Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-scale Simulations of Peridynamics on Sunway Taihulight Supercomputer

Peridynamics (PD) methods are good at describing solid mechanical behaviours and have the superiority on simulating the discontinuous problems. They can be applied to many fields, such as materials science, human health, and industrial manufacturing, etc., which motivates us to provide their efficient numerical simulations on the Sunway TaihuLight supercomputer. However, massive and complex calculations of PD simulations and the characteristics of Sunway TaihuLight bring challenges to efficient parallel PD simulations. In this paper, we present a series of performance optimization techniques to perform a large-scale parallel PD simulation application on Sunway TaihuLight. We first design the data grouping and SPM-based caching to increase the bandwidth of data transmission and reduce the time of the main memory access. Further, we design and implement vectorization and instruction-level optimization for PD applications to improve computational performance. Finally, we offer the overlapping strategies of data transmission and computation so that data transmission can be covered by computation. Our work in a core group improves the performance of the serial version on the SW26010 processor by 181 times. Compared to the serial and single-CPU Peridigm-based simulations on Intel Xeon E5-2680 V3, our work gets a speedup of 60 times and 6 times, respectively. Near linear scalability is also obtained. When testing the weak scaling, the simulation of a 296,222,720-point example achieves 1.14 PFLOPS with 8192 (532,480 cores) processes. When testing the strong scaling, 90% parallel efficiency is observed as the number of processes increases 64 times to 4096 processes.
Enabling performance portability of data-parallel OpenMP applications on asymmetric multicore processors

Asymmetric multicore processors (AMPs) couple high-performance big cores and low-power small cores with the same instruction-set architecture but different features, such as clock frequency or microarchitecture. Previous work has shown that asymmetric designs may deliver higher energy efficiency than symmetric multicores for diverse workloads. Despite their benefits, AMPs pose significant challenges to runtime systems of parallel programming models. While previous work has mainly explored how to efficiently execute task-based parallel applications on AMPs, via enhancements in the runtime system, improving the performance of unmodified data-parallel applications on these architectures is still a big challenge. In this work we analyze the particular case of loop-based OpenMP applications, which are widely used today in scientific and engineering domains, and constitute the dominant application type in many parallel benchmark suites used for performance evaluation on multicore systems. We observed that conventional loop-scheduling OpenMP approaches are unable to efficiently cope with the load imbalance that naturally stems from the different performance delivered by big and small cores. To address this shortcoming, we propose Asymmetric Iteration Distribution (AID), a set of novel loop-scheduling methods for AMPs that distribute iterations unevenly across worker threads to efficiently deal with performance asymmetry. We implemented AID in libgomp –the GNU OpenMP runtime system–, and evaluated it on two different asymmetric multicore platforms. Our analysis reveals that the AID methods constitute effective replacements of the static and dynamic methods on AMPs, and are capable of improving performance over these conventional strategies by up to 56% and 16.8%, respectively.
SeRW: Adaptively Separating Read and Write upon SSDs of Hybrid Storage Server in Clouds

Nowadays, cloud providers embrace hybrid storage servers to reap both high IO performance of solid-state drives (SSDs) and low-cost of hard disk drives (HDDs). These hybrid storage servers generally employ SSDs as primary storage directly serving requests from front-end applications while using HDDs as the secondary storage to provide sufficient storage capacity. From an actual trace analyzing on the production of Alibaba clouds, we found that writes mixed with mid/high intensive reads upon SSDs dramatically increases read-latency, especially for tail latency. These long read latencies are primarily caused by (1) write-induced-blocking and (2) write-induced-garbage-collection (GC). To fully exploit the IO potential of underutilized HDDs within hybrid storage servers, we present a SeRW scheduling approach that adaptively steers some SSD-writes to idle HDDs in running time. SeRW relieves the write-blocking read delay on SSDs at mid/high load. SeRW also reduces the amount of data written into SSDs, thus decreasing the GC frequency. We evaluate SeRW through replaying the production workload. The experimental result shows that SeRW decreases the average, 99th, 99.9th, 99.99th-percentile latencies of reads by up to 2.07x, 1.48x, 4.29x, and 4.24x, respectively, while reducing the amount of data written to SSDs by up to 37.5%. SeRW notably significantly improves both the overall system performance and SSD endurance without sacrificing the write latency.
THE EFFECTIVENESS OF WHATSAPP GROUP CHAT FOR INTERNAL COMMUNICATION AT KADOKAWA GEMPAK STARZ™

This study emphasizes on the proliferation of technological devices such as smartphones and the effectiveness of WhatsApp Group Chat in working organization such as Kadokawa Gempak Starz™ for Internal Communication. Technology is applied in work as they aid in solving problems and easy access to information. There were two research objectives which the study seeks to determine. First is to explain the adoption of WhatsApp Group Chat for Internal Communication through Technology Acceptance Model. Second, is to examine the challenges of using WhatsApp Group Chat for Internal Communication. The study employed a research design based on questionnaire with a total number of 120 respondents. Ultimately the findings showed that the respondents find it challenging to use WhatsApp Group Chat for Internal Communication and are on the fence about its usage for Internal Communication as well as the effectiveness to serve as a communication tool for internal Communication at Kadokawa Gempak Starz™, all of these variables will hugely impacted the behavioral intention towards WhatsApp Group Chat in the future.
An Online Learning-Based Task O � loading Framework for 5G Small Cell Networks

Small cells are deployed in 5G networks to complement the macro cells for improving coverage and capacity. Small cells and edge computing are natural partners which can improve users’ experience. Small cell nodes (SCNs) equipped with edge servers can support emerging computing services such as virtual reality which impose low-latency and precise contextual requirements. With the proliferation of wireless devices, there is an increasing demand for o � oading tasks to SCNs. Given limited computation and communication resources, the fundamental problem for a small cell network is how to select computing tasks to maximize e � ective rewards in an uncertain and stochastic environment. To this end, we propose an online learning framework, LFSC, which has the performance guarantee to guide task o � oading in a small cell network. LFSC balances between reward and constraint violations, and it consists of three subroutines: i) a randomized algorithm which calculates selection probability of each task based on task weights; ii) a greedy assignment algorithm which cooperatively allocates tasks among di � erent SCNs based on the selection probability; iii) an update algorithm which exploits the multi-armed bandit (MAB) technique to update task weights according to the feedback. Our theoretical analysis shows that both the regret and violations metrics of LFSC have the sub-linear property. Extensive simulation studies based on real world data con � rm that LFSC achieves a close-to-optimal reward with low violations, and outperforms many state-of-the-art
Computational Approaches for Protein Function Prediction : A Survey

Proteins are the most essential and versatile macromolecul es of ife, and the knowledge of their functions is a crucial link in the development of new drugs, better crops, and e ven the development of synthetic biochemicals such as biofuels. Experimental procedures for protein function prediction are inherently low throughput and are thus unable to annotate a non-trivial fraction of proteins that a re becoming available due to rapid advances in genome sequencing technology. This has motivated the development of computational techniques that utilize a variety of high-throughput experimental data for protein function pr ediction, such as protein and genome sequences, gene expression data, protein interaction networks and phyloge netic profiles. Indeed, in a short period of a decade, several hundred articles have been published on this topic. This survey aims to discuss this wide spectrum of approaches by categorizing them in terms of the data type the y us for predicting function, and thus identify the trends and needs of this very important field. The survey is ex pected to be useful for computational biologists and bioinformaticians aiming to get an overview of the field of co mputational function prediction, and identify areas that can benefit from further research.
Efficacy of Silver Nanoparticles, Antimicrobial peptides and Blue Light Combination on Healing Infected Wound by Methicillin Resistance Staphylococcus aureus in Rats

This study was carried out to evaluate the effectiveness of the triple combination of biologically synthesized silver nanoparticles, anti-microbial peptides and visible blue light against pathogenic clinical methicillin resistance Staphylococcus aureus (MRSA) in vivo after inducing full thickness skin injury in the initially induced diabetic rats. This experiment of in vivo study was for evaluation the role of the combinations in promoting wound healing after wound inducing infection using pathogenic MRSA (0.25ml from 1.5 ×108 cfu/ml) in diabetic rats. 108 diabetic rats (aged 8-10 weeks weighing 200-250g) were divided equally into nine groups, (treatment started after the appearance of infection signs). Group A: Negative control group, group B: Positive control group, Group C: infected treated with AgNPs 8µg/ml, group D: infected treated with Gramicidin 16µg/ml, group E: infected treated with blue light for 1 hour, group F: infected treated with combination of AgNPs 0. 25µg/ml with blue light for 1 hour, group G: infected treated with combination of Gramicidin 8 µg/ml and blue light for 1 hour, group H: infected treated with combination of AgNPs and Gramicidin 0. 25µg/ml, 0.5 µg/ml respectively, group I : infected treated with triple combination of AgNPs ,Gramicidin 0. 25µg/ml, 0.5µg/ml respectively and blue light for 1 hour. All groups treated topically daily for three days. Wound healing was assessed by measuring wound contraction at 7, 14 and 21 days post wound induction. Viable bacterial count at 21 day post wound induction. Results were according to data analysis, healing rate of treated rat with the triple combination was significantly (p < 0.001) faster (within 7 days) than treated rat with other double combination and each agent alone. All symptoms returned to near normal values at the end of treatment. In conclusion the topical therapy of the triple combination of AgNPs, visible blue light and antimicrobial peptides promote wound healing process in diabetic infected wound in a short period of time. A new approach can be used to combat serious infections caused by MRSA by combining AgNPs, blue light, and antibiotics.
Spark Streaming for Predictive Business Intelligence

Apache spark can process the data in real time with the test mining and natural language processing. The business intelligence can be improved by collecting and processing the data from web in real time. Process mining collects the data from event logs in process discovery. Then diagnosis the difference between the observed and reality through an event logs. And extended the data of the event. Dealing with huge data process mining finds difficulty in processing. Spark handles the data processing speed and real time. It receives the input data and segregated into batches put up in processing. The incoming data append into the already existing data for processing. It identifies the problems and quick report generation of processing data.
4G Wireless Networks

The better the technology, the more user friendly it becomes. This is what 4G brings to us than 3G. 4G provides us 3 timesfaster speed than 3G technology.4G is the short name of fourth generation of wireless /mobile communication That willenable things like IP –based voice, data, gamming services & high quality streamed multimedia on portable devices withcable modem like transmission speed. The expectation for the 4G technology is basically the high quality audio/videostreaming over end to end Internet Protocol. Third generation mobile, data rates are 384 Kbps (download) maximum,typically around 200kbps, and 64kbps upload. These are comparable to home broadband connections. Fourth generationmobile communications will have higher data transmission rates than 3G. 4G mobile data transmission rates are plannedto be up to 100 Mbps on the move and 1Gbps stationary, this is a phenomenal amount of bandwidth, only comparable tothe bandwidth workstations get connected
A Study on Cloud Computing Services

Cloud computing is a computing model of providing IT resources, such as application, infrastructure, and platform in the form of service by using Internet. Cloud Computing provides infrastructure for computing and processing of all types of data resources and adopted to deal with the large amounts of data. This Internet based current technology has brought flexibility, capacity and power of processing. This technology has recognized serviceoriented idea and has formed a new system in the computing world with its influence and benefits. The capabilities of Cloud computing have been able to move IT industry one step forward. Nowadays, huge and prominent enterprises have migrated to cloud computing and have relocated their processing and storage to it. In this paper, we provide an overall perception on cloud computing and draw attention to its services. Keywords—Cloud Computing, Services, Cloud providers.
Ameliorative Efficacy of Egyptian Pyramid Model Housing on Unilateral Nephrectomized Rats

Nephrectomy (Nx) is a novel intervention having first been introduced for the treatment of renal cell carcinoma in 1969 [1]. Although surgical resection remains the standard of care in the treatment of renal carcinoma, Nx is also a recognized risk factor for developing Chronic Kidney Disease (CKD) [2,3]. Indeed, nephrectomy is a widely used model to study the mechanisms leading to renal damage in CKD [4]. The intra-renal vasculature is the most affected structure, preventing an appropriate blood flow, fаvoring the glomerular sclerosis [5]. CKD will certainly lead to the progression of Chronic Renal Failure (CRF) due to gradual sclerosis of renal tissue. In addition, nephrons are gradually lost in renal failure. Remaining nephrons are hypertrophied to compensate for the loss of renal function, and hyperfiltration in these remaining nephrons leads to further nephron loss [6].
Weekly Paclitaxel and Carboplatin for Patients with Advanced Ovarian Cancer

Purpose: The purpose of this study was to evaluate the efficacy and toxicity of dose-dense weekly paclitaxel plus 3weeks carboplatin as a neoadjuvant and adjuvant therapy for primary epithelial ovarian cancer. Patients and Methods: Fifty patients with histologically proven potentially unresectable stages IIb-IV 1ry epithelial ovarian cancer attending the Outpatient Clinics of Clinical Oncology and Nuclear Medicine Department, Mansoura University Hospital during the period from Aug. 2005 to Oct. 2009 were enrolled. All patients were treated with neoadjuvant paclitaxel 100mg/m d1, 8 & 15 plus carboplatin AUC 6 given on day 1 of a 21-day cycle (PC regimen) for 3 cycles followed by interval cytoreductive surgery and an additional 3-6 cycles of adjuvant PC regimen. Results: Thirty six (72%) patients were presented with stage III-IV disease. At the end of neoadjuvant PC, 64% had a radiological complete response (CR) and 32% had a biochemical CR. Optimal interval cytoreduction was feasible in 41/50 patients (82%) and pathological CR was established in 15/50 patients (30%). Post therapy radiological CR was achieved in 43/50 patients (86%) and biochemical CR in 41/50 patients (82%). Median time to progression (TTP) and median overall survival (OS) were 22 and 49 months respectively. Neutropenia was the most common haematological toxicity developed in 34 patients of whom 12 had G3/4. Grade 3 peripheral neuropathy was observed in 2 patients and G1/2 myalgia/arthralgia in 10 patients. Ninety-two percent of patients experienced G1/2 alopecia. Conclusion: Dose dense weekly paclitaxel plus 3-weeks carpoblatin is an effective and well tolerated neoadjuvant and adjuvant regimen. Combined with interval cytoreductive surgery, it may be a better alternative treatment option to the standard 1ry debulking surgery and sequential conventional 3-week regimen in patients with advanced epithelial ovarian cancer.
OPINION MINING UNTUK ULASAN PRODUK DENGAN KLASIFIKASI NAIVE BAYES

Nowadays, micro blogs have become the most used tools for users to share many things: from just updating things to telling their conditions or thoughts. Some popular micro blogs mostly used to give comments and opinions are facebook, instagram, and twitter . Twitter has 259 million active users each month as for January until April 2017. This made twitter one of the best micro blogs to know the most updated opinions. The system uses Naive Bayes Classification to classify opinions about smartphone and computer from twitter. The sentiments are divided to positive, neutral, and negative. After that, Confusion Matrix is used to evaluate the algorithm and count the accuracy. Naive Bayes Classification gives 77.7% accuracy for Unigram, 50.7% for Bigram, and 31.7% for Trigram
Effects of Noncompliance on the Success of Alternative Designs of Marine Protected‐Area Networks for Conservation and Fisheries Management

Abstract: Studies examining the efficacy of marine protected areas (MPAs) rarely consider the potential for noncompliance. Violation of MPAs will typically occur near boundaries, so perimeter‐to‐area ratios will be important determinants of actual protection, suggesting that MPAs should be larger and likely fewer. If larval dispersal is highly localized, however, MPAs will need to be more numerous, widespread, and likely smaller in order to replenish many fished areas. Thus, there is a discord between the MPA network that would best achieve external replenishment and that which would maximize compliance. I investigated these competing criteria with a spatially structured model of a hypothetical marine fishery exploiting a sedentary reef‐dwelling organism. With full compliance, a network of several small MPAs protects a population of similar size to that in a single large MPA and produces higher fishery yield across a range of fishing mortality rates. As noncompliance increases, however, the protected population in the network of several small MPAs approaches zero, whereas the single, large MPA population declines much less. Furthermore, at high levels of fishing mortality and noncompliance, yield with the network of several small MPAs begins to mirror that with no MPAs and drops below the yield with the single large MPA. Temporal variability in both the protected population size and yield are similar between the two designs with full compliance, but the single large MPA provides much greater stability in both metrics at high fishing mortality rates as noncompliance increases. My results highlight the important effects of noncompliance in realized MPA benefits and can explain why observed and expected effects might differ. Moreover, my results support a call for increased attention to rates of noncompliance and their ecological effects and greater collaboration among natural scientists, social scientists, managers, and stakeholders in understanding and altering illegal behavior.
Crow damage to almonds increasing; no foolproof solution in sight

Of all birds causing damage to almonds in Sutter and Yuba counties, crows cause the most, according to a survey of growers. Growers also reported they are willing to spend an average of $24 an acre to reduce damage by 50% within their orchards — far less than the potential value of nuts lost.
Fusion probabiliste appliquée à la détection et classification d'opinions

Nous presentons des modeles d'apprentissage probabilistes appliques a la tâche de classification telle que definie dans le cadre du defi DEFT'07 : la classification d'un texte suivant l'opinion qu'il exprime. Pour classer les textes, nous avons utilise plusieurs classifieurs et une fusion. Une comparaison entre les resultats en validation et en tests montrent une coincidence remarquable et mettent en evidence la robustesse et performances de l'algorithme de fusion. Les resultats que nous obtenons, en termes de precision, rappel et F-score sur les sous corpus de test nous ont permis de remporter le defi.
Modelling deadlock in queueing systems

Motivated by the needs of Aneurin Bevan University Health Board, this thesis ex- plores three themes: the phenomenon of deadlock in queueing systems, the develop- ment of discrete event simulation software, and applying modelling to the evaluation of the effects of a new healthcare intervention, Stay Well Plans, for older people in Gwent. When customers in a restricted queueing network become mutually blocked, and all possible movement ceases, that system becomes deadlocked. This thesis novelly investigates deadlock. A graph theoretical method of detecting deadlock in discrete event simulations is given, analytical models of deadlocking systems are built, and these are used to investigate the effect of system parameters on the expected time until reaching deadlock. Furthermore a deadlock resolution procedure is proposed. An open source discrete event simulation software, Ciw, is developed. This software is designed and developed using best practice principles. Furthermore it permits the use of best practice, such as reproducibility, in simulation modelling. Ciw is used for the modelling of a healthcare system, in order to evaluate the effect of Stay Well Plans. During the development of these models, a number of techniques are employed to overcome the difficulties of lack of data. Insightful results from these models are obtained, indicating a shift in demand from residential care services to community care services.
Customer Segmentation Using Neural Networks

This paper shows how neural networks can be useful in process of predicting customer segments. Customer segmentation provides to companies insight in differentiating profitable from non-profitable customers so that companies can act properly on each group. The aim of this paper is to determine how neural networks are able to predict belonging of each customer to specific segment based on data from data warehouse. This paper presents research on analysing data set of customers of the company which take care of production and distribution of the range of products from a set of nuts, grains and dried fruit. As a result will be three groups of customers: group containing the most profitable customers, group of customers with a profit close to zero, and group of the least profitable customers. Depending on where specific customer belongs, company will use different marketing activities in order to retain them, move them to more profitable segment or ultimately reject them. Customer activity in first quarter will be used to predict segment in which customer belongs at the end of the year. This will show predictive ability of neural networks and how reliable they are in process of customer segmentation using incomplete data. Key-Words: customer, profitability, neural networks