Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Cannabis Use and Pain: An Experience Sampling Study

Background: Little research has tested associations of pain and MC use after long-term treatment and through methods that have external validity outside experimental settings. The study examined associations of pain, associated painful experiences, and long-term medical cannabis (MC) use in chronic pain (CP) patients using a naturalistic daily diary study that provided novel and externally valid data. Methods: Data were obtained from 78 MC users with CP three times daily over a 10-day period (nobservations = 1,688). Mixed-effects models were used to test the associations between MC use and momentary experiences of pain, affect, and fatigue. Results: Within persons, elevated experiences of pain intensity were associated with greater intention to use MC within the next hour. No evidence was found that the time lapse since last MC use was associated with pain levels, negative affect, or fatigue. Conclusions: The results imply that after long-term use, CP patients intend to use MC in response to pain experiences. Yet, they may not actually achieve the pain relief. More research is needed to examine whether continued MC use despite lack of pain relief is related to relief of other symptoms (e.g., dependence, withdrawal) or positive benefits (e.g., general sense of well-being) or tolerance.
On-Call Duties: The Perceived Impact on Veterinarians' Job Satisfaction, Well-Being and Personal Relationships

Objective: To assess the impact of on-call duties on veterinarians' job satisfaction, well-being and personal relationships. Design: Cross-sectional survey. Sample: The sample was obtained from Veterinary Information Network (VIN) members in private practice within the United States. Procedures: A link to an anonymous online survey was distributed via an email invitation to all Veterinary Information Network (VIN) members with access from August 15, 2017 to October 21, 2017. Results: A total of 1,945 responses were recorded. The majority of those who reported having on-call duties were female associates. Composite scales were created to assess the impact of on-call shifts on job satisfaction and well-being. Multiple linear regression was conducted and found that gender (p = 0.0311), associate status (p < 0.0001), and age (p = 0.0293) were all significantly associated with on-call related job satisfaction. Additionally, multiple linear regression found that gender (p = 0.0039), associate status (p < 0.0057), and age (p < 0.0001) were all significantly associated with on-call related well-being. On-call shifts were reported by many to have a negative impact on job satisfaction and well-being; this was especially pronounced for female associates. Females had on-call related job satisfaction scores that were, on average, 1.27 points lower than that of males (lower scores equates to lower job satisfaction). Further, females' average on-call related well-being scores were 1.15 points higher than that of males (lower scores equates to higher well-being). Conclusions and Clinical Relevance: This study suggests that on-call shifts have a negative impact on veterinarian job satisfaction, well-being and personal relationships. The negative impact on job satisfaction and well-being is greatest for female associates. Veterinary medicine has been identified as a stressful occupation that can lead to psychological distress. It is therefore important to critically assess current practices that appear to increase stress and reduce emotional well-being. For this reason, it is suggested that veterinary hospitals explore alternative options to traditional on-call shifts.
Building a Knowledge Graph of Events and Consequences Using Wikidata

In this short paper, we present our preliminary results on building a Knowledge Graph (KG) of events and consequences with application to event forecasting and analysis. A base KG is ﬁrst constructed using existing concepts and relations in Wikidata. Using an automated unsupervised knowledge extraction pipeline, causal knowledge is extracted from Wikipedia articles to augment the base KG. We show examples from the base and the augmented KG, and discuss a few challenges in building a high-quality KG. We also discuss a few potential directions that the Wikidata community can work on to improve the representation of event-related knowledge in Wikidata.
Reverse engineering with P-stable Abstractions

The analysis of legacy systems such as electro-mechanical circuits requires the extraction of the underlying specification from a high level point of view. We consider P -stable abstraction, an analysis for transition systems that synthesizes the properties of a given set of events in terms of their eventual and stable effects on the system state. The abstract transitions simulate convergent runs of the concrete model and correspond to a set of temporal properties which are satisfied. We show an application of the P -stable abstraction on a relay-based circuit, showing its ability to extract a high level description of the implemented behaviour.
Using Directional Arc Consistency with Asynchronous Forward-Bounding algorithm

The AFB_BJ + -AC * algorithm is one of the latest algorithms used to solve Distributed Constraint Optimization Problems (DCOPs). It is based on simple arc consistency (AC * ) to speed up the process of solving a problem by permanently removing any value that doesn’t belong to its optimal solution. In this paper, we use a directional arc consistency (DAC * ), the next higher level of AC * , to erase more values and thus to quickly reach the optimal solution of a problem. Experiments on some benchmarks show that the new algorithm, AFB_BJ + -DAC * , is better in terms of communication load and computation effort.
Grounding desire: The role of consumption and reward simulations in eating and drinking behaviour

This chapter presents a grounded cognition theory of desire and motivated behaviour, along with empirical evidence that supports it, and discusses implications for self-regulation. The grounded cognition theory of desire suggests that desire arises when an internal or external cue triggers a simulation of an earlier appetitive experience that was rewarding and that has been stored as a situated conceptualization, incorporating information about the setting, actions, events, emotions, etc. Once part of this situated representation is cued, it can re-activate its other elements via pattern completion inferences, and lead to the experience of desire and motivated behaviour. The chapter will review studies supporting this account, using behavioural, physiological, and neuroimaging methods. Research shows, for example, that food and drink cues (e.g., images, words) trigger spontaneous consumption and reward simulations (e.g., thoughts of eating and enjoying the food), that these simulations lead to desire and bodily preparations to eat (e.g., self-reported cravings, salivation), and that diffusing these simulations with mindfulness-based techniques reduces their effect on desire. The chapter also discusses related findings in research on marketing and food labelling, and addresses implications for interventions.
Mineral fertilizers and soil properties in agriculture

Maussumbayeva Aida Makenovna, candidate of Agricultural Sciences, associate professor Yerimbet Rizuan Yedilbekuly, мaster's student Oxikbayev Berikzhan Kylyshbekovich, candidate of Biology Sciences. senior lecturer Каbdrakhmanova Аinur Kanatovna, master, senior teacher Daurenbekova Sholpan Zhumabekovna, candidate of Biology Sciences, associate professor Zhetysu University named after I. Zhansugurov, Kazakhstan, Taldykorgan
The Impact of Nursing Staff Responsiveness on Patient Satisfaction: A Pilot Project

Timeliness of healthcare staff responsiveness to patient requests is multifactorial. Inadequate responsiveness to patient requests may lead to increased patient injury, increased cost, and patient dissatisfaction which may impact stakeholders and profit margin. Evidence supports that proactive, timely, staff responsiveness to patient requests and call lights may lead to improved patient safety, higher patient satisfaction, and better healthcare outcomes. Bundled staff responsiveness interventions may be implemented to improve patient safety, patient and staff satisfaction. Examples of interventions include engaging staff project buy-in with formation of a patient experience team, hardwiring hourly safety rounding, providing staff education on the importance of prompt response to patient call light requests and instructions how to implement the “no pass zone. ” The purpose of the project was to implement a bundle of evidence-based nursing interventions which included forming a patient experience team, hardwiring hourly safety rounding, and implementing the “no-pass zone. ” The project educated staff about the nurse call light system, the importance of timely staff responsiveness, increasing patient/family satisfaction, safety, and outcomes. The pilot unit was a 48-bed medical/surgical unit in a Magnetdesignated hospital. Improvement in staff responsiveness to patient requests may increase patient and staff satisfaction, optimize organizational profit margin, and improve patient outcomes.
Econometrics Lecture 8: Granger Causality and Vector Autoregressive Models

Last week we looked at simultaneous equations systems. These systems involve the imposition of two types of restriction: firstly some variables are taken as exogenous and are not explained within the model, secondly some parameters are restricted in order to achieve identification. Sims (1980) argues that both these steps involve arbitrary assumptions about the direction and timing of causal linkages in economics that may be rejected when tested against data. Instead Sims proposes a model with a very free dynamic specification which can be used to test causal linkages rather than impose them. The alternative he advocates is the vector autoregressive or VAR model. The VAR model is a multiple variable generalisation of the autoregressive model. The regressors are lags of all the variables in the model. Because there are no current endogenous variables on the right-hand side of equations, no restrictions need to be imposed to identify the system. The set of regressors is the same for each equation so that each equation is estimated efficiently using the OLS estimator. The VAR model ignores economic restrictions and so can be thought of as ‘atheoretic’. It can also be thought of as the reduced form of a simultaneous equations system where the predetermined variables are all lagged dependent variables and there are no pure exogenous variables. VAR models have often been used for forecasting and have been claimed to forecast better than simultanous equations models. The implication of this is that the theory derived over-identifying restrictions imposed on simultaneous equations systems are being rejected by the data. In the past, simultanous equations systems have tended to neglect dynamics and their poor forecasting performance may be partly the result of this. VAR models provide a benchmark against which their forecasts can be judged.
Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.
GAMIFIED INSTRUCTION AND ITS EFFECT TO STUDENT MOTIVATION AND ACHIEVEMENT IN SCIENCE ANALYN

This is a two-phase study which employed a mixed-method sequential explanatory design to explore the effects of a gamified instruction to the motivation and achievement of high school students in Science. The Science Motivation Questionnaire II was utilized to determine if gamification causes a significant increase in student motivation in Science. On the other hand, the mean normalized gains for all the pretests-posttests administration were carefully analyzed to find out if the technique causes a significant increase in student achievement in Science. Feedback from students, teacherimplementer and external observers were collected and analyzed for every phase. Both the quantitative and qualitative data collected substantiate the conclusion that gamified instruction brought about a significant increase in both student motivation and achievement in Science. It is recommended that gamified instruction be tried out in more Science classrooms with utmost caution so as to prevent students from developing wrong attitude towards assessment and towards Science as well. Future researchers may also dig deeper into the identification of the specific element of gamification that contributes to the increase of student motivation and achievement the most.
In‐Volume Laser Direct Writing of Silicon—Challenges and Opportunities

Laser direct writing is a widely employed technique for 3D, contactless, and fast functionalization of dielectrics. Its success mainly originates from the utilization of ultrashort laser pulses, offering an incomparable degree of control on the produced material modifications. However, challenges remain for devising an equivalent technique in crystalline silicon which is the backbone material of the semiconductor industry. The physical mechanisms inhibiting sufficient energy deposition inside silicon with femtosecond laser pulses are reviewed in this article as well as the strategies established so far for bypassing these limitations. These solutions consisting of employing longer pulses (in the picosecond and nanosecond regime), femtosecond‐pulse trains, and surface‐seeded bulk modifications have allowed addressing numerous applications.
FAST PATH-PLANNING ALGORITHMS FOR FUTURE MARS EXPLORATION

ESA Mars Robotic Exploration Preparation is expected to use rovers to explore the planetary surface after landing. Deciding where to land is crucial to maximize the safe rover operation and achieve the science targets of the mission. For this reason, it is important to try to plan optimal (or close to optimal) routes using the terrain information provided by the orbiters and the previous maps of Mars. In this paper we want to describe our work in anyangle path planning algorithms and the map generation parametrization in order to provide a better way to compare them.
Intentionally impacting deformable material

We envision modern robots performing impulsive tasks like kicking a ball. However, safely generating impacts with robots is challenging due to discontinuous velocity and high impact forces. If not accounted for, they can make the controller unstable or damage the robot. In our recent work, we showed how to control impacts with rigid bodies safely, however, not all objects are rigid. We here generate impacts with deformable contacts, incorporating constraints imposed by the hardware. Therefore, we learn the shock-absorbing soft dynamics. Realrobot experiments with Panda validate our approach.
Impact of Influencer Marketing on Millennials: A study

Over the years due to the growth of social media, people are more influenced by what they see in social media platforms, especially in fashion industry. With the help of platforms like Instagram, people are becoming their own magazine editors, sharing their personal style with millions of users over the internet. In the last few years there has been a huge growth in influencer marketing, with 94% of marketers believing in it’s effectiveness.Around 71% of the marketers believe that collaboration with the influencers is the most effective form, unlike other marketing techniques. The popularity of influencers is continuous and does not have a limited window of influence. The paper emphasizes on the impact of influencer marketing on millennials with special reference to fashion industry. Therefore, the research “Influencer Marketing and its impact on millennials: A study on Fashion Industry” is based on an exploratory study with the purpose of gaining an in-depth understanding of what factors that effects the millennials to follow the social media influencers. The main instrument for this study is a structured questionnaire for the primary data collection which was distributed in the city of Kolkata. Secondary data was collected from various research papers, journals, articles and websites. The sampling technique used for the study is judgemental and snowball sampling technique. The analytical techniques were employed using statistical package for social sciences (SPSS) version 20. and MS Excel. In order to make certain conclusions regarding the hypothesis framed, certain statistical tool like correlation, Regression, T test, Anova, Principal Component Analysis and simple graphs and charts were employed. Through the use of different tools and testing, it is concluded that influencers play a great role in the buying behaviour of the millennials and also helps in creation of prospective customers in fashion industry. There is a significant impact of influencer marketing on millennials. Journal of Xi'an University of Architecture & Technology
IJSRD-International Journal for Scientific Research & Development| Vol. 3, Issue 04, 2015 | ISSN (online): 2321-0613

Combining the antenna miniaturization and bandwidth preservation could increase the challenges of integrating the smaller, thinner, low profile, high efficient antennas into equipment for different applications. In this paper, we propose a new Microstrip Patch Antenna with reduced size for wireless application. We introduced Split Ring shaped slots in the patch, and found that the configuration stepped up the miniaturization by 62%. The design and analysis of the proposed antenna was carried out using Ansoft HFSS.
Population structure and genetic diversity in common bean accessions from Northeast Brazil

Common bean is of great relevance for several countries, especially those located in tropical regions. Common bean in Brazil has a wide genetic diversity, which is used by breeders to improve this crop. In this study, we used GBS methodology to genotype common bean accessions from Northeast Brazil, and to study its population structure and genetic diversity. After a filtering process, we identified 30,529 high-quality SNPs distributed in 11 linkage groups. The number of SNPs per chromosome ranged from 1,731 to 3,853. The population structure analysis separated the accessions into two subpopulations, according to K=2, one subpopulation with 26 Andean and the other with 60 Middle American accessions. Considering K=3, Middle American accessions were divided into two subpopulations with presence of allele mixture between these two groups. Based on the PCA, we were able to verify a narrow genetic base of accessions belonging to the Andean gene pool, as well as a vast genetic diversity among the accessions from the Middle American gene pool. The knowledge on the genetic diversity among the accessions is of extreme importance to subsidize the common bean breeding programs in Northeast Brazil, and to explore the variability existing in cultivars adapted to the specific bioclimatic conditions.
A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000–2019

Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.
Optical pH sensor based on polyelectrolyte complex (PEC) pectin-chitosan/methanol anthocyanin extract of Catharanthus roseus for a new optical urea biosensor development

Construction of optical pH sensor as a new platform optical urea biosensor based on polyelectrolyte complex (PEC) pectin-chitosan membrane and total phenolic (anthocyanin) of Tapak Dara flower (Catharanthus roseus) has been successfully carried out. The anthocyanin was extracted by a maceration method for 72 hours using methanol, and a total extract yield was 21.56% or 1.803 mg/L. Flavonoid and phenol tests showed positive results indicated by the formation of red and black colours. The anthocyanins showed maximum absorption at 578 nm for phosphate buffer and 575 nm for tris HCl buffer solution. The sensor fabrication was performed using a mixture of pectin and chitosan solutions with a ratio of 3:7. The anthocyanin was mixed into the solution with various concentrations. The sensor has an optimum sensitivity at the anthocyanin concentration of 0.05 mg/L (phosphate buffer) in the pH range of 7.0-9.5 and 0.025 mg/L (Tris HCl buffer) with a narrower pH range of 6.0-7.5. This sensor produced higher sensitivity, a wider linear range, and good linearity when it was exposed in 0.03M PBS. Reproducibility test with a relative standard deviation percentage (% RSD) was 9.20. The sensor showed a stable response after 5 minutes exposed to PBS solution, and it can be used to measure pH within the 20th day. The optimized optical pH sensor has been successfully developed as a urea optical biosensor by immobilizing urease on its surface. The biosensor showed a linear response in a series of 10-1-10-10 M urea concentrations and has good linearity.