Repetitive Tasks Research Articles

Prevalence of thoracic outlet syndrome among patients visiting physical therapy outpatient department of public sector Tertiary Care Hospitals, Peshawar

The indications and symptoms in the upper extremity brought on by compression of the brachial plexus, subclavian vein, and/or subclavian artery at the thoracic outlet were originally referred to as thoracic outlet syndrome (TOS). The symptoms of neurogenic thoracic outlet syndrome (NTOS) are caused by compression of the brachial plexus roots as they travel through the thoracic outlet. The most typical symptom of thoracic outlet syndrome is neurogenic thoracic outlet syndrome (NTOS). The brachial plexus is compressed and irritated, which causes its symptoms.A cross-sectional study was conducted from March 2023 to June 2023 among patients visiting Physical therapy outpatient department of public sector tertiary care hospitals Peshawar. The DASH questionnaire was distributed to all patients according to inclusion criteria from different hospitals to collect data on the prevalence of thoracic outlet syndrome.The study found that the prevalence of thoracic outlet syndrome was higher in female. The prevalence was higher among female housewives (77.77%) compared to male (63%). Risk factors associated with this pain included improper posture in same position while working, long work durations, repetitive tasks, and restricted neck movements.

A review of deep learning and artificial intelligence in dyeing, printing and finishing

This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.

Increasing Trends of Artificial Intelligence With Robotic Process Automation in Health Care: A Narrative Review.

This review explores the fast-growing importance of artificial intelligence (AI) with robotic process automation (RPA) in healthcare. AI uses intelligent algorithms to analyze data, while RPA automates repetitive tasks to improve efficiency and accuracy. These technologies are swiftly revolutionizing health care by improving diagnostic precision, accelerating administrative tasks, reducing operation timing, and improving patient care. Application of these technologies requires good technical understanding, preparedness for continuous learning, and adaptability to new challenges. This review aims to provide an in-depth study of the potential applications, present implementations, challenges, and future scope of AI with RPA in healthcare. It can provide information to researchers, professionals, and decision-makers regarding the application of the technologies under consideration for better productivity, increased security and accuracy of data, cost reduction, and personalization of healthcare provided to patients. The main results are that AI and RPA can ensure greater data security, provide supporting work in administration, like scheduling appointments and medical billing, make better decisions, enable telehealth and remote patient monitoring, reduce human error, and increase overall health outcomes. This review overviews the challenges in implementing robotics technology, focusing mainly on secondary sourcejournals, scholarly articles, and reference books. Key findings indicate that this study reveals how robotics could alleviate healthcare professionals. Further research, investment, and collaboration will be needed to enable these technologies to reach their full potential for healthcare delivery. However, challenges such as data privacy and security concerns, high implementation costs, and regulatory and ethical considerations must be addressed. The conclusion emphasizes that while these technologies are revolutionizing healthcare by increasing efficiency and personalizing patient care, ongoing research, investment, and collaboration are essential for their successful adoption.

Assessment of Lateral Epicondylitis in Butchers in Islamabad

Background: Lateral epicondylitis, or tennis elbow, is a common overuse injury affecting the extensor muscles of the forearm, particularly in professions requiring repetitive manual tasks, such as butchery.Objective: To assess the prevalence of lateral epicondylitis among butchers in Islamabad and its association with occupational risk factors.Methods: A correlational study was conducted between February and July 2022, involving 205 butchers selected through convenience sampling. Participants with at least one year of work experience were included, while those with pre-existing injuries or diabetes were excluded. Data were collected using the Pain Rating Scale, DASH questionnaire, and Cozen’s test. Descriptive and inferential statistics were performed using SPSS version 25, with a significance level of 0.05.Results: The prevalence of lateral epicondylitis was 23.41% (n=48). Butchers working more than 9 hours daily had a significantly higher prevalence (p=0.001). Mild to moderate pain was reported by 26.3% to 21.5% of participants, particularly in the arm and shoulder.Conclusion: The study found a high prevalence of lateral epicondylitis among butchers, linked to prolonged working hours and repetitive tasks. Ergonomic interventions and work modifications are recommended to reduce risk.

Prevalence of Upper Cross Syndrome Among Heavy Vehicle Drivers (HVD): A Cross-Sectional Study

Background: Heavy vehicle drivers (HVDs) are at risk for musculoskeletal disorders, including Upper Cross Syndrome (UCS), due to prolonged sitting and repetitive tasks. UCS is characterized by muscle imbalances in the neck, shoulders, and upper back, leading to pain and functional impairment.Objective: This study aimed to assess the prevalence of UCS among HVDs and identify contributing factors such as working hours and pain intensity.Methods: A cross-sectional study was conducted from March 2024 to August 2024, involving 212 male HVDs aged 25-55 years, working for over a year, and experiencing neck and back pain for more than three months. Exclusion criteria included recent spinal trauma or deformities. Data were collected using the Numeric Pain Rating Scale (NPRS) and Neck Disability Index (NDI) and analyzed using SPSS version 25. Chi-square tests and linear regression were applied.Results: UCS was prevalent in 26.4% of drivers. A significant relationship was found between pain intensity and disability (χ² = 170.126, p = 0.000). Most drivers (66.9%) were aged 25-35 years, and 30.2% reported very mild pain.Conclusion: UCS is significantly prevalent among HVDs, with long working hours and poor ergonomics contributing to its onset.

Based on Medicine, The Now and Future of Large Language Models.

This review explores the potential applications of large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4 in the medical field, aiming to encourage their prudent use, provide professional support, and develop accessible medical AI tools that adhere to healthcare standards. This paper examines the impact of technologies such as OpenAI's Generative Pre-trained Transformers (GPT) series, including GPT-3.5 and GPT-4, and other large language models (LLMs) in medical education, scientific research, clinical practice, and nursing. Specifically, it includes supporting curriculum design, acting as personalized learning assistants, creating standardized simulated patient scenarios in education; assisting with writing papers, data analysis, and optimizing experimental designs in scientific research; aiding in medical imaging analysis, decision-making, patient education, and communication in clinical practice; and reducing repetitive tasks, promoting personalized care and self-care, providing psychological support, and enhancing management efficiency in nursing. LLMs, including ChatGPT, have demonstrated significant potential and effectiveness in the aforementioned areas, yet their deployment in healthcare settings is fraught with ethical complexities, potential lack of empathy, and risks of biased responses. Despite these challenges, significant medical advancements can be expected through the proper use of LLMs and appropriate policy guidance. Future research should focus on overcoming these barriers to ensure the effective and ethical application of LLMs in the medical field.

Prevalence of Lower Back Pain and Associated Workplace and Ergonomic Factors among Mineworkers in a Nickel Mine, Zimbabwe

Lower back pain is a common occurrence among mine workers due to the nature of their work. Globally, workplace (occupational) and ergonomic risk factors have been reported to influence the prevalence of lower back pain among workers. This study aimed to determine ergonomic risk factors associated with lower back pain and associated risk factors (workplace and ergonomics) among mineworkers in a nickel mine in Zimbabwe. A cross-sectional study design was employed, and participants were randomly selected to complete a questionnaire that included the Dutch Musculoskeletal Questionnaire (DMQ). A total of 420 mine workers were interviewed, with the majority being male (n = 259, 61.7%) and aged between 31 and 44 years old (n = 159, 37.9%). The study found that the prevalence of lower back pain was 41.43% (n = 174). Several risk factors were found to be associated with lower back pain, including working overtime (AOR = 1.13, p < 0.01; 95% CI: 0.07–0.22), performing repetitive tasks (AOR = 8.06, p < 0.01; 95% CI: 4.67–13.93), bending (AOR = 7.77, p < 0.01; 95% CI: 3.97–15.22), and twisting (AOR = 3.32, p < 0.01; 95% CI: 1.83–6.03). Based on these findings, it is recommended that an ergonomic risk assessment and prevention program be implemented, which should include educational awareness about lower back pain and its prevention among mine workers.

The Impact of Physiological and Psychological Fatigue on Work Efficiency: A Case Study of Parcel Sorting Work.

The popularity of online shopping in China has increased significantly, creating new development opportunities for the express delivery industry. However, the rapid expansion of the express industry has also created challenges in the parcel sorting process. The demanding nature of parcel sorting work, which is characterized by intense and prolonged repetitive tasks, makes individuals particularly vulnerable to the effects of fatigue. Fatigue is a complex condition that encompasses both physiological and psychological exhaustion. It often results in reduced energy levels and diminished functionality, significantly impacting an individual's performance at work and their overall well-being. This study aimed to investigate how physiological and psychological fatigue affects sorting efficiency and to identify appropriate rest periods that will allow employees to maintain their performance levels. The research involved fifteen participants who took part in a 60 min continuous sorting experiment and a similar experiment with scheduled breaks. During both trials, we collected data on participants' electromyography (EMG) and electrodermal activity (EDA), as well as subjective fatigue ratings (RPE). Signal features such as the median frequency (MF) of EMG and the skin conductance level (SCL) were analyzed to assess physiological and psychological fatigue, respectively. The results show that physiological fatigue mainly affects sorting efficiency in the first 30 min, while psychological fatigue becomes more influential in the following half-hour period. In addition, subjective fatigue levels during the first 30 min are primarily determined by psychological factors, while beyond that point, both physiological and psychological fatigue contribute to subjective fatigue. Rest periods of 415-460 s, based on EDA recovery times, effectively support sorting efficiency and participants' recovery. This study highlights the complex ways in which fatigue affects parcel sorting performance and provides valuable theoretical and practical insights for establishing labor quotas and optimizing work schedules in the parcel sorting industry.

Automated machine learning with R: AutoML tools for beginners in clinical research.

Recently, interest in machine learning (ML) has increased as the application fields have expanded significantly. Although ML methods excel in many fields, establishing an ML pipeline requires considerable time and human resources. Automated ML (AutoML) tools offer a solution by automating repetitive tasks, such as data preprocessing, model selection, hyperparameter optimization, and prediction analysis. This review introduces the use of AutoML tools for general research, including clinical studies. In particular, it outlines a simple approach that is accessible to beginners using the R programming language (R Foundation for Statistical Computing). In addition, the practical code and output results for binary classification are provided to facilitate direct application by clinical researchers in future studies.

Non-Word Repetition in Arabic-speaking children with and without Autism Spectrum Disorder (ASD): A closer look into accuracy and error patterns

ABSTRACT Non-Word Repetition (NWR) tasks effectively identify language impairments and assess phonological skills across diverse populations and languages, including Arabic. Prior research revealed heterogeneity of performance in children with Autism Spectrum Disorder (ASD) on NWR tasks. The current study is the first to evaluate phonological skills of Palestinian-Arabic-speaking children with and without ASD, employing a Palestinian-Arabic NWR task. A total of 142 Palestinian-Arabic-speaking children, aged 5–11 participated in the study, including 75 children with Typical Language Development (TLD) and 67 children with ASD. The NWR task included 18 non-words of varying length (1–4 syllables) and complexity (with and without consonant clusters). Quantitative analysis examined the effects of length and phonological complexity on the NWR accuracy scores in children with TLD and ASD. Error pattern analysis accounting for phoneme and syllable substitutions/deletions/additions and lexicalisations aimed to shed light on the phonological representations of children with and without ASD. Within the ASD group, two subgroups emerged: 72% exhibited age-appropriate NWR performance, while 28% showed performance at-risk for phonological impairment. Non-word length, rather than complexity, significantly influenced the children’s performance. Consonant substitution was the most frequent error pattern across all groups. On the theoretical side, these findings extend cross-linguistic evidence of phonological skill heterogeneity in children with ASD to Arabic-speaking children. Additionally, they highlight an overall delayed but qualitatively similar pattern of phonological development in children with ASD. On the clinical side, results underscore the importance of comprehensive language assessment in children with ASD.

Speech Biomarkers in Huntington's Disease: A Longitudinal Follow-Up Study in Premanifest Mutation Carriers.

Speech alterations have been reported in manifest Huntington's disease (HD) and premanifest mutation carriers (preHD). The aim of our study was to explore these alterations in preHD and whether they can be used as biomarkers. 13 preHD mutation carriers performed reading task, sustained phonation task and syllable repetition tasks at baseline and after 21 months, as well as clinical examination and MRI. Syllable repetition capacity and self-chosen velocity of single syllable repetition differed significantly between time points. There were no changes in clinical ratings or MRI volumetry. Measurements of speech might be sensitive tools for monitoring subclinical changes in preHD.

Predictive Effect of AI on Leadership: Insights From Public Case Studies on Organizational Dynamics

The increasing integration of Artificial Intelligence (AI) in organizational leadership is transforming traditional leadership practices and dynamics. This analysis investigates the potential long-term effects of AI on leadership, focusing on how AI improves decision-making, automates repetitive tasks, and enhances employee engagement. Drawing on in-depth case studies of major companies like IBM, Google, and Amazon, this paper demonstrates the successes and challenges of incorporating AI into leadership roles. It also explores emerging AI-driven leadership skills and highlights potential future leadership frameworks that may develop as AI technologies progress, offering an optimistic view of leadership in the future. While this analysis provides valuable qualitative insights, it recognizes the need for additional empirical data to support its claims, including AI adoption rates and metrics for evaluating leadership effectiveness. Incorporating predictive models could also enhance our understanding of AI's lasting impact on leadership. The paper is a valuable resource for organizations and leaders navigating the evolving landscape of AI-augmented leadership.

Distraction impact of concurrent conversation on event-related potential based brain-computer interfaces

Objective.This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.Approach.The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main results.The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.

Considerations for identifying subtypes of speech sound disorder.

Speech sound disorders (SSDs) in children are heterogeneous. Differentiating children with SSDs into distinct subtypes is important so that each child receives a treatment approach well suited to the particular difficulties they are experiencing. To study the distinct underlying processes that differentiate phonological processing, phonological planning or motor planning deficits. The literature on the nature of SSDs is reviewed to reveal diagnostic signs at the level of distal causes, proximal factors and surface characteristics. Subtypes of SSDs may be identified by linking the surface characteristics of the children's speech to underlying explanatory proximal factors. The proximal factors may be revealed by measures of speech perception skills, phonological memory and speech-motor control. The evidence suggests that consistent phonological disorder (CPD) can be identified by predictable patterns of speech error associated with speech perception errors. Inconsistent phonological disorder (IPD) is associated with a deficit in the selection and sequencing of phonemes, that is, revealed as within-word inconsistency and poor phonological memory. The motor planning deficit that is specific to childhood apraxia of speech (CAS) is revealed by transcoding errors on the syllable repetition task and an inability to produce [pətəkə] accurately and rapidly. Children with SSDs form a heterogeneous population. Surface characteristics overlap considerably among those with severe disorders, but certain signs are unique to particular subtypes. Careful attention to underlying causal factors will support the accurate diagnosis and selection of personalized treatment options. What is already known on the subject SSD in children are heterogenous, with numerous subtypes of primary SSD proposed. Diagnosing the specific subtype of SSD is important in order to assign the most efficacious treatment approach for each child. Identifying the distinct subtype for each child is difficult because the surface characteristics of certain subtypes overlap among categories (e.g., CPD or IPD; CAS). What this paper adds to the existing knowledge The diagnostic challenge might be eased by systematic attention to explanatory factors in relation to the surface characteristics, using specific tests for this purpose. Word identification tasks tap speech perception skills; repetition of short versus long strings of nonsense syllables permits observation of phonological memory and phonological planning skills; and standard maximum performance tests provide considerable information about speech motor control. What are the potential or actual clinical implications of this work? Children with SSDs should receive comprehensive assessments of their phonological processing, phonological planning and motor planning skills frequently, alongside examinations of their error patterns in connected speech. Such assessments will serve to identify the child's primary challenges currently and as they change over developmental time.

Feature Selection and Dimensionality Reduction for Large Data set Clustering using Self-Organizing Maps

Data mining is a form of analyzing the data which relates the newline techniques from fields like statistics machine learning databases artificial newline intelligence etc Clustering is one of the most important data mining newline techniques in which cluster of objects are grouped based on their similarity newline "Clustering is the process of accumulating the data records into considerable newline subclasses clusters in a way which enhances the relationship within clusters newline and reduces the similarity among two different clusters. The activity occurs every new academic year and schools with plenty of new students registered may feel a bit overwhelmed with this grouping assignment. A decision support system which can automatically perform grouping on a list of students may be able to help the school’s staffs with this repetitive task. A self-organizing map (SOM) is an example of unsupervised learning algorithm using an artificial neural network structure to produce a low dimensional representation from a given input.

Active Molecular Gripper as a Macrocycle Synthesizer.

A confined space preorganizes substrates, which substantially changes their chemical reactivity and selectivity; however, the performance as a reaction vessel is hampered by insensitivity to environmental changes. Here, we show a dynamic confined space formed by substrate grasping of an amphiphilic host with branched aromatic arms as an active molecular gripper capable of performing substrate grasping, macrocyclization, and product release acting as a macrocycle synthesizer. The confined reaction space is formed by the substrate grasping of the molecular gripper, which is further stabilized by gel formation. Confining a linear substrate in the closed form of the gripper triggers a spontaneous ring-forming reaction to release a macrocycle product by opening. The consecutive open-closed switching enables repetitive tasks to be performed with remarkable working efficiency.

Understanding Musculoskeletal Risks in Retail Workers: An Ergonomic Assessment of Store 24H

Musculoskeletal disorders (MSDs) are prevalent in retail environments, often resulting from repetitive tasks, awkward postures, and prolonged standing. This study examined musculoskeletal discomfort and ergonomic risk factors within the retail environment of convenience stores, focusing on the prevalence of MSDs among employees of Store 24H, Dungun, Terengganu, Malaysia. The assessment began with a job task description, followed by workplace observation and worker input. Utilising a systematic approach, the research integrated two standardized assessment tools; the Cornell Musculoskeletal Questionnaire (CMQ) and the Initial Ergonomic Risk Assessment (ERA) Form Checklist, to identify the discomfort area and ergonomic risk factors associated with job tasks. Three job tasks were identified in the Store 24H environment; operating cash registers, customer service and restocking. The findings indicate a notable prevalence of MSDs associated with the identified high-risk tasks. Repetitive motions, awkward postures, prolonged standing, and forceful exertion were observed as primary ergonomic risk factors for these tasks which contribute to musculoskeletal discomfort in the neck, shoulder, back, hip, and knee. Additionally, forceful exertion and vibration exhibit high risks towards restocking tasks, resulting in restocking as the top contributor towards the MSDs. Hence, the findings highlight the need for advanced ERA and mitigation strategies, including workstation redesign, task rotation, ergonomic training and accessories, to promote employee well-being and reduce the incidence of MSDs among employees of Store 24H. Implementing these measures is essential for promoting the health and well-being of employees, decreasing the occurrence of musculoskeletal disorders, and improving overall safety and productivity in the workplace.

MULA, an affordable framework for multifunctional liquid automation in natural- and life sciences with a focus on hardware design, setup, modularity and validation

The implementation of automation has already had a considerable impact on chemical and pharmaceutical industrial laboratories. However, academic laboratories have often been more reluctant to adopt such technology due to the high cost of commercial liquid handling systems, although, in many instances, there would be a huge potential to automate repetitive tasks, resulting in elevated productivity. We present here a detailed description of the setup, validation, and utilization of a multifunctional liquid automation (MULA) system that can be used to automate various chemical and biological tasks. Considering that such a setup must be highly customizable, we also designed MULA with respect to modularity, providing detailed insight as far as possible. Including all 3D-printed parts and the used Hamilton gastight micro syringe, the total construction cost is approximately 700 €. This allows us to achieve a highly reliable and accurate system that exceeds the precision of a classical air displacement pipette while still retaining the ability to use closed vial (septa) setups. To encourage other groups to adopt this setup, detailed instructions and tips for every step of the process are provided, along with the complete CAD design of MULA and control code, which are freely available for download under the CC BY NC 3.0 license.

What Super Productive People Do Differently

This article explores seven habits shown through research to distinguish highly productive individuals from average performers. Each habit is grounded in academic literature and illustrated with real-world industry examples. The habits include planning the next day's schedule in advance to be proactive rather than reactive. Minimizing distractions like notifications to focus deeply on priorities. Batching similar tasks together to maintain cognitive momentum. Automating repetitive tasks to streamline workflow. Enforcing strict boundaries between work and personal time to prevent burnout. Dedicating time to continuous self-improvement through skills development. And scheduling regular "deep work" sessions for uninterrupted focus on complex cognitive work. The article argues that organizational leaders who foster a culture supporting these evidence-based productivity strategies can cultivate high-performing teams consistently delivering quality outcomes despite demanding workloads. adopting some principles could significantly increase organizational output.

Feasibility and Validity of Wearable Sensors for Monitoring Temporal Parameters in Manual Wheelchair Propulsion.

Upper extremity pain and injury are among the most common musculoskeletal complications manual wheelchair users face. Assessing the temporal parameters of manual wheelchair propulsion, such as propulsion duration, cadence, push duration, and recovery duration, is essential for providing a deep insight into the mobility, level of activity, energy expenditure, and cumulative exposure to repetitive tasks and thus providing personalized feedback. The purpose of this paper is to investigate the use of inertial measurement units (IMUs) to estimate these temporal parameters by identifying the start and end time of hand contact with the push-rim during each propulsion cycle. We presented a model based on data collected from 23 participants (14 males and 9 females, including 9 experienced manual wheelchair users) to guarantee the reliability and generalizability of our method. The obtained outcomes from our IMU-based model were then compared against an instrumented wheelchair (SMARTWheel) as a reference criterion. The results illustrated that our model was able to accurately detect hand contact and hand release and predict temporal parameters, including the push duration and recovery duration in manual wheelchair users, with the mean error ± standard deviation of 10 ± 60 milliseconds and -20 ± 80 milliseconds, respectively. The findings of this study demonstrate the potential of hand-mounted IMUs as a reliable and objective tool for analyzing temporal parameters in manual wheelchair propulsion. IMUs offer significant strides towards inclusivity and accessibility due to their portability and user-friendliness and can democratize health monitoring of manual wheelchair users by making it accessible to a broader range of users compared to traditional technologies.