Search results for: crisis signals

Authors: Leonardo C. Pacheco-Londoño, Nataly J Galan-Freyle, Lisandro Pacheco-Lugo, Antonio Acosta-Hoyos, Elkin Navarro, Gustavo Aroca-Martinez, Karin Rondón-Payares, Alberto C. Espinosa-Garavito, Samuel P. Hernández-Rivera

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At the Life Science Research Center at Simon Bolivar University, a primary focus is the diagnosis of various diseases, and the use of gold nanoparticles (Au-NPs) in diverse biomedical applications is continually expanding. In the present study, Au-NPs were employed as substrates for Surface-Enhanced Raman Spectroscopy (SERS) aimed at diagnosing kidney diseases arising from Lupus Nephritis (LN), preeclampsia (PC), and Hypertension (H). Discrimination models were developed for distinguishing patients with and without kidney diseases based on the SERS signals from urine samples by partial least squares-discriminant analysis (PLS-DA). A comparative study of the Raman signals across the three conditions was conducted, leading to the identification of potential metabolite signals. Model performance was assessed through cross-validation and external validation, determining parameters like sensitivity and specificity. Additionally, a secondary analysis was performed using machine learning (ML) models, wherein different ML algorithms were evaluated for their efficiency. Models’ validation was carried out using cross-validation and external validation, and other parameters were determined, such as sensitivity and specificity; the models showed average values of 0.9 for both parameters. Additionally, it is not possible to highlight this collaborative effort involved two university research centers and two healthcare institutions, ensuring ethical treatment and informed consent of patient samples.

Keywords: SERS, Raman, PLS-DA, kidney diseases

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Authors: Byomakesh Debata, Jitendra Mahakud

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The recent financial crisis has been characterized by massive monetary policy interventions by the Central bank, and it has amplified the importance of liquidity for the stability of the stock market. This paper empirically elucidates the actual impact of monetary policy interventions on stock market liquidity covering all National Stock Exchange (NSE) Stocks, which have been traded continuously from 2002 to 2015. The present study employs a multivariate VAR model along with VAR-granger causality test, impulse response functions, block exogeneity test, and variance decomposition to analyze the direction as well as the magnitude of the relationship between monetary policy and market liquidity. Our analysis posits a unidirectional relationship between monetary policy (call money rate, base money growth rate) and aggregate market liquidity (traded value, turnover ratio, Amihud illiquidity ratio, turnover price impact, high-low spread). The impulse response function analysis clearly depicts the influence of monetary policy on stock liquidity for every unit innovation in monetary policy variables. Our results suggest that an expansionary monetary policy increases aggregate stock market liquidity and the reverse is documented during the tightening of monetary policy. To ascertain whether our findings are consistent across all periods, we divided the period of study as pre-crisis (2002 to 2007) and post-crisis period (2007-2015) and ran the same set of models. Interestingly, all liquidity variables are highly significant in the post-crisis period. However, the pre-crisis period has witnessed a moderate predictability of monetary policy. To check the robustness of our results we ran the same set of VAR models with different monetary policy variables and found the similar results. Unlike previous studies, we found most of the liquidity variables are significant throughout the sample period. This reveals the predictability of monetary policy on aggregate market liquidity. This study contributes to the existing body of literature by documenting a strong predictability of monetary policy on stock liquidity in an emerging economy with an order driven market making system like India. Most of the previous studies have been carried out in developing economies with quote driven or hybrid market making system and their results are ambiguous across different periods. From an eclectic sense, this study may be considered as a baseline study to further find out the macroeconomic determinants of liquidity of stocks at individual as well as aggregate level.

Keywords: market liquidity, monetary policy, order driven market, VAR, vector autoregressive model

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Authors: Raheel Asif, Michael Frommel

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This paper focuses on analyzing the exchange rate exposure of exporting & domestic firms in (the so far rarely addressed) largest Eastern European transition economies, i.e., Russia and the three EU accession countries, Poland, Hungary, and Czech Republic (CEEC-3). It also controls for possible effects of different exchange rate regimes, Great Financial crisis (2007-08), Russian Financial crisis (2014-15), the formation of EU & turn of year effect. Substantially improving the results from the existing literature on these transition economies, we find for more than 51% of our sample firms in CEEC-3 countries and 29% in Russia shows a significant exchange rate exposure. However, the magnitude and direction of firms’ exposure depends on the particular bilateral exchange rate and differs between CEEC-3 and Russia. We find that share price increases with an appreciation of the domestic currency against the EURO and US Dollar (USD) in CEEC-3; however, the effect is more pronounced for EURO as expected. Whereas, for Russian firms share price increases with a depreciation of the domestic currency against the USD only. Those differences may result from a differing dominance of exposure channels in the respective economies, such as the country-specific export structure, competitiveness channels, and dependence on foreign debt. Finally, the switch from a pegged to a flexible exchange rate regime appears to have a less pronounced effect for the exchange rate exposure of firms in all countries except for USD in Poland and Russia.

Keywords: exchange rate exposure, transition economies, central and eastern Europe, international finance

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Authors: Faruk Ortes, Derya Karabulut, Yunus Ziya Arslan

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Myoelectric features gathering from musculature environment are considered on a preferential basis to perceive muscle activation and control human arm prostheses according to recent experimental researches. EMG (electromyography) signal based human arm prostheses have shown a promising performance in terms of providing basic functional requirements of motions for the amputated people in recent years. However, these assistive devices for neurorehabilitation still have important limitations in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyogram (EMG) is used as the control signal to command such devices. This kind of control consists of activating a motion in prosthetic arm using muscle activation for the same particular motion. Extraction of clear and certain neural information from EMG signals plays a major role especially in fine control of hand prosthesis movements. Many signal processing methods have been utilized for feature extraction from EMG signals. The specific objective of this study was to compare widely used time domain features of EMG signal including integrated EMG(IEMG), root mean square (RMS) and waveform length(WL) for prediction of externally applied forces to human hands. Obtained features were classified using artificial neural networks (ANN) to predict the forces. EMG signals supplied to process were recorded during only type of muscle contraction which is isometric and isotonic one. Experiments were performed by three healthy subjects who are right-handed and in a range of 25-35 year-old aging. EMG signals were collected from muscles of the proximal part of the upper body consisting of: biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically analyzed and merits and pitfalls of the extracted features were discussed with detail. The obtained results are anticipated to contribute classification process of EMG signal and motion control of powered human arm prosthetics control.

Keywords: assistive devices for neurorehabilitation, electromyography, feature extraction, force estimation, human arm prosthesis

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Authors: Umberto Cerasani, Luca Cerasani

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Methods for earthquakes forecasting have been intensively investigated in the last decades, but there is still no universal solution agreed by seismologists. Rock failure is most often preceded by a tiny elastic movement in the failure area and by the appearance of micro-cracks. These micro-cracks could be detected at the soil surface and represent useful earth-quakes precursors. The aim of this study was to verify whether tiny raw acceleration signals (in the 10⁻¹ to 10⁻⁴ cm/s² range) prior to the arrival of main primary-waves could be exploitable and related to earthquakes magnitude. Mathematical tools such as Fast Fourier Transform (FFT), moving average and wavelets have been applied on raw acceleration data available on the ITACA web site, and the study focused on one of the most unpredictable earth-quakes, i.e., the August 24th, 2016 at 01H36 one that occurred in the central Italy area. It appeared that these tiny acceleration signals preceding main P-waves have different patterns both on frequency and time domains for high magnitude earthquakes compared to lower ones.

Keywords: earthquake, accelerometer, earthquake forecasting, seism

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Authors: Shweta Singh, Dipali Bansal, Rashima Mahajan

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Psychoacoustics has become a potential area of research due to the growing interest of both laypersons and medical and mental health professionals. Non-invasive brain computer interface like Electroencephalography (EEG) is widely being used in this field. An attempt has been made in this paper to examine the response of EEG signals to acoustic stimuli further analysing the brain electrical activity. The real time EEG is acquired for 6 participants using a cost effective and portable EMOTIV EEG neuron headset. EEG data analysis is further done using EMOTIV test bench, EDF browser and EEGLAB (MATLAB Tool) application software platforms. Spectral analysis of acquired neural signals (AF3 channel) using these software platforms are clearly indicative of increased brain activity in various bands. The inferences drawn from such an analysis have significant correlation with subject’s subjective reporting of the experiences. The results suggest that the methodology adopted can further be used to assist patients with sleeping and depressive disorders.

Keywords: OM chant, spectral analysis, EDF browser, EEGLAB, EMOTIV, real time acquisition

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Authors: Bright Emeni

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The project is a system that allows users to locate their misplaced or lost devices by using Bluetooth technology. It utilizes the DFRobot Bettle BLE Arduino microcontroller as its main component for communication and control. By interfacing it with an LED and a buzzer, the system provides visual and auditory signals to assist in locating the target device. The search process can be initiated through an Android application, by which the system creates a Bluetooth connection between the microcontroller and the target device, permitting the exchange of signals for tracking purposes. When the device is within range, the LED indicator illuminates, and the buzzer produces audible alerts, guiding the user to the device's location. The application also provides an estimated distance of the object using Bluetooth signal strength. The project’s goal is to offer a practical and efficient solution for finding misplaced devices, leveraging the capabilities of Bluetooth technology and microcontroller-based control systems.

Keywords: Bluetooth finder, object finder, Bluetooth tracking, tracker

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Authors: Wogu Ikedinachi Ayodele Power, Agbude Godwyns, Eniayekon Eugenia, Nchekwube Excellence-Oluye, Abasilim Ugochukwu David

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Contemporary ethicists and writers on leadership, in their quest to address the problem of leadership crisis in Nigeria, have identified the absence of practical prudence -which manifests in variables such as corruption, ethnicity and greed- as one of the major factors which breeds leadership crises. These variables are further fuelled by the lack of a consistent theory of leadership among scholars that could guide the pertinent actions of political leaders, hence the rising cases of leadership crises in the country. The theoretical framework that guides this study emanates from Aristotle’s notion of prudence as contained in the Nicomachean Ethics, which states that prudence is a central moral resource for political leaders. The method of conceptual analysis shall be used to clarify the concepts of virtue, prudence and leadership. The traditional method of critical analysis and the reconstructive method of ideas in philosophy are used to conceptually and contextually analyze all relevant texts and archival materials in the subject areas of this study. The study identifies a high degree of ideological bias and logical inconsistencies inherent in the theories of leadership proposed by the realist and the moralist schools of thought. The conflicting ideologies regarding what political leadership should be among scholars of leadership is identified as one of the major factors militating against ascertaining a practicable theory of leadership, which has the capacity to guide the pertinent actions of political leaders all over the world. This paper therefore identifies the absence of practical prudence, ‘wisdom’, as the major factor associated with leadership crises in Nigeria. We therefore argue that only prudent leaders will have the capacity to identify salient aspects of political situations which leaders have obligations to consider before making political decisions. Seven frameworks were prescribed from Aristotle’s Notion of prudence to strengthen this position, they include: Disciplined reason and openness to experience; Foresight and attention to the long term, among others. We submit that leadership devoid of crisis can be attained through the application of the virtue of prudence. Where this theory is adopted, it should eliminate further leadership crises in Nigeria.

Keywords: Aristotle, leadership crisis, political leadership, prudence

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Authors: Michele Romanello

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COVID-19 has spread rapidly in Brazil since March 2020, making the country one of the most affected in the world by the pandemic. From an economic point of view, Brazil came from a pre-pandemic period characterized by low or negative growth, with a resulting increase in the number of unemployed and informal workers. This paper considers lockdown implementation in the situation of the large presence of informality in the economy. The objective of the paper is to analyze how the country has tried to help workers affected by economic crisis after the implementation of measures against COVID-19 and whether the emergency assistance from the government has been adequate to contain the increase of informal workers and unemployed. The methodology used in this paper is survival analysis. Through this methodology, the formality – informality, and informality – unemployment transitions are analyzed. This analysis draws data from the Continuous National Household Sample Survey (Continuous PNAD) and from the National Household Sample Survey COVID-19 (PNAD COVID-19) covering the period of January 2020 – July 2020. The results indicate that emergency aid has been not sufficient to reduce the transitions of workers from formal to informal jobs and from informal jobs to unemployment. Emergency aid has been not sufficient considering the previous situation of the country, with levels of poverty and inequality very high. In the next months, another fundamental determinant of the income trajectory in the context of the COVID-19 crisis will be the continuity of the emergency aid, especially considering the fiscal adjustment policy pursued by the government. Therefore, the current negative portrait may be even worse in the coming months.

Keywords: Brazil, COVID-19, informality, survival analysis

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Authors: Akshata Patel, Reenu Singh

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This paper focuses on current and upcoming fashion trends and global impact on the fashion industry due to the COVID-19 pandemic. The pandemic has had a major impact on the fashion industry worldwide. At the same time, the fashion market also faces challenges in consumer demand. As the supply chain and distribution channels are interconnected, this outbreak has a global impact due to travel restrictions and raw materials shortages. Given that this particular period represents an unprecedented market situation with almost no prior research on how the industry will recover from such a crisis and mold back to its original form, this research aims to propose new possibilities by evaluating the framework of specific segments. Based on the analysis and extensive literature review, the study develops a conceptual model that will illustrate the various connections among the different segments of the fashion industry. The findings provide actionable considerations for fashion industry pupils when implementing appropriate strategies to prevent unfavourable outcomes during times of crisis, such as the COVID-19 outbreak.

Keywords: COVID-19, fashion industry, global impact, new possibilities, pandemic

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Authors: Joel Thomas, Alexa Squirini

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The key argument of this research is that every element of systems interoperability is an enabler of regional disaster resilience, and arguably should become a focal point for standardized measurement of communities’ ability to work together. Few resilience research efforts have focused on the development and application of solutions that measurably improve communities’ ability to work together at a regional level, yet a majority of the most devastating and disruptive disasters are those that have had a regional impact. The key findings of the research include a unique theoretical, mathematical, and operational approach to tangibly and defensibly measure and assess systems interoperability required to support crisis information management activities performed by governments, the private sector, and humanitarian organizations. A most effective way for communities to measurably improve regional disaster resilience is through deliberately executed disaster preparedness activities. Developing interoperable crisis information management capabilities is a crosscutting preparedness activity that greatly affects a community’s readiness and ability to work together in times of crisis. Thus, improving communities’ human and technical posture to work together in advance of a crisis, with the ultimate goal of enabling information sharing to support coordination and the careful management of available resources, is a primary means by which communities may improve regional disaster resilience. This model describes how systems interoperability can be qualitatively and quantitatively assessed when characterized as five forms of capital: governance; standard operating procedures; technology; training and exercises; and usage. The unique measurement framework presented defines the relationships between systems interoperability, information sharing and safeguarding, operational coordination, community preparedness and regional disaster resilience, and offers a means by which to implement real-world solutions and measure progress over the course of a multi-year program. The model is being developed and piloted in partnership with the U.S. Department of Homeland Security (DHS) Science and Technology Directorate (S&T) and the North Atlantic Treaty Organization (NATO) Advanced Regional Civil Emergency Coordination Pilot (ARCECP) with twenty-three organizations in Bosnia and Herzegovina, Croatia, Macedonia, and Montenegro. The intended effect of the model implementation is to enable communities to answer two key questions: 'Have we measurably improved crisis information management capabilities as a result of this effort?' and, 'As a result, are we more resilient?'

Keywords: disaster, interoperability, measurement, resilience

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Authors: 李佳翰, CHIA-HAN LEE

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Without energy, the economy would grind to a halt. Germany's prosperity and security depend on a reliable and affordable energy supply. In recent years, Germany's energy policy has undergone major changes. Due to the sharp turn in energy, Germany cannot extend the service of nuclear power plants and can only find a rapid transition energy source: natural gas for a limited time. This study attempts to use processes of decision entrapment behavior and document analysis to explain research questions. Through primary and secondary information such as official reports, parliamentary minutes, media interview records, and speech records, the author sorted out the important events experienced by the three coalition governments (Gerhard Schröder, Angela Merkel, and Olaf Scholz) and the relationship between Nord Stream 1 and Nord Stream 2 with primary and secondary sources. Also, compare it with the processes of decision entrapment behavior, which designed in this study, and divide it into four stages to explore its key elements one by one. In this regard, the following conclusions are drawn: First, from the perspective of processes of decision entrapment behavior, Merkel’s government firmly believes that she can overcome difficulties because of her past experience in crisis management capabilities. However, the outbreak of war between Ukraine and Russia was beyond Merkel's planning. Second, in the face of the crisis, the Scholz’s government increased the import of natural gas from other countries and began to import liquefied natural gas to make up for the energy gap of Russian natural gas.

Keywords: german research, nord stream gas pipeline, energy policy, processes of decision entrapment behavior

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Authors: Mahdi Shamsi, Tohid Yousefi Rezaii, Siavash Eftekharifar

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Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising.

Keywords: compressed sensing, data denoising, model order selection, sparse representation

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Authors: Balwinder Singh, Veerpaul Kaur Mann

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The COVID-19 had a dramatic impact on several countries across the world. National governments have imposed widespread restrictions to prevent the growth of this pandemic. The new health competitive scenario induced by the COVID-19 crisis raised many issues on how business activities should be reorganized due to the difficulties of physical interactions with distributors, suppliers and customers. The pandemic has particularly affected the whole marketing processes because of the relevant issues emerged in managing physical sale channels and interactions with one another, both in the Business-to-Consumer and in the Business-to-Business markets. Recent research about the appropriate actions and strategies that could help firms overcome the crisis has highlighted the key role of digital technologies that may ensure connections and, thus, help business activities to run smoothly. This could be true, especially with the emergence of strong limitations on physical interactions during the COVID-19 pandemic. In such a scenario, the online channel becomes the most important conducive for online customers to get in contact with the firm and carry out online purchasing activities.

Keywords: COVID-19, business, digital marketing, online customers

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Authors: Salah Al-Din I. Badran, Samad Ahmadi, Ismail Shahin

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A blind source separation method is proposed; in this method we use a non-uniform filter bank and a novel normalisation. This method provides a reduced computational complexity and increased convergence speed comparing to the full-band algorithm. Recently, adaptive sub-band scheme has been recommended to solve two problems: reduction of computational complexity and increase the convergence speed of the adaptive algorithm for correlated input signals. In this work the reduction in computational complexity is achieved with the use of adaptive filters of orders less than the full-band adaptive filters, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full bandwidth, and can promote better rates of convergence.

Keywords: blind source separation, computational complexity, subband, convergence speed, mixture

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Authors: S. Hosseini, M. Ahmadi Najafabadi, M. Akhlaghi

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The health of the journal bearings is very important in preventing unforeseen breakdowns in rotary machines, and poor lubrication is one of the most important factors for producing the bearing failures. Hydrodynamic lubrication (HL), mixed lubrication (ML), and boundary lubrication (BL) are three regimes of a journal bearing lubrication. This paper uses acoustic emission (AE) measurement technique to correlate features of the AE signals to the three lubrication regimes. The transitions from HL to ML based on operating factors such as rotating speed, load, inlet oil pressure by time domain and time-frequency domain signal analysis techniques are detected, and then metal-to-metal contacts between sliding surfaces of the journal and bearing are identified. It is found that there is a significant difference between theoretical and experimental operating values that are obtained for defining the lubrication regions.

Keywords: acoustic emission technique, pressure fed journal bearing, time and frequency signal analysis, metal-to-metal contact

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Authors: Somayeh Komeylian

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Array signal processing techniques have been recently developing in a variety application of the performance enhancement of receivers by refraining the power of jamming and interference signals. In this scenario, biases induced to the antenna array receiver degrade significantly the accurate estimation of the carrier phase. Owing to the integration of frequency becomes the carrier phase, we have obtained the unbiased doppler frequency for the high precision estimation of carrier phase. The unbiased characteristic of Doppler frequency to the power jamming and the other interference signals allows achieving the highly accurate estimation of phase carrier. In this study, we have rigorously investigated the unbiased characteristic of Doppler frequency to the variation of the antenna array geometries. The simulation results have efficiently verified that the Doppler frequency remains also unbiased and accurate to the variation of antenna array geometries.

Keywords: array signal processing, unbiased doppler frequency, GNSS, carrier phase, and slowly fluctuating point target

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Stehr Melanie

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The availability of apposite information is essential for the decision-making process of organizational buyers. Due to the constraints of the Covid-19 crisis, information channels that emphasize face-to-face contact (e.g. sales visits, trade shows) have been unavailable, and usage of digitally-driven information channels (e.g. videoconferencing, platforms) has skyrocketed. This paper explores the question in which areas the pandemic induced shift in the use of information channels could be sustainable and in which areas it is a temporary phenomenon. While information and buying behavior in B2C purchases has been regularly studied in the last decade, the last fundamental model of organizational buying behavior in B2B was introduced by Johnston and Lewin (1996) in times before the advent of the internet. Subsequently, research efforts in B2B marketing shifted from organizational buyers and their decision and information behavior to the business relationships between sellers and buyers. This study builds on the extensive literature on situational factors influencing organizational buying and information behavior and uses the economics of information theory as a theoretical framework. The research focuses on the German woodworking industry, which before the Covid-19 crisis was characterized by a rather low level of digitization of information channels. By focusing on an industry with traditional communication structures, a shift in information behavior induced by an exogenous shock is considered a ripe research setting. The study is exploratory in nature. The primary data source is 40 in-depth interviews based on the repertory-grid method. Thus, 120 typical buying situations in the woodworking industry and the information and channels relevant to them are identified. The results are combined into clusters, each of which shows similar information behavior in the procurement process. In the next step, the clusters are analyzed in terms of the post and pre-Covid-19 crisis’ behavior identifying stable and dynamic information behavior aspects. Initial results show that, for example, clusters representing search goods with low risk and complexity suggest a sustainable rise in the use of digitally-driven information channels. However, in clusters containing trust goods with high significance and novelty, an increased return to face-to-face information channels can be expected after the Covid-19 crisis. The results are interesting from both a scientific and a practical point of view. This study is one of the first to apply the economics of information theory to organizational buyers and their decision and information behavior in the digital information age. Especially the focus on the dynamic aspects of information behavior after an exogenous shock might contribute new impulses to theoretical debates related to the economics of information theory. For practitioners - especially suppliers’ marketing managers and intermediaries such as publishers or trade show organizers from the woodworking industry - the study shows wide-ranging starting points for a future-oriented segmentation of their marketing program by highlighting the dynamic and stable preferences of elaborated clusters in the choice of their information channels.

Keywords: B2B buying process, crisis, economics of information theory, information channel

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Authors: Anand Choudhary

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Following the global economic crisis in 2008, businesses and more especially the big multinational conglomerates were increasingly viewed by the people world over as one of the major causes of the economic problems faced by millions globally, in terms of job loss and lifetime savings being wiped out as banks and pension funds went bankrupt and people stared at an insecure financial future. This caused a lot of resentment in the public against big businesses and fueled several protest movements by the people such as “Occupy Wall Street” in different parts of the world. This forced the big businesses to respond to the challenge by adopting more people-centric policies and initiatives for local communities in societies where they operate as part of their corporate social responsibility (CSR), in order to regain their social acceptance among the people whilst earning their ‘social license to operate’. The current paper studies many of such large MNCs across the United States of America, India and South Africa, which changed the way they did business earlier, following the global economic crisis in 2008, by incorporating capacity building initiatives for local communities as part of their CSR strategy and explores whether it has contributed to improving their financial performance. It is a conceptual research paper using secondary source data. The findings reveal that there is a positive correlation between the companies’ corporate social performance and corporate financial performance. In addition, the findings also bring to light that the MNCs examined as part of the current paper have improved their image in the eyes of their stakeholders following the change in their CSR strategy and initiatives.

Keywords: corporate social responsibility (CSR), Corporate Social Performance (CSP), Corporate Financial Performance (CFP), local communities

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Authors: Wael Bakhit, Salma Bakhit

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This paper employs a quarterly time series to determine the timing of structural breaks for interest rates in USA over the last 60 years. The Chow test is used for investigating the non-stationary, where the date of the potential break is assumed to be known. Moreover, an empirical examination of the financial sector was made to check if it is positively related to deviations from an assumed interest rate as given in a standard Taylor rule. The empirical analysis is strengthened by analysing the rule from a historical perspective and a look at the effect of setting the interest rate by the central bank on financial imbalances. The empirical evidence indicates that deviation in monetary policy has a potential causal factor in the build-up of financial imbalances and the subsequent crisis where macro prudential intervention could have beneficial effect. Thus, our findings tend to support the view which states that the probable existence of central banks has been a source of global financial crisis since the past decade.

Keywords: Taylor rule, financial imbalances, central banks, econometrics

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Authors: Bogusław Schreyer

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The paper enquires on the two methods of the wheeled robot braking torque control. Those two methods are applied when the adhesion coefficient under left side wheels is different from the adhesion coefficient under the right side wheels. In case of the select-low (SL) method the braking torque on both wheels is controlled by the signals originating from the wheels on the side of the lower adhesion. In the select-high (SH) method the torque is controlled by the signals originating from the wheels on the side of the higher adhesion. The SL method is securing stable and secure robot behaviors during the braking process. However, the efficiency of this method is relatively low. The SH method is more efficient in terms of time and braking distance but in some situations may cause wheels blocking. It is important to monitor the velocity of all wheels and then take a decision about the braking torque distribution accordingly. In case of the SH method the braking torque slope may require significant decrease in order to avoid wheel blocking.

Keywords: select-high, select-low, torque distribution, wheeled robots

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Authors: Taslim Khan, Ray Hua Horng, Rajendra Singh

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This study presents a comprehensive analysis of the design, fabrication, and performance evaluation of a solar-blind Schottky photodetector based on ZnGa₂O₄ grown via MOCVD, utilizing Ni/Au as the Schottky electrode. ZnGa₂O₄, with its wide bandgap of 5.2 eV, is well-suited for high-performance solar-blind photodetection applications. The photodetector demonstrates an impressive responsivity of 280 A/W, indicating its exceptional sensitivity within the solar-blind ultraviolet band. One of the device's notable attributes is its high rejection ratio of 10⁵, which effectively filters out unwanted background signals, enhancing its reliability in various environments. The photodetector also boasts a photodetector responsivity contrast ratio (PDCR) of 10⁷, showcasing its ability to detect even minor changes in incident UV light. Additionally, the device features an outstanding detective of 10¹⁸ Jones, underscoring its capability to precisely detect faint UV signals. It exhibits a fast response time of 80 ms and an ON/OFF ratio of 10⁵, making it suitable for real-time UV sensing applications. The noise-equivalent power (NEP) of 10^-17 W/Hz further highlights its efficiency in detecting low-intensity UV signals. The photodetector also achieves a high forward-to-backward current rejection ratio of 10⁶, ensuring high selectivity. Furthermore, the device maintains an extremely low dark current of approximately 0.1 pA. These findings position the ZnGa₂O₄-based Schottky photodetector as a leading candidate for solar-blind UV detection applications. It offers a compelling combination of sensitivity, selectivity, and operational efficiency, making it a highly promising tool for environments requiring precise and reliable UV detection.

Keywords: wideband gap, solar blind photodetector, MOCVD, zinc gallate

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Authors: K. Bensafia, A. Mansour, G. Le Maillot, B. Clement, O. Reynet, P. Ariès, S. Haddab

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This paper presents a system designed for wireless acquisition, the recording of electrocardiogram (ECG) signals and the monitoring of the heart’s health during surgery. This wireless recording system allows us to visualize and monitor the state of the heart’s health during a surgery, even if the patient is moved from the operating theater to post anesthesia care unit. The acquired signal is transmitted via a Bluetooth unit to a PC where the data are displayed, stored and processed. To test the reliability of our system, a comparison between ECG signals processed by a conventional ECG monitoring system (Datex-Ohmeda) and by our wireless system is made. The comparison is based on the shape of the ECG signal, the duration of the QRS complex, the P and T waves, as well as the position of the ST segments with respect to the isoelectric line. The proposed system is presented and discussed. The results have confirmed that the use of Bluetooth during surgery does not affect the devices used and vice versa. Pre- and post-processing steps are briefly discussed. Experimental results are also provided.

Keywords: electrocardiography, monitoring, surgery, wireless system

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Authors: Katarzyna Kamińska

Abstract:

When thinking about custodial arrangements after divorce or separation, there has been a shift from sole custody, particularly maternal preference, to joint physical custody. In many Western countries, an increasing of children with separated parents have joint physical custody, which is believed to be in the best interests of the child, as children can maintain personal relations and direct contact with both parents on a regular basis. The aim of the article is to examine joint physical custody, both from the perspective of the binding legal instruments that are relevant to joint physical custody, the Principles of European Family Law drafted by the CEFL, as well as the international research on this matter. The thesis underlying this paper is that joint physical custody is in itself neither good nor bad, and it depends on how the arrangements are managed by the parents. The paper includes a reflection on joint physical custody in the face of the COVID-19 crisis. The results indicate that in normal circumstances, joint physical custody demands broad communication, and now it times of crisis, we need over-communication about children and plans. Only a very tight and coordinated co-parenting plan make the whole family safer.

Keywords: joint physical custody, co-parenting, child welfare, COVID-19

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Authors: Bakr Ali Al-Gamrh, Ku Nor Izah B. Ku Ismail

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We investigate the relationship between corporate governance, leverage, risk, and firm performance. We use a firm level panel that spans the period 2008 to 2012 of all listed firms on Abu Dhabi Stock Exchange and Dubai Financial Market. After constructing an index of corporate governance strength, we find a negative effect of corporate governance on firm performance. We, however, discover that corporate governance strength indirectly improves the negative influence of leverage on firm performance in normal times. On the contrary, the results completely reversed when there is a black swan event. Corporate governance strength plays a significantly negative role in moderating the relationship between leverage and firm performance during the financial crisis. We also reveal that corporate governance strength increases firms’ risk and deteriorates performance during crisis. Results provide evidence that corporate governance indirectly plays a completely different role in different time periods.

Keywords: corporate governance, firm performance, risk, leverage, the UAE

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Authors: Wassamon Kun-Amornpong

Abstract:

When a financial crisis occurs, there will be a law and regulatory reform in order to manage the turmoil and prevent a future crisis. One of the most important regulatory efforts to help cope with systemic risk and a financial crisis is the third version of the Basel Accord. Basel III has introduced some measures and tools (e.g., systemic risk buffer, countercyclical buffer, capital conservation buffer and liquidity risk) in order to mitigate systemic risk. Nevertheless, the effectiveness of these measures in Basel III in adequately addressing the problem of contagious runs that can quickly spread throughout the financial system is questionable. This paper seeks to contribute to the knowledge regarding the role of the Basel Accords in mitigating systemic risk. The research question is to what extent the Basel Accords can help control systemic risk in the financial markets? The paper tackles this question by analysing the concept of systemic risk. It will then examine the weaknesses of the Basel Accords before and after the Global financial crisis in 2008. Finally, it will suggest some possible solutions in order to improve the Basel Accord. The rationale of the study is the fact that academic works on systemic risk and financial crises are largely studied from economic or financial perspective. There is comparatively little research from the legal and regulatory perspective. The finding of the paper is that there are some problems in all of the three pillars of the Basel Accords. With regards to Pillar I, the risk model is excessively complex while the benefits of its complexity are doubtful. Concerning Pillar II, the effectiveness of the risk-based supervision in preventing systemic risk still depends largely upon its design and implementation. Factors such as organizational culture of the regulator and the political context within which the risk-based supervision operates might be a barrier against the success of Pillar II. Meanwhile, Pillar III could not provide adequate market discipline as market participants do not always act in a rational way. In addition, the too-big-to-fail perception reduced the incentives of the market participants to monitor risks. There has been some development in resolution measure (e.g. TLAC and MREL) which might potentially help strengthen the incentive of the market participants to monitor risks. However, those measures have some weaknesses. The paper argues that if the weaknesses in the three pillars are resolved, it can be expected that the Basel Accord could contribute to the mitigation of systemic risk in a more significant way in the future.

Keywords: Basel accords, financial regulation, risk-based supervision, systemic risk

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Authors: John Lachapelle

Abstract:

Introduction: Neural stimulation is increasingly targeted toward treatment of back pain, PTSD, Parkinson’s disease, and for sensory perception. Sensory recording during stimulation is important in order to examine neural response to stimulation. Most neural amplifiers (headstages) focus on noise efficiency factor (NEF). Conversely, neural headstages need to handle artifacts from several sources including power lines, movement (EMG), and neural stimulation itself. In this work a layered approach to artifact rejection is used to reduce corruption of the neural ENG signal by 60dBv, resulting in recovery of sensory signals in rats and primates that would previously not be possible. Methods: The approach combines analog techniques to reduce and handle unwanted signal amplitudes. The methods include optimized (1) sensory electrode placement, (2) amplifier configuration, and (3) artifact blanking when necessary. The techniques together are like concentric moats protecting a castle; only the wanted neural signal can penetrate. There are two conditions in which the headstage operates: unwanted artifact < 50mV, linear operation, and artifact > 50mV, fast-settle gain reduction signal limiting (covered in more detail in a separate paper). Unwanted Signals at the headstage input: Consider: (a) EMG signals are by nature < 10mV. (b) 60 Hz power line signals may be > 50mV with poor electrode cable conditions; with careful routing much of the signal is common to both reference and active electrode and rejected in the differential amplifier with <50mV remaining. (c) An unwanted (to the neural recorder) stimulation signal is attenuated from stimulation to sensory electrode. The voltage seen at the sensory electrode can be modeled Φ_m=I_o/4πσr. For a 1 mA stimulation signal, with 1 cm spacing between electrodes, the signal is <20mV at the headstage. Headstage ASIC design: The front end ASIC design is designed to produce < 1% THD at 50mV input; 50 times higher than typical headstage ASICs, with no increase in noise floor. This requires careful balance of amplifier stages in the headstage ASIC, as well as consideration of the electrodes effect on noise. The ASIC is designed to allow extremely small signal extraction on low impedance (< 10kohm) electrodes with configuration of the headstage ASIC noise floor to < 700nV/rt-Hz. Smaller high impedance electrodes (> 100kohm) are typically located closer to neural sources and transduce higher amplitude signals (> 10uV); the ASIC low-power mode conserves power with 2uV/rt-Hz noise. Findings: The enhanced neural processing ASIC has been compared with a commercial neural recording amplifier IC. Chronically implanted primates at MGH demonstrated the presence of commercial neural amplifier saturation as a result of large environmental artifacts. The enhanced artifact suppression headstage ASIC, in the same setup, was able to recover and process the wanted neural signal separately from the suppressed unwanted artifacts. Separately, the enhanced artifact suppression headstage ASIC was able to separate sensory neural signals from unwanted artifacts in mouse-implanted peripheral intrafascicular electrodes. Conclusion: Optimizing headstage ASICs allow observation of neural signals in the presence of large artifacts that will be present in real-life implanted applications, and are targeted toward human implantation in the DARPA HAPTIX program.

Keywords: ASIC, biosensors, biomedical signal processing, biomedical sensors

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Authors: Sujay Kotwale, Ramasubba Reddy M.

Abstract:

Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Oluwafemi Akinlawon Atoyebi

Abstract:

This paper examines alienation and fixation as critical issues of/around mental health -crisis, sickness, and healing- through ‘Bode Ojoniyi’s dramaturgy. Two of his dramatic memoirs, arguably written to address such a life-threatening crisis between him and his employer, where he externalizes perhaps his psychological crisis, are critically analysed. This is done through a reading of the three theatrical phenomena of the actor, the character, and the audience against how he plays around the concepts of alienation and fixation within the totality of his dramaturgy beyond what could be seen as a mere academic exercise. The paper situates his apt understanding of their representations as a reflective force of a consciousness that defies psychosomatic existential conflicts. It does so by adopting a qualitative method of analysis through a critical reading of the two dramatic memoirs. It also carries out a survey on the audience that experienced the performances of the memoirs and an interview with Ojoniyi. Using Jean-Paul Sartre’s Theory of Existential Consciousness, the study discovers that there is a way the three phenomena of the actor, the character, and the audience do find expression in Ojoniyi as an existential omniscient playwright-actor-character-audience who is able to transcend the parochialism of an alienated and a fixated self; that beyond the limiting artistic purview, the theatre as a stage is a phenomenon that is capable of capturing the totality of the experiences of a man in his world and that, often time, the depressed are victims of the myopic syndrome as they probably could not see or reflect on/about their realities beyond the self and the play of a casual order. The study concludes that the therapeutic effect of Ojoniyi’s dramatic memoirs, in their reading or performance, is needed by all and should be explored in proffering cures for psychosomatic patients, for it promises to be essentially useful beyond its confine –the Arts.

Keywords: alienation, fixation, the healing theatre, theatrical personalities

Procedia PDF Downloads 141