Robust Mechanism Research Articles

Detecting review fraud using metaheuristic graph neural networks

Although online reviews play a critical role in shaping consumer behavior and establishing institutional trust, this reliance on online reviews has given rise to a proliferation of fake reviews. Consequently, there is an urgent need for the development of robust mechanisms to identify and mitigate these inauthentic reviews. In this study, we introduce an innovative approach for the detection of fake reviews, leveraging a metaheuristic graph neural network model. Our methodology begins by extracting distinctive features from review text, comprising of term frequency—inverse document frequency (TF-IDF) vectors and bidirectional encoder representation transformer (BERT) embedding vectors. We feed these features into a graph convolutional network (GCN) that uses message passing to label each review. Furthermore, we address the optimization of our graph neural network models using Harris Hawk and Cat Swarm optimization techniques. Our experiments reveal that optimization of the GNN with Harris Hawk Optimization yields a significantly enhanced model performance, further validating the efficacy of our detection framework. This research contributes to the ongoing efforts in the realm of online review authenticity and showcases the potential of advanced techniques to combat the proliferation of fraudulent reviews.

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework.

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with the blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages the blockchain's inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine-learning models like SVM, KNN, and random forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard's effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.

Foreign Experienced CFOs’ and Financial Reporting Quality

This paper aims to empirically examine whether foreign experienced CFOs (FCFOs) affect financial reporting quality (FRQ). Analyzing Chinese listed firms from 2005 to 2018, this study employs methods such as ordinary least square, fixed effect model (FEM), generalized method of moment (GMM), two-stage least square (2SLS), propensity score matching method (PSM), and change analysis to find the relationship between FCFOs and FRQ. The results show that FCFOs significantly positively impact FRQ, which means that FCFOs improve FRQ in Chinese firms. We argue that international experience imprints CFO cognition, elevates his moral standards, and makes him more transparent in dealings, leading to a better information environment and robust corporate governance mechanism. In addition, this study indicates that the relationship between FCFOs and FRQ is significant in non-state-owned enterprises (NSOEs), suggesting that NSOEs outperform state-owned enterprises (SOEs) in terms of FRQ. Finally, we find that both foreign work and study experiences of CFOs affect FRQ. The findings suggest that asymmetric information problems can be alleviated by encouraging foreign experienced individuals to the firm’s CFO position. The study provides empirical evidence that a CFO’s foreign experience determines a firm’s FRQ.

Trends in Hospital Antibacterial Consumption in Belgium (2017-2021): Evaluating the Impact of the COVID-19 Pandemic

This study aimed to evaluate the impact of the COVID-19 pandemic on antimicrobial consumption (AMC) in Belgian hospitals from 2017 to 2021, using data from the European Surveillance of Antimicrobial Consumption Network (ESAC-Net) and the Belgian Hospitals Surveillance of Antimicrobial Consumption (BeH-SAC). Antimicrobial volume was quantified in Defined Daily Doses (DDDs), and AMC was expressed in DDDs/1000 inhabitants/day (DIDs), DDDs/1000 patient days and DDDs/1000 admissions. Linear regressions were employed to analyze 5-year trends for the ATC J01 group, at the ATC-3 level and for broad-spectrum antimicrobials. Broad-spectrum antibiotics included combinations of penicillins, incl. beta-lactamase inhibitors (J01CR), second-generation cephalosporins (J01DC), third-generation cephalosporins (J01DD), macrolides, lincosamides and streptogramins (J01F, excluding erythromycin J01FA01), and fluoroquinolones (J01MA). The compound annual growth rate (CAGR) calculated for the years preceding the pandemic was used to forecast 2020 and 2021 AMC, enabling a comparison with the actual use. Hospital AMC measured as DIDs decreased by 12% from 2019 to 2020. In contrast, when expressed as DDDs/1000 patient days and DDDs/1000 admissions, a 5% and 7% increase was observed, respectively. Antibacterials for systemic use (J01) showed a significant decrease over the 5 years only when expressed in DIDs. Notable trends included a negative trend for quinolone antibacterials (J01M) when expressed in the three incidence units, as for amphenicols (J01B) when using hospital denominators only. Positive trends were observed for sulfonamides and trimethoprim (J01E) using hospital denominators and for other beta-lactam antibacterials (J01D) with the ‘patient days’ denominator. While the consumption of all J01 antimicrobial subclasses deviated negatively from predicted use both in 2020 and 2021 when expressed in DIDs, positive deviations were recorded using hospital denominators, except for macrolides (J01F). The use of broad-spectrum antimicrobials showed a notable decrease between 2017 and 2021 when expressed in DIDs. However, when using hospital denominators, the observed use of broad-spectrum antimicrobials exceeded the forecasted values in 2020, to regress below the forecasted levels in 2021 (Figure 1). Contrary to results obtained using the widely applied country’s population as the denominator, a notable surge in AMC, particularly for broad-spectrum antimicrobials, was observed in 2020 when using hospital-specific denominators. This increase coincided with the onset of the COVID-19 crisis. These findings emphasize the need for a national hospital surveillance system that uses denominators that accurately represent the specific population being monitored. Implementing robust hospital-specific surveillance mechanisms would improve the precision of evaluations and facilitate targeted interventions aimed at optimizing antimicrobial utilization.

Enhancing Academic Integrity for Bangladesh's Educational Landscape

Integrating Artificial Intelligence (AI) tools in Bangladesh's academic landscape has ignited concerns about potentially eroding students' creative writing and critical thinking abilities. While AI offers efficient and personalized learning, there is a looming risk of students using it as a shortcut to success. Educators and policymakers must emphasize the cultivation of writing skills and critical thinking while guiding students to recognize the limitations of AI. While plagiarism checking is crucial for academic integrity, it often falls short in acknowledging students' originality. Natural Language Processing (NLP) emerges as a promising solution, augmenting plagiarism detection by comprehending context, nuances, and novel expressions. This paper proposed a model based on NLP that can incorporate a robust token identification mechanism into AI tools used by the students. A unique identification token is generated at the time of system login and stored in a publicly accessible resource, acting as a distinct marker for user activity. These identification tokens support a plagiarism detection system that continually reviews and compares activity records. It also enables a thorough examination of user involvement across various academic supplementary tools, such as academic writing, presentation creation, image generation, etc.

A hotspot and mechanism of enhanced bottom intrusion on the southern New England shelf

Understanding the occurrence of the intrusion of open ocean water onto continental shelves has scientific significance and societal relevance as the intrusion can significantly disrupt the marine ecosystem and fisheries. High-resolution numerical modeling is used to investigate the spatiotemporal occurrence and mechanisms of highly anomalous bottom intrusions on the southern New England shelf. Based on multi-year numerical simulations, this study reveals a hotspot of cross-isobath bottom-intensified intrusions at a topographic trough. Examination of multiple events portrays a robust mechanism of locally enhanced bottom intrusions. Persistent upwelling-favorable winds set up an enhanced pressure gradient field at the topographic trough and drive the intrusion a large-distance onshore. Numerical experiments with and without the topographic trough show that the localized pressure gradient results from a combination of the shelf orientation and local bathymetry. Although highly anomalous waters on the shelf relate to wind forcing, correlations between the wind stress anomaly and bottom salinity anomaly at the location of the enhanced intrusion is modest, implying the need to incorporate other environmental factors to develop more deterministic prediction models for subsurface conditions on the shelf. The results have important implications for marine environment and fisheries management.

The Effectiveness of The Fertilizer Voucher Scheme in General Tinio, Nueva Ecija

This study examines the efficacy of a fertilizer voucher scheme program aimed at improving agricultural productivity and mitigating food insecurity. Through a comprehensive analysis of data collected from respondents categorized by educational attainment, employment status, and length of service, the study evaluates perceived effectiveness in terms of understanding, agricultural income, and adaptation. Employing the Kruskal-Wallis H Test, significant differences in perceived effectiveness were identified among groups based on educational attainment and employment status, highlighting variations in perspectives. Specifically, college graduates and employed individuals reported higher levels of effectiveness compared to other groups, indicating potential correlations between educational and occupational backgrounds and program perception. However, no significant differences were observed based on the length of service, suggesting that tenure within the agricultural department does not significantly influence perceptions of program effectiveness. The study further identifies challenges in the targeting, distribution, and redemption of vouchers as key hindrances to the program's overall effectiveness. Notably, misallocation of vouchers and difficulties in accessing fertilizers lead to delays in application, compromising the program's intended impact. In response to these challenges, the study proposes several recommendations. These include enhancing data collection systems for better targeting, improving rural infrastructure to facilitate voucher redemption, investing in farmer education to promote informed decision-making, and implementing robust monitoring and evaluation mechanisms to track program performance. Additionally, the study suggests leveraging mobile technology for efficient voucher distribution, which can streamline administrative processes and enhance communication between administrators and farmers. By addressing these challenges and implementing the proposed recommendations, policymakers and stakeholders can enhance the effectiveness of fertilizer voucher schemes, ultimately contributing to improved agricultural outcomes and food security in the target communities.

Analyzing defense strategies against cyber risks in the energy sector: Enhancing the security of renewable energy sources

This study investigates the cybersecurity challenges and strategies within the renewable energy sector, emphasizing the need for robust defense mechanisms against escalating cyber risks. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, industry reports, and international standards documents published between 2014 and 2024. The methodology focuses on identifying effective cybersecurity measures, innovative approaches, and the role of international cooperation in enhancing the security of renewable energy sources. Key findings reveal that the integration of renewable energy into the power grid introduces unique cybersecurity vulnerabilities, necessitating tailored defense strategies. Technological solutions, particularly artificial intelligence (AI) and machine learning (ML), emerge as promising tools for bridging existing security gaps. Moreover, international cooperation and adherence to global standards are identified as pivotal in bolstering cybersecurity resilience across the sector. The study proposes strategic recommendations for industry stakeholders, including the prioritization of comprehensive cybersecurity strategies, investment in advanced technologies, and fostering a culture of cybersecurity awareness. It also highlights the importance of developing robust cybersecurity frameworks through policy interventions and public-private partnerships. Finally, enhancing the cybersecurity of renewable energy sources requires a multifaceted approach that integrates innovative technologies, human factors, policy interventions, and international cooperation. Future research should explore the potential of emerging technologies and investigate the human and policy dimensions influencing cybersecurity in the renewable energy sector.

A COMPARATIVE STUDY OF AUDIT EXPENDITURE IN PRIVATE SECTOR AND PUBLIC SECTOR BANKS

The paper embarks on a thorough examination, comparing the audit expenditures incurred by private sector banks and public sector banks. It's not merely a financial analysis; rather, it aims to uncover whether there's a discernible variance in how these two categories of banks allocate resources to auditing. However, the focus extends beyond financial figures; it seeks to unravel the underlying attitudes and perceptions towards auditing within the banking landscape. The study endeavors to unravel the intricate fabric of the banking industry, by scrutinizing these divergent perspectives, where perceptions often shape practices and policies. The core inquiry revolves around whether private sector banks and public sector banks view auditing through the same lens of importance. The paper intends to settle query whether they both equally invested in ensuring robust audit mechanisms, or does one category regard it as a perfunctory exercise? This line of questioning opens a window into the broader ethos of banking governance and risk management. Is auditing perceived merely as a regulatory obligation, a box to be checked to fulfill compliance requirements? Or is it recognized as a fundamental pillar of organizational resilience and financial health?

Evaluating Transferability of Attacks across Generative Models

The need for adversarial sample transferability is to attack black-box deep learning models. Whereas much recent work focuses on making untargeted adversarial attacks more transferable, there has been scarce research on the creation of transferable targeted adversarial instances that can trick models into believing they are of a particular class. The present transferable targeted adversarial attacks are not transferable since they cannot sufficiently define the distribution of target classes. In this paper, we propose a generative adversarial training system consisting of a feature-label dual discriminator to identify the adversarial instances formed from the target class images and a generator to construct targeted adversarial examples. It is concluded that adversarial scenarios have significant real-world applications in safety-critical fields like biometrics and autonomous driving. In addition, it is demonstrated that the current networks' susceptibility to hostile attacks, even under the worst black-box conditions has far-reaching societal consequences. We intend to further encourage more research into the inner workings of neural networks in the face of adversarial attacks, whereby people might use this knowledge to build robust defense mechanisms.

Boko Haram terrorism and the burden of managing internal displacement in Nigeria: Is the end in sight?

Boko Haram terrorism has since its inception in 2009 claimed the lives of over 20,000 people and caused the displacement of about 3 million following violent attacks on communities and the destruction of social structure and economic livelihood opportunities for communities. Since the number of displaced persons resulting from the activities of Boko Haram is on the increase, efforts are being intensified by both the state and non-state humanitarian actors to manage the crisis that results from this displacement. Despite these efforts, very few results have been achieved. This is largely a result of problems that bedevil the Nigerian state, namely (i) inadequate resources to finance humanitarian programmes for the internally displaced persons (IDPs), (ii) poor data management system, (iii) clash of responsibilities among humanitarian delivery agencies and institutions and (iv) the lack of policy support and poor implementation of provisions of other frameworks. Utilising systems theory, the study investigates the efforts of state and non-state actors in managing Boko Haram-induced internal displacements and the effectiveness or otherwise of managing the IDPs in Nigeria. It is found that the management of IDPs in Nigeria remains poorly handled owing to the above factors. Relying on the secondary method of data collection, the study also found that prolonged Boko Haram terrorism increases the possibility of internal displacements. Based on these findings, the study concludes that the government should quicken the process of policy adoption and implementation on IDP to serve as a legal framework for the management of IDPs in Nigeria, as well as develop a robust mechanism of data management to enhance the administration of humanitarian support.

Operational Regulatory to Minimise Risks in the Malaysian Oil and Gas Industry

Unfortunate events persisted in oil and gas projects and operations despite extensive study and collective efforts, highlighting the complexity of the challenges faced by the industry. Moreover, previous studies revealed shortcomings in the regulatory framework, hindering appropriate risk management during operational activities, emphasising the crucial role of effective risk management for overall organisational performance. The persistence of disputes and unfortunate occurrences, despite existing regulations, indicated gaps in current operational processes, underscoring areas for improvement addressed by this study. Through qualitative analysis of semi-structured interviews with participants involved in project and operational roles across various oil and gas organisations, this research identified weaknesses in current systems and recommended enhancements. Recommendations included robust enforcement mechanisms, efficient resource management, enhanced awareness campaigns, streamlined procedures, and the incorporation of technology in operations. Overall, this study contributes to understanding the complexities of regulatory compliance and risk management in Malaysian O&G operations, offering insights for enhancing operational efficiency and minimising risks associated with regulatory implementation.

Electronic Cargo Tracking and Trade Facilitation Among Clearing and Forwarding Companies at Inland Container Deport Nairobi, Kenya

The increasing forces of globalization around the world require that goods and services are moved from one part of the country to the other hence the need for customs. It is the responsibility of KRA to ensure that all taxes have been collected on behalf of the government of Kenya. However, several challenges have been evident in the operations of KRA as far as the realization of its objectives of customs system automation on trade facilitation is concerned. The purpose of this study was to establish the effect of electronic cargo tracking on trade facilitation among clearing and forwarding companies at inland containers deport Nairobi, Kenya. The study was grounded by the New Trade Theory. The explanatory research design was used. The target population of this study was the 369 clearing and forwarding agents and KRA staff and transporters, and a sample size of 191 respondents. Primary data was collected through a close-ended questionnaire. The data was analyzed using descriptive and inferential statistics. The study found that electronic cargo tracking has a positive and significant effect on trade facilitation (β = 0.323; p = 0.000). The study emphasized the direct contribution of electronic cargo tracking to the enhancement of trade facilitation processes. KRA is recommended to support research initiatives that focus on the continuous improvement of customs systems and trade facilitation technologies. The study recommends that KRA establish robust monitoring and evaluation mechanisms to assess the implementation and impact of electronic cargo tracking. The study recommends that the treasury establishes and implement policies that encourage the seamless integration of advanced technological solutions within the customs and trade facilitation processes.

EVASION TECHNIQUES IN MALWARE DETECTION: CHALLENGES AND COUNTERMEASURES

In the ever-evolving digital landscape, the escalating sophistication of malware poses a substantial threat, necessitating continual advancements in detection methods. This paper addresses the pervasive challenge of evasion techniques employed by malware to circumvent standard security measures. Focused on understanding the intricate methods employed by malware developers, our study explores the dynamic nature of this cyber threat. As malicious actors continually refine their approaches, a nuanced understanding of evasion tactics becomes paramount for developing effective countermeasures. The research emphasizes the need for robust defense mechanisms capable of adapting to the constantly changing cyber threat landscape. By unraveling the complexities of evasion techniques, this paper contributes valuable insights to the development of proactive and resilient cybersecurity measures. Through an exploration of specific evasion tactics, we aim to inform and empower cybersecurity professionals, facilitating the creation of strategies capable of effectively mitigating the risks posed by these dynamic digital threats.

On the Way to SBOMs: Investigating Design Issues and Solutions in Practice

The increase of software supply chain threats has underscored the necessity for robust security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a promising solution. SBOMs, by providing a machine-readable inventory of software composition details, play a crucial role in enhancing transparency and traceability within software supply chains. This empirical study delves into the practical challenges and solutions associated with the adoption of SBOMs through an analysis of 4,786 GitHub discussions across 510 SBOM-related projects. Through repository mining and analysis, this research delineates key topics, challenges, and solutions intrinsic to the effective utilization of SBOMs. Furthermore, we shed light on commonly used tools and frameworks for SBOM generation, exploring their respective strengths and limitations. This study underscores a set of findings, for example, there are four phases of the SBOM life cycle, and each phase has a set of SBOM development activities and issues; in addition, this study emphasizes the role SBOM play in ensuring resilient software development practices and the imperative of their widespread adoption and integration to bolster supply chain security. The insights of our study provide vital input for future work and practical advancements in this topic.

A novel physical layer authentication mechanism for static and mobile 3D underwater acoustic communication networks

In this paper, we present an innovative physical layer authentication approach for underwater acoustic communication networks. Our method leverages the transmitter nodes’ positions to establish a robust authentication mechanism. We consider two scenarios based on the mobility of the nodes. In the first scenario, underwater nodes (both legitimate and malicious) are static, while the second scenario assumes that the underwater nodes are mobile moving at a certain velocity. For both scenarios, we estimate position by analyzing the signals received at reference nodes that are strategically placed within a predetermined underwater area. Once the estimates are available, we propose binary hypothesis testing based on the estimated position to determine the legitimacy of the transmitter node. Furthermore, when the nodes are mobile, we perform velocity estimation at a certain time by taking the difference of the estimated coordinates, for which we also find the uncertainty in the estimation. We use a linear Kalman filter operation to track the legitimate node’s mobility. We provide closed-form expressions of the false alarm rate and missed detection rate resulting from binary hypothesis testing. We validate our proposed mechanism through simulation, demonstrating error behavior against link quality, malicious node location, and receiver operating characteristic (ROC) curves.

Managing healthcare innovation activities in Kazakhstan for optimal effectiveness

ABSTRACT The purpose of the article is to substantiate the theoretical, methodological, and conceptual foundations for managing innovative activities in the healthcare system of the Republic of Kazakhstan and develop recommendations aimed at improving its effectiveness in modern conditions. This study employs a mixed-method approach, integrating retrospective analysis, statistical evaluation, and economic analysis to examine trends and performance in healthcare innovation within Kazakhstan. It utilizes dynamic and structural analysis to assess innovation effectiveness and identifies key factors influencing innovative activity through equity participation and ranking methods, underpinning recommendations for improved management practices. This article critically concludes that healthcare innovation in Kazakhstan must address the low effectiveness of current innovative activities, hindered primarily by insufficient demand and funding constraints. The study underscores the essential role of a robust management mechanism focused on resource allocation, service delivery efficiency, and digital technology integration. It highlights the necessity for strategic funding distribution, enhanced international cooperation, and leveraging higher education institutions for innovation drive. The findings advocate for comprehensive strategies to boost innovation’s commercial efficiency and suggest further research on implementing these strategies in the context of Kazakhstan’s unique socioeconomic landscape and healthcare needs. Priority recommendations aimed at improving the efficiency of managing the country’s healthcare innovation activities have been formed. The results obtained in the study can be useful both to the scientific community and to managers at various levels in the healthcare sector.

The Moderating Effect of Ownership Structure on the Relationship between Related Party Transactions and Earnings Quality: Evidence from Saudi Arabia

This paper seeks to investigate how earnings quality is affected by related party transactions (RPTs). The research also examines the impact of ownership structure as a moderating variable on this relationship. Panel data with the firm fixed effects model are utilized in the paper. A sample of 91 non-financial companies listed on the Saudi Stock Exchange between 2018 and 2022 were included, resulting in 429 observations of company performance over that time period. This paper finds that there is a negative association between RPTs and earnings quality. Furthermore, the study found that the adverse effect of RPTs on earnings quality is intensified when there is managerial ownership and institutional ownership as moderating variables. The study’s conclusions are robust and reliable, as the sensitivity analysis results reinforce those of the basic analysis. To the authors’ knowledge, there is relatively little available evidence on the connection between RPTs and their correlation with earnings quality, particularly in the context of ownership structure acting as a moderating variable. Moreover, the study’s findings hold important implications for enhancing earnings quality in developing economies. To the authors’ knowledge, no studies have been conducted in Saudi Arabia thus far to investigate the impact of ownership concentration, institutional ownership, managerial ownership, foreign ownership, and state ownership on the association between RPTs and earnings quality. Therefore, this paper expands the literature by modeling how the interaction between ownership structure and related party transactions may influence earnings quality. In this way, the authors contribute to the body of knowledge by unveiling a more robust control mechanism, particularly in developing economies with ineffective markets for corporate control.

Preparedness and emergency response strategies for chemical, biological, radiological and nuclear emergencies in disaster management: A qualitative systematic review

AbstractDisaster management, mainly related to chemical, biological, radiological and nuclear (CBRN) incidents, is a critical field that requires constant evaluation to improve preparedness and response strategies. This review synthesises evidence on global health sector strategies for CBRN preparedness, gathering lessons to enhance policymaking for future response and readiness. This review included original English and French research addressing healthcare preparedness for CBRN threats. Database searches, such as EBSCO, ScienceDirect, PubMed, Ovid, Lissa, and Santécom, were conducted until November 2022. The Joanna Briggs Institute's tools were utilised to assess the risk of bias. Al‐Raayan® and Nvivo® version 12 software were employed for the review and analysis. 57 articles in English and 6 in French met the inclusion criteria. The analysis identified numerous strategies, concentrating on chemical and biological incidents (Chemical and biological bioterrorism: n = 27; Epidemics and Outbreak: n = 15). “Hospitals”, “responsibility”, and “health” emerged as prominent keywords, emphasising institutional roles and health outcomes in CBRN incidents preparedness. Key findings highlighted the need for a unified global definition of bioterrorism to align international efforts. Hospital‐level initiatives were emphasised as the foundation for national preparedness, emphasising specialised training, community engagement, and robust coordination mechanisms. Geographical variability in response strategies was noted, necessitating a harmonised global approach. Continuous research, epidemic management, and addressing radiological/nuclear threats emerged as crucial elements. Among others, strategies defined under “Emergency Response Preparedness” and “Intersectoral Policy Development” outlined comprehensive preparedness contexts for CBRN incidents. The review suggests an imperative shift towards building collaborative networks and enhancing health sector operational readiness to mitigate the impacts of CBRN incidents.

Horizontally and vertically polarized kink oscillations in curved solar coronal loops

Aims. Kink oscillations are frequently observed in coronal loops. This work aims to numerically clarify the influence of the loop curvature on horizontally and vertically polarized kink oscillations. Methods. Working within the framework of ideal magnetohydrodynamics (MHD), we conduct three-dimensional (3D) simulations of axial fundamental kink oscillations in curved density-enhanced loops embedded in a potential magnetic field. Both horizontal and vertical polarizations are examined, and their oscillation frequencies are compared with Wentzel-Kramers-Brillouin (WKB) expectations. We discriminate between two different density specifications. In the first (dubbed “uniform density”), the density is axially uniform and varies continuously in the transverse direction toward a uniform ambient corona. Some further stratification is implemented in the second specification (dubbed “stratified”), allowing us to address the effect of evanescent barriers. Results. Examining the oscillating profiles of the initially perturbed uniform-density loops, we found that the frequencies for both polarizations deviate from the WKB expectation by ∼10%. In the stratified loop, however, the frequency of the horizontal polarization deviates to a larger extent (∼25%). We illustrate the lateral leakage of kink modes through wave tunneling in 3D simulations, for the first time. Despite this, in the uniform density and the stratified loops, the damping time-to-period ratios are similar and are close to the analytical predictions for straight configurations under the thin-tube-thin-boundary (TTTB) assumption. Conclusions. The WKB expectation for straight configurations can reasonably describe the eigenfrequency of kink oscillations only in loops without an asymmetrical cross-loop density profile perpendicular to the oscillating direction. Lateral leakage via wave tunneling is found to be less efficient than resonant absorption, meaning that the latter remains a robust damping mechanism for kink motions even when loop curvature is included.