In this study, a numerical investigation into heat transfer and entropy generation characteristics using confined-slot jet impingement was conducted. Comparisons were drawn between the heat transfer and entropy generation attributes of two wing ribs positioned on the heated impinging target surface and those of a rib-less surface. The influences of variations in the spacing between the stagnation point and the rib (B) of (10-30 mm), ranging from 10 to 30 mm, rib heights (A) between 0.5 to 2 mm, and a Reynolds number of the jet (Re) between 3000 to 8000 on fluid flow, heat transfer, and entropy generation were elucidated. Employing the Finite Volume Method (FVM) managed the continuity, momentum, and energy equations in adherence to the principles of the SIMPLE methodology. Results revealed that the Nusselt number $(\overline{N u})$, pressure drop, and total entropy $\left(\bar{S}_{\text {total }}\right)$ escalated in accordance with Re and A. Conversely, they diminished with reduced spacing from the stagnation point to B. Notably, a superior heat transfer rate was observed when employing a target plate integrated with wing ribs in contrast to a rib-less configuration. Performance Evaluation Criteria (PEC) values were noted to augment with rib height increment. It is demonstrated that the PEC increases as A increases. Also, the lower value of PEC equals 1.044 at A of 2 mm, B of 10 mm, and Re of 8000, while the higher value of the PEC equals 1.68 at A of 2 mm, B of 10 mm, and Re of 3000. The findings suggest that slot-Jet impingement complemented by wing ribs plays a pivotal role in enhancing the cooling efficiency of electronic devices.

Within the evolving landscape of modern business, a proficient logistics sector stands paramount in fostering regional and global competitive edges. A country's logistics performance, gauged aptly, can influence not just the business outcomes of individual enterprises but also shape the nation's holistic logistics efficacy. This study delves into an examination of logistics standards in European Union (EU) countries, viewed through the lens of the Logistics Performance Index (LPI), as reported by the World Bank. The primary focus is on the LPI data for 2023, with a subsequent exploration of the EU’s performance trajectory from 2007 to 2018. The findings illuminate that specific EU countries consistently uphold superior logistics proficiency, while striving for advancements. Beyond these front runners, many EU countries manifest commendable logistics outcomes, positioning themselves favorably on the global stage.

Purpose: In order to ensure effectiveness in State Owned Enterprises, (SOE) forensic auditing significantly plays a huge role in the detection and prevention of fraud. State owned Enterprises play a big role in any country as they provide commercial services or activities to the public. Their business involves transacting huge sums of money in their day to day operations. The state has significant control through full, majority, or significant minority ownership. There are fraudulent activities occurring in these enterprises as a result of poor controls in these organizations, hence forensic auditing plays the role. The purpose of the study was to examine how forensic auditing services aid in fraud detection in State Owned Enterprises. Methodology: Quantitative research methodology was adopted and questionnaires were used to collect data. Findings: The results indicated that forensic auditing has a significant positive correlation relationship in fraud detection in SOEs. Originality/Value: Forensic auditing although used by ZESA, is not being effectively implemented to detect and prevent fraud.

High-rise edifices are emblematic of contemporary construction, encapsulating advancements in environmental, formal, and structural design approaches. Such structures, often considered to consume substantial amounts of energy, primarily due to air conditioning and lighting, epitomise urban progression in developed regions. Concerns over energy and resource consumption have necessitated the exploration of viable alternatives for mitigating energy usage. In response, architectural endeavours have gravitated towards harnessing modern technologies to curtail energy demands, especially in high-rise constructions. Several architectural trends have subsequently emerged, each leveraging a myriad of techniques with the intent to diminish energy usage. This research, therefore, sought to elucidate the technologies deployed in energy conservation for high-rise buildings and subsequently discern their ramifications on architectural formulation. Adopting a qualitative-descriptive approach, an analytical examination was conducted on fifteen distinct cases of energy-efficient structures, aiming to gauge the influence of such technologies. Data, procured from visual and descriptive evaluations, were systematised using an observation sheet. It has been observed that certain environmentally-focused design methodologies may inadvertently compromise the architectural aesthetics of high-rise structures. Consequently, there emerges a pressing need for architects to harmonise aesthetic aspirations with contemporary energy-saving imperatives, ensuring judicious use of natural resources.

Recent financial scandals and crises have underscored the criticality of robust risk management practices, particularly in the realm of information technology (IT). This study explores the implementation of an Information Technology Risk Management (ITRM) system within an African airline, utilizing the RITM 23 methodological approach. RITM 23, a comprehensive framework, integrates standards from enterprise risk management (ISO 31000 and COSO ERM) and ITRM (COBIT 5), guiding organizations through framing the project, data collection, development of the ITRM system, and its subsequent communication and monitoring. The case study demonstrates the effective implementation of the RITM 23 framework, which led to the establishment of a complete environment for ITRM, inclusive of templates, tools, procedures, and governance processes. This implementation significantly enhanced the management of IT risks, mitigating potential catastrophic outcomes associated with unmanaged IT threats in the airline sector. The study concludes with a contemplation of future advancements, particularly the integration of artificial intelligence to further streamline and automate the ITRM process. This case study not only illustrates the successful application of RITM 23 but also sets a precedent for future ITRM implementations in similar sectors.

Fermatean fuzzy set (FRFS) is very helpful in representing vague information that occurs in real world circumstances. Their eminent characteristic of FRFS is that the degree of membership $\Im^{\ell}$ and degree of nonmembership $\beth^\gamma$ satisfy the condition $0 \leq \Im^{\ell^3}(x)+\Im^{\ell^3}(x) \leq 1$, so the space of vague information they can describe is broader. This study introduces the concept of generalized parameters into the FRFS framework and proposes a set of generalized Fermatean fuzzy average aggregation operators for the purpose of information aggregation. Subsequently, the operators are expanded to encompass a generalized parameter based on group consensus, which is derived from the perspectives of numerous experienced senior experts and observers. The present study offers a multi-criteria decision-making (MCDM) methodology, which is demonstrated using a numerical example to successfully showcase the suggested technique. In conclusion, a comparative study is undertaken to validate the efficacy of the suggested technique in relation to existing methodologies.

The paramountcy of English in the contemporary global landscape necessitates the enhancement of English language proficiency, especially in academic settings. This study addresses the disparate levels of English proficiency among college students by proposing a novel approach to evaluate English text readability, tailored for the higher education context. Employing a deep learning (DL) framework, the research focuses on developing a model based on convolutional neural networks (CNNs) to assess the readability of English texts. This model diverges from traditional methods by evaluating the difficulty of individual sentences and extending its capability to ascertain the readability of entire texts through adaptive weight learning. The methodology's effectiveness is underscored by an impressive 72% accuracy rate in readability assessment, demonstrating its potential as a transformative tool in English language education. The application of this DL-based text readability evaluation model in college English training is explored, highlighting its potential to facilitate a more nuanced understanding of text complexity. Furthermore, the study contributes to the broader discourse on enhancing English language instruction in higher education, proposing a method that not only evaluates text comprehensibility but also aligns with diverse educational needs. The findings suggest that this approach could significantly support the enhancement of English teaching methodologies, thereby promoting a deeper, more accessible learning experience for students with varying levels of proficiency.

Purpose: This study aims to develop a reliable forecasting approach for Tanzania's unemployment rate and provide policymakers with an effective tool for decision-making. Unemployment forecasting is vital for informed policymaking, particularly in countries like Tanzania. Methodology: This study employs a quantitative research design and adopts Box Jenkin's methodology and the ARIMA (AutoRegressive Integrated Moving Average) model for unemployment forecasting in Tanzania. The entire available dataset for the specified period is utilized, employing a non-probability sampling technique. Diagnostic tests, including ACF (AutoCorrelation Function), PACF (Partial AutoCorrelation Function), and unit root analysis, are conducted to guide the optimal model selection. Differencing addresses non-stationarity in the time series data by removing trend and seasonality effects. The optimal model selection is based on criteria such as AIC (Akaike Information Criterion), Schwartz, and Hannan-Quinn. Findings: The study finds that the ARIMA (3,1,4) model demonstrates superior performance in forecasting the unemployment rate in Tanzania. Diagnostic checks validate the adequacy of the model, revealing white noise residuals. The forecasts indicate a consistent downward trend in unemployment rates over the next nine years, suggesting potential labour market improvements in Tanzania. These findings enhance our understanding of Tanzania's unemployment dynamics and provide valuable insights for policymakers. Originality/Value: The study lies in its application of Box Jenkin's methodology and the ARIMA model to unemployment forecasting in Tanzania. By utilizing the entire available dataset and employing diagnostic tests for model selection, the study enhances the reliability of the forecasting approach. The study offers policymakers an informed decision-making tool by providing accurate forecasts and capturing underlying trends.

Historical records indicate that Lasem, a petite coastal town in Java, Indonesia, boasts a rich lineage commencing around 7-8 AD. Several distinct periods, encompassing the Hindu-Majapahit, Islamic, Chinese-Muslim, Colonial, Japanese, Independence, and Post-independence eras, have been identified as shaping the town's evolution. This study endeavored to elucidate the urban morphological shifts observed in Lasem over these diverse epochs, intending to derive a model for small coastal cities. Utilising a qualitative case study methodology, data was extracted from Pratiwo's sketch map, supplemented by historical maps archived in kit.nl.lv and the Tropen Museum collection. By juxtaposing the temporal modifications of Lasem's structure, connections were drawn with extant theories. The resultant findings reveal a city morphology moulded by both constant (rivers and squares) and evolving structural elements (notably the introduction of Daendels Street and the railroad during colonial rule). Distinctively, Lasem's configuration diverges from typical Southeast Asian coastal towns, primarily attributed to its modest size, which obviated the construction of Dutch defensive forts. Consequently, the formulated model for Lasem presents a four-stage developmental sequence, uniquely omitting the ‘fort city’ stage commonly observed in coastal city frameworks. This novel model furnishes profound insights into the urban morphology of comparable coastal towns, offering a robust platform for devising tailored urban planning and developmental stratagems for similar contexts.

The advent of Information and Communication Technologies (ICT) has significantly revolutionized urban logistics, particularly through the emergence of crowd-sourced platforms. This evolution has engendered substantial benefits, including cost-effectiveness, enhanced delivery speeds, and environmentally sustainable practices. Yet, the unregulated nature of such platforms poses considerable challenges, especially in Human Resource Management (HRM) within crowd distribution networks. This study, in a groundbreaking exploration, examines the complexities inherent in HRM in the context of urban crowd logistics. It primarily focuses on employment dilemmas, training intricacies, and the intricacies of salary computation, thereby illuminating areas hitherto unexplored in existing literature. It is identified that both crowd workers and pldatform operators encounter significant challenges in effective human resource administration, marking a critical area of concern. The study further discerns the regulatory lacunae prevalent in this sector, proffering prospective remedial measures and advanced HRM strategies. Such insights are pivotal in augmenting the understanding of the interplay between human resources and crowd logistics, laying a foundation for both academic research and practical application. The paper, therefore, not only contributes to scholarly discourse but also offers pragmatic guidance for optimizing HRM in crowd logistics. This comprehensive analysis serves as a crucial resource for policymakers, industry stakeholders, and academics, charting a course for future inquiry and refinement in crowd logistics HRM.

Effective engagement is a two-way process where stakeholders are not merely consulted or listened to, but where a company makes a sincere attempt to engage in a dialogue to respond to stakeholder concerns (Rossouw, 2015; Aina, 2019:60). Responding to stakeholder concerns builds trust, and experience shows that trust and relationships take time to build but are valuable assets. To build trust, the company must show that it has listened and acted in response to stakeholder concerns. This is why ongoing communication with, and reporting to, stakeholders is such an important component in any engagement strategy. The purpose of the study was to ascertain the various approaches to stakeholders’ engagement. In terms of data collection, the author sourced and reviewed literature on the topic. Among others, these sources included journal articles, books, magazines, newspapers and King IV report. The results indicated that companies use different approaches on stakeholders and engagement and the study concludes that inclusivity approach adopted in the King IV report is instrumental in developing engagements that are collaborate and may build sustainable relationships with stakeholders. Proactive continuous engagement with stakeholders brings mutual trust and builds sustainable relationships.

In recent years, a surge in the utilisation of vehicle-to-vehicle (V2V) communication has been observed, serving as a pivotal factor in facilitating automatic control of vehicles without human intervention. This advancement has notably curtailed accident rates, mitigated traffic congestions, and augmented vehicular security. Consequently, a meticulous survey has been orchestrated in the domain of Vehicle Ad-Hoc Networks (VANETs), particularly as autonomous vehicles pervade urban landscapes. The necessity for resources to assure secure and consistent operations of an escalating fleet commensurately intensifies with the enlargement of the fleet itself. Intelligent Transportation Systems (ITS) hinge upon VANETs to furnish travellers with secure and pleasant journeys, pertinent information and entertainment, traffic management, route optimisation, and accident prevention. Nevertheless, a plethora of challenges inhibits the delivery of an adequate Quality of Service (QoS) within vehicular networks, such as congested and interrupted wireless channels, a progressively saturated and sprawling spectrum, hardware inconsistencies, and the swift expansion of vehicular communication systems. Contemporary networks and energy grids are subject to strain from daily and recreational activities. As demand perpetually ascends, a necessity arises for more refined tools and methodologies for resource management and a more precise distribution system. This investigation offers an exploration of the most recent practices and trends in VANET resource allocation, with the objective of garnering insights into the existing research landscape and its impelling forces.

In the logistics domain, the selection of personnel, particularly transport managers and drivers, is pivotal to operational efficacy, demanding a selection process that transcends traditional subjectivity and expertise scarcity. Addressing this, the Best Worst Method (BWM) integrated with the Combined Compromise Solution (CoCoSo) presents a novel decision-support model, employed here for the first time to refine the recruitment process within the transportation sector. Through the application of BWM, criteria weights were ascertained, a method that has shown superior performance and reliability in deriving consistent results. Concurrently, the CoCoSo method facilitated the ranking of candidates, demonstrating greater reliability and stability compared to existing methodologies. The fusion of these methods offers a distinctive approach, enhancing reliability in diverse problems and across various hierarchical strata. A meticulous compilation of evaluation criteria has been delineated, for drivers and transport managers alike, incorporating a gamut of competencies including but not limited to communication and negotiation skills, leadership skills, swift and autonomous decision-making capabilities, resilience under pressure, educational qualifications, proficiency in computer skills, and past work experience. For transport managers, additional competencies such as spatial coordination and orientation, levels of responsibility and precision, adeptness in working under pressure, operational task efficiency, comprehensive understanding of regulations, rules, and documentation, and a history of relevant work experience have been emphasized. This research marks a theoretical and practical contribution to the literature by providing a model crafted for the nuanced requisites of logistics roles. Empirical validation confirms the model's applicability and efficiency in real-world contexts, heralding its potential to refine the Human Resource Management (HRM) landscape in logistics. Consequently, this work signifies a paradigm shift in the strategic and systematic management of human resources in the logistics sector, furnishing industry decision-makers with a robust tool for personnel selection.

Purpose: The study used the Beneish M Score to discover probable financial statement manipulation by a selected Zimbabwe Stock Exchange-listed bank. Research methodology: The Beneish M Score eight variable statistical model was applied to secondary data of the selected bank from 2011 to 2018. The model utilizes ratios in distinguishing between manipulators and non-manipulators, with a yardstick measure of -2.22. Results greater than -2.22, classify the organization as a financial statements manipulator with less than -2.22 classify it as a non-manipulator. Results: The M score model detected manipulation for the years 2011 (-0.74), 2013 (-1.84), and 2015 (-2.19), which are greater than the benchmark of -2.22. The years 2012 (-3.17), 2014 (-2.46), 2016 (-3.07), 2017 (-2.80) and 2018 (-2.42) reveal the bank as a non-manipulator as these values are less than -2.22. Limitations: The Beneish M score statistical model was modeled for manufacturing companies. The study sought to test the M Score’s applicability in the banking sector and it was restricted to the selected bank for the years 2011 to 2018. Contribution: The Beneish M score is a valuable model for users of issued annual financial statements to guard against earnings manipulation. Stakeholders rely on audited financial statements, believed to be free from manipulation, yet companies fold up with unqualified audit opinions contained in published financial statements. The study validates the Beneish M score statistical model for detecting manipulation in published annual financial statements in Zimbabwe, where there is limited research on earnings manipulation.