Miracle-dz/UniFAR-FTUs huggingface dataset:
Runtime management techniques for power- and temperature-aware computing

As integrated circuit (IC) technology advances to the sub-100nm region, power and associated thermal effects are becoming a limiting factor in high-performance circuit design. In addition to battery lifetime for mobile devices, power and temperature are emerging as concerns due to the strong temperature-dependence of leakage power, circuit performance, IC package cost and reliability. In traditional design methodologies, a constant worst-case power and temperature are commonly assumed, leading to excessive design margins (resulting in higher cost) or degraded performance due to temperature or power constraints. In reality, circuits exhibit strong workload-dependent variations (e.g. execution time, temperature) at runtime. Therefore, dynamically adapting a circuit to the behaviors of its workloads (trading off between performance, power, temperature and reliability) would enable reclamation of design margins from previous worst-case assumptions. In this dissertation, improved runtime techniques are presented to attenuate power and thermal constraints on circuits by exploiting dynamic workload variations. These techniques include efficient dynamic voltage/frequency scaling (DVS) techniques, such as using feedback control and statistical information, to reduce circuit power consumption while maintaining performance requirements. This dissertation also explores the effect of temperature variations on circuit reliability, develops a reliability model subject to dynamic thermal stress, and investigates architectural techniques to maximize circuit performance without violating IC lifetime specifications. It is shown that, using these power- and temperature-aware runtime management techniques, substantial power and performance margins can be reclaimed from methodologies using worst-case power and temperature assumptions. In addition, the dynamic models developed in this dissertation can also be used for design time optimization.
Consumer Interest and Peer Effect on Corporate Environmental Initiatives: Evidence from Web Search Volumes

With increasing consumer attention and information accessibility, firms lagging behind their peers on environmental initiatives have strong incentive to catch up. Focusing on the environmentally-sensitive industries in the U.S., we show that these “lagging” firms invest more aggressively in reducing emissions or finding eco-friendly uses of materials. Such peer pressure on CSR is not associated with a firm’s relative market share. More interestingly, this article reveals that CSR peer effect is stronger for a firm exposed to high volumes of Internet searches by consumers. In contrast, a firm’s exposure to investor interest does not have a similar impact. Finally, the relation between consumer interest and CSR peer effect is significantly weaker for a high-leverage firm. Our finding provides an additional support for the traditional theory that leverage mitigates the agency costs of overinvestment.
MULTIFUNCTIONAL COMPOSITES BASED ON VARIOUS NANOLAYERS

Summary we presented the enhanced the interfacial shear strength of composites with glass fibers deposited by nano-layer ( CNTs, ZnO). At the same time, the single modified fiber could be designed as multifunctional sensor based on various nano-layers. ABSTRACT Carbon nanotubes (CNTs) and zinc oxide (ZnO) are popular candidates as modifiers to optimize composites’ properties, because of their high strength, distinctive electrical, optical properties and high sensitivities towards gas, humidity and so on. In this report, we deposited CNTs and ZnO onto electrically insulating glass fiber surfaces by electrophoretic deposition (EPD) and atomic layer deposition (ALD) methods, respectively. Firstly, we found that the interfacial shear strength of single fiber composites has been singnificantly improved by various nanolayers. Importantly, due to a precisely controlled thickness by ALD, the effect of thickness of nanolayer on the interfacial strength was investigated for the first time, and a critical thickness of nanolayer, corresponding to the maximum interfacial strength has been firstly experimentally obtained. Subsequently, we utilized single modified fibers to produce the multifunctional sensors. The modified single fiber by CNTs demonstrated a high sensitivity to the stress/strain through the simultaneous measurement of the electrical resistance under tensile loading. The strain sensitivity factor,
Kara and Barents Sea Ice Thickness Estimation Based on CryoSat-2 Radar Altimeter and Sentinel-1 Dual-Polarized SAR

We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara Seas. From the viewpoint of tactical navigation along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from 5 the AARI ice charts, to ORAS5, PIOMAS and TOPAZ4 ocean-sea ice data assimilation system reanalyses, to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) chart and to daily MODIS (Moderate Resolution Imaging Spectroradiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments, and CS2 SIT interpolated to SAR segments with mapping the CS2 SIT distributions to correspond SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 10 or SAR data alone. The agreement with modeled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimeters and there was a signiﬁcant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions.
Enhancing the Performance Measure of Sentiment Analysis through Deep Learning Approach

: Sentiment analysis is the computational process through which one can categorize the opinions expressed in a form of text, specifically analysis is done to predict the user’s perception towards any product, movie, or any particular topic. The prediction can be negative, positive or neutral, even this can categorize to the number of class labels, it depends on the classification task. Already there are many classifiers based on machine learning and they have shown a significant result in this area, but recent years have shown a trend in deep learning due to their high performance. So this paper has also applied deep learning algorithms during the sentiment analysis. The proposed work will show how deep learning-based methods will improve the classification accuracy over machine learning algorithms. The proposed methods are evaluated on the basis of certain datasets like movie reviews, hotel reviews, and political reviews of India. In the initial phase machine learning classifiers like Naïve Bayes, Support Vector Machine, Random Forest, and logistic regression were applied later on deep learning algorithms has been implemented. With the primary focus on deep learning methods, the main contributions of numerous researchers are emphasized. The performance of the algorithm will be evaluated by the parameters like accuracy, precision, recall, and F1 score. The novelty of this work is that other than the combination of RNN and LSTM, we have implemented Deep Chrome Convolutional Neural Network for sentiment analysis and it has achieved the accuracy of 92.76% and 90.76% for a movie review and hotel review dataset respectively that is much higher than other states of the art algorithms. The analysis of sentiments will be more accurate by implementing this proposed work.
Learning Needs as an Intervention for Gen Y Employees’ Intention to Stay and the Mediating Role of Perceived Organizational Support

This paper aims to investigate the relationship between learning needs and generation Y employees’ intention to stay. This study used a cross-sectional survey to collect primary data. The target population was generation Y employees working in the banking sector in Malaysia. Data was collected from 470 respondents, where a total of 230 respondents were males and 240 were females. Results from this study revealed that learning needs have a positive effect on the intention to stay by generation Y employees in the banking sector. In addition, perceived organizational support (POS) mediated the relationship between POS and intention to stay. The results confirmed that an increase in the learning needs of generation Y employees would increase their intention to stay. The study revealed that greater attention must be given to learning needs by organizational leaders and policymakers. This study contributes to the literature in human resource practices pertaining to learning needs as a critical intervention by organizations to increase the intention to stay by generation Y employees. The study is one of the first of its kind to address learning needs and the mediating role of perceived organizational support (POS) in the context of Gen Y’s intention to stay in the banking sector in Malaysia.
The relationship between culture and counterproductive work behaviors: The moderating role of job engagement and perceived job insecurity

Abstract Counterproductive work behaviors (CWBs) have been a substantial cause for concern among organizational researchers and business executives due to its pervasiveness and ability to negatively impact organizational expenditures. Studies have shown that cultural values have an influence on an employee’s propensity to engage in CWBs and this study sought to provide additional insight into the relationship between culture and CWBs. Specifically, this study examined the relationship between cultural values (collectivism and individualism) and reported engagement in both CWBs towards the organization and towards individuals in the organization. In addition, the moderating role of both job insecurity and job engagement in this relationship was examined. Congruent with past research, individualism was shown to have a positive relationship with self-reported engagement in CWBs; however, in contrast to past research, collectivism also demonstrated a positive relationship with self-report engagement in CWBs. Moreover, the relationship between individualism and CWBs was strengthened by increased levels of job engagement. Additionally, it was observed that individuals higher in individualism and individuals higher in collectivism had lower tendencies to engage CWBs as a result of higher levels of job insecurity – as the level of job insecurity increased, the strength of the relationships increased. Implications and study limitations are discussed as well.
Relationship between Personality Traits and Knowledge Sharing Behavior: A Systematic Literature Review

This study aims to examine the relationship between big five personality traits and Knowledge Sharing (KS) behavior along with KS best practices during the KS process. Literature was searched and collected from the Web of Science, Scopus, LISTA and Google Scholar following PRISMA guidelines. The analysis revealed that except neuroticism, all other four personality traits had positive correlation with KS. The positive impact of KS practices has been reported on employee’s personalities and the employees were using varied resources including organized different seminars and workshops etc. The findings will be valuable for managers to design comprehensive and personalized knowledge management system for managers on their personality traits and KS behavior.
CONSTRAINTS IN ADOPTION OF RECOMMENDED PLANT PROTECTION PRACTICES IN GROUNDNUT AND COTTON CROPS

Groundnut and cotton both are the important crops of Saurashtra region of Gujarat state. Saurashtra region, which is known as “Groundnut Bowl of India” has greater importance for groundnut as it accounts for about 92.00 per cent of the total groundnut area of the state. Cotton “King of Fiber” is one of the most ancient and important commercial crop next only to food grains. Adoption of plant protection recommendation is one of the important aspects of controlling pests and diseases. In spite of this fact, farmers are not adopting the recommendations properly and hence, the importance of systematic use of plant protection measures to control pests and diseases cannot be neglected. A farmer faces many constraints in adoption of recommended groundnut and cotton production technology. Keeping in view the study was carried out in major groundnut and cotton growing area of Saurashtra region with 180 numbers of respondents. It was found that major constraints faced by groundnut growers were lack of adequate knowledge about groundnut plant protection practices (87.78%), inadequate knowledge about proper use of insecticide (83.33%), high price of plant protection chemicals (77.78%). Besides these all 13 constraints faced by cotton growers’ major constraints were; lack of adequate knowledge about cotton plant protection practices (83.33%), high price of plant protection chemicals (80.00%), lack of knowledge about proper diagnosis of disease / pest (76.67%). Major suggestions to overcome constraints of groundnut were; knowledge about plant protection measures should be given to groundnut growers on field by scientists and extension workers (90.00%), technical guidance should be provided regarding plant protection of groundnut crop (86.67%) and major suggestions of cotton crop were knowledge about plant protection measures should be given to cotton growers on field by scientists and extension workers (87.78%), more number of demonstrations on plant protection of cotton crop should be organized on farmers’ field (86.67%).
The Effect of Kaizen , Innovation, and Design toward Operations Performance on MSMEs the Manufacturing Sector: The Role of Output Quality as an Intervening Variable

:- This study aims to analyse the effect of kaizen , innovation, and design on output quality; analyse the effect of kaizen , innovation, design, and output quality on operations performance; and analysing the effects of kaizen , innovation, and design on operations performance through output quality. Determination of the sample using total sampling technique and obtained 183 MSMEs as the research sample. The unit of analysis is the business owner or the person in charge of production in MSMEs, the manufacturing sector, with sub-sector of the furniture industry scattered in the City of Parepare. The data analysis used Structural Equation Modeling (SEM) analysis techniques with the AMOS program's help. The results showed that kaizen , innovation, and design had a positive effect and significant on output quality; kaizen , design, and output quality have a positive effect and significant on operations performance; innovation has a positive effect but not significant on operations performance; and kaizen , innovation, and design have a positive effect and significant on operations performance through output quality. Kaizen is the most dominant variable that affects improving the output quality and operations performance, which are the findings of this study. Other findings also show that efforts to improve operations performance through innovation have not been going well. The success of implementing innovation for MSMEs actors requires sufficient entrepreneurial readiness and the ability to read opportunities and dare to take risks.
The Role of Ethics in Digital Transformation The sources of ethical challenges in digital transformation and how firms cope with them

In recent years, more and more industries have gone through the process of digital transformation. However, extant literature has not explored if and how ethics plays a role during this transformation. Based on interviews with key informants from the publishing industry, complemented with firm documents and online records, this study contributes to the extant literature on the topic of digital transformation by adding an ethical viewpoint to the discussion. It finds that firms face five sources of ethical challenges during digital transformation: (1) new technologies, (2) customer data, (3) contact with customers, (4) competitive environment, and (5) changing employee functions. In addition, this study identifies four strategies used to cope with these sources of ethical challenges: (1) being guided by a mission or code, (2) talking about ethical issues, (3) training, and (4) transparency. Each mechanism is suitable for coping with different sources of ethical challenges. By implementing the appropriate strategy, firms can cope with the ethical challenges they are confronted with during the process of digital transformation. Practically, this study recommends that managers assess the sources of ethical challenges faced by their firm, and subsequently implement the appropriate coping mechanisms.
A New Perspective on the Corporate Bond Liquidity Factor

This study documents properties of market-wide corporate bond liquidity and suggests that liquidity risk is an important determinant of returns. In market downturns, transaction costs rise for sellers and fall for buyers. The negative relation between buyer and seller liquidity motivates a new across-measure liquidity factor that incorporates an asymmetric liquidity component. Shocks to market-wide liquidity explain a large fraction of bond return variation in the time series. Primarily driven by the asymmetric component, the liquidity factor attracts a cross-sectional risk premium that is robust to controls for credit, equity, and interest rate factors as well as the illiquidity level. liquidity level the cross-section. annualized liquidity risk premium average first-stage LRF loading of benchmark
Daily Aircraft Maintenance Routing Using a Deficit Function Hollow Graph

We solve the aircraft maintenance routing problem (AMRP), with rotations for a homogeneous fleet and a repeated daily flight schedule (FS). We develop a new, more compact flight connection graph called a hollow graph. Flight connection opportunities are derived from hollow regions between maximum values of a deficit function representation of the FS. They allow a determination of the minimum aircraft fleet without need for a plane count constraint. We solve the AMRP as a dual-objective multicommodity integer linear program. The paper’s main contributions are: (1) integration of the minimum fleet size in the AMRP, (2) use of a deficit function representation of the FS, (3) reduction of aircraft connection graph and number of commodities, resulting in reduced problem size and faster run times, and (4) a dual-objective AMRP. Compared to existing formulations with larger fleets and connection networks we obtain much smaller problem sizes that execute up to 95% faster.
MIMICS-Duo: Offline & Online Evaluation of Search Clarification

Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue, we describe MIMICS-Duo, a new freely available dataset of 306 search queries with multiple clarifications (a total of 1,034 query-clarification pairs). MIMICS-Duo contains fine-grained annotations on clarification questions and their candidate answers and enhances the existing MIMICS datasets by enabling multi-dimensional evaluation of search clarification methods, including online and offline evaluation. We conduct extensive analysis to demonstrate the relationship between offline and online search clarification datasets and outline several research directions enabled by MIMICS-Duo. We believe that this resource will help researchers better understand clarification in search.
The Effectiveness of Baeg Games to Improve Early Childhood Gross Motor Skills

This research aims to develop Baeg games to improve gross motor skills in early childhood, 4-5 years old. They are using methods with qualitative and quantitative approaches and involving 20 respondents with a random sample-taking technique. The instruments used were pre-test and post-test. Data collection techniques were carried out through interviews, observation, documentation, and questionnaires with the Likers scale criteria. The data analysis technique used the T Paired test to determine the differences before treatment and after treatment. Based on the results of field tests, Baeg's game effectively improves gross motor skills in early childhood. The Baeg game is an innovation from combining the traditional games of clogs and stilts that already exist before, adapted to the characteristics of early childhood.
A Behavior Perspective of Financial Distress Anomaly: Evidence from Overnight Returns

Using measurement of overnight returns, this paper investigates if trading behaviors due to information shocks or investor sentiments can be part of determinants for distress anomaly. We find that stocks with highest (lowest) overnight returns generate greatest (smallest) distress risk premium, and accordingly, a conditional distress probability portfolio composed with double sort of overnight returns and distress probability would yield significant profitability. The regression outcomes demonstrate that probability of default significantly interacts with average overnight returns in explaining the stock returns. The conventional distress anomaly is subsumed by the conditional distress factor, but not vice versa; and only profits of conventional distress probability portfolios, not of the conditional portfolios, mainly come from stocks with characteristics that are associated with stock mispricing.
The credibility of Instagram-based information on strength and conditioning in tennis

Background: Social networks are an important and widely used resource for training information for many people. However, the content on these platforms regarding the recommendations for strength and conditioning in relation to tennis has not yet been fully evaluated. We investigated the credibility of Instagram-based information on the strength and conditioning development specifically in relation to tennis. Methods: Three experts with more then ten years of scientific and professional experience in the field of tennis (39.6±7.4 years of age) evaluated the credibility of the Instagram-based content of five selected accounts based on three aspects: information based on scientific knowledge, the practical applicability of information and the demonstration quality of the presented exercises. We calculated the means (AS) and standard deviations (SD) for each criterion. Results: The results of this research indicate a very low level of credibility in relation to the data published on social networks. The information is inaccurate and poorly verified, and as such is not a reliable source and selection for the purpose of strength and conditioning progress in tennis. Conclusion: Only validated training approaches and exercises that leverage players’ fitness abilities might serve as an avenue for strength and conditioning promotion in tennis.