This study aims to develop energy-efficient and environmentally friendly cooling solutions that are both effective and adaptable to various climates and structural forms. By leveraging computational fluid dynamics (CFD) software ANSYS and simulation software Engineering Equation Solver (EES), an innovative approach was undertaken. The investigation focused on the optimization of external air cooling via adjustable injectors operating at three distinct velocities, across three airflow rates. Concurrently, the adaptability of the cooling flow was enhanced by varying the number of turns in a coil within the heat exchanger's condenser section. This dual-phase method facilitated a comprehensive analysis across 54 scenarios, employing the EES software for the calculation of the coefficient of performance (COP) enhancement metrics. The efficiency of the cooling apparatus was rigorously evaluated by methodically altering the number of cooling tube turns and injection velocities. The apparatus comprised a loop-and-tube heat exchanger with a modifiable structure, where the second phase of the study addressed the thermal impact of air entry velocity and water spray mechanisms, featuring cooling tube adjustments ranging from five to thirteen turns. The initial phase examined the effects of air entry area and water spray techniques through variable injector configurations, with diameters of 15, 24, and 20 cm, and dimensions of 10 cm in height and 25 cm in length, alongside a conduit width of 60 mm. The findings revealed that the thermal dynamics of the heat exchanger and fluid flow are significantly influenced by the apparatus's geometry, particularly the air entry area and water spraying mechanism. Temperature and velocity contours illustrated that the number of loop turns and injections markedly affects system performance. An optimal configuration, consisting of 35 injectors and 13 coil turns, achieved a COP of 4.537 at an inlet velocity of 2.0 m/s, signifying the most effective system design identified within this study.
Food inflation presents a significant challenge in Nigeria. This study examines the volatility of four primary food items—tomatoes, yam, yellow garri, and imported rice—in Cross River State, Nigeria, utilizing data on monthly retail prices per kilogram from January 1997 to November 2023, sourced from the National Bureau of Statistics (NBS). Three asymmetric volatility models were employed: Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Threshold Autoregressive Conditional Heteroscedasticity (TARCH), and Power Autoregressive Conditional Heteroscedasticity (PARCH). The parameters of these models were estimated using three distributions of error innovations: Normal, Student's t-distribution, and Generalized Error Distribution (GED). The performance of the models was assessed based on log-likelihood for fitness and Root Mean Square Error (RMSE) for forecasting accuracy. The results indicated that non-Gaussian error innovations outperformed the normal distribution. Notably, higher persistence in volatility was observed for yam and tomatoes compared to yellow garri and imported rice. Tomatoes exhibited the highest volatility persistence among the food items analyzed. Significant Generalized Autoregressive Conditional Heteroscedasticity (GARCH) terms for tomatoes and yam suggested that past volatility has a significant positive impact on their current volatility, whereas this effect was not significant for yellow garri and imported rice (p$<$0.05). The leverage effect was found to be insignificant, indicating that positive and negative shocks in volatility exert similar effects on the volatility of these food items. These findings underscore the urgent need for incentives and adequate security measures to ensure food sufficiency in Cross River State and Nigeria at large.
The towing limits for self-propelled rail track maintenance equipment (SP-TME) are influenced by a multitude of factors, including the type and weight of the equipment, speed, braking capabilities, track and weather conditions, traction, engine power, driveline performance, coupler/towing link integrity, and safety regulations. This study investigates these variables to determine their impact on the towing limits of SP-TME. Unlike traditional rail vehicles, SP-TME possesses unique operational constraints and specifications, necessitating careful consideration of its independent mobility. An extensive analysis was conducted on the towing usage and overuse of SP-TME during travel mode, examining various scenarios that incorporate different combinations of trailing load, rail track grade, rail curvature, and weather conditions. These scenarios, ranging from normal to worst-case, aim to simulate demanding operational environments. The parameters evaluated include structural strength, traction, engine and driveline performance, wheel rolling and skidding, braking capabilities, trailing load, speed, and track and weather conditions. Results indicate that under normal and moderate conditions, the equipment can tow significantly higher loads than the defined base load. However, in special situations, such as negotiating tighter curves and steeper grades in adverse weather conditions, wheel skidding and locking emerge as limiting factors. Findings related to service and parking brake performance during steep grade descents, particularly when the trailer lacks independent braking capabilities, are also presented. Recommendations and cautions are provided to ensure safe and efficient operation of SP-TME under various conditions.
This study presents the development of a novel method for color image encryption, leveraging an enhanced Vigenère algorithm. The conventional Vigenère cipher is augmented with substantial substitution tables derived from widely used chaotic maps in the cryptography domain, including the logistic map and the A.J. map. These enhancements incorporate new confusion and diffusion functions integrated into the substitution tables. Following the Vigenère encryption process, a transition to deoxyribonucleic acid (DNA) notation is implemented, controlled by a pseudo-random crossover matrix. This matrix facilitates a genetic crossover specifically adapted for image encryption. Simulations conducted on a variety of images of diverse formats and sizes demonstrate the robustness of this approach against differential and frequency-based attacks. The substantial size of the encryption key significantly enhances the system's security, providing strong protection against brute-force attacks.
Effective waste management poses a significant challenge for transitional countries, particularly in the context of limited financial and material resources. In Bosnia and Herzegovina (BiH), the inefficiency of the waste management system at both the entity and national levels exacerbates the difficulty of establishing an integrated system resilient to natural and other hazards. This study introduces a theoretical model of comprehensive waste management (CWM) tailored for crisis situations, aiming to advance the development of a unified system across BiH. Key measures proposed include the involvement of key stakeholders, optimization of material resources, and continuous education to address irresponsible waste disposal practices and non-compliance with regulations. These issues contribute to the proliferation of illegal landfills and heighten the risk of large-scale environmental catastrophes. Specifically, in the Republic of Srpska, one of BiH's two entities, 400,000 tons of municipal waste were generated in 2020, averaging 0.95 kg per person per day, with approximately 40% being organic waste and another 40% packaging waste. Regrettably, only about 5% of this waste is recycled, largely due to an inadequate strategy and systemic approach to waste management, with about 30% of the population still lacking access to waste collection services. The proposed CWM model and the associated measures are crucial for mitigating the impacts of natural hazards, such as floods, on waste management systems.
To address the lack of clear formulae for calculating the circumferential stress in steel epoxy sleeve-reinforced pipelines under internal pressure, this study constructs a mechanical model based on the specific stress characteristics of these pipelines. Using stress solution methods and deformation compatibility relationships, theoretical formulas for circumferential stress in the pipeline layer, epoxy resin layer, and sleeve layer under internal pressure are derived. The theoretical formulas are validated through numerical simulations using ANSYS software, which includes models with and without flanges. The calculations were performed for common pipelines with outer diameters of 219mm, 660mm, and 1219mm. The results show that the discrepancies between theoretical and numerical solutions of circumferential stress in all layers of both model types are within 10%. Specifically, the circumferential stress in the pipeline layer of the flanged model is lower than that of the non-flanged model and also lower than the theoretical values. The error between the theoretical and numerical solutions for pipelines of different diameters does not exceed 10%, confirming the validity and applicability of the theoretical formulas. This suggests that using the simplified mechanical model for circumferential stress calculations ensures a conservative approach for the structural assessment of pipelines. The formulas provided herein can serve as a reference for the design and evaluation of steel epoxy sleeve-reinforced pipelines under internal pressure.
This study investigates the stability of steel columns subjected to axial compression, focusing on square hollow sections (SHS) with both uniform and non-uniform cross-sections. The stability of fixed-free end SHS columns with uniform cross-sections was initially verified using analytical equations. To obtain the critical load and design buckling resistance for each SHS column, Finite Element Analysis (FEA) was employed. The results indicate that while analytical equations can validate the stability of uniform SHS columns, they are insufficient for columns with non-uniform cross-sections. Consequently, the FEA emerges as a robust alternative for analyzing columns with varying cross-sections along their length. This study highlights the necessity of numerical methods for verifying the stability of structurally complex columns, such as those with perforations for mechanical and electrical applications. The finite element model was validated and applied to non-uniform cross-section columns, providing insights into the stability of these columns under practical conditions. This research aims to offer an alternative analytical approach for structural engineering applications where column stability is critical, especially for non-uniform cross-sectional designs that facilitate handling processes in various engineering scenarios.
This study critically evaluates Board Effectiveness (BE) within Maltese Public Sector Entities (MPSEs), with a focus on five key aspects: board selection and appointment, board role, board composition, board remuneration, and board performance evaluation. Semi-structured interviews were conducted with twenty-two participants, including eighteen MPSE board members (BMs), a representative from the Malta Institute of Directors, two corporate lawyers, and one corporate advisor. The findings indicate significant deficiencies in BE, particularly due to a lack of transparency in the selection and appointment process. This process is often influenced by political loyalties, which exclude new talent and discourage competent individuals. The identification of BMs as Politically Exposed Persons (PEPs) further restricts the inclusion of diverse talent, particularly among entrepreneurs. Additionally, insufficient training for BMs and persistent political pressures have been found to hinder the fulfilment of fiduciary duties. Female representation on MPSE boards is notably low, and foreign appointments are rare, thereby weakening the overall board composition. Moreover, the remuneration for MPSE BMs is significantly lower than that in the private sector, adversely impacting the quality of BMs. Resistance to implementing performance evaluations, which could potentially reduce political protection, has also been observed to impede BE. This study underscores the necessity of strengthening corporate governance (CG) practices to enhance BE in MPSEs, which is crucial for fostering a thriving economy and creating a positive legacy for future generations.
The study aims to examine the current state of property tax administration in Zimbabwean local authorities under the conditions of digitalization. Property taxes within the Zimbabwean local tax system are significantly under-collected, necessitating an urgent enhancement of their contribution to local authority budgets. A quantitative research approach was adopted, collecting data through questionnaires from a target population of 60 staff members within an urban local authority. Purposive sampling was employed to select Chief Executive Officers, Heads of Departments, and staff directly involved with Information and Communication Technology (ICT) and Property Tax Administration, including ICT departments, accounting and finance staff, and engineering departments. Additionally, residential and commercial property owners were conveniently sampled based on availability and willingness to participate, resulting in a total sample size of 46 respondents. The findings reveal a significant positive relationship between Information Technology and property tax administration, suggesting that policymakers should prioritize digitization to enhance effective tax administration. Furthermore, control variables such as population, trade, and GDP were found to have significant relationships with tax administration in Zimbabwe. The introduction of ICTs has been shown to improve the efficiency and effectiveness of property tax administration, underscoring its critical role in the fiscal decentralization of local governments.
In recent decades, the demand for electricity has continuously increased. Power generation facilities are predominantly situated at substantial distances from consumption centers, necessitating transmission over extensive, high-voltage lines. Such configurations lead to significant energy losses and diminished capacity and capability of transmission systems. Consequently, enhancements in transmission line performance have become a focal point for power system operators. The integration of the flexible alternating current transmission system (FACTS) technology has emerged as a pivotal solution, facilitating dynamic control over power flow and amplifying the existing capacity of power lines without the need for constructing new infrastructure. Among various FACTS devices, the static synchronous series compensator (SSSC) plays a crucial role by injecting variable capacitive or inductive reactance as required, thereby optimizing power flow and enhancing voltage stability. This review paper meticulously examines the functionality of different FACTS technologies, with a specific focus on the SSSC. Comparative analyses of transmission line performance, uncompensated, compensated through traditional series capacitors, and enhanced via SSSC, were conducted. The findings underscore the versatility of SSSC in reducing transmission losses and stabilizing network operations. This investigation not only details the operational benefits of SSSC but also explores its potential in addressing contemporary challenges in power transmission systems.
In the realm of computer vision, image recognition serves as a pivotal task with extensive applications in intelligent security, autonomous driving, and robotics. Traditional methodologies for image recognition often grapple with computational inefficiencies and diminished accuracy in complex scenarios and extensive datasets. To address these challenges, an algorithm utilizing a siamese network architecture has been developed. This architecture leverages dual interconnected neural network submodules for the efficient extraction and comparison of image features. The effectiveness of this siamese network-based algorithm is demonstrated through its application to various benchmark datasets, where it consistently outperforms conventional approaches in terms of accuracy and processing speed. By employing weight-sharing techniques and optimizing neural network pathways, the proposed algorithm enhances the robustness and efficiency of image recognition tasks. The advancements presented in this study not only contribute to the theoretical understanding but also offer practical solutions, underscoring the significant potential and applicability of siamese networks in advancing image recognition technologies.
Recent observations of global warming phenomena have necessitated the evaluation of the service performance of asphalt pavements, which is substantially influenced by surface temperature levels. This study employed twelve distinct machine learning algorithms—K-neighbors, linear regression, multi-layer perceptron, lasso, ridge, support vector regression, decision tree, AdaBoost, random forest, extra tree, gradient boosting, and XGBoost—to predict the surface temperature of asphalt pavements. Data were sourced from the Road Weather Information System of Iowa State University, comprising 12,581 data points including air temperature, dew point temperature, wind speed, wind direction, wind gust, and pavement sensor temperature. These data were segmented into training (80%) and testing (20%) datasets. Analysis of model outcomes indicated that the Extra Tree algorithm was superior, exhibiting the highest R$^2$ value of 0.95, whereas the Support Vector Regression algorithm recorded the lowest, with an R$^2$ value of 0.70. Furthermore, Shapley Additive Explanations were utilized to interpret model results, providing insights into the contributions of various predictors to model outcomes. The findings affirm that machine learning algorithms are effective for predicting asphalt pavement surface temperatures, thereby supporting pavement management systems in adapting to changing environmental conditions.
In the realm of heat transfer, the phenomenon of boiling heat transfer is paramount, especially given its efficiency in harnessing the latent heat of vaporization for significant thermal energy removal with minimal temperature alterations. This mechanism is integral to various industrial applications, including but not limited to the cooling systems of nuclear reactors, macro- and micro-electronic devices, evaporators in refrigeration systems, and boiler tubes within power plants, where the nucleate pool boiling regime and two-phase flow are prevalent. The imperative to optimize heat exchange systems by mitigating excessive heat dissipation, whilst simultaneously achieving downsizing, has consistently been a critical consideration. This research uses computational, based on Fluent software, to analyze thermal characteristics and cooling mechanisms of different concentrations of nanofluids, in conjunction with surfaces adorned with diverse fin geometries. Specifically, the study scrutinizes the thermal performance of water-based nanofluids, incorporating Copper (II) Oxide (CuO) nanoparticles at concentrations ranging from 0% to 1.4% by volume, under boiling conditions. The analyses extend to the efficacy of different fin shapes—including circular, triangular, and square configurations-within a two-dimensional geometry, under the conditions of forced convection heat transfer in both steady and transient, viscous, incompressible flows. The findings are poised to contribute to the design of more efficient heat exchange systems, facilitating enhanced heat dissipation through the strategic use of nanofluids and meticulously designed surface geometries.
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