Data Collection Phase Research Articles

Youth Satisfaction in Online Shopping: Mapping The Nexus of Influential Factors

Online shopping has become a new trend and an integral part of daily life, as it involves the process of direct browsing and purchasing products and services over the Internet using a web browser. Young people constitute the bulk of online shopping customers because purchasing has become simpler, comparatively cheaper, and more comfortable. Today, most goods and services can be purchased online, where multiple options may be offered by different sellers or brand since there are no boundaries to this shopping experience. This study aimed to look into the factors that influence 270 youths to use online shopping platforms, as most young people are highly engaged with technology and digital media. This study was conducted in Sungai Petani, the biggest town in Kedah, Malaysia. The quantitative study method was employed for the data collection phase of this study. All gathered data have been analyzed using the Statistical Package for the Social Sciences (SPSS) software. Regression, reliability, and descriptive analyses were utilized to assess, verify, and explain the findings of this study. The Pearson Correlation has indicated that the variety of selections and time-saving features were significant towards youth satisfaction in online shopping. Meanwhile, two independent variables, i.e., convenience and price fairness showed a considerable relationship. This study has found that the time-saving feature was the dominant factor among four variables that have influenced youth satisfaction in online shopping.

Utilizing Agricultural By-Products for Sustainable Agouti (Dasyprocta leporina) Diets: Evaluating Low-Cost Feed Alternatives in Trinidad and Tobago, West Indies

The agouti (Dasyprocta leporina) is a promising protein source in the Caribbean, Mexico, and South America. This study assessed the impact of three by-product diet formulations on intensively reared agoutis, including a cost analysis and nutritional evaluation as well as the nutritional composition, digestibility, and economic viability of three experimental diets. Sixteen adult male agoutis were individually housed at the University of the West Indies’ Field Station. After a two-week acclimatization, a four-week data collection phase followed. The four treatments included a control diet of commercial rabbit pellets and three experimental diets with varying by-product ratios. Key findings revealed that Treatment 2 had elevated Dry Matter, Crude Fat, and fiber but potentially compromised digestibility due to higher fiber and lignin. Treatment 1 resulted in the highest morbidity and was discontinued after two weeks due to significant weight loss but demonstrated the most cost-effective results, with the lowest variable cost per 45 kg, the lowest feeding cost to reach 3 kg, and the highest gross margin. Treatment 2 effectively maintained animal weight. Treatment 3 showed promising growth due to low levels of Neutral Detergent Fiber (NDF) and Acid Detergent Lignin (ADL). Alternative dietary feedstuffs from agricultural by-products show potential for sustaining body weight in adult male agoutis, but further research is needed to refine nutritional requirements for growing agoutis.

Predicting Emotional Experiences through Eye-Tracking: A Study of Tourists' Responses to Traditional Village Landscapes.

This study investigates the relationship between eye-tracking metrics and emotional experiences in the context of cultural landscapes and tourism-related visual stimuli. Fifty-three participants were involved in two experiments: forty-three in the data collection phase and ten in the model validation phase. Eye movements were recorded and the data were analyzed to identify correlations between four eye-tracking metrics-average number of saccades (ANS), total dwell fixation (TDF), fixation count (FC), and average pupil dilation (APD)-and 19 distinct emotional experiences, which were subsequently grouped into three categories: positive, neutral, and negative. The study examined the variations in eye-tracking metrics across architectural, historic, economic, and life landscapes, as well as the three primary phases of a tour: entry, core, and departure. Findings revealed that architectural and historic landscapes demanded higher levels of visual and cognitive engagement, especially during the core phase. Stepwise regression analysis identified four key eye-tracking predictors for emotional experiences, enabling the development of a prediction model. This research underscores the effectiveness of eye-tracking technology in capturing and predicting emotional responses to different landscape types, offering valuable insights for optimizing rural tourism environments and enhancing visitors' emotional experiences.

An Investigation of the Effect of Entrepreneurial Ecosystem on Startups Success through the Mediating Effect of Self Efficacy, Entrepreneurial Attitude and Entrepreneurial Intention

Developing an effective entrepreneurial ecosystem is viewed as a regional economic development approach that focuses on developing supportive circumstances that encourage the startup’s success. This study aims to investigate the impact of the entrepreneurial ecosystem on startups’ success and the variables that could play a mediating role in the relationship between entrepreneurial ecosystem and startups’ success. The research is based on face-to-face and online semi-structured interviews (the interview guide consisted of 9 open-ended questions) from second quarter of 2023 until the end of the third quarter of 2023 (the data collection phase was closed after meeting the criteria of data saturation). The participants were selected via purposeful sampling technique (20 owners or managers of startup companies including IT, customer service, food, transportation, etc. in Egypt). Thematic and content analysis is done by using NVivo software. From the analysis of the interviews, four themes are developed, and each theme consists of several codes. The first theme is the entrepreneurial ecosystem, with codes: competence development, education and training, enterprising culture and leadership, financial support, government policies and regulations, market openness, and intermediary services. The second theme is entrepreneurial characteristics, with codes: self-efficacy, entrepreneurial attitude, entrepreneurial intention, and entrepreneurial innovation. The third theme is entrepreneurial challenges, with codes: fierce competition, failure to plan and unrealistic expectations, knowledge and skills gaps, and ineffective marketing. The fourth theme is startup success, with codes: productivity, financial performance, and competitive advantage. This study found, that some elements are unimportant for startup’s ecosystem: government programs and support, commercial and professional infrastructure for entrepreneurs, and access to physical infrastructure for entrepreneurship. At the same time, the study confirmed that education and training, enterprising culture and leadership, financial support, government policies and regulations, and market openness are unimportant for startup’s ecosystem. Moreover, it is found that such elements as competence development and intermediate services should be additionally included in the ecosystem. From this study, the model that investigates the relationship between the entrepreneurial ecosystem and startup success was developed. Finally, some recommendations provided to decision-makers and current and future research.

Exploration of Students' Engineering Thinking Skills Through STEM Integrated PjBL Model Design Water Rocket Momentum Material

The research aims to analyse the engineering thinking skills of students by integrating the PjBL model with STEM water rocket design on momentum materials. This research is an experimental study. The design of this study was qualitative research. There are three stages to the research stage: preparation, implementation, and the final stage. The researchers collected data through observation, interviews, posttests, and documentaries. The focus of this study is on students' engineering thinking skills. The researchers interactively analyse the collected data during the phases of data collection, reduction, presentation, and conclusion. Additionally, the researchers conducted data credibility tests using triangulation techniques to assess the appropriateness of the research data. The research results predominantly classify the six indicators of engineering thinking as very good. Based on the findings and deductions, it is concluded that most students' engineering thinking abilities, when combined with the PjBL model and STEM water rocket design material, score highly on the six indicators of creative problem-solving. Students in this study experienced new knowledge and learning experiences that enabled them to solve daily problems by applying engineering thinking skills and physics concepts.

Reinforcement learning optimal control method for multi chiller HVAC system in an existing office building

Abstract Given that buildings consume approximately 33% of global energy, and HVAC systems contribute nearly half of a building’s total energy demand, optimizing their efficiency is imperative for sustainable energy use. Many existing buildings operate HVAC systems inefficiently, displaying non-stationary behavior. Current reinforcement learning (RL) training methods rely on historical data, which is often obtained through costly modeling or trial-and-error methods in real buildings. This paper introduces a novel reinforcement learning construction framework designed to improve the robustness and learning speed of RL control while reducing learning costs. The framework is specifically tailored for existing office buildings. Applying this framework to control HVAC systems in real office buildings in Beijing, engineering practice results demonstrate: during the data collection phase, energy efficiency surpasses traditional rule-based control methods from the previous year, achieving significantly improved energy performance (a 17.27% reduction) with minimal comfort sacrifices. The system achieves acceptable robustness, learning speed, and control stability. Reduced ongoing manual supervision leads to savings in optimization labor. Systematic exploration of actions required for RL training lays the foundation for RL algorithm development. Furthermore, by leveraging collected data, a reinforcement learning control algorithm is established, validating the reliability of this approach. This construction framework reduces the prerequisites for historical data and models, providing an acceptable alternative for systems with insufficient data or equipment conditions.

SERVANT LEADERSHIP AND EMPLOYEES’ KNOWLEDGE HIDING BEHAVIOR: TESTING MEDIATING ROLE OF SELF-EFFICACY

The fundamental objective of this study was to assess the effect of servant leadership on nurses' tendency to conceal knowledge in the context of a selected healthcare institution in Erzincan, Turkey. In order to evaluate the research hypotheses, we used a convenient sampling technique and collected data at two different time intervals from relevant sources. The initial data collection phase yielded a final sample of 400 valid and complete responses from nurses, which we subsequently analyzed using SPSS version 25. Our findings exposed a important relationship between servant leadership and knowledge withholding behaviour among nurses. Furthermore, the variable of self-efficacy emerged as a partial mediator between servant leadership and knowledge-hiding tendencies. This research donates to the understanding of knowledge-hiding behaviour, a critical component of knowledge management, through the lens of servant leadership, drawing on insights from both social exchange and leader-member exchange (LMX) theories

Development and Validation of the Attitude Towards Surrogacy Scale in a Turkiye Sample

Bacground: Surrogacy is one of the options a lot of ethical, legal and psychological controversy. Surveying attitudes toward surrogacy plays an important role in building awareness of this phenomenon in the society. In this study authors aimed to develop and validate a scale to assess the attitudes towards surrogacy. Methods: In this study cross-sectional design was implemented. Development process of the Attitude towards Surrogacy Scale (ATSS) included items development based on literature reviews, other existing questionnaires, confrmatory factor analysis (CFA), and reliability analysis using internal consistence coefcients. A total of 272 people residing in Büyükçekmece and Beylikdüzü districts of Istanbul were included in the study. Firstly, conceptual structure and item pool were created for the draft scale created by the researchers. Then, the question pool was submitted to expert opinion. After the expert opinion, a pilot study was applied to the statements in the draft scale. After the test-retest reliability was performed, the data collection phase started. Participants who agreed to participate in the study were reached both face-to-face and online. Convenience sampling method was used to reach the participants. Results: IBM SPSS and IBM AMOS package programmes were used to analyse the data obtained within the scope of the study. In the data analysis, firstly, item analysis was performed for internal consistency reliability. After this stage, structural validity analysis was used. As a result of the analysis, a measurement tool consisting of 4 dimensions and 24 statements expressed as “positive impact, negative impact, legal impact and economic impact” was developed. It has been statistically determined that this measurement tool is valid and reliable. Conclusion: The results obtained show that the scale developed for the perception of surrogacy has validity and reliability. Therefore, the “scale developed for the perception of surrogacy” can be used to measure the perception of surrogacy of people in our country.

Optimizing anomaly detection in 3D MRI scans: The role of ConvLSTM in medical image analysis

The analysis of Medical Images (MI), particularly the detection and classification of anomalies in 3D MRI (Magnetic Resonance Imaging) scans, plays a critical part in timely intervention and personalized therapy plans. In our paper, a comprehensive methodology for anomaly detection in 3D MRI scans of the brain is proposed that combines advanced Deep Learning (DL) techniques, including Convolutional Long Short-Term Memory (ConvLSTM) model, with efficient statistical feature extraction from segmented anomaly regions. The data collection phase utilizes three main datasets namely the Brain Tumor Segmentation Challenge (BRATS), the Federated Tumor Segmentation Challenge (FETS), and the Medical Segmentation Decathlon (MSD). The research begins with preprocessing steps including image resizing, intensity normalization, and alignment of anomaly region segmentation masks. The targeted anomaly regions within the samples are segmented using U-Net architecture and then follow statistical feature extraction procedure. Dimensionality reduction methods such as Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are utilized to streamline the feature space. The ConvLSTM is then used to classify the anomalies using both convolution and recurrent layers to capture spatiotemporal patterns in MRI data. The model is fine-tuned and iterated for better classification performance using Adam optimizer. The statistical evaluation results with accuracy of 98.9 % showed that the designed ConvLSTM method is more suitable for clinical diagnosis and treatment design in detecting anomalies in 3D MRI images.

Evaluating the Perceived Health-Related Effectiveness of 'The Daily Mile' Initiative in Irish Primary Schools.

Many Irish primary school children do not meet recommended physical activity (PA) guidelines. The Daily Mile (TDM) is a primary school initiative designed to increase children's PA output. This study evaluates TDM's perceived effect on Irish primary school children's health-related metrics. A mixed-methods study, comprising two data collection phases, used the RE-AIM framework to evaluate TDM. Phase One involved teachers, principals and classroom assistants (n = 191) from TDM-registered schools completing a questionnaire. Two TDM-registered schools participated in Phase Two, where (i) interviews were conducted with each principal, (ii) a teacher sub-sample (n = 4) participated in a focus group, and (iii) a child sub-sample (n = 14) participated in separate focus groups. TDM was perceived to positively impact the markers of children's health, including their PA behaviour, physical fitness and social well-being. Inclement weather (66.5%) and a lack of time (56.5%) were reported as the top-ranked implementation barriers. Moreover, TDM's repetitive nature often left children feeling disinterested and resulted in some teachers modifying the initiative to maintain engagement levels. Maintaining the long-term implementation of TDM and its health benefits in primary schools may require bespoke amendments to the original format to preserve children's engagement.

The quality of qualitative research

In this contribution, we discuss criteria for the quality of qualitative research. We consider reliability and validity as specifications of the comprehensive requirement for ‘intersubjective replicability’, with which qualitative research...

Symptom clusters and their impacts on the quality of life of patients with lung cancer receiving immunotherapy: A cross-sectional study.

The objective of this study was to identify symptom clusters in lung cancer patients receiving immunotherapy and explore their impact on the quality of life of patients. Immunotherapy is widely used in lung cancer; however, there is little understanding of symptom clusters and their impacts on the quality of life of this population. Cross-sectional study. The survey contained the Memorial Symptom Assessment Scale (MSAS), Quality of Life Questionnaire-Lung Cancer 43 and a self-designed General Information Evaluation Form. Symptom clusters were identified using exploratory factor analysis (EFA) based on the symptom scores. Spearman correlation analysis was performed to evaluate the associations between each symptom cluster and the patients' quality of life. Multiple linear regression analysis was employed to examine the impact of the symptom clusters on quality of life. This study adhered to the STROBE guidelines. In total, 240 participants completed the survey. Five symptom clusters were identified and named according to their characteristics: emotional-related symptom cluster, lung cancer-related symptom cluster, physical symptom cluster, skin symptom cluster and neural symptom cluster. All symptom clusters, except for the neural symptom cluster, had a significantly detrimental impact on patient quality of life. Lung cancer patients undergoing immunotherapy experience a range of symptoms, which can be categorized into five clusters. These symptom clusters have a negative impact on patients' quality of life. Future research should focus on developing interventions for each symptom cluster and their influencing factors. In the data collection phase, lung cancer patients undergoing immunotherapy were recruited to participate in the survey.

Populist Attitudes and Conspiracy Beliefs in Slovak Society: Pandemic vs Post-pandemic Data

The continued success of populist political subjects can be observed in many countries in recent years, including Slovakia. Society is polarized, and dissatisfaction, anger, and fear are coming to the fore. This has been exacerbated by the pandemic, which, as a crisis, typically increases the potential for the spread of unfounded or even conspiratorial beliefs. As the occurrence of the mentioned phenomena postulates similar questions, the study's objective is to identify the rate of occurrence of populist attitudes and trust in conspiracy content during and after the pandemic. At different stages of the pandemic course, the prevalence of populist attitudes in the sample of Slovak adult men and women (N = 1345) and the tendency to trust conspiracy beliefs both in general and specifically in the context of the COVID-19 pandemic are examined. Using the Populist Attitudes Scale, the Generic conspiracist beliefs scale, and the authors' questionnaire on belief in conspiracy claims related to the COVID-19 virus, we found a high prevalence of the variables of interest, with the highest rates observed in women, people of older age and people with lower education. Across the different phases of data collection, attitudes and beliefs were relatively stable, and a linear association was observed between them. The results demonstrate the validity of the study of populist attitudes and the tendency to believe in conspiracy content, while at the same time raising questions for further research.

Challenges and potential of quasi-experimental studies in cognitive linguistics applied to language teaching and learning

Abstract This review study focuses on the challenges faced when conducting effects-of-instruction research in Applied Cognitive Linguistics. It offers an overview of relevant research that has sought to demonstrate the effectiveness of a cognitive-based approach to second language (L2) pedagogy and provides illustrations from previous work in L2 Spanish by the author and colleagues of some of the factors that can influence findings in quasi-experimental research. More specifically, the study addresses some of the difficulties encountered in the design of materials and assessment tests, during the pedagogical intervention and the data collection and data analysis phases. These include choice of assessment, test effects, sample size, withdrawal, and time-on-task, among others. Along with these methodological issues, a discussion of possible solutions as well as pedagogical and methodological implications are discussed.

Proactive review for people with diabetes in hospital: a cluster randomised feasibility trial with process evaluation, protocol V3.1

BackgroundDiabetes inpatient specialist services vary across the country, with limited evidence to guide service delivery. Currently, referrals to diabetes inpatient specialists are usually ‘reactive’ after diabetes-related events have taken place, which are associated with an increased risk of morbidity/mortality and increased length of hospital stay. We propose that a proactive diabetes review model of care, delivered by diabetes inpatient specialist nurses, may contribute to the prevention of such diabetes-related events and result in a reduction in the risk of harm.MethodWe will conduct a cluster randomised feasibility study with process evaluation. The proactive diabetes review model (PDRM) is a complex intervention that focuses on the prevention of potentially modifiable diabetes-related harms. All eligible patients will receive a comprehensive, structured diabetes review that aims to identify and prevent potentially modifiable diabetes-related harms through utilising a standardised review structure. Reviews are undertaken by a diabetes inpatient specialist nurse within one working day of admission. This differs from usual care where patients are often only seen after diabetes-related harms have taken place. The trial duration will be approximately 32 weeks, with intervention delivery throughout. There will be an initial 8-week run-in phase, followed by a 24-week data collection phase. Eight wards will be equally randomised to either PDRM or usual care. Adult patients with a known diagnosis of diabetes admitted to an included ward will be eligible. Data collection will be limited to that typically collected as part of usual care. Data collected will include descriptive data at both the ward and patient level and glucose measures, such as frequency and results of capillary glucose testing, ketonaemia and hypoglycaemic events. The analysis aims to determine the fidelity and acceptability of the intervention and the feasibility of a future definitive trial. Whilst this study is primarily about trial feasibility, the findings of the process evaluation may lead to changes to both trial processes and modifications to the intervention. A qualitative process evaluation will be conducted in parallel to the trial. A minimum of 22 patients, nurses, doctors, and managers will be recruited with methods including direct non-participant observation and semi-structured interviews. The feasibility of a future definitive trial will be assessed by evaluating recruitment and randomisation processes, staffing resources and quality of available data.DiscussionThe aim of this cluster randomised feasibility trial with a process evaluation is to explore the feasibility of a definitive trial and identify appropriate outcome measures. If a trial is feasible and the effectiveness of PDRM can be evaluated, this could inform the future development of inpatient diabetes services nationally.Trial registrationUK Clinical Research Network, 51,167. ISRCTN, ISRCTN70402110. Registered on 21 February 2022.

Towards an understanding of the digital transformation of facility management in healthcare: perspectives from practice

PurposeFacility management (FM) in healthcare is an organisational function that provides non-medical activities, ensuring operational functionality of critical health infrastructure. FM leaders are under pressure to implement the digital transformation, confronted with a highly complex and challenging operational environment. However, the current scope and understanding of the digital transformation in FM is rather limited, with a strong focus on technology. This perspective is inadequate to realise a digital transformation in healthcare organisations and requires a new framework. To ensure the practical applicability, a clearer depiction on how FM practitioners in the field, currently understand the digital transformation, is necessary.Design/methodology/approachA sequential exploratory mixed-methods design was chosen with a qualitative data collection and quantitative data analysis phase. A total of 23 semi-structured research interviews with professionals from FM in healthcare in Switzerland were carried out. Topic modeling was used to analyse, identify and cluster topics.FindingsIn total, nine distinct digital transformation topics were generated: ‘addressing future organisational and people needs’, ‘end-user-oriented processes’, ‘digitalisation management vs. digital tools’, ‘major organisational change’, ‘information exchange and work culture’, ‘digital systems’, ‘pressure’, ‘company-wide strategy’ and ‘management support and communication involving non-medical support services’. The findings indicate that FM practitioners perceive the digital transformation beyond the purely technological viewpoint, although they lack the know-how and instruments necessary to implement the digital transformation.Originality/valueThis study employs an innovative research design, resulting in foundational insights on how FM in healthcare perceives the digital transformation. As such, the study expands the notion on what the digital transformation means for the FM field.

Deep Siamese domain adaptation convolutional neural network-based quaternion fractional order Meixner moments fostered big data analytical method for enhancing cloud data security

ABSTRACT In this paper, Quaternion Fractional Order Meixner Moments-based Deep Siamese Domain Adaptation Convolutional Neural Network-based Big Data Analytical Technique is proposed for improving Cloud Data Security (DSDA-CNN-QFOMM-BD-CDS). The proposed methodology comprises six phases: data collection, transmission, pre-processing, storage, analysis, and security of data. Big data analysis methodologies start with the data collection phase. Deep Siamese domain adaptation convolutional Neural Network (DSDA-CNN) is applied to categorize the types of attacks in the cloud database during the data analysis process. During data security phase, Quaternion Fractional Order Meixner Moments (QFOMM) is employed to protect the cloud data for encryption with decryption. The proposed method is implemented in JAVA and assessed using performance metrics, including precision, sensitivity, accuracy, recall, specificity, f-measure, computational complexity information loss, compression ratio, throughput, encryption time, decryption time. The performance of the proposed method offers 23.31%, 15.64%, 18.89% better accuracy and 36.69%, 17.25%, 19.96% less information loss. When compared to existing methods like Fractional order discrete Tchebyshev encryption fostered big data analytical model to maximize the safety of cloud data depend on Enhanced Elman spike neural network (EESNN-FrDTM-BD-CDS), an innovative scheme architecture for safe authentication along data sharing in cloud enabled Big data Environment (LZMA-DBSCAN-BD-CDS).

Development and validation of the Vaccine Barriers Assessment Tool for identifying drivers of under-vaccination in children under five years in Australia

ABSTRACT Data on routine childhood vaccination coverage can only tell us who is under-vaccinated; it cannot explain why vaccine coverage is low. Collecting data on the reasons behind under-vaccination is necessary to implement cost-effective strategies that address key barriers and target interventions appropriately. However, no instruments that measure both vaccine acceptance and access factors among parents of children <5 y have been validated in high-income countries. This study aims to develop and validate the Vaccine Barriers Assessment Tool (VBAT) for Australia. We applied three phases of mixed methods data collection and analysis. In Phase 1, we developed a comprehensive list of 80 items reflecting all potential parental barriers to childhood vaccination, derived from published literature and behavioral theory. Through cognitive interviews (n = 28), we refined this list to 45 items. In Phase 2, we conducted a two-wave online survey to test the reliability and validity of these items in an Australian sample of parents (n = 532) with structural equation modeling, further refining the list to 35 items. In Phase 3, we conducted a final parent survey (n = 156), administering these items along with the Parent Attitudes toward Childhood Vaccination (PACV) scale for comparison. We reviewed participants’ immunization register data to assess the predictive validity of the proposed models. The final 6-item short form and 15-item long form Vaccine Barriers Assessment Tool assess access, communal benefit, personal risk, equity, commitment, social norms, and trust in health-care workers. It is being applied for national surveillance in Australia and will be adapted for additional populations and vaccines.

Ramuan dan Pengobatan Tradisional Pulau Penyengat untuk Sakit yang Berkaitan dengan Mata

The manuscript of Pengobatan Tradisional pada Masyarakat Pulau Penyengat (PTpMPP) contains various types of pain along with herbal ingredients, one of which is pain related to the eyes. The purpose of this study is to explain the study of ethnomedicine contained in the text of PTpMPP. The methods used are data collection, data processing, data analysis, and presentation of the results of data analysts. The data collection phase was carried out by field studies and literature studies. The data processing stage is carried out from the description of the manuscript, transliteration, and text editing. In studying its content, researchers used the theory of ethnomedicine. Studies that combine literature and ethnomedicine can provide a better understanding of how language, culture and narrative shape medical perceptions and practices in a given society. In addition, it can help improve interdisciplinary understanding of the human complexity of understanding and addressing health issues because literary works, particularly ancient texts, can reflect and shape the traditional medical knowledge and practices of different cultures. This makes it possible to understand health and medicine in a broader and more complex cultural context. Therefore, ethnomedicine is used to reveal the kinds and functions of medicinal plants used and the treatment methods contained in ancient manuscripts. The source of the data used is PTpMPP manuscript with Jawi script and Malay script, stored in Yayasan Inderasakti, Pulau Penyengat. The results of this study explain the eight causes of pain related to the eyes, namely injured eyes, eye pain (ordinary), blurred eyes, growing eyes, meat eye pain, restung eye pain, swollen eyes, and eyes for prohibition. The medical practice of treating the eight diseases belongs to the naturalistic medical system and each disease has its own herbal ingredients.

Improving industrial security device detection with convolutional neural networks

&lt;p&gt;Employee safety is paramount in the manufacturing industry to ensure their well-being and protection. Technological advancements, particularly convolutional neural networks (CNN), have significantly enhanced this safety aspect by facilitating object detection and recognition. This project aims to utilize CNN technology to detect personal protective equipment and implement a safety implement detection system. The CNN architecture with the YOLOv5x model was employed to train a dataset. Dataset videos were converted into frames, with resolution scale adjustments made during the data collection phase. Subsequently, the dataset was labeled, underwent data cleaning, and label and bounding box revisions. The results revealed significant metrics in safety equipment detection in industrial settings. Helmet precision reached 91%, with a recall of 74%. Goggles achieved 85% precision and an 87% recall. Mask absence recorded 92% precision and an 89% recall. The YOLOv5x model exhibited commendable performance, showcasing its robust ability to accurately locate and detect objects. In conclusion, the utilization of a CNN-based safety equipment detection system, such as YOLOv5x, has yielded substantial improvements in both speed and accuracy. These findings lay a solid foundation for future industrial security applications aimed at safeguarding workers, fostering responsible workplace behavior, and optimizing the utilization of information technology resources.&lt;/p&gt;