Miracle-dz/UniFAR-FTUs huggingface dataset:
Actionable absolute risk prediction of atherosclerotic cardiovascular disease: a behavior-management approach based on data from 464,547 UK Biobank participants

Cardiovascular diseases (CVDs) are the primary cause of all global death. Timely and accurate identification of people at risk of developing an atherosclerotic CVD and its sequelae, via risk prediction model, is a central pillar of preventive cardiology. However, currently available models only consider a limited set of risk factors and outcomes, do not focus on providing actionable advice to individuals based on their holistic medical state and lifestyle, are often not interpretable, were built with small cohort sizes or are based on lifestyle data from the 1960s, e.g. the Framingham model. The risk of developing atherosclerotic CVDs is heavily lifestyle dependent, potentially making a high percentage of occurrences preventable. Providing actionable and accurate risk prediction tools to the public could assist in atherosclerotic CVD prevention. We developed a benchmarking pipeline to find the best set of data preprocessing and algorithms to predict absolute 10-year atherosclerotic CVD risk. Based on the data of 464,547 UK Biobank participants without atherosclerotic CVD at baseline, we used a comprehensive set of 203 consolidated risk factors associated with atherosclerosis and its sequelae (e.g. heart failure). Our two best performing absolute atherosclerotic risk prediction models provided higher performance than Framingham and QRisk3. Using a subset of 25 risk factors identified with feature selection, our reduced model achieves similar performance while being less complex. Further, it is interpretable, actionable and highly generalizable. The model could be incorporated into clinical practice and could allow continuous personalized predictions with automated intervention suggestions.
Staying Oriented While Driving

Do elderly drivers have more trouble finding their way and staying spatially oriented than younger drivers do? If spatial orientation is more difficult for elderly drivers it could distract attention from vehicle control resulting in a higher accident rate for elderly drivers. Moreover, it could become less desirable for older drivers to explore unfamiliar surroundings and as such diminish their action radius. In order to test our hypothesis that elderly drivers have more problems with wayfinding and spatial orientation drivers in two age groups were asked to perform a wayfinding task in an unfamiliar environment. Errors in orientation were measured and compared between age-groups. Results on this driving and wayfinding task support the original hypothesis that acquiring and maintaining spatial orientation is significantly more problematic for elderly than younger drivers, hence could be a distracting factor for vehicle control.
Urea chelated autocombused synthesis of BiFeO3 nanoparticles: application as magnetically retriable heterogeneous catalyst for synthesis of pyrano[2,3-c] pyrazoles

Abstract Herein, BiFeO3 nanoparticles are successfully established as promising catalyst of heterogeneous nature for one pot production of pyrano[2,3-c] pyrazoles. The desired BiFeO3 nanoparticles were synthesized using a facile auto combustion route in the presence of urea as a fuel. Further, the production was carried out at diverse range of temperatures of calcination (150 °C–650 °C), in order to determine the optimal temperature required for development of pure, BiFeO3 nanoparticles. Each of the prepared, BiFeO3 nanoparticles were fully characterized using XRD, FT-IR, VSM, TEM and BET. The catalytic performance of as BiFeO3 nanoparticles was then investigated for the creation of pyrano[2,3-c] pyrazoles.
Parallel Computing Framework for Optimizing Environmental and Economic Performances of Housing Units

AbstractDecision makers in the housing industry need to carefully analyze housing design and construction decisions to improve housing environmental and economic performances. Available energy optimization models are able to find minimum-cost housing design and construction decisions at different target energy-saving levels. The application of these models, however, is limited due to their time-intensive and often impractical computational requirements. This paper presents a scalable and expandable parallel computing framework to reduce the computational time that is required to optimize the trade-offs between the environmental performance of housing units and their initial cost. The framework is designed as a global parallel optimization algorithm to provide an efficient distribution of the multiobjective genetic algorithm computations over a number of parallel processors. The optimization algorithm is also coupled with an external building energy simulation engine to enable an accurate modeling of housi.
MIXED EVIDENCE FOR NICHE CONSERVATISM IN MOUNTAIN BEAVER (APLODONTIA RUFA) LINEAGES

Abstract Range shifts in response to past climatic changes have been documented in a variety of species and have often resulted in the isolation of relict populations. Understanding how these isolated populations develop local adaptations or maintain their historic climatic niche is crucial to creating effective management plans in the face of current climate change. While Mountain Beavers (Aplodontia rufa) have endured through major climatic shifts in the past, they maintain physiological constraints that limit their distribution to cool, humid climates. Increasing temperatures since the last glacial maximum likely had a strong influence in reducing their range. The species now persists as 5 genetically distinct clades, but it is not clear to what extent climatic differences have driven genetic isolation compared to other factors like topography. We compared species-distribution models (SDMs) for the 5 clades of Mountain Beaver to understand whether this species tends towards niche conservatism or adapts to local climates. Presence points from the Global Biodiversity Information Facility were divided into clades and combined with climatic layers from BioClim to develop SDMs for each clade. Niche overlap was then compared to genetic relatedness between all pairings of clades. High temperatures were a limiting factor in distribution for all clades and, despite a low level of niche overlap at broad scales, Mountain Beavers appeared to display some level of niche conservatism. These landscape level SDMs showed that some clades do exist in a warmer climate than other Mountain Beavers; however, fine-scale models for the Point Arena subspecies suggested they persist by selecting the coolest places within that range. This suggests that niche overlap between clades may be higher than what is detected at the coarser scale. Further research is needed to understand the mechanisms limiting the distribution of these subspecies.
Advanced GAF routing protocol using the goal attainment method in WSN

Various technologies have been developed to better improve lifestyles, including Wireless Sensor Networks (WSNs), used in multiple areas of research and consisting of a large number of sensor nodes. Drums in the area of interest, sensors collect data and transmit it to the base station (BS). Therefore, the key indicator for the design of WSN is the lifetime of the network. In this article, we provide an optimized GAF routing protocol that has been improved to optimize power consumption. It provides a multi-target version, which can maximize the coverage of communication between nodes to improve coverage efficiency. This is one of the key issues in the deployment of the sensor network, followed by the elected leader to minimize the proportion of active sensor nodes. The end goal is to minimize energy consumption. All these objectives are taken into account in our proposed version. The experimental results prove that in terms of performance, number of dead nodes, and energy consumption, the objectives proposed by this new version of GAF are superior to the existing basic and optimized GAF-GAF research. The practice has proven that the proposed target GAF can improve network lifetime.
DNN2FPGA: Generic Design Flow to Implement DNNs on FPGAs

Deep Neural Networks (DNNs) have recently indicated a considerable advantage in many deep learning tasks such as image classification and speech recognition. However, the achievement of high-performance DNNs has accompanied an expansion in computing and memory requirements. Due to these features, the Field-Programmable Gate Arrays (FPGA) devices are ideal for deploying DNNs, and they have the required flexibility, power efficiency, and computing performance. The implementation of DNN on FPGA is usually done using a high-level language such as python, followed by a manual transformation to Hardware Description Language (HDL), and finally, the synthesis using a vendor tool. This transformation is time-consuming and requires HDL expertise, which limits the relevance of FPGAs. The paper reviews some related works, shows the proposed design flow, then the hardware implementation, and shows two case study results: A Multi-Layer Perceptron (MLP) used to solve the classical XOR problem DNN for MNIST dataset classification. Finally, we present the conclusion and future works. This paper presents a new generic design flow of implementing DNN models automatically from the high-level language to FPGA devices, which takes the model in graph presentation as input and automatically generates the FPGA’s hardware implementations.
An IoT Based Fruit and Vegetable Sales System: A whole system including IoT based integrated intelligent scale and online shop

This paper combines people's strong demand for fruits and vegetables with the emerging intelligent sales and online mall technology, and takes fruit and vegetable retail as the breakthrough point to design an all-in-one fruit and vegetable retail machine and online market that can meet the needs of sellers and buyers and is suitable for supermarkets, fruit and vegetable markets and mobile economy. In offline purchase, all-in-one machine provides functions such as fruit and vegetable identification, automatic weighing, billing, code scanning payment, human-computer interaction, etc. The integrated shell of embedded device is convenient to carry; Edge intelligence runs through it. In online sales, cloud supermarket stores provide various information of fruits and vegetables, and support map display, real-time order, delivery and self-delivery services.
Link reciprocity in directed multiplex networks

Developing appropriate measures to characterize the important topological properties is one of the main objectives of multiplex networks. Recently many measures have been presented in context of multiplex networks. However, a nontrivial measure, namely link reciprocity which is a peculiar type of correlation and has remarkable influences on diffusions, network growth and higher-order structures such as motifs and communities, is still poorly understood in multiplex networks. Here, we generalize this crucial property to directed multiplex networks and introduce the multireciprocity measure to quantify link-reciprocity-based correlations between different pairwise layers. We first introduce a novel ‘absolute’ measure of multireciprocity to quantify correlations between pairwise layers of directed multiplexes. Then, we adopt the appropriate null models to generate its expected measure in order to obtain the ‘relative’ measure of multireciprocity. Finally, experiments on two real-world multiplex networks show that the adoption of homogeneous null models can result in misleading outcomes, highlight the significant role played by the hub distributions across the layers and show the higher accuracy of our measure for layer clustering compared with competing method on datasets.
A Hybrid Two-Level Support Vector Machine-Based Method for Churn Analysis

Customer churn is a central problem in almost every sector. Due to the diversity of the customers, products and services, and a massive amount of data being generated as a result of e-commerce tools and services, (big) data analytics and artificial intelligence-based methods have been developed and used for churn analysis in order to develop a strategy that is expected to understand the reasons behind the customer churn and subsequently to develop an effective and profitable customer retention programme. The analysis based on the data analytics and artificial intelligence methods focuses more on the profiling of customers, the classification of customer churn and identification of features that affect the churn. However, there doesn't seem many studies that would be able to help understand how much a potential customer is likely to (or not likely to) pay for the products or services when churned or not, and to predict how much a particular customer or group of customers may have paid for the products or services. Therefore, in this study, a two-level churn analysis is proposed to (1) classify the customer churn or not, and (1) predict how much the customer has paid for the service. In order to achieve it, a machine learning method, namely support vector machine (SVM), was used for the classification part whereas a monthly service charge was predicted by using support vector regression (SVR) method. In order to select the most appropriate feature sub-set for both analyses, an unsupervised feature selection method, namely the multi-cluster feature selection method was utilized. The same feature selection method was used for both analyses for the sake of consistency to understand its performance over both analyses. The proposed hybrid approach was then applied in IBM's Telcom data set with over 7000 customers in order to demonstrate the applicability and generalization ability of the proposed two-level approach. The SVM-based classification method has yielded AUC 85.6 and total classification accuracy of 81.5% being higher than those of a recent study where an aggressive set of the supervised classification methods was performed. The SVR-based prediction of the monthly charge has resulted in RMSE of 1.27, which is a reasonably acceptable outcome in the sector given the diversity of the ranges of charges as evidenced in its standard deviation. The approach presented in the study demonstrates that both the churn classification and charge prediction can be performed at the same time with a higher degree of accuracy. As the approach is open for further improvement, future analysis will be carried out to improve the accuracy for both analyses over other data sets to demonstrate its robustness and generalization ability.
Build a malware detection software for IOT network Using Machine learning

Any system based on IOT devices must provide a reliable and secure network to transmit and manipulate the data from the smallest technologies to the final server. They are the devices of nowadays, and our future for sure, it will convert all domains of our live starting from the smart home, industry, to e-healthcare systems. To achieve the quality of performances we should have an assurance of security and trustworthiness beginning by the small device used to capture information to the final terminal of the network system, using all the exist possibilities that we can implement to ensure a secure employment. Until now, machine learning become the most powerful application, which provides for any systems the capacity of auto-learning, and improving itself taking a help from the old experiences and without any human intervention or being explicit programmed. Furthermore, the usage of IOT networks, as we know is growing and evaluate enormously so the menaces keep up to date exploiting the weakness of these tools. For that, we propose to make a look about, What machine learning is coming with, to perform more security and analyses the threats, and how can it be used to detect any endanger before it can gain control. Knowing that the reproducible use of resource-constrained IOT devices, the number of IOT Malwares has exploded variously with many ways of breakthrough and then the requirement, to have an efficient malware detection adequate with this grown-up is on immense increasing importance. In this paper, we discuss the usability of machine learning applications in malwares detection software for IOT networks to elicitation the standards, which can us, use to build a powerful model to improve the network security of this small technologies answering to pervious questions.
Review on Embedded Systems and the Internet of Things: Comparative Study

Nowadays, thanks to the explosion of the Internet of Things or IoT, any object for public or industrial use can be connected to the Internet and become connected. Which requires companies to follow this explosion and to make improvements on their products to meet the evolving needs of human society. Going back before the explosion of the IoT, the business life was based on the embedded system concept. The latter is the combination of software and hardware, with fixed or programmable capabilities and it is specifically designed for a type of a particular application. This article presents a definition and a comparative study of a set of microcontrollers, boards, and operating systems the most used in IoT projects. Additionally, this comparative study helps us focus on the transition from embedded to IoT, by discussing the evolution of embedded systems.
A Greedy Load Balancing Algorithm for FaaS Platforms

In this paper, we propose a new load balancing algorithm for function-as-a-service (FaaS) platforms. We argue that the load balancing algorithm greatly affects the performance of FaaS platforms because heavy operations, such as virtualization and initialization, can be reduced using caching techniques. We demonstrate that a load balancing algorithm that provides higher locality could accelerate the FaaS platforms by increasing the cache-hit ratio, and propose a greedy load balancing algorithm optimized for FaaS. To generalize the experimental results, we conducted the experiment under three different caching policies that could be adopted in various FaaS platforms. Our evaluation reveals that load balancing algorithms with higher locality and a uniform load balance exhibit better results in all three caching policies, and our proposed algorithm achieves better performance compared to the state-of-the-art algorithms.
BMGC: A Deep Learning Approach to Classify Bengali Music Genres

Music genre classification (MGC) is the process of tagging music with their appropriate genres by analyzing music signals or the lyrics. With the accelerated surge in music data repositories, MGC can be extensively used in music recommendation systems, advertisement, and streaming services for systematic and efficient management. However, there have been many works on English music classification using different statistical and machine learning approaches, but there is no notable progress found in the arena of Bengali music. Besides, a few significant works have been found in utilizing Deep Learning (DL) methods to classify different music genres. Bengali music is significantly enriched with its contents and uniqueness. Moreover, the extent and scope of exploring the DL approach in Bengali music ground are still latent. Therefore, Bengali music genre classification is quite a new research area in the Deep learning field. In this work, we have constructed a Bengali Music Genre Classifier (BMGC) to categorize 6 Bengali music genres: ‘Adhunik’, ‘Band’, ‘Hiphop’, ‘Nazrulgeeti’, ‘Lalon’, and ‘Rabindra Sangeet’. We have created a Bengali music genre classification dataset (hereafter named BMGCD) containing 2944 Bengali music clips, and a Gated Recurrent Unit based deep learning model has been developed to predict the music genre from audio signals. Our developed model achieved an accuracy of 80.4% and 80.6% F1-score which surpasses the related existing works.
Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate

Abstract The elemental geochemical characteristics of mudstones/shales are good tracers for indicating the evolution of tectonics, paleoenvironment, and paleoclimate. Based on the continuous sampling of drilling cores from the Middle Jurassic Dameigou and Shimengou Formations in the northern Qaidam Basin, the major, trace, and rare earth elements of the 31 mudstones and shales were analyzed. The information on the evolution of tectonics, provenance, and paleoclimate during Middle Jurassic was also recovered. The results show that: (1) A couple of elements consisting of Sc, Y, V, Cr, Co, Ni, Cu, Zn, Th, and U are relatively enriched, indicating that the contents of siderophile and chalcophile elements are significantly high in the Middle Jurassic samples; (2) Changes in the chemical index of alteration, Ga/Rb, and K2O/Al2O3 ratios in the mudstone/shale samples suggest that the paleoclimate was changed from warm and humid in the early stage to cold and dry in the middle stage and to hot and arid in the late stage; (3) The Middle Jurassic provenance of the northern Qaidam Basin was predicted from upper crust and felsic rocks to the mixed felsic rocks and basic rocks; (4) The Middle Jurassic tectonic background was changed from passive continental margin to active continental margin and oceanic island arc. The paleoclimatic and paleogeographic evolution of northern Qaidam Basin were closely related to the surrounding paleo-oceanic and ancient plate activities. In the early stage of the Middle Jurassic, the extensional activity in the passive continental margin and the water vapor input was caused by the Tethys Ocean, resulting in a warm and humid paleoclimate. In the late stage of the Middle Jurassic, the tectonic background of the study area tended to be an oceanic island arc caused by compressive tectonic, which blocked the monsoon input and led to a hot and arid paleoclimate. The establishment of multiple geochemical profiles can provide a scientific basis for the climate changes in greenhouse–icehouses and source–sink systems of the Middle Jurassic in northwestern China.
Micro-coil with movable core for application in an inductive displacement sensor

A new concept for a micro-coil is presented. The micro-coil will be used as a sensor that measures the flexion of fingers. For this purpose several micro-coils are mounted on a data glove. Though the coil is fabricated by means of silicon bulk micromachining it is possible to move an iron core through the coil without destroying the windings. The inductance of the full coil is 0.25 H without a core and 7.2 H with a core. The ohmic resistance has a value of 60 .
Performance Evaluation of Linux Bridge

This paper studies a unique network feature, Ethernet bridge, in the Linux kernel and conducts an extensive experiment to measure its performance as defined in RFC2544. The result shows that Linux bridge yields satisfactory performance if the system occupancy is less than 56% where its latency and throughput are comparable to Cisco Catalyst 2950 switch. The performance is considered acceptable until system occupancy reaches 85%. We also compared the performance between Linux bridge and Linux router, and the results are almost the same as measured by latency and throughput. The contributions of this study are summarized as follows: 1. With its open source, Linux bridge is like programmable switch for education and research. We are encouraged by the performance results of this study, and plan for more advanced research on Linux bridge in load balancing and high availability. 2. The performance result shows that Linux can be deployed as an effective network device if its occupancy is properly engineered for targeted applications. One example is network firewall where the Wide Area Network (WAN) link is usually than 10M. 3. Our experiment of bridge and router configuration can be used for classroom demo and lab exercise on data network education. The use of RFC-2544 serves as a useful guide to learn network benchmark testing and performance measurements.