Repetitive Tasks Research Articles

A comprehensive assessment of a passive back support exoskeleton for load handling assistance

Various back support exoskeletons (BSEs) have been developed to reduce the workload and the risk of musculoskeletal disorders. However, the evaluations of such devices have primarily focused on specific quantitative aspects like muscle activation level or metabolic cost without any assessment of the user perception or comfort. In addition, the absence of an universal guidance or agreement on the methods for quantifying the efficacy of exoskeletons has hampered a systematic comparison among the developed devices. This study introduces a newly developed passive BSE for heavy load handling workers, and verifies its assistive effect through a rigorous and multifaceted evaluation. Fifteen young and healthy males participated in two experiment sessions. In the first session, participants lifted a 15 kg box and held it in a static position. In the second session, participants performed repetitive lifting tasks with a 10 kg box. The developed BSE reduced root mean square, peak, and integrated muscle activation with statistical significance in the key muscles. The BSE alleviated muscle fatigue by delaying spectral shift of instantaneous median frequency in the lumbar erector spinae (p < 0.001) and gluteus maximus (p < 0.001). The BSE also decreased energy expenditure by 13.6% (p < 0.001). In addition, the BSE reduced participants’ rate of perceived exertion and local musculoskeletal discomfort by 14.7% (p = 0.005) and 30.5% (p = 0.001), respectively. These results support the efficacy of the developed BSE. The multifaceted evaluation process used in this study also contributes to proposing a systematic guidance on evaluating BSEs.

A Study of Croatian Public Libraries: Challenges and Opportunities in Embracing Artificial Intelligence

ABSTRACT Artificial intelligence (AI) is gradually entering libraries worldwide. Croatian public libraries are also considering implementing selected AI-supported tools in some of their library operations and user-oriented services. Actual implementation has occurred sporadically/selectively for several popular library operations, but not for library management. AI-supported tools are perceived as time-saving aids that reduce the burden of repetitive tasks and enhance existing user services. Confidence among librarians in the current stage of AI development, as of May 2024, appears to be relatively low. The research study revealed a lack of advanced knowledge about AI among librarians. In terms of a strategic view on the future of public libraries, librarians are aware of potential changes in their roles and the possibility of job evolution or loss. AI is perceived as an important set of technologies for the development of Croatian public libraries.

Challenges and opportunities: Implementing RPA and AI in fraud detection in the banking sector

Integrating Robotic Process Automation (RPA) and Artificial Intelligence (AI) in the banking sector for fraud detection is a significant change, but it comes with challenges and opportunities. With financial institutions subjected to ever more sophisticated fraud attempts, RPA and AI present themselves as a means of increasing capabilities to detect and prevent fraud. With RPA, repetitive tasks like transaction monitoring and alert generation can be automated, freeing human analysts to analyze complex cases. Machine learning and predictive analytics enable AI to learn patterns and anomalies within large amounts of datasets, identify anomalies, and provide warnings early to fraud activity. Yet, these technologies still need to be integrated, and challenges persist. There are key issues of data privacy and security, integration with legacy systems, initial costs of implementation, and the like. Banks must operate in a world of ever-tightening regulatory control while maintaining the integrity and security of their systems. However, the opportunities are great. With RPA and AI implemented, there would additionally be an increase in the accuracy and speed of fraud detection, resulting in less financial loss and more customer faith. In addition, these technologies are scalable and flexible, which means banks can change with the changing threats. There's also a compelling cost case: reductions over time, based on improved efficiency and reduced manual intervention, make these attractive investments. Other best practices and lessons gained from these successful case studies are discussed. Also, the shift in future trends has been kept in mind, such as the future of AI technology and the changing regulatory environment, which will define the next generation of fraud detection in banking. The challenges to utilizing these technologies and their opportunities are revealed in a balanced view for the benefit of stakeholders so that they can utilize these technologies fully.

Integrating machine learning in business analytics consulting for proactive decision-making and innovation

Integrating machine learning (ML) into business analytics consulting represents a paradigm shift in enabling organizations to adopt proactive decision-making and foster innovation. As businesses face increasing complexity and competition, the demand for data-driven strategies has grown exponentially. Machine learning, with its capacity to analyse vast datasets, uncover hidden patterns, and predict future trends, has become a cornerstone of modern business analytics. This integration empowers consultants to deliver actionable insights and predictive solutions, enhancing operational efficiency and competitive advantage. Applications of ML in business analytics include customer segmentation, churn prediction, demand forecasting, and anomaly detection, all of which contribute to optimizing resource allocation and improving decision-making processes. For example, predictive models can help businesses anticipate market shifts and customer behaviours, while recommendation systems drive personalized marketing strategies. Incorporating ML also facilitates innovation by identifying untapped opportunities, automating repetitive tasks, and enabling real-time analytics. However, successful implementation requires addressing challenges such as data silos, algorithm biases, and the need for skilled professionals. Establishing robust data governance, fostering a culture of analytics adoption, and leveraging scalable cloud-based ML platforms are crucial for overcoming these barriers. This paper explores the theoretical foundations and practical applications of machine learning in business analytics consulting. It provides a framework for integrating ML into consulting practices, highlighting best practices and potential pitfalls. By adopting ML-driven approaches, consultants can help organizations navigate uncertainty, enhance strategic agility, and accelerate innovation.

Prevalence of musculoskeletal disorders among garment workers: a systematic review and meta-analysis

BackgroundGarment workers are at high risk of musculoskeletal disorders (MSDs) due to repetitive physical tasks, long working hours and varying workstations. As there is no existing epidemiological overview of MSDs...

Exploring AI Amid the Hype: A Critical Reflection Around the Applications and Implications of AI in Journalism

Over the last decade, AI has increasingly been adopted by newsrooms in the form of different tools aiming to support journalists and augment the capabilities of the profession. The main idea behind the adoption of AI is that it can make journalists’ work more efficient, freeing them up from some repetitive or routine tasks while enhancing their research and storytelling techniques. Against this idea, and drawing on the concept of “hype”, we employ a critical reflection on the lens often used to talk about journalism and AI. We suggest that the severe sustainability crisis of journalism, rooted in growing pressure from platforms and major corporate competitors, changing news consumption habits and rituals and the growing technologization of news media, leads to the obsessive pursuit of technology in the absence of clear and research-informed strategies which cater to journalism’s civic role. As AI is changing and (re)shaping norms and practices associated with news making, many questions and debates are raised pertaining to the quality and plurality of outputs created by AI. Given the disproportionate attention paid to technological innovation with little interpretation, the present article explores how AI is impacting journalism. Additionally, using the political economy framework, we analyze the fundamental issues and challenges journalism is faced with in terms of both practices and professional sustainability. In the process, we untangle the AI hype and attempt to shed light on how AI can help journalism regain its civic role. We argue that despite the advantages AI provides to journalism, we should avoid the “shiny things perspective”, which tends to emphasize productivity and profitability, and rather focus on the constructive synergy of humans and machines to achieve the six or seven things journalism can do for democracy. Otherwise, we are heading toward “alien intelligence” which is agnostic to the core normative values of journalism.

The performance of L2 French children on the LITMUS-QU Nonword repetition task during their first year of exposure: impact of age, vocabulary size, verbal-short term memory and phonological awareness

Abstract In this study, we describe the performance of 62 newly immigrated children to France at a nonword repetition task (LITMUS-QU-NWR-FR) designed to evaluate bilingual children’s syllable structure. Children were between 6;0 and 9;1 and had diverse language backgrounds. They participated in our study during their first year of exposure to French. The majority of our children exhibited a good performance on the task. The variation observed is related to: (i) the properties of the nonwords: items with complex syllables are more difficult, as are items with three syllables in length; (ii) phonological awareness: children with a more developed L2 phonological awareness perform better at the task; and (iii) receptive vocabulary size: children with a larger L2 vocabulary size perform better at the task. Overall, our findings provide support for the argument that the LITMUS-QU-NWR-FR task can be used shortly after the onset of exposure to the L2.

Research on Evaluation of Hotel Digitization Effect Assisted by Extreme Learning Machine in Artificial Intelligence Environment

Hotels streamline operations, provide more personalized service, streamline communication, automate procedures, and gather feedback for ongoing development using modern digital technologies like customer care chatbots, mobile guest applications, and property management systems. Batch, sequential, and incremental learning all make heavy use of the Extreme Learning Machine (ELM) because of its efficient and rapid learning rate, rapid convergence, strong generalizability, and simplicity of implementation. By automating repetitive tasks, Artificial Intelligence (AI) improves the hospitality sector’s efficiency and quality on service it provides to customers and employees. Using AI-driven automated systems, hotels may improve inventory management, track and manage energy use, and simplify cleaning schedules. The challenging characteristics of smart flood detection and alert systems are that they lack expertise, streamline operations and provide personalized guest services. Hence, in the proposed method, the Artificial Intelligence Environment enabled Wireless Sensor Network (AIE-WSN), which integrates a hotel digitization effect in an ELM. In addition, case studies and expert interviews provide practical insights into how hotels have used and benefited from financial management systems driven by AI. The research shows that hotels may greatly benefit from AI tools, including personalized pricing strategies, spending monitoring systems, fraud detection algorithms, and predictive analytics when making financial decisions through WSN. The hotel industry is undergoing digitalization, adopting AI-WSN solutions to track products’ provenance and monitor their history, among the many benefits of the proposed approach. It can help with supply chain management and adopting more efficient practices.

Developing a Chatbot System for PT. NG Tech Supplies based on the Python Flask Framework

This study focuses on developing and implementing a chatbot system using the Python Flask framework to enhance customer service efficiency at PT. NG Tech Supplies. The chatbot is designed to provide fast and accurate responses to customer inquiries, leveraging artificial intelligence and natural language processing technologies. The development follows a systematic approach using the Waterfall methodology, encompassing requirements analysis, design, implementation, verification, and maintenance. Key features include seamless integration with order management and CRM systems, enabling real-time updates on product and order status. Black-box testing was conducted to evaluate system performance, demonstrating an average response time of less than 2 seconds and high accuracy in handling queries. The chatbot also supports a well-organized folder structure in cPanel, ensuring efficient management and scalability. This research highlights the chatbot's potential to automate repetitive tasks, reduce workload, and improve customer satisfaction through enhanced service delivery.

Evaluating datafied time: exploring the multiple temporalities of teachers’ work with digital assessment

ABSTRACT The recent emphasis of educational policies on the development of AI tools for education is aimed to save teachers’ time of mundane and repetitive tasks for meaningful pedagogical practices. These so-called burdensome tasks (e.g. students’ roll-in presences, plagiarism detection, feedback, and grading) which are incorporated within platform infrastructures and delegated to algorithms are heavily data-driven. This paper explores the question of how data-driven automated processes of evaluation govern teachers’ evaluative practices with respect to professional judgement and decision-making by producing plural temporalities. This paper empirically scrutinizes how teachers conduct evaluative practices through digital evaluation embedded in the Moodle platform, in which practices of correction, grading and feedback giving are partially automated. By analysing the screen recordings of ten digital tests from the moment of genesis until the moment they are archived, as well as a group discussion with teachers, we identify that one of the significant performances of automated grading in student evaluation is to condense teachers’ correction time and produce immediate results. This, however, is only made possible through the extensive data work that teachers need to do while making a test.

Effects of massed and spaced task repetitions on L2 writing task performance and task emotions

Effects of massed and spaced task repetitions on L2 writing task performance and task emotions

Limitations of the Boston Carpal Tunnel Questionnaire in Assessing Severity in a Homogeneous Occupational Cohort.

Background: Carpal tunnel syndrome (CTS) is a common peripheral neuropathy, often assessed using the Boston Carpal Tunnel Questionnaire (BCTQ). The BCTQ evaluates symptom severity (SSS) and functional status (FSS) but has limitations in stratifying CTS severity, particularly in severe cases. Objective: This study aimed to evaluate the utility of the BCTQ in a homogeneous cohort of female workers engaged in repetitive manual tasks, exploring its correlation with objective clinical measures and its performance in detecting CTS severity. Methods: A cross-sectional study was conducted on 24 right-hand-dominant female workers with repetitive occupational tasks. CTS diagnosis was confirmed via clinical and electrodiagnostic criteria. Subjects completed the BCTQ, and correlations between BCTQ scores and objective measures such as median nerve cross-sectional area and nerve conduction studies were analyzed. Statistical analyses included comparisons across CTS severity groups and subgroup evaluations based on age and tenure. Results: The BCTQ demonstrated moderate correlations with objective measures, with a strong correlation between SSS and FSS scores (r = 0.86, p < 0.001). However, the sensitivity of the SSS and FSS was limited, particularly for severe CTS cases. Paradoxically lower scores in severe cases may reflect questionnaire limitations or adaptive responses. Targeted questions addressing pain and sensory symptoms showed better sensitivity (>80%) and may guide clinicians in identifying slight CTS cases. Conclusions: While the BCTQ remains a valuable tool for assessing CTS, its limitations necessitate complementary use of objective diagnostic tools, particularly for severe cases. Future refinements, such as tailored scoring systems and integration with clinical measures, could enhance its diagnostic utility and ensure comprehensive assessment of CTS severity.

Different paths to multilingualism in Autism Spectrum Disorder (ASD): Naturalistic and non-interactive.

This study is one of the few research efforts investigating unexpected non-interactive foreign language acquisition in children with Autism Spectrum Disorder (ASD). Participants included 46 English-Hebrew-speaking children (ages 4;10 to 12;0): 14 autistic children who acquired English via non-interactive input (ASD-NI); 12 autistic children (ASD-Nat), and 20 non-autistic children with typical language development (TLD-Nat) who acquired English and Hebrew naturalistically. Morpho-syntactic abilities were assessed using Sentence Repetition tasks in both languages. The results showed no group differences for morpho-syntax in English; in Hebrew, the ASD-NI group scored similarly to the ASD-Nat group but lower than the TLD-Nat group. Individual performance differences between Hebrew and English were observed across all groups. Additionally, correlations between exposure and SRep scores were found in both groups for Hebrew but not English. These findings highlight diverse paths to language acquisition in ASD, with children acquiring foreign languages via both naturalistic and non-interactive input.

Evaluating Tech Neck: A Pilot Study Using a Self-Developed Questionnaire on Symptoms, Posture, and Preventive Measures.

Musculoskeletal symptoms, such as neck pain, back pain, and eye strain, are prevalent in modern work environments, particularly among individuals engaged in prolonged sitting and repetitive tasks. This study aimed to explore the prevalence of these symptoms and the impact of ergonomic tool usage, break frequency, posture awareness, and proactive measures on symptom intensity and daily functioning. This cross-sectional pilot study used a self-developed questionnaire to assess the prevalence and intensity of musculoskeletal symptoms among participants aged 6-18 years. The survey collected data on ergonomic tool usage, break frequency, posture awareness, and proactive measures (such as taking breaks and seeking professional help). Symptom intensity was assessed across common musculoskeletal issues. Internal consistency was assessed using Cronbach's Alpha. Statistical analyses, including descriptive statistics, Chi-squared tests, ANOVA, and Spearman's correlation, were used to identify significant correlations and associations between variables. A significant proportion of participants reported moderate to severe symptoms, including neck pain, back pain, and eye strain. Participants who used ergonomic tools reported significantly lower symptom intensity (<0.001). A positive correlation was found between fewer breaks and higher symptom intensity (r = 0.908, p < 0.001). Those with higher posture awareness reported lower symptom intensity (p < 0.001). Proactive measures, including regular breaks and ergonomic interventions, were perceived as effective strategies for managing symptoms. Increased symptom intensity was associated with reduced ability to perform daily activities (p < 0.001). The study highlights the significant role of ergonomic tools, posture awareness, and regular breaks in mitigating musculoskeletal symptoms. Proactive measures, including structured interventions, can effectively reduce symptom intensity and improve daily functioning. These findings suggest that workplace health strategies focusing on ergonomics and posture education are crucial for reducing the impact of musculoskeletal discomfort on employee well-being and productivity.

The Impact of Artificial Intelligence on Digital Forensics

This paper explores artificial intelligence (AI) ‘s profound impact on digital forensics. As cybercrime grows in complexity and scale, traditional forensic methods are increasingly insufficient and must improve upon challenges. AI technologies are fundamentally revolutionizing digital forensics by significantly enhancing data analysis capabilities, automating repetitive and time-consuming tasks, and dramatically improving digital investigations’ efficiency and effectiveness. This comprehensive paper discusses the wide-ranging applications of AI in digital forensics, the challenges that arise from its implementation, and the prospects for this rapidly evolving field.

Test Fonetico per la Prima Infanzia (TFPI): A New Instrument to Assess Italian Toddlers’ Phonetic Development

The purpose was to contribute to the validation of the TFPI, a new tool to assess the phonetic development of Italian-speaking children aged 18–47 months. Since currently, norm-referenced instruments for Italian are lacking, the TFPI would fill this gap. We recruited 52 monolingual children aged 24–47 months with typical development. They were administered the complete TFPI, i.e., a naming task and repetition task; however, only their performances from the naming task were analyzed. The sessions were audio-recorded, in order to be later segmented and annotated in Praat, then manually transcribed with IPA. These data were then imported into Phon, an extremely suitable software for conducting analyses of phonological and speech data. We compiled the Phonetic Inventory (PhI) and calculated the Percentage of Consonants Correct (PCC) for each child, taking into consideration the allophones of Italian, in order to not compute them as errors. Both the PhI and the PCC improve with age, while intersubjective variability progressively decreases. Additionally, we investigated the age of the acquisition of each phone, since this domain lacks robust scientific data. Finally, our results align with previous findings, which proves the reliability and validity of the TFPI, and provides new information about the PCC, for which there are no reference values for the Italian language.

Egyedi mobilalkalmazások a kis- és középvállalkozások digitális átalakulásának motorjaként: A Z generáció elvárásainak kielégítése

The aim of the research is to investigate the impact of the automation of monotonous work processes (BPMs) on the operation of SMEs in Hajdú-Bihar, focusing on the change in the attractiveness of workplaces for Generation Z. In a case study, the effects of automating repetitive tasks in an SME were analysed by means of a test period and interviews. We found that automation significantly reduced labour demand and the number of errors, freeing up workers for more creative tasks. This change made the workplace more attractive to Generation Z, who now prefer a modern and efficient work environment. Automation has also improved business efficiency and results through labour savings and a reduction in error rates. Built-in controls and a dedicated mobile app have provided additional benefits such as strengthening the employer brand, creating a youthful image and a user-friendly interface. Interviews with participants showed that employees were more satisfied, motivated and engaged after automating monotonous tasks. Overall, the automation of monotonous BPMs is an effective tool to increase the competitiveness of SMEs, save labour and improve the attractiveness of jobs, opening up further digitisation opportunities for micro-enterprises.

The effects of heritage multilingualism on foreign language learning: a comparison of children with typical language development and developmental language disorder.

Both multilingualism and developmental language disorder (DLD) may be associated with inferior performance in the majority language, albeit for different reasons. At the same time, there is a growing body of evidence that multilingualism may have a positive effect on foreign language performance. This study tests the hypothesis that the positive effects of multilingualism on foreign language learning may be smaller in children with DLD compared to their multilingual peers with typical language development. In a 2 × 2 design, we compare the effects of multilingualism and DLD on English as a foreign language performance and majority language performance of multilinguals and monolinguals with and without DLD. The participants were primary school children (aged 9-13) acquiring Dutch as the majority language and learning English as a school subject. English skills were measured with a vocabulary test, a grammar test and the Multilingual Assessment Instrument for Narratives (MAIN). Dutch skills were assessed with the Litmus Sentence Repetition Task and the MAIN task. The MAIN narratives in both languages were analyzed for fluency, lexical diversity, syntactic complexity and grammatical accuracy. The control variables included age, working memory, declarative memory, procedural memory and (for English) amount of extracurricular exposure and length of instruction. Data were analyzed by means of multilevel linear regression. The results demonstrate that both multilingualism and DLD were associated with lower scores on the Dutch Sentence Repetition Task and lower grammatical accuracy of narratives. In English, the multilinguals outperformed monolinguals on all measures, except grammatical accuracy of narratives, and the interactions between Background and Group were not significant. Another strong predictor of EFL performance, along with the multilingual status, was extracurricular exposure to English.

Intellisearch - Intelligent Employee Data Access System

- The IntelliSearch Intelligent Employee Data Access System leverages advanced machine learning (ML) and natural language processing (NLP) techniques to modernize employee information retrieval. Designed to address inefficiencies in traditional manual methods, IntelliSearch provides secure, real-time responses to queries about roles, attendance, salaries, and performance metrics. Its modular architecture integrates with existing databases, offering robust data security through encryption and role-based access control (RBAC). By streamlining repetitive tasks and enhancing query accuracy, the system fosters organizational productivity and employee satisfaction. Future enhancements include predictive analytics, advanced performance tools, and industry-specific adaptations, paving the way for scalable, intelligent solutions in employee management Key Words: IntelliSearch, Machine Learning, Natural Language Processing, Employee Data Management, Role-based Access Control, Data Security, Organizational Efficiency, Real-time Queries, Scalable Architecture.

Deep-Learning-based Automated Identification of Ventriculoperitoneal-Shunt Valve Models from Skull X-rays.

Ventriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. The model types often need to be identified on X‑rays to assess pressure levels using amatching template. Artificial intelligence (AI), in particular deep learning, is ideally suited to automate repetitive tasks such as identifying different VPS valve models. The aim of this work was to investigate whether AI, in particular deep learning, allows the identification of VPS models in cranial X‑rays. 959 cranial X‑rays of patients with aVPS were included and reviewed for image quality and complete visualization of VPS valves. The images included four VPS model types: Codman Hakim (n = 774, 81%), Codman Certas Plus (n = 117, 12%), Sophysa Sophy Mini SM8 (n = 35, 4%) and proGAV 2.0 (n = 33, 3%). AConvolutional Neural Network (CNN) was trained using stratified five-fold cross-validation to classify the four VPS model types in the dataset. Afinetuned CNN pretrained on the ImageNet dataset as well as amodel trained from scratch were compared. The averaged performance and uncertainty metrics were evaluated across the cross-validation splits. The fine-tuned model identified VPS valve models with amean accuracy of 0.98 ± 0.01, macro-averaged F1 score of 0.93 ± 0.04, arecall of 0.94 ± 0.03 and aprecision of 0.95 ± 0.08 across the five cross-validation splits. Automatic classification of VPS valve models in skull X‑rays, using fully automatable preprocessing steps and aCNN, is feasible. This is an encouraging finding to further explore the possibility of automating VPS valve model identification and pressure level reading in skull X‑rays.