Miracle-dz/UniFAR-FTUs huggingface dataset:
Edinburgh Research Explorer Associations between reward and future-related orientations and general and specific mental health issues in adolescence

symptoms across multiple domains. These transdiagnostic factors are considered promising targets for prevention and intervention that can be leveraged to more efficiently miti - gate or improve, respectively, mental health problems across multiple different domains simultaneously. Delay discount - ing tendencies have previously been identified as correlated with diverse symptoms of mental health disorders (Amlung et al., 2019), making delay discounting processes a promising set of candidate transdiagnostic mental health factors. However, at the same time, delay discounting performance has been found to activate multiple neural system processes (McClure et al., 2004 ) and these may play different roles in relation to different mental health problems. The purpose of the present study is, therefore, to explore the extent to which (i) elements of delay discounting act as transdiagnostic predictors of mental health problems and (ii) which specific dimensions of mental health problems are uniquely predicted by different elements of delay discounting measures after accounting for the tendency for symptoms of mental health problems to co-occur.
Inhibitors of the Elastase LasB for the Treatment of Pseudomonas aeruginosa Lung Infections

Infections caused by the Gram-negative pathogen Pseudomonas aeruginosa are emerging worldwide as a major threat to human health. Conventional antibiotic monotherapy suffers from rapid resistance development, underlining urgent need for novel treatment concepts. Here, we report on a nontraditional approach to combat P. aeruginosa-derived infections by targeting its main virulence factor, the elastase LasB. We discovered a new chemical class of phosphonates with an outstanding in vitro ADMET and PK profile, auspicious activity both in vitro and in vivo. We established the mode of action through a cocrystal structure of our lead compound with LasB and in several in vitro and ex vivo models. The proof of concept of a combination of our pathoblocker with levofloxacin in a murine neutropenic lung infection model and the reduction of LasB protein levels in blood as a proof of target engagement demonstrate the great potential for use as an adjunctive treatment of lung infections in humans.
Synthesis of Solketal from glycerol catalyzed by spherical γ -Al 2 O 3 supported FeCl 3

The porous spherical alumina ( γ -Al 2 O 3 ) as catalyst support was prepared by sol-gel method, and FeCl 3 / γ - Al 2 O 3 materials were prepared by impregnation method. The FeCl 3 / γ -Al 2 O 3 materials were used as catalyst in the Solketal production process by acetalization of acetone and glycerol, showing considerable catalytic performance. When the molar ratio of glycerol and acetone was 1:10, 0.2 mol% FeCl 3 / γ -Al 2 O 3 was used as the catalyst, and the reaction was carried out at 25°C for 30 min, the conversion of glycerol was 99.89%, the selectivity of Solketal was 98.36%, and the yield of Solketal reached 98.25%. The mechanism is due to the spherical γ -Al 2 O 3 support has high thermal stability, large speci�c surface area, and rich pore structure, encouraging abundant active sites and high catalytic activity of FeCl 3 for the acetalization reaction. Thereafter, the experimental results prove that the prepared FeCl 3 / γ -Al 2 O 3 catalyst can be easily recovered and reused many times, indicating its great value for large-scale research and application in the future.
Analysis of Power-law Queues Using Maximum Entropy Framework: An Application to Performance Evaluation of Network Systems

Extensive research on traﬃc of modern communication networks including Internet and wireless establish the presence of power-law behavior. However, developing traﬃc models capturing power-law characteristics is a diﬃcult task due to analytical intractability. Adopting maximum entropy approach, a theoretical model Pow/Pow/ 1 / ∞ has been proposed recently. The paper extends the framework to build two new theoretical models Pow/Pow/ 1 /K and Pow/Pow/K/K where both inter-arrival and service times follow power law distribution. Closed form expressions of queue length distribution and various performance measures are derived in terms of traﬃc intensity ρ . An explicit expression for state probability distribution of Pow/Pow/K/K is also derived and compared with well-known Erlang loss formula. Both queue length distribution and blocking probability are found to depict power-law implying that increasing buﬀers have little impact on improving the performance of systems in these cases. Numerical computations reveal that the mean queue length explodes as ρ tends to 0.5 resulting in longer delay. The variance of number of packets also depicts sharp rise as ρ approaches to 0.3. These results are in close agreement with the empirical studies carried out on real traﬃc traces in the past. The proposed models are useful in designing networks, dimensioning resources and developing congestion control algorithms.
Analysis of Domestic Violence against Women in Pakistan: Attitudinal Acceptance of Violence and its Associated Risk Factors

Purpose: The purpose of this study is to determine the impact of attitudinal acceptance of violence (AAV) over observed violence (OV), including the impact of five factors on OV. The study aims to identify the impact of acceptance of violence and explore its consequences, if any. The independent variables (IV) in the study are self-esteem (SE), women empowerment (WE), locus of control (LoC), financial income (FI), number of people in the family (FS), and financial stress (FinST). The dependent variable (DV) is observed violence (OV). We are seeing the moderating effect of AAV over OV as well as the impact of IVs over the moderator AAV. Research design: The participants of the study are married women in the age bracket of 18–60 years. The sample size of the study is 205. The data was obtained primarily through an adopted, pre-tested, structured questionnaire. Data were analyzed using descriptive, correlation, and multiple regression. FinST and SE resulted in statistically significant impacts on OV, whereas there was no moderating impact of AAV among all the factors. Findings: The acceptance of violence by an individual woman differs vastly depending on personal perception, childhood experiences, education level, occupational type, the status of a dependency, and level of self-esteem. Women with high self-esteem are less likely to face violence; similarly, higher financial stress in the family increases the chances of observing violence. Practical Implications: The significance of this study is highlighted by the realization of the need for modification in the attitudinal acceptance of violence through changing Pakistan's educational policy. This study will help raise the bar for women and create awareness that violence should not be accepted.
Effect of Foliar Application with Silicon and Seaweed Extract on the Vegetative Growth, Bunch Quality and Some Fungal Diseases of Red Globe and Superior Seedless Grapevines

: This study was carried out during two consecutive seasons (2016 & 2017) to study the effect of the foliar application of potassium silicate and/or seaweed extract at (0.1 or 0.2%) in single or combination on Superior Seedless and Red Globe grapevines. Both cultivars are planted at 1.75 × 3 meters apart and trellised by Gable supporting system. The vines were pruned during the last week of December for Superior Seedless and the second week of January for Red Globe grapevines. Results showed that foliar application of seaweed extract and potassium silicate in combination or alone have positive effects on the vegetative growth characteristics, bunch and berry quality for both cultivars compared with the control. The best results for enhancing the vegetative growth characteristics, bunch and berry quality for both cultivars were obtained by using of foliar application with seaweed extract and potassium silicate in combination at 0.2%. Also, the best results of anthocyanin content for Red Globe were obtained from treatment of seaweed extract and potassium silicate in combination at 0.2%. Indeed, foliar spray of seaweed extract and potassium silicate in combination or alone was reducing percentage of powdery mildew infection in grape berries of Superior Seedless grapevines and reducing percentage of bunch rot of Red Globe grapevines compared with control. Eventually, the best results for controlling of powdery mildew and bunch rot were obtained from treatment of seaweed extract and potassium silicate in combination at a rate of 0.2%. Finally, it could be recommended to foliar application of silicon and seaweed extract for both cultivars to improving yield, berry quality and control the fungal diseases, thus, economic return.
EVALUATION OF MANUAL AND ROBOTIC MIRNA EXTRACTION AND DETECTING MIR-SEPS4 IN HUMAN PLASMA FOR EARLY SEPSIS DIAGNOSIS

Sepsis is a life-threatening condition in which a dysregulated immune response causes damage to the patient’s organs. The condition moves fast leaving little time for diagnosis and starting treatment. Early diagnosis would help save lives and reduce the amount of antibiotics used. The aim of this study was to compare manual and robotic extractions for miRNA extraction and to quantify miR-seps4, a possible biomarker for sepsis, from human plasma. During the first part of the study manual extractions done by hand and robotic extractions done using QIAcube (Qiagen) of miRNA using the miRNeasy Serum/Plasma Advanced Kit (Qiagen) were compared using the quantity and purity of RNA they produced. Then two-tailed RT-qPCR was optimised for human plasma spiked with synthetic miR-seps4. The final objective was to use these methods to test whether two-tailed RT-qPCR could be used to identify and quantify miR-seps4 from non-spiked human plasma using the absolute quantification method. The QIAcube (Qiagen) extractions gave a higher concentration of RNA while manual RNA extractions had a higher purity A successfully optimised standard curve was made using synthetic miR-seps4 though the melt curve showed contamination and possible non-specific products. Non-spiked RNA extractions showed successful amplification of miR-seps4 that could be quantified through the absolute quantification method. This makes miR-seps4 a good target for further research and a possible addition to a multi-marker panel for early diagnosis of sepsis.
30 YEARS 30 YEARS

Objective: The study was performed to reveal experiences of adolescents and their families in the short-term after scoliosis surgery. Methods: This study had a qualitative descriptive design, and data were collected from adolescents with scoliosis surgery (n=17) and their family members (n=9) at in-depth interviews. Obtained data were analysed with content analysis. Results: Five main themes emerged: physical complaints, unfamiliar environment (operating room and intensive care unit), emotional changes, wanting their parents to stay with them and worries about future. Both the adolescents and their families reported that the adolescents experienced not only physical and emotional problems but also worries about their future after surgery. Conclusion: Nurses should meet the adolescents and their families and allow them to express their feelings before surgery. Informing them before surgery is important in terms of relieving their anxiety and enhancing their adaptation. Creating an appropriate environment in recovery rooms and intensive care units where the adolescents can frequently see their parents may help
Opleiding Informatica Expanding Side-Channel Data Sets Using Conditional Generative Adversarial Networks

Side-channel analyses using deep learning usually require large data sets to perform well. Obtaining these data sets can prove to be quite difficult, as manufactures of chips implement certain safeguards against obtaining the required measures. The performance of side-channel analyses can be improved by generating synthetic traces from the original data set of traces instead of measuring directly from the target device. This research proposes using conditional generative adversarial networks (cGAN) to generate the synthetic traces, on which a deep-learning model can be trained to execute a Deep-Learning Side-Channel Analysis. We will compare the effectiveness of various cGAN models and investigate the effect of certain hyperparameters on the performance of our analysis. The experiments will be performed on an ATMega8515 chip running AES 128-bit encryption. To compare our models, we will calculate the guessing entropy of the predictions made by a MLP model, trained on the synthetic traces generated by our cGAN model. We will also compare models using the Signal-To-Noise ratio of the synthetic traces and the real traces (the SNR should be similar). Our results show much promise in using cGAN models to enhance existing data sets and using them perform Side-Channel Analyses.
SENTIMENT ANALYSIS OF INDONESIAN E-COMMERCE PRODUCT REVIEWS USING SUPPORT VECTOR MACHINE BASED TERM FREQUENCY INVERSE DOCUMENT FREQUENCY

E-commerce transaction has grown eight times every year in Indonesia. Meanwhile, the number of Indonesian e-commerce customers grows twice every year. The Indonesian e-commerce market has attracted prospects both in terms of management strategies and customer opinions. Therefore, this study aims to provide another perspective to e-commerce management that product reviews reviewed by users can be parameters in determining management strategy. Existing reviews can be collected, processed, and predicted into product segmentation data using the CRISP-DM method. To achieve this goal, the authors compare several machine learning algorithms to determine the best way for sentiment analysis on product reviews contained in five E-Commerce. in Indonesia. Data were collected from product reviews in 3 broad categories: telephone, groceries, and fashion. Then this data is placed by comparing four algorithms, i.e., Decisions Tree, Random Forest, Gradient Boost, and Super Vector Machine). It can be denied that the SVM method is the best method with an accuracy rate of 95.875%. After the prediction model is modelled, the deployment involves connecting the prediction to the dashboard software to display it to E-commerce management and related sellers.
Semi‐volatile aerosols in Beijing (R.P. China): Characterization and influence on various PM2.5 measurements

[1] During summer, the Beijing urban area meets contrasting meteorological conditions including warm and humid monsoon winds from the southeast bringing high levels of pollution, with PM2.5 mass concentrations often exceeding 100 μg/m3. The specific weather conditions and aerosol chemical composition observed at that time offer a unique opportunity to address the question of the contribution of semi-volatile material (SVM) in the continuous PM records available for the south East Asian urban regions. Different PM2.5 measurements were evaluated during a 3-week field campaign performed in Beijing downtown during the summer 2004, and consisting of a Rupprecht & Patashnik (R&P TEOM) (heating air sample at 50°C), R&P TEOM-FDMS (enabling SVM measurement), optical GRIMM counter, and filter weighing. A good agreement was found between the different TEOM measurements during the campaign with the exception of the periods of high Relative Humidity (RH), which exhibited SVM levels (derived from PM2.5 loss in the TEOM heated at 50°C) as high as 140 μg/m3. Continuous artefact-free PM2.5 nitrate measurements were performed simultaneously and showed a close relationship with the TEOM-derived SVM, accounting for half of this SVM. To better document the role of RH on SVM, estimates of liquid water content (LWC) in aerosols were derived from light scattering coefficient and integrated aerosol volume measurements performed in the field at different RH. LWC is shown to be related in a quantitative way to the levels of SVM and nitrate in aerosols, and thus is believed to play a major role in the gas-particle partitioning of semi-volatile species in Beijing aerosols.
Opleiding Informatica Performance of Reinforcement Learning Agents within Gameworlds of differing Coarsnesses

Games are meant to be played by humans, but even so AI’s have been created to play games too. Many games are built as a gridworld where elements are kept within designated tiles. This would be good for implementing Reinforcement Learning(RL) agents that use a state-space to save its exploration data and choose actions according to it. So a tabular gameworld can be easily converted into a state-space. However this becomes more difficult when the player character is separate from the gridworld, where it can exist anywhere between multiple states. With a continuous location for the player it is hard to compute with a RL agent, but it has been done before using approximation algorithms and deep learning. But what happens when the player is bound by this grid, when the player position is contained by tiles or subdivisions of tiles? In this thesis, the effects on RL agents are explored when the RL agents are bound by a grid that can be subdivided. With a Python program gameworlds can be created where a player can interact with the world elements and its physics. Using a variable called Coarseness the movement of the player can be changed through changing the amount of subdivisions of a tile. By implementing RL agents, Q-learning, SARSA and Expected SARSA, they can traverse the world and learn from the environment at multiple coarseness settings. After testing the results are accumulated into multiple graphs as well as the shortest path each agent was able to achieve. In small and simple worlds all agents perform comparatively with only execution time going up with each coarseness setting. As the complexity of the worlds rise so does the difficulty of the agents reaching the goal. Q-Learning and Expected SARSA often still perform similar with Expected SARSA usually performing a little bit better than Q-learning, both of them capable of learning more complex world design. SARSA performs comparatively badly, unable to finish some of the tests and sometimes unable to create efficient shortest paths where Q-Learning and Expected SARSA do both. All in all with a sufficiently well coded agent they can be able to traverse gameworlds with many subdivisions normally given enough time. Q-learning and Expected SARSA show this, but SARSA is only good at low complexity. The biggest impact is execution time, as the tiles get more subdivided the state-space grows with it. Using smaller and less complex worlds reduces this execution time, a high exploration perimeter helps finding the goal. Future research could be sub-rewards for reaching new areas, player inputs during subdivided tile movement and adjusting the state-space implementation.
Review on Prevalence of Bacillus cereus Enterotoxigenic Genes Isolated from Food of Animal Origin and its Public Health Significance

: Bacillus cereus is a ubiquitous and highly resistant food poisoning bacterium that is an important food safety concern. It can contaminate a variety of foods throughout the world. In this systematic review and meta-analysis, a complete search was conducted on the prevalence of B. cereus , worldwide. Between 1990 and 2020, a total of 6035 articles were collected from the databases. By applying explicit criteria, the number of 98 studies was included in this study. The overall prevalence of B. cereus obtained in this study was 23.746% with a confidence interval of (95%) 22.979-24.513. However, the prevalence of B. cereus varied according to food type, identification method and continents. Our findings showed that the highest prevalence of B. cereus among eight groups of foods belonged to the cereals and beans, with 41.489% and 44.901%, respectively. Its prevalence was also high in vegetables (37.279%) and dairy products (36.385%). The detection methods used also affected reporting the prevalence of B. cereus . In general, the molecular tests such as PCR, reported twice the prevalence compared to other methods. The prevalence of B. cereus was also the highest in the Americas and lowest in Australia. However, with some exceptions, the prevalence of B. cereus in developing countries was significantly higher than in the developed countries. This study indicates that although the prevalence of B. cereus is generally high, depending on the type of food, the method of bacterial detection and also geographically, its prevalence can be quite different. The high prevalence of B. cereus in different food types can be attributed to B. cereus origin, the soil and non-compliance with hygienic protocols from farm to the fork, so it is still possible to prevent food contamination to this bacterium by observing hygienic principles. In addition, it is recommended to use molecular detection methods along with the Culture/Biochemical methods to obtain a more accurate prevalence of food contamination to B. cereus .
Burnout and Satisfaction With Work-Life Balance Among US Physicians Relative to the General US Population

Background : Despite extensive data about physician burnout, to our knowledge, no national study has evaluated rates of burnout among US physicians, explored differences by specialty, or compared physicians with US workers in other fields. Methods : We conducted a national study of burnout in a large sample of US physicians from all specialty disciplines using the American Medical Association Physician Masterfile and surveyed a probability-based sample of the general US population for comparison. Burnout was measured using validated instruments. Satisfaction with work-life balance was explored. Results : Of 27 276 physicians who received an invitation to participate, 7288 (26.7%) completed surveys. When assessed using the Maslach
DEEPFAKE DETECTION BASED ON THE XCEPTION MODEL

Thanks to artificial intelligence, everybody can easily create deepfakes without any particularly technic knowledge. By analyzing faces movements. But these will become more and more realistic as technological developments progress, and therefore more and more problematic . The rapid evolution in synthetic image generation and manipulation has now come to a point where it raises significant concerns on the implication on the society. With the advent of fake news and its harmful effects on social networks, the dissemination of deepfakes on the web therefore constitutes a new technological threat. Manipulation, disinformation, humiliation, defamation . the dangers of deepfakes will be more and more numerous. In this post, first, we will describe in brief how deep learning with Depthwise Separable Convolutions can be the most useful and promising techniques to detect deepfakes