Search results for: robust estimators

Authors: Nathalia Florencia Barros Azeredo, Josué Martins Gonçalves, Pamela De Oliveira Rossini, Koiti Araki, Lucio Angnes

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This work demonstrates the electroanalysis of uric acid (UA) at very low working potential (0 V vs Ag/AgCl) directly in body fluids such as saliva and sweat using electrodes modified with mixed -Ni0.75Zn0.25(OH)2 nanoparticles exhibiting stable electrocatalytic responses from alkaline down to weakly acidic media (pH 14 to 3 range). These materials were prepared for the first time and fully characterized by TEM, XRD, and spectroscopic techniques. The electrochemical properties of the modified electrodes were evaluated in a fast and simple procedure for uric acid analyses based on cyclic voltammetry and chronoamperometry, pushing down the detection and quantification limits (respectively of 2.3*10-8 and 7.6*10-8 mol L-1) with good repeatability (RSD = 3.2% for 30 successive analyses pH 14). Finally, the possibility of real application was demonstrated upon realization of unexpectedly robust and sensitive modified FTO (fluorine doped tin oxide) glass and screen-printed sensors for measurement of uric acid directly in real saliva and sweat samples, with no significant interference of usual concentrations of ascorbic acid, acetaminophen, lactate and glucose present in those body fluids (Fig. 1).

Keywords: nickel hydroxide, mixed catalyst, uric acid sensors, biofluids

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Authors: Endale Fantahun Tadesse, Sabika Khalid

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The global call for increased male representation in early childhood education (ECE) has garnered significant attention. Emerging studies have indicated that involving men in ECE can yield positive outcomes for children's physical and psychological development. Challenging the prevailing misconception and stereotype that women dominate the ECE sector is crucial. In light of this, the present study undertakes a systematic review of nine studies on males working in ECE, revealing a dearth of male presence in the field in China as well. To address this issue, substantial structural changes must be implemented to enhance the inadequate pay and working conditions that dissuade both men and women from pursuing a sustainable career in ECE. It is recommended that school leadership raise awareness among female teachers and parents, encouraging them to support and uphold virtuous values for male teachers. Additionally, governing bodies should provide explicit guidelines during training programs to address concerns regarding potential abuse and gender biases. The findings of this review underscore the need for future studies to examine the self-identities of male teachers from various stakeholders' perspectives and explore the consequences of being in the profession through rigorous and robust methodologies that can inform policymakers.

Keywords: male teachers, Early Childhood Education (ECE), self-identity, perception of stakeholders

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Authors: Yan Yu, Wang Yu, Chen Xintong, Liu Yi, Zhang Yanzhong, Wang Yanji, Chen Xingyu, Zhang Miaocheng, Tong Yi

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Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to its high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO₂-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO₂-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.

Keywords: leaky integrate-and-fire, memristor, spiking neural networks, spiking-time-dependent-plasticity

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: Alisha Fisher

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Sex trafficking is a worldwide crisis that requires trauma-informed and survivor-centered media attention to accurate disseminate information. Much of the previous literature on sex trafficking tends to focus on the frequency of incidents, intervention, and support strategies for survivors, with few of them looking to how the media is conducting their reporting on sex trafficking cases to the public. Utilizing data of reports from the media of cases of sex trafficking in the two Canadian provinces with the highest cases of sex trafficking, Ontario and Nova Scotia, the authors sought to analyze the similarities and differences of how sex trafficking cases were being reported. A total of twenty articles were examined, with ten based within the province of Ontario and the remaining ten from the province of Nova Scotia. The authors coded in two processes, first, who the article was about, and second, the framing and content inclusion. The results suggest that there is high usage and reliance of voices and images of authority, with male people of color being shown as the perpetrators and white women being shown as the survivors. These findings can aid in the expansion of trauma-informed, survivor-centered media literacy of reports of sex trafficking to provide accurate insights and further developing robust methods to intersectional approaches to reporting cases of sex trafficking.

Keywords: sex trafficking, media coverage, Canada sex trafficking, content analysis

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Authors: Shu-Hui Wen, Wei-Chuan Chang, Hsien-Chang Wu

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Background: Little is known about the benefits and risks of use of Chinese herbal medicine (CHM) in conditions related to hormone therapy (HT) use on the risk of ischemic stroke (IS). The aim of this study is to explore the risk of IS in menopausal women treated with HT and CHM. Materials and methods: A total of 32,441 menopausal women without surgical menopause aged 40- 65 years were selected from 2003 to 2010 using the 2-million random samples of the National Health Insurance Research Database in Taiwan. According to the medication usage of HT and CHM, we divided the current and recent users into two groups: an HT use-only group (n = 4,989) and an HT/CHM group (n = 9,265). Propensity-score matching samples (4,079 pairs) were further created to deal with confounding by indication. The adjusted hazard ratios (HR) of IS during HT or CHM treatment were estimated by the robust Cox proportional hazards model. Results: The incidence rate of IS in the HT/CHM group was significantly lower than in the HT group (4.5 vs. 12.8 per 1000 person-year, p < 0.001). Multivariate analysis results indicated that additional CHM use was significant with a lower risk of IS (HR = 0.3; 95% confidence interval, 0.21-0.43). Further subgroup analyses and sensitivity analyses had similar findings. Conclusion: We found that combined use of HT and CHM was associated with a lower risk for IS than HT use only. Further study is needed to examine possible mechanism underlying this association.

Keywords: Chinese herbal medicine, hormone therapy, ischemic stroke, menopause

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Authors: Mehdi Basirat, Mohammad Rouhani, Shahla Borzouei, Majid Zarangi, Asma Abolghasemi, Mohammad Fazel Soltani, Mohammad Gorji, Mohammad Amin Samih

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The common pistachio psylla, Agonoscena pistaciae Burckhardt and Lauterer (Hemiptera: Aphalaridae), as one of the most detrimental pests in all pistachio producing regions, causes great economic damages to pistachio trees. Nowadays, various pesticides are used to control the common pistachio psylla and robust pesticide exposure has occurred in orchards. In this study, field experiments were conducted during 2018–2021 to assess the effects of sulfur on A. pistaciae. This study compared sulfur with asafoetida extract and pesticide (acetamiprid) on A. pistaciae based on complete randomized blocks with three replications. The analysis results of variance showed that the effect of treatments on egg (F2,24 = 17.61, P = 0.00) and nymphs (F2,24 = 18.29, P = 0.00) had a significant difference at a 1% level. The results demonstrated that sulfur had the highest measure of control on eggs and nymphs significantly compared to the plant extract and pesticide (negative control). These results provide support to the potential use of sulfur as an alternative pest management tool against A. pistaciae. The results clearly indicated that sulfur could control the common pistachio psylla population for six weeks at least.

Keywords: Agonoscena pistaciae, pesticide exposure, pistachio, sulfur

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Authors: Minh Nguyen

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Today, QR or Quick Response Codes are prevalent, and mobile/smart devices can efficiently read and understand them. Therefore, we can see their appearance in many areas, such as storing web pages/websites, business phone numbers, redirecting to an app download, business location, social media. The popularity of the QR Code is mainly because of its many advantages, such as it can hold a good amount of information, is small, easy to scan and read by a general RGB camera, and it can still work with some damages on its surface. However, there are still some issues. For instance, some areas needed to be kept untouched for its successful decode (e.g., the “Finder Patterns,” the “Quiet Zone,” etc.), the capability of built-in auto-correction is not robust enough, and it is not flexible enough for many application such as Augment Reality (AR). We proposed a new Colour Quick Response Code that has several advantages over the original ones: (1) there is no untouchable area, (2) it allows up to 40% of the entire code area to be damaged, (3) it is more beneficial for Augmented Reality applications, and (4) it is back-compatible and readable by available QR Code scanners such as Pyzbar. From our experience, our Colour Quick Response Code is significantly more flexible on damage compared to the original QR Code. Our code is believed to be suitable in situations where standard 2D Barcodes fail to work, such as curved and shiny surfaces, for instance, medical blood test sample tubes and syringes.

Keywords: QR code, computer vision, image processing, 2D barcode

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Authors: Ikediobi Lottanna Samuel

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Days are gone when perpetrators of core international crimes hide under the cloak of sovereignty to go with impunity. The principle of international criminal responsibility is a reality. This move to end impunity for violation of human rights has led to the creation of international and hybrid tribunals, a permanent international criminal court, and increased prosecution of human rights violations in domestic courts. This article examines the attempts by the international community to bring perpetrators of heinous crimes to book. The work reveals the inadequacy of the current international mechanism for prosecuting core international crimes in order to end the culture of impunity and entrench the culture of accountability. It also identifies that ad hoc international criminal tribunals and the international criminal court face similar challenges ranging from lack of cooperation by nation states, non-existence of hierarchy of crimes, lack of effective enforcement mechanism, limited prosecutorial capacity and agenda, difficulty in apprehending suspects, difficulty in blending different legal tradition, absence of a coherent sentencing guideline, distant location of courts, selective indictment, etc. These challenges adversely affect the functioning of these courts. It is suggested that a more helpful way to end impunity would be to have a more robust and synergistic relationship between national, regional, and international approaches to prosecuting core international crimes.

Keywords: prosecution, criminal, international, tribunal, justice, ad hoc

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Authors: Robert Saputra, Tomas Havlicek

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This study discusses the design of collaborative rural governance strategies to enhance the rural economy through Village-owned Enterprises (VOE) in Riau Province, Indonesia. Using Soft Systems Methodology (SSM) combined with Textual Network Analysis (TNA) in the Rich Picture stage of SSM, we investigated the current state of VOE management. Significant obstacles identified include insufficient business feasibility analyses, lack of managerial skills, misalignment between strategy and practice, and inadequate oversight. To address these challenges, we propose a collaborative strategy involving regional governments, academic institutions, NGOs, and the private sector. This strategy emphasizes community needs assessments, efficient resource mobilization, and targeted training programs. A dedicated working group will ensure continuous monitoring and iterative improvements. Our research highlights the novel integration of SSM with TNA, providing a robust framework for improving VOE management and demonstrating the potential of collaborative efforts in driving rural economic development.

Keywords: village-owned enterprises (VOE), rural economic development, soft system methodology (SSM), textual network analysis (TNA), collaborative governance

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Authors: Fayzan Ahmed, Saad Bin Saeed, Abdul Qadir Jangda

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Gafchromic™ sheet are extensively used for the QA of intensity modulated radiation therapy and other in-vivo dosimetry. Intra-sheet Non-uniformity of scanner as well as film causes undesirable fluctuations which are reflected in dosimetry The aim of this study is to define a systematic and robust method to investigate the intra-sheet uniformity of the unexposed Gafchromic Sheets and the region of interest (ROI) of the scanner. Sheets of lot No#: A05151201 were scanned before and after the expiry period with the EPSON™ XL10000 scanner in the transmission mode, landscape orientation, and 72 dpi resolution. ROI of (8’x 10’ inches) equal to the sheet dimension in the center of the scanner is used to acquire images with full transmission, block transmission and with sheets in place. 500 virtual grids, created in MATALB® are imported as a macros in ImageJ (1.49m Wayne Rasband) to analyze the images. In order to remove the edge effects, the outer 86 grids are excluded from the analysis. The standard deviation of the block transmission and full transmission are 0.38% and 0.66% confirming a higher uniformity of the scanner. Expired and non-expired sheets have standard deviations of 2.18% and 1.29%, show that uniformity decreases after expiry. The results are promising and indicate a good potential of this method to be used as a uniformity check for scanner and unexposed Gafchromic sheets.

Keywords: IMRT, film dosimetry, virtual grids, uniformity

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Authors: A. Alao Olumuyiwa

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Health hazards reported to be associated with exposure to electromagnetic radiations which include brain tumors, genotoxic effects, neurological effects, immune system deregulation, allergic responses and some cardiovascular effects are discussed under a closed tabular model in this study. This review however showed that there is strong and robust evidence that chronic exposures to electromagnetic frequency across the spectrum, through strength, consistency, biological plausibility and many dose-response relationships, may result in brain cancer and other carcinogenic disease symptoms. There is therefore no safe threshold because of the genotoxic nature of the mechanism that may however be involved. The discussed study explains that the cell phone has induced effects upon the blood –brain barrier permeability and the cerebellum exposure to continuous long hours RF radiation may result in significant increase in albumin extravasations. A physical Biomodeling approach is however employed to review this health effects using Specific Absorption Rate (SAR) of different GSM machines to critically examine the symptoms such as a decreased loco motor activity, increased grooming and reduced memory functions in a variety of animal spices in classified grouped and sub grouped models.

Keywords: brain cancer, electromagnetic radiations, physical biomodeling, specific absorption rate (SAR)

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Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

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Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

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Authors: Mazharuddin Syed Ahmed

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This research presents a framework used to develop The Ara Polytechnic College of Architecture Studies building “Kahukura” which is Green Building certified. This framework integrates the development of a smart building digital twin by utilizing Building Information Modelling (BIM) and its BIM maturity levels, including Levels of Development (LOD), eight dimensions of BIM, Heritage-BIM (H-BIM) and Facility Management BIM (FM BIM). The research also outlines a structured approach to building performance analysis and integration with the circular economy, encapsulated within a five-level digital twin framework. Starting with Level 1, the Descriptive Twin provides a live, editable visual replica of the built asset, allowing for specific data inclusion and extraction. Advancing to Level 2, the Informative Twin integrates operational and sensory data, enhancing data verification and system integration. At Level 3, the Predictive Twin utilizes operational data to generate insights and proactive management suggestions. Progressing to Level 4, the Comprehensive Twin simulates future scenarios, enabling robust “what-if” analyses. Finally, Level 5, the Autonomous Twin, represents the pinnacle of digital twin evolution, capable of learning and autonomously acting on behalf of users.

Keywords: building information modelling, circular economy integration, digital twin, predictive analytics

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Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

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Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.

Keywords: big data, big data ecosystem, classification of big data actors, big data actors roles, definition of government (big) data ecosystem, data-driven government, eGovernment, gaps in data ecosystems, government (big) data, public administration, systematic literature review

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Authors: Imene Skhakhfa, Lahbaci Ouerdachi

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To ensure the overall coherence of simulated results, it is necessary to develop a robust validation process. In many applications, it is no longer content to calibrate and validate the model only in relation to the hydro graph measured at the outlet, but we try to better simulate the functioning of the watershed in space. Therefore the timing also performs compared to other variables such as water level measurements in intermediate stations or groundwater levels. As part of this work, we limit ourselves to modeling flood of short duration for which the process of evapotranspiration is negligible. The main parameters to identify the models are related to the method of unit hydro graph (HU). Three different models were tested: SNYDER, CLARK and SCS. These models differ in their mathematical structure and parameters to be calibrated while hydrological data are the same, the initial water content and precipitation. The models are compared on the basis of their performance in terms six objective criteria, three global criteria and three criteria representing volume, peak flow, and the mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent and also highlight the problems associated with the simulation of low flow events and intermittent precipitation.

Keywords: model calibration, intensity, runoff, hydrograph

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad

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Power systems are innately uncertain systems. To face with such uncertain systems, robust uncertainty assessment tools are appealed. This paper inspects the uncertainty assessment formulation of the load flow (LF) problem considering different kinds of uncertainties, developed in its companion paper through some case studies. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. The load and wind power generation are considered as probabilistic uncertain variables and the electric vehicles (EVs) and gas turbine distributed generation (DG) units are considered as possibilistic uncertain variables. The cumulative distribution function (CDF) of the system output parameters obtained by the pure probabilistic method lies within the belief and plausibility functions obtained by the joint propagation approach. Furthermore, the imprecision in the DG parameters is explicitly reflected by the gap between the belief and plausibility functions. This gap, due to the epistemic uncertainty on the DG resources parameters grows as the penetration level increases.

Keywords: electric vehicles, joint possibilistic- probabilistic uncertainty modeling, uncertain load flow, wind turbine generator

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

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Authors: Keven A. Prusak, William F. Christensen, Zack Beddoes

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Background: For more than a decade, the Sport Motivation Scale (SMS) has been used to measure contextual motivation across a variety of sporting and physical education (PE) settings but not without criticism as to its tenability. Consequently, a new version of the sport motivation scale (SMS-II) was created to address certain weakness of the original SMS. Purpose: The purpose of this study is to assess the suitability of the SMS-II in the secondary PE setting. Methods: Three hundred and twenty (204 females, and 116 males; grades 7-9) completed the 18-item, six-subscale SMS-II at the end of a required PE class. Factor means, standard deviations, and correlations were calculated and further examined via confirmatory factor analysis (CFA). Model parameters were estimated maximum likelihood function. Results: Results indicate that participants held generally positive perceptions toward PE as a context (more so for males than females). Reliability analysis yielded adequate alphas (rα = 0.71 to 0.87, Mα = 0.78) with the exception of the AM subscale (αAM = .64). Correlation analysis indicated some support for the SIMPLEX pattern, but distal ends of the motivation continuum displayed no relationship. CFA yielded robust fit indices to the proposed structure of the SMS-II for PE. A small but significant variance across genders was noted and discussed. Conclusions: In all, the SMS-II suitably accesses PE context-specific motivational indices within Jr. High PE.

Keywords: motivation, self-determination theory, physical education, confirmatory factor analysis

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Authors: Zhenming Wang, Jun Zhu, Yuchen Yang, Ning Zhao

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In the present paper, an alternative framework is proposed to construct a class of finite difference multi-resolution nested weighted essentially non-oscillatory (WENO) schemes with an increasingly higher order of accuracy for solving inviscid Euler equations. These WENO schemes firstly obtain a set of reconstruction polynomials by a hierarchy of nested central spatial stencils, and then recursively achieve a higher order approximation through the lower-order precision WENO schemes. The linear weights of such WENO schemes can be set as any positive numbers with a requirement that their sum equals one and they will not pollute the optimal order of accuracy in smooth regions and could simultaneously suppress spurious oscillations near discontinuities. Numerical results obtained indicate that these alternative finite-difference multi-resolution nested WENO schemes with different accuracies are very robust with low dissipation and use as few reconstruction stencils as possible while maintaining the same efficiency, achieving the high-resolution property without any equivalent multi-resolution representation. Besides, its finite volume form is easier to implement in unstructured grids.

Keywords: finite-difference, WENO schemes, high order, inviscid Euler equations, multi-resolution

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Authors: Urmi Khatri

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Capital asset pricing model (CAPM) draws a direct relationship between the risk and the expected rate of return. There was a criticism on the beta and the assumptions of CAPM, as they are not applicable in the real world. Fama French Three Factor Model and Fama French Five Factor Model have given different factors, which have an impact on the return of any asset like size, value, investment and profitability. This study proposes to see Capital Asset pricing Model through the lenses of the quality aspect. In the study, the six factors are studied. The Fama French Five Factor Model and addition of the quality dimension are studied. Here, Graham’s seven quality and quantity criteria are measured to determine the score of the sample firms. Thus, this study tries to check the model fit. The beta coefficient of the quality dimension and the R square value is seen to determine validity of the proposed model. The sample is drawn from the firms listed on Indian Stock Exchange (BSE). For the study, only nonfinancial firms are been selected. The time period of the study is from January 1999 to December 2019. Hence, the primary objective of the study is to check how robust the model becomes after giving the quality dimension to the capital asset pricing model in addition to the size, value, profitability and investment.

Keywords: asset pricing model, CAPM, Graham’s score, G-score, multifactor model, quality

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Authors: Faik Derya Ince, Ugur Topgul, Alp Gunay, Can Bayoglu, Dante J. Dorantes-Gonzalez

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Explosive attacks are arguably the most lethal threat that may occur in terrorist attacks. In order to counteract this issue, explosive ordnance disposal operators put their lives on the line to dispose of a possible improvised explosive device. Robots can make the disposal process more accurately and saving human lives. For this purpose, there is a demand for more accurate and dexterous manipulating robotic hands that can be teleoperated from a distance. The aim of this project is to design a robotic hand that contains two active and two passive DOF for each finger, as well as a minimum set of tools for mechanical cutting and screw driving within the same robotic hand. Both hand and toolset, are teleoperated from a distance from a haptic robotic glove in order to manipulate dangerous objects such as improvised explosive devices. SolidWorks® Computer-Aided Design, computerized dynamic simulation, and MATLAB® kinematic and static analysis were used for the robotic hand and toolset design. Novel, dexterous and robust solutions for the fingers were obtained, and six servo motors are used in total to remotely control the multi-tooled robotic hand. This project is still undergoing and presents currents results. Future research steps are also presented.

Keywords: Explosive Manipulation, Robotic Hand, Tele-Operation, Tool Integration

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Authors: Jie Zhang, Chaomin Zhang, Jihua Zhang, Haitong Li

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Using the theory of optimal distinctiveness, this study examines the effects of conformity and differentiation within corporate social responsibility (CSR) practices on capital market performance. Analysing data from Chinese A-share listed firms from 2007 to 2022, this paper demonstrates that when firms conform to the expected scope of CSR, such behaviour enhances investor attention and market acceptance, thereby boosting stock liquidity. Conversely, emphasising differentiation in CSR practices more effectively mitigates stock price crash risk by addressing principal–agent problems and decreasing information asymmetry. This paper also investigates how organisational and environmental factors moderate the relationship between conformity and differentiation in CSR practices and their impact on capital market performance. The results also show that the influence of conformity on stock liquidity is accentuated in smaller firms and environments with stringent legal oversight. By contrast, the benefits of differentiation in reducing stock price crash risk are amplified in firms with robust corporate governance and markets characterised by high uncertainty.

Keywords: corporate social responsibility, social responsibility practices, capital market performance, optimal distinctiveness

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Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

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Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

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Authors: Srinivas Vangari

Abstract:

With the technological advancements around the globe, artificial intelligence (AI) has boosted supply chain management (SCM) by improving efficiency, sensitivity, and promptness. Artificial intelligence-based SCM provides comprehensive perceptions of consumer behavior in dynamic market situations and trends, foreseeing the accurate demand. It reduces overproduction and stockouts while optimizing production planning and streamlining operations. Consequently, the AI-driven SCM produces a customer-centric supply with resilient and robust operations. Intending to delve into the transformative significance of AI in SCM, this study focuses on improving efficiency in SCM with the integration of AI, understanding the production demand, accurate forecasting, and particular production planning. The study employs a mixed-method approach and expert survey insights to explore the challenges and benefits of AI applications in SCM. Further, a case analysis is incorporated to identify the best practices and potential challenges with the critical success features in AI-driven SCM. Key findings of the study indicate the significant advantages of the AI-integrated SCM, including optimized inventory management, improved transportation and logistics management, cost optimization, and advanced decision-making, positioning AI as a pivotal force in the future of supply chain management.

Keywords: artificial intelligence, supply chain management, accurate forecast, accurate planning of production, understanding demand

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

Abstract:

To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

Procedia PDF Downloads 316

Authors: Pratyush Agnihotri

Abstract:

The space missions are very costly because the transportation to the space is highly expensive and therefore there is the need to achieve complete re-usability in our launch vehicles to make the missions highly economic by cost cutting of the material recovered. Launcher reusability is the most efficient approach to decreasing admittance to space access economy, however stays an incredible specialized hurdle for the aerospace industry. Major concern of the difficulties lies in guidance and control procedure and calculations, specifically for those of the controlled landing stage, which should empower an exact landing with low fuel edges. Although cutting edge ways for navigation and control are present viz hybrid navigation and robust control. But for powered descent and landing of first stage of launch vehicle the guidance control is need to enable on board optimization. At first the CAD model of the launch vehicle I.e. space x falcon 9 rocket is presented for better understanding of the architecture that needs to be identified for the guidance and control solution for the recovery of the launcher. The focus is on providing the landing phase guidance scheme for recovery and re usability of first stage using retro propulsion. After reviewing various GNC solutions, to achieve accuracy in pre requisite landing online convex and successive optimization are explored as the guidance schemes.

Keywords: guidance, navigation, control, retro propulsion, reusable rockets

Procedia PDF Downloads 92

Authors: Harry Burton, Alexios Dimitrios Iliadis, Neil Jones, Aaron Saini, Nicola Bystrzonowski, Alexandros Vris, Georgios Pafitanis

Abstract:

This article portrays the authors’ experience with a complex lower limb bone and soft tissue defect, following chronic osteomyelitis and pathological fracture, which was managed by the multidisciplinary orthoplastic team. The decision for functional amputation versus limb salvage was deemed necessary, enhanced by the principles of “spares parts” in reconstructive microsurgery. This case describes a successful use of the osteocutaneous distal tibia turn-over fillet flap that allowed ‘lowering the level of the amputation’ from a through knee to the conventional level of a below-knee amputation to preserve the knee joint function. This case demonstrates the value of ‘spare-parts’ surgery principles and how these concepts refine complex orthoplastic approaches when limb salvage is not possible to enhance function. The osteocutaneous distal tibia turn-over fillet flap is a robust technique for modified BKA reconstructions that provides sufficient bone length to achieve a tough, sensate stump and functional knee joint.

Keywords: osteocutaneous flap, fillet flap, spare-parts surgery, Below knee amputation

Procedia PDF Downloads 166