Miracle-dz/UniFAR-FTUs huggingface dataset:
Institutional pressures and firms’ environmental management behavior: the moderating role of slack resources

Firms’ environmental management behavior is divided into two dimensions: substantive and symbolic. The effects of coercive pressure, mimetic pressure, and normative pressure on these two dimensions are explored based on institutional theory. The moderating role of slack resources in the above relationships is examined using a resource-based view. Data from 193 Chinese listed companies in the heavy-polluting industries from 2014 to 2018 are used to test the hypotheses. The results show the strongest facilitator of substantive environmental management behavior is coercive pressure, while the strongest promoter of symbolic environmental management behavior is mimetic pressure. Slack resources positively moderate institutional pressures and firms’ environmental management behavior relationships partially. The conclusions are significant to both environmental practitioners and managers.
The globalization of values: A comparison between Bangladesh and the USA

Globalization is creating similar environments for younger consumers around the world. With younger individuals being socialized in more similar environments than their parents, the former should have more comparable values than the latter. The goal of the current study was to empirically test this argument. 154 consumers served as voluntary subjects (84 from the USA and 70 from Bangladesh). Questionnaires were administered to respondents for gathering the necessary data. The respondents ranged in age from 19 to 80. Based on data gathered from 120 subjects across two countries, the results support growing similarities in values between younger consumers in comparison to older consumers. These results suggest that marketers might be able to adopt standardized marketing practices with younger individuals, whereas standardization might not have been an option with their parents. JEL Classification Code: D46, F01, M31
HIGH POWER PULSED TESTS OF A BETA=0.5 5-CELL 704 MHZ SUPERCONDUCTING CAVITY

A  = 0.5 5-cell 704 MHz cavity was developed in the framework of European R&D programs on high intensity pulsed proton injectors. Medium beta elliptical cavities are known to be sensitive to Lorentz detuning, which can become difficult to deal with in pulsed operation. The cavity was optimized to reduce the Lorentz detuning by means of two series of rings welded around the irises, and equipped with a piezo tuning system. In order to test the cavity in pulsed mode, a power coupler with 1 MW capability was connected to the cavity. We report here on the fully equipped cavity tests at 1.8 K carried out in the horizontal cryostat Cryholab at Saclay to study its RF and mechanical behavior in pulsed mode, mostly with 2 ms pulses at a 50 Hz repetition rate. The compensation of Lorentz force detuning has been achieved at an accelerating gradient of 13 MV/m (44 MV/m peak surface electric field).
Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning

Simple Summary In approximately 50% of prostate cancer patients undergoing surgical treatment, cancer has extended beyond the prostate boundary (i.e., extraprostatic extension). The aim of our study was to expand artificial intelligence (AI) models that identify cancer in the prostate to also identify the cancer that spreads outside the boundary of the prostate. By combining past models with image post-processing steps and clinical decision rules, we built an autonomous approach to detect the extension of the cancer beyond the prostate boundary using prostate MRI. Our study included 123 prostate cancer patients (38 with extraprostatic extension), and our proposed method can detect cancer outside the prostate boundary in more cases than radiologists. Abstract The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of 0.72 at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning.
Does Board Gender Diversity Improve
Environmental Disclosure of Multinational
Corporations? A Cross-Cultural Analysis

The relationship between board gender diversity and environmental disclosure has received considerable scholarly attention. However, the influence of national cultural dimensions on this relationship has not been extensively studied. Therefore, we investigate the data of multinational corporations (MNCs) to examine the relationship between board gender diversity and the environmental disclosure of MNCs, and the moderating effect of national culture. We find that (1) board gender diversity positively affects the environmental disclosure of MNCs, and (2) power distance, individualism, masculinity, and uncertainty avoidance negatively moderate this relationship and show how board members’ gender differences influence the board’s stakeholder management, as well as the effects of national cultures. We offer suggestions for policymakers to improve corporate environmental responsibility when formulating related policies by considering board gender diversity and national culture. We also provide suggestions for MNCs on ways to promote the positive effects of female directors and national cultural characteristics to maintain corporate reputation.
Solar Based Reciprocating Pump

Abstract: The soul of India lives in the villages and the backbone of that soul is the agricultural sector Improvement in irrigation is necessary to strengthen the economic background in agriculture. In present times, more power is required for people to use new equipment. Solar energy must be used optimally to achieve the expected results that it is environmentally friendly and can be run with less power consumption than central pumps and fuel-based pumps. Its ultimate objective is to lift water from the level discharged through water resources. Therefore, electric energy is powered by the help of PV panels and batteries charged by the Sun rays and electric power is obtained to drive the DC motor Which provides power to the reciprocating pump with mechanical power to the connecting rod. The system consists of a single solar panel, battery, motor, crankshaft, exchange pump, valve and tank
Paradox or Mitigation? Childless and Parent Gender Gaps across British, Finnish, and German Wage Distributions

Abstract:Part of the welfare paradox is that generous family policies increase private sector employer discrimination particularly against higher-wage women. We argue instead that bundles of generous policies mitigate gender productivity differences among parents, and in turn the discrimination also affecting childless women. We test these assertions by estimating the two gaps across the British, Finnish, and German private sector wage distributions using 2000–2018 panel data and unconditional quantile regression. Because of smaller motherhood penalties below the median, parenthood gaps are smallest in Finland and Germany. In contrast, fatherhood premiums constitute most of the parenthood gap for high-wage German and British women, whereas high-wage British women are disadvantaged by motherhood penalties and fatherhood premiums. The childless gap is also smaller across the bottom of the Finnish and German wage distributions. Overall, our advanced modeling strategy finds strong support for the mitigating effects of generous family policies on gender wage gaps.
Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
A Bayesian adaptive algorithm (
 QUEST
 ) to estimate olfactory threshold in hyposmic patients

Objectives The sense of smell is important as a warning system, in social communication and in guiding food intake. Impairment is common, and cases are increasing following COVID-19. Olfactory dysfunction may lead to decreased quality of life. There are several established ways to assess olfaction including the "Sniffin' Sticks" which are a validated test for healthy and diseased populations. Methods The odor threshold is traditionally determined using a single staircase procedure, with narrow or wide step. We investigated a Bayesian adaptive algorithm (QUEST) to estimate olfactory threshold in a hyposmic population compared with a healthy control group. Thresholds were measured using the three procedures in two sessions (Test and Retest). Results All the tested methods showed considerable overlap in both groups: there was a positive correlation between the QUEST procedure and classic staircase method (r = 0.88), and high test-retest reliability for all three methods used (Sniffin' Sticks narrow: r = 0.81;Sniffin' Sticks wide: r = 0.95;QUEST: r = 0.80). Conclusions Results from these approaches exhibit considerable overlap with all of them being suitable for clinical use. An advantage of the QUEST method can be the defined number of trials needed to determine an odor threshold.
Java Code Clone Detection by Exploiting Semantic and Syntax Information From Intermediate Code-Based Graph

Code clone detection plays a critical role in the field of software engineering. To achieve this goal, developers are required to have rich development experience for finding the “functional” clone code. However, this is unfriendly to novice developers. Although many approaches were proposed to automatically detect code clones, the results are not satisfactory. A major reason is that it is difficult to extract syntax and semantic information from the source code. To resolve this problem, in this article, we develop a novel graph representation approach based on intermediate code to detect the functional code clones. This graph representation is built based on intermediate code compiled from the source code. By using it, we can easily utilize graph embedding techniques to extract syntactic and semantic features from abstract syntax tree, control flow graph, and DFG generated from intermediate code. After that, we use the Softmax classifier to detect functional code clone pairs. We evaluate the performance of the proposed graph representation approach based on intermediate code for the code clone detection task on the BigCloneBench dataset. In order to improve performance, the embedded representation of intermediate code is initialized based on pretrained vectors learned from the collected LLVM IR dataset in advance. The experimental results show that our proposed intermediate code-based graph approach performs better than existing functional code clone detection approaches. Especially for the type-4 code clone detection, our approach outperforms the baseline approaches by an average of 33.49% in the term of F1 score.
PREDICTION OF THE STRESS DISTRIBUTION ON A LEADING EDGE INFLATABLE KITE UNDER AERODYNAMIC LOAD

The auxiliary propulsion of commercial ships requires huge kites for huge propulsive loads. Numerical tools must be implemented in order to evaluate the stress level within the kite fabric. In the present study, the kite fluid modelling is done using the 3D lifting line method. The load distribution is transferred to the structural modelling. The finite element analysis of the kite is based on shell elements for the fabric and beam elements for the laths. The stress distribution within the kite fabric is computed for the case of a F-one Revolt kite in a static flight case.
Development of DNA markers that distinguish male and female haploid germlings of the brown alga, Cladosiphon okamuranus

Okinawa mozuku, Cladosiphon okamuranus, is one of many edible brown algae, yielding 22 Kton annually and contributing ¥5 billion to the Japanese economy. The life cycle of C. okamuranus is complicated, since the alga has self‐cloning life cycles in both haploid (N, male and female) and diploid (2N) conditions, but only diploid “seeds” (germlings) become edible sporophytes. Because haploid and diploid germlings are morphologically indistinguishable, haploid germlings are often mistakenly combined with diploid germlings for cultivation, which results in less efficient harvesting of mozuku. Sexual identification of haploid germlings is essential to develop better diploid strains by crossbreeding. With this aim, we performed RNA‐seq analysis of haploid germlings of C. okamuranus. Using its decoded diploid genome and transcriptomic information, we identified 269 genes that are expressed specifically in male or female haploids. BLAST analysis with Ectocarpus siliculosus gene models revealed that nine of 269 genes were putative sex determination‐related genes of C. okamuranus. A unique set of polymerase chain reaction primers for these nine genes was designed, and DNA amplification using primers enabled us to distinguish male and female haploid and diploid germlings. This tool will enable mozuku farmers to select diploid germlings free of haploid germlings. Using this DNA marker technique, the amount of mozuku cultivated in Okinawa is expected to increase.
A Comparative Study of Canine Mesenchymal Stem Cells Isolated from Different Sources

Simple Summary The present study describes differences in the isolation yield, morphology, presence of surface markers and proliferation capacity but not in the multilineage potential of canine MSCs isolated from bone marrow, adipose tissue and amnion. Among all the MSCs analysed, AT-MSCs showed the highest isolation yield, phenotype homogeneity, proliferation capacity and osteogenic and chondrogenic potential. In addition, for BM-MSCs and AM-MSCs, we uncovered some differences that need to be considered during isolation, expansion and phenotyping prior to their possible application in targeted regenerative veterinary medicine. Abstract In this study, we provide comprehensive analyses of mesenchymal stem cells (MSCs) isolated from three types of canine tissues: bone marrow (BM-MSCs), adipose tissue (AT-MSCs) and amniotic tissue (AM-MSCs). We compare their morphology, phenotype, multilineage potential and proliferation activity. The BM-MSCs and AM-MSCs showed fibroblast-like shapes against the spindle shape of the AT-MSCs. All populations showed strong osteogenic and chondrogenic potential. However, we observed phenotypic differences. The BM-MSCs and AT-MSCs revealed high expression of CD29, CD44, CD90 and CD105 positivity compared to the AM-MSCs, which showed reduced expression of all the analysed CD markers. Similarly, the isolation yield and proliferation varied depending on the source. The highest isolation yield and proliferation were detected in the population of AT-MSCs, while the AM-MSCs showed a high yield of cells, but the lowest proliferation activity, in contrast to the BM-MSCs which had the lowest isolation yield. Thus, the present data provide assumptions for obtaining a homogeneous MSC derived from all three canine tissues for possible applications in veterinary regenerative medicine, while the origin of isolated MSCs must always be taken into account.
COVID-19: Employee Experience and Adjustment at a State Owned Company in South Africa

The study explored employees’ experience and adjustment of working during the COVID-19 pandemic. The study followed a qualitative research approach and used a convenience sampling method. The sample contained 12 employees (women = 6; men = 6; working from home = 6; working from the office = 1; hybrid workers = 5; median age = 36; Mage = 39). Data was collected through semi-structured interviews that occurred virtually on Microsoft teams. Data were analyzed using the Braun and Clarke’s thematic analysis. Six themes were established: The study found that work content increased, employees had to work remotely and use online platforms to communicate and deliver work, received adequate support from the organization, achieved positive work-life balance, and faced personal challenges in grief and emotional distress. In fulfilling the goals and objectives of the study, HR and management can be better placed to recognize the effect of the pandemic on workers and the role they need to play in supporting these workers throughout this time.
ASAD: Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles

Anomaly detection is the foundation of intelligent operation and maintenance (O&M), and detection objects are evaluated by key performance indicators (KPIs). For almost all computer O&M systems, KPIs are usually the machine-level operating data. Moreover, these high-frequency KPIs show a non-Gaussian distribution and are hard to model, i.e., they are intricate KPI profiles. However, existing anomaly detection techniques are incapable of adapting to intricate KPI profiles. In order to enhance the performance under intricate KPI profiles, this study presents a seasonal adaptive KPI anomaly detection algorithm ASAD (Adaptive Seasonality Anomaly Detection). We also propose a new eBeats clustering algorithm and calendar-based correlation method to further reduce the detection time and error. Through experimental tests, our ASAD algorithm has the best overall performance compared to other KPI anomaly detection methods.
Students’ Career Decision-Making During Online Learning: The Mediating Roles of Self-Efficacy in Vocational Education

In the last decade, vocational education in Indonesia has experienced problems in making career decisions for students, which was exacerbated by the COVID-19 pandemic. Therefore, this research aims to examine the role of self-efficacy and mediate digital literacy, social environment, and counselling guidance in influencing career decision-making. This is an ex-post-facto research design with data collected from a sample of 566 vocational education students in Indonesia through a questionnaire method distributed online using Google Form. The collected data was then analyzed using structural equation modelling (SEM) with path analysis and bootstrap methods. The results revealed that self-efficacy plays a vital role in mediating digital literacy and guiding students in career decision-making. On the other hand, digital literacy, guidance, and counselling have a significant direct effect on self-efficacy and career decision-making. Meanwhile, the social environment only has a significant direct effect on students’ career decision-making. The real role of all elements of vocational education in strengthening self-efficacy, growing digital literacy, monitoring social environment interactions, and providing counselling guidance to students is needed to increase optimism and the quality of career decision-making in vocational education.
Concept and Measurement of the Food System Sustainability: A Bibliometric Research

In view of climate change and population growth, making food system sustainable is a global concern. A lot of policies regulate this issue, but recent data show that global (and national) food systems are still far from the sustainability. Improvement of existing policies in the field of food system regulation requires a clear understanding of the food system sustainability concept itself: specification of its traits, attributes, measurement indicators, and goals. These issues are reflected in a lot of research papers, but none of these articles summarise the major trends, content, and features of the food system sustainability concept’s evolution. This study for the first time summarises history and contents of research in the field of concept and measurement of food system sustainability through bibliometric analysis of Scopus indexed papers for 1991-2022. The growth of scientific interest in this area, led by researchers from the USA, Italy, and France, was found to be wave-like with peaks following the global food crises appearance patterns. The results of this study show that modern perception of the concept (since 2018) is complicated and intertwined in the notions of systems and system thinking, sustainability, and life cycle assessment, contrasting to the previous views (1991-2018) focused on food production system and food security. In general, a little attention is given to social and economic aspects of food system sustainability, in contrast to environmental, food, and agri-food issues. The elaboration of one-size-fits-all policy and solutions favouring food systems sustainability is suggested to be unlikely due to the complexity of the concept's perception. Governance of food system sustainability should go in line with concrete institutional, economic, social, and natural environments that need to be comprehensively studied in a future. Studies from developing countries are of special interest in this context