Miracle-dz/UniFAR-FTUs huggingface dataset:
Two novel cache management mechanisms on CPU-GPU heterogeneous processors

Heterogeneous multicore processors that take full advantage of CPUs and GPUs within the samechip raise an emerging challenge for sharing a series of on-chip resources, particularly Last-LevelCache (LLC) resources. Since the GPU core has good parallelism and memory latency tolerance,the majority of the LLC space is utilized by GPU applications. Under the current cache managementpolicies, the LLC sharing of CPU applications can be remarkably decreased due to the existence ofGPU workloads, thus seriously affecting the overall performance. To alleviate the unfair contentionwithin CPUs and GPUs for the cache capability, we propose two novel cache supervision mechanisms:static cache partitioning scheme based on adaptive replacement policy (SARP) and dynamiccache partitioning scheme based on GPU missing awareness (DGMA). SARP scheme first uses cachepartitioning to split the cache ways between CPUs and GPUs and then uses adaptive cache replacementpolicy depending on the type of the requested message. DGMA scheme monitors GPU’s cacheperformance metrics at run time and set appropriate threshold to dynamically change the cache ratioof the mutual LLC between various kernels. Experimental results show that SARP mechanismcan further increase CPU performance, up to 32.6% and an average increase of 8.4%. And DGMAscheme improves CPU performance under the premise of ensuring that GPU performance is not affected,and achieves a maximum increase of 18.1% and an average increase of 7.7%.
Determinants of Capitalization of Islamic Banking: Evidence from Select Islamic Banks in Asia

Capitalization of Islamic banking is one key of the capital structure of Islamic banks. This study attempts to determine the internal and external factors that effect on capitalization of Islamic banking in terms of equity-to-total assets ratio using data of select Islamic banks in Asian countries where these banks operate in (Bahrain, Iran, Turkey and Malaysia) in the period of 2011– 2020. Panel data method is used in the practical analysis. Equity Ratio (ER) is used as a proxy of capitalization in this study. Descriptive statistics, correlation and regression analysis are applied to the variables under study. The results of analysis show that Return on Average Assets (ROAA) and GDP Growth Rate (GDPGR) have a significant and positive effect on Equity Ratio (ER) of select Islamic Banks; both are consistent with trade-off theory. While, Return on Average Equity (ROAE) and Growth Rate of Total Assets (GRTA) have a significant and negative effect on Equity Ratio (ER) of select Islamic banks; both are consistent with pecking order theory. The other variables e.g., Total Assets (nlogTA), Tangibility Ratio (TR), Liquidity Ratio (LQR), Bank Age (BA), Inflation Growth Rate (IGR) and Type of Banking System (TBS) have a non-significant effect on Equity Ratio (ER) of select Islamic banks. Index Terms Capital Structure, Capitalization, Equity Ratio, Islamic Banking, Internal and External Variables.
Air Quality Monitoring And Prediction Using IOT And Machine Learning Approaches

- Air pollution today is on the rise in urban areas mainly due to vehicular emissions, manufacturing plants and industries accumulating harmful pollutants such as particulate matter, carbon monoxide and ozone posing a serious health risk to the people. The nature of air is also influenced by multi-dimensional elements including area, time, period during the year and many other factors. Implementing a cost effective, efficient air quality monitoring and forecasting system which gather information and provide evaluations on air pollution is hence required, with numerous experts utilizing the big data analytics approach for contemplating, assessing, and predicting air quality recently. In this study an air quality monitoring device using arduino based sensors was developed to capture air quality parameters followed by the calibration of sensors to improve accuracy. Using data from the air quality monitor over 3 months, forecast models for pollutant concentrations were built using random forest as the base model to forecast PM2.5 air quality index. The IoT device developed performed with a significant improvement in accuracy after calibration of sensors. Using random forest prediction model as base model outperformed
A General Parameter Optimization Method for a Capacitive Power Transfer System with an Asymmetrical Structure

Capacitive power transfer (CPT) is an attractive wireless power transfer (WPT) technology and it has been widely studied in many applications. Symmetrical structures and high-order compensation networks are always produced as optimization results and common configurations for high-efficiency CPT systems. However, in space-limited scenarios, an asymmetric structure tends to be a better choice. The related large number of high-order asymmetric system parameters is a key problem in parameter design. In this study, a general parameter design method that is based on reactive power optimization is proposed for an electric field resonance-based CPT system with an asymmetric six-plate coupler. The reactive power in the compensation network was analyzed and optimized under the constraint of transferred power. With equal reactive power, the optimization complexity was significantly reduced and the optimized system parameters were provided. To validate the effectiveness of the proposed method, a 1 MHz, 3.2 kW asymmetric CPT protype with 100 mm gap distance was implemented. The results indicate that, with the optimized parameters, high system efficiency can be achieved when the system’s volume is reduced. At the rated power, about 95% DC–DC overall efficiency was achieved through a 6-pF coupling capacitor.
Green Synthesis of Silver Nanoparticles Using Pleurotus ostreatus

Aims: The study aimed to synthesize silver nanoparticles (AgNPs) from aqueous AgNO3 using phytochemicals present in Pleurotus ostreatus and assess the AgNPs antibacterial activity on Bacillus subtilis and Providencia rettgeri. Study design: Experimental/Analytical. Place and Duration of Study: Bells University of Technology between December 2020 and August 2021. Methodology: The mushroom was washed, dried, pulverized and 5g stirred into 100ml deionized water. The solution was sonicated using ultrasonic cleaner at 40 oC for 40 min, centrifuged at 4000 rpm for 10 min. The supernatant was filtered, and 1ml filtrate was mixed with 9 ml of 10mM AgNO3. After the reaction period, the mixture was centrifuged at 15,000 rpm for 15 min. The residues were washed thrice with deionized water and dried. Synthesis of AgNPs was monitored by UV–Vis spectrophotometer, characterized by Fourier transform infrared spectroscopy, scanning electron microscopy and X-ray diffraction analysis. Antibacterial analysis was done by agar well diffusion using gentamicin as control. Results: A dark brown colour change and UV visible spectroscopy peak at 400 nm confirmed the formation of AgNPs. Fourier transform infrared spectroscopy analysis showed the presence of functional groups involved in the reduction of AgNO3. GCMS performed on the methanolic extract of Pleurotus ostreatus showed the presence of 37 organic compounds, among them were cathecol, hydroquinone and phenols etc. Antimicrobial activity revealed that AgNPs inhibited the growth of B. subtilis and P. rettgeri. Conclusion: The study revealed that Pleurotus ostreatus effectively synthesized AgNPs and the AgNPs inhibited the growth of Providencia rettgeri and Bacillus subtilis and can play major roles in the field of medical and pharmaceutical nanotechnology.
Potential Antiviral Activity of Lactiplantibacillus plantarum KAU007 against Influenza Virus H1N1

The development of antiviral resistance has exacerbated a growing threat to public health. As a result, there is increasing demand for unconventional antivirals that can effectively replace the presently in-use drugs. Lactic acid-producing bacteria (LAB) are among the most common bacteria used in the food industry. These bacteria play an essential role in the fermentation of many foods and feed. Additionally, these bacteria are considered more economical, efficient, and safe “nutraceuticals” in the health care arsenal. Therefore, we carried out the screening and molecular characterization of raw camel milk LAB isolates and tested their inhibitory activity against influenza virus H1N1. The strain that exhibited the highest antiviral activity against the H1N1 virus, confirmed by hemagglutination assay, was identified as Lactiplantibacillus plantarum KAU007. The study also confirmed the non-cytotoxic behavior of CFCS isolated from KAU007 against MDCK cells, approving its safety concern against the mammalian cells. Besides, CFCS at 5 and 10 mg/mL significantly decreased the level of IFN-γ (p < 0.001 and p < 0.001) and IL-6 (p < 0.001 and p < 0.005) in a dose-dependent manner, respectively. This is a preliminary report about the anti-influenza activity of KAU007 isolated from camel milk. This study reinforces that camel milk contains beneficial LAB isolates with antagonistic properties against the H1N1 influenza virus.
A community call to action: mitigating COVID pandemic’s impact on mental health

Wahid Ullah1, Muhammad Ilyas*,1,2 , Mukhtar Alam3, Jong Bhak4 & Peter J Tonellato5 1Centre for Omic Sciences, Islamia College University Peshawar, 25120, Pakistan 2Department of Bioengineering, University of Engineering and Applied Sciences, Swat, 19060, Pakistan 3Dean Faculty of Sciences, University of Swabi-23430, Pakistan 4School of Life Sciences, Ulsan National Institute of Science & Technology (UNIST), Ulsan, 44919, South Korea 5School of Medicine, University of Missouri, Columbia, MO 65212, USA *Author for correspondence: Tel. : +92 300 574 0568; milyaskh@hotmail.com
Empirical Testing of Co-integration of International Financial Markets with Reference to India, the USA, Japan, and Hong Kong

This article examines the co-integration among the financial markets of four countries (India, the USA, Japan, and Hong Kong) from April 2010 to May 2021. Using the Johansen co-integration test, we establish the integration between the stock markets of these countries. The test clearly shows that there exists long-run co-integration between these countries. Furthermore, we checked the relationship of one country with another by applying the multivariate Granger causality test, which shows the influence of the US market over the other countries. The results significantly show the existence of long-run co-integration and linkages between these financial markets. Applying the Vector error correction model and variance decomposition analysis for the period confirms the result obtained from multivariate Granger causality. As the study shows, the stock market of the countries is not entirely inter-linked. However, some tests provide the pairwise linkages but not when talking about all the nations together. That means the rise in one stock market does not necessarily impact the surge in other stock exchanges, which means investors can profit from diversifying between these stock exchanges. It provides the opportunity for portfolio diversification to investors. The study shows that “the international stock market is neither fully integrated nor completely segmented,” giving international diversification an opportunity. As in our study, the “U.S. market is the most exogenous,” and the Japanese market is relatively isolated from the other market, where the impact from the different markets is not that significant.
Study on the Mechanical Properties and Design Parameters of Floor Slabs Waste Subgrade Filler

In recent years, there have been more and more studies on the use of construction and demolition waste (CDW) as subgrade filler. The physical performance indexes of a single material of floor slabs waste (FSW) are studied, which meet the requirements of subgrade specification. From the perspective of construction quality control, it is proposed that when FSW is used as subgrade filler, the compaction thickness and the maximum allowable particle size should be controlled according to different layers, and the gradation of subgrade filler is evaluated by using the nonuniformity coefficient (Cu) and curvature coefficient (Cc). The compaction test of FSW shows that the maximum dry density and optimum moisture content of different gradations do not change much. The crushing value under the freeze-thaw cycle is proposed to evaluate the frost resistance of FSW. The crushing value of FSW after water absorption and frost heaving changes a little. The CBR and resilience modulus tests were carried out on the FSW such that the results show that they have a good relationship and the relevant expressions under different compaction degrees are fitted. When performing on-site compaction testing, it is proposed that the upper roadbed adopts the sand replacement method according to the different layers of the FSW subgrade and the settlement discrepancy method should be adopted for other parts. Through laboratory tests and construction field detective, the quality control requirements, road performance indexes, and design indexes of FSW materials are put forward.
Latest Trends in Pollutant Accumulations at Threatening Levels in Energy-Efficient Residential Buildings with and without Mechanical Ventilation: A Review

Improving the energy efficiency of buildings is a major target in developed countries toward decreasing their energy consumption and CO2 emissions. To meet this target, a large number of countries have established energy codes that require buildings to be airtight. While such a retrofitting approach has improved health outcomes in areas with heavy traffic, it has worsened the health outcomes in Nordic countries and increased the risk of lung cancer in areas with high levels of radon emissions. This review highlights the importance of adapting the characteristics of energy-efficient residential buildings to the location, age, and health of inhabitants to guarantee healthy indoor pollutant levels. The implementation of mechanical ventilation in new energy-efficient buildings has solved some of these problems; however, for others, a decrease in the level of outdoor pollutants was still required in order to achieve a good indoor air quality. A good balance between the air exchange rate and the air humidity level (adapted to the location) is key to ensuring that exposure to the various pollutants that accumulate inside energy-efficient buildings is low enough to avoid affecting inhabitants′ health. Evidence of the protective effect of mechanical ventilation should be sought in dwellings where natural ventilation allows pollutants to accumulate to threatening levels. More studies should be carried out in African and Asian countries, which, due to their rapid urbanization, use massive volumes of unproven/unrated building materials for fast-track construction, which are frequent sources of formaldehyde and VOC emissions.
Incremental End-to-End Learning for Lateral Control in Autonomous Driving*

The use of high-quality data is required to complete the job of lateral control utilizing Behavioral Cloning (BC) through an End-to-End (E2E) learning system. The majority of E2E learning systems gather this high-quality data all at once before beginning the training phase (i.e., the training process does not start until the end of the data collection process). The demand for high-quality data necessitates a large amount of human effort and substantial time and money spent waiting for data collection to be completed. As a result, it is critical to find a viable option to reduce both the time and cost of data collecting while also maintaining the performance of a trained vehicle controller. This paper offers a novel behavioral cloning approach for lateral vehicle control to address the aforementioned problems. The proposed technique begins by collecting the least amount of human driving data possible. The data from human drivers are utilized for training a convolutional neural network for lateral control. The trained neural network is subsequently deployed to the vehicle’s automated driving controller, replacing a human driver. At this point, a human driver is out of the loop, and an automated driving controller, trained by the initial data from a human driver, drives the vehicle to collect further training data. The driving data obtained are sent into a convolutional neural network training module, then the newly trained neural network is deployed to the automated driving controller that will drive the vehicle further. The data collection alternates neural network training processes using the collected data until the neural network learns to correctly associate an image input with a steering angle. The proposed incremental approach was extensively tested in simulated environments, and the results are promising. The incrementally trained neural networks using data collected by automated controllers were able to drive the vehicle in two different tracks successfully.
Regionalization of low flow for chosen catchments of the upper Vistula river basin using non-hierarchical cluster analysis

The aim of this work was the regionalization of low flow for chosen catchments located in the upper Vistula river basin using non-hierarchical cluster analysis. Next, with such creative clusters, the regional relationships were determined between the specific low flow discharge q95 and the meteorological and physiographic parameters of the catchment. The study evaluated regional regression models for low flow (specific q95 discharge) in selected, 30 catchments located in the upper Vistula river basin. The data for calculations were a series of observations of daily discharge from the multiannual period of 1963–2016 and were obtained from the Institute of Meteorology and Water Management – National Research Institute in Warsaw. The study showed, that the k-means method can be used for regional regression determination. The parameters which influenced the catchments grouping in clusters were the specific low flow discharge q95, precipitation, median catchment altitude, mean catchment slope, soil, and land use. The study indicated that k-means method may be an effective tool for evaluating low flow in rivers of the southern parts of Poland.
Hybrid Heuristic for the Multi-Depot Static Bike Rebalancing and Collection Problem

The bike rebalancing problem is one of the major operational challenges in the urban bike-sharing system, which involves the redistribution of bikes among stations to prevent stations from being empty or overloaded. This paper investigates a new bike rebalancing problem, which considers the collection of broken bikes in the multi-depot system. The proposed problem can be classified as a two-commodity vehicle routing problem with pick-up and delivery. An integer programming model is formulated to find the optimal vehicle assignment and visiting sequences with the minimum total working time and fixed cost of vehicles. A hybrid heuristic algorithm integrating variable neighborhood search and dynamic programming is proposed to solve the problem. The computational results show that the proposed method can find 26 best solutions out of 36 instances, while the CPLEX obtains 16 best solutions. Impact of broken bikes collection and distribution of depots is examined. Comparison of different practical strategies indicates that the number of vehicles can be significantly reduced by allowing multiple visits to depots. Allowing vehicles to return to different depots can help reduce the total working time.
Evolution-Driven Randomized Graph Convolutional Networks

Randomized neural networks (NNs), such as random vector functional link (RVFL) and extreme learning machine (ELM), have been widely applied in various classification problems owing to their computational efficiency and universal approximation capability. However, such approaches are designed for regular Euclidean data and lack the ability to generalize to complex structured data. Moreover, their randomly generated parameters often lead to a suboptimal decision boundary with a growing requirement of hidden neurons. In this article, we first propose a plain framework, termed randomized graph convolutional networks (RGCNs), to generalize the classic randomized NNs to the non-Euclidean domain. Then, a hybrid framework called evolution-driven RGCN (EvoRGCN) is presented by using adaptive differential evolution with novelty search strategy to seek the globally optimal graph embedding for the plain RGCN. Finally, we recast the classic ELM and RVFL under the proposed frameworks, resulting in four novel semi-supervised models, including the plain models [i.e., graph convolutional extreme learning machines (GCELMs) and graph convolutional RVFL (GCRVFL)] and the optimized models (i.e., O-GCELM and O-GCRVFL). We show that our approaches are the natural generalization of the traditional randomized NNs in the non-Euclidean domain. Furthermore, our approaches not only retain the advantages of the classic approaches but also enable them to handle graph data. We compare our approaches against many existing methods across regular datasets and graph benchmarks, demonstrating that the proposed approaches dramatically outperform the compared methods with better generalization ability and robustness. Particularly, we quantitatively show the performance ranking of different randomized NNs, i.e., O-GCRVFL <inline-formula> <tex-math notation="LaTeX">$> $ </tex-math></inline-formula> O-GCELM <inline-formula> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula> GCRVFL <inline-formula> <tex-math notation="LaTeX">$> $ </tex-math></inline-formula> GCELM <inline-formula> <tex-math notation="LaTeX">$\approx $ </tex-math></inline-formula> RVFL <inline-formula> <tex-math notation="LaTeX">$> $ </tex-math></inline-formula> ELM.
Case Studies of Japanese Universities’ Collaborations with ASEAN, China, and Mongolia

This paper elaborates on recent trends in Japanese higher education partnerships through three cases of collaboration between Japanese universities and higher education institutions in China, Mongolia, and the Southeast Asia (hereinafter referred to as ASEAN). Our analysis shows that partnerships were shaped by both top-down government policies and bottom-up institutional visions and past relations. Japan’s educational quality and reputation, particularly in science and technology, is highly regarded by higher education institutions (HEIs) interested in developing competitive international programs to prepare a globally skilled workforce. On one hand, Japan is striving to deepen and expand its partnerships with HEIs, particularly within Asia. A number of domestic and international factors - such as Japan’s population decline; increasing competition among HEIs both on the domestic and international front; an ambition to remain competitive in a twenty-first century, knowledge-based economy; and Japan’s strategy to strengthen its influence in Asia - drive the partnerships. On the other hand, while earlier partnerships were limited to student and faculty exchange or joint research projects, institutions increasingly prioritize more comprehensive strategic partnerships. Such strategic partnerships are important to overcome barriers during the COVID-19 pandemic that limit physical movement and interaction, and they may drive even greater collaboration and integration among Asian higher education institutions.