Search results for: fast Fourier algorithms

Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan

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Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.

Keywords: ADAS, home zone parking pilot, object detection, visual SLAM

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Authors: Baleeqs, O. Adegoke, Adeola, O. Aburime

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Juvenile delinquency is fast becoming one of the largest problems facing many societies due to many different factors ranging from parental factors to bullying at schools all which had led to different theoretical notions by different scholars. Delinquency is an illegal or immoral behaviour, especially by the young person who behaves in a way that is illegal or that society does not approve of. The purpose of the study was to investigate causes and effects of delinquent behaviours among adolescent in juvenile home in Osun State. A descriptive survey research type was employed. The random sampling technique was used to select 100 adolescents in Juvenile home in Osun State. Questionnaires were developed and given to them. The data collected from this study were analyzed using frequency counts and percentage for the demographic data in section A, while the two research hypotheses postulated for this study were tested using t-test statistics at the significance level of 0.05. Findings revealed that the greatest school effects of delinquent behaviours among adolescent in juvenile home in Osun by respondents were their aggressive behaviours. Findings revealed that there was a significant difference in the causes and effects of delinquent behaviours among adolescent in juvenile home in Osun State. It was also revealed that there was no significant difference in the causes and effects of delinquent behaviours among secondary school students in Osun based on gender. These recommendations were made in order to address the findings of this study: More number of teachers should be appointed in the observation home so that it will be possible to provide teaching to the different age group of delinquents. Developing the infrastructure facilities of short stay homes and observation home is a top priority. Proper counseling session’s interval is highly essential for these juveniles.

Keywords: behaviour, delinquency, juvenile, random sampling, statistical techniques, survey

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Anuradha Chug, Sunali Gandhi

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Regression Testing is a very expensive and time-consuming process carried out to ensure the validity of modified software. Due to the availability of insufficient resources to re-execute all the test cases in time constrained environment, efforts are going on to generate test data automatically without human efforts. Many search based techniques have been proposed to generate efficient, effective as well as optimized test data, so that the overall cost of the software testing can be minimized. The generated test data should be able to uncover all potential lapses that exist in the software or product. Inspired from the natural behavior of bat for searching her food sources, current study employed a meta-heuristic, search-based bat algorithm for optimizing the test data on the basis certain parameters without compromising their effectiveness. Mathematical functions are also applied that can effectively filter out the redundant test data. As many as 50 Java programs are used to check the effectiveness of proposed test data generation and it has been found that 86% saving in testing efforts can be achieved using bat algorithm while covering 100% of the software code for testing. Bat algorithm was found to be more efficient in terms of simplicity and flexibility when the results were compared with another nature inspired algorithms such as Firefly Algorithm (FA), Hill Climbing Algorithm (HC) and Ant Colony Optimization (ACO). The output of this study would be useful to testers as they can achieve 100% path coverage for testing with minimum number of test cases.

Keywords: regression testing, test case selection, test case prioritization, genetic algorithm, bat algorithm

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Authors: Kulin Dave, Kapil Mohan

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Earthquakes are considered to be the most destructive rapid-onset disasters human beings are exposed to. The amount of loss it brings in is sufficient to take careful considerations for designing of structures and facilities. Seismic Hazard Analysis is one such tool which can be used for earthquake resistant design. Ground Response Analysis is one of the most crucial and decisive steps for seismic hazard analysis. Rapar district of Kutch, Gujarat falls in Zone 5 of earthquake zone map of India and thus has high seismicity because of which it is selected for analysis. In total 8 bore-log data were studied at different locations in and around Rapar district. Different soil engineering properties were analyzed and relevant empirical correlations were used to calculate maximum shear modulus (Gmax) and shear wave velocity (Vs) for the soil layers. The soil was modeled using Pressure-Dependent Modified Kodner Zelasko (MKZ) model and the reference curve used for fitting was Seed and Idriss (1970) for sand and Darendeli (2001) for clay. Both Equivalent linear (EL), as well as Non-linear (NL) ground response analysis, has been carried out with Masing Hysteretic Re/Unloading formulation for comparison. Commercially available DEEPSOIL v. 7.0 software is used for this analysis. In this study an attempt is made to quantify ground response regarding generated acceleration time-history at top of the soil column, Response spectra calculation at 5 % damping and Fourier amplitude spectrum calculation. Moreover, the variation of Peak Ground Acceleration (PGA), Maximum Displacement, Maximum Strain (in %), Maximum Stress Ratio, Mobilized Shear Stress with depth is also calculated. From the study, PGA values estimated in rocky strata are nearly same as bedrock motion and marginal amplification is observed in sandy silt and silty clays by both analyses. The NL analysis gives conservative results of maximum displacement as compared to EL analysis. Maximum strain predicted by both studies is very close to each other. And overall NL analysis is more efficient and realistic because it follows the actual hyperbolic stress-strain relationship, considers stiffness degradation and mobilizes stresses generated due to pore water pressure.

Keywords: DEEPSOIL v 7.0, ground response analysis, pressure-dependent modified Kodner Zelasko model, MKZ model, response spectra, shear wave velocity

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Authors: Maxime A. J. Kuitche, Ruxandra M. Botez, Arthur Guillemin

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As the Unmanned Aerial Systems have found diverse utilities in both military and civil aviation, the necessity to obtain an accurate aerodynamic model has shown an enormous growth of interest. Recent modeling techniques are procedures using optimization algorithms and statistics that require many flight tests and are therefore extremely demanding in terms of costs. This paper presents a procedure to estimate the aerodynamic behavior of an unmanned aerial system from a numerical approach using computational fluid dynamic analysis. The study was performed using an unstructured mesh obtained from a grid convergence analysis at a Mach number of 0.14, and at an angle of attack of 0°. The flow around the aircraft was described using a standard k-ω turbulence model. Thus, the Reynold Averaged Navier-Stokes (RANS) equations were solved using ANSYS FLUENT software. The method was applied on the UAS-S45 designed and manufactured by Hydra Technologies in Mexico. The lift, the drag, and the pitching moment coefficients were obtained at different angles of attack for several flight conditions defined in terms of altitudes and Mach numbers. The results obtained from the Computational Fluid Dynamics analysis were compared with the results obtained by using the DATCOM semi-empirical procedure. This comparison has indicated that our approach is highly accurate and that the aerodynamic model obtained could be useful to estimate the flight dynamics of the UAS-S45.

Keywords: aerodynamic modelling, CFD Analysis, ANSYS FLUENT, UAS-S45

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Authors: Chitralekha Ngangbam, Aniruddha Mondal, Naorem Khelchand Singh

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Aluminium (Al) doped In2O3 (Indium Oxide) nanocolumn array was synthesized by glancing angle deposition (GLAD) technique on Si (n-type) substrate for photodetector application. The sample was characterized by scanning electron microscopy (SEM). The average diameter of the nanocolumn was calculated from the top view of the SEM image and found to be ∼80 nm. The length of the nanocolumn (~500 nm) was calculated from cross sectional SEM image and it shows that the nanocolumns are perpendicular to the substrate. The EDX analysis confirmed the presence of Al (Aluminium), In (Indium), O (Oxygen) elements in the samples. The XRD patterns of the Al-doped In2O3 nanocolumn show the presence of different phases of the Al doped In2O3 nanocolumn i.e. (222) and (622). Three different peaks were observed from the PL analysis of Al doped In2O3 nanocolumn at 365 nm, 415 nm and 435 nm respectively. The peak at PL emission at 365 nm can be attributed to the near band gap transition of In2O3 whereas the peaks at 415 nm and 435 nm can be attributed to the trap state emissions due to oxygen vacancies and oxygen–indium vacancy centre in Al doped In2O3 nanocolumn. The current-voltage (I–V) characteristics of the Al doped In2O3 nanocolumn based detector was measured through the Au Schottky contact. The devices were then examined under the halogen light (20 W) illumination for photocurrent measurement. The Al-doped In2O3 nanocolumn based optical detector showed high conductivity and low turn on voltage at 0.69 V under white light illumination. A maximum photoresponsivity of 82 A/W at 380 nm was observed for the device. The device shows a high internal gain of ~267 at UV region (380 nm) and ∼127 at visible region (760 nm). Also the rise time and fall time for the device at 650 nm is 0.15 and 0.16 sec respectively which makes it suitable for fast response detector.

Keywords: glancing angle deposition, nanocolumn, semiconductor, photodetector, indium oxide

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Authors: Damian Stanislaw Nakonieczny, Grazyna Simha Martynkova, Marianna Hundakova, G. Kratosová, Karla Cech Barabaszova

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The main aim of the research was to functionalize the surface of spherical aluminum silicates in the form of so-called cenospheres. Cenospheres are light ceramic particles with a density between 0.45 and 0.85 kgm-3 hat can be obtained as a result of separation from fly ash from coal combustion. However, their occurrence is limited to about 1% by weight of dry ash mainly derived from anthracite. Hence they are very rare and desirable material. Cenospheres are characterized by complete chemical inertness. Mohs hardness in range of 6 and completely smooth surface. Main idea was to prepare the surface by chemical etching, among others hydrofluoric acid (HF) and hydrogen peroxide, caro acid, silanization using (3-aminopropyl) triethoxysilane (APTES) and tetraethyl orthosilicate (TEOS) to obtain the maximum development and functionalization of the surface to improve chemical and mechanical connection with biomedically used polymers, i.e., polyacrylic methacrylate (PMMA) and polyetheretherketone (PEEK). These polymers are used medically mainly as a material for fixed and removable dental prostheses and PEEK spinal implants. The problem with their use is the decrease in mechanical properties over time and bacterial infections fungal during implantation and use of dentures. Hence, the use of a ceramic filler that will significantly improve the mechanical properties, improve the fluidity of the polymer during shape formation, and in the future, will be able to support bacteriostatic substances such as silver and zinc ions seem promising. In order to evaluate our laboratory work, several instrumental studies were performed: chemical composition and morphology with scanning electron microscopy with Energy-Dispersive X-Ray Probe (SEM/EDX), determination of characteristic functional groups of Fourier Transform Infrared Spectroscopy (FTIR), phase composition of X-ray Diffraction (XRD) and thermal analysis of Thermo Gravimetric Analysis/differentia thermal analysis (TGA/DTA), as well as assessment of isotherm of adsorption with Brunauer-Emmett-Teller (BET) surface development. The surface was evaluated for the future application of additional bacteria and static fungus layers. Based on the experimental work, it was found that orated methods can be suitable for the functionalization of the surface of cenosphere ceramics, and in the future it can be suitable as a bacteriostatic filler for biomedical polymers, i.e., PEEK or PMMA.

Keywords: bioceramics, composites, functionalization, surface development

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Authors: Youngjae Jin, Daeshik Kim

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This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.

Keywords: auto-encoder, behavior model simulation, digital hardware design, pre-route simulation, Unsupervised feature learning

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Authors: Rahul Jarariya

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The textile industry uses various types of dyes and discharges a lot of highly coloured wastewater. It impacts the environment like allergic reaction, respiratory, skin problems, irritation to a mucous membrane, the upper respiratory tract has to the fore, Intoxicated dye discharges 40 to 50,000 tons with great concern. Spinel ferrites gained a lot of attention due to their wide application area from biomedical to wastewater treatment. Generally, spinel ferrite is known as M-Fe2O4. Spinel type nanoparticles possess high suspension stability. The synthesis method of Microwave solution combustion (MC) method is effective for nanoscale materials, including oxides, metals, alloys, and sulfides, works as fast and energy-efficient during the process. The review focuses on controlling, nanostructure and doping. The influence of the fuel concentration and the post-treatment temperature on the structural and magnetic properties. The effects of amounts of fuel and phase changes, particle size and shape, and magnetic properties can be characterized by various techniques. Urea is the most commonly used fuel. Ethanol or n-butanol is apt for removing impurities. As a result of the materials gives fine purity. Photocatalysis phenomena act with catalyst dosage to degrade dye from wastewater. Visible light responsive produces a large amount of hydroxyl (•OH) radical made the degradation efficiency of Rh21 type dye. It develops a narrow bandgap to make it suitable for enhanced photocatalytic activity.

Keywords: microwave solution combustion method, normal spinel, doped spinels, magnetic property, Rb21

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Asghar Zamani, Mostafa Falahmorad

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Virtualization is established to combine and use the unique capabilities of employees to increase productivity and agility to provide services regardless of location. Adapting to fast and continuous change and getting maximum access to human resources are reasons why virtualization is happening. The distance problem is solved by information. Flexibility is the most important feature of virtualization, and information will be the main focus of virtualized companies. In this research, we used the Covid-19 opportunity window to assess the productivity of the companies that had been going through more virtualized management before the Covid-19 in comparison with those that just started planning on developing infrastructures on virtual management after the crises of pandemic occurred. The research process includes financial (profitability and customer satisfaction) and behavioral (organizational culture and reluctance to change) metrics assessment. In addition to financial and CRM KPIs, a questionnaire is devised to assess how manager and employees’ attitude has been changing towards the migration to virtualization. The sample companies and questions are selected by asking from experts in the IT industry of Iran. In this article, the conclusion is that companies open to virtualization based on accurate strategic planning or willing to pay to train their employees for virtualization before the pandemic are more agile in adapting to change and moving forward in recession. The prospective companies in this research, not only could compensate for the short period loss from the first shock of the Covid-19, but they could also foresee new needs of their customer sooner than other competitors, resulting in the need to employ new staff for executing the emerging demands. Findings were aligned with the literature review. Results can be a wake-up call for business owners especially in developing countries to be more resilient toward modern management styles instead of continuing with traditional ones.

Keywords: virtual management, virtual organization, competitive advantage, KPI, profit

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Authors: Omar Alsetoohy, Mohamed Ezzat, Mahmoud Abou Kamar

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The success of a restaurant or hotel depends very much on the quality and quantity of its human resources. Thus, establishing a competitive edge through human assets requires careful attention to the practices that best leverage these assets. Usually, hotel or restaurant employees recognize customer defection as an unfavorable or unpleasant occurrence associated with failure. These failures could be in handling, communication, learning, or encouragement. Besides, employees could be afraid of blame from their colleagues and managers, which prevents them from freely discussing these mistakes with them. Such behaviors, in turn, would push employees to withdraw from the workplace. However, we have a good knowledge of the leadership outcomes, but less is known about how and why these effects occur. Accordingly, mindful leaders usually analyze the causes and underlying mechanisms of failures for work improvement. However, despite the excessive literature in the field of leadership and employee behaviors, to date, no research studies had investigated the impact of a failure-tolerant restaurant culture on the employees’ withdrawal behaviors considering the moderating role of psychological empowerment and mindful leadership. Thus, this study seeks to investigate the impact of a failure-tolerant culture on the employees’ withdrawal behaviors in fast-food restaurants in Egypt considering the moderating effects of employee empowerment and mindful leaders. This study may contribute to the existing literature by filling the gap between failure-tolerant cultures and employee withdrawal behaviors in the hospitality literature. The study may also identify the best practices for restaurant operators and managers to deal with employees' failures as an improvement tool for their performance.

Keywords: failure-tolerant culture, employees’ withdrawal behaviors psychological empowerment, mindful leadership, restaurants

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: P. Byrnes, F. A. DiazDelaO

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The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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Authors: Kumlachew Zelalem Walle, Chun-Chen Yang

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Solid-state Lithium metal batteries (SSLMBs) that contain polymer and ceramic solid electrolytes have received considerable attention as an alternative to substitute liquid electrolytes in lithium metal batteries (LMBs) for highly safe, excellent energy storage performance and stability under elevated temperature situations. Here, a novel fast Li-ion conducting material, LiTa₂PO₈ (LTPO), was synthesized and electrochemical performance of as-prepared powder and LTPO-incorporated composite solid polymer electrolyte (LTPO-CPE) membrane were investigated. The as-prepared LTPO powder was homogeneously dispersed in polymer matrices, and a hybrid solid electrolyte membrane was synthesized via a simple solution-casting method. The room temperature total ionic conductivity (σt) of the LTPO pellet and LTPO-CPE membrane were 0.14 and 0.57 mS cm-1, respectively. A coin battery with NCM811 cathode is cycled under 1C between 2.8 to 4.5 V at room temperature, achieving a Coulombic efficiency of 99.3% with capacity retention of 74.1% after 300 cycles. Similarly, the LFP cathode also delivered an excellent performance at 0.5C with an average Coulombic efficiency of 100% without virtually capacity loss (the maximum specific capacity is at 27th: 138 mAh g−1 and 500th: 131.3 mAh g−1). These results demonstrates the feasibility of a high Li-ion conductor LTPO as a filler, and the developed polymer/ceramic hybrid electrolyte has potential to be a high-performance electrolyte for high-voltage cathodes, which may provide a fresh platform for developing more advanced solid-state electrolytes.

Keywords: li-ion conductor, lithium-metal batteries, composite solid electrolytes, liTa2PO8, high-voltage cathode

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Authors: Francis Kwame Bukari, Maclean Acheampong Yeboah

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From collective hunting to contemporary financial markets, the concept of risk sharing in insurance has evolved significantly. In today's insurance landscape, statistical analysis plays a pivotal role in determining premiums and assessing the likelihood of insurance claims. Accurately estimating motor insurance claims remains a challenge, allowing insurance companies to pull much of their money to cover claims, which in the long run will affect their reserves and impact their profitability. Advanced machine learning algorithms can enhance accuracy and profitability. The primary objectives of this study encompassed the prediction of motor insurance claims through the utilization of Artificial Neural Networks (ANN) and Random Forest (RF). Additionally, a comparative analysis was conducted to assess the performance of these two models in the domain of claim prediction. The study drew upon secondary data derived from motor insurance claims, employing a range of techniques, including data preprocessing, model training, and model evaluation. To mitigate potential biases, a random over-sampler was used to balance the target variable within the preprocessed dataset. The Random Forest model outperformed the ANN model, achieving an accuracy rate of 90.33% compared to the ANN model's accuracy of 86.33%. This study highlights the importance of modern data-driven approaches in enhancing accuracy and profitability in the insurance industry.

Keywords: risk, insurance claims, artificial neural network, random forest, over-sampler, profitability

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Authors: Lutfi AuliaRahman, Hari Rizki Ananda

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The traditional food is a typical food of a certain region that has a taste of its own unique and typically consumed by a society in certain areas, one of which is Gudeg, a regional specialties traditional food of Yogyakarta and Central Java which is made of young jackfruit cooked in coconut milk, edible with rice and served with thick coconut milk (areh), chicken, eggs, tofu and sambal goreng krecek. However, lately, the image of traditional food has declined among people, so with gudeg, which today's society, especially among young people, tend to prefer modern types of food such as fast food and some other foods that are popular. Moreover, traditional food usually only preferred by consumers of local communities and lack of demand by consumers from different areas for different tastes. Thus, the traditional food producers increasingly marginalized and their consumers are on the wane. This study aimed to evaluate the management used by producers of traditional food with a case study of Gudeg Sagan which located in the city of Yogyakarta, with the ability of their management in creating core competencies, which includes the competence of cost, competence of flexibility, competence of quality, competence of time, and value-based competence. And then, in addition to surviving and continuing to exist with the existing external environment, Gudeg Sagan can increase the number of consumers and also reach a broader segment of teenagers and adults as well as consumers from different areas. And finally, in this paper will be found positive impact on the creation of the core competencies of the existence and progress of the traditional food business based on case study of Gudeg Sagan.

Keywords: Gudeg Sagan, traditional food, core competencies, existence

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Authors: Boularas El Alia, H. Rezk-Allah, S. Chaoui, A. Chama, B. Rezk-Allah

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Aims: To assess the current exposure to the PHA among various workers in the sector of asphalt and road paving. Methods: The assessment of the exposure to PHA has been performed on workers (n=14) belonging to two companies, allocated into several activities such as road paving, manufacturing of coated bituminous warm, manufacturing of asphalt cut-back, manufacturing of emulsion of asphalt. A group of control subjects (n=18) was associated. The internal exposure to PHA was investigated by measurement of the urinary excretion of 2-naphtol, urine metabolite of naphtalene, one of the biomarkers of total PHA exposure. Urine samples were collected from the exposed workers, at the beginning of the week, at the beginning of the work shift (BWBS) and at the end of the work shift, at the end of the week (ESEW). In the control subjects, single samples of urine were collected after the end of the work shift.Every subject was invited to answer a questionnaire for the collection of technical and medical data as well as smoking habits and food intake. The concentration of 2-naphtol in the hydrolysate of urine was determined spectrophotometrically, after its reaction with the Fast Blue BB salt (diazotized 4-benzoylamino-2,5-diethoxyaniline). Results: For all the workers included in the study, the 2-urinary naphtol concentrations were higher than those in the control subjects (Median=9,55 µg/g creatinine) whether it is at (BWBS) (Md=16,2 µg/g creatinine) or at (ESEW) (n=18,Median=32,22 µg/g creatinine). Considerable differences are observed according to the category of job. The concentrations are also higher among smokers. Conclusion:The results show a significant exposure, mainly during manual laying, reveals an important risk particularly for the respiratory system.Considering the current criteria, carcinogenic risk due to the PHA seems not insignificant.

Keywords: PHA, asphalt, assessment, occupational, exposure

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Sudip Mondal, Biswanath Mondal, Sudit S. Mukhopadhyay, Apurba Dey

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Bioactive materials improve devices for a long lifespan but have mechanical limitations. Mechanical characterization is one of the very important characteristics to evaluate the life span and functionality of the scaffold material. After implantation of scaffold material the primary stage rejection of scaffold occurs due to non biocompatible effect of host body system. The second major problems occur due to the effect of mechanical failure. The mechanical and biocompatibility failure of the scaffold materials can be overcome by the prior evaluation of the scaffold materials. In this study chemically treated Labeo rohita scale is used for synthesizing hydroxyapatite (HAp) biomaterial. Thermo-gravimetric and differential thermal analysis (TG-DTA) is carried out to ensure thermal stability. The chemical composition and bond structures of wet ball-milled calcined HAp powder is characterized by Fourier Transform Infrared spectroscopy (FTIR), X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), Transmission Electron Microscopy (TEM), Energy Dispersive X-ray (EDX) analysis. Fish scale derived apatite materials consists of nano-sized particles with Ca/P ratio of 1.71. The biocompatibility through cytotoxicity evaluation and MTT assay are carried out in MG63 osteoblast cell lines. In the cell attachment study, the cells are tightly attached with HAp scaffolds developed in the laboratory. The result clearly suggests that HAp material synthesized in this study do not have any cytotoxic effect, as well as it has a natural binding affinity for mammalian cell lines. The synthesized HAp powder further successfully used to develop porous scaffold material with suitable mechanical property of ~0.8GPa compressive stress, ~1.10 GPa a hardness and ~ 30-35% porosity which is acceptable for implantation in trauma region for animal model. The histological analysis also supports the bio-affinity of processed HAp biomaterials in Wistar rat model for investigating the contact reaction and stability at the artificial or natural prosthesis interface for biomedical function. This study suggests the natural sourced fish scale-derived HAp material could be used as a suitable alternative biomaterial for tissue engineering application in near future.

Keywords: biomaterials, hydroxyapatite, scaffold, mechanical property, tissue engineering

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Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

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Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

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Authors: Aya Belal, Ahmed Maher Eltair, Maggie Ahmed Mashaly

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Geopolymer concrete is gaining recognition as a sustainable alternative to conventional Portland Cement concrete due to its environmentally friendly nature, which is a key goal for Smart City initiatives. It has demonstrated its potential as a reliable material for the design of structural elements. However, the production of Geopolymer concrete is hindered by batch-to-batch variations, which presents a significant challenge to the widespread adoption of Geopolymer concrete. To date, Machine learning has had a profound impact on various fields by enabling models to learn from large datasets and predict outputs accurately. This paper proposes an integration between the current drift to Artificial Intelligence and the composition of Geopolymer mixtures to predict their mechanical properties. This study employs Python software to develop machine learning model in specific Decision Trees. The research uses the percentage oxides and the chemical composition of the Alkali Solution along with the curing conditions as the input independent parameters, irrespective of the waste products used in the mixture yielding the compressive strength of the mix as the output parameter. The results showed 90 % agreement of the predicted values to the actual values having the ratio of the Sodium Silicate to the Sodium Hydroxide solution being the dominant parameter in the mixture.

Keywords: decision trees, geopolymer concrete, machine learning, smart cities, sustainability

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Authors: Bagul Abhijeet

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Background: Client-Server model is the backbone of today’s internet communication. In which normal user can not have control over particular website or server? By using the same processing model one can have unauthorized access to particular server. In this paper, we discussed about application scenario of hacking for simple website or server consist of unauthorized way to access the server database. This application emerges to autonomously take direct access of simple website or server and retrieve all essential information maintain by administrator. In this system, IP address of server given as input to retrieve user-id and password of server. This leads to breaking administrative security of server and acquires the control of server database. Whereas virus helps to escape from server security by crashing the whole server. Objective: To control malicious attack and preventing all government website, and also find out illegal work to do hackers activity. Results: After implementing different hacking as well as non-hacking techniques, this system hacks simple web sites with normal security credentials. It provides access to server database and allow attacker to perform database operations from client machine. Above Figure shows the experimental result of this application upon different servers and provides satisfactory results as required. Conclusion: In this paper, we have presented a to view to hack the server which include some hacking as well as non-hacking methods. These algorithms and methods provide efficient way to hack server database. By breaking the network security allow to introduce new and better security framework. The terms “Hacking” not only consider for its illegal activities but also it should be use for strengthen our global network.

Keywords: Hacking, Vulnerabilities, Dummy request, Virus, Server monitoring

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Authors: Wang Qiaowei, Gao Yujiang

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With the economic growth and rapid urbanization after the reform and openness, some of China's fast-growing cities have demolished former dwellings and built modern residential quarters. The blind, incomplete reference to western modern cities and the one-off construction lacking feedback mechanism have intensified such phenomenon, causing the citizen gradually expanded their living scale with the popularization of car traffic, and the peer-to-peer lifestyle gradually settled. The construction of large-scale commercial centers has caused obstacles to small business around the residential areas, leading to space for residents' interaction has been compressed. At the same time, the advocated Central Business District (CBD) model even leads to the unsatisfactory reconstruction of many historical blocks such as the Hangzhou Southern Song Dynasty Imperial Street. However, the popularity of historical spaces such as Wuzhen and Hongcun also indicates the collective memory and needs of the street space for Chinese residents. The evolution of Shanghai TianZiFang also proves the importance of the motivation of space participants in space construction in the context of the “top-down” construction model in China. In fact, there are frequent occurrences of “reconstruction”, which may redefine the space, in various residential areas. If these activities can be selectively controlled and encouraged, it will be beneficial to activate the public space as well as the residents’ intercourse, so that the traditional Chinese street space can be reconstructed in the context of modern cities.

Keywords: rapid urbanization, traditional street space, space re-construction, bottom-up design

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Authors: Riad Benelmir, Muhammad Shoaib Ahmed Khan

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The socio-economic importance of the olive oil production is significant in the Mediterranean region, both in terms of wealth and tradition. However, the extraction of olive oil generates huge quantities of wastes that may have a great impact on land and water environment because of their high phytotoxicity. Especially olive mill wastewater (OMWW) is one of the major environmental pollutants in olive oil industry. This work projects to design a smart and sustainable integrated thermochemical catalytic processes of residues from olive mills by hydrothermal carbonization (HTC) of olive mill wastewater (OMWW) and fast pyrolysis of olive mill wastewater sludge (OMWS). The byproducts resulting from OMWW-HTC treatment are a solid phase enriched in carbon, called biochar and a liquid phase (residual water with less dissolved organic and phenolic compounds). HTC biochar can be tested as a fuel in combustion systems and will also be utilized in high-value applications, such as soil bio-fertilizer and as catalyst or/and catalyst support. The HTC residual water is characterized, treated and used in soil irrigation since the organic and the toxic compounds will be reduced under the permitted limits. This project’s concept includes also the conversion of OMWS to a green diesel through a catalytic pyrolysis process. The green diesel is then used as biofuel in an internal combustion engine (IC-Engine) for automotive application to be used for clean transportation. In this work, a theoretical study is considered for the use of heat from the pyrolysis non-condensable gases in a sorption-refrigeration machine for pyrolysis gases cooling and condensation of bio-oil vapors.

Keywords: biomass, olive oil extraction, adsorption cooling, pyrolisis

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Authors: Sajjad Esmaeili Aghdam

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Economics typically conceptualizes individual behavior as the consequence of external states, for example, budgets and prices (or respective beliefs) and choices. As the main goal, we focus on the influence of a range of Behavioral Economics factors on Strategies of Digital Marketing, evaluation of strategies and deformation of it into highly prospective marketing strategies. The different forms of behavioral prospects all lead to the succeeding two main results. First, the steadiness of the economic dynamics in a currency union be contingent fatefully on the level of economic incorporation. More economic incorporation leads to more steady economic dynamics. Electronic word-of-mouth (eWOM) is “all casual communications focused at consumers through Internet-based technology connected to the usage or characteristics of specific properties and services or their venders.” eWOM can take many methods, the most significant one being online analyses. Writing this paper, 72 articles have been gathered, focusing on the title and the aim of the article from research search engines like Google Scholar, Web of Science, and PubMed. Recent research in strategic management and marketing proposes that markets should not be viewed as a given and deterministic setting, exogenous to the firm. Instead, firms are progressively abstracted as dynamic inventors of market prospects. The use of new technologies touches all spheres of the modern lifestyle. Social and economic life becomes unbearable without fast, applicable, first-class and fitting material. Psychology and economics (together known as behavioral economics) are two protruding disciplines underlying many theories in marketing. The wide marketing works papers consumers’ none balanced behavior even though behavioral biases might not continuously be steadily called or officially labeled.

Keywords: behavioral economics, digital marketing, marketing strategy, high impact strategies

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Authors: Md. Kumruzzaman, Anutosh Das, Md. Mosharraf Hossain

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Historically, rapid urban growth activities are considered one of the main culprits behind urban floods or waterlogging. The increased unplanned urbanization of many areas of Dhaka has resulted in waterlogging, urban floods, and increasing groundwater depth. To determine potential groundwater recharge from precipitation, the study is being conducted to examine the changes in land use/land cover (LULC) and urban runoff extent based on the NRCS-CN from 2005–2021. Four kinds of land use are used to examine the LULC change: built-up, bare land, vegetation, and water body. These categories are used for the years 2005, 2010, 2015, and 2021. The built-up area is growing at a relatively fast rate: 7.43%, 17.4%, and 5.21%, respectively, between the years 2005 and 2010, 2010 and 2015, and 2015 and 2021. As the amount of impervious surface rose in Dhaka city, stormwater discharge increased from 2005 to 2021. In 2005, 2010, 2015, and 2021, heavy stormwater runoff regions made up around 24.873%, 32.616%, 49.118%, and 55.986% of the entire Dhaka city. Stormwater runoff accounted for around 53.738%, 55.092%, 63.472%, and 67.061% of the total rainfall in 2005, 2010, 2015, and 2021, respectively. Between 2005 and 2021, a significant portion of the natural land cover was altered because of the expanding impervious surface, which also harmed the natural drainage system. Due to careless growth, the potential for stormwater runoff and groundwater recharge in Dhaka city worsens every year. Concerning this situation, a sustainable urban drainage system (SUDS) can be the best possible solution for minimizing the stormwater runoff and groundwater recharge problem.

Keywords: LULC, impervious surface, stormwater runoff, groundwater recharge, SUDS

Procedia PDF Downloads 83