Miracle-dz/UniFAR-FTUs huggingface dataset:
A qualitative study on people with opioid use disorders’ perspectives on smoking and smoking cessation interventions

Introduction Smoking-related diseases are major contributors to disability and shorter life expectancy among opioid-dependent patients. Smoking prevalence is considerably higher for opioid-dependent persons than among the general population, and only a minority quit smoking in treatment settings. Studies show that pharmacological smoking cessation interventions have modest success rates. This study aimed to investigate patients’ receiving opioid agonist therapy perspectives on factors affecting behavior and decisions related to smoking cessation, and their experiences with smoking cessation. Methods This is a qualitative study using semi-structured individual interviews. The participants were asked, among others, to elaborate on the participants’ thoughts about smoking, previous attempts to quit tobacco use, and what could prompt a smoking cessation attempt. We analyzed the transcripts with systematic text condensation. The Standards for Reporting Qualitative Research and the Consolidated Criteria for Reporting Qualitative Research guidelines were followed. Opioid-dependent patients receiving opioid agonist therapy in outpatient clinics were invited to participate using a purposive sampling method. In total, fourteen individuals participated in this study. Results We identified six themes which were: (1) reflections on how smoking affected decisions, (2) smoking and its impact on physical and mental health, (3) the economy as a motivator to stop smoking, (4) emotions, desires, and habits related to smoking, (5) knowledge of smoking, smoking cessation, and quit attempts, and (6) social factors influencing the participants’ choices and activities. The participants were well informed about the consequences of smoking and had some knowledge and experience in quitting. The participants’ pulmonary health was an important motivational factor for change. Withdrawal symptoms, anxiety, and fear of using other substances discouraged several from attempting to quit smoking. In contrast, social support from partners and access to meaningful activities were considered important factors for success. Few reported being offered help from health professionals to make a smoking cessation attempt. Discussion Experiencing social support, being encouraged to quit smoking, and patients’ concerns for their physical health were important reasons for wanting to quit smoking. Smoking cessation interventions based on patient preferences and on the behavior change wheel may enable a higher success rate among patients receiving opioid agonist therapy.
Participation in extracurricular activities and associated factors among Jaffna medical students

Extracurricular activities (ECA) help to nurture the core competencies of a doctor. This study assessed participation in ECA, barriers and attitudes towards participation, and associated sociodemographic and academic factors among medical students of the University of Jaffna.This descriptive cross-sectional study was carried out at the Jaffna Medical Faculty. Stratified random sampling was used ensuring proportionate representation from each batch. Data were collected with a self-administered questionnaire. Frequencies, proportions, and the Chi squared test were used in the analysis with SPSS (v21). In total, 332 students participated (response rate 96.5%); 65.1% reported participating in ECA. Among them 44.9% participated in sports, while 47.2% were involved in cultural/ faith-based activities. The majority participated in ECA on less than 3 days per week (68.1%), and for less than 2 hours per day (61%). Lack of time (27.5%) and transport (24.6%) were key barriers to participation. Many agreed that ECA enabled new friendships (63.2%) and enhanced social skills (60.2%). Ethnicity (p=0.03) and household income (p=0.045) were significantly associated with the level of participation; Sinhala and Muslim students and those with lower income participated at a lower level. Although female students reported lower participation, there was no evidence of an association between gender and ECA participation (p>0.05). Programme phase (p<0.001) and first examination results (p=0.019) were significantly associated with level of participation; students in the para-clinical phase and those who achieved class honours reported a lower level of participation.Over a third of students do not participate in any type of ECA at the Jaffna Medical Faculty. Targeted interventions are needed to improve participation with consideration to the barriers and groups who are less likely to participate in ECA.
Canada Agricultural Situation This Week In Canadian Agriculture, Issue 17 2001

Report Highlights: "What’s New, Eh? " * Ag Minister Targets U.S. Farm Bill at Sparks Conference * Canadian Farmers Call for Trade Injury Compensation * CWB Minister Calls U.S. Investigations "Harassment" * Scramble to Beat Lumber Duties Causes Rail Car Shortage * B.C. to Launch Forest Marketing Campaign * Maine and New Brunswick Politicians Meet to Improve Cross Border Trade * Starlink Found in Canadian Corn * Sask. Agriculture Forecasts Increased Cereal Acreage *
Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer’s disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer’s prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.
Caffeine Intake and Its Association with Mental Health Status among Pharmacy Students at UiTM Puncak Alam

Introduction: Caffeine is a psychoactive substance widely consumed over the past decades. The effect of caffeine can be either beneficial or harmful. It increases cognitive performances, including attention, alertness and concentration. However, high caffeine intake may also induce an anxiogenic effect, causing symptoms such as rapid heart rate, restlessness and nervousness. This study aimed to determine the association between caffeine intake and mental health disorders such as anxiety and depression among undergraduate pharmacy students at UiTM Puncak Alam. Methods: This was a cross-sectional study with stratified random convenience sampling. A total of 270 undergraduate pharmacy students in UiTM Puncak Alam, Selangor participated in this study. A set of questionnaires was distributed using the Google Form platform. Standard General Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) scoring were used to assess the level of anxiety and depression among the respondents, respectively. SPSS version 27.0 was used to analyse the data. Results: About 70.4% of the students consumed caffeine, while 29.6% of the students did not consume caffeine. No significant association was found between caffeine status and the mental health scoring of GAD-7 (χ2=4.639, p=0.200) and PHQ-9 (χ2=5.256, p=0.262). Conclusion: Non-daily consumption and a low dose of caffeine intake patterns are good practises to prevent the development of anxiety or depression conditions, although the associations were not significant. Public awareness on possible anxiogenic effect and mental related disorders due to caffeine consumption need to be initiated, as nowadays, the caffeine intake behaviour has become a trendy lifestyle among the young adults.
The influence of Internal Control effectiveness on Audit pricing: based on the empirical Evidence of Shenzhen Stock Exchange Manufacturing Industry

In recent years, incidents such as chairman Kant being arrested by the police for suspected crimes occur frequently. In the development process of these incidents, the negligence of internal control has played a guiding role. This warns us that listed companies in addition to timely disclosure of internal control reports, establish and improve the internal control system construction, but also the introduction of external supervision system, timely audit work, and the effectiveness of enterprise internal control on the follow-up audit work of audit pricing is worth discussing. This paper takes the data of 354 manufacturing companies listed in 2017 and 2018 in Shenzhen Stock Exchange from 2015 to 2017 in 2018 as samples to explore the impact of the effectiveness of internal control on audit pricing. The empirical results show that there is no significant negative correlation between the effectiveness of internal control of this year and the audit pricing of this year, and a significant negative correlation between the effectiveness of internal control of the previous year and the audit pricing of this year, indicating that the effectiveness of internal control of the previous year is an important influencing factor of the audit pricing of this year. Starting from the effectiveness of internal control, this paper discusses the influencing factors of audit pricing, enriches the research on the influencing factors of audit pricing, and provides relevant reference information for the government to standardize audit pricing.
In-Hospital Mortality Outcomes of ST-Segment Elevation Myocardial Infarction: A Cross-Sectional Study from a Tertiary Academic Hospital in Johannesburg, South Africa

In sub-Saharan Africa, the burden of atherosclerotic cardiovascular disease (ASCVD) is increasing. This study aimed to describe the clinical characteristics of patients with ST-segment elevation myocardial infarction (STEMI) and estimate the in-hospital all-cause mortality rate. We conducted a cross-sectional retrospective single-centre study of STEMI patients who underwent diagnostic coronary angiography with or without percutaneous coronary intervention (PCI) between January 2015 and December 2019. We compared demographic and clinical parameters between survivors and non-survivors with descriptive statistics. Univariable and multivariable logistic regression analyses were performed to determine the predictors of all-cause mortality. The study population consisted of 677 patients with a mean age of 55.5 ± 11.3 years. The in-hospital all-cause mortality rate was 6.2% [95% confidence interval (CI): 4.5–8.3%]. Risk factors for ASCVD included smoking (56.1%), hypertension (52.8%), dyslipidemia (40.0%), and a family history of coronary artery disease (32.7%). A pharmaco-invasive management strategy (treatment with thrombolytic therapy and PCI) was implemented in 36.5% of patients and reduced all-cause mortality risk (OR: 0.16; CI: 0.04–0.71, p = 0.015). The in-hospital all-cause mortality rate in STEMI patients was 6.2%, and a pharmaco-invasive management strategy proved to be an effective approach.
Taverns, inns and alehouses? : an archaeology of consumption practices in the City of London, 1666-1780

This thesis set out to explore the changing nature of consumption patterns in seventeenth- and eighteenth-century London through the analysis of archaeological evidence previously excavated by the Museum of London Archaeology. The aim of this research was to address existing gaps and limitations within the existing methodology related to the excavation and analysis of these environments, to establish a more holistic method of approaching consumption practices from this period, and to explore the complexities which were being performed within the setting of these establishments. To do this, a typological system for artefact classification was developed which enabled the categorization of material by their fabric, form and their associated functions. The distribution patterns of the various types and functions across three sites and five establishments in the City of London were analyzed. The material was then assessed for patterns indicating changes in consumption.Linkages from these patterns are then made between historical themes and theoretical frameworks outlined within the thesis. Particular focus will be given to developing a better understanding of how these venues changed over time based on the degree of variation that can be perceived between the late seventeenth to the late eighteenth centuries. By exploring the character of consumption practices, I will demonstrate how they work together to provide a more complete picture of the complex systems at work. During the course of this research, specific objectives have been achieved and conclusions reached which make original contributions to the wider dialogues surrounding how meaningful patterns of consumption can be perceived and interpreted through material goods from establishments of social or public consumption. The focus on the everyday materials from closed deposits related to clearance episodes (Pearce 2000) from these establishments and their how they relate to emergent and shifting patterns of social trends in consumption is what separates this thesis from other scholarship on these and similar spaces. Significantly, this research differs from the previous examples by attempting to detect social change across a variety of classes and in a variety of different settings, all brought together in relatively modest atmospheres of social and public consumption. This has allowed for both the subtle and the overt shifts in social patterns to be detected, and from there, conclusions are drawn regarding wider social ideology.
Distributed training for spatio-temporal neural networks

For the training of spatio-temporal sensing data, most of the existing methods are centralized or combined with federated learning, but in some scenarios only fully distributed training can be performed. And the existing methods basically assume that the topology of the agent is fixed, but sometimes the topology changes dynamically. Therefore, this paper combines the (sub)gradient projection method to realize a distributed training method for spatio-temporal sensing data. Our method is not only suitable for scenarios where the agent topology is fixed, but also for scenarios where the topology changes dynamically.
Medz: Innovative Drug Store Information System

In the rapidly evolving landscape of healthcare, efficient information management is paramount. This study probes into the transformative potential of the "Medz: Innovative Drug Store Information System" in reshaping pharmacy operations. Through a comprehensive analysis of data gathered from surveys, interviews, and observations, this study highlights the limitations of conventional paper-based systems and the need for innovation. The "Medz" system's features, encompassing real-time prescription processing, streamlined inventory management, and enhanced patient record keeping, are examined in detail. User feedback from pharmacists and customers underscores the system's tangible impact, with statistically significant improvements in prescription processing speed and patient experiences. Comparative analysis showcases the system's accuracy and user-friendliness, setting it apart from traditional methods and existing digital alternatives. Collectively, these findings underscore a new era in pharmacy management, one defined by technological innovation, precision, and improved patient care. The "Medz" system emerges as a beacon of progress, illustrating the transformative power of technology in enhancing healthcare efficiency and efficacy.
Aerosol predictions and their links to weather forecasts through online interactive atmospheric modeling and data assimilation

This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of 150 μg/m (24 h running averages). The forecasting system is based on accurately simulating carbon monoxide (CO) as a PM10/PM2.5 surrogate, since during episodes and within the city there is a high correlation (over 0.95) among these pollutants. Thus, by accurately forecasting CO, which behaves closely to a tracer on this scale, a PM estimate can be made without involving aerosol-chemistry modeling. Nevertheless, the very stable nocturnal conditions over steep topography associated with maxima in concentrations are hard to represent in models. Here we propose a forecast system based on the WRF-Chem model with optimum settings, determined through extensive testing, that best describe both meteorological and air quality available measurements. Some of the important configurations choices involve the planetary boundary layer (PBL) scheme, model grid resolution (both vertical and horizontal), meteorological initial and boundary conditions and spatial and temporal distribution of the emissions. A forecast for the 2008 winter is performed showing that this forecasting system is able to perform similarly to the authority decision for PM10 and better than persistence when forecasting PM10 and PM2.5 high pollution episodes. Problems regarding false alarm predictions could be related to different uncertainties in the model such as day to day emission variability, inability of the model to completely
CCD Image-Based Pixel-Level Identification Model for Pavement Cracks Under Complex Noises Using Artificial Intelligence

Existing manual detection methods have limitations, particularly for pavement cracks in complex backgrounds, which are manifested in low recognition accuracy, high misjudgment rate, and long time-consuming. To overcome these problems, artificial intelligence technology and charge-coupled devices (CCD) imaging technology are combined to construct an automatic identification method for pavement hidden cracks under complex background interference conditions. First, the classic semantic segmentation model U-net is selected as the basic model, and the MobileNet lightweight network is utilized to replace the encoder part of U-Net with huge parameters, to realize the lightweight of the model and improve the segmentation effect of pavement cracks. On this basis, the Atrous Channel Pyramid Attention (ACPA) mechanism is introduced into the U-net to further improve contextual information capability to focus on selectively relevant features. A pavement crack data set containing different complex and diverse crack types and background noise is used to evaluate the effectiveness and scope of application of the developed model. Quantitative evaluation results show that the developed model achieves an overall performance in the test set with a precision of 88.84%, recall of 89.76%, accuracy of 98.87%, and IoU of 89.95%, respectively. Combined with the analysis of the results of the comparison experiment and the ablation experiment, it can be inferred that the utilization of the MobileNet lightweight network to replace the encoder part of U-net can effectively construct a lightweight model while the ACPA module can effectively perform multi-scale and long-distance cross-channel interaction, help suppress useless features, strengthen useful features, and help the network learn stronger feature representations of hidden areas of pavement cracks.
Bearing‐only containment control of multi‐agent systems

Bearing‐only containment control means to achieve containment using only the bearing information, rather than the complete relative position information including both the bearing and the distance. Compared with the most existing relative position based containment control algorithms, the bearing‐only containment control algorithm is nonlinear due to the nonlinearity of the bearing, which renders the stability analysis challenging. In this article, the bearing‐only containment control problem of multi‐agent systems with dynamically changing topologies is investigated for two cases, where the leaders are stationary or dynamic. First, when the leaders are stationary, it is shown that the nonlinearity of the bearing leads to the unknown nonlinear time‐varying parameters in the system. Based on the boundedness of the nonlinear parameters and the connectivity of the switching directed sensing graphs, it is proven that all the followers converge to the stationary convex hull formed by the leaders asymptotically. Second, when the leaders are dynamic, a distributed estimator is proposed for each follower to keep a local estimate of the velocity of the leaders. Based on the optimality of the projection and the shift invariance of the normal cone, it is proven that all the followers converge to the dynamic convex hull formed by the leaders asymptotically under the conditions on the switching directed sensing and communication graphs. It is shown from the two cases that the distance information is not necessary for containment control, and containment can be achieved as long as the bearing information is provided. Numerical examples are presented to illustrate the theoretical results.
Study on the relationship between tsunami waves in dam break state and initial water levels

Tsunami wave characteristics are greatly influenced by the initial water level when they attack structures. In this study, experimental and numerical investigations were conducted to investigated the relationship between tsunami wave characteristics and initial water levels. Results showed that, the wave height, wave velocity, and Froude number increase with the increase of tsunami wave intensity; the time history of water levels were influenced by the different initial water level conditions; the analytical solution proposed by Chanson (2005) may be extended to wet-bed conditions (for initial water level < 0.36 tsunami bore height in our experimental set-up). Due to the limitations of experimental ranges in the laboratory, the validated numerical model can provide more results for wider experimental ranges for tsunami bore investigations. It was observed from numerical results that, tsunami bore height increases with the increase of reservoir water level; tsunami bore velocity decreases with the increased initial water level on the bed; as the initial water level on the bed gradually increases, the mean tsunami bore Froude number shows a downward trend.