Search results for: predictive performance

Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Hossein Emari, Hossein Vazifehdoust, Hashem Nikoo Maram

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This study aims to develop an original framework of Environmental Consciousness (EC) to explore the positive effect of environmental consciousness on financial performance through the partial mediator - green intellectual capital. A questionnaire survey on the environmental consciousness, intellectual capital, and financial performance of Iran’s manufacturing firms was conducted, and 324 samples were analyzed. This study utilizes structural equation modeling to explore the direct and indirect influences of EC on financial performance. Research results reveal that environmental consciousness had an indirect impact on financial performance through investment in green intellectual capital. It was thus known that green intellectual capital is a mediator of the relationship between environmental consciousness and financial performance. This paper may serve as a reference for firms mapping out future environmental policies and provide an input of various perspectives and arguments into the discipline of green management.

Keywords: environmental consciousness, social responsibility, green intellectual capital, financial performance

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Authors: Khadija Mushtaq, Mian Saqib Mehmood

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This paper attempts to determine the impact of leadership style and learning orientation on organizational performance in Pakistan. A sample of 158 middle managers selected from sports and surgical factories from Sialkot. The empirical estimation is based on a multiple linear regression analysis of the relationship between leadership style, learning orientation and organizational performance. Leadership style is measure through transformational leadership and transactional leadership. The transformational leadership has insignificant impact on organizational performance. The transactional leadership has positive and significant relation with organizational performance. Learning orientation also has positive and significant relation with organizational performance. Linear regression used to estimate the relation between dependent and independent variables. This study suggests top manger should prefer continuous process for improvement for any change in system rather radical change.

Keywords: transformational leadership, transactional leadership, learning orientation, organizational performance, Pakistan

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Authors: Kemalettin Eryesil, Osman Esmen, Aykut Beduk

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In this study, it has been studied to determine the relationship between business performance and strategic flexibility, which is defined to be the strategic choice that provides the ability of rapidly responding the changes of the dynamic environment of the companies, for having competitive advantages. In this context a field study has been conducted over 56 companies, which are active in informatics and electronics sectors in TEKNOKENT. As a result of the study it has been determined that; strategic flexibility has an effect on business performance and there is a positive and statistically significant relationship between strategic flexibility and business performance.

Keywords: sustainable competition advantage, strategic flexibility, firm performance, TEKNOKENT

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Authors: Omar Al Ali

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The current study was aimed to explore the relationships between emotional intelligence, cognitive ability, and leader's performance. Data were collected from 260 senior managers from UAE. The results showed that there are significant relationships between emotional intelligence and leadership performance as measured by the annual internal evaluations of each participant (r = .42, p < .01). Data from regression analysis revealed that both variables namely emotional intelligence (beta = .31, p < .01), and cognitive ability (beta = .29, p < .01), predicted leadership competencies, and together explained 26% of its variance. Data suggests that EI and cognitive ability are significantly correlated with leadership performance. In depth implications of the present findings for human resource development theory and practice are discussed.

Keywords: emotional intelligence, cognitive ability, leadership, performance

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Authors: Leilei Liang

Abstract:

Leadership is commonly classified as formal leadership and informal leadership, which ignores and neglects the effects of 3rd type leadership. The emergence of 3rd type of leadership is closely related to special relations. To figure out the mechanism and effects of 3rd type leadership as well as the impacts of formal leadership and informal leadership on employee performance, this study collects data from 350 participants through a survey and proposes three hypotheses respectively from the perspective of expectation theory. The analytical results provide strong evidence for two of the three hypotheses, which demonstrate the positive correlation between formal leadership and individual performance and the negative relationship between 3rd type leadership and individual performance. This study contributes to leadership literature by putting forward the concept of the 3rd type of leadership. In addition, the effects of formal leadership, informal leadership, and 3rd type leadership on individual performance are discussed respectively in this study.

Keywords: formal leadership, informal leadership, 3rd leadership, individual performance, expectation theory

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Authors: Asma Ammar

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Being different in theory and practice from their conventional counterparts, this research aims to assess the performance of Islamic banks beyond the financial performance by emphasizing their ethical and social identity based on the higher purposes of Islamic law, namely Maqasid al-Shariah. Using Imam al-Ghazali’s theory of Maqasid al-Shariah and Sekaran’s (2000) method, we develop a Maqasid-based index including the five objectives of Shariah (preservation of life, religion, intellect, posterity, and wealth). Our sample covers 9 Islamic banks considered among the largest Islamic banks in the world. For the five years of study (2017-2021), our results reveal that the highest score is performed by Bank Muamalat while the least score is given to Dubai Islamic Bank. The overall Maqasid performance of the sample is unimpressive, indicating that there is a lack of achievement in Maqasid al-Shariah performance of Islamic banks. Consequently, serious measures should be taken by Islamic banks to improve their Maqasid performance and thus contribute effectively to the socio-economic development of the countries in which they operate.

Keywords: Maqasid al-Shariah, Maqasid al-Shariah index, Islamic banks, performance assessment

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Authors: Andrew Gray, Zhibing Zhang

Abstract:

The ability for consumer products to deliver a sustained perfume response can be a key driver for a variety of applications. Many compounds in perfume oils are highly volatile, meaning they readily evaporate once the product is applied, and the longevity of the scent is poor. Perfume capsules have been introduced as a means of abating this evaporation once the product has been delivered. The impermeable capsules are aimed to be stable within the formulation, and remain intact during delivery to the desired substrate, only rupturing to release the core perfume oil through application of mechanical force applied by the consumer. This opens up the possibility of obtaining an olfactive response hours, weeks or even months after delivery, depending on the nature of the desired application. Tailoring the properties of the polymeric capsules to better address the needs of the application is not a trivial challenge and currently design of capsules is largely done by trial and error. The aim of this work is to have more predictive methods for capsule design depending on the consumer application. This means refining formulations such that they rupture at the right time for the specific consumer application, not too early, not too late. Finding the right balance between these extremes is essential if a benefit is sought with respect to neat addition of perfume to formulations. It is important to understand the forces that influence capsule rupture, first, by quantifying the magnitude of these different forces, and then by assessing bulk rupture in real-world applications to understand how capsules actually respond. Samples were provided by an industrial partner and the mechanical properties of individual capsules within the samples were characterized via a micromanipulation technique, developed by Professor Zhang at the University of Birmingham. The capsules were synthesized such as to change one particular physicochemical property at a time, such as core: wall material ratio, and the average size of capsules. Analysis of shell thickness via Transmission Electron Microscopy, size distribution via the use of a Mastersizer, as well as a variety of other techniques confirmed that only one particular physicochemical property was altered for each sample. The mechanical analysis was subsequently undertaken, showing the effect that changing certain capsule properties had on the response under compression. It was, however, important to link this fundamental mechanical response to capsule performance in real-world applications. As such, the capsule samples were introduced to a formulation and exposed to full scale stresses. GC-MS headspace analysis of the perfume oil released from broken capsules enabled quantification of what the relative strengths of capsules truly means for product performance. Correlations have been found between the mechanical strength of capsule samples and performance in terms of perfume release in consumer applications. Having a better understanding of the key parameters that drive performance benefits the design of future formulations by offering better guidelines on the parameters that can be adjusted without worrying about the performance effects, and singles out those parameters that are essential in finding the sweet spot for capsule performance.

Keywords: consumer products, mechanical and physicochemical properties, perfume capsules, rupture behaviour

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

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Authors: Chico Horacio Jose Sambo

Abstract:

Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Austin Oduor Otieno

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There is an ingrained belief that the use of performance-enhancing drugs by athletes enable them to perform better. While this has been found to be truth, it also raises ethical and health issues. This paper analyzes the health hazards associated with performance enhancing drugs. It seeks to achieve this through the analysis of different academic journals as well as publications on the relationship between doping in sports and health. It concludes that there are inherent health hazards associated with the use of performance-enhancing drugs as they affect the physical and psychological health and wellbeing of a user (athlete).

Keywords: doping, health hazards, athletes, drugs

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Authors: Jiang Fan

Abstract:

With growth of PV market in tropical region, it is necessary to investigate the performance of different types of PV technology under the tropical weather conditions. Singapore Polytechnic was funded by Economic Development Board (EDB) to set up a solar PV test-bed for the research on performance of different types of PV modules in the country. The PV test-bed installed the nine different types of PV systems that are integrated to power utility grid for monitoring and analyzing their operating performances. This paper presents the 12 months operational data of nine different PV systems and analyses on performances of installed PV systems using energy yield and performance ratio. The nine types of PV systems under test have shown their energy yields ranging from 2.67 to 3.36 kWh/kWp and their performance ratios (PRs) ranging from 70% to 88%.

Keywords: monocrystalline, multicrystalline, amorphous silicon, cadmium telluride, thin film PV

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Authors: Joseph Salim

Abstract:

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: Derya Yilmaz, Ali Murat Tanyer, Irem Dikmen Toker

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There are many studies addressing the discrepancy between the planned and actual performance of buildings, which is defined as the energy performance gap. The difference between expected and actual project results usually depends on risky events and how these risks are managed throughout the project. This study presents a systematic review of the literature about the energy performance gap in buildings. First of all, a brief history and definitions of the energy performance gap are given. The initial search string is applied on Scopus and Web of Science databases. Research activities in years, main research interests, the co-occurrence of keywords based on average publication year are given. Scientometric analyses are conducted using Vosviewer. After the review, the papers are grouped to thematic relevance. This research will create a basis for analyzing the research focus, methods, limitations, and research gaps of key papers in the field.

Keywords: energy performance gap, discrepancy, energy efficient buildings, green buildings

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Authors: Emin Gundogar, Aysegul Yilmaz

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Strategic performance scoring is a significant procedure in management. There are various methods to improve this procedure. This study introduces an information system that is developed to score performance for municipal management. The application of the system is clarified by exemplifying municipal processes.

Keywords: management information system, municipal management, performance scoring

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Authors: M. Celeska, K. Najdenkoski, V. Dimchev, V. Stoilkov

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Accurate determination of wind turbine performance is necessary for economic operation of a wind farm. At present, the procedure to carry out the power performance verification of wind turbines is based on a standard of the International Electrotechnical Commission (IEC). In this paper, nonparametric statistical inference is applied to designing a simple, inexpensive method of verifying the power performance of a wind turbine. A statistical test is explained, examined, and the adequacy is tested over real data. The methods use the information that is collected by the SCADA system (Supervisory Control and Data Acquisition) from the sensors embedded in the wind turbines in order to carry out the power performance verification of a wind farm. The study has used data on the monthly output of wind farm in the Republic of Macedonia, and the time measuring interval was from January 1, 2016, to December 31, 2016. At the end, it is concluded whether the power performance of a wind turbine differed significantly from what would be expected. The results of the implementation of the proposed methods showed that the power performance of the specific wind farm under assessment was acceptable.

Keywords: canonical correlation analysis, power curve, power performance, wind energy

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Inayat Ur Rehman, Georgia Sakellari

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Cloud computing has evolved into a vital technology for businesses, offering scalability, flexibility, and cost-effectiveness. However, maintaining high availability and optimal performance in the cloud is crucial for reliable services. This paper explores the significance of cloud monitoring and performance optimization in sustaining the high availability of cloud-based systems. It discusses diverse monitoring tools, techniques, and best practices for continually assessing the health and performance of cloud resources. The paper also delves into performance optimization strategies, including resource allocation, load balancing, and auto-scaling, to ensure efficient resource utilization and responsiveness. Addressing potential challenges in cloud monitoring and optimization, the paper offers insights into data security and privacy considerations. Through this thorough analysis, the paper aims to underscore the importance of cloud monitoring and performance optimization for ensuring a seamless and highly available cloud computing environment.

Keywords: cloud computing, cloud monitoring, performance optimization, high availability

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Authors: Ahmad Badawi Saluy, Novawiguna Kemalasari

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This study aims to examine and analyze the influence of organizational culture, organizational commitment, and transformational leadership style on employee performance. This study used descriptive survey method with quantitative approach, and questionnaires as a tool used for basic data collection. The sampling technique used is proportionate stratified random sampling technique; all respondents in this study were 70 respondents. The analytical method used in this research is multiple linear regressions. The result of determination coefficient of 52.3% indicates that organizational culture, organizational commitment, and transformational leadership style simultaneously have a significant influence on the performance of employees, while the remaining 47.7% is explained by other factors outside the research variables. Partially, organization culture has strong and positive influence on employee performance, organizational commitment has a moderate and positive effect on employee performance, while the transformational leadership style has a strong and positive influence on employee performance and this is also the variable that has the most impact on employee performance.

Keywords: organizational culture, organizational commitment, transformational leadership style, employee performance

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Authors: Johannes Barlang

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Adhesive bonding plays a pivotal role in various industrial applications, ranging from automotive manufacturing to aerospace engineering. The peel strength of adhesives, a critical parameter reflecting the ability of an adhesive to withstand external forces, is crucial for ensuring the integrity and durability of bonded joints. This study provides a synopsis of the methodologies, influencing factors, and significance of peel testing in the evaluation of adhesive performance. Peel testing involves the measurement of the force required to separate two bonded substrates under controlled conditions. This study systematically reviews the different testing techniques commonly applied in peel testing, including the widely used 180-degree peel test and the T-peel test. Emphasis is placed on the importance of selecting an appropriate testing method based on the specific characteristics of the adhesive and the application requirements. The influencing factors on peel strength are multifaceted, encompassing adhesive properties, substrate characteristics, environmental conditions, and test parameters. Through an in-depth analysis, this study explores how factors such as adhesive formulation, surface preparation, temperature, and peel rate can significantly impact the peel strength of adhesively bonded joints. Understanding these factors is essential for optimizing adhesive selection and application processes in real-world scenarios. Furthermore, the study highlights the role of peel testing in quality control and assurance, aiding manufacturers in maintaining consistent adhesive performance and ensuring the reliability of bonded structures. The correlation between peel strength and long-term durability is discussed, shedding light on the predictive capabilities of peel testing in assessing the service life of adhesive bonds. In conclusion, this study underscores the significance of peel testing as a fundamental tool for characterizing adhesive performance. By delving into testing methodologies, influencing factors, and practical implications, this study contributes to the broader understanding of adhesive behavior and fosters advancements in adhesive technology across diverse industrial sectors.

Keywords: adhesively bonded joints, cleavage resistance, elastic adhesives, peel strength

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Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

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Authors: V. Naciti, L. Pulejo, F. Cesaroni

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This study empirically analyzes whether the composition of the board of directors (BoD) enhances sustainability performance, in order to understand how the BoD contribute to the integration of Sustainable Development Goals (SDGs) in their businesses. Hypotheses are developed based on the agency theory and stakeholder theory. Using a system generalized method of the moment (SGMM) two-step estimator, with data from Sustainalytics and Compustat databases for 362 firms in six regions, we find that firms with more diversity on the board and a separation of chair and CEO roles have higher sustainability performance. Moreover, our findings provide that a higher number of independent directors is negatively associated with sustainability performance. This study contributes to the literature on corporate governance and the firm’s performance by demonstrating that the composition of the board of directors contributes to a better sustainability performance: by the implementation of a particular corporate governance mechanism, it is possible to integrate SDGs in the corporate strategy.

Keywords: sustainable development goals, corporate governance, board of directors, sustainability performance

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Authors: Bahram Fathi, Mahdi Khodaparast Mashhadi, Masuod Homayounifar

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Global awareness on energy security and climate change has created much interest in assessing energy efficiency performance. A number of previous studies have contributed to evaluate energy efficiency performance using different analytical techniques among which data envelopment analysis (DEA) has recently received increasing attention. Most of DEA-related energy efficiency studies do not consider undesirable outputs such as CO2 emissions in their modeling framework, which may lead to biased energy efficiency values. Within a joint production frame work of desirable and undesirable outputs, in this paper we construct energy efficiency performance index for measuring energy efficiency performance by using environmental DEA model with CO2 emissions. We finally apply the index proposed to assess the energy efficiency performance in OPEC over time.

Keywords: energy efficiency, environmental, OPEC, data envelopment analysis

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Authors: Yingjie Zhang, Ming Li, Ying Zhang, Jing Zhang, Zuolei Hu

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In the actual start-up process of DC motors, the DC drive system often faces a conflict between energy consumption and acceleration performance. To resolve the conflict, this paper proposes a comprehensive performance index that energy consumption index is added on the basis of classical control performance index in the DC motor starting process. Taking the comprehensive performance index as the cost function, particle swarm optimization algorithm is designed to optimize the comprehensive performance. Then it conducts simulations on the optimization of the comprehensive performance of the DC motor on condition that the weight coefficient of the energy consumption index should be properly designed. The simulation results show that as the weight of energy consumption increased, the energy efficiency was significantly improved at the expense of a slight sacrifice of fastness indicators with the comprehensive performance index method. The energy efficiency was increased from 63.18% to 68.48% and the response time reduced from 0.2875s to 0.1736s simultaneously compared with traditional proportion integrals differential controller in energy saving.

Keywords: comprehensive performance index, energy consumption, acceleration performance, particle swarm optimal control

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Authors: P. F. Wong, H. Salleh, F. A. Rahim

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The adoption of building information modeling (BIM) is increasing in the construction industry. However, quantity surveyors are slow in adoption compared to other professions due to lack of awareness of the BIM’s potential in their profession. It is still unclear on how BIM application can enhance quantity surveyors’ work performance and project performance. The aim of this research is to identify the capabilities of BIM in quantity surveying practices and examine the relationship between BIM capabilities and project performance. Questionnaire survey and interviews were adopted for data collection. Literature reviews identified there are eleven BIM capabilities in quantity surveying practice. Questionnaire results showed that there are several BIM capabilities significantly correlated with project performance in time, cost and quality aspects and the results were validated through interviews. These findings show that BIM has the capabilities to enhance quantity surveyors’ performances and subsequently improved project performance.

Keywords: Building Information Modeling (BIM), quantity surveyors, capability, project performance

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Authors: Yan Wang, Yuan George Shan

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This study examines whether earnings management reduces firm performance and how other comprehensive income (OCI) disclosure and strong corporate governance restrain earnings management. Using a data set comprising 6,260 firm-year observations from listed companies on the Shanghai and Shenzhen Stock Exchanges during 2009–2015, the results indicate that OCI disclosure generally improves firm performance, but earnings management lowers firm performance. The study also finds that OCI disclosure and corporate governance are complementary in restraining earnings manipulation and promote firm performance. The implications of the findings are relevant policy-makers and regulators in assisting them evaluate the consequences of convergence of Chinese Accounting Standards with the International Financial Reporting Standards.

Keywords: other comprehensive income, corporate governance, earnings management, firm performance, China

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Authors: Shun Horikoshi, Tohru Kawabe

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This study presents performance analysis results of SMC (Sliding mode control) with changing the chattering functions applied to slip suppression problem of electric vehicles (EVs). In SMC, chattering phenomenon always occurs through high frequency switching of the control inputs. It is undesirable phenomenon and degrade the control performance, since it causes the oscillations of the control inputs. Several studies have been conducted on this problem by introducing some general saturation function. However, study about whether saturation function was really best and the performance analysis when using the other functions, weren’t being done so much. Therefore, in this paper, several candidate functions for SMC are selected and control performance of candidate functions is analyzed. In the analysis, evaluation function based on the trade-off between slip suppression performance and chattering reduction performance is proposed. The analyses are conducted in several numerical simulations of slip suppression problem of EVs. Then, we can see that there is no difference of employed candidate functions in chattering reduction performance. On the other hand, in slip suppression performance, the saturation function is excellent overall. So, we conclude the saturation function is most suitable for slip suppression sliding mode control.

Keywords: sliding mode control, chattering function, electric vehicle, slip suppression, performance analysis

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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