System Environment Research Articles

Effect of a vehicle's mobility on SNR and SINR in vehicular optical camera communication systems.

The widespread use of light-emitting diodes (LEDs) and cameras in vehicular environments provides an excellent opportunity for optical camera communication (OCC) in intelligent transport systems. OCC is a promising candidate for the Internet of Vehicles (IoV), and it uses LEDs as the transmitter and cameras as the receiver. However, the mobility of vehicles has a significant detrimental impact on the OCC system's performance in vehicular environments. In this paper, a traffic light that uses multiple-input multiple-output (MIMO) technology serves as the transmitter, and the receiver is a camera mounted on a moving vehicle. The impact of vehicle mobility on the vehicular MIMO-OCC system in the transportation environment is then examined using precise point spread function (PSF) analysis. The experimental results are used to evaluate the proposed PSF. A good agreement between the laboratory's recorded videos and this PSF model's simulations is observed. Moreover, the signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR) values are evaluated. It is shown that they are greatly influenced by the vehicle's speed, direction of motion, and position of the camera. However, since the angular velocity in a typical transportation environment is low, it does not have a significant impact on the performance of the vehicular OCC systems.

Face Recognition Based Attendance System

The integration of face recognition technology into attendance management systems has emerged as a transformative solution for monitoring and optimizing attendance processes in educational institutions and organizations. This report presents an in-depth analysis of a project dedicated to the development and implementation of a Face Recognition Attendance System (FRAS) and examines its efficiency and security in various real-world scenarios. The report begins by discussing the significance of attendance tracking systems in educational and corporate environments, highlighting the shortcomings of traditional methods and the potential benefits of face recognition technology. It explores the underlying principles of face recognition, focusing on the algorithms and techniques used to capture and process facial data. The project methodology and implementation details are outlined, covering the hardware and software components used to create the FRAS. Special emphasis is placed on the challenges encountered during the system’s development and the strategies employed to overcome them. A comprehensive evaluation of the system’s performance and efficiency is presented, including accuracy, speed, and scalability, with comparisons to traditional attendance systems. Moreover, the report addresses security concerns associated with the FRAS, such as data protection, privacy, and vulnerability to spoofing. The report concludes with a discussion of the practical implications and future prospects of implementing face recognition technology in attendance systems. It highlights the system’s potential to streamline administrative tasks, reduce errors, and enhance security, while also emphasizing the need for ongoing research and development to address emerging challenges. Overall, this report provides a valuable insight into the evolving landscape of attendance management and the role that face recognition technology can play in revolutionizing these processes, balancing efficiency with security considerations. KeyWords: FRAS : Face recognition attendance systems SDLC: Software Development Life Cycle

Digital Twin and Deep Reinforcement Learning-Driven Robotic Automation System for Confined Workspaces: A Nozzle Dam Replacement Case Study in Nuclear Power Plants

Robotic automation has emerged as a leading solution for replacing human workers in dirty, dangerous, and demanding industries to ensure the safety of human workers. However, practical implementation of this technology remains limited, requiring substantial effort and costs. This study addresses the challenges specific to nuclear power plants, characterized by hazardous environments and physically demanding tasks such as nozzle dam replacement in confined workspaces. We propose a digital twin and deep-reinforcement-learning-driven robotic automation system with an autonomous mobile manipulator. The study follows a four-step process. First, we establish a simplified testbed for a nozzle dam replacement task and implement a high-fidelity digital twin model of the real-world testbed. Second, we employ a hybrid visual perception system that combines deep object pose estimation and an iterative closest point algorithm to enhance the accuracy of the six-dimensional pose estimation. Third, we use a deep-reinforcement-learning method, particularly the proximal policy optimization algorithm with inverse reachability map, and a centroidal waypoint strategy, to improve the controllability of an autonomous mobile manipulator. Finally, we conduct pre-performed simulations of the nozzle dam replacement in the digital twin and evaluate the system on a robot in the real-world testbed. The nozzle dam replacement with precise object pose estimation, navigation, target object grasping, and collision-free motion generation was successful. The robotic automation system achieved a 92.0%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$92.0\\%$$\\end{document} success rate in the digital twin. Our proposed method can improve the efficiency and reliability of robotic automation systems for extreme workspaces and other perilous environments.

Enhancing Student Experience through an Integrated Portal System

The integration of face recognition technology into attendance management systems has emerged as a transformative solution for monitoring and optimizing attendance processes in educational institutions and organizations. This report presents an in-depth analysis of a project dedicated to the development and implementation of a Face Recognition Attendance System (FRAS) and examines its efficiency and security in various real-world scenarios. The report begins by discussing the significance of attendance tracking systems in educational and corporate environments, highlighting the shortcomings of traditional methods and the potential benefits of face recognition technology. It explores the underlying principles of face recognition, focusing on the algorithms and techniques used to capture and process facial data. The project methodology and implementation details are outlined, covering the hardware and software components used to create the FRAS. Special emphasis is placed on the challenges encountered during the system’s development and the strategies employed to overcome them. A comprehensive evaluation of the system’s performance and efficiency is presented, including accuracy, speed, and scalability, with comparisons to traditional attendance systems. Moreover, the report addresses security concerns associated with the FRAS, such as data protection, privacy, and vulnerability to spoofing. The report concludes with a discussion of the practical implications and future prospects of implementing face recognition technology in attendance systems. It highlights the system’s potential to streamline administrative tasks, reduce errors, and enhance security, while also emphasizing the need for ongoing research and development to address emerging challenges. Overall, this report provides a valuable insight into the evolving landscape of attendance management and the role that face recognition technology can play in revolutionizing these processes, balancing efficiency with security considerations. KeyWords: FRAS : Face recognition attendance systems SDLC: Software Development Life Cycle

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments.

In complex and dynamic environments, traditional pursuit-evasion studies may face challenges in offering effective solutions to sudden environmental changes. In this paper, a bio-inspired neural network (BINN) is proposed that approximates a pursuit-evasion game from a neurodynamic perspective instead of formulating the problem as a differential game. The BINN is topologically organized to represent the environment with only local connections. The dynamics of neural activity, characterized by the neurodynamic shunting model, enable the generation of real-time evasive trajectories with moving or sudden-change obstacles. Several simulation and experimental results indicate that the proposed approach is effective and efficient in complex and dynamic environments.

Management of infodemics in outbreaks or health crises: a systematic review.

The World Health Organization (WHO) defined an infodemic as an overabundance of information, accurate or not, in the digital and physical space, accompanying an acute health event such as an outbreak or epidemic. It can impact people's risk perceptions, trust, and confidence in the health system, and health workers. As an immediate response, the WHO developed the infodemic management (IM) frameworks, research agenda, intervention frameworks, competencies, and processes for reference by health authorities. This systematic review explored the response to and during acute health events by health authorities and other organizations operating in health. It also assessed the effectiveness of the current interventions. On 26 June 2023, an online database search included Medline (Ovid), Embase, Cochrane Library, Scopus, Epistemonikos, and the WHO website. It included English-only, peer-reviewed studies or reports covering IM processes applied by health organizations that reported their effectiveness. There was no restriction on publication dates. Two independent reviewers conducted all screening, inclusion, and quality assessments, and a third reviewer arbitrated any disagreement between the two reviewers. Reviewers identified 945 records. After a final assessment, 29 studies were included in the review and were published between 2021 and 2023. Some countries (Pakistan, Yemen, Spain, Italy, Hong Kong, Japan, South Korea, Singapore, United Kingdom, United States, New Zealand, Finland, South Korea, and Russia) applied different methods of IM to people's behaviors. These included but were not limited to launching media and TV conservations, using web and scientific database searches, posting science-based COVID-19 information, implementing online surveys, and creating an innovative ecosystem of digital tools, and an Early AI-supported response with Social Listening (EARS) platform. Most of the interventions were effective in containing the harmful effects of COVID-19 infodemic. However, the quality of the evidence was not robust. Most of the infodemic interventions applied during COVID-19 fall within the recommended actions of the WHO IM ecosystem. As a result, the study suggests that more research is needed into the challenges facing health systems in different operational environments and country contexts in relation to designing, implementing, and evaluating IM interventions, strategies, policies, and systems.

The Effects of Natural Disasters on Art Through the Eyes of Art Students

Natural disasters are events with devastating effects that cause significant damage to people, the environment or economic systems as a result of natural events. These events are phenomena that are not usually under the control of humans, and take place in the natural balance of nature. These events not only affect human life, the environment and society, but also have both negative and positive effects on art, artists and works of art. However, these effects are not always limited to material damage or changes. Disasters can cause the destruction of works of art, which are our cultural heritage, as well as nourish art. Art has such a wide spectrum that it can reflect all kinds of experiences people have. This research is about how art students see the positive and negative effects of natural disasters on art. With the answers given to the semi-structured questions, the opinions of the university students receiving art education about the effects of natural disasters on art were determined and interpreted. As a result of these comments, it was aimed to determine the awareness level of the youth as natural disasters are expected to increase gradually due to factors such as climate change, depletion of natural resources, and increase in human population. Therefore, it is necessary to be prepared for natural disasters, develop risk management strategies, mitigate the negative effects of disasters, and most importantly raise awareness among young people.

Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

Background The addition of biofeedback and artificial intelligence (AI) in simulation training and serious games has shown promising results in improving the effectiveness of training and can lead to increased engagement, motivation, and retention of information. This systematic literature review explores the integration of biofeedback and artificial intelligence into eXtended reality (XR) training scenarios and is the first review to provide a consolidated overview of applied biofeedback and AI technologies in this area. Method This review was conducted using keywords related to biofeedback, AI, XR, and training and included papers that: contained the use of biofeedback and AI in XR training scenarios; reported on at least one outcome related to training effectiveness; were published in English; were peer-reviewed; date from 1 January 2016 – 7 February 2022. Results The results indicate that many studies collect two or more biosignals using a single biosensing device. This is particularly relevant in applied settings, where ease of use and minimal interference in training/education activities is desired. Also, that light, portable devices such as wrist bands, wireless straps, or headbands are preferred. Additionally, eye tracking, electrodermal activity (EDA), and photoplethysmograms (PPG) present as particularly useful biomarkers of stress and/or cognitive load in XR training contexts. A wide variety of machine learning (ML) approaches were used to support biofeedback systems in XR environments. However, a limited number of studies employed real-time analysis of biosignals (just 1% of studies) which indicates current challenges in implementing such systems. Conclusion The majority of papers meeting the selection criteria were from the fields of education and healthcare. Further research in other domains, such as defense and general industry, is needed to gain a comprehensive understanding of the potential for biofeedback and AI integration in XR training scenarios used in these domains.

Smart Hydroponic Farming System Integrated with LED Grow Lights

Vertical farming, including hydroponics, is a growing trend in the agricultural sector due to the increasing demand for food and urbanisation. Thus, hydroponics can save space and achieve faster plant growth compared to traditional farming methods. The concept of smart farming has been applied in this study to improve the ease of control and monitoring of hydroponic systems. The effects of light-emitting diodes (LEDs), light distance, and colour (purple and white) on water spinach growth in a hydroponic system were investigated. Additionally, an Internet of Things (IoT) controller was developed and implemented to facilitate the use of the system in an indoor hydroponic-based environment system. Based on the results, the distance between the LED light of 15 cm and the plants and the colour of the LED light (white) can positively impact plant growth in a hydroponic system. Using an IoT controller also allows for continuous monitoring and control of factors that influence plant growth. Hence, this research would catalyse the local smart hydroponic farming system for improved deliverables.

Assessing High-Voltage Shore Connection Safety: An In-Depth Study of Grounding Practices in Shore Power Systems

There is growing concern regarding air pollutants (NOx, SOx, and PM) and carbon emissions from ocean-going vessels in harbor areas and the role of high-voltage shore connection (HVSC) systems in mitigating these emissions during vessel berthing. The HVSC operates as a TN grounding system in humid environments, and it needs a proper grounding design to ensure safety when faults occur. This article intends to examine the overvoltage resulting from fault currents and its implications for the safety of operators when a single line-to-ground fault takes place within the design of HVSC grounding systems. The assessment is carried out by employing actual scenarios and parameters from a container berth at Kaohsiung Harbor in Taiwan. Considering site conditions, such as the wet ground surface, human body resistance, and electric shock duration, the tolerable safe voltage level is derived using IEEE Std. 80 and IEC 60479-1. Based on the shore power system grounding architecture specified in IEEE/IEC 80005-1, an equivalent circuit model is constructed to calculate the fault currents using symmetrical component analysis. The actual touch voltages generated in various locations are analyzed under scenarios of connecting or disconnecting the equipotential bonding between the ship and the shore using neutral grounding resistor (NGR) designs. This article delves into the scenarios of electric shock that may occur during the operation of an actual container ship’s shore power system. It evaluates whether various contact voltage values exceed current international standards and verifies the grounding design and safety voltage specifications of IEEE/IEC 80005-1. According to the results of this study, the use of NGR and protective earthed neutral (PEN) conductors in HVSC is crucial. This can limit fault currents, reduce touch voltage, and ensure the safety of personnel and equipment. Therefore, ensuring and monitoring equipment conductors and adopting NGRs of appropriate sizes are crucial elements in maintaining electrical safety in HVSC systems.

A study on real-time quantitative thermal analysis of composite tapes

Generating vast volumes of data by continuously moving systems in industrial environments presents a significant challenge. The carbon fiber reinforced polymer (CFRP) tapes control exemplifies this issue, with a strongly increasing production these last decades, and yielding several tens of meters to inspect within a short time. It leads to a pressing need for rapid and effective quality control and decision-making systems. This study introduces an innovative method for mitigating data overload by focusing on the defective sections of the tape by creating a corresponding virtual twin to assess fiber content variations. Data analysis techniques are employed to differentiate flawless from faulty regions and thermal methods are applied to characterize observed defects. This article presents a comprehensive approach for enabling the analysis of tens of meters of tapes at 25 cm/s speed scroll within a competitive timeframe.

On the derivation of the delta formulation for decision value

This paper contains decision analytical approaches, conditions and models for the quantification of decision values for built environment systems. Specifically, (1) delta formulations of objective functions for decision value quantification are introduced, (2) conditions for decision value are identified and (3) action value analysis formulations are further developed. The delta objective functions are formulated with differences in utility, cost, and probabilities for consistent decision identification by expected utility and value. The delta formulations facilitate the direct calculation of action and information values and the explication of conditions for a positive decision value. Action value objective functions are derived in delta formulation for the action types of utility and system state actions and with action implementation states and action uncertainty models. The delta formulations are exemplified for predicted information and predicted actions values. The paper closes with a synthesis and discussion of decision values and with findings encompassing a computational effort reduction and the identification of predicted information value mechanisms.

Energy Innovation and Sustainability on Campus: Implementation of A Biogas System at IFBA Salvador

Objective: To investigate the feasibility and benefits of implementing a biogas system at IFBA Salvador campus, focusing on energy sustainability and efficient waste management. Theoretical Framework: Addresses the importance of energy sustainability in educational environments, with an emphasis on the use of biogas as an alternative to LPG. Examines the environmental impact of using fossil fuels and the effectiveness of biogas-based solutions. Method: Uses an exploratory approach to collect and analyze data on LPG consumption and organic waste generation at the IFBA Salvador campus cafeteria and restaurant. The methodology includes quantitative analyses of GHG emissions and the assessment of the infrastructure needed to implement the biogas system. Results: Reveals significant potential for reducing LPG consumption and GHG emissions through the implementation of biogas. Identifies environmental and economic benefits, including the estimated annual savings and reduction of CO2 emissions. Research Implications: Highlights the importance of the project for energy sustainability and environmental awareness in the educational context. Emphasizes the role of waste management and renewable energy in reducing environmental impact and promoting sustainable practices. Originality/Value: Presents an innovation in the Brazilian educational context, demonstrating the applicability and benefits of biogas systems in academic environments. Contributes to the advancement of knowledge in energy sustainability and waste management in the educational sector.

Effect of impurities and carrier concentration on conductivity of bilayer graphene double layer system in different dielectric environments

The electronic transport properties of the bilayer graphene double layer (BLGDL) system at [Formula: see text][Formula: see text]K using the Boltzmann transport equation are reported here. The conductivity of the BLGDL system has been calculated with varying parameters as relative carrier concentration [Formula: see text], short-range (point defect) and long-range (Coulomb charge) impurity concentration, relative dielectric constant [Formula: see text] and interlayer distance [Formula: see text]. The maximum conductivity has been achieved in the absence of short-range impurity. The short-range impurity plays a non-trivial meaningful role in high dielectric and high carrier concentration regimes and limits the conductivity. For higher [Formula: see text] and higher [Formula: see text] values, the nature of the conductivity curve turns out to be of a sublinear nature, while in all other cases, it shows a superlinear trend. Suitable selection of relative dielectric constants, relative carrier concentration and interlayer distance helps to improve the conductivity of the BLGDL system. If the dielectric constant of the spacer is higher than the substrate material, the thickness of the spacer material helps remarkably in enhancing conductivity. Moreover, a comparison of BLGDL with our previously reported MLG–MLG double layer graphene system (DLGS) confirms that higher conductivity is achieved in the case of BLG–BLG system compared to DLGS.

Data-driven integrated study on operational performance degradation detection and recovery control of VFDR

The Variable Flow Ducted Rocket (VFDR) serves as the preferred propulsion system for hypersonic aircraft. However, it is prone to health degradation issues due to component failures, aging, and changes in operational conditions, which significantly challenge flight safety. This study introduces a data-driven integrated control approach for modeling, diagnosing, and remedying performance deterioration. Initially, a method for open-set modeling of performance degradation is developed using the Linear Parameter-Varying (LPV) modeling technique and gap metric theory, tackling the hurdles of data collection, overlapping fault classes, and modeling of unidentified faults in the VFDR system; thus, providing vital data support for detecting performance degradation. Moreover, employing the LPV model for performance degradation, a hybrid deep neural network combining multi-scale CNN and LSTM is trained to diagnose system performance issues online using multi-dimensional time-domain sequential data. Subsequently, a smooth transition control method utilizing sliding mode compensation is applied to regain control of system performance based on diagnostic outcomes and a pre-established VFDR performance recuperation controller. Simulations of three types of performance deterioration scenarios caused by changes in actuators, sensors, and the system's operational environment are conducted to demonstrate the efficiency of the proposed approach. The simulation outcomes affirm that the proposed approach is capable of effectively modeling system performance deterioration, diagnosing faults, and controlling recovery, offering promising technical support for the secure operation of intricate closed-loop control systems with stringent precision and reliability requirements.

Availability and maintenance strategy under time-varying environments for redundant repairable systems with PH distributions

Systems commonly operate in time-varying environments which have essential impact on system performance, thus reliability modeling and maintenance strategy are faced with new challenges. In this study, a cold standby repairable system in a time-varying environment is investigated based on different operational levels and maintenance strategies, in which repairman's vacation and maintenance rules vary with environments. The evolution of dynamic environments is governed by a Markov process, and the successive vacation time is governed by Markovian arrival processes (MAPs). Using Phase-type (PH) distribution not only increases the flexibility of analytical properties because it can approximate to any probability distribution defined on a nonnegative real line, but also could effectively ensure the applicability of the model in engineering practice, specially all the lifetimes of components and maintenance time are subject to disparate PH distributions in diverse environments. By employing the matrix-analytic method, Kronecker operator theory and aggregated stochastic processes, some reliability indexes and the system average benefit are obtained respectively. Finally, an example of a redundant power supply is given to illustrate the validity and applicability of the proposed model.

Blockchain-based group signature for secure authentication of IoT systems in smart home environments

ABSTRACT Current solutions that rely on a single-server architecture have privacy, anonymity, integrity, and confidentiality limitations. Blockchain-based solutions can address some of these issues but face challenges regulating behaviour and protecting access policy privacy. This study proposes a new approach to securing a smart home environment to overcome these limitations.The proposed architecture is based on a group signature scheme, which allows multiple IoT devices to securely exchange keys and authenticate each other without needing a central authority. Our experimental results indicate the effectiveness and efficiency of the proposed architecture and provide insights into its security and privacy features compared to existing IoT authentication methods.

Cloud media video encoding: review and challenges

AbstractIn recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user.

Performance improvement of vapor compression heat pump with superhydrophobic finned-tube evaporator

Residential buildings frequently employ vapor compression heat pump for winter heating. Its advantages over electric and coal-fired heating include great energy effectiveness and low emissions. However, the outside evaporator's frosting negatively impacts the performance of heat pump systems in low-temperature and high-humidity environments. To ameliorate the frosting problem, a finned-tube heat exchanger with superhydrophobic surface wettability prepared by the wet chemical etching method was applied to a vapor compression heat pump in this study. The performance of a commercial evaporator unit and the superhydrophobic evaporator unit under five frosting conditions was investigated through experiments. The maximum heat transfer rate of the superhydrophobic heat exchanger unit was increased by 17%–44% compared with that of the commercial unit. The COP improvement of the superhydrophobic unit reached 11.2%, 14.3%, 18.8%, 20.9% and 31.9%, respectively. The total mass of frost formation of the superhydrophobic unit experienced a 59.2%–76.3% decrease. The operation time of the superhydrophobic evaporator unit before defrosting determination was significantly extended, reducing the number of defrosting times during a long-time operation. This study proves the advantages of superhydrophobic finned-tube evaporators used in heat pumps in terms of heat transfer performance and frost suppression performance, promoting the application of superhydrophobic finned-tube evaporators in heat pump air-conditioning systems.

Performance of General Bureau Civil Servants in The Regional Secretariat of North Sulawesi Province

The aim of this research is to describe, interpret and analyze the performance of Civil Servants (PNS) in the Administration Sub-Section for Leadership and Expert Staff of the General Bureau of the Regional Secretariat of North Sulawesi Province seen from the aspects of Employee Performance Targets (SKP), Discipline and the application of Civil Service principles State (ASN) "BerAKHLAK" (Service Oriented, Responsible, Competent, Harmonious, Loyal, Adaptive and Collaborative). The research method applied is a descriptive qualitative approach using observation, interview, and document analysis techniques. The research results revealed a performance gap among civil servants, where some succeeded in achieving satisfactory results while others did not meet expectations. Factors such as effective training, motivation, conducive work environment, and reward and punishment systems were identified as important determinants in achieving optimal performance. The obstacles faced involve the quality and quantity of Human Resources (HR), with uncertainty that all civil servant members can contribute collectively. Therefore, a more thorough and objective performance measurement approach is needed. This research suggests an increase in the implementation of intensive training, improvements in the motivation system through structured rewards, evaluation of work environment conditions, as well as collaborative efforts with external parties to enrich technological understanding and skills.