Search results for: fuzzy aggregation operator

Authors: Yuan-Lin Chen

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This paper presents an approaching forward collision probability index (AFCPI) for alerting and assisting driver in keeping safety distance to avoid the forward collision accident in highway driving. The time to collision (TTC) and time headway (TH) are used to evaluate the TTC forward collision probability index (TFCPI) and the TH forward collision probability index (HFCPI), respectively. The Mamdani fuzzy inference algorithm is presented combining TFCPI and HFCPI to calculate the approaching collision probability index of the vehicle. The AFCPI is easier to understand for the driver who did not even have any professional knowledge in vehicle professional field. At the same time, the driver’s behavior is taken into account for suiting each driver. For the approaching index, the value 0 is indicating the 0% probability of forward collision, and the values 0.5 and 1 are indicating the 50% and 100% probabilities of forward collision, respectively. The AFCPI is useful and easy-to-understand for alerting driver to avoid the forward collision accidents when driving in highway.

Keywords: approaching index, forward collision probability, time to collision, time headway

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Authors: Bongs Lainjo

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The evolution and escalation of bike-sharing programs (BSP) continue unabated. Since the sixties, many countries have introduced different models and strategies of BSP. These include variations ranging from dockless models to electronic real-time monitoring systems. Reasons for using this BSP include recreation, errands, work, etc. And there is all indication that complex, and more innovative rider-friendly systems are yet to be introduced. The objective of this paper is to analyze current variables established by different operators and streamline them identifying the most compelling ones using analytics. Given the contents of available databases, there is a lack of uniformity and common standard on what is required and what is not. Two factors appear to be common: user type (registered and unregistered, and duration of each trip). This article uses historical data provided by one operator based in the greater Washington, District of Columbia, USA area. Several variables including categorical and continuous data types were screened. Eight out of 18 were considered acceptable and significantly contribute to determining a useful and reliable predictive model. Bike-sharing systems have become popular in recent years all around the world. Although this trend has resulted in many studies on public cycling systems, there have been few previous studies on the factors influencing public bicycle travel behavior. A bike-sharing system is a computer-controlled system in which individuals can borrow bikes for a fee or free for a limited period. This study has identified unprecedented useful, and pragmatic parameters required in improving BSP ridership dynamics.

Keywords: sharing program, historical data, parameters, ridership dynamics, trip duration

Procedia PDF Downloads 138

Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Dylber Qema

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Driven by recent developments in technology and the growing concern about the sustainability and environmental impact of conventional fuel use, the possibility of producing clean and sustainable energy in significant quantities from renewable energy sources has sparked interest all over the world. Solar energy is one of the sources for the generation of electricity, with no emissions or environmental pollution. The electricity produced by photovoltaics can supply a home or business and can even be sold or exchanged with the grid operator. A very positive effect of using photovoltaic modules is that they do not produce greenhouse gases and do not produce chemical waste, unlike all other forms of energy production. Photovoltaics are becoming one of the largest investments in the field of renewable generating units. Improving the reliability of the electric power system is one of the most important impacts of the installation of photovoltaics (PV). Renewable energy sources are so large that they can meet the energy demands of the whole world, thus enabling sustainable supply as well as reducing local and global atmospheric emissions. Albania is rated by experts as one of the most favorable countries in Europe for the production of electricity from solar panels. But the country currently produces about 1% of its energy from the sun, while the rest of the needs are met by hydropower plants and imports. Albania has very good characteristics in terms of solar radiation (about 1300–1400 kW/m2). Solar energy has great potential and is a permanent source of energy with greater economic efficiency. Photovoltaic energy is also seen as an alternative, as long periods of drought in Albania have produced crises and high costs for securing energy in the foreign market.

Keywords: capacity, ministry of tourism and environment, obstacles, photovoltaic energy, sustainable

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Authors: Raana Babadi Fathipour

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Biofilms, those intricate structures of microbial aggregation that emerge as microorganisms adhere to animate or inanimate surfaces, possess an innate capacity to shield their inhabitants from adversities within the environment whilst fortifying their endurance against antimicrobial agents. This remarkable aspect facilitates the persistence and virulence of said microorganisms, establishing biofilm formation as an integral component of bacterial survival mechanisms. However, should foodborne pathogens adopt this mode of existence, the potentiality for foodborne disease infections becomes alarmingly intensified—an alarming prospect that harbors significant public health hazards and engenders deleterious economic ramifications. Thus, due to these consequences lurking on the horizon, extensive research concentrating upon comprehending biofilms and devising efficacious removal strategies assumes a position imbued with paramount importance within the realm of the food industry. The problem of food waste resulting from spoilage in the food industry continues to present a widespread challenge to both environmental sustainability and the security of our food supplies. In this comprehensive analysis, we delve into the formation of bacterial biofilms, highlighting the specific issues they pose within the realm of food production. Additionally, we provide an overview of various types of common foodborne pathogens that tend to thrive in these biofilms. Furthermore, we summarize existing strategies aimed at tackling or managing detrimental bacterial biofilm growth. We also introduce contemporary approaches that show promise in terms of controlling this issue and highlight their potential for further advancement. Ultimately, our focus lies on outlining prospects for future development as they pertain specifically to combatting bacterial biofilms within the field.

Keywords: foodborne pathogens, food safety, biofilm, resistance, quorum-sensing

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Authors: Huan Lin, Liwen Pang

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In order to reduce the ambiguity and uncertainty of product form bionic design, a product form bionic design method based on eye tracking is proposed. The eye-tracking experiment is designed to calculate the average time ranking of the specific parts of the bionic shape that the subjects are looking at. Key bionic shape is explored through the experiment and then applied to a desk lamp bionic design. During the design case, FAHP (Fuzzy Analytic Hierachy Process) and SD (Semantic Differential) method are firstly used to identify consumer emotional perception model toward desk lamp before product design. Through investigating different desk lamp design elements and consumer views, the form design factors on the desk lamp product are reflected and all design schemes are sequenced after caculation. Desk lamp form bionic design method is combined the key bionic shape extracted from eye-tracking experiment and priority of desk lamp design schemes. This study provides an objective and rational method to product form bionic design.

Keywords: Bionic design; Form; Eye tracking; FAHP; Desk lamp

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Authors: Abiodun Ladanu Ajala, Wuyi Oke

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Mathematical simulation and optimization models packaged within interactive computer programs provide a common way for planners and managers to predict the behaviour of any proposed water resources system design or management policy before it is implemented. Modeling presents a principal technique of predicting the behaviour of the proposed infrastructural designs or management policies. Models can be developed and used to help identify specific alternative plans that best meet those objectives. This study discusses various types of models, their development, architecture, data requirements, and applications in the field of engineering. It also outlines the advantages and limitations of each the optimization and simulation models presented. The techniques explored in this review include; dynamic programming, linear programming, fuzzy optimization, evolutionary algorithms and finally artificial intelligence techniques. Previous studies carried out using some of the techniques mentioned above were reviewed, and most of the results from different researches showed that indeed optimization and simulation provides viable alternatives and predictions which form a basis for decision making in building engineering structures and also in engineering planning and management.

Keywords: linear programming, mutation, optimization, simulation

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Authors: Jonathan Iworiso

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Forecasting the equity premium out-of-sample is a major concern to researchers in finance and emerging markets. The quest for a superior model that can forecast the equity premium with significant economic gains has resulted in several controversies on the choice of variables and suitable techniques among scholars. This research focuses mainly on the application of Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regression models involving model complexity was employed. The RT models include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net, and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the RT models demonstrates significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.

Keywords: regression training, out-of-sample forecasts, expanding window, statistical predictability, economic significance, utility gains

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Authors: Georgiana Onicescu, Yuqian Shen

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Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered.

Keywords: Lasso, Bayesian analysis, spatial analysis, variable selection

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Liwiusz Wojciechowski

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Research background: The current knowledge does not give a clear answer to the question of the impact of FDI on productivity. Results of the empirical studies are still inconclusive, no matter how extensive and diverse in terms of research approaches or groups of countries analyzed they are. It should also take into account the possibility that FDI and productivity are linked and that there is a bidirectional relationship between them. This issue is particularly important because on one hand FDI can contribute to changes in productivity in the host country, but on the other hand its level and dynamics may imply that FDI should be undertaken in a given country. As already mentioned, a two-way relationship between the presence of foreign capital and productivity in the host country should be assumed, taking into consideration the endogenous nature of FDI. Purpose of the article: The overall objective of this study is to determine the causality between foreign direct investment and total factor productivity in host county in terms of different relative absorptive capacity across countries. In the classic sense causality among variables is not always obvious and requires for testing, which would facilitate proper specification of FDI models. The aim of this article is to study endogeneity of selected macroeconomic variables commonly being used in FDI models in case of Visegrad countries: main recipients of FDI in CEE. The findings may be helpful in determining the structure of the actual relationship between variables, in appropriate models estimation and in forecasting as well as economic policymaking. Methodology/methods: Panel and time-series data techniques including GMM estimator, VEC models and causality tests were utilized in this study. Findings & Value added: The obtained results allow to confirm the hypothesis states the bi-directional causality between FDI and total factor productivity. Although results differ from among countries and data level of aggregation implications may be useful for policymakers in case of providing foreign capital attracting policy.

Keywords: endogeneity, foreign direct investment, multi-equation models, total factor productivity

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Authors: A. Zaher, A. Traore, F. Thiéry, T. Talbert, B. Shaer

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In this paper, an intelligent approach is proposed to optimize the orientation of continuous solar tracking systems on cloudy days. Considering the weather case, the direct sunlight is more important than the diffuse radiation in case of clear sky. Thus, the panel is always pointed towards the sun. In case of an overcast sky, the solar beam is close to zero, and the panel is placed horizontally to receive the maximum of diffuse radiation. Under partly covered conditions, the panel must be pointed towards the source that emits the maximum of solar energy and it may be anywhere in the sky dome. Thus, the idea of our approach is to analyze the images, captured by ground-based sky camera system, in order to detect the zone in the sky dome which is considered as the optimal source of energy under cloudy conditions. The proposed approach is implemented using experimental setup developed at PROMES-CNRS laboratory in Perpignan city (France). Under overcast conditions, the results were very satisfactory, and the intelligent approach has provided efficiency gains of up to 9% relative to conventional continuous sun tracking systems.

Keywords: clouds detection, fuzzy inference systems, images processing, sun trackers

Procedia PDF Downloads 192

Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

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Authors: Sina Mahdavi, Mahdi Asghari Ozma

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Multiple sclerosis (MS) is the most common inflammatory autoimmune disease of the CNS that affects the myelination process in the central nervous system (CNS). Complex interactions of various "environmental or infectious" factors may act as triggers in autoimmunity and disease progression. The association between viral infections, especially human Varicella-zoster virus (VZV) and MS is one potential cause that is not well understood. This study aims to summarize the available data on VZV retrovirus infection in MS disease progression. For this study, the keywords "Multiple sclerosis", " Human Varicella-zoster virus ", and "central nervous system" in the databases PubMed, Google Scholar, Sid, and MagIran between 2016 and 2022 were searched and 14 articles were chosen, studied, and analyzed. Analysis of the amino acid sequences of HNRNPA1 with VZV proteins has shown a 62% amino acid sequence similarity between VZV gE and the PrLD/M9 epitope region (TNPO1 binding domain) of mutant HNRNPA1. A heterogeneous nuclear ribonucleoprotein (hnRNP), which is produced by HNRNPA1, is involved in the processing and transfer of mRNA and pre-mRNA. Mutant HNRNPA1 mimics gE of VZV as an antigen that leads to autoantibody production. Mutant HnRNPA1 translocates to the cytoplasm, after aggregation is presented by MHC class I, followed by CD8 + cells. Of these, antibodies and immune cells against the gE epitopes of VZV remain due to the memory immune response, causing neurodegeneration and the development of MS in genetically predisposed individuals. VZV expression during the course of MS is present in genetically predisposed individuals with HNRNPA1 mutation, suggesting a link between VZV and MS, and that this virus may play a role in the development of MS by inducing an inflammatory state. Therefore, measures to modulate VZV expression may be effective in reducing inflammatory processes in demyelinated areas of MS patients in genetically predisposed individuals.

Keywords: multiple sclerosis, varicella-zoster virus, central nervous system, autoimmunity

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Authors: Pawan Kumar Gupta, Sumana Ghosh

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Parkinson's Disease (PD) is a long-term neurodegenerative movement disorder of the central nervous system with vast symptoms related to the motor system. The common symptoms of PD are tremor, rigidity, bradykinesia/akinesia, and postural instability, but the clinical symptom includes other motor and non‐motor issues. The motor symptoms of the disease are consequence of death of the neurons in a region of the midbrain known as substantia nigra pars compacta, leading to decreased level of a neurotransmitter known as dopamine. The cause of this neuron death is not clearly known but involves formation of Lewy bodies, an abnormal aggregation or clumping of the protein alpha-synuclein in the neurons. Unfortunately, there is no cure for PD, and the management of this disease is challenging. Therefore, it is critical for a patient to be diagnosed at early stages. A limited choice of drugs is available to improve the symptoms, but those become less and less effective over time. Apart from that, with rapid growth in the field of science and technology, other methods such as multi-area brain stimulation are used to treat patients. In order to develop advanced techniques and to support drug development for treating PD patients, an accurate mathematical model is needed to explain the underlying relationship of dopamine secretion in the brain with the hand tremors. There has been a lot of effort in the past few decades on modeling PD tremors and treatment effects from a computational point of view. These models can effectively save time as well as the cost of drug development for the pharmaceutical industry and be helpful for selecting appropriate treatment mechanisms among all possible options. In this review paper, an effort is made to investigate studies on PD modeling and analysis and to highlight some of the key advances in the field over the past centuries with discussion on the current challenges.

Keywords: Parkinson's disease, deep brain stimulation, tremor, modeling

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Authors: O. P. Rahi, Manoj Kumar

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Power transformers are essential and expensive equipments in electrical power system. Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by conventional method is not always an easy task due to variability of gas data and operational variables. In this paper, an extension theory based power transformer fault diagnosis method is presented. Extension theory tries to solve contradictions and incompatibility problems. This paper first briefly introduces the basic concept of matter element theory, establishes the matter element models for three-ratio method, and then briefly discusses extension set theory. Detailed analysis is carried out on the extended relation function (ERF) adopted in this paper for transformer fault diagnosis. The detailed diagnosing steps are offered. Simulation proves that the proposed method can overcome the drawbacks of the conventional three-ratio method, such as no matching and failure to diagnose multi-fault. It enhances diagnosing accuracy.

Keywords: DGA, extension theory, ERF, fault diagnosis power transformers, fault diagnosis, fuzzy logic

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Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

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The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Uroš Durlević, Ivan Novković, Nina Čegar, Stefanija Stojković

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Snow avalanches represent one of the most dangerous natural phenomena in mountain regions worldwide. Material and human casualties caused by snow avalanches can be very significant. In this study, using geographic information systems and remote sensing, the natural conditions of the Šar Mountains were analyzed for geospatial modeling of dry slab avalanches. For this purpose, the Fuzzy Analytic Hierarchy Process (FAHP) multi-criteria analysis method was used, within which fifteen environmental criteria were analyzed and evaluated. Based on the existing analyzes and results, it was determined that a significant area of the Šar Mountains is very highly susceptible to the occurrence of dry slab avalanches. The obtained data can be of significant use to local governments, emergency services, and other institutions that deal with natural disasters at the local level. To our best knowledge, this is one of the first research in the Republic of Serbia that uses the FAHP method for geospatial modeling of dry slab avalanches.

Keywords: GIS, FAHP, Šar Mountains, snow avalanches, environmental protection

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Authors: Gerard Bray, Derek Mao, Arya Bahadori, Sachinka Ranasinghe

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The EAU currently recommends partial nephrectomy as the preferred management for localised cT1 renal tumours, irrespective of surgical approach. With the advent of robotic assisted partial nephrectomy, there is growing evidence that warm ischaemia time may be reduced compared to the traditional laparoscopic approach. There is still no clear differences between the two approaches with regards to other peri-operative and oncological outcomes. Current limitations in the field denote the lack of single surgeon series to compare the two approaches as other studies often include multiple operators of different experience levels. To the best of our knowledge, this study is the first single surgeon series comparing peri-operative outcomes of robotic assisted and laparoscopic PN. The current study aims to reduce intra-operator bias while maintaining an adequate sample size to assess the differences in outcomes between the two approaches. We retrospectively compared patient demographics, peri-operative outcomes, and renal function derangements of all partial nephrectomies undertaken by a single surgeon with experience in both laparoscopic and robotic surgery. Warm ischaemia time, length of stay, and acute renal function deterioration were all significantly reduced with robotic partial nephrectomy, compared to laparoscopic nephrectomy. This study highlights the benefits of robotic partial nephrectomy. Further prospective studies with larger sample sizes would be valuable additions to the current literature.

Keywords: partial nephrectomy, robotic assisted partial nephrectomy, warm ischaemia time, peri-operative outcomes

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Authors: G. M. Contessa, L. Lepore, G. Gandolfo, C. Poggi, N. Cherubini, R. Remetti, S. Sandri

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Practical guidelines are provided in this work for the safe use of a portable d-t Thermo Scientific MP-320 neutron generator producing pulsed 14.1 MeV neutron beams. The neutron generator’s emission was tested experimentally and reproduced by MCNPX Monte Carlo code. Simulations were particularly accurate, even generator’s internal components were reproduced on the basis of ad-hoc collected X-ray radiographic images. Measurement campaigns were conducted under different standard experimental conditions using an LB 6411 neutron detector properly calibrated at three different energies, and comparing simulated and experimental data. In order to estimate the dose to the operator vs. the operating conditions and the energy spectrum, the most appropriate value of the conversion factor between neutron fluence and ambient dose equivalent has been identified, taking into account both direct and scattered components. The results of the simulations show that, in real situations, when there is no information about the neutron spectrum at the point where the dose has to be evaluated, it is possible - and in any case conservative - to convert the measured value of the count rate by means of the conversion factor corresponding to 14 MeV energy. This outcome has a general value when using this type of generator, enabling a more accurate design of experimental activities in different setups. The increasingly widespread use of this type of device for industrial and medical applications makes the results of this work of interest in different situations, especially as a support for the definition of appropriate radiation protection procedures and, in general, for risk analysis.

Keywords: instrumentation and monitoring, management of radiological safety, measurement of individual dose, radiation protection of workers

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Authors: Amr Mohamed Bakry Ibrahim, Yingzhou Ni, Hao Cheng, Li Liang

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Tuna oil (omega-3 oil) has become increasingly popular in the last ten years, because it is considered one of the treasures of food which has many beneficial health effects for the humans. Nevertheless, the susceptibility of omega-3 oils to oxidative deterioration, resulting in the formation of oxidation products, in addition to organoleptic problems including “fishy” flavors, have presented obstacles to the more widespread use of tuna oils in the food industry. This study sought to evaluate the potential impact of Mentha piperita oil on physicochemical characteristics and oxidative stability of tuna oil microcapsules formed by spray drying using the partial substitution to whey protein isolate by carboxymethyl cellulose and pullulan. The emulsions before the drying process were characterized regarding size and ζ-potential, viscosity, surface tension. Confocal laser scanning microscopy showed that all emulsions were sphericity and homogeneous distribution without any visible particle aggregation. The microcapsules obtained after spray drying were characterized regarding microencapsulation efficiency, water activity, color, bulk density, flowability, scanning surface morphology and oxidative stability. The microcapsules were spherical shape had low water activity (0.11-0.23 aw). The microcapsules containing both tuna oil and Mentha piperita oil were smaller than others and addition of pullulan into wall materials improved the morphology of microcapsules. Microencapsulation efficiency of powdered oil ranged from 90% to 94%. Using Mentha piperita oil in the process of microencapsulation tuna oil enhanced the oxidative stability using whey protein isolate only or with carboxymethyl cellulose or pullulan as wall materials, resulting in improved storage stability and mask fishy odor. Therefore, it is foreseen using tuna-Mentha piperita oil mixture microcapsules in the applications of the food industries.

Keywords: Mentha piperita oil, microcapsule, tuna oil, whey protein isolate

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Authors: G. Blázquez, A. Gálvez-Pérez, M. Calero, I. Iáñez-Rodríguez, M. A. Martín-Lara, A. Pérez

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The production of olive oil is considered as one of the most important agri-food industries. However, some of the by-products generated in the process are potential pollutants and cause environmental problems. Consequently, the management of these by-products is currently considered as a challenge for the olive oil industry. In this context, several technologies have been developed and tested. In this sense, the autohydrolysis of these by-products could be considered as a promising technique. Therefore, this study focused on autohydrolysis treatments of a solid residue from the olive oil industry denominated olive cake. This one comes from the olive pomace extraction with hexane. Firstly, a water washing was carried out to eliminate the water soluble compounds. Then, an experimental design was developed for the autohydrolysis experiments carried out in the hydrothermal pressure reactor. The studied variables were temperature (30, 60 and 90 ºC) and time (30, 60, 90 min). On the other hand, aliquots of liquid obtained fractions were analysed by HPLC to determine the fructose and sucrose contents present in the liquid fraction. Finally, the obtained results of sugars contents and the yields of the different experiments were fitted to a neuro-fuzzy and to a polynomial model.

Keywords: ANFIS, olive cake, polyols, saccharides

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Authors: Gang Wang

Abstract:

Education is widely believed to be a prerequisite for democracy and civil society, but the causal link between education and outcome variables is usually hardly to be identified. This study applies a fuzzy regression discontinuity design to examine the effects of college education on democratic attitudes in the Chinese context. In the analysis treatment assignment is determined by students’ college entry years and thus naturally selected by subjects’ ages. Using a sample of Chinese college students collected in Beijing in 2009, this study finds that college education actually reduces undergraduates’ motivation for political development in China but promotes political loyalty to the authoritarian government. Further hypotheses tests explain these interesting findings from two perspectives. The first is related to the complexity of politics. As college students progress over time, they increasingly realize the complexity of political reform in China’s authoritarian regime and rather stay away from politics. The second is related to students’ career opportunities. As students are close to graduation, they are immersed with job hunting and have a reduced interest in political freedom.

Keywords: china, college education, democratic attitudes, regression discontinuity

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Authors: Paulo Lopes Henriques, Carla Curado

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According to literature, knowledge sharing behaviors are influenced by organizational values and structures, namely organizational climate. The manuscript examines the antecedents of the knowledge sharing oriented organizational climate. According to theoretical expectations the study adopts the following explanatory conditions: knowledge sharing costs, knowledge sharing incentives, perceptions of knowledge sharing contributing to performance and tenure. The study confronts results considering two groups of firms: nondigital (firms without intranet) vs digital (firms with intranet). The paper applies fsQCA technique to analyze data by using fsQCA 2.5 software (www.fsqca.com) testing several conditional arguments to explain the outcome variable. Main results strengthen claims on the relevancy of the contribution of knowledge sharing to performance. Secondly, evidence brings tenure - an explanatory condition that is associated to organizational memory – to the spotlight. The study provides an original contribution not previously addressed in literature, since it identifies the sufficient conditions sets to knowledge sharing oriented organizational climate using fsQCA, which is, to our knowledge, a novel application of the technique.

Keywords: fsQCA, knowledge sharing oriented organizational climate, knowledge sharing costs, knowledge sharing incentives

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Authors: Divya Agarwal, Pushpendra S. Bharti

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Autonomous mobile robots (AMR) are expected as smart tools for operations in every automation industry. Path planning and obstacle avoidance is the backbone of AMR as robots have to reach their goal location avoiding obstacles while traversing through optimized path defined according to some criteria such as distance, time or energy. Path planning can be classified into global and local path planning where environmental information is known and unknown/partially known, respectively. A number of sensors are used for data collection. A number of algorithms such as artificial potential field (APF), rapidly exploring random trees (RRT), bidirectional RRT, Fuzzy approach, Purepursuit, A* algorithm, vector field histogram (VFH) and modified local path planning algorithm, etc. have been used in the last three decades for path planning and obstacle avoidance for AMR. This paper makes an attempt to review some of the path planning and obstacle avoidance algorithms used in the field of AMR. The review includes comparative analysis of simulation and mathematical computations of path planning and obstacle avoidance algorithms using MATLAB 2018a. From the review, it could be concluded that different algorithms may complete the same task (i.e. with a different set of instructions) in less or more time, space, effort, etc.

Keywords: path planning, obstacle avoidance, autonomous mobile robots, algorithms

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Authors: Ahsan Khan, Nazish Shah, Muhammad Salman

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The present study deals with the toxicological effects of copper, lead and chromium on brain and liver tissues of grass carp (Ctenopharyngodon idella). The average length of experimental fish was 8.5 ± 5.5 cm and weighed 9.5 ± 6.5 g. Grass carp was exposed to lethal concentration (LC₁₅) of copper, lead and chromium for 24, 48, 72 and 96 hours respectively. (LC₁₅) for copper was 1.5, 1.4, 1.2 and 1mgL⁻¹. Similarly, LC₁₅ of lead was 250, 235, 225 and 216mgL⁻¹ while (LC₁₅) for chromium was 25.5, 22.5, 20 and 18mgL⁻¹ respectively. During the time of exposure against various doses of heavy metals the grass carp showed some behavioral changes. In the initial stages of experiment, the rapid movements and gulping of air were observed. Several times the fish tried to jump to scat from the toxic median. In addition, the accumulation of heavy metals in different tissues of grass carp particularly in liver and brain tissues were observed. Lead was highly accumulated in brain tissue after the exposure of fish for 24 and 48 hours, while highly accumulated in liver tissues after the exposure of fish for 72 and 96 hours. Chromium was highly accumulated in the liver tissues after the exposure of fish for 24 hours while its accumulation was found highly in the brain tissues after the exposure of fish for 48, 72 and 96 hours. Similarly, accumulation of copper concentration was found highly in brain tissues after the exposure of 48 and 96 hours while its accumulation was high in liver tissues after the exposure of 24 and 72 hours. Comparatively maximum accumulation of lead was found in brain and liver tissues of grass carp followed by chromium and copper. Furthermore, accumulation of these metals caused many abnormalities like gliosis, destruction of cell, change in cell shape and shrinkage of cells in brain tissue while in liver tissues aggregation in hepatocytes, widen space between cells and also destruction of cell was observed. These experiments and observations can be useful to monitor the aquatic pollution and quality of aquatic environment system.

Keywords: brain, grass carp, liver, lethal concentration, toxicity

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Authors: Kemal Polat

Abstract:

In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Yuxi Liu, Boris Belousov, Mehrzad Esmaeili Charkhab, Oliver Tessmann

Abstract:

This paper presents a computational solution for designing reconfigurable interlocking structures that are fully assembled with SL Blocks. Formed by S-shaped and L-shaped tetracubes, SL Block is a specific type of interlocking puzzle. Analogous to molecular self-assembly, the aggregation of SL blocks will build a reversible hierarchical and discrete system where a single module can be numerously replicated to compose semi-interlocking components that further align, wrap, and braid around each other to form complex high-order aggregations. These aggregations can be disassembled and reassembled, responding dynamically to design inputs and changes with a unique capacity for reconfiguration. To use these aggregations as architectural structures, we developed computational tools that automate the configuration of SL blocks based on architectural design objectives. There are three critical phases in our work. First, we revisit the hierarchy of the SL block system and devise a top-down-type design strategy. From this, we propose two key questions: 1) How to translate 3D polyominoes into SL block assembly? 2) How to decompose the desired voxelized shapes into a set of 3D polyominoes with interlocking joints? These two questions can be considered the Hamiltonian path problem and the 3D polyomino tiling problem. Then, we derive our solution to each of them based on two methods. The first method is to construct the optimal closed path from an undirected graph built from the voxelized shape and translate the node sequence of the resulting path into the assembly sequence of SL blocks. The second approach describes interlocking relationships of 3D polyominoes as a joint connection graph. Lastly, we formulate the desired shapes and leverage our methods to achieve their reconfiguration within different levels. We show that our computational strategy will facilitate the efficient design of hierarchical interlocking structures with a self-replicating geometric module.

Keywords: computational design, SL-blocks, 3D polyomino puzzle, combinatorial problem

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