widespread issue, various social media platforms, such as Facebook, have become a platform where netizens shared their grievances and seeking support from others who have faced similar situations. Nonetheless, the academic research evaluating netizens' responses to Umrah fraud are scantly documented. Most existing research does not deeply explore into how individuals discuss and respond to such fraud on digital platforms. This research aims to fill the gap by gathering and analyzing comments and grievances from netizens regarding their perspective of Umrah fraud. Using the Fraud Triangle (FTT), this research extracted the response and feedback from online newspaper and Facebook (Umrah group) to better understand the behaviors and attitudes of those involved. This research also collected suggestions and recommendations from netizens on how to prevent from future fraud and improve the overall Umrah experience and process. The findings from this research are expected to be valuable for future Umrah pilgrims, as it provided them with insight into common fraud and the precautionary measures that they can take. Additionally, the research was great use to policymakers, enabling them to develop targeted policies and strategies to curb Umrah fraud and ensure safer travel experiences for Muslims undertaking the pilgrimage. By addressing the concerns raised by the netizens, the research could contribute to more effective and preventative measures in the future

Development of a High-Sensitivity Triple-Band Nano-Biosensor Utilizing Petahertz Metamaterials for Optimal Absorption in Early-Stage Leukemia Detection

This research focuses on designing a novel miniaturized biosensor for early-stage detection of leukemia. The proposed sensor operates in the low Petahertz (PHz) range. The primary prerequisites for the development process include multi-band operation, compact physical size, near-perfect absorption at resonant frequencies, and insensitivity to the angle of incidence. These features are crucial for ensuring high-performance operation in microwave imaging (MWI) and have been achieved through meticulous design of the absorber’s geometry and dimensions. The sensor incorporates a dipole and two rings made of gold or silver, implanted on a silicon dioxide dielectric substrate with a fully metallic backplane. The evolution of the biosensor’s topology is detailed, along with comprehensive simulation studies conducted using a commercial full-wave electromagnetic (EM) solver. The operating principles are explained through parametric studies, analysis of absorption and refraction characteristics, and discussions of electric and magnetic fields, as well as surface current density distributions. The device’s suitability for blood cancer diagnostics is demonstrated through full-wave analysis of the MWI system, highlighting the sensor’s ability to discriminate between healthy and cancerous samples through noticeable frequency shifts in absorption responses. Extensive comparisons with the state-of-the-art biosensors reported in recent literature show that the proposed device significantly improves spectral properties and achieves remarkable spatial resolution due to its PHz range operation when compared to the benchmark

Transition Metal-Doped 2D GaN as Single-Atom Electrocatalysts for Lithium–Sulfur Batteries: Insights from First-Principles Calculations

The commercial adoption of lithium–sulfur (Li–S) batteries is primarily limited by the shuttle effect and slow kinetics of the sulfur reduction reaction (SRR), which involves a complex 16-electron conversion process. Single-atom catalysts (SACs) show great potential as electrocatalysts to improve reaction kinetics in Li–S batteries. Using first-principles methods, we conducted computational screening of a series of transition metal (TM) atoms doped into two-dimensional (2D) GaN to enhance the SRR activity. Our results indicate that the important SRR step which involves liquid–solid transformation of Li2S4 into Li2S is correlated linearly with the SRR overpotential via 2.7ΔGLi2S* – ΔGLi2S4*. Based on the volcano plot, two catalysts, namely Pd@GaN and Cu@GaN, are identified as the most effective electrocatalysts, with an overpotential of 0.43 V. These doped atoms remain stable on the 2D GaN even at high temperatures. In addition, both Pd@GaN and Cu@GaN exhibit strong binding energies for high order Li2Sn (n = 4, 6, 8), ranging from −1.81 to −2.99 eV, effectively mitigating the shuttle effect. This study offers theoretical insights into the SRR mechanism on TM-doped 2D GaN and guides the rational design of single-atom catalysts (SACs) for Li–S batteries

Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning

Introduction: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection. Methods: This study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. State of the art machine learning models i.e., Random Forest, Support Vector Machine, and Gradient Boosting are utilized, while deep learning models such as Transformers and Autoencoders are selected for their robust feature-extraction capabilities. Traditional methods for training machine learning and deep learning models raises data privacy concerns and require significant computational resources. To address these issues, the study applies a split learning framework. In this framework, an ensemble learning technique has been utilized that combines the best performing machine and deep learning models. Results: Results demonstrate a commendable classification performance with certain ensemble methods, and a Transformer-Random Forest combination achieved 99% accuracy. In addition, to address data-sharing constraints, a split learning framework is implemented across three clients, yielding high accuracy (over 95%) while preserving privacy. The best client recorded 96.23% accuracy, underscoring the robustness of combining Transformers with Random Forest under resource-constrained conditions. Discussion: These findings demonstrate that distributed deep learning pipelines can deliver precise MDD detection from EEG data without compromising data security. Proposed framework keeps data on local nodes and only exchanges intermediate representations. This approach meets institutional privacy requirements while providing robust classification outcomes

Exploring the Psychological and Behavioral Effects of Mobile Payment Systems on Consumer Spending: A Theoretical Perspective

The rapid growth of mobile payments has made changes in the way people spend their money, bringing in notable psychological and behavioral changes. This paper aims to analyze the theoretical perspectives on mobile payment applications, and most importantly how these applications affect people’s spending habits, financial consciousness and parental discipline. The ‘pain of paying’ model and decoupling theory are the two approaches used in this study that seek to understand the ways in which mobile payment systems encourage spending by eliminating the psychological costs associated with spending and why such reckless spending behavior is encouraged. When one uses cash, the money is physically removed, which maintains discipline because loss is felt. In contrast, mobile payment systems are seamless and encourage the risk of overspending. This theoretical discussion carries practical implications for people, policy makers and mobile money services in light of the need to cater for financial education and restraint during spending through their applications. The results of this research represent a starting point for more advanced empirical investigations of these behavior alterations, that are necessary for adjusting the financial behavior in the conditions of scarcity of cash

ERBM: A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic, where three membership functions are created to classify all the network traffic features as low, medium, or high to remain situationally aware of the environment. Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty. Also, for further classification, machine learning classifiers such as Decision Tree (DT), Random Forest (RF), and Neural Networks (NN) learn complex ways of attacks and make the detection process more precise. A thorough performance evaluation using different metrics, including accuracy, precision, recall, F1 Score, detection rate, and false-positive rate, verifies the supremacy of ERBM over classical IDS. Under extensive experiments, the ERBM enables a remarkable detection rate of 99% with considerably fewer false positives than the conventional models. Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions, the ERBM system provides a unique, scalable, data-driven approach to IoT intrusion detection. This research presents a major enhancement initiative in the context of rule-based IDS, introducing improvements in accuracy to evolving IoT threats

Environmental Reporting and Financial Performance: Evidence From the Banking Sector in BRICS Countries

This study analyzes the incidence of environmental reporting on the financial performance (FP) of top banks in Brazil, Russia, India, China and South Africa (“BRICS”) countries using data from 50 leading banks from 2018 to 2023. Using panel regression analysis, the findings indicate that environmental reporting significantly impacts accounting-based financial indicators, specifically return on assets (ROA) and return on equity (ROE). Conversely, it shows a negative but statistically insignificant effect on diluted earnings per share (EPS). Environmental reporting significantly lowers Tobin's Q for market-based performance indicators, whereas its impact on the market-to-book ratio (MBR) is positive yet not statistically significant. This research offers a unique contribution to the limited body of literature on the financial impacts of environmental reporting within the BRICS banking sector. It provides nuanced insights into how sustainability practices influence financial outcomes in emerging economies, highlighting varied effects across different financial metrics

Quality of care in mental health services: does patient engagement play a role?

Purpose – The mental healthcare is experiencing an ever-growing surge in understanding the consumer (e.g., patient) engagement paradox, aiming to vouch for the quality of care. Despite this surge, scant attention has been given in academia to conceptualize and empirically investigate this particular aspect. Thus, drawing on the Stimulus-Organism-Response (S-O-R) paradigm, the study explores how patients engage with healthcare service providers and how they perceive the quality of the healthcare services. Design/methodology/approach – Data were collected from 279 respondents, and the derived conceptual model was tested by using Smart PLS 3.2.7 and PROCESS. To complement the findings of partial least squares (PLS)-based structural equation modeling (SEM), the present study also applied fuzzy set qualitative comparative analysis (fsQCA) to identify the necessary and sufficient conditions to explore substitute conjunctive paths that emerge. Findings – Findings show that patients’ perceived intimacy (PI), cohesion and privacy enhance the quality of mental healthcare service providers. The results also suggest that patients’ PI, cohesion and privacy have indirect effects on the perceived quality of care (PQC) by the service providers through consumer engagement. The fsQCA results derive that the relationship among conditions leading to patients’ perception of the quality of care in regard to mental healthcare service providers is complex and is best reflected as multiple and conjectural causation configurations. Research limitations/implications – The findings from this research contribute to the advancement of studies on patients’ experiences by empirically examining the unique dynamics of interaction between consumers (patients) and mental healthcare service providers, thereby enriching both the literature on social interactions and the understanding of the consumer–provider relationship. Practical implications – The results of this study provide practical implications for mental healthcare service providers on how to combine the study variables to enhance the quality of care and satisfy more patients. Originality/value – A significant research gap has ascertained the inter-relationship between PI, cohesion, privacy, engagement and PQC from the perspective of mental healthcare service providers. This research is one of the primary studies from a managerial and methodological standpoint. The study contributes by combining symmetric and asymmetric statistical tools in service marketing and healthcare research. Furthermore, the application of fsQCA helps to understand the interactions that might not be immediately obvious through traditional symmetric methods

The mediating role of knowledge management processes on the relationship between knowledge-based HRM practices and open innovation in SMEs

Purpose Studies have demonstrated the role of human resource management (HRM) practices and knowledge management processes (KMPs) in innovative performance. However, there is limited focus on the role of HRM practices in facilitating KMPs in organizations, most especially in small and medium enterprises (SMEs) that are constrained by lack of adequate resources, making them dependent on external sources of knowledge. In addressing this gap, this study aims to investigate the link between knowledge-based HRM practices and open innovation (OI) activities through KMPs in Jordanian SMEs. Design/methodology/approach Following the survey method, 500 manufacturing SMEs in Jordan were randomly selected as participants, with a total of 335 responses collated. The structural equation modeling technique, based on AMOS, was used in analyzing the collected data. Findings The findings revealed a significant positive relationship between knowledge-based HRM practices and OI. In addition, KMPs was determined to be a significant mediator of the relationship between knowledge -based HRM practices and OI. Originality/value The study contributes to the literature by emphasizing the organizational elements that boost OI in SMEs. The findings hold significant implications for enhancing the performance of innovativeness, competitiveness and the socioeconomic advancement in the SMEs sector

The role of financial literacy in driving sustainable entrepreneurial success: A case study of Lapo Microfinance Institution (MFI), Nigeria

Even with access to microfinance loans, many small and medium enterprises (SMEs) in Nigeria still find it challenging to achieve sustainable growth; thus, this systematic study investigates the role of financial literacy in driving the sustainable entrepreneurial success of clients of Lapo Microfinance Institution (MFI), Nigeria. A representative sample of Lapo MFI clients who have participated in their financial literacy training programmes was surveyed and interviewed, providing qualitative and quantitative data. The findings demonstrate a positive correlation between participation in Lapo MFI's financial literacy training programmes and sustainable business success among their clients. The novelty of this research is that it establishes a link between financial literacy and SME success. This study offersvaluable direction or guidance for other MFIs in developing targeted financial literacy interventions to support the sustainable growth of their clients' businesse