Error Handling Research Articles

System Integration Failures and Their Impact on Patient Safety in Critical Care Settings

Robotic surgery systems offer precision and minimally invasive procedures, enhancing surgical outcomes in critical care settings. However, these systems' complexity, with integrated hardware and software, can lead to integration failures, compromising patient safety. This paper examines the system integration challenges in robotic surgery, explores their effects on patient safety, and suggests solutions to overcome them. Rigorous testing strategies during development and continuous monitoring of system performance are crucial in detecting failures early and ensuring reliable system operation. Keywords: Robotic Surgery Systems, System Integration, Patient Safety, Software Failures, Hardware Failures, Critical Care, Integration Testing, Failover Mechanisms, Robotic Arms, Sensor Calibration, Real-Time Data Processing, Medical Robotics, Surgical Precision, Failure Detection, Stress Testing, Fault-Tolerant Design, Error Handling, Modular Design, Quality Assurance, ISO 13485, Robotic Surgery Failures, Calibration Errors, Mako System, Da Vinci Surgical System, CyberKnife System, Automated Testing, Robotic Surgery Technology.

Customer Service Chatbot With ML

In the modern business landscape, customer support plays a critical role in shaping customer experiences and building brand loyalty. With the growing demand for instant responses and 24/7 availability, organizations are increasingly exploring artificial intelligence (AI) solutions to enhance the efficiency and effectiveness of their customer service operations. This work aims to design and develop an intelligent customer support chatbot that leverages machine learning (ML) and natural language processing (NLP) techniques to deliver automated, yet personalized, customer assistance. The core objective is to build a machine learning-based chatbot capable of understanding and responding to a wide array of customer queries in real-time. Traditional customer service models often require significant human resources, leading to high operational costs and long response times. By integrating an AI powered chatbot into the customer support process, businesses can streamline operations, reduce response time, and provide customers with timely, accurate information. Keywords: · Natural Language Understanding (NLU): The chatbot uses NLTK and Speech recognition to process and understand the user's input in natural language. It can identify the intent and extract key details from user queries to provide relevant responses[6]. · Integration in Flask Backend Flask supports the light-weight fast backend with requests that routes those to the correct machine learning model. It manages all the interactions with the users by processing inputs and passing on[4]. · Model Training of Machine Learning Model: The OpenAI models are trained on labelled data for the improvement in recognizing intents of the chatbot. user and giving suitable responses of the model keeps learning and evolving as it processes new data of the users[3]. · Real-Time Error Handling: The system handles errors such as unrecognized queries or technical issues efficiently. It logs errors and provides feedback to the user, ensuring a smooth experience[2]. · Sentiment Analysis: Based on NLTK, it analysis the sentiment of all user inputs to establish possible emotions such as frustration and satisfaction. This will provide the chatbot with means to respond in addition to adjusting its tone[4].

Personalized Mathematics Teaching with The Support of AI Chatbots to Improve Mathematical Problem-Solving Competence for High School Students in Vietnam

The digital age has sparked interest among educators in utilizing information technology, especially artificial intelligence (AI) chatbots. Due to the constant technological involvement, students must also acquire solid digital skills, especially AI proficiency, for learning and everyday life. However, there are few studies on models applying AI in teaching to develop mathematical abilities for high school students. Therefore, this paper proposes a theoretical framework for incorporating AI chatbots into education, boosting students’ mathematical problem-solving competence. Based on student data analysis, this framework will cover teaching, assessment, feedback, and dynamic learning activity adjustment. The paper then explains the operations of AI chatbots to provide personalized feedback. This process emphasizes the importance of error handling and information security, ensuring safety and efficiency in the learning process. This theoretical model supports the integration of AI chatbots in personalized teaching, specifically for improving mathematical skills.

Automation of Remote Laboratory Device Configuration File Management

This study develops a Python-based configuration file management application to automate backup and recovery of network devices in the Universitas Kristen Duta Wacana laboratory, which uses Cisco and Mikrotik devices. The goal is to understand the impact of automation on the effectiveness of backup and recovery, support for remote configuration file management, reduction of recovery time, and its impact on operational sustainability. The Waterfall method is applied with the stages of analysis, design, implementation, testing, and evaluation, using network topology data, device information, and network protocols collected through observation, interviews, documentation, and literature studies. Testing includes scenarios of full and partial backup and recovery, error handling (credentials, lost files, unresponsive devices), and FTP server and database failure conditions on a network topology consisting of four identical blocks with Mikrotik routers and switches. The test results show that the application functions as expected, with all scenarios running smoothly without significant failures, including the ability to filter data according to parameters and detect credential errors. This system is concluded to have met the test objectives and is ready to be implemented.

The Transformative Impact of AI on Modern System Integration

This article examines the transformative impact of Artificial Intelligence on modern system integration, exploring how AI technologies are revolutionizing how organizations connect and manage their enterprise applications. The article investigates the core AI technologies driving this transformation, including supervised learning, unsupervised learning, and reinforcement learning algorithms, and their applications in data mapping, error handling, and API management. The article demonstrates how AI-enabled integration platforms significantly improve operational efficiency, security, and scalability by analyzing implementation strategies, technical architectures, and best practices. The findings reveal that organizations implementing AI-driven integration solutions experience substantial reductions in deployment time, enhanced data accuracy, and improved system reliability while maintaining robust security and compliance frameworks. The article also highlights the importance of structured change management approaches and proper architectural considerations in successful AI integration implementations.

Significant handling errors and education gaps regarding the use of the emergency kit among adult patients with Hymenoptera venom allergy.

In case of systemic anaphylactic reactions after Hymenoptera stings, patients should be provided with an adrenaline autoinjector (AAI). We aimed to evaluate the education and handling competence of patients in a real-world setting. Patients with Hymenoptera venom allergy presenting for the first time in our clinic with a previously prescribed emergency kit including an AAI were interviewed using a standardized questionnaire and were asked to demonstrate the AAI use with a dummy. 82 patients (62.2% female, mean age 52.0 ± 17.3 years) with allergy to wasp venom (85.3%), bee venom (9.8%), or hornet venom (4.9%) were included. 37.8% reported to have received a practical training on the AAI upon prescription. 59.8% of all patients showed significant handling errors which would have led to misinjections in 30.6%. Our data demonstrate a considerable lack of education, significant handling errors of the emergency kit, and a high risk of misinjections of the AAI. As the emergency kit is potentially lifesaving, the awareness for a sufficient education and training needs to be risen.

A Training for Parents Prevents Clinically Relevant Handling Errors in the Use of Long-Term Antiseizure Medication.

Medication handling errors (ME) in long-term antiseizure medication (ASM) compromise patient safety. Training programs to prevent those errors by parents are scarce. The intervention concept is designed for parents of children and adolescents aged 0-18 years with at least one long-term ASM. In a controlled prospective study, we assessed ME of ASM during home visits 3-6 weeks after a patient's in- or outpatient hospital visit (outpatient neuropediatric clinic and Social-Pediatric Center (SPZ)). We investigated the effectiveness of a patient specific, risk-adapted training (intervention group, IG) compared to routine care (control group, CG). For 54 ME in ASM handling, an expert panel classified the clinical risk ranging from Score-0 (no risk) to Score-6 (maximum risk) with the lowest risk actually classified as Score-3. We analyzed data from 83 parents in the CG and 85 in the IG who administered 140 ASM per group. The intervention reduced ME per patient from 5 (median; Q25/Q75 3/9) to 4 (2/8; p=0.018). A total number of 589 ME occurred in the CG, 432 in the IG. ME in ASM handling rated Score-6 occurred once in the CG and not in the IG. A relative-risk-reduction (RRR) of ME (with p<0.001) was observed, with a RRR of 55.0% for Score-5, 27.6% for Score-4, and 23.1% for Score-3. RRR was 56.6% for ASM preparation (p<0.001) and 22.4% for oral administration (p=0.045). Compared to controls, ME with high clinical risk significantly decreased in the IG after the training. Drug safety in chronically ill children with ASM was thereby improved.

Autonomous Maneuvering Decision-Making Algorithm for Unmanned Aerial Vehicles Based on Node Clustering and Deep Deterministic Policy Gradient

Decision-making for autonomous maneuvering in dynamic, uncertain, and nonlinear environments represents a challenging frontier problem. Deep deterministic policy gradient (DDPG) is an effective method to solve such problems, but it is found that complex strategies require extensive computation and time in the learning process. To address this issue, we propose a node clustering (NC) method, inspired by grid clustering, integrated into the DDPG algorithm for the learning of complex strategies. In the NC method, the node membership degree is defined according to the specific characteristics of the maneuvering decision-making problem, and error handling strategies are designed to reduce the number of transitions in the replay database effectively, ensuring that the most typical transitions are retained. Then, combining NC and DDPG, an autonomous learning and decision-making algorithm of maneuvering is designed. The algorithm flow and the pseudo-code of the algorithm are given. Finally, the NC_DDPG algorithm is applied to a typical short-range air combat maneuvering decision problem for verification. The results show that the NC_DDPG algorithm significantly accelerates the autonomous learning and decision-making process under both balanced and disadvantageous conditions, taking only about 77% of the time required by Vector DDPG. The scale of NC impacts learning speed; the simulation results across five scales indicate that smaller clustering scales significantly increase learning time, despite a high degree of randomness. Compared with Twin Delayed DDPG (TD3), NC_DDPG consumes only 0.58% of the time of traditional TD3. After applying the NC method to TD3, NC_DDPG requires approximately 20–30% of the time of NC_TD3.

MODELING THE STRENGTH-TO-MASS RATIO OF THE FUSED FILAMENT FABRICATED ABS POLYMER WITH ANN

The present study investigated the Strength-to-Mass ratio (StMr) yielded by Acrylonitrile Butadiene Styrene (ABS) specimens, fabricated with the Fused Filament Fabrication (FFF) method. A universal testing machine was utilized for the Ultimate-Tensile-Strength (UTS) measurement of the specimens, whereas a precision balance was used to measure their mass. The experiments were designed according to the Central-Composite-Design (CCD), by considering four process parameters: the infill, the layer thickness, the line direction of the top and bottom layers, as well as the pattern. In addition, a shallow Artificial Neural Network (ANN) was developed to predict the StMr, which was then compared to the empirical model generated by the CCD method. The analysis revealed a strong correlation between the two models, with the Mean Absolute Percentage Error (MAPE) being below 1%. Finally, verification testing was performed to evaluate the absolute error handling of the ANN model, which was found to be 6%.

Multimedia Transmission Technique for Smart Ambulance with multi-carrier OFDM in a V2V and V2I Channel model using Software Defined Radio Technology

This research explores the implementation of a cutting-edge Software Defined Radio (SDR) framework to transmit multimedia files that can be assumed to be medical data in smart ambulances. The system utilizes multi-carrier Orthogonal Frequency-Division Multiplexing (OFDM) across V2V and V2I channels. The research is based on the notion that adaptive real-time communication is essential for the uninterrupted supply of key patient data to medical facilities and vehicles in transit, in order to address the problems posed by high mobility and dynamic environmental conditions. A comprehensive SDR system has been constructed and assessed in comparison to conventional communication mechanisms, demonstrating notable advancements in data accuracy and uninterrupted transmission. Our system successfully established stable connections in V2I channels, even in the presence of environmental obstacles. It maintained average power levels of approximately 32.074 dBm and a Peak-to-Average Power Ratio (PAPR) of 1.037 dB. These results indicate a constant signal envelope that promotes optimal signal transmission with excellent fidelity. In V2V scenarios, we successfully maintained data integrity with a low Peak-to-Average Power Ratio (PAPR) of 3.316 dB, even while vehicles were moving at a speed of 20 km/h. Additionally, we secured a high likelihood (94.5%) that the signal power remained close to the average, showing the robustness of our system against Doppler effects and signal dispersion. Text transmissions experienced errors when subjected to a Doppler shift of 20 km/h, which impacted the decoding of the received text. Similarly, image transmissions revealed limitations in bandwidth, as a transmitted image of 3640 KB was received with a degraded 4 KB. This emphasizes the importance of implementing effective error handling and recovery mechanisms. The results illustrate the efficacy of the suggested system in maintaining a high Quality of Service (QoS), offering proof of the effectiveness of contemporary wireless communication technologies in improving emergency medical services and setting new standards in smart ambulance capabilities.

Exploring pedagogies, opportunities and challenges of teaching and learning programming in business school

PurposeThis study aims to develops an interdisciplinary business and computer science pedagogy for teaching and learning computer programming in business schools at higher education institutions and explores its associated benefits, challenges and improvement.Design/methodology/approachBased on a body of theories, an interdisciplinary pedagogy is developed and tested for programming education in a business context. Meanwhile, based on the unified theory of acceptance and use of technology, the authors used observation study and thematic analysis to explore opportunities, challenges and future improvements associated with this interdisciplinary pedagogy.FindingsThe developed pedagogy includes integrating humanism and construction theory, problem-based learning, cognitive development, active instructional strategies, synergy of individual and group programming tasks and creating an encouraging and inclusive learning environment. This study shows that business students perceive this novel pedagogy as highly valuable because it enhances their logical thinking and problem-solving abilities while giving them a sense of accomplishment. Although students face challenges in data preprocessing, error handling and translating theoretical knowledge, they find it useful to review teaching materials, seek peer support and learn independently through online resources. Further improvements in pedagogy include incorporating collaborative code reviews, using shared documents for troubleshooting and grouping students based on their prior programming experience.Practical implicationsThis interdisciplinary pedagogy can guide business schools to improve the quality of programming-related modules, enhance students’ performance and prepare them for future careers.Originality/valueThis is the first interdisciplinary study investigating teaching programming in a business context.

Leveraging Databases for Effective Policing: A Strategic Approach to Modern Law Enforcement

The incorporation of databases in law enforcement has significantly revolutionized policing by improving operational efficiencies, enhancing decision-making, and advancing crime prevention strategies. This study explores how databases contribute to effective policing by assessing their utility in crime mapping, predictive analytics, criminal identification, and evidence management. It highlights critical challenges, including worries about data privacy, the necessity for collaboration between agencies, data falsification, errors in data handling, inadequate infrastructure, insufficiently trained personnel, and technological constraints. Additionally, the study offers recommendations for the ethical optimization of database usage, emphasizing the importance of balancing technological advancements with the protection of individual rights. By investigating these aspects, the research aims to provide insights into the best practices for leveraging database capabilities in policing while addressing ethical considerations and challenges.

Autonomy in acquisition: Exploring the transformative effects of self-error correction on ESL learners’ proficiency and motivation

This study explores the transformative effects of self-error correction on language proficiency and motivation among ESL college students. The research investigates how self-directed error handling influences ESL learners' linguistic development and engagement in learning activities through a mixed-methods approach involving surveys and interviews. Preliminary findings indicate significant enhancements in language skills and motivational levels, substantiating the role of self-correction in promoting educational outcomes and learner independence.

From the Operating Theater to the Pathology Laboratory: Failure Mode, Effects, and Criticality Analysis of the Biological Samples Transfer.

Introduction: The frozen section intra-operative consultation is a pathology procedure that provides real-time evaluations of tissue samples during surgery, enabling quick and informed decisions. In the pre-analytical phase, errors related to sample collection, transport, and identification are common, and tools like failure mode, effects, and criticality analysis help identify and prevent risks. This study aims to enhance patient safety and diagnostic quality by analyzing risks and optimizing sample management. Materials and Methods: The failure mode, effects, and criticality analysis was conducted by a multidisciplinary team to analyze the workflow of frozen section sample handling from collection in the operating theater to acceptance at the pathology lab. Six steps were identified, each assigned tasks and responsibilities, with risks assessed through the risk priority number, calculated from severity, occurrence, and detectability. Severity was classified based on the WHO framework, ranging from "No Harm" to "Death", to prioritize risks effectively. Results: The study identified 12 failure modes across 11 sub-processes, prioritized by risk. Key failures included missing patient identification, incorrect sample retrieval, missing labels, misdirected samples, and samples sent to the wrong lab. Discussion: Pre-analytical errors in pathology pose risks to diagnosis and patient care, with most errors occurring in this phase. A multidisciplinary team identified key issues, such as sample mislabeling and delays due to staff unavailability, and implemented corrective actions, including improved signage, staff re-training, and sample tracking systems. Monitoring and regular checks ensured ongoing adherence to protocols and reduced the risks of misidentification, transport delays, and procedural errors. Conclusions: The frozen section intra-operative consultation is vital in surgical pathology, with the pre-analytical phase posing significant risks due to potential errors in sample handling and labeling. Failure mode, effects, and criticality analysis has proven effective in identifying and prioritizing these failures, despite resource demands, by allowing corrective actions that enhance patient safety and healthcare quality.

Orchestration Workflows in Distributed Systems: A Systematic Analysis of Efficiency Optimization and Service Coordination

This article presents a comprehensive analysis of orchestration workflows in modern software architectures, examining their role in optimizing service efficiency and coordination across distributed systems. Through a systematic investigation of implementation patterns and operational strategies, we demonstrate how orchestrated workflows enhance service integration, automate complex processes, and improve system reliability. The article employs a mixed-method approach, combining quantitative performance metrics from real-world implementations with qualitative analysis of architectural patterns across diverse enterprise environments. The findings reveal that organizations implementing robust orchestration workflows experienced a 47% reduction in service integration failures, 35% improvement in resource utilization, and 62% faster deployment cycles. The article also identifies critical success factors in orchestration implementation, including dynamic resource management, automated error handling, and comprehensive monitoring frameworks. Furthermore, The article proposes a novel framework for evaluating orchestration effectiveness in cloud-native environments, considering factors such as service mesh integration, container orchestration, and distributed tracing. This article contributes to the growing body of knowledge in service optimization by providing empirical evidence of orchestration benefits and offering practical guidelines for implementing scalable, resilient software systems through advanced workflow orchestration strategies.

Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving

We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves as the central coordinator, managing an action generator and instruction-tuned small-scale language models (expert models). The action generator decomposes complex problems into sub-tasks and identifies suitable expert models to execute each, delivering precise solutions for multi-step problem-solving. Key techniques include advanced knowledge modeling using property graphs for improved information retrieval, facilitating more accurate and contextually relevant solutions. Additionally, the framework utilizes a teacher-student transfer-learning approach with GPT-4 (Omni) to fine-tune the action generator and expert models for domain adaptation, alongside an iterative problem-solving mechanism with sophisticated error handling. Custom datasets were developed to evaluate the framework against leading proprietary language models on various engineering tasks. The results demonstrate the framework’s effectiveness in automating calculations, accelerating prototyping, and providing AI-augmented decision support for industrial processes, marking a significant advancement in process engineering capabilities.

Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount.

Data Stream Protocols for Real-Time Messaging : Optimizing Performance and Reliability

This article explores the design and implementation of data stream protocols for real-time messaging, focusing on key aspects that contribute to their performance and reliability. It examines protocol architectures, data formatting and serialization techniques, error handling mechanisms, and quality of service features. The article compares popular protocols such as WebSocket and MQTT, discusses performance optimization techniques, and addresses security considerations. Emerging trends like WebTransport, HTTP/3, and edge computing integration are also explored, highlighting their potential to reshape the landscape of real-time messaging. By providing a comprehensive overview of current practices and future directions, this article aims to equip developers and system architects with the knowledge needed to design efficient and reliable real-time communication systems.

Visual Servoing for Aerial Vegetation Sampling Systems

This research describes a vision-based control strategy that employs deep learning for an aerial manipulation system developed for vegetation sampling in remote, dangerous environments. Vegetation sampling in such places presents considerable technical challenges such as equipment failures and exposure to hazardous elements. Controlling aerial manipulation in unstructured areas such as forests remains a significant challenge because of uncertainty, complex dynamics, and the possibility of collisions. To overcome these issues, we offer a new image-based visual servoing (IBVS) method that uses knowledge distillation to provide robust, accurate, and adaptive control of the aerial vegetation sampler. A convolutional neural network (CNN) from a previous study is used to detect the grasp point, giving critical feedback for the visual servoing process. The suggested method improves the precision of visual servoing for sampling by using a learning-based approach to grip point selection and camera calibration error handling. Simulation results indicate the system can track and sample tree branches with minimum error, demonstrating that it has the potential to improve the safety and efficiency of aerial vegetation sampling.

A Framework for Enhancing Maintainability of CMS in Kenyan Universities' LMS

The maintainability of Content Management Systems (CMS) within Learning Management Systems (LMS) is critical in educational institutions to ensure efficient content management, system scalability, and security. This paper presents a framework aimed at enhancing the maintainability of CMS within LMS in Kenyan universities. The research utilizes Cognitive Load Theory and Software Evaluation Theory to address CMS challenges, focusing on perfective, corrective, adaptive, and preventive maintenance. This paper also discusses key factors influencing CMS maintainability, including system documentation, modularity, and error handling. Findings from this study contribute to the development of a framework and advancing CMS design in LMS, providing a robust, scalable, and secure platform for educators and administrators.