Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting and influencing driver emotions using psycho- physiological sensors and ambient light

Driving is a sensitive task that is strongly affected by the driver’s emotions. Negative emotions, such as anger, can evidently lead to more driving errors. In this work, we introduce a concept of detecting and influencing driver emotions using psycho-physiological sensing for emotion classification and ambient light for feedback. We detect arousal and valence of emotional responses from wearable bio-electric sensors, namely brain-computer interfaces and heart rate sensors. We evaluated our concept in a static driving simulator with a fully equipped car with 12 participants. Before the rides, we elicit negative emotions and evaluate driving performance and physiological data while driving under stressful conditions. We use three ambient lighting conditions (no light, blue, orange). Using a subject-dependent random forests classifier with 40 features collected from physiological data we achieve an average accuracy of 78.9% for classifying valence and 68.7% for arousal. Driving performance was enhanced in conditions where ambient lighting was introduced. Both blue and orange light helped drivers to improve lane keeping. We discuss insights from our study and provide design recommendations for designing emotion sensing and feedback systems in the car.
DOI: 10.22078/jpst.2018.3348.1535

http://jpst.ripi. ir Journal of Petroleum Science and Technology 2018, 8(4), 3-15 © 2018 Research Institute of Petroleum Industry (RIPI) ABSTRACT Aerogels are a special class of porous material, which have excellent physicochemical properties such as low density, high porosity, high surface area and adjustable surface chemistry. Aerogels were first prepared several decades ago, but never truly commercialized due to their high cost. Technological advancements in the production and quality of different types of aerogel cut costs down and market factors increase demand. Therefore, the viability of this porous material on the several applications has been widely studied. Among the various high-performance applications, aerogel has attracted significant attention as a chemical sorbent and catalyst for CO2 capture and conversion as well as methane conversion to value-added products. Although aerogel performance for zero CO2 emission to the environment is promising, some drawbacks of aerogels such as complicated drying process, mechanically weak structure and processing cost should also be considered in material development. Ultimately this review article will cover an overview regarding the aerogels synthesis, processing and their recent applications in CO2 capture and conversion.
Pricing strategies in the era of digitalisation and the perceived shift in consumer behaviour of youth in Poland

The advent of industry 4.0 along with the spread of Information and Communication Technology has brought about many pivotal changes in the ECommerce segment. Technology driven pricing strategies like dynamic pricing has become very common across different industries all over the world. Today, online pricing has evolved into a very efficient and sophisticated pricing strategy where product prices are personalised and tailored to the last conceivable individual buying unit possessing similar characteristics. This study examines various traits exhibited by online consumers in a dynamic pricing environment and figure out the reasons for the display of strategic purchase behaviour by the consumers in response to the dynamic pricing strategy adopted by the sellers. The study was conducted among the Polish millennials as Poland has the median online market size and growth rate among the Central and Eastern European Received: January, 2019 1st Revision: April, 2019 Accepted: September, 2019 DOI: 10.14254/20718330.2019/12-3/7 Journal of International Studies S ci en ti fi c P a pe rs © Foundation of International Studies, 2019 © CSR, 2019 Vijay Victor, Jose Joy Thoppan, Maria Fekete-Farkas, Janusz Grabara Pricing strategies in the era of digitalisation and the perceived shift in consumer.
"European Philanthropic Behavior Patterns: Charitable Giving, Non-Profit and Welfare Regimes in the European Union "

The connection that links patterns of phil­anthropic behavior with the organization of state delivery of welfare and the development of civil society has been theorized recently. Off this line of thought we refer to the Esping-Andersen’s well-known welfare regimes: Liberal, Conserva­tive and Social-Democratic (1990), respectively, more recently, to the philanthropic regimes elab­orated by Pennerstorfer and Neumeyr (2017): Liberal, Social-Democratic, Corporatist, Mediter­ranean and Eastern European regimes. In order to test the taxonomy of the non-profit regimes de­veloped by the aforementioned authors, we em­ployed the more recent data of the 2010 74.1 Eu­robarometer. In our approach we classified the 29 countries and territories using the hierarchical clustering technique and identified three main classes of countries, named by us, regimes of philanthropic practices: Regime 1 (Great Givers), Regime 2 (Direct Givers) and Regime 3 (NGO Donors and Volunteers). A comparison of our classification with the one proposed by Penner­storfer and Neumeyr shows similarities as well as the limits of the original taxonomy which can be partly overcome due to the larger and more vari­ate pool of cases. Interpretations of the results in comparison with Esping’s welfare regimes or to the alternative models of Crowding-out and Crowding-in of NGOs by state are also provided.
DMSO_A_232958 117..138

Ehab Mudher Mikhael 1,2 Mohamed Azmi Hassali Saad Abdulrahman Hussain 3 1Department of Clinical Pharmacy, College of Pharmacy, University of Baghdad, Baghdad, Iraq; 2Department of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia; 3Department of Pharmacology and Toxicology, Faculty of Pharmacy, Al-Rafidain University College, Baghdad, Iraq Aim: This review study aimed to determine the effectiveness and factors affecting the success of DSME programs in T2DM patients living in ME countries. Methods: An extensive manual literature search was conducted using PubMed and Google Scholar for clinical trials assessing the effect of diabetes self-management education (DSME) for type 2 diabetes mellitus patients in Middle East countries. Information from the included studies was summarized in relation to study population, sample size, duration of follow-up, characteristics of DSME program, and follow-up time, besides in addition to parameters used in assessment, results, and conclusions. The risk of bias in the included studies was assessed using the Cochrane risk of bias tool. The effect of DSME on clinical and patient-reported outcomes was measured by calculation of the percentage of DSME studies that produce a significant improvement in these outcomes for patients in intervention group as compared to those in control group. Additionally, the effect of DSME on each clinical outcome was assessed by calculating the mean for the absolute effect of DSME on that outcome. Results: Twelve studies were included in this review. Heterogeneity was found among included studies in terms of DSME program characteristics, the enrolled patients, duration of follow-up, assessment methods, and obtained outcomes. All clinical glycemic outcomes (glycosylated hemoglobin, fasting, and non-fasting blood glucose), lipid profile (total cholesterol and triglycerides), and body mass index were significantly improved for patients in intervention group as compared to those in control group in at least 60% of the included studies. All patients' reported outcomes (medication adherence, self-management behavior, knowledge, self-efficacy, health belief and quality of life) were significantly improved by the DSME program. Conclusion: DSME programs are highly effective in improving glycemic control, lipid profile and BMI, and modestly effective in improving BP. Thus, they can reduce the risks of developing diabetes complications. Patient diabetes knowledge, DSM behaviors, adherence to medications, self-efficacy, and quality of life can also be significantly improved by DSME.
Dynamics of Changes in Isometric Strength and Muscle Imbalance in the Treatment of Women with Low back Pain

The aim of the study was to evaluate the dynamics of isometric changes in strength and muscular lumbar-pelvic imbalances in the treatment of women with low back pain. Forty-one women, nineteen in the study group (A) and twenty-two in the control group (B), participated in the study. Magnetic resonance imaging (MRI) was performed to assess the degree of degenerative changes in the lumbar spine. The diagnosis of isometric muscle strength and their imbalances was performed with the Tergumed 700 device. After six weeks of therapy in the study group (A), there was a significant improvement in the strength of all the examined muscle groups. However, in the control group (B), significant improvement occurred only in the strength of the lumbar flexor muscles and the flexor muscles on the left side. Furthermore, there was a significant intensification of the imbalance of left flexor muscle strength compared to right flexor strength in group B. Significant differences in favour of the study group (A) concerned the strength of the rotator muscles to the left, the strength of the extensor muscles of the lumbar spine, the strength of the flexors of the lumbar spine to the right, and the balance of the strength of the lumbar spine flexors to the left compared to the strength of the flexor muscles to the right. Therapy with the Tergumed 700 system leads to an increase in the muscle strength of the lumbar and pelvic complex, compensating for its imbalance, bringing beneficial effects in the treatment of low back pain.
Economic opportunities of AMI implementation : a review

Advanced Metering Infrastructure (AMI) is rapidly becoming a key element for the modernization and disruption of power grids, generating benefits and opportunities to all actors involved in its implementation. In order to guarantee a correct deployment of AMI, a wide knowledge of its advantages and challenges is needed, that takes into account previous experiences and latest advances that have been made in the field. In this paper, a review of literature is used as a mean to collect the relevant information concerning AMI, so as to conclude which are the opportunities that AMI provides to all parties involved. This is achieved by searching in the most important data bases and specialized sources such as IEEE and IEA. It was found that this infrastructure, does indeed help improve efficiency and leads to positive economic effects impacting variables like costs and prices.
RISOTTO-A Novel Hybrid Approach for Enhancing Classical Apriori Algorithm

Discovering frequent itemsets is the computationally intensive step in the task of mining association rules and Apriori is one of the most significant algorithms for finding the frequent itemsets. The main challenge in the classical Apriori is that, the mining often needs to generate a huge number of candidate itemsets and requires more number of database scans which increases time and decreases efficiency. It also increase the I/O cost and requires more memory. To eradicate these issues a lot of improvements to Apriori have been proposed in the literature. In this series, this research work also introduces a refinement to the Apriori which uses a data structure called prefixed-itemset and the horizontal data format approach. Based on the comparative analysis with the classical Apriori, the proposed approach truncates the number of database scans and reduces the time required for finding candidate generation.
Bacillus thuringiensis - Mediated Priming Induces Jasmonate/Ethylene and Salicylic Acid-Dependent Defense Pathways Genes in Tomato Plants

Bacillus thuringiensis Berliner as a biological control agent can play a crucial role in the integrated management of a wide range of plant pests and diseases. B. thuringiensis is expected to elicit plant defensive response through plant recognition of microbe-associated molecular patterns (MAMPs), however, there is little information on the molecular base of induced systemic resistance priming of tomatoes. Using q-RT-PCR technique, the transcription rate of the genes responsive to salicylic acid, SA (Chi9, Chi3, PR1), jasmonic acid, JA (Pin2), and of the signaling regulatory genes of jasmonate/ ethylene, JA/ ET hormones (WRKY33, ERF1, MYC2) were studied at the time of 6, 12, 24, 48, 72, and 96 hours after inoculation of tomato plants with B. thuringiensis strain IBRC-M 11096 as the promoting plant growth factor. The bacterial strain could prime tomato cultivar of Early Urbana through induction of all three hormonal signaling pathways (SA, JA, and ET) involved in the resistance to a broad range of necrotrophic as well as biotrophic pathogens. However, further transcription of WRKY33, ERF1, MYC2, and Pin2 genes in the inoculated plants, indicated that the observed priming effect was mainly based on JA/ ET signaling pathway. These promising results indicate high potential of superior isolates of B. thuringiensis in the field management of the crops.
Margin-restricted Mean-Variance Portfolio Deleveraging Under Market Impact*

We consider the problem of deleveraging a large long-short portfolio of risky assets in a relatively short trading period under liquidity impact on prices. Given an adverse outlook on uncertainty in the one-period investment horizon, an optimally-deleveraged portfolio must be determined according to specified risk aversion and satisfying stricter leverage and margin policy constraints. Liquidity costs are considered due to both volume and trading intensity, leading to temporary and permanent impact on asset prices. The resulting portfolio model generalizes the usual mean-variance model; however, it has no closed-form solution nor is it possible to solve numerically with standard methods. One main contribution of the paper is to show how an efficient solution methodology is developed to obtain an optimally-deleveraged portfolio. The second main contribution is to employ the methodology to perform an empirical evaluation of the deleveraging model to develop insights on setting leverage and margin policy in deteriorating markets in which large and leveraged portfolios become highly-vulnerable, such as those witnessed during recent financial crises. We demonstrate our approach using a portfolio of ETF assets, and analyze the sensitivity of the optimal deleveraging strategy to leverage and margin limits under scenarios of market illiquidity. The study of MV efficient frontiers under liquidity costs allows us to develop managerial insight for setting deleveraging policy parameters. Empirical quantification of the relative gain from our approach, in comparison to ignoring market liquidity, under out-of-sample analysis is considered in our continuing research.
Hybrid Modular Multilevel Converter With Arm-Interchange Concept for Zero-/Low- Frequency Operation of AC Drives

DC-AC Modular Multilevel Converter (MMC) is a promising candidate for high-power AC drive applications. The main challenge of operating the MMC in AC drive applications is its performance during low-frequency conditions, as high voltage ripple is associated with the low-frequency operation, which results in voltage stresses on the involved semiconductor devices. To keep capacitor voltages balanced and bounded in low-frequency operation, different techniques have been proposed in literature, which can be classified into software and hardware approaches. In this paper, a hybrid MMC with arm-interchange concept is proposed to ensure operating successfully during low-frequency conditions. The hybrid MMC consists of two-stages. The first stage is a front-end Integrated Gate Commutated Thyristor (IGCT)-based H-bridge converter, while the second stage is a Full-Bridge Sub-Module (FBSM)-based MMC, where FBSMs is able to generate positive, zero, and negative voltage states. Detailed illustration and design of the hybrid MMC are introduced. Simulation results of the proposed converter are presented to show the effectiveness during low-frequency and normal frequency conditions in AC drive applications. Finally, a scaled-down prototype is employed for experimental validation.
Cart Path Recognition in a Golf Course Using Deep Fully Convolutional Networks

Autonomous driving vehicles are a growing reality. Autonomous driving related industries are also growing. We believe that an unmanned golf cart is an appropriate application area for such autonomous driving. So, we have commenced the project of the golf cart the purpose of which is to provide an assistant to golf players. The golf cart includes almost all the functions the Autonomous driving should have. Image processing and recognition capability are of course one of the key functions that a golf cart needs. To develop such a kind of a golf cart would take much time. Thus, we have started the research and development of a demonstration prototype, which can perform some of the main tasks of the golf cart. In this paper, we will deal with the recognition of a cart path by themselves, trained end-to-end, pixels-to-pixels. We have collected and annotated the cart path of challenging scenes captured in a golf course. And we developed a deep learning network for a cart path. As a result, we achieve 93% accuracy and 50ms inference time.
Use of C-reactive protein in the evaluation of Widal test and Typhoid stripe test in the diagnosis of typhoid fever

Introduction The typhoid and paratyphoid fever stay important public health issues globally and major causes of morbidity in the developing world (1). Typhoid and paratyphoid fever are acute and sometimes serious febrile diseases caused by systemic infection with the Salmonella enterica serotype typhi and paratyphi, respectively (2). Clinical symptoms are similar but paratyphoid fever tends to have a more benign course of illness. Typhoid fever has a case-fatality rate of 10–30% without effective treatment. This rate is reduced to 1–4% in those receiving proper therapy (3).
Psycho-Social Disorders in Childhood, and Their Outcome in Adult Life

\ ^ ̂ a normal part of growing-up for children to encounter and usually overcome a variety of psycho-social stresses. However, for a minority, adult |ife is blighted by the persisting ill-effects of childhood disorders. If any lrnPression is to be made on this toll of adult handicaps that are the sequelae ? f difficulties in early life, it is essential to differentiate between those disorders that are usually only temporary and those that are likely to have a persistent effect. In considering this differentiation, it is convenient to subdivide dis0rders into those shown by the child himself, and those he meets in his environment.
Understanding Privacy Awareness in Android App Descriptions Using Deep Learning

Permissions are a key factor in Android to protect users' privacy. As it is often not obvious why applications require certain permissions, developer-provided descriptions in Google Play and third-party markets should explain to users how sensitive data is processed. Reliably recognizing whether app descriptions cover permission usage is challenging due to the lack of enforced quality standards and a variety of ways developers can express privacy-related facts. We introduce a machine learning-based approach to identify critical discrepancies between developer-described app behavior and permission usage. By combining state-of-the-art techniques in natural language processing (NLP) and deep learning, we design a convolutional neural network (CNN) for text classification that captures the relevance of words and phrases in app descriptions in relation to the usage of dangerous permissions. Our system predicts the likelihood that an app requires certain permissions and can warn about descriptions in which the requested access to sensitive user data and system features is textually not represented. We evaluate our solution on 77,000 real-world app descriptions and find that we can identify individual groups of dangerous permissions with a precision between 71% and 93%. To highlight the impact of individual words and phrases, we employ a model explanation algorithm and demonstrate that our technique can successfully bridge the semantic gap between described app functionality and its access to security- and privacy-sensitive resources.
THE EFFECTS OF SUPERCRITICAL CARBON DIOXIDE ON THE DEGRADATION AND ANTIMICROBIAL PROPERTIES OF PLA BIOCOMPOSITE

Biopolymer products that is biodegradable presently attracting an attention from researchers and industry. The biodegradable packaging based on polylactic acid (PLA), durian skin fibre (DSF), epoxidized palm oil (EPO) and incorporated with cinnamon essential oil (CEO) as antimicrobial agent have been developed and showed to be a promising field of research. This paper reported the effects of supercritical carbon dioxide on the degradation and antimicrobial properties of PLA biocomposite films produced via solvent casting. The biocomposites underwent supercritical carbon dioxide (SCCO2) treatment at two different conditions under 40 °C temperature and at 100 bar and 200 bar pressure. Water absorption test showed that the untreated PLA biocomposite absorbed most water as compared to treated PLA biocomposite with SCCO2 at 5.1%. This is due to the hydrophilic nature of the fibre that absorbed water molecules. Soil burial test showed that the treated PLA biocomposite possessed the highest value of weight losses after 80 days with 97.8%. Biocomposite with the presence of CEO demonstrated antimicrobial activity against both gram-positive and gram-negative bacteria. This showed that SCCO2 significantly improved the properties of PLA biocomposite films. The supercritical fluid treatment of PLA biocomposite could be an alternative for active packaging industries to ensure that the packaging product meets the requirement by consumers as well as being an eco-friendly product. ABSTRAK: Produk biopolimer yang biodegradasi pada masa ini menarik perhatian dari penyelidik dan industri. Pembungkusan biodegradasi berasaskan polilaktik asid (PLA), serat kulit durian (DSF), minyak kelapa sawit terepoksi (EPO) dan ditambah dengan
Executive Compensation, Individual-Level Tax Rates, and Insider Trading Aggressiveness*

We consider insider trading aggressiveness as a means for executives to shift the impact of changes in individual-level tax rates to shareholders. Consistent with predictions from a simple model, we find a positive association between abnormal insider trading profits and changes in tax rates around the enactments of two recent tax acts in the U.S. The changes in insider trading profits offset between 12% and 17% of the effect of changes in tax rates for an average executive. Using a difference-in-differences analysis, we show that our findings apply to executives who are subject to taxation in the U.S. and not to executives in foreign firms, who are not subject to U.S. tax laws. We conclude that executives pass a portion of the tax effects onto shareholders through insider trading aggressiveness. JEL Codes: H24; H31; J33; M12.