Search results for: systems approach

Authors: T. Gomes, J. Manzi

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The inclusion of stirring systems in the calculation and optimization procedures has been undergone a significant lack of attention, what it can reflect in the results because such systems provide an additional energy to the process, besides promote a better distribution of mass and energy. This is meaningful for the reactive systems, particularly for the Continuous Stirred Tank Reactor (CSTR), for which the key variables and parameters, as well as the operating conditions of stirring systems, can play a pivotal role and it has been showed in the literature that neglect these factors can lead to sub-optimal results. It is also well known that the sole use of the First Law of Thermodynamics as an optimization tool cannot yield satisfactory results, since the joint use of the First and Second Laws condensed into a procedure so-called entropy generation minimization (EGM) has shown itself able to drive the system towards better results. Therefore, the main objective of this paper is to determine the effects of key parameters of the stirring system in the optimization procedures by means of EGM applied to the reactive systems. Such considerations have been possible by dimensional analysis according to Rayleigh and Buckingham's method, which takes into account the physical and geometric parameters and the variables of the reactive system. For the simulation purpose based on the production of propylene glycol, the results have shown a significant increase in the conversion rate from 36% (not-optimized system) to 95% (optimized system) with a consequent reduction of by-products. In addition, it has been possible to establish the influence of the work of the stirrer in the optimization procedure, in which can be described as a function of the fluid viscosity and consequently of the temperature. The conclusions to be drawn also indicate that the use of the entropic analysis as optimization tool has been proved to be simple, easy to apply and requiring low computational effort.

Keywords: stirring systems, entropy, reactive system, optimization

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Authors: Michael Lognoul

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In April 2021, the European Commission released its AI Act Proposal, which is the first policy proposal at the European Union level to target AI systems comprehensively, in a horizontal manner. This Proposal notably aims to achieve an ecosystem of trust in the European Union, based on the respect of fundamental rights, regarding AI. Among many other requirements, the AI Act Proposal aims to impose several generic transparency obligationson all AI systems to the benefit of natural persons facing those systems (e.g. information on the AI nature of systems, in case of an interaction with a human). The Proposal also provides for more stringent transparency obligations, specific to AI systems that qualify as high-risk, to the benefit of their users, notably on the characteristics, capabilities, and limitations of the AI systems they use. Against that background, this research firstly presents all such transparency requirements in turn, as well as related obligations, such asthe proposed obligations on record keeping. Secondly, it focuses on a legal analysis of their scope of application, of the content of the obligations, and on their practical implications. On the scope of transparency obligations tailored for high-risk AI systems, the research notably notes that it seems relatively narrow, given the proposed legal definition of the notion of users of AI systems. Hence, where end-users do not qualify as users, they may only receive very limited information. This element might potentially raise concern regarding the objective of the Proposal. On the content of the transparency obligations, the research highlights that the information that should benefit users of high-risk AI systems is both very broad and specific, from a technical perspective. Therefore, the information required under those obligations seems to create, prima facie, an adequate framework to ensure trust for users of high-risk AI systems. However, on the practical implications of these transparency obligations, the research notes that concern arises due to potential illiteracy of high-risk AI systems users. They might not benefit from sufficient technical expertise to fully understand the information provided to them, despite the wording of the Proposal, which requires that information should be comprehensible to its recipients (i.e. users).On this matter, the research points that there could be, more broadly, an important divergence between the level of detail of the information required by the Proposal and the level of expertise of users of high-risk AI systems. As a conclusion, the research provides policy recommendations to tackle (part of) the issues highlighted. It notably recommends to broaden the scope of transparency requirements for high-risk AI systems to encompass end-users. It also suggests that principles of explanation, as they were put forward in the Guidelines for Trustworthy AI of the High Level Expert Group, should be included in the Proposal in addition to transparency obligations.

Keywords: aI act proposal, explainability of aI, high-risk aI systems, transparency requirements

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Authors: Bill D. Geis

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Even with significantly increased spending, suicide rates continue to climb—with alarming increases among traditionally low-risk groups. This has caused clinicians and researchers to call for a complete rethinking of all assumptions about suicide prevention, assessment, and intervention. A form of therapy--Internal Family Systems Therapy--affords tremendous promise in sustained diminishment of lethal suicide risk. Though a form of therapy that is most familiar to trauma therapists, Internal Family Systems Therapy, involving direct work with suicidal parts, is a promising therapy for meaningful and sustained reduction in suicide deaths. Developed by Richard Schwartz, Internal Family Systems Therapy proposes that we are all influenced greatly by internal parts, frozen by development adversities, and these often-contradictory parts contribute invisibly to mood, distress, and behavior. In making research videos of patients from our database and discussing their suicide attempts, it is clear that many persons who attempt suicide are in altered states at the time of their attempt and influenced by factors other than conscious intent. Suicide intervention using this therapy involves direct work with suicidal parts and other interacting parts that generate distress and despair. Internal Family Systems theory posits that deep experiences of pain, fear, aloneness, and distress are defended by a range of different parts that attempt to contain these experiences of pain through various internal activities that unwittingly push forward inhibition, fear, self-doubt, hopelessness, desires to cut and engage in destructive behavior, addictive behavior, and even suicidal actions. These suicidal parts are often created (and “frozen”) at young ages, and these very young parts do not understand the consequences of this influence. Experience suggests that suicidal parts can create impulsive risk behind the scenes when pain is high and emotional support reduced—with significant crisis potential. This understanding of latent suicide risk is consistent with many of our video accounts of serious suicidal acts—compiled in a database of 1104 subjects. Since 2016, consent has been obtained and records kept of 23 highly suicidal patients, with initial Intention-to-Die ratings (0= no intent, 10 = conviction to die) between 5 and 10. In 67% of these cases using IFST parts-work intervention, these highly suicidal patients’ risk was reduced to 0-1, and 83% of cases were reduced to 4 or lower. There were no suicide deaths. Case illustrations will be offered.

Keywords: suicide, internal family systems therapy, crisis management, suicide prevention

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Authors: Akintunde O. Osibamowo, Abdulrasaq O. Olusanya

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The present study was designed to examine the effect of problem-solving approach as a medium of instruction in teaching and learning of mathematics to improve the achievement of the student. One Hundred (100) students were randomly chosen from five (5) Junior Secondary School in Ijebu-Ode Local Government Area of Ogun State, Nigeria. The data was collected through Mathematics Achievement Test (MAT) on the two groups (experimental and control group). The study confirmed that there is a significant different in the achievement of students exposed to problem-solving approach than those not exposed. The result also indicated that male students, however, had a greater mean-score than the female with no significant difference in their achievement. The result of the study supports the use of problem-solving approach in the teaching and learning of mathematics in secondary schools.

Keywords: problem, achievement, teaching phases, experimental control

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Authors: O. Maximova, I. L. Danilovich, E. B. Deeva, K. Y. Bukhteev, A. A. Vorobyova, I. V. Morozov, O. S. Volkova, E. A. Zvereva, I. V. Solovyev, S. A. Nikolaev, D. Phuyal, M. Abdel-Hafiez, Y. C. Wang, J. Y. Lin, J. M. Chen, D. I. Gorbunov, K. Puzniak, B. Lake, A. N. Vasiliev

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The low-dimensional magnetic systems tend to reveal exotic spin liquid ground states or form peculiar types of long-range order. Among systems of vivid interest are those characterized by the triangular motif in two dimensions. The realization of either ordered or disordered ground state in a triangular, honeycomb, or kagome lattices is are dictated by the competition of exchange interactions, also being sensitive to anisotropy and the spin value of magnetic ions. While the low-spin Heisenberg systems may arrive at a spin liquid long-range entangled quantum state with emergent gauge structures, the high-spin Ising systems may establish the rigid non-collinear structures. This study presents the case of chiral non-coplanar inverted umbrella-type ferrimagnet formed in cobalt nitrate Co(NO₃)₂ below T

Keywords: chiral magnetic structures, low dimensional magnetic systems, umbrella-type ferrimagnets, chiral non-coplanar magnetic structures

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Authors: Peng Zhou, Peter Pudney

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Driver advice systems are used by many rail operators to help train drivers to improve timekeeping while minimising energy use. These systems typically operate independently of the safeworking system, because information on how far the train is allowed to travel -the “limit of authority"- is usually not available as real-time data that can be used when generating driving advice. This is not an issue when there is sufficient separation between trains. But on systems with low headways, driving advice could conflict with safeworking requirements. We describe a method for generating driving advice that takes into account a moving limit of authority that is communicated to the train in real-time. We illustrate the method with four simulated examples using data from the Zhengzhou Metro. The method will allow driver advice systems to be used more effectively on railways with low headways.

Keywords: railway transportation, energy efficient train operation, optimal train control, safe separation

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: C. A. Bradley, D. Patsios, D. Berridge

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This paper presents evidence to better understand the risk and protective factors related to sibling conflict and the patterns of association between sibling conflict and negative adjustment outcomes by incorporating additional familial and societal factors within statistical models of risk and adjustment. It was conducted through the secondary analysis of a large representative cross-sectional dataset of children in the UK. The original study includes proxy interviews for young children and self-report interviews for adolescents. The study applies an ecological systems framework for the analyses. Hierarchical regression models assess risk and protective factors and adjustment outcomes associated with sibling conflict. Interactions reveal differential effect between contextual risk factors and the social context of influence. The general pattern of findings suggested that, although factors affecting likelihood of experiencing sibling conflict were often determined by child age, some remained consistent across childhood. These factors were often conditional on each other, reinforcing the importance of an ecological framework. Across both age-groups, sibling conflict was associated with siblings closer in age; male sibling groups; most advantaged socio-economic group; and exposure to community violence, such as witnessing violent assault or robbery. The study develops the evidence base on the influence of ethnicity and socio-economic group on sibling conflict by exploring interactions between social context. It also identifies key new areas of influence – such as family structure, disability, and community violence in exacerbating or reducing risk of conflict. The study found negative associations between sibling conflict and young children’s mental well-being and adolescents' mental well-being and anti-social behaviour, but also more context specific associations – such as sibling conflict moderating the negative impact of adversity and high risk experiences for young children such as parental violence toward the child.

Keywords: adjustment, conflict, ecological systems, family systems, risk and protective factors, sibling

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Authors: K. D. Candido, A. Lissounov, I. Knezevic, N. Knezevic

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Background: The most commonly performed pain procedures in the USA is Lumbar Epidural Steroid Injections (LESI). There are three main types of these procedures: transforaminal (TF), interlaminar (IL) and caudal injections. It is expected for TF injections to have better outcomes than IL injections, based on the recently published systematic review. The studies presented in that review used a midline IL approach, but those with parasagittal IL approach were not taken into consideration. Our aim is to emphasize the efficacy of the lateral parasagittal (paramedian) IL approach in this review. Methods: We included five studies in this systematic review, which compared Parasagittal-IL (PIL) with either Midline-IL (MIL) or TF LESI. Total of 296 patients who had undergone different types of LESI were observed across the five studies, and the average pain and functional improvements were calculated and compared among groups. Results: Pain and function improvements with PIL approach is superior on 12 months follow up to MIL approach (53.4% vs. 14.7%) and (55% vs. 27.7%), respectively. A 12 months follow-up results between PIL and TF shows a near equivalent effectiveness for pain (58.9% vs. 63.2%) and function improvement (47.3% vs. 48.1%). An average follow-up of 17.1 days have shown better short-term pain relief for PIL than TF approach (45.8% vs. 19.2%), respectively. Number of repeated injections is lower for PIL injections than MIL. Number of weeks between 1st and 2nd injections: PIL averaged 15.8 weeks and MIL averaged 9.7 weeks. Third LESI injection is more common in TF group (30%) than PIL group (18.8%). Conclusion: Higher complication rates are associated with TF injections for which FDA7 issued an official warning. We have recorded better outcomes in pain and function improvement of Parasagittal-IL LESI as compared to midline-IL injection, in the presented systematic review. Parasagittal and TF injections have equivalent efficacy in Pain and Function improvements thus we advocate for Parasagittal-IL approach consideration as an alternative for TF injections.

Keywords: parasagital approach, lumbar, back pain, epidural steroid injection

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Authors: Seyed Saeid Tabaee, Omid Bahar

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Nowadays, using energy dissipation devices has been commonly used in structures. A high rate of energy absorption during earthquakes is the benefit of using such devices, which results in damage reduction of structural elements specifically columns. The hysteretic damping capacity of energy dissipation devices is the key point that it may adversely complicate analysis and design of such structures. This effect may be generally represented by equivalent viscous damping. The equivalent viscous damping may be obtained from the expected hysteretic behavior under the design or maximum considered displacement of a structure. In this paper, the hysteretic damping coefficient of a steel moment resisting frame (MRF), which its performance is enhanced by a buckling restrained brace (BRB) system has been evaluated. Having the foresight of damping fraction between BRB and MRF is inevitable for seismic design procedures like Direct Displacement-Based Design (DDBD) method. This paper presents an approach to calculate the damping fraction for such systems by carrying out the dynamic nonlinear time history analysis (NTHA) under harmonic loading, which is tuned to the natural frequency of the system. Two steel moment frame structures, one equipped with BRB, and the other without BRB are simultaneously studied. The extensive analysis shows that proportion of each system damping fraction may be calculated by its shear story portion. In this way, the contribution of each BRB in the floors and their general contribution in the structural performance may be clearly recognized, in advance.

Keywords: buckling restrained brace, direct displacement based design, dual systems, hysteretic damping, moment resisting frames

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Authors: S. H. Lee, M. J. Park, O. M. Kwon

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In this paper, reliable consensus of multi-agent systems with sampled-data is investigated. By using a suitable Lyapunov-Krasovskii functional and some techniques such as Wirtinger Inequality, Schur Complement and Kronecker Product, the results of this systems are obtained by solving a set of Linear Matrix Inequalities(LMIs). One numerical example is included to show the effectiveness of the proposed criteria.

Keywords: multi-agent, linear matrix inequalities (LMIs), kronecker product, sampled-data, Lyapunov method

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Authors: Agah Tuğrul Korucu, Handan Atun

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Diversity and usefulness of information that used in education are have increased due to development of technology. Web technologies have made enormous contributions to the distance learning system especially. Mobile systems, one of the most widely used technology in distance education, made much easier to access web technologies. Not bounding by space and time, individuals have had the opportunity to access the information on web. In addition to this, the storage of educational information and resources and accessing these information and resources is crucial for both students and teachers. Because of this importance, development and dissemination of web technologies supply ease of access to information and resources are provided by web technologies. Dynamic web technologies introduced as new technologies that enable sharing and reuse of information, resource or applications via the Internet and bring websites into expandable platforms are commonly known as Web 2.0 technologies. Cloud systems are one of the dynamic web technologies that defined as a model provides approaching the demanded information independent from time and space in appropriate circumstances and developed by NIST. One of the most important advantages of cloud systems is meeting the requirements of users directly on the web regardless of hardware, software, and dealing with install. Hence, this study aims at using cloud services in education and investigating the services provided by the cloud computing. Survey method has been used as research method. In the findings of this research the fact that cloud systems are used such studies as resource sharing, collaborative work, assignment submission and feedback, developing project in the field of education, and also, it is revealed that cloud systems have plenty of significant advantages in terms of facilitating teaching activities and the interaction between teacher, student and environment.

Keywords: cloud systems, cloud systems in education, online learning environment, integration of information technologies, e-learning, distance learning

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Authors: Suyeb Ahmed Khan, Mahmood Mian

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Simulation plays an important role during all phases of the design and engineering of communications systems, from early stages of conceptual design through the various stages of implementation, testing, and fielding of the system. In the present paper, a simulation model has been constructed for the WCDMA system in order to evaluate the performance. This model describes multiusers effects and calculation of BER (Bit Error Rate) in 3G mobile systems using Simulink MATLAB 7.1. Gaussian Approximation defines the multi-user effect on system performance. BER has been analyzed with comparison between transmitting data and receiving data.

Keywords: WCDMA, simulations, BER, MATLAB

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Authors: Tomoaki Hashimoto

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Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and a moving terminal time. This paper examines the stability of model predictive control for linear discrete-time systems with additive stochastic disturbances. A sufficient condition for the stability of the closed-loop system with model predictive control is derived by means of a linear matrix inequality. The objective of this paper is to show the results of computational simulations in order to verify the validity of the obtained stability condition.

Keywords: computational simulations, optimal control, predictive control, stochastic systems, discrete-time systems

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Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

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Authors: Ricky Leung

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Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

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Authors: Natalia Marulanda Grisales

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The most significant changes in the characteristics of consumers have contributed to the development and adoption of methodologies and tools that enable organizations to be more competitive in the marketplace. One of these methodologies is the integration of Knowledge Management (KM) phases and Environmental Management Systems (EMS). This integration allows companies to manage and share the required knowledge for EMS adoption, from the place where it is generated to the place where it is going to be exploited. The aim of this paper is to identify the relationship between KM phases as a tool for the adoption of EMS in HEI. The methodology has a descriptive scope and a qualitative approach. It is based on a case study and a review of the literature about KM and EMS. We conducted 266 surveys to students, professors and staff at Minuto de Dios University (Colombia). Data derived from the study indicate that if a HEI wants to achieve an adequate knowledge acquisition and knowledge transfer, it must have clear goals for implementing an EMS. Also, HEI should create empowerment and training spaces for students, professors and staff. In the case study, HEI must generate alternatives that enhance spaces of knowledge appropriation. It was found that 85% of respondents have not received any training from HEI about EMS. 88% of respondents believe that the actions taken by the university are not efficient to knowledge transfer in order to develop an EMS.

Keywords: environmental management systems, higher education institutions, knowledge management, training

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Authors: E. Karami, M. Bohloul, P. Najmadi

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The superconducting system is a suitable system for quantum computers. Quantum entanglement is a fundamental phenomenon that is key to the power of quantum computers. Quantum entanglement has been studied in different superconducting systems. In this paper, we are investigating a superconducting two-qubit system as a macroscopic system. These systems include two coupled Quantronium circuits. We calculate quantum entanglement and thermalization for system evolution and compare them. We observe, thermalization and entanglement have different behavior, and equilibrium thermal state has maximum entanglement.

Keywords: macroscopic system, quantum entanglement, thermalization, superconducting system

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Authors: Ercan Kızılay, Mustafa Demi̇rel, Selçuk Coşkun

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Railway signaling systems consist of vital products that regulate railway traffic and provide safe route arrangements and maneuvers of trains. SIL 4 signal lamps are produced by many manufacturers today. There is a need for systems that enable these signal lamps to be controlled by commands from the interlocking. These systems should act as fail-safe and give error indications to the interlocking system when an unexpected situation occurs for the safe operation of railway systems from the RAMS perspective. In the past, driving and proving the lamp in relay-based systems was typically done via signaling relays. Today, the proving of lamps is done by comparing the current values read over the return circuit, the lower and upper threshold values. The purpose is an analog electronic object controller with the possibility of easy integration with vital systems and the signal lamp itself. During the study, the EN50126 standard approach was considered, and the concept, definition, risk analysis, requirements, architecture, design, and prototyping were performed throughout this study. FMEA (Failure Modes and Effects Analysis) and FTA (Fault Tree) Analysis) have been used for safety analysis in accordance with EN 50129. Concerning these analyzes, the 1oo2D reactive fail-safe hardware design of a controller has been researched. Electromagnetic compatibility (EMC) effects on the functional safety of equipment, insulation coordination, and over-voltage protection were discussed during hardware design according to EN 50124 and EN 50122 standards. As vital equipment for railway signaling, railway signal object controllers should be developed according to EN 50126 and EN 50129 standards which identify the steps and requirements of the development in accordance with the SIL 4(Safety Integrity Level) target. In conclusion of this study, an analog railway signal object controller, which takes command from the interlocking system, is processed in driver cards. Driver cards arrange the voltage level according to desired visibility by means of semiconductors. Additionally, prover cards evaluate the current upper and lower thresholds. Evaluated values are processed via logic gates which are composed as 1oo2D by means of analog electronic technologies. This logic evaluates the voltage level of the lamp and mitigates the risks of undue dimming.

Keywords: object controller, railway electronic, analog electronic, safety, railway signal

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Authors: Cheng Li, Jie Shen, Yutian Tang

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Embracing the upcoming era of urbanization with the challenges of limitation of resources, disappearing cultural identities and conflicts among different groups of stakeholders, new integrated approaches are offered in our urban practice to help decision-makers and stakeholders frame and develop well-conceived, practical strategies for urban developing trajectories to approach urban-level sustainability in multiple social, cultural, ecological dimensions. Through bottom-up participation, we take advantage of tourism and leisure industries as driving forces for urbanization in China to promote integrated sustainable systems, with the hope of approaching both historical and ecological aspects of urban sustainability; and also thanks to top-down participation, we have codes, standards and rules established by the governments to strengthen the implementation of ecological urban sustainability. The results are monitored and evaluated experimentally and multidimensionally and the sustainable systems we constructed with local stakeholder groups turned out to be effective. The presentation of our selected projects would indicate our different focuses on urban sustainability.

Keywords: urban sustainability, integrated urban strategy, tourism and leisure industries, history, modern technology

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Authors: Roberto de Lieto Vollaro, Emanuele de Lieto Vollaro, Gianluca Coltrinari

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The building sector is responsible, in many industrialized countries, for about 40% of the total energy requirements, so it seems necessary to devote some efforts in this area in order to achieve a significant reduction of energy consumption and of greenhouse gases emissions. The paper presents a study aiming at providing a design methodology able to identify the best configuration of the system building/plant, from a technical, economic and environmentally point of view. Normally, the classical approach involves a building's energy loads analysis under steady state conditions, and subsequent selection of measures aimed at improving the energy performance, based on previous experience made by architects and engineers in the design team. Instead, the proposed approach uses a sequence of two well known scientifically validated calculation methods (TRNSYS and RETScreen), that allow quite a detailed feasibility analysis. To assess the validity of the calculation model, an existing, historical building in Central Italy, that will be the object of restoration and preservative redevelopment, was selected as a case-study. The building is made of a basement and three floors, with a total floor area of about 3,000 square meters. The first step has been the determination of the heating and cooling energy loads of the building in a dynamic regime by means of TRNSYS, which allows to simulate the real energy needs of the building in function of its use. Traditional methodologies, based as they are on steady-state conditions, cannot faithfully reproduce the effects of varying climatic conditions and of inertial properties of the structure. With TRNSYS it is possible to obtain quite accurate and reliable results, that allow to identify effective combinations building-HVAC system. The second step has consisted of using output data obtained with TRNSYS as input to the calculation model RETScreen, which enables to compare different system configurations from the energy, environmental and financial point of view, with an analysis of investment, and operation and maintenance costs, so allowing to determine the economic benefit of possible interventions. The classical methodology often leads to the choice of conventional plant systems, while RETScreen provides a financial-economic assessment for innovative energy systems and low environmental impact. Computational analysis can help in the design phase, particularly in the case of complex structures with centralized plant systems, by comparing the data returned by the calculation model RETScreen for different design options. For example, the analysis performed on the building, taken as a case study, found that the most suitable plant solution, taking into account technical, economic and environmental aspects, is the one based on a CCHP system (Combined Cooling, Heating, and Power) using an internal combustion engine.

Keywords: energy, system, building, cooling, electrical

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Authors: Manlika Ratchagit

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This paper is concerned with robust mean square stability of uncertain stochastic switched discrete time-delay systems. The system to be considered is subject to interval time-varying delays, which allows the delay to be a fast time-varying function and the lower bound is not restricted to zero. Based on the discrete Lyapunov functional, a switching rule for the robust mean square stability for the uncertain stochastic discrete time-delay system is designed via linear matrix inequalities. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: robust mean square stability, discrete-time stochastic systems, hybrid systems, interval time-varying delays, Lyapunov functional, linear matrix inequalities

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Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

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Authors: Aziz Ahmad, Guillermo Ramirez-Prado

Abstract:

Governments around the world encourage the use of solar water heating in residential houses due to the low maintenance requirements and efficiency of the solar collector water heating systems. The aim of this work is to study a domestic solar water heating system in a residential building to develop a model of the entire solar water heating system including flat-plate solar collector and storage tank. The proposed model is adaptable to any households and location. The model can be used to test different types of controllers and can provide efficiency as well as economic analysis. The proposed model is based on the heat and mass transfer equations along with assumptions applied in the model which can be modified for a variety of different solar water heating systems and sizes. Simulation results of the model were compared with the actual system which shows similar trends.

Keywords: solar thermal systems, solar water heating, solar collector model, hot water tank model, solar controllers

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Authors: Virender Ranga, Mayank Dave, Anil Kumar Verma

Abstract:

Recently wireless sensor networks (WSNs) are used in many real life applications like environmental monitoring, habitat monitoring, health monitoring etc. Due to power constraint cheaper devices used in these applications, the energy consumption of each device should be kept as low as possible such that network operates for longer period of time. One of the techniques to prolong the network lifetime is an intelligent grouping of sensor nodes such that they can perform their operation in cooperative and energy efficient manner. With this motivation, we propose a novel approach by organize the sensor nodes in cooperative multihop mini-groups so that the total global energy consumption of the network can be reduced and network lifetime can be improved. Our proposed approach also reduces the number of transmitted messages inside the WSNs, which further minimizes the energy consumption of the whole network. The experimental simulations show that our proposed approach outperforms over the state-of-the-art approach in terms of stability period and aggregated data.

Keywords: clustering, cluster-head, mini-group, stability period

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Authors: C. L. Wu, W. W. Tseng

Abstract:

From the National Health Insurance (NHI) system was introduced in Taiwan in 2000, there have been some problems in transformed small-scale hospitals, such as mobility of patients, shortage of nursing staff, medical pipelines breaking fire compartments and insufficient fire protection systems. Due to shrinking of the funding scale and the aging society, fire safety in small-scale hospitals has recently given cause for concern. The aim of this study is to determine fire risk index for small-scale hospital through a systematic approach The selection of fire safety mitigation methods can be regarded as a multi-attribute decision making process which must be guaranteed by expert groups. First of all, identify and select safety related factors and identify evaluation criteria through literature reviews and experts group. Secondly, application of the Fuzzy Analytic Hierarchy Process method is used to ascertain a weighted value which enables rating of the importance each of the selected factors. Overall, Sprinkler type and Compartmentation are the most crucial indices in mitigating fire, that is to say, structural approach play an important role to decrease losses in fire events.

Keywords: Fuzzy Delphi Method, fuzzy analytic hierarchy, process risk assessment, fire events

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Authors: Zviad Ghadua, Biswa Bhattacharya

Abstract:

The Flash Flood Guidance (FFG) provides the rainfall amount of a given duration necessary to cause flooding. The approach is based on the development of rainfall-runoff curves, which helps us to find out the rainfall amount that would cause flooding. An alternative approach, mostly experimented with Italian Alpine catchments, is based on determining threshold discharges from past events and on finding whether or not an oncoming flood has its magnitude more than some critical discharge thresholds found beforehand. Both approaches suffer from large uncertainties in forecasting flash floods as, due to the simplistic approach followed, the same rainfall amount may or may not cause flooding. This uncertainty leads to the question whether a probabilistic model is preferable over a deterministic one in forecasting flash floods. We propose the use of a Bayesian probabilistic approach in flash flood forecasting. A prior probability of flooding is derived based on historical data. Additional information, such as antecedent moisture condition (AMC) and rainfall amount over any rainfall thresholds are used in computing the likelihood of observing these conditions given a flash flood has occurred. Finally, the posterior probability of flooding is computed using the prior probability and the likelihood. The variation of the computed posterior probability with rainfall amount and AMC presents the suitability of the approach in decision making in an uncertain environment. The methodology has been applied to the Posina basin in Italy. From the promising results obtained, we can conclude that the Bayesian approach in flash flood forecasting provides more realistic forecasting over the FFG.

Keywords: flash flood, Bayesian, flash flood guidance, FFG, forecasting, Posina

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Authors: Rubén Valcarce-Álvarez, Francisco Jáñez-Martino, Rocío Alaiz-Rodríguez

Abstract:

Cybersecurity challenges, including scams, drug sales, the distribution of child sexual abuse material, fake news, and hate speech on both the surface and deep web, have significantly increased over the past decade. Users who post such content often employ strategies to evade detection by automated filters. Among these tactics, text obfuscation plays an essential role in deceiving detection systems. This approach involves modifying words to make them more difficult for automated systems to interpret while remaining sufficiently readable for human users. In this work, we aim at spotting obfuscated words and the employed techniques, such as leetspeak, word inversion, punctuation changes, and mixed techniques. We benchmark Named Entity Recognition (NER) using models from the BERT family as well as two large language models (LLMs), Llama and Mistral, on XX_NER_WordCamouflage dataset. Our experiments evaluate these models by comparing their precision, recall, F1 scores, and accuracy, both overall and for each individual class.

Keywords: natural language processing (NLP), text obfuscation, named entity recognition (NER), deep learning

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Authors: Manlika Rajchakit

Abstract:

This paper is concerned with robust mean square stability of uncertain stochastic switched discrete time-delay systems. The system to be considered is subject to interval time-varying delays, which allows the delay to be a fast time-varying function and the lower bound is not restricted to zero. Based on the discrete Lyapunov functional, a switching rule for the robust mean square stability for the uncertain stochastic discrete time-delay system is designed via linear matrix inequalities. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: robust mean square stability, discrete-time stochastic systems, hybrid systems, interval time-varying delays, lyapunov functional, linear matrix inequalities

Procedia PDF Downloads 431