Search results for: system models

Authors: Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Strahinja Z. Kovačević

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Benzimidazoles are a group of compounds with significant antibacterial, antifungal and anticancer activity. The studied compounds consist of the main benzimidazole structure with different combinations of substituens. This study is based on the two-dimensional and three-dimensional molecular modeling and calculation of molecular descriptors (physicochemical and lipophilicity descriptors) of structurally diverse benzimidazoles. Molecular modeling was carried out by using ChemBio3D Ultra version 14.0 software. The obtained 3D models were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. The obtained set of molecular descriptors was used in principal component analysis (PCA) of possible similarities and dissimilarities among the studied derivatives. After the molecular modeling, the quantitative structure-property relationship (QSPR) analysis was applied in order to get the mathematical models which can be used in prediction of pKb values of structurally similar benzimidazoles. The obtained models are based on statistically valid multiple linear regression (MLR) equations. The calculated cross-validation parameters indicate the high prediction ability of the established QSPR models. This study is financially supported by COST action CM1306 and the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina.

Keywords: benzimidazoles, chemometrics, molecular modeling, molecular descriptors, QSPR

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Authors: Amr Mohamadien, Ali Imanpour, Sylvester Agbo, Nader Yoosef-Ghodsi, Samer Adeeb

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The fracture toughness resistance curve (e.g., J-R curve and crack tip opening displacement (CTOD) or δ-R curve) is important in facilitating strain-based design and integrity assessment of oil and gas pipelines. This paper aims to present laboratory experimental data to characterize the fracture behavior of pipeline steel. The influential parameters associated with the fracture of API 5L X52 pipeline steel, including different initial crack sizes, were experimentally investigated for a single notch edge bend (SENB). A total of 9 small-scale specimens with different crack length to specimen depth ratios were conducted and tested using single edge notch bending (SENB). ASTM E1820 and BS7448 provide testing procedures to construct the fracture resistance curve (Load-CTOD, CTOD-R, or J-R) from test results. However, these procedures are limited by standard specimens’ dimensions, displacement gauges, and calibration curves. To overcome these limitations, this paper presents the use of small-scale specimens and a 3D-digital image correlation (DIC) system to extract the parameters required for fracture toughness estimation. Fracture resistance curve parameters in terms of crack mouth open displacement (CMOD), crack tip opening displacement (CTOD), and crack growth length (∆a) were carried out from test results by utilizing the DIC system, and an improved regression fitting resistance function (CTOD Vs. crack growth), or (J-integral Vs. crack growth) that is dependent on a variety of initial crack sizes was constructed and presented. The obtained results were compared to the available results of the classical physical measurement techniques, and acceptable matchings were observed. Moreover, a case study was implemented to estimate the maximum strain value that initiates the stable crack growth. This might be of interest to developing more accurate strain-based damage models. The results of laboratory testing in this study offer a valuable database to develop and validate damage models that are able to predict crack propagation of pipeline steel, accounting for the influential parameters associated with fracture toughness.

Keywords: fracture toughness, crack propagation in pipeline steels, CTOD-R, strain-based damage model

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Authors: Ahmad Shah Irshad, Naqibullah Kargar, Wais Samadi

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Building an integrated photovoltaic (BIPV) system is a new and modern technique for solar energy production in Kandahar. Due to its location, Kandahar has abundant sources of solar energy. People use both monocrystalline and polycrystalline silicon solar PV modules for the grid-connected solar PV system, and they don’t know which technology performs better for the BIPV system. This paper analyses the parameters described by IEC61724, “Photovoltaic System Performance Monitoring Guidelines for Measurement, Data Exchange and Analysis,” to evaluate which technology shows better performance for the BIPV system. The monocrystalline silicon BIPV system has a 3.1% higher array yield than the polycrystalline silicon BIPV system. The final yield is 0.2%, somewhat higher for monocrystalline silicon than polycrystalline silicon. Monocrystalline silicon has 0.2% and 4.5% greater yearly yield factor and capacity factors than polycrystalline silicon, respectively. Monocrystalline silicon shows 0.3% better performance than polycrystalline silicon. With 1.7% reduction and 0.4% addition in collection losses and useful energy produced, respectively, monocrystalline silicon solar PV system shows good performance than polycrystalline silicon solar PV system. But system losses are the same for both technologies. The monocrystalline silicon BIPV system injects 0.2% more energy into the grid than the polycrystalline silicon BIPV system.

Keywords: photovoltaic technologies, performance analysis, solar energy, solar irradiance, performance ratio

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: M. Jerold, V. Sivasubramanian

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In this study, nano zero valent iron Sargassum swartzii (nZVI-SS) biocomposite a marine algal based biosorbent was used for the removal of simulated crystal violet (CV) in batch and continuous fixed bed operation. The Box-Behnen design (BBD) experimental results revealed the biosoprtion was maximum at pH 7.5, biosorbent dosage 0.1 g/L and initial CV concentration of 100 mg/L. The effect of various column parameters like bed depth (3, 6 and 9 cm), flow rate (5, 10 and 15 mL/min) and influent CV concentration (5, 10 and 15 mg/L) were investigated. The exhaustion time increased with increase of bed depth, influent CV concentration and decrease of flow rate. Adam-Bohart, Thomas and Yoon-Nelson models were used to predict the breakthrough curve and to evaluate the model parameters. Out of these models, Thomas and Yoon-Nelson models well described the experimental data. Therefore, the result implies that nZVI-SS biocomposite is a cheap and most promising biosorbent for the removal of CV from wastewater.

Keywords: algae, biosorption, zero-valent, dye, wastewater

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Authors: Noura Al-Ajmi, Mohammed A. Almulla

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With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit.

Keywords: headache diagnosis system, prescription recommender system, expert system, backward rule-based system

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Authors: Ramanpreet Kaur, Upasana Sharma

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The purpose of this paper is to address the challenges facing the water supply for the Machine Learning Database (MLDB) system at the piston foundry plant. In the MLDB system, one main unit, i.e., robotic, is connected by two sub-units. The functioning of the system depends on the robotic and water supply. Lack of water supply causes system failure. The system operates at full capacity with the help of two sub-units. If one sub-unit fails, the system runs at a low capacity. Reliability modeling is performed using semi-Markov processes and regenerative point techniques. Several system effects such as mean time to system failure, availability at full capacity, availability at reduced capacity, busy period for repair and expected number of visits have been achieved. Benefits have been analyzed. The graphical study is designed for a specific case using programming in C++ and MS Excel.

Keywords: MLDB system, robotic, semi-Markov process, regenerative point technique

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Authors: Wenhao Wang, Xinzhi Xu, Limin Feng, Wei Cong

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This paper comprehensively investigates current development status of global power grids and fossil energy pipelines (oil and natural gas), proposes a standard visual platform of global power and fossil energy based on Geographic Information System (GIS) technology. In this visual platform, a series of systematic visual models is proposed with global spatial data, systematic energy and power parameters. Under this visual platform, the current Global Power Grids Map and Global Fossil Energy Pipelines Map are plotted within more than 140 countries and regions across the world. Using the multi-scale fusion data processing and modeling methods, the world’s global fossil energy pipelines and power grids information system basic database is established, which provides important data supporting global fossil energy and electricity research. Finally, through the systematic and comparative study of global fossil energy pipelines and global power grids, the general status of global fossil energy and electricity development are reviewed, and energy transition in key areas are evaluated and analyzed. Through the comparison analysis of fossil energy and clean energy, the direction of relevant research is pointed out for clean development and energy transition.

Keywords: energy transition, geographic information system, fossil energy, power systems

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Authors: Bo-Wei Song, Yun-Jung Lee

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This paper presents an intention interface and controller for a wire-driven heavy object lifting system that assists the operator with moving a heavy object. The handler is designed to allow a comfortable working posture for the operator. Plus, as a human assistive system, the operator is involved in the control loop, where a fuzzy control system is used to consider the human control characteristics. The effectiveness and performance of the proposed system are proved by experiments.

Keywords: fuzzy controller, handler design, heavy object lifting system, human-assistive device, human-in-the-loop system

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Authors: Burak Yildirim, Muhsin Tunay Gençoğlu

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A microgrid (MG) is a small power grid composed of localized medium or low level power generation, storage systems, and loads. In this paper, the effects of a MG on power systems voltage stability are shown. The MG model, designed to demonstrate the effects of the MG, was applied to the IEEE 14 bus power system which is widely used in power system stability studies. Eigenvalue and modal analysis methods were used in simulation studies. In the study results, it is seen that MGs affect system voltage stability positively by increasing system voltage instability limit value for buses of a power system in which MG are placed.

Keywords: eigenvalue analysis, microgrid, modal analysis, voltage stability

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Authors: Syed Auon Raza, Tahir Mahmood, Syed Basit Ali Bukhari

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In power system properly coordinated protection scheme is designed to make sure that only the faulty part of the system will be isolated when abnormal operating condition of the system will reach. The complexity of the system as well as the increased user demand and the deregulated environment enforce the utilities to improve system reliability by using a properly coordinated protection scheme. This paper presents overview of over current relay coordination techniques. Different techniques such as Deterministic Techniques, Meta Heuristic Optimization techniques, Hybrid Optimization Techniques, and Trial and Error Optimization Techniques have been reviewed in terms of method of their implementation, operation modes, nature of distribution system, and finally their advantages as well as the disadvantages.

Keywords: distribution system, relay coordination, optimization, Plug Setting Multiplier (PSM)

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Ayman M. Mansour

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In this paper, we propose a multi-agent intelligent system that is used for monitoring the health conditions of elderly people. Monitoring the health condition of elderly people is a complex problem that involves different medical units and requires continuous monitoring. Such expert system is highly needed in rural areas because of inadequate number of available specialized physicians or nurses. Such monitoring must have autonomous interactions between these medical units in order to be effective. A multi-agent system is formed by a community of agents that exchange information and proactively help one another to achieve the goal of elderly monitoring. The agents in the developed system are equipped with intelligent decision maker that arms them with the rule-based reasoning capability that can assist the physicians in making decisions regarding the medical condition of elderly people.

Keywords: fuzzy logic, inference system, monitoring system, multi-agent system

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Authors: Misho Matsankov, Stoyan Petrov

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The paper presents the methodology and the obtained mathematical models for determining the value of the grounding resistance of a disconnected conductor in a three-phase power line, for which the contact voltage is safe, by taking into account the potentials, induced by the non-disconnected phase conductors. The mathematical models have been obtained by implementing the experimental design techniques.

Keywords: contact voltage, experimental design, induced voltage, safety

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Authors: Nathaniel Deboever, Malcolm Breeze, Adrian Sheen

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Basic surgical and suturing techniques are a fundamental requirement for all doctors. In order to gain confidence and competence, doctors in training need to obtain sufficient teaching and just as importantly: practice. Young doctors with an apt level of expertise on these simple surgical skills, which are often used in the Emergency Department, can help alleviate some pressure during a busy evening. Unfortunately, learning these skills can be quite difficult during medical school or even during junior doctor years. The aim of this project was to adequately train medical students attending University of Sydney’s Nepean Clinical School through a series of workshops highlighting practical skills, with hopes to further extend this program to junior doctors in the hospital. The sessions instructed basic skills via tutorials, demonstrations, and lastly, the sessions cemented these proficiencies with practical sessions. During such an endeavor, it is fundamental to employ models that appropriately resemble what students will encounter in the clinical setting. The sustainability of workshops is similarly important to the continuity of such a program. To address both these challenges, the authors have developed models including suturing platforms, knot tying, and vessel ligation stations, as well as a shave and punch biopsy models and ophthalmologic foreign body device. The unique aspect of this work is that we utilized hands-on teaching sessions, to address a gap in doctors-in-training and junior doctor curriculum. Presented to you through this poster are our approaches to creating models that do not employ animal products and therefore do not necessitate particular facilities or discarding requirements. Covering numerous skills that would be beneficial to all young doctors, these models are easily replicable and affordable. This exciting work allows for countless sessions at low cost, providing enough practice for students to perform these skills confidently as it has been shown through attendee questionnaires.

Keywords: medical education, surgical models, surgical simulation, surgical skills education

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Authors: Pimploi Tirastittam, Phutthiwat Waiyawuththanapoom, Sawanath Treesathon

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As there are several countries which import agriculture product from Thailand, those countries demand Thailand to establish the traceability system. The traceability system is the tool to reduce the risk in the supply chain in a very effective way as it will help the stakeholder in the supply chain to identify the defect point which will reduce the cost of operation in the supply chain. This research is aimed to design the traceability system for Tubtimjun roseapple for exporting to China, and it is the qualitative research. The data was collected from the expert in the tuntimjun roseapple and fruit exporting industry, and the data was used to design the traceability system. The design of the tubtimjun roseapple traceability system was followed the theory of supply chain which starts from the upstream of the supply chain to the downstream of the supply chain to support the process and condition of the exporting which included the database designing, system architecture, user interface design and information technology of the traceability system.

Keywords: design information, technology system, traceability, tubtimjun roseapple

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Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

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Authors: Saud Hassan

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This paper shows simulation for the prediction of the flow around the backlight angle of the passenger vehicle. The CFD simulations are carried out on different car models. The Ahmed model “bluff body” used as the stander model to study aerodynamics of the backlight angle. This paper described the airflow over the different car models with different backlight angles and also on the Ahmed model to determine the trailing vortices with the varying backlight angle of a passenger vehicle body. The CFD simulation is carried out with the Ahmed body which has simplified car model mainly used in automotive industry to investigate the flow over the car body surface. The main goal of the simulation is to study the behavior of trailing vortices of these models. In this paper the air flow over the slant angle of 0,5o, 12.5o, 20o, 30o, 40o are considered. As investigating on the rear backlight angle two dimensional flows occurred at the rear slant, on the other hand when the slant angle is 30o the flow become three dimensional. Above this angle sudden drop occurred in drag.

Keywords: aerodynamics, Ahemd vehicle , backlight angle, finite element method

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Authors: Nauman Idrees

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Modeling and simulation during the conceptual design phase are the most effective means of system testing resulting in time and cost savings as compared to the testing of hardware prototypes, which are mostly not available during the conceptual design phase. This paper uses the model-based design (MBD) method in designing the fly-by-wire flight controls system of a fighter aircraft using Simulink. The process begins with system definition and layout where modeling requirements and system components were identified, followed by hierarchical system layout to identify the sequence of operation and interfaces of system with external environment as well as the internal interface between the components. In the second step, each component within the system architecture was modeled along with its physical and functional behavior. Finally, all modeled components were combined to form the fly-by-wire flight controls system of a fighter aircraft as per system architecture developed. The system model developed using this method can be simulated using any simulation software to ensure that desired requirements are met even without the development of a physical prototype resulting in time and cost savings.

Keywords: fly-by-wire, flight controls system, model based design, Simulink

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Nivedha Rajaram

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Hybrid Quantum solvers have been given prime focus in recent days by computation problem-solving domain industrial applications. D’Wave Quantum Computers are one such paragon of systems built using quantum annealing mechanism. Discrete Quadratic Models is a hybrid quantum computing model class supplied by D’Wave Ocean SDK - a real-time software platform for hybrid quantum solvers. These hybrid quantum computing modellers can be employed to solve classic problems. One such problem that we consider in this paper is finding a network connectivity knowledge hub in a huge network of systems. Using this quantum solver, we try to find out the prime system hub, which acts as a supreme connection point for the set of connected computers in a large network. This paper establishes an innovative problem approach to generate a connectivity system hub plot for a set of systems using DWave ocean SDK hybrid quantum solvers.

Keywords: quantum computing, hybrid quantum solver, DWave annealing, network knowledge graph

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Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

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Authors: Mohd Aftar Abu Bakar, Noratiqah Mohd Ariff, Abdul Aziz Jemain

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Storm Event Analysis (SEA) provides a method to define rainfalls events as storms where each storm has its own amount and duration. By modelling daily probability of different types of storms, the onset, offset and cycle of rainfall seasons can be determined and investigated. Furthermore, researchers from the field of meteorology will be able to study the dynamical characteristics of rainfalls and make predictions for future reference. In this study, four categories of storms; short, intermediate, long and very long storms; are introduced based on the length of storm duration. Daily probability models of storms are built for these four categories of storms in Peninsular Malaysia. The models are constructed by using Bernoulli distribution and by applying linear regression on the first Fourier harmonic equation. From the models obtained, it is found that daily probability of storms at the Eastern part of Peninsular Malaysia shows a unimodal pattern with high probability of rain beginning at the end of the year and lasting until early the next year. This is very likely due to the Northeast monsoon season which occurs from November to March every year. Meanwhile, short and intermediate storms at other regions of Peninsular Malaysia experience a bimodal cycle due to the two inter-monsoon seasons. Overall, these models indicate that Peninsular Malaysia can be divided into four distinct regions based on the daily pattern for the probability of various storm events.

Keywords: daily probability model, monsoon seasons, regions, storm events

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Authors: Liyu Adhi Kasari Sulung

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Poverty has been a major problem in Indonesia. Islamic micro finance (IMF) named Baitul Maal Wat Tamwil (Bmt) plays a prominent role to eradicate this. Indonesia as the biggest muslim country has many successful applied products such as worldwide adopt group-based lending approach, flexible financing for farmers, and gold pawning. The Problems related to these models are operation risk management and internal control system (ICS). A proper ICS will help an organization in preventing the occurrence of bad financing through detecting error and irregularities in its operation. This study aims to seek a proper risk management scheme of credit system in Bmt and internal control system’s rank for every stage. Risk management variables are obtained at the first In-Depth Interview (IDI) and Focus Group Discussion (FGD) with Shariah supervisory boards, boards of directors, and operational managers. Survey was conducted covering nationwide data; West Java, South Sulawesi, and West Nusa Tenggara. Moreover, Content analysis is employed to build the relationship among these variables. Research Findings shows that risk management Characteristics in Indonesia involves ex ante, credit process, and ex post strategies to deal with risk in credit system. Ex-ante control consists of Shariah compliance, survey, group leader reference, and islamic forming orientation. Then, credit process involves saving, collateral, joint liability, loan repayment, and credit installment controlling. Finally, ex-post control includes shariah evaluation, credit evaluation, grace period and low installment provisions. In addition, internal control order sort three stages by its priority; Credit process as first rank, then ex-post control as second, and ex ante control as the last rank.

Keywords: internal control system, islamic micro finance, poverty, risk management

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Authors: Sanjay M. Gulhane, Athar Ravish Khan, Umesh W. Kaware

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Orthogonal frequency and code division multiplexing (OFCDM) has received large attention as a modulation scheme to realize high data rate transmission. Multiband (MB) Orthogonal frequency division multiplexing (OFDM) Ultra Wide Band (UWB) system become promising technique for high data rate due to its large number of advantage over Singleband (UWB) system, but it suffer from coherent frequency diversity problem. In this paper we have proposed MB-OFCDM UWB system, in which two-dimensional (2D) spreading (time and frequency domain spreading), has been introduced, combining OFDM with 2D spreading, proposed system can provide frequency diversity. This paper presents the basic structure and main functions of the MB-OFCDM system, and evaluates the bit error rate BER performance of MB-OFDM and MB-OFCDM system under UWB indoor multi-path channel model. It is observe that BER curve of MB-OFCDM UWB improve its performance by 2dB as compare to MB-OFDM UWB system.

Keywords: MB-OFDM UWB system, MB-OFCDM UWB system, UWB IEEE channel model, BER

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Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

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Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

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Authors: Jing Jiang, Yuguang Gao, Yang Yu

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Facing increasingly challenging insurance payment pressures, the Chinese healthcare system is undergoing significant transformations, akin to the implementation of DRG payment models by the United States' Medicare. Consequently, there is a pressing need for Chinese hospitals to establish optimizations in departmental operations tailored to the ongoing healthcare payment reforms. This abstract delineates the meticulous construction of a scientifically rigorous and comprehensive index system at the departmental level in China strategically aligned with the evolving landscape of healthcare payment reforms. Methodologically, it integrates key process areas and maturity assessment theories, synthesizing relevant literature and industry standards to construct a robust framework and indicator pool. Employing the Delphi method, consultations with 21 experts were conducted, revealing a collective demonstration of high enthusiasm, authority, and coordination in designing the index system. The resulting model comprises four primary indicators -technical capabilities, cost-effectiveness, operational efficiency, and disciplinary potential- supported by 14 secondary indicators and 23 tertiary indicators with varied coefficient adjustment for department types (platform or surgical). The application of this evaluation system in a Chinese hospital within the northeastern region yielded results aligning seamlessly with the actual operational scenario. In conclusion, the index system comprehensively considers the integrity and effectiveness of structural, process, and outcome indicators and stands as a comprehensive reflection of the collective expertise of the engaged experts, manifesting in a model designed to elevate the operational management of hospital departments. Its strategic alignment with healthcare payment reforms holds practical significance in guiding departmental development positioning, brand cultivation, and talent development.

Keywords: Chinese healthcare system, Delphi method, departmental management, evaluation indicators, hospital operations, weight coefficients

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Authors: Sarah Abdourraziq

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To improve the efficiency of photovoltaic pumping system, more attention has been paid to their setting up. This paper presents an optimal technique to establish an efficient system under different conditions of irradiance and temperature. The state of place should be carefully studied before stage of installation of the over system: local climate, boreholes, soil, crops and water resources. The studied system consists of a PV panel, a DC-DC boost converter, a DC motor-pump, and storage tank. The concepts shown in this paper presents a support for an optimal installation of each solar pump.

Keywords: photovoltaic pumping system, optimal implementation, boost converter, motor-pump

Procedia PDF Downloads 351

Authors: Samar Raffoul, Martin Heywood, Dimitrios Moutaftsis, Michael Rowell

Abstract:

This paper comprises an experimental investigation into the structural performance of cold formed steel (CFS) and timber board composite floor systems. The tests include a series of small-scale pushout tests and full-scale bending tests carried out using a refined loading system to simulate uniformly distributed constant load. The influence of connection details (screw spacing and adhesives) on floor performance was investigated. The results are then compared to predictions from relevant existing models for composite floor systems. The results of this research demonstrate the significant benefits of considering the composite action of the boards in floor design. Depending on connection detail, an increase in flexural stiffness of up to 40% was observed in the floor system, when compared to designing joists individually.

Keywords: cold formed steel joists, composite action, flooring systems, shear connection

Procedia PDF Downloads 130

Authors: Joseph C. Chen

Abstract:

Machine vision system provides automatic inspection to reduce manufacturing costs considerably. However, only a few principles have been found to optimize machine vision system and help it function more accurately in industrial practice. Mostly, there were complicated and impractical design techniques to improve the accuracy of machine vision system. This paper discusses implementing the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) approach to optimize the setup parameters of machine vision system when it is used as a direct measurement technique. This research follows a case study showing how Six Sigma DMAIC methodology has been put into use.

Keywords: DMAIC, machine vision system, process capability, Taguchi Parameter Design

Procedia PDF Downloads 440