bluuebunny/crossref_metadata_2025_split huggingface dataset:
Public Awareness on How Vape is used as a Tool for Hacking
Authors: Rahman Nor Azlina Abd , Roszlan Mohammad Haziq , Khaled Serry , Alasaad Ameer A.N. , Salem Mohammed 
Published: December 2020

Abstract
Nowadays e-cigarettes and vaping devices are more popular than tobacco cigarettes among the youngsters. Most of the youngsters falsely believe that vaping is safer than tobacco and it is trendier. This research paper is reviewing on how vape is used as a tool for hacking. Based on research, vaping is not only give bad impact to the health but it is also can be used as a medium in transferring malware and to breach the computer system security. A few tweaks and modification of the vape device could make the attacker to transfer the malware to the computer system. Literature review being done on several electronic smoking devices such as e-cigarettes, vaping and JUUL. Statistic on vape users and vape hackers are being done. On top of that questionnaire being prepared and distributed to the public to analyse on public awareness on vape hacking. The method of vape hacking and protection from vaping hackers are also being discuss in this paper. Since vape is using USB to charge the device hence security measures focus more on USB security on how to protect the computer system from the vaping hackers. Several methods can be applied such as disable USB drives and mass storage devices using registry, keep security patches updated, create strong password, control the USB port through device manager or by using a free tool USB drive Disabler/Enabler. There are many others security protection can be implemented but this paper only focuses on several methods as discussed.
Analysis of Surface Quality Measurement With Classification Approach
Authors: Flaih Laith R. , Baha al_Deen Shaimaa Awadh , Ayoobkhan Mohamed Uvaze Ahamed 
Published: December 2020

Abstract
This investigation provides a methodology for surface quality measurement. In machine based vision, an optical inspection is validated to identify defects over materials. As well, normalization approach is used to process homogeneous thickness. With compensation procedures flaws are identified and analyzed. However, after defect identification, decision rules are defected for appropriate classification which offers optimal performance and diminishes tuning complexity. The anticipated approach is effectual and fulfils inspection requirements. Experimental outcomes may validate performance of anticipated approach to recognition rate and inspection speed.
MathFun: Examining the Effectiveness of Calculic Model in Designing App for Dyscalculia Children
Authors: Rohizan Rizawati , Soon Lim Hean , Mubin Siti Azreena 
Published: December 2020

Abstract
Learning disability can vary from dyslexia (reading), dyscalculia (math) and dysgraphia (writing) where it focuses on certain learning disability that is being faced by the children. Dyscalculia is one of the common learning disabilities where in this category children are lack in ability of studying the math. However, with the enhancement of technologies, variety of application can be created in aiding the children learning process such as mobile learning by using mobile app. This paper focuses on examining the design model (Calculic Model) in designing a mobile app for Dyscalculia children. MathFun is a mobile app created by the mobile app developer while following the Calculic Model approach for Malaysia Dyscalculia children. The outcomes of this paper view on the effectiveness of the model towards building a mobile application for these children. Usability was performed in order to assess the usability and verifying the effectiveness of MathFun. This study involved 3 teachers and three children. Descriptive analysis was performed from the collected data. Based on the outcome, it’s shows that by using the suggested model there is an increased in the acceptance and usability of the application by the children.
E-Commerce System for Sale Prediction Using Machine Learning Technique
Authors: Singh Karandeep , Booma P M , Eaganathan Umapathy 
Published: December 2020

Abstract
E-commerce is a platform where people are able to buy and sell goods. The main purpose of e-commerce is to provide convenience to the customers where they do not have to go to a physical store to make a purchase. As the will be able to make the purchase online and the item will be in their door step in the following days. In 2019, a total of $603 billion worth of sales were done via e-commerce in the United States compared to 3.17 billion in retail sales in the United States. The purpose of this study was to build machine learning algorithms which are able to forecast the sales of the e-commerce platform. A research was being done to understand the literature reviews based on similar systems and similar studies that relates to the researcher project. The purpose of doing this literature review is to understand which machine learning model was being used by other studies so the researcher will be able to select some of the best machine learning models for this study. Once the researcher has selected the models, he will them build the models and test their accuracy, error and performance. At the end, the researcher will compare all of the model’s accuracy and errors to get the best model which have low error and high accuracy for forecasting sales. The model which have been fulfil the criteria, will be integrated into the system which is being built by the researcher. The system will give a view of the current and forecasted sales.
An appraisal on the methods and techniques of recommender models for personalised marketing campaigns
Authors: Wong Ann-Nee , Raheem Mafas 
Published: December 2020

Abstract
Recommender models for personalized marketing empower businesses to provide personalized recommendations of goods or services to customers to fulfil their requirements, thus ultimately improves the customer buying experience. Various recommender models powered by robust machine learning algorithms were reviewed on the methods and techniques to appraise its performance concerning the personalized marketing campaigns. Recommender models can be broadly categorized into four types such as content-based, collaborative-based, knowledge-based and hybrid-based. The content-based recommendation is suitable when the system, user or product is new where classification and regression algorithms are mostly implemented. The collaborative-based recommendation is suitable when a more accurate prediction is required where Neighbour-based models, Bayesian methods, rule-based models, decision trees, and latent matrix factorization models may be implemented in this scenario. Knowledge-based recommenders are well suited for recommendations that address explicitly defined user requirements. Different types of recommenders use different sources of data and inherently have different strengths and weaknesses. Selecting the suitable recommender model with the consideration of the scenario and domain of application is very crucial. Therefore, an in-depth research is required and done on the emphasis of the application of recommender models in the personalized marketing especially on the hybrid models with a more efficient deployment for mass applications in this contemporary data-driven business world.
Optimising e-commerce customer satisfaction with machine learning
Authors: Wong Ann-Nee , Marikannan Booma Poolan 
Published: December 2020

Abstract
Customer insighs is the key to the success of e-commerce. Therefore, factors affecting customer satisfaction leading to product purchase and re-purchase should be studied extensively. This study intends to identify the key drivers that influence the satisfaction and the model which can predict the likelihood of customer satisfaction. The outcome would provide insights to prioritise factors that are significant, as well as to provide advice to a wide range of sellers. Four classification machine learning algorithms decision tree, random forest, artificial neural network and support vector machine are evaluated to classify customer satisfaction based on a 3-year historical data from an e-commerce retailer. There were a few challenges with the dataset, such as imbalanced, skewed and missing. Data pre-processing was conducted, and different techniques were evaluated. Of the algorithms evaluated, the best result is achieved by Random Forest with the highest accuracy and reasonable processing time. Meeting the estimated delivery date and the number of days taken to deliver an order is found to be the top two important factors affecting customer satisfaction.
Strategies for reducing the costs of clean-energy technologies in buildings in Nigeria
Authors: Ekung Samuel , Ohama Victor , Ajiero Reginald 
Published: December 2020

AbstractThe costs of clean-energy technologies are currently very high and their adoption in buildings is voluntary. This study evaluated strategies for improving the cost performance of photovoltaic (PV) electricity applied in buildings in Nigeria using a questionnaire survey involving 415 targets. The efficacy of each strategy and consensus in respondents’ perceptions were determined using Fuzzy Set Theory and Kruskal–Wallis tests. The top four strategies for achieving PV-cost reduction are mandating green buildings, standardization of building designs and PV components, facilitating import licensing and massive public education. Developing these strategies to improve the PV value chain will increase the supply capacity of clean energy in emerging markets.
Longitudinal Trajectory of Fatigue in Patients With Inflammatory Bowel Disease: A Prospective Study
Authors: Borren Nienke Z , Long Millie D , Sandler Robert S , Ananthakrishnan Ashwin N 
Published: December 2020

Abstract

Background
Fatigue is a disabling symptom in patients with inflammatory bowel disease (IBD). Its prevalence, mechanism, and impact remain poorly understood. We determined changes in fatigue status over time and identified predictors of incident or resolving fatigue.

Methods
This was a prospective study nested within the IBD Partners cohort. Participants prospectively completed the Multidimensional Fatigue Inventory and the Functional Assessment of Chronic Illness Therapy-Fatigue at baseline, 6 months, and 12 months. A Functional Assessment of Chronic Illness Therapy-Fatigue score ≤43 defined significant fatigue. Multivariable regression models using baseline covariates were used to identify risk factors for incident fatigue at 6 months and to predict the resolution of fatigue.

Results
A total of 2429 patients (1605 with Crohn disease, 824 with ulcerative colitis) completed a baseline assessment, and 1057 completed a second assessment at 6 months. Persistent fatigue (at baseline and at 6 months) was the most common pattern, affecting two-thirds (65.8%) of patients. One-sixth (15.7%) of patients had fatigue at 1 timepoint, whereas fewer than one-fifth (18.5%) of patients never reported fatigue. Among patients not fatigued at baseline, 26% developed fatigue at 6 months. The strongest predictor of incident fatigue was sleep disturbance at baseline (odds ratio, 2.91; 95% confidence interval, 1.48–5.72). In contrast, only 12.3% of those with fatigue at baseline had symptom resolution by month 6. Resolution was more likely in patients with a diagnosis of ulcerative colitis, quiescent disease, and an absence of significant psychological comorbidity.

Conclusions
Fatigue is common in patients with IBD. However, only a few fatigued patients experience symptom resolution at 6 or 12 months, suggesting the need for novel interventions to ameliorate its impact.
TOI-257b (HD 19916b): a warm sub-saturn orbiting an evolved F-type star
Authors: Addison Brett C , Wright Duncan J , Nicholson Belinda A , Cale Bryson , Mocnik Teo , Huber Daniel , Plavchan Peter , Wittenmyer Robert A , Vanderburg Andrew , Chaplin William J , Chontos Ashley , Clark Jake T , Eastman Jason D , Ziegler Carl , Brahm Rafael , Carter Bradley D , Clerte Mathieu , Espinoza Néstor , Horner Jonathan , Bentley John , Jordán Andrés , Kane Stephen R , Kielkopf John F , Laychock Emilie , Mengel Matthew W , Okumura Jack , Stassun Keivan G , Bedding Timothy R , Bowler Brendan P , Burnelis Andrius , Blanco-Cuaresma Sergi , Collins Michaela , Crossfield Ian , Davis Allen B , Evensberget Dag , Heitzmann Alexis , Howell Steve B , Law Nicholas , Mann Andrew W , Marsden Stephen C , Matson Rachel A , O’Connor James H , Shporer Avi , Stevens Catherine , Tinney C G , Tylor Christopher , Wang Songhu , Zhang Hui , Henning Thomas , Kossakowski Diana , Ricker George , Sarkis Paula , Schlecker Martin , Torres Pascal , Vanderspek Roland , Latham David W , Seager Sara , Winn Joshua N , Jenkins Jon M , Mireles Ismael , Rowden Pam , Pepper Joshua , Daylan Tansu , Schlieder Joshua E , Collins Karen A , Collins Kevin I , Tan Thiam-Guan , Ball Warrick H , Basu Sarbani , Buzasi Derek L , Campante Tiago L , Corsaro Enrico , González-Cuesta L , Davies Guy R , de Almeida Leandro , do Nascimento Jose-Dias , García Rafael A , Guo Zhao , Handberg Rasmus , Hekker Saskia , Hey Daniel R , Kallinger Thomas , Kawaler Steven D , Kayhan Cenk , S. Kuszlewicz James , Lund Mikkel N , Lyttle Alexander , Mathur Savita , Miglio Andrea , Mosser Benoit , Nielsen Martin B , Serenelli Aldo M , Aguirre Victor Silva , Themeßl Nathalie 
Published: December 2020

ABSTRACT
We report the discovery of a warm sub-Saturn, TOI-257b (HD 19916b), based on data from NASA’s Transiting Exoplanet Survey Satellite (TESS). The transit signal was detected by TESS and confirmed to be of planetary origin based on radial velocity observations. An analysis of the TESS photometry, the Minerva-Australis, FEROS, and HARPS radial velocities, and the asteroseismic data of the stellar oscillations reveals that TOI-257b has a mass of MP = 0.138 ± 0.023 $\rm {M_J}$ (43.9 ± 7.3 $\, M_{\rm \oplus}$), a radius of RP = 0.639 ± 0.013 $\rm {R_J}$ (7.16 ± 0.15 $\, \mathrm{ R}_{\rm \oplus}$), bulk density of $0.65^{+0.12}_{-0.11}$ (cgs), and period $18.38818^{+0.00085}_{-0.00084}$ $\rm {days}$. TOI-257b orbits a bright (V = 7.612 mag) somewhat evolved late F-type star with M* = 1.390 ± 0.046 $\rm {M_{sun}}$, R* = 1.888 ± 0.033 $\rm {R_{sun}}$, Teff = 6075 ± 90 $\rm {K}$, and vsin i = 11.3 ± 0.5 km s−1. Additionally, we find hints for a second non-transiting sub-Saturn mass planet on a ∼71 day orbit using the radial velocity data. This system joins the ranks of a small number of exoplanet host stars (∼100) that have been characterized with asteroseismology. Warm sub-Saturns are rare in the known sample of exoplanets, and thus the discovery of TOI-257b is important in the context of future work studying the formation and migration history of similar planetary systems.
Adjunctive Aspirin vs Placebo in Patients With Schizophrenia: Results of Two Randomized Controlled Trials
Authors: Weiser Mark , Zamora Daisy , Levi Linda , Nastas Igor , Gonen Ilan , Radu Paull , Matei Valentin , Nacu Anatol , Boronin Larisa , Davidson Michael , Davis John M 
Published: December 2020

Abstract
Two previous randomized controlled trials (RCTs) suggested that adjunctive aspirin is efficacious in treating schizophrenia. We conducted two 16-week double-blind randomized placebo-controlled RCTs of adjunctive 1000 mg aspirin vs placebo in schizophrenia. Study 1 included 200 patients, with Positive and Negative Syndrome Scale (PANSS) total score as the primary outcome. Study 2 included 160 patients with C-reactive protein (CRP) &gt;1 mg/L at baseline; the primary outcome was PANSS-positive score. Dropout rates for aspirin/placebo were 12% in study 1 and 20% in study 2. Differences in outcome between aspirin and placebo were calculated with linear regression, adjusting for the baseline value of the outcome. No statistically significant between-group differences were found in primary or secondary outcomes in either study. Study 1: mean difference in PANSS at 16 weeks was −3.9 (95% CI: −8.4 to 0.5, P = .10, effect size (ES) = −0.25) and at 8 weeks was −3.5 (95% CI: −7.5 to 0.5, P = .11, ES = −0.22). Study 2: mean difference in PANSS at 16 weeks was 0.3 (95% CI: −4.1 to 4.7, P = .90, ES = 0.02) and in positive PANSS was 0.5 (95% CI: −1.0 to 2.1, P = .50, ES = 0.11). A meta-analysis of these data with the existing studies, excluding one with large baseline differences in total PANSS, found that the overall estimate of the effect of adjunctive aspirin on the PANSS total score comparing group means at the end of the study was −2.9 (95% CI: −6.6 to 0.7; P = .21), favoring aspirin. Our studies and meta-analysis failed to find a statistically significant improvement in the symptoms of schizophrenia from adjunctive aspirin therapy in comparison to placebo in schizophrenia. Trial registration: study 1: Clinicaltrials.gov: NCT01320982; study 2 (high CRP): EudraCT Number: 2014-000757-36.
Impact of COVID-19 Pandemic on the Outcomes in Patients With Critical Limb Threatening Ischaemia and Diabetic Foot Infection
Authors: Zayed Hany , Musajee Mustafa , Thulasidasan Narayanan , Sayed Mohamed , Francia Federica , Green Meryl , Arissol Martin , Lakhani Alpa , Biasi Lukla , Patel Sanjay 
Published: December 2020

Objective:
Examine the impact of COVID-19 pandemic on the outcomes in patients with CLTI or DFI.

Background:
Patients with CLTI and/or DFI are at risk of amputations if not treated in a timely manner.

Methods:
We compared the outcomes in patients with CLTI or DFI during 2 periods; Period 1[P1] (15/03/2019-31/05/2019) and period 2[P2] (15/03/ 2020-31/05/2020- corresponding to COVID-19 pandemic).

Results:
One hundred thirty-nine patients were treated in P1 [mean age 70 years (±11), Male:Female = 102:37] whereas 95 patients were treated in P2 [mean age 67 (±12), Male:Female = 64:31]. The 2 cohorts were matched regarding Rutherford category (P = 0.25) and GLASS classification (P = 0.38). Notably, the time from onset of symptom to clinical presentation was significantly longer [31 (1-105) days vs 27 (0–78) days, (P = 0.017)], whereas the time from presentation to first intervention was significantly shorter [3 (0–61) days vs 5 (0–65) days, (P = 0.013)] in P2 compared to P1. There was a significantly higher white cell count (P = 0.014) and CRP (P = 0.004) on admission in P2. Having treatment for CLTI or DFI in P2 was an independent predictor of worse primary patency rate and freedom from major adverse limb events. At 90 days, amputation-free survival and limb salvage were noticeably worse in P2 compared to P1 (amputation-free survival was 80% and 87% whereas limb salvage was 64% and 72% in P2 and P1, respectively).

Conclusions:
Patients with CLTI and DFI experienced a significantly delayed presentation with features of sepsis on admission in P2. Treatment in P2 was a predictor of worse primary patency and freedom from major adverse limb events and therefore close and long follow-up is advisable.
Automated Analysis of Low-Field Brain MRI in Cerebral Malaria
Authors: Tu Danni , Goyal Manu S. , Dworkin Jordan D. , Kampondeni Samuel , Vidal Lorenna , Biondo-Savin Eric , Juvvadi Sandeep , Raghavan Prashant , Nicholas Jennifer , Chetcuti Karen , Clark Kelly , Robert-Fitzgerald Timothy , Satterthwaite Theodore D. , Yushkevich Paul , Davatzikos Christos , Erus Guray , Tustison Nicholas J. , Postels Douglas G. , Taylor Terrie E. , Small Dylan S. , Shinohara Russell T. 
Published: December 2020

AbstractA central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images (MRI). These methods, however, may not translate to low resolution images acquired on MRI scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria were imaged using low-field 0.35 Tesla MRI. We integrate multi-atlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to cerebral malaria.
A quantitative demonstration of NADP+/NADPH redox homeostasis in cyanobacterial cells
Authors: Tanaka Kenya , Shimakawa Ginga , Tabata Hiro , Kusama Shoko , Miyake Chikahiro , Nakanishi Shuji 
Published: December 2020

AbstractIn photosynthetic organisms, it is recognized that the intracellular NADP+/NADPH ratio is regulated within an appropriate range for the cooperative function of a wide variety of physiological processes. However, despite its importance, there is large variability in the values of the NADP+/NADPH ratio quantitatively estimated to date. In the present study, the light-response of the NADP+/NADPH ratio was investigated by applying a novel NADP(H) extraction method using phenol / chloroform / isoamyl alcohol (PCI) in the cyanobacteriumSynechocystissp. PCC 6803. The light-response of NADP(H) observed using PCI extraction was qualitatively consistent with the NADPH fluorescence time course measuredin vivo. Moreover, the results obtained by PCI extraction and the fluorescence-based methods were also consistent in a mutant lacking the ability to oxidize NAD(P)H in the respiratory chain, and exhibiting a unique NADPH light-response. These observations indicate that the PCI extraction method allowed quantitative determination of NADP(H) redox. Notably, the PCI extraction method showed that not all NADP(H) was oxidized or reduced by light-dark transition, indicating that some NADP(H) is not light-responsive. Specifically, 64% of total NADP(H) was observed as non-light-responsive in the wild-type cells. The variation of the intracellular NADP+/NADPH ratio is limited to a narrow range due to the presence of non-light-responsive NADP(H).