Development Of System Research Articles

Unveiling Crash Casualties: A Neural Network Model for Crash Type Classification Using Traffic Datasets

Abstract: Traffic accidents, a significant public health burden with substantial economic costs, necessitate proactive measures for sustainable transportation. This study investigates the efficacy of neural networks in predicting crash types using traffic data. We propose a deep learning model that leverages the power of recurrent neural networks (RNNs) for sequential traffic pattern analysis and convolutional neural networks (CNNs) for extracting spatial features from traffic data. The model, trained on a labeled traffic dataset, learns to identify patterns and relationships between traffic characteristics (e.g., speed, volume, weather) and different crash types (e.g., rear-end collision, single-vehicle). The evaluation focuses on the model's accuracy in predicting crash types using metrics like precision, recall, and F1-score. This research contributes to the development of intelligent traffic management systems (ITMS) that can proactively identify and mitigate potential accidents, ultimately enhancing road safety and promoting a more sustainable transportation future.The implementation results indicate that the proposed method yields an accuracy of 82.93% approximately.

Enhancement Network Security Features Through Intrusion Detection Systems

Abstract: In pursuit of its objectives, the project "Enhancement Network Security features through Intrusion Detection Systems" undertakes a comprehensive exploration of the intricacies surrounding network security, delving into the nuances of intrusion detection methodologies. By dissecting the mechanisms behind both anomaly- and signature-based IDS, the initiative aims not only to implement these systems but also to grasp their underlying principles deeply. Through rigorous experimentation and analysis, the project endeavours to uncover the strengths and limitations of each approach, thereby paving the way for the development of a hybrid system that leverages the best of both worlds. This meticulous approach ensures that the resulting IDS is not only robust but also finely tuned to adapt to the ever-evolving landscape of cyber threats. Furthermore, the integration of machine learning techniques represents a pivotal advancement in the realm of intrusion detection. By harnessing the power of algorithms capable of autonomously learning from data, the IDS transcends static rule-based detection methods, ushering in a new era of adaptive security measures. Through continuous training on vast datasets comprising diverse threat scenarios, the machine learning-enabled IDS hones its ability to discern subtle patterns indicative of malicious activity, thus bolstering its efficacy in safeguarding network assets. Moreover, the implementation of machine learning algorithms fosters a proactive stance against emerging threats, enabling the system to anticipate and mitigate potential breaches before they materialize into fullfledged attacks. This proactive approach not only enhances the overall security posture of organizations but also instils confidence in their ability to stay ahead of the curve in an increasingly hostile digital landscape. The project's goal is to provide companies with a stronger protection against cyberattacks by guaranteeing the availability, confidentiality, and integrity of their networked systems.

Traffic Sign Recognition Using Deep Learning

Abstract: Traffic sign recognition is a crucial component in advanced driver assistance systems and autonomous vehicles, enhancing road safety and overall transportation efficiency. Deep learning, specifically convolutional neural networks (CNNs), has emerged as a powerful tool for image based recognition tasks. The proposed deep learning-based traffic sign recognition system exhibits promising results, providing a foundation for the development of intelligent transportation systems. The trained model demonstrates remarkable accuracy in recognizing a wide range of traffic signs under different scenarios, including challenging lighting conditions and occlusions. The system's real-time performance is evaluated on both simulated and actual road scenarios, showcasing its feasibility for deployment in practical driving environments. The integration of such technology into vehicles holds great potential for enhancing road safety, reducing accidents, and contributing to the evolution of autonomous driving technology.

Comprehensive Survey of Autonomous Driving Systems and Advanced Technologies: Literature Review, Gap Analysis and Future Directions

Abstract: This article presents a literature review of numerous research papers focusing around the topic ‘Development of Autonomous Driving Vehicle System’, and related works. The advent of autonomous driving technology has revolutionized the automotive industry, promising enhanced safety, efficiency, and convenience on the road. A critical aspect of autonomous vehicle development is the assessment of their performance ina controlled and risk-free environment. Virtual environments, enabled by cutting-edge technologies, offer a dynamic plat- form for rigorous testing and training of autonomous vehicles. This paper aims to study and analyze different approaches and methodologies that are implemented for the development of Autonomous Driving Systems(ADS). Some of the primary aspects that are found common in most papers are Virtual Environment, pedestrians’ safety, privacy, security, real-time datapresentation, precise torque control, Reinforcement Learning, Deep Learning, Hardware-in-the-loop (HIL)- simulation, Model- in-the-loop (MIL)-simulation, etc. The sole idea of extracting valuable information from a virtual environment ensures a sense of safety as there are no humans involuntarily involved to take part in the development. However, the data extracted from the virtual environment must be highly accurate and reliable, as it will be trained and tested in real environments post deployment. Collision scenarios need to be carefully studied, for which relative positioning of vehicle and pedestrians should be taken into account, so as to examine their velocities, time to collision, and appropriately taking actions. Above all, it should be noted that the safety of human lives holds the highest priority, if a method suggests a high risk factor for a human life, then it should be either discarded, or improved

Intelligent Robotic Arm-Assisted Electrical Furnace for Efficient Metal Casting

Abstract: This project proposes the development of an intelligent robotic arm-assisted electrical furnace system for efficient and precise metal casting. Electric furnace is used for heating purpose in various industrial production processes. Electric furnaces are used where more accurate temperature control is required. By combining the strengths of robotic automation with the clean and controllable environment of an electric furnace, the system aims to achieve Enhanced quality and reduced waste, Improved safety and reduced hazards, increased efficiency and productivity, Greater flexibility, and versatility, Reduced environmental impact. The project will explore the integration of sensors enhancing efficiency and quality control. The integration of these technologies aims to enhance productivity, precision, and safety while reducing operational costs. The robotic arm is designed to perform loading and unloading tasks, handling raw materials and moulds with exceptional accuracy and repeatability. The robotic arm can be attached to the electrical furnace. With the help of sensors, robots can manage to avoid obstacles on their paths and achieve their goals with or without prior planning, depending on the complexity of their implemented program.

Enhancing Event Attendance Tracking System with Stranger Detection and Alerting Mechanism

Abstract: This paper presents the development and implementation of an innovative attendance tracking system coupled with a stranger detection and alerting mechanism for event venues. The proposed system utilizes a combination of frontend development in PHP, backend development using Python for facial recognition, and hardware integration for stranger detection using ESP8266 module, LED lighting, and buzzer. By seamlessly integrating these technologies, the system not only accurately records attendee presence but also alerts event organizers in real-time in case of unrecognized individuals, enhancing security measures and attendee safety.

Health monitoring system using ECG and PPG techniques

The demand for portable health monitoring systems has grown rapidly in recent years due to the increasing prevalence of chronic diseases and the need for continuous health monitoring. This article introduces the development and implementation of a portable health monitoring system using photo plethysmography (PPG) and electrocardiogram (ECG) technologies. The system integrates the Node MCU ESP8266 microcontroller, AD8232 ECG sensor, and MAX30100 sensor to measure vital signs such as heart rate and blood oxygen saturation level. The proposed system provides a cost-effective and efficient solution for real-time health monitoring, enabling early detection of abnormalities and timely intervention. The feasibility and performance of the system were proven through experimental tests.

Enhancing registration systems: Implementing database systems in digital boarding schools

Islamic boarding schools, known as pondok pesantren are specialized schools for Islamic education in Indonesia. With the community's increasing interest in religious education, some pesantren have begun to offer online learning opportunities. Pondok Pesantren Bayt Tamyiz is a Islamic boarding school that has started developing online pesantren education under the name Tamyiz Digittren. This program aims to enhance understanding and translation of the Qur'an using the Tamyiz method. The pondok pesantren requires significant changes in its administrative systems, including the registration process. Information system development is often challenging for the pesantren staffs and software developers. One of the fundamental difficulties lies in the user requirement process. Typically, pesantren need help to provide the information system they need. Such difficulties hinder the development process as developers must conduct numerous experiments. This research aims to design and implement a suitable database for the new student registration system with minimal information and limited time development. The research follows a database development method divided into three stages: Requirement Collection and Analysis, Conceptual Database Design, and Logical Database Design. Database development utilizes MySQL as the Database Management System (DBMS) and PHP as the programming language. This research results in a database design capable of storing and managing student data, payments, and other registration information. We expect the results of this database development to be effective in supporting the registration system and in the future development of information systems at Pondok Pesantren Bayt Tamyiz.

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Банковская система России в 1987-1994: новая организация и новые проблемы

The article delves into the evolution of the banking institutions in Russia from 1987 to 1994. The necessity to move away from a single-level banking system by the late 1980s became apparent as it failed to meet the demands of a market economy. The emergence of specialized banks significantly expanded the functionality of banking operations, accompanied by the establishment of numerous regional branches. However, their dependence on the government persisted, and the banks' own capital increased slowly. The advent of commercial banks marked the beginning of the development of a full-fledged two-level structure of the Russian banking system. The number of commercial banks increased rapidly, attributed to the low requirements for obtaining licenses to conduct banking operations. Many banks encountered either liquidation or merging with the others as control over their activities was tightened and new regulations were introduced. The article outlines the legislative history regulating the monetary and credit system. The development and establishment of the banking system in Russia constitute a complex and contradictory process, including the criminalization of this sector in the early 90s, linked to the insufficient regulatory activity of the Central Bank of Russia. This led to citizens mistrust in banks, especially during the growth and subsequent collapse of financial pyramids. Unresolved issues in the banking and financial sectors led to another institutional crisis in 1995.

Novel nano magnesium oxide modified hybrid emulsion for antimicrobial coating

Purpose The purpose of this paper is to develop an inorganic-organic hybrid emulsion polymer using grafted hyperbranched alkyd modified with nanometal oxide and to study the performance of the developed hybrid emulsion in paint formulation with respect to antimicrobial and other properties. Design/methodology/approach A novel hybrid emulsion polymer was synthesized by grafting of vinyl acetate (VAM), vinyl ester of versatic acid (VeoVa 10) monomers onto hyperbranched alkyd resin and incorporation of nano magnesium oxide (MgO) into the hybrid resin matrix during dispersion of resin in water. Subsequently, paint was prepared by using this hybrid emulsion followed by a performance study. Findings The performance of nano MgO modified into VAM and VeoVa 10 grafted hyperbranched alkyd resulted in unique properties of coating especially antimicrobial activity. Research limitations/implications In the present study, soya fatty acid, polyol and di basic acid have been used to prepare hyperbranched alkyd by condensation polymerization. Monomers like VAM and VeoVa 10 used for grafting onto hyperbranched alkyd and nano MgO have been incorporated into hybrid resin matrix during emulsification of hybrid resin in water. Practical implications Grafting of VAM and VeoVa 10 onto hyperbranched alkyd along with in situ incorporation of nano MgO in resin matrix is an effective technique to achieve excellent coating properties. Originality/value Nano MgO modified VAM and VeoVa 10 grafted hyperbranched alkyd-based hybrid emulsion can be used as binder in water-based metal primer, direct to metal (DTM) coating as well as topcoat application. The developed system has antimicrobial properties as well as superior mechanical properties.

The banking system of Ukraine as a factor of social stability in the war

Before the collapse of the USSR, Ukraine was one of the 15 socialist republics. Along with Belarus, the Russian Federation, and the USSR, Ukraine is one of the founders of the United Nations. Ukraine has gone through transformations from a command-administrative to a market system in 30 years. The driving force behind this development was the national democratic forces, the leaders of which were former dissidents of the USSR. One of the main factors of market transformations in Ukraine is the national private banking business. In the conditions of Russia’s direct military aggression against Ukraine in 2022, the banking business of Ukraine has shown its institutional capacity and is one of the stabilizing factors of society. Thanks to the integration of Ukraine’s banking business into the global financial system, millions of Ukrainian refugees have the opportunity to use their accumulated funds in the national currency abroad. The purpose of this article is to analyze the impact of the banking sector of Ukraine on the country’s democratic development, and the contribution of the banking system to countering Russian aggression. The article considers further ways of development of the banking system of Ukraine in the post-war period.

Investigation of the Stability Mechanism of Stimulus-Responsive Wormlike Micelle-CO2 Foams in the Oil Phase.

Foam flooding is an important tool for reservoir development. This study aims to further investigate the interaction between stimulus-responsive wormlike micelle (WLM)-CO2 foams and crude oil. We performed micromorphology experiments as our major studies and used molecular dynamics simulations as an auxiliary tool for interfacial analysis. We utilized foam generation, liquid separation, and defoaming as the entry points of experimental research and energy as the quantitative assessment index to investigate the dynamic process of the action of different oil contents and oil phase types in a DOAPA@NaSal-H+ foam system. We also examined the role of NaSal in the generation and development of the foam system. Results indicated that the law of crude oil's effect on foam could be summarized as "low contents are beneficial and high contents are harmful." In addition, although the DOAPA@NaSal-H+ foam system has high compatibility for saturated and aromatic hydrocarbons, it is highly suitable for application in reservoir environments with relatively high asphaltene and resin contents. Through combined experimental and simulation approaches, we clarified the law governing the stability of the DOAPA@NaSal-H+ foam system in different oil-containing environments, identified the key role of NaSal, and provided a reference for the targeted application of the DOAPA@NaSal-H+ foam system in different oil reservoirs.

Accessibility of digital systems in information retrieval by users with visual impairment

Purpose This paper aims to evaluate the accessibility of digital information systems as a key usability attribute in information retrieval by users with visual impairment (VI), to inform development of accessible information systems. Design/methodology/approach This study used a cross-sectional survey design based on the quantitative research paradigm. Data was collected from 117 students with visual impairments from selected public universities in Kenya, using a semi-structured questionnaire adapted from Web Content Accessibility Guidelines. Findings This study revealed that gender did not influence screen reader compatibility, alternative format preferences, perceptions of accessible content or digital library navigability. Instead, type of visual impairment, prior assistive technology (AT) training and digital library use frequency significantly impacted these attributes. Blind students reported positive experiences, favouring alternative formats, and expressed positive views on accessible content while those with low vision faced more challenges. Prior training in assistive technology and frequent digital library use resulted in higher screen reader compatibility and better navigability. Increased digital library use was linked to access to alternative content for those with prior assistive technology training. Research limitations/implications This study contributes to the existing literature on accessibility of digital information systems for users with visual impairments. Further research is needed to explore other factors that may influence the accessibility of digital systems, such as the design of user interfaces as well as an evaluation of accessibility of digital systems for other categories of disabilities such as hearing impairment, physical impairment, autism and intellectual disability, among others. Practical implications The findings of this study have practical implications for the design and development of digital systems such as digital libraries. Developers can leverage findings of this study to enhance compatibility of digital platforms with screen readers and provide alternative formats and accessible content. User-centred design principles can be used to create interfaces that are easy to navigate and friendly to users with visual impairments. Training programmes on assistive technology should be provided to users to enhance their skills and confidence in using digital systems. Originality/value This study contributes towards design and creation of navigable systems that are compatible with screen readers. The study also points out the importance of creating more inclusive and usable digital environments for persons with visual impairments.

Extent of foetal exposure to maternal elexacaftor/tezacaftor/ivacaftor during pregnancy.

Cystic fibrosis (CF) patients are living longer and healthier due to improved treatments, e.g. cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI), with treatment possibly occurring in pregnancy. The risk of ETI to foetuses remain unknown. Thus the effect of maternally administered ETI on foetal genetic and structural development was investigated. Pregnant Sprague Dawley rats were orally treated with ETI (6.7mg·kg-1·day-1 elexacaftor + 3.5mg·kg-1·day-1 tezacaftor + 25 mg·kg-1·day-1 ivacaftor) for 7days from E12 to E19. Tissue samples collected at E19 were analysed using histology and RNA sequencing. Histological changes and differentially expressed genes (DEG) were assessed. No overt structural abnormalities were found in foetal pancreas, liver, lung and small intestine after 7-day ETI exposure. Very few non-functionally associated DEG in foetal liver, lung and small intestine were identified using RNA-seq. 29 DEG were identified in thymus (27 up-regulated and two down-regulated) and most were functionally linked to each other. Gene ontology enrichment analysis revealed that multiple muscle-related terms were significantly enriched. Many more DEG were identified in cortex (44 up-regulated and four down-regulated) and a group of these were involved in central nervous system and brain development. Sub-chronic ETI treatment in late pregnancy does not appear to pose a significant risk to the genetic and structural development of many foetal tissues. However, significant gene changes in foetal thymic myoid cells and cortical neuronal development requires future follow-up studies to assess the risk to these organs.

Development and validation of a risk scoring system to identify patients with lupus nephritis in electronic health record data

ObjectiveAccurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient...

Semiautomated electronic medical record based system for monitoring delayed adverse events in pediatric endoscopy and sedation.

To develop a semiautomated electronic medical record (EMR) system to track pediatric endoscopic procedural adverse event (AE) at a tertiary referral children's hospital. We developed an automated EMR based query for postprocedure AEs. Main outcome measurements within 30 days of procedure: return to emergency department, return to surgery, unplanned admissions and admissions with longer than intended stays. Events were graded using a recently described classification system for postendoscopy events and tracked for a 36-month period, from January 2017 to December 2019. Development of a semi-automated system was successful in comprehensive identification of endoscopy and sedation related AE. A total of 193 AEs (2%) were identified in all three categories. Seventy cases (0.7%) were noted to be a direct result of an endoscopic procedure. Of these cases, 31 (44%) were noted to be Grade 3, 5 cases (7%) Grade 4, and no Grade 5 AE occured. Higher rates of AE were observed after therapeutic procedures versus diagnostic (2.6% vs. 0.3%, p = <0.00001). AEs related to sedation occurred in 0.5% of procedures with the majority (84%) reported in patients with American Society of Anesthesia classification of 3 or greater. Diagnostic endoscopy remains a safe procedure and risk of both endoscopy and sedation related AE are low. Therapeutic procedures carry a higher risk but are still overwhelmingly safe. Institutional investment in this EMR based system allowed for sustainability and comprehensive tracking of endoscopy related AE.

Analysis of gesture recognition applications based on deep learning

With the advent of artificial intelligence, deep learning has been continuously evolving. It has progressed from its initial stages, exemplified by AlphaGo, to the development of related technologies exclusively centred around deep learning for the game of Go. Subsequently, deep learning has found applications in an increasing number of domains, including healthcare, sports, and more. This paper delves into the profound influence and development of deep learning as the foundation for analysing its impact on gesture recognition. Deep learning has brought about significant advancements in this field, enabling more precise and versatile gesture recognition systems. Furthermore, we explore specific use cases and contexts where deep learning has been harnessed, such as in medical diagnostics and sports performance analysis. While scrutinizing these applications, a promising future for deep learning becomes evident, with the potential to revolutionize various industries and enhance technology interaction through the development of more intuitive and sophisticated gesture recognition systems. The continuous growth and evolution of deep learning offer a bright prospect for the future of human-machine interfaces and artificial intelligence-driven solutions.

Research progress of energy regenerative suspension systems

As energy saving and recycling in the automobile industry becoming a hot topic today, the energy recovery and regeneration system attracts the attention of a large number of researchers. However, unlike the energy recycling system of the braking system, the suspension is a relatively more immature technology. This article focuses on the summary of the current development of the energy recycling suspension system. It starts with a brief introduction to suspension systems, including their definition and several types of typical suspension systems with their structures and principles. After that is the description of four types of energy recycling suspension systems: hydraulic type, air pressure type, electric type, and mechanical type. It shares their principles, structures, workings, and the research achievements of others contemporary. The following is a comparison of the four types of energy recovery and regeneration suspension systems from several different aspects. The last part of this article is the summary conclusion of this research, mainly about their development prospect in the future.

Cognitive control in infancy: Attentional predictors using a tablet-based measure.

Cognitive control is a predictor of later-life outcomes and may underpin higher order executive processes. The present study examines the development of early cognitive control during the first 24-month. We evaluated a tablet-based assessment of cognitive control among infants aged 18- and 24-month. We also examined concurrent and longitudinal associations between attentional disengagement, general cognitive skills and cognitive control. Participants (N=60, 30 female) completed the tablet-task at 18- and 24-month of age. Attentional disengagement and general cognitive development were assessed at 5-, 8-, 12-, 18- and 24-month using an eye-tracking measure and the Mullen Scales of Early Learning (MSEL), respectively. The cognitive control task demonstrated good internal consistency, sensitivity to age-related change in performance and stable individual differences. No associations were found between infant cognitive control and MSEL scores longitudinally or concurrently. The eye-tracking task revealed that slower attentional disengagement at 8-month, but faster disengagement at 18-month, predicted higher cognitive control scores at 24-month. This task may represent a useful tool for measuring emergent cognitive control. The multifaceted relationship between attention and infant cognitive control suggests that the rapid development of the attentional system in infancy results in distinct attentional skills, at different ages, being relevant for cognitive control development.