Teachers face challenges when conducting their jobs (stress, work overload, lack of resources, lack of time, etc.), which may lead them to psychological problems. With the current information overload and sparsity of resources, it can be challenging to find relevant educational resources promptly. Recommender systems are designed to address the issue of information overload by filtering relevant information from a large volume of data based on user preferences, interests, or observed behavior. A recommender system can help mitigate teachers’ struggles by recommending personalized resources based on teachers’ needs. This paper presents previous works related to recommender systems in education. It highlights their techniques and limitations. Some papers relied on machine learning and/or ontology for building recommender systems, while others relied on a hybrid system comprising several techniques. The most employed recommendation techniques include collaborative filtering (CF), content-based (CB), and knowledge-based (KB) approaches. Each approach has its advantages and limitations. To overcome these limitations, several advanced recommendation methods have been proposed, such as social network-based recommender systems, fuzzy recommender systems, context awareness-based recommender systems, and group recommender systems. Our analysis reveals that existing recommender systems are learner-centered, often lacking an understanding of the teacher’s context. The continuous advancement of recommendation approaches and techniques has led to the implementation of numerous recommender systems and the development of numerous real-world applications. A context-aware personalized recommender system for teachers should consider personal and professional development goals and psychosocial state when presenting a recommendation. Years of experience, access to equipment, and commute time are some of the aspects that should be considered when designing such a system. Moreover, the studies surveyed provided detailed information about their evaluation methodologies. However, the evaluation of these systems is typically conducted using simulated or nonreal students, along with various assessment approaches such as algorithmic performance tests, statistical analysis, questionnaires, and qualitative observations.
In the realm of epidemiological modeling, the intricacies of epidemic dynamics are elucidated through the lens of compartmental models, with the SIR (Susceptible-Infectious-Recovered) and its variant, the SIS (Susceptible-Infectious-Susceptible) model, being pivotal. This investigation delves into both deterministic and stochastic frameworks, casting the SIR model as a continuous-time Markov chain (CTMC) in stochastic settings. Such an approach facilitates simulations via Gillespie's algorithm and integration of stochastic differential equations. The latter are formulated through a bivariate Fokker-Planck equation, originating from the continuous limit of the master equation. A focal point of this study is the distribution of extinction time, specifically, the duration until recovery in a population with an initial count of infected individuals. This distribution adheres to a Gumbel distribution, viewed through the prism of a birth and death process. The stochastic analysis reveals several insights: firstly, the SIR model as a CTMC encapsulates random fluctuations in epidemic dynamics. Secondly, stochastic simulation methods, either through Gillespie's algorithm or stochastic differential equations, offer a robust exploration of disease spread variability. Thirdly, the precision of modeling is enhanced by the incorporation of a bivariate Fokker-Planck equation. Fourthly, understanding the Gumbel distribution of extinction time is crucial for gauging recovery periods. Lastly, the non-linear nature of the SIR model, when analyzed stochastically, enriches the comprehension of epidemic dynamics. These findings bear significant implications for epidemic mitigation and recovery strategies, informing healthcare resource planning, vaccine deployment optimization, implementation of social distancing measures, public communication strategies, and swift responses to epidemic resurgences.
To investigate the high-quality and efficient development of the manufacturing industry in the Central Plains Urban Agglomeration (CPUA), this paper uses the three-stage Data Envelopment Analysis (DEA) and Malmquist index model to evaluate and analyze the manufacturing development efficiency of 30 prefecture-level cities in five provinces of China in the CPUA from 2017 to 2022. First, the DEA model is applied to evaluate the comprehensive efficiency of the manufacturing industry in 30 regions of the CPUA; second, the Stochastic Frontier Analysis (SFA) regression model is used in conjunction with the technical efficiency and scale efficiency of the manufacturing industry to deeply explore and adjust the causes of the current situation in various regions; finally, after re-analyzing the efficiency with the corrected input-output data, the Malmquist index model is used to analyze the total factor productivity index and its decomposed efficiency of the manufacturing industry in the CPUA from 2017 to 2022. The study shows that the pure technical efficiency (PTE) of the 30 prefecture-level cities in the CPUA from 2017 to 2022 is stable and relatively good, and the main reason for the overall low comprehensive efficiency is the poor scale efficiency; after excluding the interference of environmental factors, the average comprehensive efficiency of each city is lower than before the adjustment, with environmental factors and random errors having a significant impact on the manufacturing industry, especially in Bengbu City; the main factor in the decline of the total factor productivity of the manufacturing industry in the CPUA is the hindrance of technological progress; the spatial distribution of the comprehensive efficiency of the manufacturing industry in the CPUA generally shows a pattern of “higher efficiency in the middle, lower efficiency at the edges”, and there is a situation of regional development imbalance in the high-quality development level of the manufacturing industry in the 30 regions.
This study investigates the impact of the high turnover rate of articled clerks on audit quality within BDO Zimbabwe Chartered Accountants. The departure of these professionals from the firm has raised significant concerns, prompting a comprehensive analysis. The research adopts a mixed-method approach, combining qualitative and quantitative elements, to provide a holistic understanding of the phenomenon. The sample, drawn from a population of 56 BDO Chartered Accountants members through stratified simple random sampling, consisted of 30 individuals. The findings reveal a notable negative correlation between the turnover of articled clerks and the overall quality of audits. This correlation suggests that frequent departures of these clerks adversely affect the firm's audit standards. It was determined that BDO Zimbabwe lacks adequate capacity management strategies, leading to increased staff turnover. To mitigate this issue, the study recommends the implementation of a mandatory tenure system for articled clerks. This system would ensure the retention of expertise and skills essential for maintaining high audit quality. Such a measure could prove instrumental in stabilizing the workforce and preserving the integrity of audit processes. This research contributes to the understanding of staff turnover's impact on audit quality, offering valuable insights for firms in similar contexts.
This study presents a comprehensive analysis of the influence of defense expenditures on the Gross Domestic Product (GDP) in Turkey from 1974 to 2021. Defense spending, crucial for national security, often diverges from regular civic investments such as education, healthcare, and transportation. The significance of these expenditures becomes evident in times of international tension, terrorist threats, and warfare. Globally, defense budgets are escalating, and Turkey, a North Atlantic Treaty Organization (NATO) member, is no exception. Recent trends show a decline in Turkey's public defense spending, with current levels lower than in the 1960s yet higher than the NATO average during 2014-2021. Concurrently, private sector investment in the defense industry has risen, underscoring Turkey's involvement in global defense dynamics. This research adopts the Autoregressive Distributed Lag (ARDL) bounds test and the Toda-Yamamoto causality test to scrutinize the long-term and causal relationships between defense spending and economic growth. The ARDL bounds test reveals a long-term negative cointegration relationship, while the Toda-Yamamoto test indicates a unidirectional causal relationship from defense expenditures to GDP at a 10% significance level. These findings affirm the Neoclassical economic theory's postulation of a negative impact of defense spending on growth. Despite this, the paper argues for the necessity of sustained defense expenditures in Turkey, given its unique historical and geopolitical context. The study navigates through various theoretical perspectives, notably the Keynesian and neoliberal approaches, and their specific adaptations in defense economics: military Keynesianism and private military services. It critically assesses these frameworks, integrating their critiques into the analysis. The study contributes to the discourse on defense economics by providing empirical evidence from a critical NATO member, balancing the theoretical debate with practical insights from Turkey's experience. This dual approach, combining empirical analysis with theoretical exploration, offers a nuanced understanding of the complex interplay between defense spending and economic growth, particularly in geopolitically sensitive regions.
This study examines innovative box-plate prefabricated steel structures, where stiffened steel plates serve as primary load-bearing walls and floors. In contrast to traditional stiffened steel plate walls, which typically exhibit significant hysteresis, pronounced out-of-plane deformation, and rapid stiffness degradation, these advanced systems demonstrate superior performance. A pivotal feature of these structures is the intensive use of welding to connect stiffened steel plates during assembly. This study introduces a novel composite stiffened steel plate wall, addressing concerns of traditional systems, and executes a comprehensive numerical simulation to assess the influence of welding on joint integrity and overall structural performance. It is observed that the height-to-thickness ratio of steel plate walls significantly influences load-bearing capacity, with a lower ratio yielding enhanced capacity. However, the stiffness ratio of ribs is found to have minimal impact. An increase in bolt quantity and density correlates with improved ultimate bearing capacity. Moreover, the adoption of staggered welding techniques bolsters shear strength, though the positioning of welds has negligible influence on this parameter. The number of welded joints moderately affects shear strength, while the size of staggered welding joints is identified as a crucial factor, with larger sizes leading to more pronounced reductions in shear strength. This study highlights the importance of construction details, particularly in welding practices, in the structural integrity and performance of box-plate prefabricated steel structures. The findings offer significant insights for optimizing design and construction methodologies to maximize the load-bearing capacities of these innovative systems.
In the realm of enterprise technology, Internet of Things (IoT)-based wireless devices have witnessed significant advancements, enabling seamless interactions among machines, sensors, and physical objects. A critical component of IoT, Wireless Sensor Networks (WSN), have proliferated across various real-time applications, influencing daily life in both critical and non-critical domains. These WSN nodes, typically small and battery-operated, necessitate efficient energy management. This study focuses on the integration of crow search optimization and firefly algorithms to optimize energy efficiency in IoT-WSN systems. It has been observed that the energy reserve (RE) of a node and its communication costs with the base station are pivotal in determining its likelihood of becoming a Cluster Head (CH). Consequently, energy-saving data aggregation techniques are paramount to prolonging network longevity. To this end, a hybrid approach combining crow search and firefly optimization has been proposed. The crow search algorithm plays a significant role in enhancing data transfer efficiency, while the firefly algorithm is instrumental in selecting optimal cluster heads. This integrated methodology not only promises to extend the network's lifespan but also ensures a balance between energy conservation and data transmission efficacy.
In the rapid urbanization experienced globally, traffic congestion emerges as a critical challenge, detrimentally affecting economic performance and the quality of urban life. This study delves into the deployment of machine learning (ML) and deep learning (DL) methodologies for mitigating traffic congestion within smart city frameworks. An extensive literature review coupled with empirical analysis is conducted to scrutinize the application of these advanced technologies in various transportation domains, including but not limited to traffic flow prediction, optimization of routing, adaptive control of traffic signals, dynamic management of traffic systems, implementation of smart parking solutions, enhancement of public transportation systems, anomaly detection, and seamless integration with the Internet of Things (IoT) and sensor networks. The research methodology encompasses a detailed outline of data sources, the selection of ML and DL models, along with the processes of training and evaluation. Findings from the experiments underscore the efficacy of these technological interventions in real-world settings, highlighting notable advancements in the precision of traffic predictions, the efficiency of route optimization, and the responsiveness of adaptive traffic signal controls. Moreover, the study elucidates the pivotal role of ML and DL in facilitating dynamic traffic management, anomaly detection, smart parking, and the optimization of public transportation. Through illustrative case studies and examples from cities that have embraced these technologies, practical insights into their applicability and the consequential impact on urban mobility are provided. The research also addresses challenges encountered, offering a discourse on potential avenues for future research to further refine traffic congestion management strategies in smart cities. This contribution significantly enriches the existing corpus of knowledge, presenting pragmatic solutions for urban planners and policy makers to foster more efficient and sustainable transportation infrastructures.
In response to the mechanical performance alterations of PVC-P geomembranes due to improper handling or subgrade particle action during construction and operation, a series of axial tensile tests on PVC-P geomembranes with various scratch damages were conducted. Multifactorial variance analysis was performed using Python, and a multivariate regression model for the fracture strength and elongation at break of scratched PVC-P geomembranes was developed using SPSS. The precision of the regression model was evaluated using parameters such as the coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results indicated that the fracture strength and elongation at break of PVC-P geomembranes are significantly affected by a combination of scratch angle, length, and depth. The impact on elongation at break is greater than on fracture strength, with the scratch angle having the most significant effect. The developed multivariate regression model yielded R2 values of 0.98 and 0.97 for fracture strength and elongation at break, respectively. The MAEs were 0.62 kN/m and 7.96%, and the MAPEs were 3.06% and 5.13%, respectively. The RMSEs were 0.84 kN/m and 12.08%. The high fitting accuracy of the model suggests its utility for evaluating the mechanical performance of PVC-P geomembranes with scratch damage.
This study examines the interrelation between foreign equity investments (FEIs) and the BIST 100 index, a pivotal indicator of the Turkish economy's overall performance. Given Turkey's emergence as a market with considerable potential returns, this analysis is particularly pertinent. The focus is on the volume of shares held by international investors in Borsa Istanbul and its impact on the BIST 100 index. Data spanning 924 weeks, from 2006 to 2023, form the empirical basis of this investigation. Stationarity of variables was assessed using the Augmented Dickey-Fuller (ADF) and Phillips-Peron (PP) unit root tests. The direction of causality between the studied variables was determined via a Granger causality test, employing a Vector Autoregressive (VAR) model. Findings reveal a bidirectional causality between the returns of the BIST 100 index and FEIs, aligning with prevailing hypotheses that posit a connection between foreign equity flows and stock market indices. This relationship underscores the integral role of international investments in shaping market dynamics within emerging economies. The study contributes to the understanding of financial market interdependencies, highlighting the significance of foreign investments in the context of a developing economy's stock market.
In the realm of ground transportation, high-speed maglev trains stand out due to their exceptional stability, rapid velocity, and environmental benefits, such as low pollution and noise. However, the aerodynamic challenges faced by these lightweight, high-velocity trains significantly impact their safety and comfort, making aerodynamics a critical aspect in their design. This research delves into the dynamic aerodynamic behavior of high-speed maglev trains in the presence of crosswinds. A simulation analysis was conducted on a simplified model of a three-car maglev train, with an established aerodynamic model for the train and track beam in crosswind scenarios. The study employed three-dimensional, steady-state, incompressible $N-S$ equations, complemented by a $k-\varepsilon$ dual-equation turbulence model. The finite volume method was utilized to assess the flow field structure around the train and the pressure distribution on its surface under varying combinations of train speed and wind velocity. The investigation summarized the patterns and trends in aerodynamic loads across diverse conditions. Results demonstrate that at a speed of 600 km/h, the tail car is subjected to the highest aerodynamic drag, while the head car bears the maximum lateral force and overturning moment. As crosswind speeds increase from 5 m/s to 20 m/s, the tail car exhibits the largest increment in drag, reaching 16.6 kN. The front car shows the most significant rise in lateral force and overturning moment, measured at 34.11 kN and 52.45 kN·m, respectively. It is observed that the behavior of aerodynamic forces at lower and medium speeds aligns fundamentally with the patterns noted at higher speeds.
In this investigation, the enhancement of heat transfer in pipes facilitated by Fe3O4-distilled water nanofluid under the influence of magnetic fields is comprehensively studied. The research primarily focuses on examining the alterations in the thermal boundary layer and fluid flow patterns caused by the application of magnetic fields. It is observed that magnetic fields induce the formation of vortexes, thereby actively influencing the flow patterns within the fluid. These vortexes play a pivotal role in promoting thermal diffusion, resulting in an improved heat transfer rate. The core aim of this study is to quantitatively assess the impact of magnetic nanofluids on the coefficient of heat transfer. A model tube, possessing an inner diameter of 25.4 mm and a length of 210 mm, serves as the basis for the simulations. The investigation encompasses a range of inlet velocities (0.05, 0.1, and 0.5 m/s) and exit pressures to analyze the magnetic field's effect on heat transfer and fluid dynamics. Magnetic flux intensities of one, two, and three Tesla are employed. Notably, the highest temperature of 349 K is recorded in the presence of three magnets, indicating an escalation in temperature with an increase in magnetic strength. However, a diminishing temperature rise is noted over a specified distance with additional magnets. For instance, at a distance of 100 mm, the temperature peaks at 340 K with one magnet, whereas with two magnets, this temperature is attained at a mere 50 mm, suggesting enhanced magnetizer efficiency. Furthermore, the introduction of a magnetic field at the tube's center reveals that high flow velocities tend to counteract the magnetic influence due to their superior force, which impedes the incorporation of metal particles into the fluid. As the magnetic flux value escalates, the nanofluid's magnetic particles either congregate or disperse, thereby obstructing flow and intensifying channel vortices. This phenomenon results in heightened turbulence, instigated by the magnets, which in turn precipitates a rapid increase in fluid flow velocity, thereby impeding the fluid's capacity to adequately absorb heat for efficient heating.
An innovative approach utilizing 3D laser scanning technology has been introduced in open-pit mining for capturing spatial data of blast piles. RANSAC for plane fitting and DBSCAN for clustering are applied to outline rock block contours accurately. Quick calculation of rock block volumes and maximum particle sizes is enabled through 3D convex hulls and Oriented Bounding Boxes (OBB). Delaunay triangulation of 3D point cloud data is used to create a detailed mesh model for precise volume estimation of blast piles. Indoor testing revealed relative errors of approximately 4.61% for block volumes and 4.75% for particle sizes, while field applications showed an average rock block identification accuracy of 80.4%, increasing with block size. Estimated versus actual blast pile volumes showed a relative error of 4.85%, with computational errors for the pile's height, forward throw distance, and lateral extent being 2.92%, 3.91%, and 4.29%, respectively.
Open Access
