Manual Tasks Research Articles

Peran Sistem Informasi dalam Meningkatkan Kualitas Audit Keuangan Pemerintah Daerah

This study aims to examine the role of Information Systems (SI) in improving the quality of audits of local government financial statements. Audit of local government financial statements is a critical process in evaluating public finances and ensuring the reliability of financial information and compliance with applicable regulations. In recent years, the use of SI has become an important factor in improving audit quality by strengthening the efficiency, effectiveness, and accuracy of the audit process. A review of the literature reveals that the use of SI in auditing local government financial statements provides significant benefits. First, automation of the audit process using SI replaces error-prone manual tasks, thereby increasing audit efficiency. Second, SI facilitates efficient data collection and analysis, allowing auditors to access more accurate and comprehensive financial information. Third, SI strengthens internal supervision through real-time monitoring of financial activities and early detection of risks or policy violations. Finally, SI also provides protection against the security and integrity of sensitive data in auditing practices. However, the use of SI in auditing local government financial statements is also faced with challenges. These challenges include limited resources, complexity of system integration, security of sensitive data, and the need for increased auditor competence in understanding and using SI effectively. Through this research, it is hoped that a deeper understanding of the role of SI in improving the quality of audits of local government financial statements can be obtained. The results of this study can provide practical guidance and recommendations for auditors and local governments in making optimal use of SI, as well as overcoming challenges that may arise. Thus, it is expected that the practice of auditing local government financial statements can be more effective, accurate, and transparent, as well as provide significant benefits in public financial management.

AUTOMATIC ASSEMBLY OF INTERLOCKING BUILDING CUBES BY USING VISUAL SERVOING APPROACH

Most of the robotic arms are set up for operation by the teach-and-repeat (T&R) technique, also known as coordinate-based control, using pre-recorded coordination values to generate robotic arm trajectories. This technique is inefficient because it requires teaching a robotic arm every single coordinate point to complete an operation. It is a manual, tedious and time-consuming task. Moreover, this task needs to be repeated if there are slight changes in the operation. This paper studied the visual servoing approach to replace the T&R technique in assembling different interlocking building cubes (IBC) combinations. The proposed method uses an image-based visual servo (IBVS) with a dynamic look-and-move system, and the eye-to-hand system, which requires computing the incremental joint angles of the robotic arm, a novel kinematic formula with a combination of Trigonometry formulas has been proposed in this paper. The experimental results show the feasibility of using a visual servoing approach to complete the assembly tasks without relying on the T&R technique and achieved an average success rate of 90% and an average of 40.2 seconds for task duration. Thus, this eliminates the tedious and time-consuming tasks of teaching the coordinate points to the robotic arms. Further improvement on the servo motor, camera and computer processor can be done to increase the accuracy, computation time and task duration in future.

Artificial intelligence in pharmacovigilance - Opportunities and challenges.

Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time-consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Processing of the enormous volume and variety of data, making its sensible use and separating "needles from haystack," is a challenge for key stakeholders such as pharmaceutical firms, regulatory authorities, medical and PV experts, and National Pharmacovigilance Program managers. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. Similarly, there has been a growing interest to adopt AI tools to complement and automate the PV process. The advanced technology can certainly complement the routine, repetitive, manual task of case processing, and boost efficiency; however, its implementation across the PV lifecycle and practical impact raises several questions and challenges. Full automation of PV system is a double-edged sword and needs to consider two aspects - people and processes. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. What is important is to emphasize and ensure that AI brings more benefits to PV rather than challenges. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system.

LEVERAGING DIGITAL TRANSFORMATION AND ERP FOR ENHANCED OPERATIONAL EFFICIENCY IN MANUFACTURING ENTERPRISES

Purpose: The process of making the most of digital tools in the workplace to support an organization's success is known as "leveraging on technology." By automating manual tasks more quickly, modern ERP systems free up time and funds for other important projects. Data can be aggregated by contemporary ERP systems for simpler management, access, and analysis. Method: Systems for enterprise resource planning, or ERPs, have become a vital component of contemporary manufacturing processes. These integrated software solutions are essential for improving, streamlining, and controlling many aspects of manufacturing, including financial control, customer service, production and inventory management. Manufacturers can effectively manage vital resources like labor, equipment, and materials with the help of ERP systems. ERP helps eliminate waste, minimize production delays, and maximize resource utilization by offering real-time visibility into resource allocation and availability. Result and Conclusion: Manufacturing process synchronization and automation are made easier by ERP software. Work orders, production schedules, and quality control can all be managed by it, resulting in seamless and effective manufacturing processes. In manufacturing, keeping the proper amount of inventory on hand is a difficult task. ERP systems give producers insights into demand trends, order lead times, and reorder points, which helps them optimize inventory levels. In addition to lowering carrying costs and preventing stockouts, this minimizes superfluous inventory. Implication of Research: An organization's operational model is altered by digital transformation. This process involves systems, workflow, processes, and culture. Every level of an organization is impacted by this transition, which unifies data from different departments to collaborate more successfully. Originality/Value: Enhanced Efficiency: Businesses are able to allocate resources more effectively thanks to automation and streamlined processes, which also lower operational costs and increase productivity.

Riding to the Rescue: A Comprehensive Review of Health and Safety Measures in Ambulance Cars

Paramedics are registered health professionals in numerous countries. Whilst ambulance services must prioritize their patients, they also require personnel to be healthy to perform at their utmost to provide optimal patient care. Ambulance transport is associated with predictable and likely preventable occupant hazards. Ambulance services provide care 24 hours a day, 7 days a week, and shift work is known to be a risk factor for poor health. Working unsociable hours has detrimental consequences on the health and well-being of healthcare workers and can ultimately result in compromised patient care. Ambulance service utilization rates are high, with paramedics working in unpredictable environments, and undertaking complex clinical and manual handling tasks whilst caring for critically unwell patients; thus, there are several reasons postulated for poor health in this professional group. These include injuries, serious infections, high rates of musculoskeletal diseases, stress, and mental health problems. This review article aims to identify the occupational risks in an ambulance service, assess their impact on the health of employees, and explore control measures that can be implemented to reduce the risks and improve the safety of healthcare workers and patients.

Development and Evaluation of Drug Management Information System in Mijen Community Health Center Demak

Background: Drug management information systems can serve as tools for decision-making. The effectiveness of such a system can be assessed through evaluation. One of the evaluation methods used is the PIECES method.Objectives: This research aims to evaluate and develop a drug management information system using the PIECES method.Methods: The research conducted was a qualitative descriptive study with inductive analysis. Sampling for this research was conducted purposive sampling. System development in this study utilized the prototype method, with interviews conducted for Management Information System (MIS) evaluation using the PIECES Framework. The design of the MIS system was created using Data Flow Diagrams and Entity Relationship Diagrams. The designed MIS system development was evaluated using the same method. Data processing in qualitative research consists of valid, clear, and accountable word descriptions, sentences, or narratives provided by the researchers, followed by data validation using triangulation techniques and Nvivo 12 Plus software.Results: Evaluation results using the PIECES Framework indicate that the implemented MIS system benefits and aids staff performance in daily drug dispensing. However, there are still manual tasks and lacking features such as distribution, drug expiration warnings, and minimum stock on the dashboard. The proposed design for MIS system development includes several features such as stock in, stock out, and distribution, complemented with drug expiration warnings, minimum stock alerts on the dashboard, and reports for each feature.Conclusion: Implementing the proposed MIS system design can assist and streamline work processes by providing comprehensive information, thus serving as a decision-making tool.

Estimating Compressive and Shear Forces at L5-S1: Exploring the Effects of Load Weight, Asymmetry, and Height Using Optical and Inertial Motion Capture Systems.

This study assesses the agreement of compressive and shear force estimates at the L5-S1 joint using inertial motion capture (IMC) within a musculoskeletal simulation model during manual lifting tasks, compared against a top-down optical motion capture (OMC)-based model. Thirty-six participants completed lifting and lowering tasks while wearing a modified Plug-in Gait marker set for the OMC and a full-body IMC set-up consisting of 17 sensors. The study focused on tasks with variable load weights, lifting heights, and trunk rotation angles. It was found that the IMC system consistently underestimated the compressive forces by an average of 34% (975.16 N) and the shear forces by 30% (291.77 N) compared with the OMC system. A critical observation was the discrepancy in joint angle measurements, particularly in trunk flexion, where the IMC-based model underestimated the angles by 10.92-11.19 degrees on average, with the extremes reaching up to 28 degrees. This underestimation was more pronounced in tasks involving greater flexion, notably impacting the force estimates. Additionally, this study highlights significant differences in the distance from the spine to the box during these tasks. On average, the IMC system showed an 8 cm shorter distance on the X axis and a 12-13 cm shorter distance on the Z axis during lifting and lowering, respectively, indicating a consistent underestimation of the segment length compared with the OMC system. These discrepancies in the joint angles and distances suggest potential limitations of the IMC system's sensor placement and model scaling. The load weight emerged as the most significant factor affecting force estimates, particularly at lower lifting heights, which involved more pronounced flexion movements. This study concludes that while the IMC system offers utility in ergonomic assessments, sensor placement and anthropometric modeling accuracy enhancements are imperative for more reliable force and kinematic estimations in occupational settings.

Comparing fine motor performance among young children with autism spectrum disorder, intellectual disability, attention-deficit/hyperactivity disorder, and specific developmental disorder of motor function.

The acquisition of fine motor skills is considered to be a crucial developmental milestone throughout early childhood. This study aimed to investigate the fine motor performance of young children with different disability diagnoses. We enrolled a sample of 1,897 young children under the age of 6 years who were at risk of developmental delays and were identified by a transdisciplinary team. A series of standardized developmental assessments included the Bayley Scales of Infant Development-Third Edition, Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition, Peabody Developmental Motor Scale-Second Edition, and Movement Assessment Battery for Children-Second Edition were used. Retrospective chart reviews were conducted on all children to identify specific developmental disorders. The number of autism spectrum disorder (ASD), intellectual disability (ID), attention-deficit/hyperactivity disorder (ADHD), comorbidity, motor dysfunction, and unspecified developmental delays (DD) were 363 (19.1%), 223 (11.8%), 234 (12.3%), 285 (15.0%), 128 (6.7%), and 590 (31.1%), respectively. Young children with ID, comorbidity, and motor dysfunction demonstrated significant difficulty in performing manual dexterity and visual motor integration tasks and scored significantly lower in these areas than children with ASD, ADHD, and unspecified DD. In addition, fine motor performance was associated with cognitive ability in children with different disability diagnoses, indicating that young children showed better fine motor performance when they demonstrated better cognitive ability. Our findings support that differences in fine motor performance differ by disability type. Close links between fine motor performance and cognitive ability in children under the age of 6 years were seen in all disability types.

Unlocking Business Potential: Artificial Intelligence and Machine Learning Capabilities in SAP S/4HANA

This article explores the transformative role of artificial intelligence (AI) and machine learning (ML) capabilities within SAP S/4HANA, a leading digital platform for enterprise resource planning (ERP). This article elucidates how AI and ML technologies embedded in SAP S/4HANA empower businesses to drive innovation, optimize operations, and gain actionable insights. From predictive analytics and intelligent automation to advanced decision support systems, SAP S/4HANA's AI and ML capabilities are revolutionizing the way organizations operate in today's fast-paced business landscape. SAP S/4HANA, a leading enterprise resource planning (ERP) platform, stands at the forefront of this digital transformation, offering a comprehensive suite of AI and ML capabilities designed to empower businesses to optimize operations, enhance decision-making, and unlock new growth opportunities. This article provides a succinct overview of the transformative role of AI and ML in SAP S/4HANA, highlighting key functionalities and their potential impact on business operations. SAP S/4HANA's AI and ML capabilities encompass a range of functionalities, including predictive analytics, intelligent automation, and decision support systems. Intelligent automation features, such as robotic process automation (RPA) and machine learning-based algorithms, automate manual tasks, reduce errors, and accelerate decision- making processes, thereby enhancing operational efficiency and agility. Moreover, SAP S/4HANA's decision support systems leverage real-time data insights to provide actionable recommendations to users across the organization, enabling organizations to make informed decisions and drive strategic initiatives. Through real- world examples and case studies, this article illustrates how organizations across industries are leveraging SAP S/4HANA's AI and ML capabilities to drive business transformation, streamline processes, and achieve measurable outcomes. This article aims to provide a comprehensive exploration of the AI and ML capabilities within SAP S/4HANA, examining their potential impact on business operations and outlining best practices for implementation and utilization. Through real-world examples, case studies, and expert insights, readers will gain a deeper understanding of how SAP S/4HANA's AI and ML functionalities can unlock new opportunities, drive innovation, and propel business growth.

MUSCULOSKELETAL DISORDERS RISK ASSESSMENT AMONG SMALL TOFU FACTORY WORKERS IN TANGERANG CITY: AN INDONESIAN CASE STUDY

Introduction: Conventional tofu production involves various manual handling tasks which increases the risk of musculoskeletal disorders (MSDs). A previous study found a very high risk of MSDs in tofu manufacturing, especially in the filtering task with a REBA score of 13. Aims: This study aims to evaluate the risk of MSDs in tofu factory workers. Methods: Workplace observations and unstructured interviews were conducted at two traditional tofu factories (X and Y) and a more modern silken tofu factory to identify the risk of MSDs. The Nordic Musculoskeletal Questionnaire and the Quick Exposure Check (QEC) method were used to determine the prevalence and risk of MSDs. A total of 52 workers voluntarily participated in this study. Results: The traditional and silken tofu manufacturing processes involved seven and fifteen tasks, respectively. This study found that workers in the traditional tofu factories had a higher prevalence of MSDs than those in the silken tofu factory. In the past 12 months, traditional tofu factory workers reported MSDs in their lower back (94.4%), right shoulder (77.8%), and neck (61.1%). On the other hand, silken tofu factory workers reported MSDs in their lower back (81.3%), right shoulder (62.5%), and upper back region (56.3%). According to the QEC method, grinding and filtering were identified as two tasks with a very high risk of MSDs in the traditional factories. In addition, transferring tofu crates from cold water to hot water tub in the silken factory was identified as a very high risk of MSDs. Conclusion: Intervention is necessary for tasks with a very high risk of MSDs in small tofu factories.

3D visualization systems improve operator efficiency during difficult laparoscopic cholecystectomy: a retrospective blinded review of surgical videos.

3D visualization systems in laparoscopic surgery have been proposed to improve manual task handling compared to 2D, however, few studies have compared the intra-operative efficacy in laparoscopic cholecystectomy (LC). The aim of this study is to determine if there is a benefit in intra-operative efficiency when using a 3D visualization system in difficult LC compared to traditional 2D visualization systems. Retrospective analysis of 'difficult' LCs (Grades 3 or 4) was completed. The assessor was blinded as all cases were recorded and viewed in 2D only. Variables collected included time to complete steps, missed hook diathermy attempts, failed grasp attempts, missed clip attempts and preparation steps for intra-operative cholangiogram (IOC). Multiple linear regression was undertaken for time variables, Poisson regression or negative binomial regression was completed for continuous variables. Fifty-two operative videos of 'difficult' LC were reviewed. 3D systems were associated with reduced operative times, although this was not statistically significant (CI: -2.93-14.93, P-value = 0.183). Dissection of the anterior fold to achieve the critical view of safety was significantly faster by 3.55 min (CI: 1.215-9.206, P-value = 0.002), and with considerably fewer errors when using 3D systems. Fewer IOC preparation errors were observed with a 3D system compared with a 2D system. 3D systems appear to enhance operator efficiency, allowing faster completion of critical steps with fewer errors. This pilot study underscores the utility of video annotation for intra-operative assessment and suggests that, in larger multi-centre studies, 3D systems may demonstrate superior intra-operative efficiency over 2D systems during a 'difficult' LC.

RANCANG BANGUN SISTEM INFORMASI INVENTORY MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT (RAD) PADA TOKO ABADI JAYA

Toko Abadi Jaya is a trading company in the furniture industry that uses technology to enhance its operations. The company recognizes the importance of technology in accessing fast and accurate information to solve various challenges. Currently, many manual tasks, such as marketing, finance, administration products management, and checking product stock availability can take a lot of time and accuracy. For example, employees need to call other employees in the inventory warehouse to inquire about stock availability. This process hinders efficiency and effectiveness. To resolve this issue, the company has developed an application-based inventory information system. This system enables employees to quickly check the availability of goods without wasting time. The application also generates reports, which aid the owner in decision-making. Therefore, it is necessary to have carefully organized data stored in the database so that the work process is easy in making reports. The design of this application uses the Rapid Application Development (RAD) method and the Unified Modeling Language (UML) design model, with Visual Studio Code as the text editor tool and Laravel as the PHP framework. This aims to facilitate the process of monitoring the stock of incoming goods and outgoing goods and provide benefits to the work process of inputting inventory of goods and providing precise and accurate reports at Toko Abadi Jaya.

Different Dual-Task Paradigm Reduce Postural Control Ability and Dynamic Stability of Healthy Young Adults during Stair Descent.

This study aimed to compare the impacts of different dual-task paradigms on the postural control ability and dynamic stability of the youth during stair descent. Twenty young adults without regular exercise habits were randomly recruited to perform stair descent tasks with three different paradigms: single-task, cognitive dual-task, and manual dual-task. Kinematic and dynamic data were collected using an 8 Vicon motion analysis system and a Kistler force plate to evaluate postural control ability and dynamic stability during stair descent. The variation trends of lower limb joint moment were similar under the three task models. Compared with a single-task, both dual-task paradigms significantly reduced the mechanical parameters and dynamic stability during stair descent. The dual-task paradigm increases the risk of stair-related falls. Both cognitive and manual tasks have similar impacts on postural control ability and dynamic stability during stair walking. It is recommended that people avoid performing dual tasks during stair descent.

Future of software development with generative AI

Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be used for many purposes, from creative dimensions to replacing repetitive and manual tasks. The number of opportunities increases with the capabilities of large-language models (LLMs). This has raised concerns about ethics, education, regulation, intellectual property, and even criminal activities. We analyzed the potential of generative AI and LLM technologies for future software development paths. We propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations. The motivation for this research is clear: the software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it.

Human-exoskeleton cooperation for reducing the musculoskeletal load of manual handling tasks in orchid farms

Work-related musculoskeletal diseases of the lower back and shoulders are frequently caused by lifting, carrying and trunk bending. Therefore, a hybrid exoskeleton (HExo) that provides external torque to the upper extremities and back may reduce discomfort in the lower back and shoulder tendinopathies. In this study, we designed and fabricated HExo, a novel upper-limb and lower-back combined exoskeleton, and evaluated its impact in reducing the musculoskeletal load on the shoulder and back during agricultural work in orchid farms. The human motion tracking technique was utilised to collect the joint angles of orchid farmers during their work, and the joint angle data were subsequently used for the design of HExo. Electromyography (EMG) and centre of pressure (CoP) measurements were taken for 5 participants performing farm tasks and 16 participants performing laboratory-standardised tasks both with and without the HExo. The amplitude probability density function and Wilcoxon signed rank test were utilised in the data analysis to verify the advantage in wearing HExo. The use of HExo resulted in a reduction in erector spinae, latissimus dorsi and anterior deltoid muscle activation of up to 36.92 % (p = 0.05), 31.43 % (p = 0.017), and 57.59 % (p = 0.012), respectively. Furthermore, its use did not affect the ability of participants to maintain balance. Hence, HExo could be a promising for reducing solution musculoskeletal load.

Neural Oscillations and Functional Significances for Prioritizing Dual-Task Walking in Parkinson's Disease.

Task prioritization involves allocating brain resources in a dual-task scenario, but the mechanistic details of how prioritization strategies affect dual-task walking performance for Parkinson's disease (PD) are little understood. We investigated the performance benefits and corresponding neural signatures for people with PD during dual-task walking, using gait-prioritization (GP) and manual-prioritization (MP) strategies. Participants (N = 34) were asked to hold two inter-locking rings while walking and to prioritize either taking big steps (GP strategy) or separating the two rings (MP strategy). Gait parameters and ring-touch time were measured, and scalp electroencephalograph was performed. Compared with the MP strategy, the GP strategy yielded faster walking speed and longer step length, whereas ring-touch time did not significantly differ between the two strategies. The MP strategy led to higher alpha (8-12 Hz) power in the posterior cortex and beta (13-35 Hz) power in the left frontal-temporal area, but the GP strategy was associated with stronger network connectivity in the beta band. Changes in walking speed and step length because of prioritization negatively correlated with changes in alpha power. Prioritization-related changes in ring-touch time correlated negatively with changes in beta power but positively with changes in beta network connectivity. A GP strategy in dual-task walking for PD can enhance walking speed and step length without compromising performance in a secondary manual task. This strategy augments attentional focus and facilitates compensatory reinforcement of inter-regional information exchange.

Industrial Engineering Using IBM SPSS Statistics

Industrial Engineering, Most historians agree that the Industrial Revolution is when the profession of industrial engineering had its start. The invention, the spinning jenny, and—possibly most significantly—the steam engine were among the innovations that helped mechanize traditional manual tasks in the textile industry. These innovations also created scale savings that for the first time that made commercial production in centralized facilities appealing. The factories built as a result of these developments are where the idea of something like the production system originated. Also, it has been proposed that Leonardo Vinci may have been the first manufacturing engineer as there is proof that he used science to analyze human labor by observing how quickly a laborer could shovel soil approximately in 1500.Both the producers and the final consumers of a product value industrial engineering. To improve efficiency, industrial engineers enhance designs and procedures. This benefits businesses since it conserves people, raw materials, time, and money. IBM SPSS Statistics Your firm can easily derive useful insights from your data because to its user-friendly interface and extensive feature set. High accuracy and good decision-making are ensured by advanced statistical techniques. The entire analytics lifecycle is covered, from data administration and preparation to reporting and analysis. Perceived quality, Customer expectations, Perceived value, Customer satisfaction, Customer complaints, and Customer loyalty. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .581 which indicates 58% reliability. From the literature review, the above 46% Cronbach's Alpha value model can be considered for analysis. the outcome of Cronbach's Alpha Reliability. The model's total Cronbach's Alpha score is .581, which denotes a 58% dependability level. The 46% Cronbach's Alpha value model mentioned above from the literature review may be used for analysis.

Handedness and effector strength modulate a compatibility effect between stimulus size and response position with manual and vocal responses.

Humans respond more quickly with the left hand to a small stimulus, and with the right hand to a large stimulus, as compared to the reverse mapping (spatial-size association of response codes [SSARC] effect). We investigated the hypothesis that strength differences between the hands contribute to the origin of this effect. Therefore, 80 left-handers and 80 right-handers participated in two experiments. In Experiment 1, participants performed a manual choice-response task in which we manipulated the mapping between physical stimulus size and responding hand. In addition, we measured the strengths of participants' left and right effectors (i.e., finger, hand, and arm). In Experiment 2, we measured the SSARC effect in vocal responses of the same sample. There were four main results. First, participants' dominant effectors were stronger than their nondominant effectors. Second, the SSARC effect occurred in manual and vocal responses with similar size. Third, in both modalities, the SSARC effect was larger in right-handers than in left-handers. Finally, strength differences between effectors (fingers and hands) correlated with the size of the SSARC effect. In sum, results support the hypothesis that functional differences between the hands contribute to the origin of the SSARC effect. In addition, the results suggest that size-space associations have generalized across motor systems, and formed a modality-independent association. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Information retrieval from textual data: Harnessing large language models, retrieval augmented generation and prompt engineering

This paper describes how recent advancements in the field of Generative AI (GenAI), and more specifically large language models (LLMs), are incorporated into a practical application that solves the widespread and relevant business problem of information retrieval from textual data in PDF format: searching through legal texts, financial reports, research articles and so on. Marketing research, for example, often requires reading through hundreds of pages of financial reports to extract key information for research on competitors, partners, markets and prospective clients. It is a manual, error-prone and time-consuming task for marketers, where until recently there was little scope for automation, optimisation and scaling. The application we have developed combines LLMs with a retrieval augmented generation (RAG) architecture and prompt engineering to make this process more efficient. We have developed a chatbot that allows the user to upload multiple PDF documents and obtain a summary of predefined key areas as well as to ask specific questions and get answers from the combined documents’ content. The application’s architecture begins with the creation of an index for each of the PDF files. This index includes embedding the textual content and constructing a vector store. A query engine, employing a small-to-big retrieval method, is then used to accurately respond to a set of predefined questions for each PDF to create the summary. The prompt has been designed in a manner that minimises the risk of hallucination which is common in this type of model. The user interacts with the model via a chatbot feature. It utilises similar small-to-big retrieval techniques over the indices for straightforward queries, and a more complex sub-questions engine for in-depth analysis, providing a comprehensive and interactive tool for document analysis. We have estimated that the implementation of this tool would reduce the time spent on manual research tasks by around 60 per cent, based on the discussions we have had with potential users.

A Comprehensive Review of the Significance of Low-code Automation in Risk Management for Banks

This study provides a comprehensive examination of the influence of low-code development platforms in the risk management area within the banking sector. This research delves into the tactical utilization of low-code automation in the realm of risk management, with a particular emphasis on compliance monitoring, fraud detection, credit risk evaluation, and operational risk management. Additionally, the study centers on the metamorphosis of software design, development, and deployment processes, made possible by the easy accessibility of low-code platforms. The objective of the study is to illustrate how the digitization and automation of manual tasks in these domains augment service. provision, expedite the identification of compliance violations, and simplify credit risk evaluation, thereby conferring advantages to both banks and their clientele. Through meticulous analysis, this investigation seeks to furnish important perspectives on the uptake and influence of low-code development platforms in the banking sector, illuminating the potential for improved operational efficiency, regulatory adherence, and superior risk management.