Miracle-dz/UniFAR-FTUs huggingface dataset:
Techno-Economic Analysis of Photovoltaic-Hydrogen Fuel Cell/Pumped Hydro Storage System for Micro Grid Applications: Case Study in Cyprus

Renewable energy resources such as solar resources are suitable alternatives for the use of fossil fuels as they are abundant, can be harnessed in affordable ways and are considered environmentally friendly. However, renewable energy resources fluctuate with time which decreases the matching between the energy produced by the renewable energy system and the demand and also decreases the reliability of the power supply. There are several potential ways to increase the matching and reliability of renewable energy systems such as the hybridization of renewable energy resources and the integration of energy storage. A techno-economic analysis of different configurations of Photovoltaic, Hydrogen Fuel Cell (HFC) and Pumped Hydro Storage (PHS) is carried out where Middle East Technical University Northern Cyprus Campus (METU NCC) is the case study. The optimal configurations of the PV system with different energy storage system configurations for the university are found based on maximizing the renewable energy (RES) fraction with Levelized Cost of Electricity (LCOE) equals to the grid tariff. However, the objective of the optimization becomes the maximization of the RES fraction with the minimum LCOE if there is no a feasible configuration. The results show that the integration of HFC and PHS system with the PV system increases the RES fraction and the demand-supply fraction from 36.2% to 45.4% and from 23.9% to 35.1%, respectively. The proposed system consists of 2.57 MW PV, 1.16 MWh HFC and 4.14 MWh PHS where such a system has LCOE of 0.181 USD/kWh.
Sensation and pain thresholds of transcranial stimulation with direct and alternating electric current

Transcranial electrical stimulation with direct and alternating current (tDCS and tACS) is non-invasive methods used for therapeutic purposes as well as cognitive effects. The sensation and the pain thresholds as well as the adverse effects were recorded for anodal tDCS, cathodal tDCS, tACS at 6 Hz, 10 Hz, 20 Hz and 40 Hz administered to healthy participants via sponge electrodes placed at AF7 and AF8. The lowest sensation thresholds were found for 10-40 Hz tACS. The pain thresholds less than 2.0 mA were reached only with anodal tDCS in a few participants. Mild prickle, itching, tickling or tingling were referred by all participants for all kinds of stimulations with current thresholds highly scattered. Feelings of burning or tingling were observed in more than half participants mostly for anodal tDCS. Phosphenes were referred by most participants with tACS even for very low currents.
Privacy Protection, Personalized Medicine, and Genetic Testing

This paper explores how state genetic privacy laws affect the diffusion of personalized medicine, using data on genetic testing for cancer risks. State genetic privacy laws take three alternative approaches to protecting patient privacy: Requiring informed consent on the part of the individual; restricting discriminatory usage of genetic data by employers, health care providers or insurance companies; or limiting redisclosure without the consent of the individual or defining genetic data as the ‘property’ of the individual. We find empirically that approaches to genetic and health privacy that give users control over redisclosure encourage the spread of genetic testing, but that the informed consent approach deters individuals from obtaining genetic tests. We present some evidence that the latter reflects costs imposed on the supply of genetic testing by hospitals. We find no effects of state or federal genetic anti-discrimination laws on genetic testing rates. ∗Economics Department, University of Virginia, Charlottesville, VA, IZA and NBER. †MIT Sloan School of Management, MIT, Cambridge, MA and NBER. ‡We thank the CDC for providing access to the confidential NHIS data used in this study and Alex Marthews for constructing databases with EEOC claims counts and state genetic privacy laws. We thank the NSF for research funding and Scott Laughery for excellent research assistance. The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the Research Data Center, the National Center for Health Statistics, the Centers for Disease Control and Prevention, or the National Science Foundation.
Millimeter-Wave MIMO Balanced Antipodal Vivaldi Antenna Design for Autonomous Cars

This paper introduces a millimeter-wave multiple-input-multiple-output (MIMO) antenna for autonomous (self-driving) cars. The antenna is a modified four-port balanced antipodal Vivaldi which produces four directional beams and provides pattern diversity to cover 90 deg angle of view. By using four antennas of this kind on four corners of the car's bumper, it is possible to have a full 360 deg view around the car. The designed antenna is simulated by two commercially full-wave packages and the results indicate that the proposed method can successfully bring the required 90 deg angle of view.
Clone-Based Variability Management in the Android Ecosystem

Mobile app developers often need to create variants to account for different customer segments, payment models or functionalities. A common strategy is to clone (or fork) an existing app and then adapt it to new requirements. This form of reuse has been enhanced with the advent of social-coding platforms such as Github, cultivating a more systematic reuse. Different facilities, such as forks, pull requests, and cross-project traceability support clone-based development. Unfortunately, even though, many apps are known to be maintained in many variants, little is known about how practitioners manage variants of mobile apps. We present a study that explores clone-based reuse practices for open-source Android apps. We identified and analyzed families of apps that are maintained together and that exist both on the official app store (Google Play) as well as on Github, allowing us to analyze reuse practices in depth. We mined both repositories to identify app families and to study their characteristics, including their variabilities as well as code-propagation practices and maintainer relationships. We found that, indeed, app families exist and that forked app variants fall into the following categories: (i) re-branding and simple customizations, (ii) feature extension, (iii) supporting of the mainline app, and (iv) implementation of different, but related features. Other notable characteristic of the app families we discovered include: (i) 72.7% of the app families did not perform any form of code propagation, and (ii) 74% of the app families we studied do not have common maintainers.
Performance Analysis of RDBMS and Hadoop Components with Their File Formats for the Development of Recommender Systems

A recommender system is a software that can suggest users through prediction based on their previous data usage in the shortest amount of time. Present recommender systems are designed using complex techniques like collaborative filtering, content-based filtering etc. but a similar system can be built by applying complex queries using different query tools. Performance of these query tools depends upon various factors like data size, file formats of the dataset, aggregate search etc. In this paper, we compare four query tools like Hive, Impala, SparkSQL and MySQL to design a fast and an efficient recommender system. Analysis of these tools is done by comparing the execution time of complex queries on data stored in different file formats like text, CSV, AVRO, PARQUET, RC and ORC. The results obtained indicate that a fast recommender system can be built using a query tool like Impala on a dataset saved in AVRO file format.
A new method of classification with rejection applied to building images recognition based on Transfer Learning

The present paper1 proposes a new method of classification with rejection for the scenario of building images recognition based on the probability vector generated by the classifier. Also, it is performed an evaluation of a set of pre-trained models of convolutional neural networks (CNN). Transfer Learning technique is used for features extraction (feature vectors), these are used to feed the classifier. Similarly, an evaluation is conducted on a set of classifiers with the objective of identifying the most optimal machine learning algorithm during the scene of buildings images recognition. The experiments are evaluated on the first version of the Cusco Building Image Dataset (CuscoBID). Finally, it is developed the second version of CuscoBID, composed of 4560 images of 14 different historical buildings, available to the entire scientific community.
Price-Based Resource Allocation in Massive MIMO H-CRANs With Limited Fronthaul Capacity

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.
TC and PPGL Detection Based on Machine Learning Models

Cancer is one of the most dangerous diseases that can pose a great threaten to human health and well-being. Studies indicate that diagnosing cancers comparatively early can increase the survival rate of patients. Hence, the early detection screen for cancers is very significant for patients' survival time and quality. With the development of metabonomics, the study of metabonomics for diagnosis of cancer diseases has become a research focus in the current medical field. In this paper, some kinds of machine learning algorithms including K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are applied to analyze the data from clinical metabonomics for diagnosis of cancers. We used the machine learning methods to detect thyroid carcinoma (TC) and PPGL (a medical term composing of two kinds of tumors called pheochromocytoma and paraganglioma respectively). The experiment results show that machine learning methods can discriminate the samples of thyroid cancer and PPGL from normal subjects. It has been found that the SVM classifier has the best classification performance, which shows an accuracy of 99.15%.
Falcarinol Is a Potent Inducer of Heme Oxygenase-1 and Was More Effective than Sulforaphane in Attenuating Intestinal Inflammation at Diet-Achievable Doses

Nuclear factor- (erythroid-derived 2) like 2 (Nrf2) is a transcription factor that regulates the expression of a battery of antioxidant, anti-inflammatory, and cytoprotective enzymes including heme oxygenase-1 (Hmox1, Ho-1) and NADPH:quinone oxidoreductase-1 (Nqo1). The isothiocyanate sulforaphane (SF) is widely understood to be the most effective natural activator of the Nrf2 pathway. Falcarinol (FA) is a lesser studied natural compound abundant in medicinal plants as well as dietary plants from the Apiaceae family such as carrot. We evaluated the protective effects of FA and SF (5 mg/kg twice per day in CB57BL/6 mice) pretreatment for one week against acute intestinal and systemic inflammation. The phytochemical pretreatment effectively reduced the magnitude of intestinal proinflammatory gene expression (IL-6, Tnfα/Tnfαr, Infγ, STAT3, and IL-10/IL-10r) with FA showing more potency than SF. FA was also more effective in upregulating Ho-1 at mRNA and protein levels in both the mouse liver and the intestine. FA but not SF attenuated plasma chemokine eotaxin and white blood cell growth factor GM-CSF, which are involved in the recruitment and stabilization of first-responder immune cells. Phytochemicals generally did not attenuate plasma proinflammatory cytokines. Plasma and intestinal lipid peroxidation was also not significantly changed 4 h after LPS injection; however, FA did reduce basal lipid peroxidation in the mesentery. Both phytochemical pretreatments protected against LPS-induced reduction in intestinal barrier integrity, but FA additionally reduced inflammatory cell infiltration even below negative control.
Finding SQL Injection and Cross Site Scripting Vulnerabilities with Diverse Static Analysis Tools

The use of Static Analysis Tools (SATs) is mandatory when developing secure software and searching for vulnerabilities in legacy software. However, the performance of the various SATs concerning the detection of vulnerabilities and false alarm rate is usually unknown and depends on many factors. The simultaneous use of several tools should increase the detection capabilities, but also the number of false alarms. In this paper, we study the problem of combining several SATs to best meet the developer needs. We present results of analyzing the performance of diverse static analysis tools, based on a previously published dataset that resulted from the use of five diverse SATs to find two types of vulnerabilities, namely SQL Injections (SQLi) and Cross-Site Scripting (XSS), in 132 plugins of the WordPress Content Management System (CMS). We present the results based on well-established measures for binary classifiers, namely sensitivity and specificity for all possible diverse combinations that can be constructed using these 5 SAT tools. We then provide empirically supported guidance on which combinations of SAT tools provide the most benefits for detecting vulnerabilities with low false positive rates.
The co‐administration of estradiol/17&agr;‐methyltestosterone leads to male fate in the protogynous orange‐spotted grouper, Epinephelus coioides

Abstract Estrogen plays a pivotal role in the sex differentiation of teleosts, whereas the precise function of androgens is more controversial. In this study, orange‐spotted grouper (Epinephelus coioides) fry were treated with letrozole (an aromatase inhibitor, AI), 17&agr;‐methyltestosterone (MT), or MT and 17&bgr;‐estradiol (E2) simultaneously, during the period of gonadal formation and sex differentiation. MT feeding at 50 days after hatching resulted in gonadal dysgenesis, which could be rescued by E2 supplementation. Different doses of AI treatment led to different phenotypes: undifferentiated gonads were maintained in the AI group fed a low dose (5 mg/kg diet), whereas female‐to‐male sex reversal was observed in the AI group fed a high dose (100 mg/kg diet). MT and MT + E2 treatment could induce female‐to‐male sex reversal during sex differentiation (90 days after hatching). The expression of female pathway genes was suppressed, while the expression of genes in the male pathway was up‐regulated in the MT + E2 group. Consistent with the expression of sex‐related genes, the serum 11‐ ketotestosterone level was also upregulated in MT and MT + E2 group. Finally, we examined the expression of male‐specific mark (DMRT1) and proliferating cell nuclear antigen in MT and MT + E2 induced sex reversal, and the result indicated that male germ cells and somatic cells may origin from the gonium and proliferative somatic cells surrounding the efferent duct, respectively. Overall, our data suggested that estrogen acts as a natural inducer of female differentiation, and that the co‐administration of estrogen and androgen during sex differentiation leads to a male sex fate in the protogynous orange‐spotted grouper.
Performance enhancement of wireless sensor networks using an efficient coding approach

The Random Linear Network Coding (RLNC) has been investigated as a promising solution for improve transmission reliability; however, it is highly susceptible to errors. In wireless sensor networks (WSN), The problem resemblance to a column vector on the generator matrix of Low-Density Parity-Check (LDPC) matrix is caused by the important similarity between received symbols from the cluster head (CH). To deal with this problem, we have to reduce BER. The principal objective of this paper is to use an efficient coding approach based on the RNC and LDPC codes using TDMA (Time Division Multiple Access) technology. The coding techniques employed in this paper are focused on enhance planning to report error-bit (BER) vs. signal-to-noise (SNR), between CHs and the base station (BS) node in dense WSN, while describing each point examining effective use of the proposed algorithm.
Gankyrin Drives Malignant Transformation of Gastric Cancer and Alleviates Oxidative Stress via mTORC1 Activation

Gastric cancer, as a malignant epithelial tumor, is a major health threat leading to poor overall survival and death. It is usually diagnosed at an advanced stage due to asymptomatic or only nonspecific early symptoms. The present study demonstrated that gankyrin contributes to the early malignant transformation of gastric cancer and can be selected to predict the risk of gastric cancer in those patients harboring the precancerous lesions (dysplasia and intestinal metaplasia). In addition, a new insight into gastric cancer was provided, which stated that gankyrin alleviates oxidative stress via mTORC1 pathway activation. It can potentiate the mTORC1 by PGK1-AKT signaling that promotes the tumor process, and this phenomenon is not completely consistent with the previous report describing colorectal cancer.
CASS: A Cooperative Hybrid Storage System Consisting of an SSD and a SMR Drive

Shingled Magnetic Recording (SMR) can effectively increase the capacity of hard disk drives (HDDs). However, failing to support in-place updates in SMR drives often leads to performance degradation. Although flash based SSDs can be used as caches to improve the performance of SMR drives, the garbage collection (GC) processes in SSDs bring negative impact on the performance. In this paper, we design a cooperative hybrid storage system called CASS, which uses a flash based SSD as a cache for a SMR drive. We first propose a global GC algorithm to coordinate the SSD and SMR drive to avoid severe performance degradation. And then, we design a hybrid GC algorithm for the SMR drive by making full use of idle time. The hybrid GC algorithm combines the intra-band GC and inter-band GC together to improve GC efficiency. Experimental results show that the global GC algorithm can improve the performance of CASS up to 47%, and CASS can perform up to 75% and 93% better than SSD-Sblock and SSD-HSR respectively.
Execution-Efficient Response Time Analysis on Global Multiprocessor Platforms

Response time analysis (RTA) is an important and fundamental tool for analyzing the schedulability of real-time tasks on multiprocessor platforms, and many promising techniques have been developed during the past few years. However, most of the existing researches focus on improving the analysis precision, while less has been done on enhancing the execution efficiency. In this paper, we take, to our best knowledge, the first effort towards improving the efficiency of the state-of-the-art RTA methods for sporadic tasks under both global fixed-priority (G-FP) and global earliest deadline first (G-EDF) scheduling. Specifically, three factors that impact the efficiency of existing global RTA tests are identified: 1) pessimistic initial value when computing the worst-case response time (WCRT) of tasks under both G-FP and G-EDF; 2) conservative interference upper bound under both G-FP and G-EDF; and 3) unnecessary recalculation of WCRTs when there is update of any task WCRT under G-EDF. By addressing these three limitations, we propose two efficient RTA methods for G-FP and G-EDF scheduling, respectively, which achieve better run-time performance but without sacrificing any analysis precision. Experimental evaluations with randomly generated task sets show that the proposed methods exhibit remarkable performance improvements and can save on average 60 and 61 percent run time, as compared to the state-of-the-art technologies under G-FP and G-EDF scheduling, respectively.
Design and Implementation of ConvNet for Handwritten Digits Classification on Graphical Processing Unit

Convolutional Neural Network (CNN) or ConvNet is the leading-edge deep learning model that has achieved phenomenal successes in the tasks of image classification, object recognition, speech recognition and natural language processing. ConvNets are inherently complex architectures and training ConvNets requires significant amount of computation. There is a need to determine whether GPU or CPU provide the effective method for implementation of ConvNets. Very few studies have come up with the comparison of the implementation of ConvNets on both CPU and GPU. The present work examines the impact of GPU on the implementation of ConvNets. ConvNet is trained on MNIST dataset to perform classification of handwritten digits. The experiments have been performed on both CPU and GPU and observed that there is a performance improvement of 5 times in terms of training time speedup on GPU. The proposed work also investigates the effect of regularization and the results show that regularization indeed reduces the problem of overfitting.