Search results for: predictive tracking

Authors: Ramachandra C. G., Amarnath. M., Prashanth Pai M., Nagesh S. N.

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The machine's performance level drops down over a period of time due to the wear and tear of its components. The early detection of an emergent fault becomes very vital in order to obtain uninterrupted production in a plant. Maintenance is an activity that helps to keep the machine's performance at an anticipated level, thereby ensuring the availability of the machine to perform its intended function. At present, a number of modern maintenance techniques are available, such as preventive maintenance, predictive maintenance, condition-based maintenance, total productive maintenance, etc. Condition-based maintenance or condition monitoring is one such modern maintenance technique in which the machine's condition or health is checked by the measurement of certain parameters such as sound level, temperature, velocity, displacement, vibration, etc. It can recognize most of the factors restraining the usefulness and efficacy of the total manufacturing unit. This research work is conducted on a Batch Mill in a tire production unit located in the Southern Karnataka region. The health of the mill is assessed using amplitude of vibration as a parameter of measurement. Most commonly, the vibration level is assessed using various points on the machine bearing. The normal or standard level is fixed using reference materials such as manuals or catalogs supplied by the manufacturers and also by referring vibration standards. The Rio-Vibro meter is placed in different locations on the batch-off mill to record the vibration data. The data collected are analyzed to identify the malfunctioning components in the batch off the mill, and corrective measures are suggested.

Keywords: availability, displacement, vibration, rio-vibro, condition monitoring

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Authors: Avinoam Rabinovich

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CO₂ geological storage is a proven technology for reducing anthropogenic carbon emissions, which is paramount for achieving the ambitious net zero emissions goal. Core flooding experiments are an important step in any CO₂ storage project, allowing us to gain information on the flow of CO₂ and brine in the porous rock extracted from the reservoir. This information is important for understanding basic mechanisms related to CO₂ geological storage as well as for reservoir modeling, which is an integral part of a field project. In this work, a different method for constructing accurate models of CO₂-brine core flooding will be presented. Results for synthetic cases and real experiments will be shown and compared with numerical models to exhibit their predictive capabilities. Furthermore, the various mechanisms which impact the CO₂ distribution and trapping in the rock samples will be discussed, and examples from models and experiments will be provided. The new method entails solving an inverse problem to obtain a three-dimensional permeability distribution which, along with the relative permeability and capillary pressure functions, constitutes a model of the flow experiments. The model is more accurate when data from a number of experiments are combined to solve the inverse problem. This model can then be used to test various other injection flow rates and fluid fractions which have not been tested in experiments. The models can also be used to bridge the gap between small-scale capillary heterogeneity effects (sub-core and core scale) and large-scale (reservoir scale) effects, known as the upscaling problem.

Keywords: CO₂ geological storage, residual trapping, capillary heterogeneity, core flooding, CO₂-brine flow

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Authors: Jigg Pelayo, Ricardo Villar

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Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

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Authors: Anisa Luli, Alessandra Santona

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Wanting a child and experiencing the impossibility to conceive is a painful condition that often is linked to infertility and often leads infertile individuals to experience psychological, relational and social problems. In this situation, infertile couples have to review their choices and take into consideration new ones. Few studies have focused on the decision-making style used by infertile individuals to solve their problem and on the factors that influences it. The aim of this paper is to define the style of decision-making used by infertile persons to give a solution to the “problem” and the predictive role of the attachment, of the representations of the relationship with parents in childhood and of the dyadic adjustment. The total sample is composed by 251 participants, divided in two groups: the experimental group composed by 114 participants, 62 males and 52 females, age between 25 and 59 years, and the control group composed by 137 participants, 65 males and 72 females, age between 22 and 49 years. The battery of instruments comprises: General Decision Making Style (GDMS), Experiences in Close Relationships Questionnaire Revised (ECR-R), Dyadic Adjustment Scale (DAS), Parental Bonding Instrument (PBI) and Symptom Checklist-90-R (SCL-90-R). The results from the analysis of the samples showed a prevalence of the rational decision-making style for both males and females, experimental and control group. There have been founded significant statistical relationships between the attachment scales, the representations of the parenting style, the dyadic adjustment and the decision-making styles. These results contribute to enrich the literature on the subject of decision-making in infertile people and show the relationship between the attachment and decision-making styles, confirming the few results in literature.

Keywords: attachment, decision-making style, infertility, dyadic adjustment

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Authors: G. Espriú-Pérez, F. A. Vargas-Oviedo, I. Zenteno-Aguirrezábal, M. D. Moya-Bencomo

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One of the injuries with a higher incidence among university-level athletes in the North of Mexico is presented in the knee. This injury generates absenteeism in training and competitions for at least 8 weeks. There is no active quantitative methodology, or protocol, that directly contributes to the clinical evaluation performed by the medical personnel at the prevalence of knee injuries. The main objective is to contribute with a quantitative tool that allows further development of preventive and corrective measures to these injuries. The study analyzed 55 athletes for 6 weeks, belonging to the disciplines of basketball, volleyball, soccer and swimming. Using a motion capture system (Nexus®, Vicon®), a three-dimensional analysis was developed that allows the measurement of the range of movement of the joint. To focus on the performance of the lower limb, eleven different movements were chosen from the Functional Performance Test, Functional Movement Screen, and the Cincinnati Jump Test. The research identifies the profile of the natural movement of a healthy knee, with the use of medical guidance, and its differences between each sport. The data recovered by the single-leg crossover hop managed to differentiate the type of knee movement among athletes. A maximum difference of 60° of offset was found in the adduction movement between male and female athletes of the same discipline. The research also seeks to serve as a guideline for the implementation of protocols that help identify the recovery level of such injuries.

Keywords: Cincinnati jump test, functional movement screen, functional performance test, knee, motion capture system

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Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

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Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

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Authors: Aarushi Agarwal, Tara Singh, I.L Singh, Anju Lata Singh, Trayambak Tiwari

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Simple discrimination in near absence of attention has been widely observed. Dual-task studies with natural scenes studies have been claimed as being preattentive in nature that facilitated categorization simultaneously with the attentional demanding task. So in this study, multiple images at the periphery are presented, initiating parallel processing in near absence of attention. For the central demanding task rotated letters were presented in both conditions, while in periphery natural and animal images were presented. To understand the breakpoint of ability to perform in near absence of attention one, two and three peripheral images were presented simultaneously with central task and subjects had to respond when all belong to the same category. Individual participant performance did not show a significant difference in both conditions central and peripheral task when the single peripheral image was shown. In case of two images high-level parallel processing could take place with little attentional resources. The eye tracking results supports the evidence as no major saccade was made in a large number of trials. Three image presentations proved to be a breaking point of the capacities to perform outside attentional assistance as participants showed a confused eye gaze pattern which failed to make the natural and animal image discriminations. Thus, we can conclude attention and awareness being independent mechanisms having limited capacities.

Keywords: attention, dual task pardigm, parallel processing, break point, saccade

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Authors: S. Aberkane, N. Djabali, S. Fafi, A. Baghezza

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Chronic illnesses affect many Algerians. It is possible to investigate the impact of illness representations and coping on quality of life and whether illness representations are indirectly associated with quality of life through their influence on coping. This study aims at investigating the relationship between illness perception, coping strategies and quality of life with chronic illness. Illness perceptions are indirectly associated with the quality of life through their influence on coping mediation. A sample of 316 participants with chronic illness living in the region of Batna, Algeria, has been adopted in this study. A correlation statistical analysis is used to determine the relationship between illness perception, coping strategies, and quality of life. Multiple regression analysis was employed to highlight the predictive ability of the dimensions of illness perception and coping strategies on the dependent variables of quality of life, where mediation analysis is considered in the exploration of the indirect effect significance of the mediator. This study provides insights about the relationship between illness perception, coping strategies and quality of life in the considered sample (r = 0.39, p < 0.01). Therefore, it proves that there is an effect of illness identity perception, external and medical attributions related to emotional role, physical functioning, and mental health perceived, and these were fully mediated by the asking for assistance (c’= 0.04, p < 0.05), the guarding (c’= 0.00, p < 0.05), and the task persistence strategy (c’= 0.05, p < 0.05). The findings imply partial support for the common-sense model of illness representations in a chronic illness population. Directions for future research are highlighted, as well as implications for psychotherapeutic interventions which target unhelpful beliefs and maladaptive coping strategies (e.g., cognitive behavioral therapy).

Keywords: chronic illness, coping, illness perception, quality of life, self- regulation model

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Authors: Manuel Rosales, Francisco Agrela, Julia Rosales

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The development of concrete pavements that include recycled waste with active and predictive safety features is a possible approach to mitigate the harmful impacts of the construction industry, such as CO2 emissions and the consumption of energy and natural resources during the construction and maintenance of road infrastructure. This study establishes the basis for formulating new smart materials for concrete pavements and carrying out the in-situ implementation of an experimental road section. To this end, a comprehensive recycled pavement solution is developed that combines eco-hybrid cement made with 25% mixed recycled aggregate powder (pMRA) and biomass bottom ash powder (pBBA) and a 30% substitution of natural aggregate by MRA and BBA. This work is grouped in three lines. 1) construction materials with high rates of use of recycled material, 2) production processes with efficient consumption of natural resources and use of cleaner energies, and 3) implementation and monitoring of road section with sustainable concrete made from waste. The objective of this study is to ensure satisfactory rheology, mechanical strength, durability, and CO2 capture of pavement concrete manufactured from waste and its subsequent application in real road section as well as its monitoring to establish the optimal range of recycled material. The concrete developed during this study are aimed at the reuse of waste, promoting the circular economy. For this purpose, and after having carried out different tests in the laboratory, three mixtures were established to be applied on the experimental road.

Keywords: biomass bottom ash, construction and demolition waste, recycled concrete pavements, full-scale experimental road, monitoring

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Authors: Akram Sadat Jafari Roodbandi, Parisa Kahani, Fatollah Rahimi Bafrani, Ali Dehghan, Nava Seyedi, Vafa Feyzi, Zohreh Forozanfar

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Introduction: Movement function is considered as the ability to produce and maintain balance, stability, and movement throughout the movement chain. Having a score of 14 and above on 7 sub-tests in the functional movement screening (FMS) test shows agility and optimal movement performance. On the other hand, the person's body is an important factor in physical fitness and optimal movement performance. The aim of this study was to identify effective anthropometric dimensions in increasing motor function. Methods: This study was a descriptive-analytical and cross-sectional study using simple random sampling. FMS test and 25 anthropometric dimensions and subcutaneous in five body regions measured in 139 healthy students of Bam University of Medical Sciences. Data analysis was performed using SPSS software and univariate tests and linear regressions at a significance level of 0.05. Results: 139 students were enrolled in the study, 51.1% (71 subjects) and the rest were female. The mean and standard deviation of age, weight, height, and arm subcutaneous fat were 21.5 ± 1.45, 12.6 ± 64.3, 168.7 ± 9.8, 15.3 ± 7, respectively. 17 subjects (12.2%) of the participants in the study have a score of less than 14, and the rest were above 14. Using regression analysis, it was found that exercise and arm subcutaneous fat are predictive variables associated with obtaining a high score in the FMS test. Conclusion: Exercise and weight loss are effective factors for increasing the movement performance of individuals, and this factor is independent of the size of other physical dimensions.

Keywords: functional movement, screening test, anthropometry, ergonomics

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Authors: Kheira Hamaida, Mohamed Salah Halati

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In this current paper, our aim work is to link as possible the obtained simulation results and the other experimental ones, just focusing on the electronic and optical properties of ZnSe. The predictive spectra of the total and partial densities of states using the Full Potential Linearized/Augmented Plane Wave method with the newly Tran-Blaha (TB) modified Becke-Johnson (mBJ) exchange-correlation potential (EXC). So the upper valence energy (UVE) levels contain the relative contribution of Se-(4p and 3d) states with considerable contribution from the electrons of Zn-2s orbital. The dielectric function of w-ZnSe, with its two parts, appears with a noticeable anisotropy character. The microscopic origins of the electronic states that are responsible for the observed peaks in the spectrum are determined through the decomposition of the spectrum to the individual contributions of the electronic transitions between the pairs of bands, where Vi is an occupied state in the valence band, and Ci is an unoccupied state in the conduction band. X-PES (X Ray-Photo Electron Spectroscopy) is an important technique used to probe the homogeneity, stoichiometry, and purity state of the title compound. In order to check the electron transitions derived from simulations and the others from Reflected Electron Energy Loss Spectroscopy (REELS) technique which was of great sensitivity, is used to determine the interband electronic transitions. In the optical window (Eg), all the electron energy states created were also determined through the specific gaussian deconvolution of the photoluminescence spectrum (PLS) that probed under a room temperature (RT).

Keywords: spectroscopy, WIEN2K, IIB-VIA semiconductors, dielectric function

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Husham Bayazed

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Immune responses following surgical trauma play a pivotal role in predicting postoperative outcomes from healing and recovery to postoperative complications. Postoperative complications, including infections and protracted recovery, occur in a significant number of about 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on the healthcare system in any community. The accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain major clinical provocations. Recent Findings: Recent studies are focusing on immune dysregulation mechanisms that occur in response to surgical trauma as a key determinant of postoperative complications. Antecedent studies mainly were plunging into the detection of inflammatory plasma markers, which facilitate in providing important clues regarding their pathogenesis. However, recent Single-cell technologies, such as mass cytometry or single-cell RNA sequencing, have markedly enhanced our ability to understand the immunological basis of postoperative immunological trauma complications and to identify their prognostic biological signatures. Summary: The advent of proteomic technologies has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers and providing patients and surgeons with information to improve surgical outcomes. However, more studies are required to accurately predict the risk of postoperative complications in individual patients.

Keywords: immune dysregulation, postoperative complications, surgical trauma, flow cytometry

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Authors: Paria Soleimani, Arezoo Neshati

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Myocardial infarction is one of the leading causes of ‎death in the world. Some of these deaths occur even before the patient ‎reaches the hospital. Myocardial infarction occurs as a result of ‎impaired blood supply. Because the most of these deaths are due to ‎coronary artery disease, hence the awareness of the warning signs of a ‎heart attack is essential. Some heart attacks are sudden and intense, but ‎most of them start slowly, with mild pain or discomfort, then early ‎detection and successful treatment of these symptoms is vital to save ‎them. Therefore, importance and usefulness of a system designing to ‎assist physicians in the early diagnosis of the acute heart attacks is ‎obvious.‎ The purpose of this study is to determine how well a predictive ‎model would perform based on the only patient-reportable clinical ‎history factors, without using diagnostic tests or physical exams. This ‎type of the prediction model might have application outside of the ‎hospital setting to give accurate advice to patients to influence them to ‎seek care in appropriate situations. For this purpose, the data were ‎collected on 711 heart patients in Iran hospitals. 28 attributes of clinical ‎factors can be reported by patients; were studied. Three logistic ‎regression models were made on the basis of the 28 features to predict ‎the risk of heart attacks. The best logistic regression model in terms of ‎performance had a C-index of 0.955 and with an accuracy of 94.9%. ‎The variables, severe chest pain, back pain, cold sweats, shortness of ‎breath, nausea, and vomiting were selected as the main features.‎

Keywords: Coronary heart disease, Acute heart attacks, Prediction, Logistic ‎regression‎

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Authors: Farhad Aalizadeh, Ali Moosavi

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This paper presents a two-dimensional Computational Fluid Dynamics (CFDs) simulation for the steady, particle tracking. The purpose of this paper is applied magnetic field effect on Magnetic Nanoparticles velocities distribution. It is shown that the permeability of the particles determines the effect of the magnetic field on the deposition of the particles and the deposition of the particles is inversely proportional to the Reynolds number. Using MHD and its property it is possible to control the flow velocity, remove the fouling on the walls and return the system to its original form. we consider a channel 2D geometry and solve for the resulting spatial distribution of particles. According to obtained results when only magnetic fields are applied perpendicular to the flow, local particles velocity is decreased due to the direct effect of the magnetic field return the system to its original fom. In the method first, in order to avoid mixing with blood, the ferromagnetic particles are covered with a gel-like chemical composition and are injected into the blood vessels. Then, a magnetic field source with a specified distance from the vessel is used and the particles are guided to the affected area. This paper presents a two-dimensional Computational Fluid Dynamics (CFDs) simulation for the steady, laminar flow of an incompressible magnetorheological (MR) fluid between two fixed parallel plates in the presence of a uniform magnetic field. The purpose of this study is to develop a numerical tool that is able to simulate MR fluids flow in valve mode and determineB0, applied magnetic field effect on flow velocities and pressure distributions.

Keywords: MHD, channel clots, magnetic nanoparticles, simulations

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Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

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Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

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Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

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Authors: Nafiseh Ebrahimi, Amir Jafari

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The study of humanoid character is of great interest to researchers in the field of robotics and biomechanics. One might want to know the forces and torques required to move a limb from an initial position to the desired destination position. Inverse dynamics is a helpful method to compute the force and torques for an articulated body limb. It enables us to know the joint torques required to rotate a link between two positions. Our goal in this study was to control a human-like articulated manipulator for a specific task of path tracking. For this purpose, the human arm was modeled with a three-link planar manipulator activated by Hill muscle model. Applying a proportional controller, values of force and torques applied to the joints were calculated by inverse dynamics, and then joints and muscle forces trajectories were computed and presented. To be more accurate to say, the kinematics of the muscle-joint space was formulated by which we defined the relationship between the muscle lengths and the geometry of the links and joints. Secondary, the kinematic of the links was introduced to calculate the position of the end-effector in terms of geometry. Then, we considered the modeling of Hill muscle dynamics, and after calculation of joint torques, finally, we applied them to the dynamics of the three-link manipulator obtained from the inverse dynamics to calculate the joint states, find and control the location of manipulator’s end-effector. The results show that the human arm model was successfully controlled to take the designated path of an ellipse precisely.

Keywords: arm manipulator, hill muscle model, six-muscle model, three-link lodel

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Authors: T. Keiller, E. Hindman, P. Hassmen, K. Radford, L. Lavrencic

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Background: Psychosocial disadvantage is associated with impaired cognitive abilities, with executive functioning (EF) abilities particularly vulnerable. EF abilities strongly predict general daily functioning, educational and career prospects, and health choices. A reliable and valid assessment of EF is important to support appropriate care and intervention strategies. However, evidence-based EF assessment tools for use with Aboriginal Australians are limited. Aim and Method: This research aims to develop and validate a culturally appropriate EF tool for use with indigenous Australians. To this end, Study One aims to review current literature examining the benefits and disadvantages of current EF assessment tools for use with Indigenous Australians. Study Two aims to collate expert opinion on the strengths and weaknesses of various current EF assessment tools for use with Indigenous Australians using Delphi methodology with experienced psychologists (n = 10). The initial two studies will inform the development of a culturally appropriate assessment tool. Study Three aims to evaluate the psychometric properties of the tool with an Indigenous sample living in the New South Wales Mid-North Coast. The study aims to quantify the predictive validity of this tool via comparison to functionality predictors and neuropsychological assessment scores. Study Four aims to collect qualitative data surrounding the feasibility and acceptability of the tool among indigenous Australians and health professionals. Expected Results: Findings from this research are likely to inform cognitive assessment practices and tool selection for health professionals conducting cognitive assessments with Indigenous Australians. Improved assessment of EF will inform appropriate care and intervention strategies for individuals with EF deficits.

Keywords: aboriginal Australians, assessment tool, cognition, executive functioning

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Authors: Adem Öğüt, M. Tahir Demirsel

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Rapid changes and developments today, besides the opportunities and facilities they offer to the organization, may also be a source of danger and difficulties due to the uncertainty. In order to take advantage of opportunities and to take the necessary measures against possible uncertainties, organizations must always follow the changes and developments that occur in the business environment and develop flexible structures and strategies for the alternative cases. Perceived environmental uncertainty is an outcome of managers’ perceptions of the combined complexity, instability and unpredictability in the organizational environment. An environment that is perceived to be complex, changing rapidly, and difficult to predict creates high levels of uncertainty about the appropriate organizational responses to external circumstances. In an uncertain and complex environment, organizations experiencing cutthroat competition may be successful by developing their corporate entrepreneurial ability. Corporate entrepreneurship is a process that includes many elements such as innovation, creating new business, renewal, risk-taking and being predictive. Successful corporate entrepreneurship is a critical factor which has a significant contribution to gain a sustainable competitive advantage, to renew the organization and to adapt the environment. In this context, the objective of this study is to investigate the effect of perceived environmental uncertainty of managers on corporate entrepreneurship performance. The research was conducted on 222 business executives in one of the major industrial zones of Turkey, Konya Organized Industrial Zone (KOS). According to the results, it has been observed that there is a positive statistically significant relationship between perceived environmental uncertainty and corporate entrepreneurial activities.

Keywords: corporate entrepreneurship, entrepreneurship, industrial zone, perceived environmental uncertainty, uncertainty

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Authors: P. Intaracharoen, P. Chantraket, C. Detyothin, S. Kirtsaeng

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Radar reflectivity data from Phimai weather radar station of DRRAA (Department of Royal Rainmaking and Agricultural Aviation) were used to analyzed the rainstorm characteristics via Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN) algorithm. The Phimai weather radar station was situated at Nakhon Ratchasima province, northeastern Thailand. The data from 277 days of rainstorm events occurring from May 2016 to May 2017 were used to investigate temporal distribution characteristics of convective individual rainclouds. The important storm properties, structures, and their behaviors were analyzed by 9 variables as storm number, storm duration, storm volume, storm area, storm top, storm base, storm speed, storm orientation, and maximum storm reflectivity. The rainstorm characteristics were also examined by separating the data into two periods as wet and dry season followed by an announcement of TMD (Thai Meteorological Department), under the influence of southwest monsoon (SWM) and northeast monsoon (NEM). According to the characteristics of rainstorm results, it can be seen that rainstorms during the SWM influence were found to be the most potential rainstorms over northeastern region of Thailand. The SWM rainstorms are larger number of the storm (404, 140 no./day), storm area (34.09, 26.79 km²) and storm volume (95.43, 66.97 km³) than NEM rainstorms, respectively. For the storm duration, the average individual storm duration during the SWM and NEM was found a minor difference in both periods (47.6, 48.38 min) and almost all storm duration in both periods were less than 3 hours. The storm velocity was not exceeding 15 km/hr (13.34 km/hr for SWM and 10.67 km/hr for NEM). For the rainstorm reflectivity, it was found a little difference between wet and dry season (43.08 dBz for SWM and 43.72 dBz for NEM). It assumed that rainstorms occurred in both seasons have same raindrop size.

Keywords: rainstorm characteristics, weather radar, TITAN, Northeastern Thailand

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Authors: Mohamed Fathy Heweage, Xiao Wen, Ayman Mokhtar, Ahmed Eldamarawy

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In this paper, we are interested in modeling, simulation, and measurement of the laser spot position with a quadrant detector. We enhance detection and tracking of semi-laser weapon decoding system based on microcontroller. The system receives the reflected pulse through quadrant detector and processes the laser pulses through a processing circuit, a microcontroller decoding laser pulse reflected by the target. The seeker accuracy will be enhanced by the decoding system, the laser detection time based on the receiving pulses number is reduced, a gate is used to limit the laser pulse width. The model is implemented based on Pulse Repetition Frequency (PRF) technique with two microcontroller units (MCU). MCU1 generates laser pulses with different codes. MCU2 decodes the laser code and locks the system at the specific code. The codes EW selected based on the two selector switches. The system is implemented and tested in Proteus ISIS software. The implementation of the full position determination circuit with the detector is produced. General system for the spot position determination was performed with the laser PRF for incident radiation and the mechanical system for adjusting system at different angles. The system test results show that the system can detect the laser code with only three received pulses based on the narrow gate signal, and good agreement between simulation and measured system performance is obtained.

Keywords: four quadrant detector, pulse code detection, laser guided weapons, pulse repetition frequency (PRF), Atmega 32 microcontrollers

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Authors: Ryota Kuzuki, Mitsuhiro Kohara, Noboru Kizuki, Satoshi Yoshida, Hidetaka Hirai, Yuta Nezasa

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About fifty years have passed since Japan's gas supply industry became the first in the world to switch from coal and oil to LNG as a city gas feedstock. Since the Japanese government target of net-zero carbon emission in 2050 was announced in October 2020, it has now entered a new era of challenges to commit to the requirement for decarbonization. This paper describes the situation that synthetic methane, produced from renewable energy-derived hydrogen and recycled carbon, is a promising national policy of transition toward net-zero society. In November 2020, the Japan Gas Association announced the 'Carbon Neutral Challenge 2050' as a vision to contribute to the decarbonization of society by converting the city gas supply to carbon neutral. The key technologies is methanation. This paper shows that methanation is a realistic solution to contribute to the decarbonization of the whole country at a lower social cost, utilizing the supply chain that already exists, from LNG plants to burner chips. The challenges during the transition period (2030-2050), as CO2 captured from exhaust of thermal power plants and industrial factories are expected to be used, it is proposed that a system of guarantee of origin (GO) for H2 and CO2 should be established and harmonize international rules for calculating and allocating greenhouse gas emissions in the supply chain, a platform is also needed to manage tracking information on certified environmental values.

Keywords: synthetic methane, recycled carbon fuels, methanation, transition period, environmental value transfer platform

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Veronika Solopova, Oana-Iuliana Popescu, Tim Landgraf, Christoph Benzmüller

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Beginning in 2014, slowly building geopolitical tensions in Eastern Europe led to a full-blown conflict between the Russian Federation and Ukraine that generated an unprecedented amount of news articles and data from social media data, reflecting the opposing ideologies and narratives as a background and the essence of the ongoing war. These polarized informational campaigns have led to countless mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for many readers all over the world. In this study, we analyzed scraped news articles from Ukrainian, Russian, Romanian and English-speaking news outlets, on the eve of 24th of February 2022, compared to day five of the conflict (28th of February), to see how the media influenced and mirrored the changes in public opinion. We also contrast the sources opposing and supporting the stands of the Russian government in Ukrainian, Russian and Romanian media spaces. In a data-driven way, we describe how the narratives are spread throughout Eastern and Central Europe. We present predictive linguistic features surrounding war propaganda. Our results indicate that there are strong similarities in terms of rhetoric strategies in the pro-Kremlin media in both Ukraine and Russia, which, while being relatively neutral according to surface structure, use aggressive vocabulary. This suggests that automatic propaganda identification systems have to be tailored for each new case, as they have to rely on situationally specific words. Both Ukrainian and Russian outlets lean towards strongly opinionated news, pointing towards the use of war propaganda in order to achieve strategic goals.

Keywords: linguistic, news, propaganda, Russia, ukraine

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Authors: Mohamed Asaad Abdelrazek, Amir Taher El-Sheikh, M. Zayan, A.M. Elhady

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The successful realization of complex systems is dependent not only on the technology issues and the process for implementing them, but on the management issues as well. Managing the systems development lifecycle requires technical management. Systems engineering management is the technical management. Systems engineering management is accomplished by incorporating many activities. The three major activities are development phasing, systems engineering process and lifecycle integration. Systems engineering management activities are performed across the system development lifecycle. Due to the ever-increasing complexity of systems as well the difficulty of managing and tracking the development activities, new ways to achieve systems engineering management activities are required. This paper presents a systematic approach used as a design management tool applied across systems engineering management roles. In this approach, Transdisciplinary System Development Lifecycle (TSDL) Model has been modified and integrated with Quality Function Deployment. Hereinafter, the name of the systematic approach is the Transdisciplinary Quality System Development Lifecycle (TQSDL) Model. The QFD translates the voice of customers (VOC) into measurable technical characteristics. The modified TSDL model is based on Axiomatic Design developed by Suh which is applicable to all designs: products, processes, systems and organizations. The TQSDL model aims to provide a robust structure and systematic thinking to support the implementation of systems engineering management roles. This approach ensures that the customer requirements are fulfilled as well as satisfies all the systems engineering manager roles and activities.

Keywords: axiomatic design, quality function deployment, systems engineering management, system development lifecycle

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Authors: Ademola Ajibade

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Previous studies have explored several rationales for hedging strategies, but the evidence provided by these studies remains ambiguous. Using a hand-collected dataset of 2460 observations of non-financial firms in eight African countries covering 2013-2022, this paper investigates the determinants and extent of corporate hedge use. In particular, this paper focuses on the link between country-specific conditions and the corporate hedging behaviour of firms. To our knowledge, this represents the first African studies investigating the association between country-specific factors and corporate hedging policy. The evidence based on both univariate and multivariate reveal that country-level corruption and government quality are important indicators of the decisions and extent of hedge use among African firms. However, the connection between country-specific factors as a rationale for corporate hedge use is stronger for firms located in highly corrupt countries. This suggest that firms located in corrupt countries are more motivated to hedge due to the large exposure they face. In addition, we test the risk management theories and observe that CEOs educational qualification and experience shape corporate hedge behaviour. We implement a lagged variables in a panel data setting to address endogeneity concern and implement an interaction term between governance indices and firm-specific variables to test for robustness. Generally, our findings reveal that institutional factors shape risk management decisions and have a predictive power in explaining corporate hedging strategy.

Keywords: corporate hedging, governance quality, corruption, derivatives

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Authors: Silvia Arrate, Waldo Salud, Eloy París

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The wear of cutting tools is one of the most decisive elements when planning tunneling works, programming the maintenance stops and saving the optimum stock of spare parts during the evolution of the excavation. Being able to predict the behavior of cutting tools can give a very competitive advantage in terms of costs and excavation performance, optimized to the needs of the TBM itself. The incredible evolution of data science in recent years gives the option to implement it at the time of analyzing the key and most critical parameters related to machinery with the purpose of knowing how the cutting head is performing in front of the excavated ground. Taking this as a case study, Metro Line 3 of Guadalajara in Mexico will develop the feasibility of using Specific Energy versus data science applied over parameters of Torque, Penetration, and Contact Force, among others, to predict the behavior and status of cutting tools. The results obtained through both techniques are analyzed and verified in the function of the wear and the field situations observed in the excavation in order to determine its effectiveness regarding its predictive capacity. In conclusion, the possibilities and improvements offered by the application of digital tools and the programming of calculation algorithms for the analysis of wear of cutting head elements compared to purely empirical methods allow early detection of possible damage to cutting tools, which is reflected in optimization of excavation performance and a significant improvement in costs and deadlines.

Keywords: cutting tools, data science, prediction, TBM, wear

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Authors: Chong-Yang Qiao

Abstract:

Mobile augmented reality (MAR) tracking targets from the surroundings and aids operators for interactive data and procedures visualization, potential equipment and system understandably. Operators remotely communicate and coordinate with each other for the continuous tasks, information and data exchange between control room and work-site. In the routine work, distributed control system (DCS) monitoring and work-site manipulation require operators interact in real-time manners. The critical question is the improvement of user experience in cooperative works through applying Augmented Reality in the traditional industrial field. The purpose of this exploratory study is to find the cognitive model for the multiple task performance by MAR. In particular, the focus will be on the comparison between different tasks and environment factors which influence information processing. Three experiments use interface and interaction design, the content of start-up, maintenance and stop embedded in the mobile application. With the evaluation criteria of time demands and human errors, and analysis of the mental process and the behavior action during the multiple tasks, heuristic evaluation was used to find the operators performance with different situation factors, and record the information processing in recognition, interpretation, judgment and reasoning. The research will find the functional properties of MAR and constrain the development of the cognitive model. Conclusions can be drawn that suggest MAR is easy to use and useful for operators in the remote collaborative works.

Keywords: mobile augmented reality, remote collaboration, user experience, cognition model

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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