Internal audits serve as critical assurance services that support the enhancement of operational efficiency and financial performance within organizations. This study examines the role of internal auditing in improving these aspects in privatised financial institutions, specifically focusing on BLESSING Finance. Given the profit-driven orientation of management in such institutions, there is a pressing need to identify strategies that maximize profitability. Enhancing operational efficiency is pivotal, as it reduces operational costs while increasing productivity. Internal auditing contributes significantly by identifying deficiencies within internal controls and providing audit opinions that inform management in drafting appropriate policies and procedures. This research utilized a mixed-methods approach, combining qualitative data from interviews and quantitative data from questionnaires, to assess the impact of internal auditing on operational efficiency and financial performance. The findings demonstrate that internal audits have a positive and significant effect on both operational efficiency and financial performance, highlighting the value of internal audits as a strategic tool for financial institutions. It is recommended that BLESSING Finance’s management prioritize the recruitment of qualified auditors with the necessary skills and expertise to perform audits effectively and efficiently, thereby further enhancing the institution’s operational efficiency and financial outcomes. The study underscores the importance of robust internal audit functions as a key driver of strategic and financial success in financial institutions.
A detailed investigation into the axial bearing load of the revolving platform in a hydraulic excavator equipped with a shovel attachment was presented in this study. A mathematical model was formulated to assess the forces acting on the bearing under various operational conditions. The analysis focuses on a 100,000 kg excavator with a 6.5 m³ bucket, examining the contributions of kinematic chains and drive mechanisms to axial loads. Simulations of multiple positions within the working range were carried out, calculating the load spectrum, including boundary resistance, to ensure machine stability. An optimization program was developed to refine the bearing selection process by identifying equivalent loads and moments. These calculations were benchmarked against manufacturer capacity diagrams, allowing for precise selection of appropriate bearing sizes. The findings underscore the critical role of accurate load calculations in enhancing the performance, reliability, and design optimization of hydraulic excavators. This approach provides engineers with a framework for selecting bearings that can withstand complex operational stresses, thereby improving the efficiency and longevity of hydraulic machinery.
The traditional manufacturing sector in China is increasingly challenged by rising labour costs and the diminishing demographic advantage. These issues exacerbate existing inefficiencies, such as limited value addition, high resource consumption, prolonged production cycles, inconsistent product quality, and inadequate automation. To address these challenges, a production scheduling framework is proposed, guided by three key objectives: the prioritisation of high-value orders, the reduction of total processing time, and the earliest possible completion of all orders. This study introduces a multi-objective constrained greedy model designed to optimise scheduling by balancing these objectives through maximum weight allocation, shortest processing time selection, and adherence to the earliest deadlines. The proposed approach incorporates comprehensive reward and penalty factors to account for deviations in performance, thus fostering a balance between operational efficiency and product quality. By implementing the optimised scheduling strategy, it is anticipated that significant improvements will be achieved in production efficiency, workforce motivation, product quality, and organisational reputation. The enhanced operational outcomes are expected to strengthen the core competitiveness of enterprises, particularly within the increasingly complex landscape of pull production systems. This research offers valuable insights for manufacturers seeking to transition towards more efficient, automated, and customer-centric production models, addressing both short-term operational challenges and long-term strategic objectives.
This study aims to investigate the academic community’s engagement with research on green policies within higher education institutions. The study examines the evolving landscape of green policy research in universities, seeking to elucidate trends in academic interest, key contributors, and thematic developments. Employing bibliometric analysis, this research scrutinizes publications indexed in the Web of Science database from 1994 to 2024, with a particular focus on keywords, co-authorship networks, and institutional affiliations. The findings indicate a notable increase in publications, particularly post-2016, reflecting a transition from broad conceptual themes to more specific applications of green policies, including sustainable management practices and performance evaluation. Central themes identified include “green”, “sustainability”, “performance”, and “management”, highlighting a shift from theoretical exploration to practical implementation. Prominent contributors, such as Wang Y and Zhang Y, alongside institutions like Tsinghua University, have significantly advanced the field. Furthermore, the study underscores a robust correlation between the growth in scientific output and the emergence of specialized sustainability journals, indicating an escalating academic demand for focused publication platforms. The results suggest that research on green policies in universities is increasingly characterized by interdisciplinary collaboration and the integration of innovative technologies and methodologies to effectively address sustainability challenges. The field of green policy application in higher education is rapidly expanding, with a well-connected, collaborative research community generating impactful work that harmonizes modern technologies and methodologies to confront sustainability issues at both global and local levels.
The thermal behavior and fluid dynamics of Nano-Enhanced Phase Change Materials (NEPCM) in enclosed systems have been investigated using numerical simulations, focusing on the effects of time-varying temperature profiles and nanoparticle concentration. The analysis reveals that the inclusion of nanoparticles significantly enhances the fluid flow velocity and streamlining within the enclosure, particularly for aluminium oxide (Al2O3), copper oxide (CuO), and zinc oxide (ZnO) nanoparticles. The results indicate that an increase in nanoparticle concentration leads to an acceleration in fluid flow and improved heat transfer efficiency, with distinct phase change dynamics observed across different concentrations. The study demonstrates that nanomaterials hold substantial potential for enhancing the thermal performance of NEPCM systems. These enhancements can contribute to greater efficiency in thermal energy storage (TES) and heat transfer processes, particularly in industrial applications requiring energy optimization. The findings align with previous research, emphasizing the positive correlation between nanoparticle concentration and velocity streamlining. This work provides valuable insights for the future exploration of different nanoparticle types and concentrations, paving the way for the development of more efficient NEPCM systems in advanced thermal systems.
Logistics performance plays a pivotal role in fostering economic growth and enhancing global competitiveness. This study aims to evaluate the logistics performance of G8 nations through multi-criteria decision-making (MCDM) models. Standard Deviation (SD) has been applied to determine the weights of evaluation criteria, while the Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) has been employed to rank the countries based on their performance. The findings indicate that Timeliness emerges as the most critical factor influencing logistics efficiency. Among the G8 nations, Germany achieves the highest logistics performance, reflecting the robustness of its logistical infrastructure and operational efficiency. The results reinforce the premise that logistics performance is instrumental to both international trade and economic competitiveness. Nations demonstrating strong logistical capabilities are better positioned to excel in global markets, while those with underdeveloped logistics systems may face increased economic vulnerabilities. Enhancing logistical frameworks, including infrastructure and systems, is therefore essential for nations striving to improve their global standing. The insights presented underscore the importance of strategic investment in logistics infrastructure as a key policy instrument for enhancing economic resilience and international trade potential.
The determinants influencing marketing performance in small and medium-sized enterprises (SMEs) have garnered increasing scholarly attention due to their critical role in driving economic development. SMEs face multifaceted challenges in optimizing market strategies, necessitating a comprehensive understanding of the factors underpinning marketing success. Through a systematic literature review (SLR) adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols, this study synthesizes insights from 44 empirical studies published between 2019 and 2023. Key determinants identified include entrepreneurial orientation, marketing capabilities, innovation, and digital strategies. Entrepreneurial orientation and marketing capabilities were found to exhibit a strong correlation with marketing performance, highlighting their importance in shaping SME competitiveness. Furthermore, innovative practices and the strategic use of digital marketing tools were observed to significantly bolster market positioning, enabling SMEs to achieve competitive differentiation. Enhanced marketing performance is shown to contribute to consistent revenue generation, organizational resilience, and financial stability, thereby promoting long-term sustainability in competitive industries. This investigation advances the academic discourse by proposing an integrated conceptual framework to guide future research on SME marketing performance. Additionally, evidence-based recommendations are provided to assist enterprises in leveraging identified determinants to enhance marketing efficacy and achieve sustainable growth.
The traditional channel scheduling methods in short-range wireless communication networks are often constrained by fixed rules, resulting in inefficient channel resource utilization and unstable data communication. To address these limitations, a novel multi-channel scheduling approach, based on a Q-learning feedback mechanism, was proposed. The architecture of short-range wireless communication networks was analyzed, focusing on the core network system and wireless access network structures. The network channel nodes were optimized by deploying Dijkstra's algorithm in conjunction with an undirected graph representation of the communication nodes within the network. Multi-channel state characteristic parameters were computed, and a channel state prediction model was constructed to forecast the state of the network channels. The Q-learning feedback mechanism was employed to implement multi-channel scheduling, leveraging the algorithm’s reinforcement learning capabilities and framing the scheduling process as a Markov decision-making problem. Experimental results demonstrate that this method achieved a maximum average packet loss rate of 0.03 and a network throughput of up to 4.5 Mbps, indicating high channel resource utilization efficiency. Moreover, in low-traffic conditions, communication delay remained below 0.4 s, and in high-traffic scenarios, it varied between 0.26 and 0.4 s. These outcomes suggest that the proposed approach enables efficient and stable transmission of communication data, maintaining both low packet loss and high throughput.
Jayapura City, Indonesia, presents significant potential for solar energy utilisation, driven by its high solar radiation levels. However, the presence of urban obstacles, such as buildings, trees, and varied topography, can obstruct the direct transmission of solar radiation to the ground, thereby reducing its efficiency for solar energy systems. This study aims to develop a methodology for predicting and assessing the shade projection of solar radiation intensity across Jayapura City. A quantitative descriptive approach was employed, involving the measurement of elevation and azimuth angles using Global Positioning System (GPS) technology. Data were analysed using RETScreen and Sun Locator Pro (SLP) software. The analysis of the collected data facilitated the generation of a detailed shade projection map, which can be utilised to optimise the placement of solar panels and enhance the performance of the city's Solar Power Generation System (SPGS). The findings indicated that the highest elevation angle occurred at 12:00 pm in March. In September, the sun's position was nearly directly above the equator, leading to a minimal shadow ratio (SR = 0.08), with the projection closely aligned with the object. The azimuth angle, measured at noon, exhibited an extreme angular shift, reflecting the standard reference towards the north (180° at noon). This study demonstrates the potential of this methodology to inform the strategic placement of solar infrastructure, improving the efficiency and efficacy of solar power systems in urban environments characterised by complex topographies.
The substitution of cement with coal gangue powder (CGP) offers significant potential for energy conservation, emission reduction, and environmental sustainability. To optimize the mechanical properties of coal gangue cement paste, a modified response surface methodology (RSM) model was developed, incorporating grinding parameters as independent variables and compressive strength as the response variable. The feasibility of the model was validated through coefficient estimation, variance analysis, and fitting statistics. The analysis revealed that milling speed was the most significant factor influencing the compressive strength at 20% substitution, while the ball-to-material ratio predominantly affected the strength at 50% substitution. An increase in milling speed was observed to significantly broaden the particle size distribution, with larger particles (15.14$\mathrm{\mu m}$ to 275.42$\mathrm{\mu m}$) serving primarily as micro-aggregates, and smaller particles (0.32$\mathrm{\mu m}$ to 15.14$\mathrm{\mu m}$) functioning as fillers within ultra-fine pores. Scanning Electron Microscopy (SEM) further corroborated these findings. Numerical optimization based on the RSM model identified optimal grinding parameters: a ball-to-material ratio of 1.40, a milling time of 0.843 hours, and a milling speed of 300 rpm. These parameters are recommended to achieve the target compressive strengths of 25 MPa at 20% CGP substitution and 10 MPa at 50% CGP substitution. This study provides a cost-effective and feasible approach for the utilization of coal gangue in cementitious materials, contributing to the advancement of sustainable construction practices.
The rapid evolution of drone-assisted logistics for urban last-mile (ULM) delivery has garnered significant interest from both academia and industry. This article presents a comprehensive review of the state-of-the-art research and practical implementations of ULM systems, focusing on the use of unmanned aerial vehicles (UAVs) for the final stage of goods and parcel delivery in urban environments. The applicability of UAV-based logistics across various contexts, including urban and rural areas, is examined, with real-world case studies highlighted to demonstrate practical uses. Key methodologies and models employed in optimising UAV routing and operations are discussed, particularly those that enhance the efficiency and reliability of ULM. The critical advantages and limitations of drone-assisted last-mile logistics are analysed, providing insights into the operational, regulatory, and technological challenges. The discussion is further expanded by addressing emerging trends in UAV technology, as well as innovations in drone deployment strategies and the evolving regulatory landscape. In conclusion, potential theoretical advancements and future applications of ULM systems are outlined, with an emphasis on integrating drones into broader logistics networks and smart city frameworks. The insights offered aim to guide future research and practical developments in this rapidly advancing field.
This paper examines the integration of Information and Communication Technology (ICT) in vocational education and training (VET) institutions, highlighting its significance in aligning education with the demands of a rapidly digitising world. The Council of the European Union recognises VET's role in equipping individuals with skills for digital and green transitions. Amid the COVID-19 pandemic, ICT adoption in education became imperative, accelerating the digital revolution within the sector. Albania, as an EU candidate country, has emphasised ICT in its national strategies, aligning with European frameworks to foster modernisation in VET. The research employs a quantitative methodology, utilising a questionnaire tailored to the Albanian VET context, to gather insights from public vocational schools and training centers, with a total number of n=46 institutions participating in the research. Findings reveal that VET institutions recognise ICT-supported modernisation as vital for improving teaching quality, management, communication, and students' transition to the labour market. Despite this, challenges such as inadequate infrastructure, outdated devices, and limited digital skills among teaching staff impede the full realisation of ICT's potential. To address these barriers, the study recommends targeted interventions, including teacher training and infrastructure development. This research contributes to the discourse on digital transformation in VET, underscoring the importance of strategic investments in ICT for enhancing vocational education's quality and relevance in Albania's evolving educational landscape.
To address the challenges in detecting surface defects on insulator iron caps, particularly due to the complex backgrounds that hinder accurate identification, an improved defect detection algorithm based on YOLOv8n, whose full name is You Only Look Once version 8 nano, was proposed. The C2f convolutional layers in both the backbone and neck networks were replaced by the C2f-Spatial and Channel Reconstruction Convolution (SCConv) convolutional network, which strengthens the model's capacity to extract detailed surface defect features. Additionally, a Convolutional Block Attention Module (CBAM) was incorporated after the Spatial Pyramid Pooling - Fast (SPPF) layer, enhancing the extraction of deep feature information. Furthermore, the original feature fusion method in YOLOv8n was replaced with a Bidirectional Feature Pyramid Network (BiFPN), significantly improving the detection accuracy. Extensive experiments conducted on a self-constructed dataset demonstrated the effectiveness of this approach, with improvements of 2.7% and 2.9% in mAP@0.5 and mAP@0.95, respectively. The results confirm that the proposed algorithm exhibits strong robustness and superior performance in detecting insulator iron cap defects under varied conditions.
The Energy Roadmap 2050 necessitates the active participation of all sectors—including energy, construction, industry, transport, and agriculture—in a transformative energy paradigm. Over the past decades, there has been a notable increase in energy-related regulations, directives, protocols, and communications, which underscore the urgency of infrastructure interventions. Intelligent networks and energy storage systems are recognized as pivotal elements in enhancing sustainability and efficiency. This study presents a comprehensive technical-managerial program aimed at improving energy performance and minimizing consumption at the University of Basilicata (UNIBAS) campus in Potenza, southern Italy. An initial energy audit identified various energy-saving techniques, while ISO 50001 standards were employed to facilitate the establishment of energy performance objectives and strategies for consumption reduction. A dynamic simulation model was developed to assess the potential integration of photovoltaic and solar thermal systems, in conjunction with heat pumps. An Energy Baseline was established to evaluate the impact of these technologies. The strategies proposed to optimize both technological and managerial practices for the major energy variables were examined, with the effects tracked over time using established energy performance indicators (EnPIs). An economic assessment of the proposed strategies was conducted to evaluate their viability. Communication initiatives aimed at enhancing awareness regarding light rationalization and systems shutdown represent immediate interventions, while more invasive efficiency improvements are classified as medium- and long-term strategies. Compliance with European and Italian legislation mandates advancements in building envelopes and distribution systems, as well as the incorporation of renewable energy sources for thermal and electrical applications, alongside automation of building-plant systems through smart grids and actuators. It is anticipated that experts in energy management processes will adapt and expand the planned actions to ensure the energy sustainability of the university throughout the period from 2022 to 2050.
Open Access
