Search results for: systems approach

Authors: Thiago Ribeiro De Alencar, Jacyro Gramulia Junior, Patricia Teixeira Leite Asano

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The area of the electricity sector that deals with energy needs by the hydropower and thermoelectric in a coordinated way is called Planning Operating Hydrothermal Power Systems. The aim of this area is to find a political operative to provide electrical power to the system in a specified period with minimization of operating cost. This article proposes a computational tool for solving the planning problem. In addition, this article will be introducing a methodology to find new transfer points between reservoirs increasing energy production in hydroelectric power plants cascade systems. The computational tool proposed in this article applies: i) genetic algorithms to optimize the water transfer and operation of hydroelectric plants systems; and ii) Ant Colony algorithm to find the trajectory with the least energy pumping for the construction of pipes transfer between reservoirs considering the topography of the region. The computational tool has a database consisting of 35 hydropower plants and 41 reservoirs, which are part of the southeastern Brazilian system, which has been implemented in an individualized way.

Keywords: ant colony system, genetic algorithms, hydroelectric, hydrothermal systems, optimization, water transfer between rivers

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Authors: Seyed Mohammad Hashemi, Shahrokh Barati

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The problem of fault-tolerant control (FTC) by accommodation method has been studied in this paper. The fault occurs in any system components such as actuators, sensors or internal structure of the system and leads to loss of performance and instability of the system. When a fault occurs, the purpose of the fault-tolerant control is designate strategy that can keep the control loop stable and system performance as much as possible perform it without shutting down the system. Here, the section of fault detection and isolation (FDI) system has been evaluated with regard to actuator's fault. Designing a fault detection and isolation system for a multi input-multi output (MIMO) is done by an unknown input observer, so the system is divided to several subsystems as the effect of other inputs such as disturbing given system state equations. In this observer design method, the effect of these disturbances will weaken and the only fault is detected on specific input. The results of this approach simulation can confirm the ability of the fault detection and isolation system design. After fault detection and isolation, it is necessary to redesign controller based on a suitable modification. In this regard after the use of unknown input observer theory and obtain residual signal and evaluate it, PID controller parameters redesigned for iterative. Stability of the closed loop system has proved in the presence of this method. Also, In order to soften the volatility caused by Annie variations of the PID controller parameters, modifying Sigma as a way acceptable solution used. Finally, the simulation results of three tank popular example confirm the accuracy of performance.

Keywords: fault tolerant control, fault detection and isolation, actuator fault, unknown input observer

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Authors: Nasser S. Shebka

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Fuzzy logic is used in complex adaptive systems where classical tools of representing knowledge are unproductive. Nevertheless, the incorporation of fuzzy logic, as it’s the case with all artificial intelligence tools, raised some inconsistencies and limitations in dealing with increased complexity systems and rules that apply to real-life situations and hinders the ability of the inference process of such systems, but it also faces some inconsistencies between inferences generated fuzzy rules of complex or imprecise knowledge-based systems. The use of fuzzy logic enhanced the capability of knowledge representation in such applications that requires fuzzy representation of truth values or similar multi-value constant parameters derived from multi-valued logic, which set the basis for the three t-norms and their based connectives which are actually continuous functions and any other continuous t-norm can be described as an ordinal sum of these three basic ones. However, some of the attempts to solve this dilemma were an alteration to fuzzy logic by means of non-monotonic logic, which is used to deal with the defeasible inference of expert systems reasoning, for example, to allow for inference retraction upon additional data. However, even the introduction of non-monotonic fuzzy reasoning faces a major issue of conflict resolution for which many principles were introduced, such as; the specificity principle and the weakest link principle. The aim of our work is to improve the logical representation and functional modelling of AI systems by presenting a method of resolving existing and potential rule conflicts by representing temporal modalities within defeasible inference rule-based systems. Our paper investigates the possibility of resolving fuzzy rules conflict in a non-monotonic fuzzy reasoning-based system by introducing temporal modalities and Kripke's general weak modal logic operators in order to expand its knowledge representation capabilities by means of flexibility in classifying newly generated rules, and hence, resolving potential conflicts between these fuzzy rules. We were able to address the aforementioned problem of our investigation by restructuring the inference process of the fuzzy rule-based system. This is achieved by using time-branching temporal logic in combination with restricted first-order logic quantifiers, as well as propositional logic to represent classical temporal modality operators. The resulting findings not only enhance the flexibility of complex rule-base systems inference process but contributes to the fundamental methods of building rule bases in such a manner that will allow for a wider range of applicable real-life situations derived from a quantitative and qualitative knowledge representational perspective.

Keywords: fuzzy rule-based systems, fuzzy tense inference, intelligent systems, temporal modalities

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Authors: Danny Barash

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Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

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Authors: Bronwen Wade

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Over the last half-century, child welfare systems have increasingly relied on quantitative risk assessment tools, such as actuarial or predictive risk tools. These tools are developed by performing statistical analysis of how attributes captured in administrative data are related to future child maltreatment. Some scholars argue that attributes in administrative data can serve as proxies for race and that quantitative risk assessment tools reify racial bias in decision-making. Others argue that these tools provide more “objective” and “scientific” guides for decision-making instead of subjective social worker judgment. This study performs a systematic review of the literature on the impact of quantitative risk assessment tools on racial disproportionality; it examines methodological biases in work on this topic, summarizes key findings, and provides suggestions for further work. A search of CINAHL, PsychInfo, Proquest Social Science Premium Collection, and the ProQuest Dissertations and Theses Collection was performed. Academic and grey literature were included. The review includes studies that use quasi-experimental methods and development, validation, or re-validation studies of quantitative risk assessment tools. PROBAST (Prediction model Risk of Bias Assessment Tool) and CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) were used to assess the risk of bias and guide data extraction for risk development, validation, or re-validation studies. ROBINS-I (Risk of Bias in Non-Randomized Studies of Interventions) was used to assess for bias and guide data extraction for the quasi-experimental studies identified. Due to heterogeneity among papers, a meta-analysis was not feasible, and a narrative synthesis was conducted. 11 papers met the eligibility criteria, and each has an overall high risk of bias based on the PROBAST and ROBINS-I assessments. This is deeply concerning, as major policy decisions have been made based on a limited number of studies with a high risk of bias. The findings on racial disproportionality have been mixed and depend on the tool and approach used. Authors use various definitions for racial equity, fairness, or disproportionality. These concepts of statistical fairness are connected to theories about the reason for racial disproportionality in child welfare or social definitions of fairness that are usually not stated explicitly. Most findings from these studies are unreliable, given the high degree of bias. However, some of the less biased measures within studies suggest that quantitative risk assessment tools may worsen racial disproportionality, depending on how disproportionality is mathematically defined. Authors vary widely in their approach to defining and addressing racial disproportionality within studies, making it difficult to generalize findings or approaches across studies. This review demonstrates the power of authors to shape policy or discourse around racial justice based on their choice of statistical methods; it also demonstrates the need for improved rigor and transparency in studies of quantitative risk assessment tools. Finally, this review raises concerns about the impact that these tools have on child welfare systems and racial disproportionality.

Keywords: actuarial risk, child welfare, predictive risk, racial disproportionality

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Authors: Ritu Vashistha, Jitender Kumar

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Clustering using the K-means algorithm is a very common way to understand and analyze the obtained output data. When a similar object is grouped, this is called the basis of Clustering. There is K number of objects and C number of cluster in to single cluster in which k is always supposed to be less than C having each cluster to be its own centroid but the major problem is how is identify the cluster is correct based on the data. Formulation of the cluster is not a regular task for every tuple of row record or entity but it is done by an iterative process. Each and every record, tuple, entity is checked and examined and similarity dissimilarity is examined. So this iterative process seems to be very lengthy and unable to give optimal output for the cluster and time taken to find the cluster. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses the Euclidian distance formula as well melanosis to find the minimum distance between slots as technically we called clusters and the same approach we have also applied to Ant Colony Optimization(ACO) algorithm, which results in the production of two and multi-dimensional matrix.

Keywords: ant colony optimization, data clustering, centroids, data mining, k-means

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham

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In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

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Authors: S. K. Ashiquer Rahman

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The innovative knowledge threshold significantly shifted education from traditional to an online version which was an emergent state of arts for academic programs of any higher education institutions; the substantive situation thus raises the importance of deliberative integration of education, Knowledge, technology and sustainability as well as knowledge platforms, e.g., ePLANETe. In fact, the concept of 'ePLANETe' an innovative knowledge platform and its functionalities as an experimental digitized platform for contributing sustainable assessment of academic programs of higher education institution(HEI). Besides, this paper assessed and define the common sustainable development challenges of higher education(HE) and identified effective approach and tools of 'ePLANETe’ that is enable to practices sustainability assessment of academic programs through the deliberation methodologies. To investigate the effectiveness of knowledge tools and approach of 'ePLANETe’, I have studied sustainable challenges digitized pedagogical content as well as evaluation of academic programs of two public universities in France through the 'ePLANETe’ evaluation space. The investigation indicated that the effectiveness of 'ePLANETe’s tools and approach perfectly fit for the quality assessment of academic programs, implementation of sustainable challenges, and dynamic balance of ecosystem within the university communities and academic programs through 'ePLANETe’ evaluation process and space. The study suggests to the relevant higher educational institution’s authorities and policymakers could use this approach and tools for assessing sustainability and enhancing the sustainability competencies of academic programs for quality education

Keywords: ePLANETe, deliberation, evaluation, competencies

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Authors: Kohtaroh Miyamoto

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As enterprise computing becomes more and more complex, the costs and technical challenges of IT system maintenance and support are increasing rapidly. One popular approach to managing IT system maintenance is to prepare and use an FAQ (Frequently Asked Questions) system to manage and reuse systems knowledge. Such an FAQ system can help reduce the resolution time for each service incident ticket. However, there is a major problem where over time the knowledge in such FAQs tends to become outdated. Much of the knowledge captured in the FAQ requires periodic updates in response to new insights or new trends in the problems addressed in order to maintain its usefulness for problem resolution. These updates require a systematic approach to define the exact portion of the FAQ and its content. Therefore, we are working on a novel method to hierarchically structure the FAQ and automate the updates of its structure and content. We use structured information and the unstructured text information with the timelines of the information in the service incident tickets. We cluster the tickets by structured category information, by keywords, and by keyword modifiers for the unstructured text information. We also calculate an urgency score based on trends, resolution times, and priorities. We carefully studied the tickets of one of our projects over a 2.5-year time period. After the first 6 months, we started to create FAQs and confirmed they improved the resolution times. We continued observing over the next 2 years to assess the ongoing effectiveness of our method for the automatic FAQ updates. We improved the ratio of tickets covered by the FAQ from 32.3% to 68.9% during this time. Also, the average time reduction of ticket resolution was between 31.6% and 43.9%. Subjective analysis showed more than 75% reported that the FAQ system was useful in reducing ticket resolution times.

Keywords: FAQ system, resolution time, service incident tickets, IT system maintenance

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Authors: A. T. Zaki

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The flipped classroom approach as a mode of blended learning was formally introduced to students of the English language modules at the British University in Egypt (BUE) at the start of the academic year 2015/2016. This paper aims to study the impact of the flipped classroom approach after three semesters of implementation. It will restrict itself to the examination of students’ achievement rates, student satisfaction, and how different student cohorts have benefited differently from the flipped practice. The paper concludes with recommendations of how the experience can be further developed.

Keywords: achievement rates, developmental experience, Egypt, flipped classroom, higher education, student cohorts, student satisfaction

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Authors: Essardi Omar, Razzouk Redouane

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The concept of freedom requires notice of countries all over the world to consider welfare and the quality of life. Despite, many economics efforts in the field of development literature, they have often failed to incorporate the ideas of freedom and rights into their theoretical and empirical work. However, with Amartya Sen’s approach of capability and researches, we can provide a basis for moving forward in theory and measure of development. Indeed, with an approach based on the correlation and the analysis of data, particularly on the tool of principle component analysis, we are going to study assessments of World Bank, Freedom House, Fraster institute, and MINEFE experts. Our empirical objective is to reveal the existence of the institutional and freedom characteristics related to the development of the emergent countries. In order to help us to explain the recent performance reached by Central and Eastern Europe and Latine America in compared with the case of countries of North Africa. To do this, first we will try to build indicators based on dilemma liberties /institutions. Second we will introduce institutional variables and freedom variables to make comparisons in freedom, quality of institutions and development in the countries observed.

Keywords: freedoms, institutions, development, approach of capability, principle component analysis

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Authors: Helene Thieblemont, Fariborz Haghighat

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Energy storage systems play a crucial role in decreasing building energy consumption during peak periods and expand the use of renewable energies in buildings. To provide a high building thermal performance, the energy storage system has to be properly controlled to insure a good energy performance while maintaining a satisfactory thermal comfort for building’s occupant. In the case of passive discharge storages, defining in advance the required amount of energy is required to avoid overheating in the building. Consequently, anticipatory supervisory control strategies have been developed forecasting future energy demand and production to coordinate systems. Anticipatory supervisory control strategies are based on some predictions, mainly of the weather forecast. However, if the forecasted hourly outdoor temperature may be found online with a high accuracy, solar radiations predictions are most of the time not available online. To estimate them, this paper proposes an advanced approach based on the forecast of weather conditions. Several methods to correlate hourly weather conditions forecast to real hourly solar radiations are compared. Results show that using weather conditions forecast allows estimating with an acceptable accuracy solar radiations of the next day. Moreover, this technique allows obtaining hourly data that may be used for building models. As a result, this solar radiation prediction model may help to implement model-based controller as Model Predictive Control.

Keywords: anticipatory control, model predictive control, solar radiation forecast, thermal storage

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

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Authors: Sima Aznavi, Poria Fajri, Hanif Livani

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Smart homes are small energy systems which may be equipped with renewable energy sources, storage devices, and loads. Energy management strategy plays a main role in the efficient operation of smart homes. Effective energy scheduling of the renewable energy sources and storage devices guarantees efficient energy management in households while reducing the energy imports from the grid. Nevertheless, despite such strategies, independently day ahead energy schedules for multiple households can cause undesired effects such as high power exchange with the grid at certain times of the day. Therefore, the interactions between multiple smart home day ahead energy projections is a challenging issue in a smart grid system and if not managed appropriately, the imported energy from the power network can impose additional burden on the distribution grid. In this paper, a central energy management strategy for a network consisting of multiple households each equipped with renewable energy sources, storage devices, and Plug-in Electric Vehicles (PEV) is proposed. The decision-making strategy alongside the smart home energy management system, minimizes the energy purchase cost of the end users, while at the same time reducing the stress on the utility grid. In this approach, the smart home energy management system determines different operating scenarios based on the forecasted household daily load and the components connected to the household with the objective of minimizing the end user overall cost. Then, selected projections for each household that are within the same cost range are sent to the central decision-making system. The central controller then organizes the schedules to reduce the overall peak to average ratio of the total imported energy from the grid. To validate this approach simulations are carried out for a network of five smart homes with different load requirements and the results confirm that by applying the proposed central energy management strategy, the overall power demand from the grid can be significantly flattened. This is an effective approach to alleviate the stress on the network by distributing its energy to a network of multiple households over a 24- hour period.

Keywords: energy management, renewable energy sources, smart grid, smart home

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Authors: P. Srisuruk, P. Narot

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The purpose of this research were 1) to develop the curriculum and instructional approach that are suitable for children with autism, attention deficit hyperactivity disorder and learning disability as well as to arrange the instructional approach that can be integrated into inclusive classroom 2) to increase the competency of the children in these group. The research processes were to a) study related documents, b) arrange workshops to clarify fundamental issues in developing core curriculum among the researchers and experts in curriculum development, c) arrange workshops to develop the curriculum, submit it to the experts for criticism and editing, d) implement the instructional approach to examine its effectiveness, e) select the schools to participate in the project and arrange training programs for teachers in the selected school, f) implement the instruction approach in the selected schools in different regions. The research results were 1) the core curriculum to enhance the competency of children with autism, attention deficit hyperactivity disorder and learning disability , and to be used as a guideline for teachers, and these group of children in order to arrange classrooms for students with special needs to study with normal students, 2) teaching and learning methods arranged for students with autism, attention deficit, hyperactivity disorder and learning disability to study with normal students can be used as a framework for writing plans to help students with parallel problems by developing teaching materials as part of the instructional approach. However, the details of how to help the students in each skill or content differ according to the demand of development as well as the problems of individual students or group of students. Furthermore; it was found that most of target teacher could implement the instructional approach based on the guideline model developed by the research team. School in each region does not have much difference in their implementation. The good point of the developed instructional model is that teacher can construct a parallel lesson plan. So teacher did not fell that they have to do extra work it was also shown that students in regular classroom enjoyed studying with the developed instructional model as well.

Keywords: instructional approach, autism, attention deficit hyperactivity disorder, learning disability

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Authors: Vardan Melkonyan, Arman Azizyan, Astghik Boyajyan

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Virtual reality (VR) exposure therapy is a form of cognitive-behavioral therapy that uses immersive virtual environments to expose individuals to the feared stimuli or situations that trigger their phobia. VR exposure therapy has become an increasingly popular treatment for phobias, including fear of heights, public speaking, and flying, due to its ability to provide a controlled and safe environment for individuals to confront their fears while also allowing therapists to tailor the virtual exposure to the specific needs and goals of each individual. It is also a cost-effective and accessible treatment option, as it can be delivered remotely and does not require the use of drugs. Overall, VR exposure therapy has the potential to be a valuable tool for therapists in the treatment of phobias. But current methods may be improved by incorporating advanced technology such as photorealistic VR environments, tactile feedback suits, and responsive systems. The aim of this study was to identify the most effective approach for enhancing VR exposure therapy for the treatment of phobias. Photorealistic VR environments and stimuli can greatly enhance the effectiveness of VR exposure therapy for the treatment of phobias. By creating immersive, realistic virtual environments that closely mimic the real-life situations that trigger phobia responses, patients are able to more fully engage in the therapeutic process and confront their fears in a controlled and safe manner. This can help to reduce the severity of phobia symptoms and increase treatment outcomes. The use of tactile feedback suits and responsive systems can further enhance the VR exposure therapy experience by adding a physical element to the virtual environment. These suits, which can mimic the sensations of touch, pressure, and movement, allow patients to fully immerse themselves in the virtual world and feel as if they are physically present in the situation. This can help to increase the realism of the virtual environment and make it more effective in reducing phobia symptoms. Additionally, responsive systems can be used to trigger specific events or responses within the virtual environment based on the patient's actions, providing a more interactive and personalized treatment experience. A comprehensive literature review was conducted, including studies on VR exposure therapy for phobias and the use of advanced technology to enhance the therapy. Results indicate that incorporating these enhancements may significantly increase the effectiveness of VR exposure therapy for phobias. Further research is needed to fully understand the potential of these enhancements and to determine the optimal combination and implementation.

Keywords: virtual reality, mental health, phobias, fears, treatment, photorealistic, immersive, phobia

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Authors: Hanania Nasan Shokry Abdelmasih

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The discipline of regulation is pretty complex and has its own terminology. other than written legal guidelines, there's also dwelling regulation, which refers to prison exercise. primary legal rules purpose at the happiness of individuals in social existence and feature different characteristics in unique branches including public or non-public regulation. on the other hand, law is a countrywide phenomenon. The law of 1 state and the legal device implemented at the territory of another state can be completely exceptional. individuals who are professionals in a specific discipline of regulation in a single united states may have inadequate know-how within the regulation of every other united states. today, similarly to the neighborhood nature of regulation, worldwide and even supranational regulation rules are implemented as a way to defend basic human values and make sure the protection of human rights around the sector. systems that offer algorithmic answers to prison problems using synthetic intelligence (AI) gear will perhaps serve to produce very meaningful consequences in phrases of human rights. but, algorithms to be used need to no longer be evolved with the aid of only pc professionals, however additionally want the contribution of folks who are familiar with law, values, judicial choices, and even the social and political culture of the society to which it'll provide answers. otherwise, even supposing the set of rules works perfectly, it may not be well suited with the values of the society in which it is applied. The present day traits involving using AI techniques in legal systems suggest that artificial law will come to be a brand new subject within the area of law.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Adeshina Francis Akindutre, Adebolarin Adekanle

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Sexual assault is often considered as the most extreme form of violence that degrades and humiliates women in society. It is a widespread public health and psychological problem in Nigeria. Criminologically, sexual assaults have been considered as one of the several violent crimes targeted specifically at women and perpetrated by men. This paper attempts to examine the types of sexual assaults in Nigeria, the strategies used by the offenders, the causes, the psychological effects on the victims and the possible solutions of sexual assaults. This work also, examines the law prohibiting sexual assault in Nigeria. The authors made use of three theories: the victim precipitation approach, the feminist approach, and the psychological approach which explain why sexual assault takes place in society. Finally, it takes the Stockholm Syndrome into consideration (the treatment of victims).

Keywords: feminist, victims, offenders, psychological, sexual assault, Stockholm Syndrome

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Authors: Edward Schwalb, Matthias Dehmer, Michael Schlenkrich, Farzaneh Taslimi, Ketron Mitchell-Wynne, Horen Kuecuekyan

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We present a generalized feature extraction approach for supporting Machine Learning (ML) algorithms which perform tasks similar to Finite-Element Analysis (FEA). We report results for estimating the Head Injury Categorization (HIC) of vehicle engine compartments across various impact scenarios. Our experiments demonstrate that models learned using features derived with a simple discretization approach provide a reasonable approximation of a full simulation. We observe that Decision Trees could be as effective as Neural Networks for the HIC task. The simplicity and performance of the learned Decision Trees could offer a trade-off of a multiple order of magnitude increase in speed and cost improvement over full simulation for a reasonable approximation. When used as a complement to full simulation, the approach enables rapid approximate feedback to engineering teams before submission for full analysis. The approach produces mesh independent features and is further agnostic of the assembly structure.

Keywords: mechanical design validation, FEA, supervised decision tree, convolutional neural network.

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Authors: T. Delval, C. Sauvage, Q. Jullien, R. Viano, T. Diallo, B. Collignan, G. Picinbono

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In the context of the international spread of COVID-19, the Centre Scientifique et Technique du Bâtiment (CSTB) has led a joint research with the French government authorities Hauts-de-Seine department, to analyse the risk in school spaces according to their configuration, ventilation system and spatial segmentation strategy. This paper describes the main results of this joint research. A multidisciplinary team involving experts in indoor air quality/ventilation, pedestrian movements and IT domains was established to develop a COVID risk analysis tool based on Building Information Model. The work started with specific analysis on two pilot schools in order to provide for the local administration specifications to minimize the spread of the virus. Different recommendations were published to optimize/validate the use of ventilation systems and the strategy of student occupancy and student flow segmentation within the building. This COVID expertise has been digitized in order to manage a quick risk analysis on the entire building that could be used by the public administration through an easy user interface implemented in a free BIM Management software. One of the most interesting results is to enable a dynamic comparison of different ventilation system scenarios and space occupation strategy inside the BIM model. This concurrent engineering approach provides users with the optimal solution according to both ventilation and pedestrian flow expertise.

Keywords: BIM, knowledge management, system expert, risk management, indoor ventilation, pedestrian movement, integrated design

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Authors: Maria Ahmadnezhad, Mohammad Reza Soltanpour

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Electrohydraulic servo systems have been used in industry in a wide number of applications. Its dynamics ‎are highly nonlinear and also have large extent of model uncertainties and external disturbances. In this ‎thesis, a robust back-stepping control (RBSC) scheme is proposed to overcome the problem of ‎disturbances and system uncertainties effectively and to improve the tracking performance of EHS ‎systems. In order to implement the proposed control scheme, the system uncertainties in EHS systems ‎are considered as total leakage coefficient and effective oil volume. In addition, in order to obtain the ‎virtual controls for stabilizing system, the update rule for the system uncertainty term is induced by the ‎Lyapunov control function (LCF). To verify the performance and robustness of the proposed control ‎system, computer simulation of the proposed control system using Matlab/Simulink Software is ‎executed. From the computer simulation, it was found that the RBSC system produces the desired ‎tracking performance and has robustness to the disturbances and system uncertainties of EHS systems.‎

Keywords: electro hydraulic servo system, back-stepping control, robust back-stepping control, Lyapunov redesign

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Authors: M. Cabrera, W. Tizani, J. Ninic, F. Wang

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Open section beam to concrete-filled tubular column structures has been increasingly utilized in construction over the past few decades due to their enhanced structural performance, as well as economic and architectural advantages. However, the use of this configuration in construction is limited due to the difficulties in connecting the structural members as there is no access to the inner part of the tube to install standard bolts. Blind-bolted systems are a relatively new approach to overcome this limitation as they only require access to one side of the tubular section to tighten the bolt. The performance of these connections in concrete-filled steel tubular sections remains uncharacterized due to the complex interactions between concrete, bolt, and steel section. Over the last years, research in structural performance has moved to a more sophisticated and efficient approach consisting of machine learning algorithms to generate metamodels. This method reduces the need for developing complex, and computationally expensive finite element models, optimizing the search for desirable design variables. Metamodels generated by a data fusion approach use numerical and experimental results by combining multiple models to capture the dependency between the simulation design variables and connection performance, learning the relations between different design parameters and predicting a given output. Fully characterizing this connection will transform high-rise and multistorey construction by means of the introduction of design guidance for moment-resisting blind-bolted connections, which is currently unavailable. This paper presents a review of the steps taken to develop metamodels generated by means of artificial neural network algorithms which predict the connection stress and stiffness based on the design parameters when using Extended Hollo-Bolt blind bolts. It also provides consideration of the failure modes and mechanisms that contribute to the deformability as well as the feasibility of achieving blind-bolted rigid connections when using the blind fastener.

Keywords: blind-bolted connections, concrete-filled tubular structures, finite element analysis, metamodeling

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Authors: Hashem Shariatmadar, Mozhgansadat Momtazdargahi

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The purpose of control structure against earthquake is to dissipate earthquake input energy to the structure and reduce the plastic deformation of structural members. There are different methods for control structure against earthquake to reduce the structure response that they are active, semi-active, inactive and hybrid. In this paper two different combined control systems are used first system comprises base isolator and multi tuned mass dampers (BI & MTMD) and another combination is hybrid base isolator and multi tuned mass dampers (HBI & MTMD) for controlling an eight story isolated benchmark steel structure. Active control force of hybrid isolator is estimated by fuzzy logic algorithms. The influences of the combined systems on the responses of the benchmark structure under the two near-field earthquake (Newhall & Elcentro) are evaluated by nonlinear dynamic time history analysis. Applications of combined control systems consisting of passive or active systems installed in parallel to base-isolation bearings have the capability of reducing response quantities of base-isolated (relative and absolute displacement) structures significantly. Therefore in design and control of irregular isolated structures using the proposed control systems, structural demands (relative and absolute displacement and etc.) in each direction must be considered separately.

Keywords: base-isolated benchmark structure, multi-tuned mass dampers, hybrid isolators, near-field earthquake, fuzzy algorithm

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Authors: Kldiashvili Ekaterina, Burduli Archil, Ghortlishvili Gocha

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Introduction – Medical information system (MIS) is at the heart of information technology (IT) implementation policies in healthcare systems around the world. Different architecture and application models of MIS are developed. Despite of obvious advantages and benefits, application of MIS in everyday practice is slow. Objective - On the background of analysis of the existing models of MIS in Georgia has been created a multi-user web-based approach. This presentation will present the architecture of the system and its application for image based second opinion consultations. Methods – The MIS has been created with .Net technology and SQL database architecture. It realizes local (intranet) and remote (internet) access to the system and management of databases. The MIS is fully operational approach, which is successfully used for medical data registration and management as well as for creation, editing and maintenance of the electronic medical records (EMR). Five hundred Georgian language electronic medical records from the cervical screening activity illustrated by images were selected for second opinion consultations. Results – The primary goal of the MIS is patient management. However, the system can be successfully applied for image based second opinion consultations. Discussion – The ideal of healthcare in the information age must be to create a situation where healthcare professionals spend more time creating knowledge from medical information and less time managing medical information. The application of easily available and adaptable technology and improvement of the infrastructure conditions is the basis for eHealth applications. Conclusion - The MIS is perspective and actual technology solution. It can be successfully and effectively used for image based second opinion consultations.

Keywords: digital images, medical information system, second opinion consultations, electronic medical record

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Authors: Sharjeel Aslam, Vassil Vassilev, Karim Ouazzane

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Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web.

Keywords: distributed ontologies, parallel querying, semantic indexing, shared vocabulary, SPARQL

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Authors: Carlos E. Rodríguez-Rodríguez, Karla Ruiz-Hidalgo, Kattia Madrigal-Zúñiga, Juan Salvador Chin-Pampillo, Mario Masís-Mora, Elizabeth Carazo-Rojas

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One of the main causes of contamination linked to agricultural activities is the spillage and disposal of pesticides, especially during the loading, mixing or cleaning of agricultural spraying equipment. One improvement in the handling of pesticides is the use of biopurification systems (BPS), simple and cheap degradation devices where the pesticides are biologically degraded at accelerated rates. The biologically active core of BPS is the biomixture, which is constituted by soil pre-exposed to the target pesticide, a lignocellulosic substrate to promote the activity of ligninolitic fungi and a humic component (peat or compost), mixed at a volumetric proportion of 50:25:25. Considering the known ability of lignocellulosic fungi to degrade a wide range of organic pollutants, and the high amount of lignocellulosic waste used in biomixture preparation, the bioaugmentation of biomixtures with these fungi represents an interesting approach for improving biomixtures. The present work aimed at evaluating the effect of the bioaugmentation of rice husk based biomixtures with the fungus Trametes versicolor in the removal of the insectice/nematicide carbofuran (CFN) and to optimize the composition of the biomixture to obtain the best performance in terms of CFN removal and mineralization, reduction in formation of transformation products and decrease in residual toxicity of the matrix. The evaluation of several lignocellulosic residues (rice husk, wood chips, coconut fiber, sugarcane bagasse or newspaper print) revealed the best colonization by T. versicolor in rice husk. Pre-colonized rice husk was then used in the bioaugmentation of biomixtures also containing soil pre-exposed to CFN and either peat (GTS biomixture) or compost (GCS biomixture). After spiking with 10 mg/kg CBF, the efficiency of the biomixture was evaluated through a multi-component approach that included: monitoring of CBF removal and production of CBF transformation products, mineralization of radioisotopically labeled carbofuran (14C-CBF) and changes in the toxicity of the matrix after the treatment (Daphnia magna acute immobilization test). Estimated half-lives of CBF in the biomixtures were 3.4 d and 8.1 d in GTS and GCS, respectively. The transformation products 3-hydroxycarbofuran and 3-ketocarbofuran were detected at the moment of CFN application, however their concentration continuously disappeared. Mineralization of 14C-CFN was also faster in GTS than GCS. The toxicological evaluation showed a complete toxicity removal in the biomixtures after 48 d of treatment. The composition of the GCS biomixture was optimized using a central composite design and response surface methodology. The design variables were the volumetric content of fungally pre-colonized rice husk and the volumetric ratio compost/soil. According to the response models, maximization of CFN removal and mineralization rate, and minimization in the accumulation of transformation products were obtained with an optimized biomixture of composition 30:43:27 (pre-colonized rice husk:compost:soil), which differs from the 50:25:25 composition commonly employed in BPS. Results suggest that fungal bioaugmentation may enhance the performance of biomixtures in CFN removal. Optimization reveals the importance of assessing new biomixture formulations in order to maximize their performance.

Keywords: bioaugmentation, biopurification systems, degradation, fungi, pesticides, toxicity

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Authors: Evrime Fawzy Ishak Gadelsayed

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This paper presents resource intake accounting as an inventive manner to cope with control accounting, which concentrates on administrators as the crucial customers of the information and offers satisfactory statistics of conventional control accounting. This machine underscores that the association's asset motivates prices; as a consequence, in costing frameworks, the emphasis ought to be on assets and their usage. Resource consumption accounting consolidates two costing methodologies, action-based totally and the German cost accounting approach called GPK. This methodology, however, is a danger to managers when making the management accounting undertaking operational. The motive for this article is to clarify the concept of resource intake accounting, its elements and highlights and use of this approach in associations. Inside the first area, we present useful resource consumption accounting, the basis, reasons for its improvement, and the issues that are faced beyond costing frameworks. At that point, we deliver the requirements and presumptions of this approach; ultimately, we depict the execution of this approach in associations and its preferences over other costing techniques.

Keywords: financial statement fraud, forensic accounting, fraud prevention and detection, auditing, audit expectation gap, corporate governance resource consumption accounting, management accounting, action based method, German cost accounting method

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Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

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Authors: S. Gokul Prassad, S. Aakash, K. Malar Mohan

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In the process to cope with the challenges faced by the automobile industry in providing ride comfort, the electronics and control systems play a vital role. The control systems in an automobile monitor various parameters, controls the performances of the systems, thereby providing better handling characteristics. The automobile suspension system is one of the main systems that ensure the safety, stability and comfort of the passengers. The system is solely responsible for the isolation of the entire automobile from harmful road vibrations. Thus, integration of the control systems in the automobile suspension system would enhance its performance. The diverse road conditions of India demand the need of an efficient suspension system which can provide optimum ride comfort in all road conditions. For any passenger vehicle, the design of the suspension system plays a very important role in assuring the ride comfort and handling characteristics. In recent years, the air suspension system is preferred over the conventional suspension systems to ensure ride comfort. In this article, the ride comfort of the adaptive suspension system is compared with that of the passive suspension system. The schema is created in MATLAB/Simulink environment. The system is controlled by a proportional integral differential controller. Tuning of the controller was done with the Particle Swarm Optimization (PSO) algorithm, since it suited the problem best. Ziegler-Nichols and Modified Ziegler-Nichols tuning methods were also tried and compared. Both the static responses and dynamic responses of the systems were calculated. Various random road profiles as per ISO 8608 standard are modelled in the MATLAB environment and their responses plotted. Open-loop and closed loop responses of the random roads, various bumps and pot holes are also plotted. The simulation results of the proposed design are compared with the available passive suspension system. The obtained results show that the proposed adaptive suspension system is efficient in controlling the maximum over shoot and the settling time of the system is reduced enormously.

Keywords: automobile suspension, MATLAB, control system, PID, PSO

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Authors: Ala Hanetite

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The purpose of this article is to present, by means of a model, the determinants of organisational culture which influence creativity and innovation. A literature study showed that a model, based on the open systems theory and the work of Schein, can offer a holistic approach in describing organisational culture. The relationship between creativity, innovation and culture is discussed in this context. Against the background of this model, the determinants of organisational culture were identified. The determinants are strategy, structure, support mechanisms, behaviour that encourages innovation, and open communication. The influence of each determinant on creativity and innovation is discussed. Values, norms and beliefs that play a role in creativity and innovation can either support or inhibit creativity and innovation depending on how they influence individual and group behaviour. This is also explained in the article.

Keywords: attitudes, creativity, innovation, organisational culture

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