User Training Research Articles

Streamlining Document Processing: Basis foran Online Document Management System

This study addresses the critical need for an online document management system aimed at streamlining document submission, approval, and storing processes among the faculty and staff of Saint Michael College of Caraga (SMCC) located in Nasipit, Agusan del Norte, Philippines. By utilizing a mixed-methodology approach that integrates qualitative and quantitative techniques, the shortcomings and challenges of the current manual system were assessed through online survey and interviews. The research respondents, who were selected by convenience sampling, were primarily college instructors with 1 to 5 years of work experience. The investigation revealed significant inefficiencies in the current system, identifying key issues such as error-prone processes and delayed communication. The weighted mean scores indicated high level of agreement among the respondents for the proposed system that can potentially enhance process workflow, restructure communication channels, and promote transparency within the institution. The recommendations also include prioritizing primary functions such as user authentication, backup and recovery, and user training. Overall, it can be concluded the proposed online DMS can offer substantial improvements in document management practices, aligning with global trends and best practices in educational administration thus providing a potential model for other institutions facing similar challenges

Implementation of Digital Library Applications at SMK Wira Buana 2 Citayem Bogor

Libraries have long been at the heart of the educational environment, playing a vital role in providing the resources necessary for learning and research. However, with the advent of digital technology, traditional libraries have faced new challenges in providing easy and efficient access to educational resources. Therefore, this research aims to investigate the implementation of digital library applications at SMK Wira Buana 2 Citayem as an effort to improve library services and expand access to learning resources for students and teaching staff. This study will describe the process of implementing digital library applications at SMK Wira Buana 2 Citayem, including platform selection, content development, user training, and integration with other supporting systems. The research method used is a combination of literature studies to understand the basic concepts of digital library applications and case studies to analyze ongoing implementation at SMK Wira Buana 2 Citayem. It is hoped that the results of this research will provide a better understanding of the benefits and challenges in implementing digital library applications in secondary education environments. Apart from that, it is hoped that this study will also provide insight for other schools that are considering carrying out digital transformation in their library services.

Participation of the Central Regional Scientific Library in the Educational Process of Universities

The emergence of new information channels and methods of distributing scientific publications, active development of digital technologies are changing the tasks and forms of work of scientific libraries, which form documentary and information resources and have extensive experience in meeting the information needs of representatives of the scientific and educational complex. The directions of scientific libraries’ activities are complemented by purposeful training of users in the basics of information culture.In scientific libraries – libraries of universities and central scientific libraries of the subjects of the Russian Federation – much attention is paid to servicing students. The change in the educational model, strengthening of the self-educational component in the educational process of the university, the increasing importance of the information culture of the individual change the status of scientific libraries: from an element of the information infrastructure, the library turns into a subject of educational activity, interacting with faculties and departments on the principles of partnership.The purpose of this article is to present the results of the survey of the information behavior of students in Chelyabinsk, who use in their work, along with information resources of university libraries, document and information resources of the Chelyabinsk State Universal Scientific Library and to determine areas of cooperation between the Central Regional Scientific Library and higher educational institutions.

Patient-specific placental vessel segmentation with limited data

A major obstacle in applying machine learning for medical fields is the disparity between the data distribution of the training images and the data encountered in clinics. This phenomenon can be explained by inconsistent acquisition techniques and large variations across the patient spectrum. The result is poor translation of the trained models to the clinic, which limits their implementation in medical practice. Patient-specific trained networks could provide a potential solution. Although patient-specific approaches are usually infeasible because of the expenses associated with on-the-fly labeling, the use of generative adversarial networks enables this approach. This study proposes a patient-specific approach based on generative adversarial networks. In the presented training pipeline, the user trains a patient-specific segmentation network with extremely limited data which is supplemented with artificial samples generated by generative adversarial models. This approach is demonstrated in endoscopic video data captured during fetoscopic laser coagulation, a procedure used for treating twin-to-twin transfusion syndrome by ablating the placental blood vessels. Compared to a standard deep learning segmentation approach, the pipeline was able to achieve an intersection over union score of 0.60 using only 20 annotated images compared to 100 images using a standard approach. Furthermore, training with 20 annotated images without the use of the pipeline achieves an intersection over union score of 0.30, which, therefore, corresponds to a 100% increase in performance when incorporating the pipeline. A pipeline using GANs was used to generate artificial data which supplements the real data, this allows patient-specific training of a segmentation network. We show that artificial images generated using GANs significantly improve performance in vessel segmentation and that training patient-specific models can be a viable solution to bring automated vessel segmentation to the clinic.

Assessing the outcome of mobility assistive technology (OMAT) in daily living: results in an Italian sample

Background Despite the widely recognised benefits of assistive technology (AT) in everyday life, AT abandonment rate due to unmet user needs or expectations is high. World Health Organisation emphasised the importance of evaluating AT outcomes as a top research priority, but comprehensive approaches to assess users’ needs and AT impact are still limited. Aim The research study OMAT (outcomes of mobility assistive technology in rehabilitation pathways) aimed to evaluate the feasibility of a new AT assessment protocol for mobility AT, spanning from an initial evaluation of users’ and relatives’ needs to a follow-up after real-world use of assistive products (APs). Material and Methods An observational single-arm study involving 95 AT users was conducted at Fondazione Don Gnocchi (Milan, Italy). The OMAT protocol encompassed baseline assessment, user training, prescription, authorisation, delivery and follow-up. The evaluations blend standard clinical evaluations with validated AT outcome measures for perceived effectiveness of AT, user satisfaction and changes in quality of life. Results The feasibility of OMAT in clinical settings was established, with 67 user participants completing the protocol. Data showed positive impacts on user satisfaction and perceived AT effectiveness by all participants (users and relatives), with an improvement in the perceived degree of severity in problems. Quality of life outcomes, however, showed mixed results across various domains. Conclusion OMAT is a feasible protocol for assessing mobility APs in clinical settings, underscoring the importance of involving users in the prescription process and the need for comprehensive AT monitoring mechanisms. This approach could enhance AT service delivery, exceeding the current standards of the Italian-National-Health-Service. Implication for rehabilitation Personalise assistive technology (AT) based on user needs for higher effectiveness. Include structured follow-up in rehabilitation for better AT outcomes. Enhance patient involvement in AT selection to improve use. Focus on AT’s role in enhancing overall quality of life. Use validated tools for AT impact assessment and monitoring.

User-agnostic adaptation of human locomotion intent: Leveraging Teacher-Student-Learning and ensemble modeling

Wearable robot control in complicated contexts requires an understanding of human locomotion intent and behaviors. However, signals from human–robot interfaces are typically reliant on the user, leading to poor performance of the model trained on training user when applied to new end-point users. This study aims to address this problem by developing a novel Teacher-Student-Learning (TSL) approach using Heterogeneous Ensemble Hypotheses (HEH) to achieve unsupervised user-agnostic adaptation. The motivation behind using the Teacher-Student-Learning architecture is to leverage the diverse features extracted by HEH while maintaining computational efficiency. HEH reduces the difference between labeled training users and unlabeled end-point users by incorporating multiple diverse feature generators to extract a wide range of features, thereby increasing classification accuracy and reducing variance. However, this approach sacrifices efficiency due to the ensemble nature of HEH. To address this trade-off, the knowledge from HEH is distilled into a single network using TSL, ensuring precision and efficiency. The proposed approach is evaluated on two publicly available human locomotion datasets and a 2D moon dataset. Experimental results demonstrate the effectiveness of the proposed method, outperforming all other methods. Tested with three datasets, the proposed method can classify end-point users’ data with an average accuracy of 98.9%, 96.7%, and 96.9% while yielding a low processing time (1 ms). Compared to a benchmark method, the suggested strategy improves the average accuracy by 1.5% and 7.2% for categorizing the target users’ locomotor modes and stabilizes the learning curves. The proposed method represents a significant contribution to the field of human activity recognition and human intent prediction for human–robot interaction systems, enhancing the efficiency and generalization capacity of these systems.

Technology enhanced multidisciplinary cancer conferences in gynecologic oncology.

e13652 Background: Multidisciplinary Cancer Conferences (MCCs) improve quality of care and patient outcomes. The goals of our study were to investigate the impact of an information technology platform, Navify Tumor Board Solution (NTBS) on the (1) efficiency of preparing and conducting MCCs and (2) educational quality of the gynecologic oncology MCCs. Methods: We conducted a prospective, mixed methods study involving participants of the biweekly gynecologic oncology MCC at a comprehensive cancer center. NTBS was introduced to the gynecologic oncology MCC for the preparation and presentation of tumor board cases. Data were collected before and after the introduction of NTBS. We used a validated observational tool to assess the impact of the platform on the quality of case presentation and discussion, as well as decision making (as measured by a composite mean score). We also evaluated compliance of care plans with national guidelines, changes in care plans, and concordance of actual treatment received with MCC recommendations. The survey consisted of five qualitative and two demographic questions. Interviews lasting 10 minutes were recorded using Microsoft Teams. Analyses were conducted using SAS v9.4 (Cary, NC) and Dedoose v9.0.17, (Los Angeles, CA). Results: Pre- and Post-NTBS cohorts consisted of 49 and 60 patients, respectively. When comparing pre- versus post-NTBS cohorts, there was a difference in the mean composite score (50.57 vs 54.11; p=0.016). While there was no statistical difference between cohorts, patient care plans were changed in 13% of the cases and were 92% concordant with treatment received. Additionally, MCC recommendations were 100% compliant with national guidelines. Surveys of 13 MCC participants revealed that post-NTBS, the presentation format was more cohesive, concise, and structured. Common themes from gynecologic oncology fellows’ responses included improvement of case preparation time and simplified process while using NTBS. However, trainees reported limited use of NTBS features, difficulty incorporating graphical, and lack of a comprehensive list of NCCN guidelines. All respondents had educational goals for their participation and reported a high educational value of the MCC. These goals included knowledge acquisition of clinical diagnosis, management of standard of care versus complex situations, sentinel clinical trials underlying NCCN treatment guidelines, primary literature review, management of preparation time and flow of discussion, and preparation for oral boards. Conclusions: NTBS contributed positively to the gynecologic oncology MCC and clinical care for patients. Overall, NTBS had a positive impact on case formatting, preparation time and quality of trainee education. Comprehensive user training in the NTBS features could further enhance its utilization within the MCC.

Wireless Sensor Network for Smart Education: Design, Architecture, and Implementation

Abstract: This paper delineates the design, architecture, and implementation of a Wireless Sensor Network (WSN) tailored for smart education environments. As educational institutions increasingly integrate technology to enhance pedagogical practices and operational efficiency, the deployment of WSNs presents substantial advantages. The study initiates with an examination of fundamental design considerations, encompassing energy efficiency, scalability, data security, and user experience. It proposes a comprehensive architecture that includes sensor nodes, communication protocols, network topology, base stations, and cloud integration to facilitate effective data collection and processing. Key implementation strategies are detailed, including sensor node deployment, communication protocol configuration, cloud server setup, and user training. The paper underscores the suitability of routing protocols such as LEACH, AODV, and Directed Diffusion for educational applications, highlighting their efficacy in achieving energy-efficient and reliable data transmission. This research elucidates the potential of WSNs to foster interactive, adaptive, and resource-optimized educational environments, providing valuable insights into their practical deployment and benefits in contemporary educational settings.

Assistive Orthosis for Paralysis People Using 3d Printing Model

Abstract: The Monoplegia is a one type of paralysis. The Monoplegia patients do not do the regularElbow Extension and Flexion activities, because weakens muscle movements, mainly due to nervedamage. This orthosis is used to externally assist patients elbow extension and flexion movements.An elbow extension and flexion assist orthosis is automatic functioning system. When the patientis shaking the fingers, vibration sensor detects the vibration and the device is extending the hand by using Linear actuator and flexion is also done by a same procedure. The prototype is design in 3d printing model which was a make the design lightweight as much possible. Consider incorporating a user-friendly control interface that allows the patient to adjust the level ofassistance or control the device based on their comfort and needs. Implement safety features to prevent sudden or unintended movements that could cause discomfort or injury .Design the orthosis to be easily adjustable to accommodate different arm sizes and shapes. Allow for customization based on the individual's specific range of motion and strength. efficient and lightweight power source, considering the need for mobility and comfort. Optimize the battery lifeto ensure the device can be used throughout the day without frequent recharging. Ensure that the materials used in the 3D printing model are durable, hypoallergenic, and lightweight .Pay attentionto the ergonomic design to maximize comfort during prolonged use. Develop a comprehensive user training program to help patients become familiar with the device and optimize its use. Ensurethat the design complies with relevant medical device regulations and standards to guarantee safetyand efficacy. Collaborate with healthcare professionals, therapists, and end-users to gather feedback and make continuousimprovements to the design. Thus the prototype will help to recoveras soon as possible from the monoplegia condition.

The Effect of Training on Localizing HoloLens-Generated 3D Sound Sources.

Sound localization is a crucial aspect of human auditory perception. VR (virtual reality) technologies provide immersive audio platforms that allow human listeners to experience natural sounds based on their ability to localize sound. However, the simulations of sound generated by these platforms, which are based on the general head-related transfer function (HRTF), often lack accuracy in terms of individual sound perception and localization due to significant individual differences in this function. In this study, we aimed to investigate the disparities between the perceived locations of sound sources by users and the locations generated by the platform. Our goal was to determine if it is possible to train users to adapt to the platform-generated sound sources. We utilized the Microsoft HoloLens 2 virtual platform and collected data from 12 subjects based on six separate training sessions arranged in 2 weeks. We employed three modes of training to assess their effects on sound localization, in particular for studying the impacts of multimodal error, visual, and sound guidance in combination with kinesthetic/postural guidance, on the effectiveness of the training. We analyzed the collected data in terms of the training effect between pre- and post-sessions as well as the retention effect between two separate sessions based on subject-wise paired statistics. Our findings indicate that, as far as the training effect between pre- and post-sessions is concerned, the effect is proven to be statistically significant, in particular in the case wherein kinesthetic/postural guidance is mixed with visual and sound guidance. Conversely, visual error guidance alone was found to be largely ineffective. On the other hand, as far as the retention effect between two separate sessions is concerned, we could not find any meaningful statistical implication on the effect for all three error guidance modes out of the 2-week session of training. These findings can contribute to the improvement of VR technologies by ensuring they are designed to optimize human sound localization abilities.

The Impact of Visual and Kinesthetic Motor Imagery on Mental Fatigue and Classification Performance in Untrained Participants

Motor imagery (MI) is a critical component of Brain-Computer Interface (BCI) technology, but the effective use of MI-BCI requires significant user training. In order to improve the training effect, it is necessary to choose a stable and easy to use paradigm. To explore the effects of different paradigms on training effectiveness, 18 untrained participants were recruited to perform kinesthetic motor imagery (KMI) and visual motor imagery (VMI) experiments. The participants’ subjective vividness and mental fatigue levels were measured using a subjective scale and (θ + α)/β value in the experiments, respectively. Additionally, the Common Spatial Pattern (CSP) algorithm and the Support Vector Machine (SVM) algorithm were used to extract EEG signal features and classify them. The results showed that the majority of participants demonstrated a greater aptitude for excelling in the VMI paradigm, where the average accuracy rate was 84.7%, compared to the KMI paradigm, where the average accuracy rate was 79.6%. In addition to this, participants with higher levels of sports proficiency showed better adaptability in the KMI paradigm. Compared to KMI, VMI can enable most inexperienced participants to achieve a better user experience with less fatigue and improve training performance.

Less-proficient EFL students' use of Grammarly in writing: Behavior, cognition, and affect

This multiple case study examines less-proficient Intermediate EFL students' attitudes toward the use of Grammarly's free version in writing. The study explored the participants' behavior, cognition, and affect, aiming to provide detailed insights into their experiences and perspectives. Utilizing Zoom recordings, stimulated recall, and semi-structured interviews, the study reveals that the less-proficient learners were product-oriented in revising their writing, showing low cognitive engagement with Automated Written Corrective Feedback (AWCF). Although one participant did not fully trusted Grammarly’s accuracy, all of them accepted its suggestions quickly and uncritically. The desire to increase the scores seemed to play a significant role in their engagement with AWCF. Despite this, the study primarily highlights how inadequate linguistic knowledge posed challenges for the students in effectively utilizing the technology. Nonetheless, the students expressed overall satisfaction with Grammarly's assistance, recognizing its ability to improve their drafts. To optimize the technology's benefits for less-proficient EFL students, the study the need for providing metalinguistic knowledge and user training to enhance their language learning through its feedback.

#2485 CellMatchR—RNA-seq based matching of kidney cell lines to kidney reference cell types

Abstract Background and Aims Cultured kidney cell lines are broadly used to model the physiology and pathophysiology of the kidney. Due to immortalization, passaging and culture conditions, a cell line's gene expression profile might differ greatly from the primary cells it was originally derived from. This might be further enhanced by experimental treatments with compounds or genetic modifications. It is therefore a relevant question if a given cell culture system is still the appropriate model for a certain disease or scientific investigations. Reasoning that RNA sequencing (RNA-seq) based transcriptomes are generated as part of many experimental setups and hence may be used to address this question, we developed and tested an RNA-seq based approach, CellMatchR. This approach matches kidney cell lines, primary cells and even tissue specimen to known kidney reference cell types to determine which primary kidney cell type is the most similar and to what degree global gene expression or the expression of selected marker genes differs. Method CellMatchR uses published murine and human kidney single cell- and tubule-level transcriptomic datasets from healthy mice and human donors as references. Single cell datasets were further processed to pseudobulk references for each of the contained cell types. Then RNA-seq data from cell lines or tissues of interest (test data) was compared to the reference (pseudo)bulk data with Spearman correlations of gene counts-per-million values (CPM) or Euclidean distance using log-transformed CPM values. Both approaches were systematically tested for various combinations of test and reference datasets across different species and using global gene expression (i.e. all genes measured in reference and test datasets) and a set of 315 manually curated, kidney cell type marker genes. Results We sequentially used different kidney tissue types, primary cells and cell lines as positive controls and matched them to our reference datasets. Spearman correlations of gene expression rankings showed the highest correlation coefficients with biologically correct reference cell types (examples in Fig. 1A-C). We found Spearman correlations to be superior to Euclidean distances as method of comparison. Analyses based on global gene expression compared to our curated set of 315 kidney cell type marker genes yielded similar results. Notably, correlation coefficients were higher and showed less variation between reference cell types when using the global gene set. Matchings across species, i.e. using murine test and human reference data or vice versa still yielded mostly correct results. However, correlation coefficients were generally lower (i.e. rho = 0.9 vs. 0.6) and varied more across reference datasets if comparisons were performed across species. Using published RNA-seq data for different cell lines of the proximal tubule (e.g. human HK-2 and opossum OKH cells) we observed low similarities with proximal tubule cells (Fig. 1D), whereas two tested cell lines of the collecting duct (mIMCD-3 and mpkCCD cells) plausibly showed the highest similarity to medullary cell types like collecting duct and Loop of Henle cells. The most similar reference cell types did not change in mIMCD-3 cells when kept in 2-dimensional vs. 3-dimensional culture conditions, and in mpkCCD cells when the osmolality of the culture medium was changed from 300 to 600 mosmol/kg. Also, a Pkd1 knockout in mIMCD-3 cells did not change the most similar reference cell type in our analyses (Fig. 1E and F). Conclusion Our CellMatchR approach uses publicly available kidney single cell and bulk RNA-seq datasets and combines these with simple, computationally fast yet effective statistical methods to determine similarities of kidney cell lines and tissue samples to reference cell types of interest. It can easily be implemented by trained users or integrated into online resources for usage with a visual interface. It relies on RNA-seq data from the cell lines of interest and hence presents a feasible complementary method to check the general similarity of cell culture models to kidney cell types and assess their stability across experimental conditions.

Implementasi Pendataan Kawin Belum Tercatat : Solusi Melalui Aplikasi Kalimasada untuk Peningkatan Kualitas Data Penduduk di Kelurahan Kedungdoro

Community Service in English means community service, which is an activity of helping the community in the form of several activities without expecting any form of reward. Community service is one of the pillars of the Tri Dharma of Higher Education, namely: education, research and community service. Universities not only carry out education for their students, but also carry out research and develop innovation, as well as preserve and develop superior knowledge that is beneficial to society. Data collection on marriages that have not been registered is a problem that is often faced in managing population data in many sub-districts. This article discusses the proposed solution through the development of the Kalimasada application to improve the quality of population data in Kedungdoro Village. The service method used involves the process of data collection, needs analysis, application development, and user training. The approach used is the use of mobile application-based information technology to facilitate the process of collecting data on unregistered marriages. The Kalimasada application is designed with an intuitive and easy-to-use interface, allowing sub-district officials to quickly record data on unregistered marriages. In addition, this application is equipped with an automatic data validation feature to reduce input errors and increase data accuracy. During the service process, training was conducted for the community about the importance of reporting unregistered marriages to subdistrict officers via the Kalimasada application. The results of implementing the Kalimasada application show a significant increase in data collection on unregistered marriages in Kedungdoro Village. With this application, the data collection process becomes more efficient and accurate, producing more valid and reliable data. Apart from that, public participation in reporting unregistered marriages has also increased, so that the data collected becomes more complete. In conclusion, developing the Kalimasada application is an effective solution for improving the quality of population data in Kedungdoro Village. The use of information technology in public administration can help speed up the data collection process and increase data accuracy. The next step is to expand the implementation of this application to other sub-districts and continue to carry out monitoring and evaluation to ensure its sustainability and effectiveness in the long term.

When Young Scholars Cooperate with LLMs in Academic Tasks: The Influence of Individual Differences and Task Complexities

As a novel AI-powered conversational system, large language models (LLMs) have the potential to be used in various applications. Recent advances in LLMs like ChatGPT have made LLM-based academic tools possible. However, most of the existing studies on the adoption of LLM for academic tasks were based on theoretical or qualitative analyses, which failed to provide empirical evidence on the effects of LLMs on users’ behaviors. Additionally, although previous work has investigated users’ acceptance of conventional conversational systems, little is known about how scholars evaluate LLMs when they are used for academic tasks. Hence, we conducted an empirical field experiment to assess the performance of 48 early-stage scholars on two core academic activities (paper reading and literature reviews) under varying time constraints. Prior to the tasks, participants underwent different training programs about LLM capabilities and limitations. Then, we built a hierarchy dependency network using the Bayesian network. Statistical regression analyses were further conducted to quantify relationships among influential factors of task performance and users’ attitudes toward the LLMs. It was found that young scholars have upheld relatively high academic integrity when using LLMs for academic tasks, and user-LLM performance varied with the task type and time pressure but not with the type of training we used. Further, scholars’ traits can also affect their performance in academic tasks and attitudes towards the LLMs. This work can inspire the future development of LLM-related user training and guide the optimization of LLMs.

Implementasi Nilai-Nilai Kebangsaan Berbasis UUD 1945 Guna Mendukung Sistem Cyber Security Dalam Penggunaan Sistem Informasi Akademik (SIA) Di Institusi Perguruan Tinggi

Cyber Security for Higher Education Academic Information System (AIS) data is an increasingly important issue along with the increasing dependence of higher education on information technology. Students have their education data in the Academic Information System (AIS) which is owned by each campus. Campuses are obliged to give their students the right to privacy and the right to information. The right to privacy and limitations on the right to information include state protection of personal data. In this way, students have the right to receive protection for their personal data in the academic information system (AIS). This is regulated by the 1945 Constitution (UUD 1945) and the Personal Data Protection Law (UU PDP), which are an important foundation for regulations on data management and cyber security systems. This research uses a journal literature review method which aims to investigate the security threats faced by Academic Information System (AIS) data and to design effective protection strategies. The methods used include risk analysis, identification of system vulnerabilities, and development of appropriate security policies. The findings show that sensitive data such as student personal information and academic records are the main targets for attackers. Therefore, the need to implement security measures such as data encryption, use of double authentication, strict system monitoring, as well as training for information system users is very important. Through a policy analysis approach and literature study, this research aims to identify strategies and best practices in integrating national values into cyber security policies and practices. It is hoped that this research will provide an overview of the importance of national principles in terms of cyber security in higher education. It will also provide recommendations for institutions to improve academic information systems in accordance with the country's constitution.

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

Analisis Penerapan dan Pengembangan Aplikasi SIMKASDA (Sistem Informasi Manajemen Kas Daerah) dalam Meningkatkan Efisiensi Keberhasilan dan Pelaporan Keuangan: Studi Kasus BPPKAD Kota Probolinggo

This study aims to analyze the impact of implementing and developing the Regional Cash Management Information System (SIMKASDA) on efficiency, success, and financial reporting at the regional level. The research method used qualitative research, focusing on the use of SIMKASDA application in BPPKAD Probolinggo City. The results showed that the implementation of SIMKASDA significantly improved the efficiency of the regional cash management process. The system allows for more accurate monitoring of the DTH/RTH Reconciliation. This directly contributes to the success of local financial management by providing faster and more accurate information for stakeholders. Such improved success is also reflected in the transparency and accuracy of financial reporting. SIMKASDA provides a platform that enables faster and structured preparation of financial reports, minimizes human error, and strengthens accountability in the management of public funds. The discussion in this journal also identified some challenges faced during implementation, such as the need for intensive training and regular maintenance of the system. Research suggestions include increased training for users, regular system updates, and integration of the latest technology to ensure the continued success of SIMKASDA in the future. Thus, this study provides in-depth insight into the contribution of SIMKASDA in improving operational efficiency, financial management success, and financial reporting quality at the local level.

THE IMPACT OF THE USE OF ACCOUNTING SOFTWARE ON THE ACCURACY OF FINANCIAL INFORMATION AN ANALYTICAL STUDY OF THE OPINIONS OF A SAMPLE OF EMPLOYEES AT AL-MURABBA SOFTWARE SOLUTIONS COMPANY

In today's dynamic business landscape, the use of accounting software has become increasingly widespread, promising to enhance efficiency and accuracy in financial management processes. This study aims to know the impact of using accounting software on the accuracy of financial information within organizations. To achieve the research objectives, a questionnaire was distributed to employees of Al-Murabba Software Solutions Company, and by compiling results from both theoretical frameworks and practical insights, this research provides valuable implications for companies that aim to benefit from accounting software for financial management. It provides recommendations to improve program implementation strategies, enhance user training programs, and enhance data security measures to increase the accuracy and reliability of financial information. This study also contributes to the ongoing discourse on the role of technology in modern accounting practices and emphasizes the importance of aligning software adoption with organizational goals to achieve greater financial accuracy and effective decision-making.

Evaluation of augmented reality training for a navigation device used for CT-guided needle placement.

Numerous navigation devices for percutaneous, CT-guided interventions exist and are, due to their advantages, increasingly integrated into the clinical workflow. However, effective training methods to ensure safe usage are still lacking. This study compares the potential of an augmented reality (AR) training application with conventional instructions for the Cube Navigation System (CNS), hypothesizing enhanced training with AR, leading to safer clinical usage. An AR-tablet app was developed to train users puncturing with CNS. In a study, 34 medical students were divided into two groups: One trained with the AR-app, while the other used conventional instructions. After training, each participant executed 6 punctures on a phantom (204 in total) following a standardized protocol to identify and measure two potential CNS procedural user errors: (1) missing the coordinates specified and (2) altering the needle trajectory during puncture. Training performance based on train time and occurrence of procedural errors, as well as scores of User Experience Questionnaire (UEQ) for both groups, was compared. Training duration was similar between the groups. However, the AR-trained participants showed a 55.1% reduced frequency of the first procedural error (p > 0.05) and a 35.1% reduced extent of the second procedural error (p < 0.01) compared to the conventionally trained participants. UEQ scores favored the AR-training in five of six categories (p < 0.05). The AR-app enhanced training performance and user experience over traditional methods. This suggests the potential of AR-training for navigation devices like the CNS, potentially increasing their safety, ultimately improving outcomes in percutaneous needle placements.