Search results for: statistical model

Authors: Hyun-Woo Cho

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For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

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Authors: Kashif Laeeq, Zubair A. Shaikh

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The paradigm of education is drastically changing from conventional to e-learning model. Due to ease of learning with various other benefits, several educational institutions are adopting the e-learning models. Some institutions are still willing to transform their educational system on to e-learning, but due to limited resources, they are still compromising on the old traditional system. The cloud computing could be one of the best solutions to overcome this problem by providing hardware, software, and infrastructure resources with cost efficient manner. The adoption of cloud computing in education will bring revolution in this paradigm. This paper introduces various positive features of e-learning and presents a way how cloud computing technology can be provisioned e-learning model. This paper also investigates the numerous challenges and opportunities that would be observed in cloud computing adoption in e-learning domain. The concept and knowledge present in this paper may create a new direction of research in the domain of cloud-based e-learning.

Keywords: cloud-based e-learning, e-learning, cloud computing application, smart learning

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Authors: Mohammed S. Assiri

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In EFL programs, rating scales used in writing assessment are often constructed by intuition. Intuition-based scales tend to provide inaccurate and divisive ratings of learners’ writing performance. Hence, following an empirical approach, this study attempted to develop a rating scale for elementary-level writing at an EFL program in Saudi Arabia. Towards this goal, 98 students’ essays were scored and then coded using comprehensive taxonomy of writing constructs and their measures. An automatic linear modeling was run to find out which measures would best predict essay scores. A nonparametric ANOVA, the Kruskal-Wallis test, was then used to determine which measures could best differentiate among scoring levels. Findings indicated that there were certain measures that could serve as either good predictors of essay scores or differentiators among scoring levels, or both. The main conclusion was that a rating scale can be empirically developed using predictive and discriminative statistical tests.

Keywords: analytic scoring, rating scales, writing assessment, writing constructs, writing performance

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Authors: Jana Ismail, Layla Fallatah, Maha Alshmaisi

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ChatGPT, a cutting-edge language model built on the GPT-3.5 architecture, has garnered attention for its profound natural language processing capabilities, holding promise for transformative applications in customer service and content creation. This study delves into ChatGPT's architecture, aiming to comprehensively understand its strengths and potential limitations. Through systematic experiments across diverse domains, such as general knowledge and creative writing, we evaluated the model's coherence, context retention, and task-specific accuracy. While ChatGPT excels in generating human-like responses and demonstrates adaptability, occasional inaccuracies and sensitivity to input phrasing were observed. The study emphasizes the impact of prompt design on output quality, providing valuable insights for the nuanced deployment of ChatGPT in conversational AI and contributing to the ongoing discourse on the evolving landscape of natural language processing in artificial intelligence.

Keywords: artificial Inelegance, chatGPT, open AI, NLP

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Authors: Nurul Liyana Abdul Aziz, Nur Aidya Hanum Aizam

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Course timetabling problems occur every semester in a university which includes the allocation of resources (subjects, lecturers and students) to a number of fixed rooms and timeslots. The assignment is carried out in a way such that there are no conflicts within rooms, students and lecturers, as well as fulfilling a range of constraints. The constraints consist of rules and policies set up by the universities as well as lecturers’ and students’ preferences of courses to be allocated in specific timeslots. This paper specifically focuses on the preferences of the course timetabling problem in one of the public universities in Malaysia. The demands will be considered into our existing mathematical model to make it more generalized and can be used widely. We have distributed questionnaires to a number of lecturers and students of the university to investigate their demands and preferences for their desired course timetable. We classify the preferences thus converting them to construct one mathematical model that can produce such timetable.

Keywords: university course timetabling problem, integer programming, preferences, constraints

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Authors: N. Paparisteidi, D. Rodamitou

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This article focuses on the skills development and path planning of students studying computer science in EPITECH: french private institute of Higher Education. The researchers examine students’ points of view and experience in a blended learning model based on a skills development curriculum. The study is based on the collection of four main categories of data: semi-participant observation, distribution of questionnaires, interviews, and analysis of internal school databases. The findings seem to indicate that a skills-based program on active learning enables students to develop their learning strategies as well as their personal skills and to actively engage in the creation of their career path and contribute to providing additional information to curricula planners and decision-makers about learning design in higher education.

Keywords: active learning, blended learning, higher education, skills development

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Authors: Alexander Babin, Leonid Savin

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In the present paper, increasing of energy efficiency of a thrust hybrid bearing with a central feeding chamber is considered. The mathematical model was developed to determine the pressure distribution and the reaction forces, based on the Reynolds equation and static characteristics’ equations. The boundary problem of pressure distribution calculation was solved using the method of finite differences. For various types of lubricants, geometry and operational characteristics, axial gaps can be determined, where the minimal friction coefficient is provided. The next part of the study considers the application of servovalves in order to maintain the desired position of the rotor. The report features the calculation results and the analysis of the influence of the operational and geometric parameters on the energy efficiency of mechatronic fluid-film bearings.

Keywords: active bearings, energy efficiency, mathematical model, mechatronics, thrust multipad bearing

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

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Authors: Mireya Mateo Gomez

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The purpose of this study was to examine the perceptions first-generation Mexican origin mothers living in San Diego have on postpartum depression (PPD), with a special focus on social influences and cultural beliefs towards those meanings. This study also aimed to examine possible PPD help-seeking behaviors that first-generation Mexican origin mothers can perform. The Health Belief Model (HBM) and Social Ecological Model (SEM) were the guiding theoretical frameworks for this study. Data for this study were collected from three focus groups, four in-depth interviews, and the distribution of an acculturation survey (ARSMA II). There were a total of 15 participants, in which participant’s mean age was 45, and the mean age migrated to the United States being 22. Most participants identified as being married, born in Southern or Western Mexico, and with a strong Mexican identity in relation to the ARSMA survey. Participants identified four salient PPD perceptions corresponding to the interpersonal level of SEM. These four main perceptions were: 1) PPD affecting the identity of motherhood; 2) PPD being a natural part of a mother’s experience but mitigated by networks; 3) PPD being a U.S. phenomenon due to family and community breakdown; and 4) natural remedies as a preferred PPD treatment. In regard to themes relating to help seeking behaviors, participants identified seven being: 1) seeking help from immediate family members; 2) practicing home remedies; 3) seeking help from a medical professional; 4) obtaining help from a clinic or organization; 5) seeking help from God; 6) participating in PPD support groups; and 7) talking to a friend. It was evident in this study that postpartum depression is not a well discussed topic within the Mexican immigrant population. In relation to the role culture and social influences have on PPD perceptions, most participants shared hearing or learning about PPD from their family members or friends. Participants also stated seeking help from family members if diagnosed with PPD and seeking out home remedies. This study as well provides suggestions to increase the awareness of PPD among the Mexican immigrant community.

Keywords: cultural beliefs, health belief model, Mexican origin mothers, perceptions, postpartum depression social ecological model

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Authors: Itiola Kehinde Adeniran

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The study examined the effect of employees’ training on the performance of small scale enterprises in Oyo State. A structured questionnaire was used to collect data from 150 respondents through purposive sampling method. Linear regression was used with the aid of statistical package for social science (SPSS) version 20 to analyze the data collected in order to examine the effect of independent variable, employees’ training on dependent variable, performance (profit) of small scale enterprises. The result revealed that employees’ training has a significant effect on the performance of small scale enterprises. It was concluded that predictor variable namely (training) is 55.5% variance of enterprises performance (profitability). Therefore, the paper recommended that all small scale enterprises in Nigeria should embrace manpower training and development in order to improve employees’ performance leading to organizational profitability.

Keywords: training, employee performance, small scale enterprise, organizational profitability

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Authors: Huseyin Guler, Cigdem Kosar

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The aim of this study is to propose Bridge estimator for seasonal unit root tests. Seasonality is an important factor for many economic time series. Some variables may contain seasonal patterns and forecasts that ignore important seasonal patterns have a high variance. Therefore, it is very important to eliminate seasonality for seasonal macroeconomic data. There are some methods to eliminate the impacts of seasonality in time series. One of them is filtering the data. However, this method leads to undesired consequences in unit root tests, especially if the data is generated by a stochastic seasonal process. Another method to eliminate seasonality is using seasonal dummy variables. Some seasonal patterns may result from stationary seasonal processes, which are modelled using seasonal dummies but if there is a varying and changing seasonal pattern over time, so the seasonal process is non-stationary, deterministic seasonal dummies are inadequate to capture the seasonal process. It is not suitable to use seasonal dummies for modeling such seasonally nonstationary series. Instead of that, it is necessary to take seasonal difference if there are seasonal unit roots in the series. Different alternative methods are proposed in the literature to test seasonal unit roots, such as Dickey, Hazsa, Fuller (DHF) and Hylleberg, Engle, Granger, Yoo (HEGY) tests. HEGY test can be also used to test the seasonal unit root in different frequencies (monthly, quarterly, and semiannual). Another issue in unit root tests is the lag selection. Lagged dependent variables are added to the model in seasonal unit root tests as in the unit root tests to overcome the autocorrelation problem. In this case, it is necessary to choose the lag length and determine any deterministic components (i.e., a constant and trend) first, and then use the proper model to test for seasonal unit roots. However, this two-step procedure might lead size distortions and lack of power in seasonal unit root tests. Recent studies show that Bridge estimators are good in selecting optimal lag length while differentiating nonstationary versus stationary models for nonseasonal data. The advantage of this estimator is the elimination of the two-step nature of conventional unit root tests and this leads a gain in size and power. In this paper, the Bridge estimator is proposed to test seasonal unit roots in a HEGY model. A Monte-Carlo experiment is done to determine the efficiency of this approach and compare the size and power of this method with HEGY test. Since Bridge estimator performs well in model selection, our approach may lead to some gain in terms of size and power over HEGY test.

Keywords: bridge estimators, HEGY test, model selection, seasonal unit root

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Authors: Rossouw De Bruin, Sebastiaan H. Von Solms

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The state of national security is an evolving concern. Companies, organizations, governments, states and individuals are aware of the security of their information and their assets however, they may not always be aware of the risks present. These risks are not only limited to non-existence of security procedures. Existing security can be severely flawed, especially if there is non-conformance towards policies, practices and procedures. When looking at humanitarian actions, we can easily identify these flaws. Unfortunately, humanitarian aid has to compete with factors from within the states, countries and continents they are working in. Furthermore, as technology improves, so does our connectivity to the internet and the way in which we use the internet. However, there are times when security is overlooked and humanitarian agencies are some of the agencies that do not always take security into consideration. The purpose of this paper will be to introduce the importance of cybersecurity and cybersecurity governance with respect to humanitarian work. We will also introduce and briefly discuss a model that can be used by humanitarian agencies to assess, manage and maintain their cybersecurity efforts.

Keywords: humanities, cybersecurity, cybersecurity governance, maturity, cybersecurity maturity, maturity model

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

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Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

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Authors: Berk Ecer, Ebru Akcapinar Sezer

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Traditional management models of intersections, such as no-light intersections or signalized intersection, are not the most effective way of passing the intersections if the vehicles are intelligent. To this end, Dresner and Stone proposed a new intersection control model called Autonomous Intersection Management (AIM). In the AIM simulation, they were examining the problem from a multi-agent perspective, demonstrating that intelligent intersection control can be made more efficient than existing control mechanisms. In this study, autonomous intersection management has been investigated. We extended their works and added a potential-based lane organization layer. In order to distribute vehicles evenly to each lane, this layer triggers vehicles to analyze near lanes, and they change their lane if other lanes have an advantage. We can observe this behavior in real life, such as drivers, change their lane by considering their intuitions. Basic intuition on selecting the correct lane for traffic is selecting a less crowded lane in order to reduce delay. We model that behavior without any change in the AIM workflow. Experiment results show us that intersection performance is directly connected with the vehicle distribution in lanes of roads of intersections. We see the advantage of handling lane management with a potential approach in performance metrics such as average delay of intersection and average travel time. Therefore, lane management and intersection management are problems that need to be handled together. This study shows us that the lane through which vehicles enter the intersection is an effective parameter for intersection management. Our study draws attention to this parameter and suggested a solution for it. We observed that the regulation of AIM inputs, which are vehicles in lanes, was as effective as contributing to aim intersection management. PLO-AIM model outperforms AIM in evaluation metrics such as average delay of intersection and average travel time for reasonable traffic rates, which is in between 600 vehicle/hour per lane to 1300 vehicle/hour per lane. The proposed model reduced the average travel time reduced in between %0.2 - %17.3 and reduced the average delay of intersection in between %1.6 - %17.1 for 4-lane and 6-lane scenarios.

Keywords: AIM project, autonomous intersection management, lane organization, potential-based approach

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Authors: Jac J. W. Andrews, James B. Hale

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Effective practicing psychologists require ongoing skill development that is constructivist and recursive in nature, with mentor, colleague, co-worker, and patient feedback critical to successful acquisition and maintenance of professional competencies. This paper will provide an overview of the nature and scope of psychologist skill development through multisource feedback (MSF) or 360 degree evaluation, present a rationale for its use for assessing practicing psychologist performance, and advocate its use in psychology given the demonstrated model utility in other health professions. The paper will conclude that an international research design is needed to assess the feasibility, reliability, and validity of MSF system ratings intended to solicit feedback from mentors, colleagues, coworkers, and patients about psychologist competencies. If adopted, the MSF model could lead to enhanced skill development that fosters patient satisfaction within and across countries.

Keywords: psychologist, multisource feedback, psychologist competency, professionalism

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Authors: Michael B. C. Khoo, J. L. Khoo, W. C. Yeong, W. L. Teoh

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The Shewhart Xbar control chart is a useful process monitoring tool in manufacturing industries to detect the presence of assignable causes. However, it is insensitive in detecting small process shifts. To circumvent this problem, adaptive control charts are suggested. An adaptive chart enables at least one of the chart’s parameters to be adjusted to increase the chart’s sensitivity. Two common adaptive charts that exist in the literature are the double sampling (DS) Xbar and variable sampling interval (VSI) Xbar charts. This paper compares the performances of the DS and VSI Xbar charts, based on the average time to signal (ATS) criterion. The ATS profiles of the DS Xbar and VSI Xbar charts are obtained using the Mathematica and Statistical Analysis System (SAS) programs, respectively. The results show that the VSI Xbar chart is generally superior to the DS Xbar chart.

Keywords: adaptive charts, average time to signal, double sampling, charts, variable sampling interval

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Authors: Sina Keyhanian, Abbas Ahmadi, Behrooz Karimi

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We present a multi-objective binary programming model which considers surgical cases are scheduling among operating rooms and the configuration of surgical instruments in limited capacity hospital trays, simultaneously. Many mathematical models have been developed previously in the literature addressing different challenges in health-care logistics such as assigning operating rooms, leveling beds, etc. But what happens inside the operating rooms along with the inventory management of required instruments for various operations, and also their integration with surgical scheduling have been poorly discussed. Our model considers the minimization of movements between trays during a surgery which recalls the famous cell formation problem in group technology. This assumption can also provide a major potential contribution to robotic surgeries. The tray configuration problem which consumes surgical instruments requirement plan (SIRP) and sequence of surgical procedures based on required instruments (SIRO) is nested inside the bin packing problem. This modeling approach helps us understand that most of the same-output solutions will not be necessarily identical when it comes to the rearrangement of surgeries among rooms. A numerical example has been dealt with via a proposed nested simulated annealing (SA) optimization approach which provides insights about how various configurations inside a solution can alter the optimal condition.

Keywords: health-care logistics, hospital tray configuration, off-line bin packing, simulated annealing optimization, surgical case scheduling

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Authors: Jackeline Kafie-Martinez, Peter B. Keating

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A three-dimensional finite element model is developed to capture the stress field generated in connected plates during the installation of hot-driven rivets. Clamping stress is generated when a steel rivet heated to approximately 1000 °C comes in contact with the material to be fastened at ambient temperature. As the rivet cools, thermal contraction subjects the rivet into tensile stress, while the material being fastened is subjected to compressive stress. Model characteristics and assumptions, as well as steel properties variation with respect to temperature are discussed. The thermal stresses developed around the rivet hole are assessed and reported. Results from the analysis are utilized to detect possible regions for fatigue crack propagation under cyclic loads.

Keywords: clamping stress, fatigue, finite elements, rivet, riveted railroad bridges

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Authors: B. Vadiraj, S. N. Omkar, B. Kapil Bharadwaj, Yash Vardhan Gupta

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During manned exploration of space, missions will require astronaut crewmembers to perform Extra Vehicular Activities (EVAs) for a variety of tasks. These EVAs take place after long periods of operations in space, and in and around unique vehicles, space structures and systems. Considering the remoteness and time spans in which these vehicles will operate, EVA system operations should utilize common worksites, tools and procedures as much as possible to increase the efficiency of training and proficiency in operations. All of the preparations need to be carried out based on studies of astronaut motions. Until now, development and training activities associated with the planned EVAs in Russian and U.S. space programs have relied almost exclusively on physical simulators. These experimental tests are expensive and time consuming. During the past few years a strong increase has been observed in the use of computer simulations due to the fast developments in computer hardware and simulation software. Based on this idea, an effort to develop a computational simulation system to model human dynamic motion for EVA is initiated. This study focuses on the simulation of an astronaut moving the orbital replaceable units into the worksites or removing them from the worksites. Our physics-based methodology helps fill the gap in quantitative analysis of astronaut EVA by providing a multisegment human arm model. Simulation work described in the study improves on the realism of previous efforts, incorporating joint stops to account for the physiological limits of range of motion. To demonstrate the utility of this approach human arm model is simulated virtually using ADAMS/LifeMOD® software. Kinematic mechanism for the astronaut’s task is studied from joint angles and torques. Simulation results obtained is validated with numerical simulation based on the principles of Newton-Euler method. Torques determined using mathematical model are compared among the subjects to know the grace and consistency of the task performed. We conclude that due to uncertain nature of exploration-class EVA, a virtual model developed using multibody dynamics approach offers significant advantages over traditional human modeling approaches.

Keywords: extra vehicular activity, biomechanics, inverse kinematics, human body modeling

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Authors: Yuan Tian, Benhe Gao

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This paper aims to investigate a two stage supply chain of one leading supplier and one following retailer that experiences two factors perturbation out of supplier's production cost, retailer's marginal cost and retail price in stochastic demand environment. Granted that risk neutral condition has long been discussed, little attention has been given to disruptions under the premise of risk neutral supplier and risk aversion retailer. We establish the optimal order quantity and revealed the profit distribution coefficient in risk-neutral static model, make adjustment under disruption scenario, and then select utility function method for risk aversion model. Using buy-back contract policy, the improvement of parameters can achieve channel coordination where Pareto optimal is realized.

Keywords: supply chain coordination, disruption management, buy-back contract, lose aversion

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Authors: Sriram Kashyap Prasad, Ionut Florescu

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This study models the intraday asset prices as driven by Markov process. This work identifies the latent states of the Hidden Markov model, using limit order book data (trades and quotes) to continuously estimate the states throughout the day. This work builds a trading strategy using estimated states to generate signals. The strategy utilizes current state to recalibrate buy/ sell levels and the transition between states to trigger stop-loss when adverse price movements occur. The proposed trading strategy is tested on the Stevens High Frequency Trading (SHIFT) platform. SHIFT is a highly realistic market simulator with functionalities for creating an artificial market simulation by deploying agents, trading strategies, distributing initial wealth, etc. In the implementation several assets on the NASDAQ exchange are used for testing. In comparison to a strategy with static buy/ sell levels, this study shows that the number of limit orders that get matched and executed can be increased. Executing limit orders earns rebates on NASDAQ. The system can capture jumps in the limit order book prices, provide dynamic buy/sell levels and trigger stop loss signals to improve the PnL (Profit and Loss) performance of the strategy.

Keywords: algorithmic trading, Hidden Markov model, high frequency trading, limit order book learning

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Authors: Tarek Aboueldahab, Hanan Farag

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Cost minimization of Multiple Vehicle Routing Problem becomes a critical issue in the field of transportation because it is NP-hard optimization problem and the search space is complex. Many researches use the hybridization of artificial intelligence (AI) models to solve this problem; however, it can not guarantee to reach the best solution due to the difficulty of searching the whole search space. To overcome this problem, we introduce the hybrid model of Discrete Particle Swarm Optimization (DPSO) with a passive congregation which enable searching the whole search space to compromise between both local and global search. The practical experiment shows that our model obviously outperforms other hybrid models in cost minimization.

Keywords: cost minimization, multi-vehicle routing problem, passive congregation, discrete swarm, passive congregation

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Authors: Rafael Andres Rincon Barrera

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Safety in global aviation plays a preponderant role in organizations that seek to avoid accidents in an attempt to preserve their most precious assets (the people and the machines). Human factors-based programs have shown to be effective in managing human-generated risks. The importance of training on human factors in maintenance has not been indifferent to the Colombian Air Force (COLAF). This research, which has a mixed quantitative, qualitative and descriptive approach, deals with its absence of structuring an instruction program in Human Factors in Aeronautical Maintenance, which serves as a tool to improve Operational Safety in the military air units of the COLAF. Research shows the trends and evolution of human factors programs in aeronautical maintenance through the analysis of a data matrix with 33 sources taken from different databases that are about the incorporation of these types of programs in the aeronautical industry in the last 20 years; as well as the improvements in the operational safety process that are presented after the implementation of these ones. Likewise, it compiles different normative guides in force from world aeronautical authorities for training in these programs, establishing a matrix of methodologies that may be applicable to develop a training program in human factors in maintenance. Subsequently, it illustrates the design, validation, and development of a human factors knowledge measurement instrument for maintenance at the COLAF that includes topics on Human Factors (HF), Safety Management System (SMS), and aeronautical maintenance regulations at the COLAF. With the information obtained, it performs the statistical analysis showing the aspects of knowledge and strengthening the staff for the preparation of the instruction program. Performing data triangulation based on the applicable methods and the weakest aspects found in the maintenance people shows a variable crossing from color coding, thus indicating the contents according to a training program for human factors in aeronautical maintenance, which are adjusted according to the competencies that are expected to be developed with the staff in a curricular format established by the COLAF. Among the most important findings are the determination that different authors are dealing with human factors in maintenance agrees that there is no standard model for its instruction and implementation, but that it must be adapted to the needs of the organization, that the Safety Culture in the Companies which incorporated programs on human factors in maintenance increased, that from the data obtained with the instrument for knowledge measurement of human factors in maintenance, the level of knowledge is MEDIUM-LOW with a score of 61.79%. And finally that there is an opportunity to improve Operational Safety for the COLAF through the implementation of the training program of human factors in maintenance for the technicians working in this area.

Keywords: Colombian air force, human factors, safety culture, safety management system, triangulation

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Authors: Joseph Salim

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This paper proposes the development of a supervised machine learning system to predict the consequences of untreated bruxism (teeth grinding) on gingival (gum) recession and jaw pain (most often bilateral jaw pain with possible headaches and limited ability to open the mouth). As a general dentist in a multi-specialty practice, the author has encountered many patients suffering from these issues due to uncontrolled bruxism (teeth grinding) at night. The most effective treatment for managing this problem involves wearing a nightguard during sleep and receiving therapeutic Botox injections to relax the muscles (the masseter muscle) responsible for grinding. However, some patients choose to postpone these treatments, leading to potentially irreversible and costlier consequences in the future. The proposed machine learning model aims to track patients who forgo the recommended treatments and assess the percentage of individuals who will experience worsening jaw pain, gingival (gum) recession, or both within a 3-to-5-year timeframe. By accurately predicting these outcomes, the model seeks to motivate patients to address the root cause proactively, ultimately saving time and pain while improving quality of life and avoiding much costlier treatments such as full-mouth rehabilitation to help recover the loss of vertical dimension of occlusion due to shortened clinical crowns because of bruxism, gingival grafts, etc.

Keywords: artificial intelligence, machine learning, predictive insights, bruxism, teeth grinding, therapeutic botox, nightguard, gingival recession, gum recession, jaw pain

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Authors: Ovais S. Qadri, Abhaya K. Srivastava

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Present experiments were carried to study the drying kinetics and quality of microwave foam-mat dried guava powder. Guava pulp was microwave foam mat dried using 8% egg albumin as foaming agent and then dried at microwave power 480W, 560W, 640W, 720W and 800W, foam thickness 3mm, 5mm and 7mm and inlet air temperature of 40˚C and 50˚C. Weight loss was used to estimate change in drying rate with respect to time. Powdered samples were analysed for various physicochemical quality parameters viz. acidity, pH, TSS, colour change and ascorbic acid content. Statistical analysis using three-way ANOVA revealed that sample of 5mm foam thickness dried at 800W and 50˚C was the best with 0.3584% total acid, 3.98 pH, 14min drying time, 8˚Brix TSS, 3.263 colour change and 154.762mg/100g ascorbic acid content.

Keywords: foam mat drying, foam mat guava, guava powder, microwave drying

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Authors: R. Karami, A. Gharaei

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The aim of this study was to investigate ecotourism status in Chabahar coastal zone using swot analysis and strategic planning. Firstly, the current status of region was studied by literature review, field survey and statistical analysis. Then strengths and weaknesses (internal factors) were identified as well as opportunities and threats (external factors) using Delphi Method. Based on the obtained results, the total score of 2.46 in IFE matrix and 2.33 in the EFE matrix represents poor condition related to the internal and external factors respectively. This condition means both external and internal factors have not been utilized properly and the zone needs defensive plan; thus appropriate planning and organizational management practices are required to deal with these factors. Furthermore strategic goals, objectives and action plans in short, medium and long term schedule were formulated in attention to swot analysis.

Keywords: tourism, SWOT analysis, strategic planning, Chabahar

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Authors: Sujing Wang, Song Wang, Jian Zhang, Qiang Xu

Abstract:

Flaring emissions during abnormal operating conditions such as plant start-ups, shut-downs, and upsets in chemical process industries (CPI) are usually significant. Flare minimization can help to save raw material and energy for CPI plants, and to improve local environmental sustainability. In this paper, a systematic methodology based on plant-wide dynamic simulation is presented for CPI plant flare minimizations under abnormal operating conditions. Since off-specification emission sources are inevitable during abnormal operating conditions, to significantly reduce flaring emission in a CPI plant, they must be either recycled to the upstream process for online reuse, or stored somewhere temporarily for future reprocessing, when the CPI plant manufacturing returns to stable operation. Thus, the off-spec products could be reused instead of being flared. This can be achieved through the identification of viable design and operational strategies during normal and abnormal operations through plant-wide dynamic scheduling, simulation, and optimization. The proposed study includes three stages of simulation works: (i) developing and validating a steady-state model of a CPI plant; (ii) transiting the obtained steady-state plant model to the dynamic modeling environment; and refining and validating the plant dynamic model; and (iii) developing flare minimization strategies for abnormal operating conditions of a CPI plant via a validated plant-wide dynamic model. This cost-effective methodology has two main merits: (i) employing large-scale dynamic modeling and simulations for industrial flare minimization, which involves various unit models for modeling hundreds of CPI plant facilities; (ii) dealing with critical abnormal operating conditions of CPI plants such as plant start-up and shut-down. Two virtual case studies on flare minimizations for start-up operation (over 50% of emission savings) and shut-down operation (over 70% of emission savings) of an ethylene plant have been employed to demonstrate the efficacy of the proposed study.

Keywords: flare minimization, large-scale modeling and simulation, plant shut-down, plant start-up

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Authors: Zuliziana Suif, Nordila Ahmad, Sengheng Hul

Abstract:

This paper evaluates the impact of climate change on suspended sediment changes in the Mekong River Basin. In this study, the distributed process-based sediment transport model is used to examine the potential impact of future climate on suspended sediment dynamic changes in the Mekong River Basin. To this end, climate scenarios from two General Circulation Model (GCMs) were considered in the scenario analysis. The simulation results show that the sediment load and concentration shows 0.64% to 69% increase in the near future (2041-2050) and 2.5% to 95% in the far future (2090- 2099). As the projected climate change impact on sediment varies remarkably between the different climate models, the uncertainty should be taken into account in sediment management. Overall, the changes in sediment load and concentration can have a great implication for related sediment management.

Keywords: climate change, suspended sediment, Mekong River Basin, GCMs

Procedia PDF Downloads 436

Authors: Xiao Wang, Qinge Zhang

Abstract:

Background: This randomized, open-label study examined the therapeutic effects of computerized cognitive training (CCT) combined with selective serotonin reuptake inhibitors (SSRIs) on cognitive impairment among patients with late-life depression (LLD). Method: Study data were collected from May 5, 2021, to April 21, 2023. Outpatients who met diagnostic criteria for major depressive disorder according to the fifth revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria (i.e., a total score on the 17-item Hamilton Depression Rating Scale (HAMD-17) ≥ 18 and a total score on the Montreal Cognitive Assessment scale (MOCA) <26) were randomly assigned to receive up to 12 weeks of CCT and SSRIs treatment (n=57) or SSRIs and Control treatment (n=61). The primary outcome was the change in Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) scores from baseline to week 12 between the two groups. The secondary outcomes included changes in the HAMD-17 score, Hamilton Anxiety Scale (HAMA) score and Neuropsychiatric Inventory (NPI) score. Mixed model repeated measures (MMRM) analysis was performed on modified intention-to-treat (mITT) and completer populations. Results: The full analysis set (FAS) included 118 patients (CCT and SSRIs group, n=57; SSRIs and Control group, n =61). Over the 12-week study period, the reduction in the ADAS-cog total score was significant (P < 0.001) in both groups, while MMRM analysis revealed a significantly greater reduction in cognitive function (ADAS-cog total scores) from baseline to posttreatment in the CCT and SSRIs group than in the SSRI and Control group [(F (1,115) =13.65, least-squares mean difference [95% CI]: −2.77 [−3.73, −1.81], p<0.001)]. There were significantly greater improvements in depression symptoms (measured by the HAMD-17) in the CCT and SSRIs group than in the control group [MMRM, estimated mean difference (SE) between groups −3.59 [−5.02, −2.15], p < 0.001]. The least-squares mean changes in the HAMA scores and NPI scores between baseline and week 8 were greater in the CCT and SSRIs group than in the control group (all P < 0.05). There was no significant difference between groups on response rates and remission rates by using the last-observation-carried-forward (LOCF) method (all P > 0.05). The most frequent adverse events (AEs) in both groups were dry mouth, somnolence, and constipation. There was no significant difference in the incidence of adverse events between the two groups. Conclusions: CCT combined with SSRIs was efficacious and well tolerated in LLD patients with cognitive impairment.

Keywords: late-life depression, cognitive function, computerized cognitive training, SSRIs

Procedia PDF Downloads 45