Miracle-dz/UniFAR-FTUs huggingface dataset:
The mediating role of big data analytics in enhancing firms’ commitment to sustainability

The era of big data has overturned the classical model of how businesses work. Companies are now dealing with data as one of their most valuable assets that fuels their digital strategies to overcome the competition. As the potential of big data analytics is endless, looking for any impact from this cutting-edge technology on sustainability could lead to new insights about this paradigm. This paper represents a quantitative study that included 41 Moroccan firms from diverse industries. By using a Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach, the results showed that big data analytics may enhance companies’ commitment to sustainability (p-value =0) if the firm crafted the right digital strategy fuelled by data (p-value=0), established a data-driven decision-making culture (p-value =0.008), and acquired the right talents with soft skills (p-value = 0.029). Against all expectations, the results showed that technical skills do not play a significant role and do not boost the mediating role of big data regarding sustainability (p-value=0.124).
Visual Performance under Varying Illumination Conditions while using an Indigenously Developed Wrist Wearable Computer

Ambient illumination conditions have significant impact on users’ visual performance while carrying out onscreen reading tasks on visual display units, especially smaller screen sizes. Present study assessed the visual performance responses of different ambient illumination levels during onscreen reading on Wrist Wearable Computer (WWC) developed for the command-control-communication between the control room and the soldiers operating in remote locations. Ten (10) Indian Infantry soldiers performed two different types of loud reading tasks on the display of WWC under three different ambient illumination (mean ±SEM) conditions namely, Indoor controlled (450.00±10.00 lx), Outdoor daylight (11818.7±582.91 lx) and Indoor dark (0.12±0.03 lx) environments. While reading, participants wore an eye tracking glass which recorded the eye movement responses. Visualisation techniques were used to predict the association of illumination levelof surrounding with visual performance of the user. Subjective legibility rating was also applied to understand participants’ preferences towards physical attributes of the onscreen information and illumination level. Results indicated that illumination had a significant effect on eye movement parameters like fixation frequency, fixation duration and scanpath length while completing the tasks. Overall, participants performed better under indoor controlled illumination conditions in terms of fixation profile and scanpath length, apart from improved subjective legibility ratings as compared to other two illumination conditions. Future research attempts need to be directed towards the optimum performance of the display across wide range of ambient illumination conditions and to establish how the display of indigenously developed wearable computer performs in comparison to other such displays available across the globe.
Application of digital signal processing and machine learning for Electromyography: A review

This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.
A remote self-directed psychological intervention for the public: The PAUSE programme protocol

Background: This protocol outlines procedures for the development and evaluation of a remotely accessible intervention tool known as the ‘Psychology And yoU: Self-Enhancement programme’ (i.e., PAUSE programme). The PAUSE programme aims to support and promote psychological well-being using positive psychological concepts and principles. The programme has been developed in the context of the coronavirus disease 2019 pandemic, where effective and evidence-based remote interventions are needed. The PAUSE programme will provide users with valuable tools and skills that they may choose to implement in their daily lives, in order to foster and support positive mental well-being. The programme includes six modules: Well-being and Happiness; Healthy Body and Mind; Being Grateful and Savouring Life; Thought and Action; Strengthening Relationships; and Overcoming Challenges. Methods: Participants will be recruited using media outlets, social media, and professional networking websites in Ireland. Those who choose to participate in this study will be asked to complete a set of measures at baseline, immediate follow-up, and six weeks post-intervention. This will allow for changes in subjective well-being scores to be analysed and interpreted over time. This study adopts a Groups x Time design, with participants being randomly assigned to either an intervention or wait-list control group. Ethical approval is currently under review at the host institution. Dissemination: The dissemination strategy will follow the Evidence-based model for the Transfer and Exchange of Research Knowledge (EMTReK) and study findings will be prepared in line with various formats (e.g. study newsletters, conferences/meetings) in order to meet the needs of different audiences. Targeted and timely dissemination activities are anticipated, and the team intends to disseminate research in an ongoing manner, throughout the lifetime of the project. Registration: This RCT protocol is pre-results and has been registered with an international database resulting in an International Standard Randomised Controlled Trials Number (ISRCTN14772616)
Fish species recognition using transfer learning techniques

Article history Received October 10, 2020 Revised December 5, 2020 Accepted January 6, 2021 Available online July 31, 2021 Marine species recognition is the process of identifying various species that help in population estimation and identifying the endangered types for taking further remedies and actions. The superior performance of deep learning for classification is due to the property of estimating millions of parameters that have to be extracted from many annotated datasets. However, many types of fish species are becoming extinct, which may reduce the number of samples. The unavailability of a large dataset is a significant hurdle for applying a deep neural network that can be overcome using transfer learning techniques. To overcome this problem, we propose a transfer learning technique using a pre-trained model that uses underwater fish images as input and applies a transfer learning technique to detect the fish species using a pre-trained Google Inception-v3 model. We have evaluated our proposed method on the Fish4knowledge(F4K) dataset and obtained an accuracy of 95.37%. The research would be helpful to identify fish existence and quantity for marine biologists to understand the underwater environment to encourage its preservation and study the behavior and interactions of marine animals.
The Let’s Eat Healthy and Move at School program for adolescents in South Korea: Program design, implementation, and evaluation plan using intervention mapping

Purpose This study aimed to systematically develop an obesity prevention program for adolescents to promote healthy eating and physical activity in schools. Methods The development of the Let's Eat Healthy and Move at School program for adolescents followed the six steps of intervention mapping (IM). IM is a widely used protocol for developing systematic and effective interventions based on theories and evidence. Results To better understand the problem and identify the needs of adolescents, interviews were conducted with teachers, school nurses, and students (step 1). In step 2, the desired behaviors and their determinants were established and combined into a matrix comprising 16 change objectives. In step 3, theoretical methods such as persuasive communication and consciousness-raising were chosen. The program was segmented into three educational activity sessions in step 4. In step 5, an implementation manual was developed for program instructors to ensure effective and accurate implementation. Finally, practices for evaluating the program's effectiveness and procedures were designed in step 6. Conclusion The Let’s Eat Healthy and Move at School program will provide adolescents with guidelines to promote healthy living and prevent obesity in everyday life using strategies for sustainable adolescent obesity prevention and management.
Comparative Study of Three DNA-based Information Hiding Methods

Cryptography is the science of protecting information by transforming data into formats that cannot be recognized by unauthorized users. Steganography is the science of hiding information using different media such as image, audio, video, text, and deoxyribonucleic acid (DNA) sequence. The DNA-based steganography is a newly discovered information security technology characterized by high capacity, high randomization, and low modification rate that leads to increased security. There are various DNA-based methods for hiding information. In this paper, we compared three DNA-based techniques (substitution, insertion, and complementary) in terms of its capacity, cracking property, Bit Per Nucleotide (BPN), and payload. The selected algorithms combine DNA-based steganography and cryptography techniques. The results show that the substitution technique offers the best BPN for short secret messages and offers the best imperceptibility feature. We also found that both the substitution and the complementary method have a threshold BPN. On the other hand, the insertion method does not have a threshold BPN and it is more difficult to crack.
Pre-emptive PPAR-γ Activation Abolishes Development of Nerve Injury-induced Behavioral Hypersensitivity: Elucidating Underlying Mechanisms

Background: Neuropathic pain is a chronic incapacitating painful condition for which there is no effective treatment. The peroxisome proliferator-activated receptors (PPARs) are ligand-activated nuclear transcription factors that play key roles in modulating immune and inflammatory responses. The antinociceptive properties of PPAR-γ activation on development of neuropathic pain are not fully known. Objective: To determine the role of PPAR-γ activation on the development of neuropathic pain following chronic constriction injury and to elucidate underlying mechanisms. Methodology: Neuropathy was induced by chronic constriction injury of sciatic nerve in rats. Cold allodynia and thermal hyperalgesia were assessed and the markers of inflammation and nitrosooxidative stress were estimated. Original Research Article Thakur et al.; JPRI, 33(39B): 23-37, 2021; Article no.JPRI. 72258 24 Results: Pre-emptive administration of pioglitazone, a PPAR- agonist (3, 10 or 30 mg/kg, i.p. 1 hr before surgery and continued once daily for 2 weeks) dose-dependently attenuated paw withdrawal latency to cold (allodynia) and thermal (hyperalgesia) stimuli. Pioglitazone significantly reduced elevated TBARS, protein carbonylation, nitrite levels and markedly restored depleted GSH, and reduction in activities of SOD and catalase in injured nerves. Further, pioglitazone markedly reduced plasma extravasation and levels of pro-inflammatory cytokines TNF-α and IL-1β following nerve injury. Moreover, pioglitazone did not alter the locomotor activity. Pretreatment with PPAR-γ antagonist BADGE (30 mg/kg, i.p.) blocked the beneficial effects of pioglitazone. Essentially, pioglitazone promoted the long-lasing recovery and also prevented the development of neuropathic pain even after treatment termination. Conclusion: Pioglitazone, a PPAR- agonist receptor-dependently abolished the development of traumatic neuropathic pain and exerted long-lasting antinociceptive effects through reducing nitroso-oxidative stress and inflammation. Our findings strongly suggest that pre-emptive activation of PPAR- prevented or at least delayed the development of nerve injury-induced pain hypersensitivity.
A Comparative Study on Partition-based Clustering Methods

: Clustering analysis is one of the essential data analysis tools that separate a group of data objects into similar sets called clusters. In Partition-based clustering method, identifying the initial centroid is challenging task. This paper presents a review of some partition-based clustering method that improves the selection of initial centroid value and enhances the quality of clustering to some extent.
PROSPECTIVE PHILOLOGISTS’ TRANSLATION ASSESSMENT TRIANGULATION: SCREEN VIDEO RECORDING AND THINK ALOUD PROTOCOL COMBINATION

Purpose. The article strives for the enhancement of the efficiency of translation competence assessment in philologists’ university training with the help of the triangulation method. It is deemed in the concurrent involvement of different assessment agents (teacher, peer and self) into integrated and collaborative translation performance evaluation from two perspectives, i.e. translation product quality and process workflow, with the use of diversified methods based on different theoretical approaches. This research aims at the study of students’ video screen recording contribution combined with think-aloud protocols (TAPs) to increase the assessment objectivity and reliability of the received translation product. Method. A mixed research design was developed and implemented. It involved 40 third-year university students majoring in Philology, who were asked to perform a written translation of the excerpt of English popular science article in Marketing (c. 250 words) into Ukrainian in MS Word using any reference sources at hand, video record the process of their translation and accompany it with their comments. The research was completed with the questionnaire on students’ attitude to the screen recording and TAP involvement into translation task performance and assessment. Findings. The findings of this study reported on the positive impact of screen recording on the assessment accuracy, informative value of the collected data and formative effect of triangulated assessment method on students’ translation competence acquisition. Implications for research and practice. The received results can serve for the optimisation of the procedures of translation task difficulty measurement and competence assessment in the translation classroom.
Can Multistep Nonparametric Regressions Beat Historical Average in Predicting Excess Stock Returns?

Several economic and financial variables are said to have predictive power over excess stock returns. Empirically there is little consensus among academics, whether these variables have predictive power or not. Results are often sensitive to the econometric model of choice. The econometric models can produce biased results due to the high degree of persistence in predictive variables. Apart from high persistence, the relationship between stock return and the predictive variable may also be misspecified in the model. In order to address possible non-linearities and endogeneity between the residuals and persistent independent variables in predictive regressions, multi-step non-parametric and semiparametric regressions are explored in this paper. In these regressions, the conditional mean and the residuals are estimated separately and then added to obtain the predicted excess stock returns. Goyal and Welch's (2008) predictive variables are used to predict excess S&P 500 returns. The predictive performance of both in-sample and out-of-sample of the two proposed models are compared with the historical average, Ordinary Least Squares (OLS) and non-parametric regressions. The performance of the models is evaluated using Root Mean Squared Errors (RMSEs). The explored models, particularly the two-step nonparametric model, outperform the compared models in-sample. Out-of-sample several variables are found to have predictive ability.
The Complementary Effect between Private Consumption and Government Spending: Effect on the Economy

This paper analyzes the relationship between final consumption and government spending, using the correlation and linear regression model. The objective of the research is whether government expenditures are complementary, substitute, or not related to private consumption to give positive effects on the economy. Methodology, quantitative secondary data were obtained from the World Bank, for five countries and processed with SPSS, 21. The empirical analysis was verified through, Bivariate and Partial correlation and normality test. The Result, first, shows that government spending complements and replaces final consumption. Second, it is also confirmed that even when interacting with other variables, the complementary effect of final consumption is not eliminated despite the shocks coming from government spending. Third, by adding other variables to the model, the issue of complementarity and substitutability of the two main variables is not lost. As a result, findings, confirm that private consumption (InCt) and government spending (InGt), Gross Savings (GS), and per capita income (GDPpc), are in statistically significant and positive relationships with each other. The novelty of the paper is, government expenditures cause an increase in private consumption is to high value, showing the complementary effect of government expenditures on private consumption. Based on health expenditures, education, public order, internet provided by the state have increased the demand of families for these services, causing an increase in the share of services provided by the private sector. JEL Classification: E21; H5; E2; R0.
Diseased Body and Diseased Mind: Mind Your Psychological Health during Lockdown

The novel Corona virus also known as 2019-nCoV that originated in China in December 2019 has quickly spread all over the world. In absence of specific treatment or vaccine, with no natural immunity, we are forced to rely on old public health measures like Quarantine, Social distancing and Isolation for prevention of spread among community. Although isolated incidents of epidemics have required isolation and quarantine in the past, no pandemic has required social distancing to a national and international scale before. Despite the obvious physical health and economic concerns of this situation, all the above measures bring forth challenges of emotional and psychological nature which can be especially severe in special populations like elderly, young children and adolescents, health care workers, persons with mental illness. Also, certain aspects like death, role of mass media, and ethics also arise during isolation and quarantine. With mass media playing a significant role in drumming up the perceived threat, people may resort to panic behaviours like hoarding essential day to day use items and masks and other medical supplies leading to shortage. Problem of fake news related to COVID 19 lead to Govt. of India launching of AAROGYA SETU app as well as websites to centralise all COVID 19 related information to people of India. Death in isolation deprives family members and friends of opportunity to grieve for near and dear ones lost due infection or due to natural causes. The requirement of isolation and quarantine during this distressing pandemic has paved way for recognising the importance and advantages of telemedicine as a whole which has led to various government bodies including the Government of India to draw primary guidelines for medical practitioners to follow and ensure ethical concerns are addressed.
Response Surface Methodology Based Desirability Function Approach To Investigate Optimal Mixture Ratio of Silver Nanoparticles Synthesis Process

Silver nanoparticles are known especially because their antimicrobial properties can be used as conductive ink or adhesives for different electronics in compliance with their unique electronic and optical properties. As they are well-known, these practices require smaller nanoparticle formation. A response surface methodology based desirability function approach has been first applied to achieve the desired size of silver nanoparticles with the green synthesis methods. It is notable that the improvement rates at 7.9% by the mean of particle size distribution in AgNPs manufacturing process have been obtained with the utilization of utilizing the proposed methodology. When these results are evaluated, it can be seen that an 89.3% lower standard deviation has been obtained in comparison with the studies in literature. The results also show that the minimization of the nanosilver production variance should be performed to obtain the high quality and large scale manufacturing process.
AGGREGATE EARNINGS AND RETURNS IN BRAZIL

We used the method employed in Kothari, Lewellen and Warner (2006) to show the relationship between aggregate earnings and market returns in Brazil in the period from 1995 to 2017. Considering the findings found by Kothari, Lewellen and Warnet (2006), our results indicate that the theory of Bernard and Thomas (1990) is more consistent with the US market than with the Brazilian market, signaling that the aggregate post-earnings announcement drift tends to be larger in markets with higher earnings persistence, like Brazil. Our findings also indicate that the relationship between aggregate returns and earnings in Brazil tends to be positive for the current period and the next two quarters, corroborating the Sadka and Sadka (2009) study. Considering that the predictability of earnings in the US market is higher than that in Brazil, our results also support the argument by He and Hu (2014) that the relationship between aggregate earnings and returns is linked to each country’s level of disclosure. However, new evidences reveal the influence of high interest rates on financial market results, suggesting that expectations of increased interest rates tend to reduce aggregate current returns in Brazil due to the possible migration of capital to lower risk, given the attractiveness of their returns in an environment of high inflation. 1 Associate Professor of Accounting and Finance at FUCAPE Business School. Av. Fernando Ferrari, 1358 Boa Vista, Vitória ES, Brazil. E-mail: tallesbrugni@fucape.br. https://orcid.org/0000-0002-9025-9440 2 Associate Professor of Finance at PUC-RIO (IAG Business School). Rua Marquês de São Vicente, 225 Gávea, Rio de Janeiro RJ, Brazil. E-mail: klotzle@iag.puc-rio.br. https://orcid.org/0000-0002-5463-6333 3 Associate Professor of Finance at PUC-RIO (IAG Business School). Rua Marquês de São Vicente, 225 Gávea, Rio de Janeiro RJ, Brazil. E-mail: figueiredo@iag.puc-rio.br. https://orcid.org/0000-0002-1452-1240 4 Associate Professor of Applied Econometrics at University of São Paulo (FEA/USP). Av. Prof. Luciano Gualberto, 908 Butantã, São Paulo SP, Brazil. E-mail: lpfavero@usp.br. https://orcid.org/0000-0001-8516-6701 5 PhD (Accounting), Wuhan University. Assistant Professor at COMSATS University Islamabad (CUI). E-mail: safdar.sial786@gmail.com. https://orcid.org/0000-0002-5473-8882 Aggregate Earnings and Returns in Brazil Revista Contabilidade Vista & Revista, ISSN 0103-734X, Universidade Federal de Minas Gerais, 39 Belo Horizonte, v. 32, n. 2, p. 38-58, maio/ago. 2021.