Maintain System Integrity Research Articles

Scalable Parking Management Using Cloud Infrastructure and DevOps

This project presents a scalable parking management system built using PHP and SQL, integrated with cloud infrastructure and DevOps methodologies. The system dynamically scales to handle high traffic using cloud auto-scaling and load balancing techniques, ensuring reliable performance. By leveraging containerization, continuous integration, and monitoring tools, the project automates deployments and ensures minimal downtime. The goal is to provide an efficient and resilient solution to parking management challenges, enabling seamless user experience during peak times while maintaining system integrity and availability.

Keamanan Siber dalam Era Digital: Tantangan dan Solusi

Cybersecurity is becoming a major challenge in today's digital era along with the increasing reliance on information and communication technology. This paper explores various cybersecurity threats such as DDoS attacks, malware, and ransomware and the solutions that can be applied, including the implementation of Vulnerability Assessment and Penetration Testing (VAPT) using tools such as Nmap and Nikto. This research aims to provide insight into the best techniques in cyber threat mitigation and discuss the relevance of VAPT practices in maintaining system integrity.

Review on Grounds state Hanle effect on paraffin coated alkali atoms under condition EIT and EIA

IntroductionThis review is specifically aimed at a higher level of understanding of the ground-state Hanle effect through novel methods of atomic vapor interaction with resonant light. Electromagnetically Induced Transparency (EIT) and absorption (EIA) are the primary phenomena in this context. EIT is a process in which destructive interference at absorption frequencies within a medium is caused by a control laser and makes the medium clear to the probe laser light. It should be noted that this transparency is crucial for turning on/off the light pulses, slowing down or stopping in several cases, thus enhancing the effectiveness of atomic magnetometers. However, constructive interference that occurs in EIA raises the density of the medium, which is advantageous for profit-making precise measurements and the improvement of atomic clocks. ObjectiveThe objective of this study is to review the fundamental principles of the Hanle effect, EIT, EIA, and coating methods for alkali atoms. These principles and techniques are critical for improving the sensitivity and functionality of atomic magnetometers and other quantum devices. By understanding and leveraging these phenomena, significant progress has been made in the development of high-precision measurement tools and quantum technologies. ResultsAdvancements in atomic magnetometer sensitivity represent a rapidly growing area of interest, driving significant progress in the development of quantum devices. This review consolidates the existing knowledge and recent findings, offering a thorough understanding of the fundamental principles and practical applications of the Hanle effect, EIT, EIA, and coating methods for alkali atoms. This review highlights how advancements in EIT and EIA have contributed to the sensitivity improvements of atomic magnetometers, and underscores the importance of coating methods for maintaining system integrity and performance. These developments are essential to the evolution of modern quantum technologies and precision measurement devices.

Environmentally Acceptable Lubricants for Stern Tube Application: Shear Stability and Friction Factor

Stern tube lubricants are essential in maritime operations, safeguarding ship propeller shafts from wear and corrosion while ensuring efficient propulsion. Their role in reducing friction and maintaining system integrity is critical. With growing environmental concerns, the adoption of environmentally acceptable lubricants (EALs) for stern tubes has gained importance, balancing operational performance with environmental protection. This study investigates the rheological and tribological properties of EALs formulated for ship propeller stern tube applications. The primary focus is on comparing these EALs with conventional mineral oils to assess their suitability in marine environments. EALs are increasingly favored due to their biodegradability and reduced environmental impact. Key parameters such as shear stability, friction factor, and temperature dependency were evaluated using a range of experimental methods including rotational viscometry and tribological analysis. The results indicate that the newly formulated EALs based on synthetic esters exhibit the highest viscosity index, a higher range of shear stability, and lower friction factors, compared to commercially available mineral oils, especially under varying operational conditions. These findings contribute to the ongoing efforts to promote eco-friendly lubricants in maritime industries, aligning with global environmental protection initiatives.

Flow Performance Analysis of Non-Return Multi-Door Reflux Valve: Experimental Case Study

Non-return multi-door reflux valves are essential in fluid control systems to prevent reverse flow and maintain system integrity. This study experimentally analyzes the flow performance of multi-door check valves under different operating conditions, focusing on pressure testing and evaluating their effectiveness in preventing backflow. A wide-ranging experimental setup was designed and implemented to simulate real-world scenarios, facilitating accurate measurement of flow rates, pressure differences, and valve response times. The collected experimental data were analyzed to evaluate the valve’s performance in terms of flow capacity, pressure drop, and hydraulic efficiency. Additionally, the effects of factors such as valve size, valve configuration, and fluid properties (water) on performance were considered. It was found that the non-return multi-door reflux valve has been proven effective and reliable in preserving system integrity and maintaining unidirectional flow at the same time during pressure testing. It exhibits no backflow, remains stable and constant across varied flow conditions, and demonstrates a low pressure drop and high flow capacity, making it suitable for critical pressure testing applications. The response curve revealed that valve opening takes longer to reach higher flow rates than closing, indicating pressure instability during transition periods. This non-linear relationship indicates possible irregularities in pressure drop response to flow rate changes, highlighting potential areas for further investigation.

Advancing maintenance, monitoring, and control techniques for the optimization of power electronic systems

This paper explores the innovative methodologies and technologies in predictive maintenance, real-time monitoring, fault detection, and advanced control strategies for power electronic devices. Initially, we delve into data acquisition and preprocessing techniques crucial for ensuring the quality and reliability of data used in predictive models. These models, leveraging machine learning and time-series analysis, predict the remaining useful life of devices, guiding proactive maintenance strategies. Furthermore, we discuss the significance of risk assessment and decision support systems in prioritizing maintenance tasks and allocating resources efficiently. The narrative then shifts to real-time monitoring and fault detection, emphasizing the role of sensor integration, data fusion, anomaly detection, and diagnostics in maintaining system integrity. Condition-based maintenance strategies, underscored by real-time data analytics, are presented as a means to optimize maintenance activities and enhance operational performance. The paper concludes with a detailed examination of advanced control strategies, including model predictive control, reinforcement learning, and distributed control and optimization techniques, highlighting their potential to improve system efficiency, reliability, and adaptability. Through comprehensive research and analysis, this work aims to provide valuable insights into the development of sophisticated maintenance and control mechanisms for power electronic systems, ultimately contributing to their longevity and operational efficacy.

Detecting and Patching Loopholes in Windows Operating System

Widely used in personal and business environments, the Windows operating system is a complex software platform that has historically been vulnerable to various intrusions and vulnerabilities. These breaches can be seen as software vulnerabilities, configuration vulnerabilities, higher validity issues, or architectural flaws, which can pose a serious security risk. This research report provides a comprehensive analysis of Windows vulnerabilities and examines their types, key examples such as EternalBlue and Stuxnet, and the potential impact they can have on individuals and organizations. This white paper examines mitigation strategies and best practices, including patch management, system hardening, vulnerability assessment, and the use of third-party security tools. It also addresses the challenges associated with blockchain, including the complexity of legacy code, zero-day vulnerabilities, targeted attacks, and the growing threat landscape. The paper also reviews future ideas, including secure software development practices, architectural improvements, collaboration and information sharing, automated vulnerability detection and remediation, and continuing education and security awareness. By taking a proactive approach to reducing intrusions, organizations can strengthen the security of their Windows systems, protect sensitive data, and maintain system integrity against threats and crimes.

Energy-efficient classification strategy for detecting interference and malicious sensor nodes in wireless body area Networks

Wireless Body Area Networks (WBANs) play a vital role in healthcare monitoring, using wireless sensors to track physiological parameters and predict illness onset. This study proposes a novel approach for detecting interference and malicious sensor nodes in WBANs, crucial for maintaining system integrity and performance. The method combines feature-based techniques with classification strategies to accurately identify anomalies. Features are taken from WBAN nodes and used to train Support Vector Machine (SVM) classifiers, which makes interference detection work well. A neurofuzzy inference system (ANFIS) classifier is also used to train the system on trusted and untrusted nodes at the start, which makes classification easier in real-world WBAN situations. Link failures due to rogue sensor nodes can severely impact WBAN performance, emphasizing the need for efficient detection and correction mechanisms. The proposed strategy introduces a weight metric to identify broken links, enhancing system reliability. Evaluation metrics, including LFD latency and packet delivery ratio, are analyzed to assess the efficacy of the approach. By improving interference detection and addressing link failures, this study contributes to enhancing the efficiency and reliability of WBAN networks, critical for advancing healthcare monitoring technologies.

The Effect of Ice Floe on the Strength, Stability, and Fatigue of Hybrid Flexible Risers in the Arctic Sea

Flexible risers have proven to be a popular choice for deepwater exploration due to their ability to withstand functional and environmental stress while maintaining system integrity. In the challenging arctic conditions, lightweight hybrid composite flexible risers are likely to be employed to mitigate the increase in effective tension. This study investigates the strength and stability performance of production hybrid composite flexible risers with composite pressure armour in the harsh environmental conditions of the Arctic seas. At a water depth of 340 m, the flexible riser was analysed in various global configurations to evaluate the static, dynamic, and lamina-scale performance of its carbon fibre-reinforced thermoplastic polymer composite layer. The drifting ice in the region generated additional load on the riser system, and the effects of this ice on the riser design and its dynamic and lamina-level performances were also analysed. The results indicate that the current riser design incorporating the composite layer is insufficient to ensure system integrity without mitigating the effects of ice loading. The carbon fibre direction in each lamina must be optimised for excess axial stress emanating from the combined action of hoop, axial, and bending stresses. Finally, recommendations on how to improve the life of the lightweight hybrid composite riser in arctic conditions are provided.

In situ three-dimensional cross-linked carbon nanotube-interspersed SnSb@CNF as freestanding anode for long-term cycling sodium-ion batteries

Alloying-based material has achieved tremendous appealing being anode used in sodium‑ion batteries (SIBs) by virtue of its relatively giant sodium storage specific capacity along with low discharge platforms. Nevertheless, the sluggish ion transmission dynamics and the huge volume change during cycling induced irreparable particle pulverization and agglomeration result in collapse of electrode structure and deterioration of cycling properties. In this work, three-dimensional cross-linked carbon nanotube-interspersed SnSb@CNF integrated structure (SnSb@CNF/CNT) is designed and synthesized. In this ingenious nanostructure, CNTs interspersed between CNF frames play the role of electron transport and diffusion “bridges” for enhancing the electrical conductivity of anode materials and relieve aggregation of SnSb alloy particles, the N doped-CNFs serve as external frame could efficiently mitigate dramatic volume effect to maintain system integrity. Based on these constructive advantages, the elastic conductive system can be directly available as anode for SIBs, showing ultralong cycling performance of 210 mAh g−1 over 700 sodiation/desodiation processes with a current density of 0.5 A g−1, even 161 mAh g−1 following 1000 cycling under a high current density of 1 A g−1 accompanied by an almost 100% superb capacity retention ratio, as well as distinguished high rate performance (470 mAh g−1 with a returned current density of 0.05 A g−1). This work shed distinctive insight to construct in situ three-dimensional cross-linked freestanding alloying-based anode materials used in alternative electrochemical grid-scale application.

Development of methods for the simplification of complex group built causal loop diagrams: A case study of the Rechna doab

Complex systems are generally challenging to model due to many variables whose relationships and dependencies are often difficult and time-consuming to comprehend. Simplifying complex systems can remove barriers to adaptation and application by marginalized stakeholders. The main aim of the proposed research is to compare two simplification methods, i.e., Endogenisation, Encapsulation and Order-Oriented Reduction (EEOR) and Thematic Maps Development (TMD), in terms of maintaining system integrity. A case study was conducted in the Rechna Doab region of Pakistan. This was a qualitative system dynamics model study, which included 79 variables, 32 duplicate or ghost variables and 15 loops. EEOR simplifies Causal Loop Diagrams (CLDs) by removing exogenous variables and adjusting loops in successive steps. In this study, EEOR eliminated 46 variables and 6 critical feedback loops during the simplification of the complex case study CLD and did not maintain the system's integrity. The main risk of this technique is eliminating variables without understanding their significance. Accordingly, despite the goal of a better understanding, EEOR led to a loss of 60% of the final merged CLD. TMD divided the final integrated case study CLD into four thematic sub-modules: agriculture, environmental, social and industrial, and government subsidies. Each variable and loop were placed in their respective thematic sub-modules. This separation enhanced the understanding and evaluation of individual thematic sub-modules. When recombined into a single diagram, these sub-modules were able to represent the entire system without losing any information. This research can help to simplify complex system models to help system understanding by novice stakeholders, while maintaining the integrity of the system. Consequently, this simplification approach can help further increase stakeholder engagement in participatory modelling exercises.

A METHOD FOR DETERMINING THE EFFECTIVENESS OF A DISTRIBUTED SYSTEM FOR DETECTING ABNORMAL MANIFESTATIONS

mandatory step, which is carried out to confirm the correctness, feasibility and feasibility of the developed solutions, including architecture, method of maintaining system integrity.
 In order to conduct a study on the effectiveness of the use of self-organized distributed systems, anomalies in computer systems were identified in relation to the evaluation criteria. Determining the specifics of the application of the system also affects the choice of criteria for evaluating effectiveness.
 The method of determining the effectiveness of the proposed solutions for the developed self-organized distributed system for detecting anomalies in computer systems has been developed. Software has been developed to ensure the functioning of a self-organized distributed anomaly detection system in computer systems to confirm the feasibility of the proposed solutions.
 Experimental studies with the developed implementation of a self-organized distributed system for detecting anomalies in computer systems according to the obtained coefficients confirmed the effectiveness of the proposed solutions and the developed distributed system for its operation in the computer network.

Simulation of a Low-Voltage Direct Current System Using T-SFCL to Enhance Low Voltage Ride through Capability

Owing to the increasing penetration level of distributed energy resources (DER) and direct current (DC) load, the usage of low-voltage direct current (LVDC) systems has expanded to achieve efficient operations. However, because the LVDC system reaches the peak fault current at a faster rate than the alternating current (AC) system, a solution that protects the system components is necessary to maintain system integrity. It is required by the low-voltage ride-through (LVRT) that the DERs maintain their interconnections with the LVDC system and support fault recovery. In this study, a method is proposed to allow the application of the superconducting fault current limiter (SFCL) to reduce the fault current and enhance the LVRT capability. However, when the DER maintain a connection to support fault recovery, the conventional resistive-type SFCL must withstand the burden of high-temperature superconducting (HTSC) operation during fault state dependence on LVRT. Therefore, this study proposes a trigger-type SFCL to reduce the burden of the HTSC element and enhance the LVRT capability. The voltage sag related to the LVRT was improved owing to the SFCL. The proposed solution was confirmed using PSCAD/EMTDC, which is a commercial software.

Assessment of the wind power generation uncertainty on the steady-state operation of electric power systems

Nowadays, the large-scale integration of wind energy conversion systems in transmission networks is one of the most effective and practical options for generating electricity from renewable sources. However, the increase in wind energy penetration causes the operation of electrical power systems to become more dependent and vulnerable to variations in wind speed. Within this context, it is necessary to perform probabilistic power flow (PPF) studies to maintain system integrity in the face of stochastic wind power generation. This paper describes and implements a power flow method that considers the uncertainties that arise from wind generation behavior. This PPF approach, based on a point estimation technique, is validated against thousands of Monte Carlo simulations.

Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity

This paper presents a new quantum-behaved particle swarm optimization (QPSO) algorithm for the trajectory planning task of mobile robotic vehicles in static and dynamic environments—it is called enhanced diversity particle swarm optimization (EDPSO). The main characteristic of this algorithm is that it has peaks of diversity in its population, making it possible to escape from local minima effectively, avoiding stagnation. Through the proposed PSO, it is possible to obtain safe and efficient routes, avoiding energy waste and maintaining system integrity in several possible applications. The parameters of the proposed algorithm were tuned using the benchmarking functions. The same functions were used to compare the algorithm with those already established in the literature. Once the proposed algorithm showed promising results, it was simulated in four environments, each with different complexities, presenting dangerous regions and terrains unsuitable for robot navigation, and a large number of obstacles or even moving objects. Further, the algorithms used for comparison were also simulated and the EDPSO presented satisfactory results. Through simulations it was possible to notice that the proposed approach resulted in collision-free and planned routes, and the algorithm presented increased exploration features owing to the diversity peaks that occur during the optimization.

Enhancing robustness of DC microgrid protection during weather intermittency and source outage for improved resilience and system integrity

Despite the advantages pertaining to efficiency, stability, and ease of integration with renewable energy sources, the wider acceptance of DC microgrids has been hindered by the complexity of the protection task. In addition to the absence of zero crossings, the weather-dependent fluctuations in the operation of renewable sources and the high probability of distributed energy resources (DERs) outage further complicate the fault detection and classification task. This paper proposes a reliable DC microgrid protection approach with improved robustness against weather intermittency and possible source outages. The protection framework involves identifying the network topology from local information followed by executing a set of convolution neural networks (CNNs) to perform the protection tasks. The proposed CNN-based scheme reduces the complexity and the associated computational cost of feature extraction for performing the intended protection tasks. The novelty of the scheme pertains to the inclusion of the stochastic model for representing weather variation and a mechanism for adapting the relaying mechanism during changes in the network structure. The same allows for avoiding relay malfunction during weather intermittency and source outage, thereby improving grid resilience and maintaining system integrity. The DC microgrid model has been simulated using MATLAB and Simulink. The proposed scheme has been extensively validated for various operating scenarios involving variations in wind speed, solar irradiance, fault parameters, and network configuration. Improvement toward reduction in memory requirement is still a challenge for the hardware implementation of CNN, which can be considered as a potential area for future research.

High-Risk Cases at the Intersection of Domestic/Family Violence and Child Protection: Learning from Practice

Domestic/family violence (DFV) and child protection co-occurrence of risk is common, with related homicides remaining a grave public concern. Recognising and prioritising high-risk cases that pose a lethal risk remains a complex challenge for practitioners across a range of services and legal jurisdictions. There are significant gaps in practice knowledge about how to assess and respond to high-risk cases involving child protection and DFV concerns, while working safely and effectively. This paper reports on qualitative research conducted with practitioners from a range of justice and service delivery organisations in Queensland, Australia. A community of practice, supported by the U.S. based Safe & Together Institute, provided the forum for data to be collected on participants’ reflections and observations. An action research framework was employed to connect practice learning into research data through an iterative cycle of reflection and review. Ethnographic note taking was used to document arising policy and practice issues. Discussions in the CoP were also recorded and transcribed verbatim. Thematic analysis was undertaken combining inductive and deductive techniques. Key findings include: learning from and partnering with women; applying a perpetrator pattern-based approach in high-risk cases; engaging with men as fathers; and improving the role of the judicial system. Practice needs to be informed by centring the victim/survivor perspective, which requires a collaborative approach while maintaining system integrity to hold the perpetrator within system responses to ensure accountability. There are important learnings for developing service sector responses and future research on high-risk cases.

Innovative solutions to enable flexibility and retail markets in distribution grids: The Platone approach

The present study describes the characteristics of the European project Platone that proposes an innovative approach to joint data management for both higher levels of observability and exploitation of flexibility of the distribution grids. In the project, a layered platform will fulfil the needs of system operators, aggregators, and end users. A blockchain-based platform is the access layer to generators' and customers' flexibilities able to break traditional access barriers by providing certified measures to all the players. Certified data and signals will be used for an innovative distribution system operator (DSO) platform to locally maintain system integrity fostering confidence in flexibility operations. An upper layer will make data available to market platforms: also in this case it is envisioned the possibility of a blockchain approach to link the local system to the transmission system operator domains and enhance the overall system cost efficiency. Platone puts the grid users at the centre, investigates their needs and expectations and uses the underlying blockchain to unlock the potentials of higher dynamics of response. The platform will be tested in pilots hosted by three larger DSOs in Europe and analysed in cooperation with a large University in Canada.

Digital Forensics using Blockchain

On considering the integrity of electronic evidence, in particular,we can see that such evidence needs to be protected from a number of undesirable outcomes namely, alteration or destruction. We need to guard against these events and others when trying to maintain system integrity and preserve the purity of evidence so that it could be acceptable in the court. Chain of Custody is nothing but the consecutive documentation of records. The Chain of Custody has all the necessary steps that a crime investigator must follow to make sure whether the information is honest. The Chain of Custody is significant because it cannot be proven that evidence was not altered during the time between collection and its usage in court. Then the collected evidence is not credible. Blockchain technology,a decentralized network currently used by Bitcoins and other Cryptocurrency networks, helps provide a secure database with the help of hashing the data and storing it in blocks.We propose to implement blockchain technology for the process of Chain of Custody, which helps in tracking the people whoaccess the data and assist in assuring the credibility of the data provided during the time of submission in court.

Information fusion architecture for secure cyber physical systems

The security essentially governs the usability and stability of Cyber Physical Systems (CPSs) whose cyber and physical components of the system are integrated. Security solutions to this effect are threefold - detecting attacks, preventing them and finally, maintaining system integrity under attacks. In this paper, we provide a solution to the problem of detecting collaborative attacks by proposing an information fusion architecture, which utilizes the strengths of Bayesian estimation in determining causalities as conditional probabilities. We propose a Time-Varying Dynamic Bayesian Network (TVDBN) to ascertain system state information, eventually enabling the system administrator to make control decisions and maintain system stability under security attacks. The control information to the physical components such as actuators is sent over by the sensors, which are the cyber components. As such, the focus of this paper is to provide a solution to uphold the stability of a CPS based on control theoretic aspects that can be adversely affected by compromised cyber components. Theoretical aspects touched in this paper for this purpose are - bounds on transmission delays, quantization errors and sampling time. Using our proposed information fusion architecture, we were able to detect the presence of both, the stand-alone and collaborative attacks on the CPS. Our experimental results showed that our fusion architecture can detect collaborative attacks and profile them with an average accuracy of 91.7%. Given the difficulty in detecting the presence of collaborative attacks in CPS, this level of accuracy is considered high to protect CPS applications.