Miracle-dz/UniFAR-FTUs huggingface dataset:
Corporate Social Responsibility: Impact of Individual Attitude on Implementation of Company’s Policy

Author Adetola Adegbenle Title CSR: Impact of Individual Attitudes on the Implementation of Organizational Policies Year 2018 Language English Pages 30 + 3 Appendices Name of Supervisor Thomas Sabel Many multinational companies that have laudable CSR policies have ended up polluting the environment by their operational activities. This is the reason this research work has focused on the personal attitudes of individual employees towards the environment and how this can affect their level of productivity in relation to the organizational CSR policies and its implementation. The theoretical background is based on the value-based theory by Stern & Dietz, value scale by Schwartz et al, and NEP Scale designed by Dunlap & Van-Liere. The quantitative method of collecting data was used and involved the use of a questionnaire and subsequent data analysis with SPSS application. This research focused on the employees of an international company in Nigeria. The analyzed result showed that factors such as age, region, and grade level (position) have impacted on individual attitudes which in turn, has affected their view of the environment negatively or positively. Therefore, such factors should be considered before employing individuals to work in the CSR department of a company. Keyword Corporate Social Responsibility CSR
EFFECTIVENESS OF RHIZOBIA ISOLATES FROM NJORO SOILS ( KENYA ) AND COMMERCIAL INOCULANTS IN NODULATION OF COMMON BEANS ( PHASEOLUS VULGARIS )

Bean production in Kenya barely meets half the demand because of low soil fertility among other factors. While use of rhizobia inoculants can substantially increase bean yields, less than 1% of the farming population is aware of inoculants. The objectives of this study were, one, to isolate and evaluate indigenous rhizobia populations in two agro ecological zones in Njoro, and two, to test the efficacy of the isolates against commercial rhizobia inoculants in relation to nodulation and shoot dry matter of Rose Coco bean cultivar. Rhizobia isolates were collected from field 8 of Egerton University and Kerma farm in Njoro. The isolates were tested against Biofix (market available inoculant) and USDA 9030 (pure culture of Rhizobia tropici) under greenhouse conditions. Rhizobia inoculation had no effect on shoot dry weight (SDW), nodule dry weight (NDW) and shoot N content. However, significant differences were observed among rhizobia strains used, where Biofix produced a higher NDW and Kerma isolate resulted in higher shoot N content than other strains. Symbiotic effectiveness of 111%, 107%, 97% and 92% was observed for Kerma isolate, Field 8 isolate, Biofix and USDA 9030 respectively. Our results indicate that rhizobia isolates from Njoro had comparable symbiotic effectiveness to commercial inoculants and should be tested further using other bean varieties to assess their potential.
Neural Attention Recommender Systems

Recommender systems, predictive models that provide lists of personalized suggestions, have become increasingly popular in many web-based businesses. By presenting potential items that may interest a user, these systems are able to better monetize and improve users’ satisfaction. In recent years, the most successful approaches rely on capturing what best define users and items in the form of latent vectors, a numeric representation that assumes all instances can be described by their respective affiliation towards a set of hidden features. However, recommendation methods based on latent features still face some realworld limitations. The data sparsity problem originates from the unprecedented variety of available items, making generated suggestions irrelevant to many users. Furthermore, many systems have been recently expected to accompany their suggestions with corresponding reasoning. Users who receive unjustified recommendations they do not agree with are susceptible to stop using the system or ignore its suggestions. In this work we investigate the current trends in the field of recommender systems and focus on two rising areas, deep recommendation and explainable recommender systems. First we present Textual and Contextual Embedding-based Neural Recommender (TCENR), a model that mitigates the data sparsity problem in the area of point-of-interest (POI) recommendation. This method employs different types of deep neural networks to learn varied perspectives of the same user-location interaction, using textual reviews, geographical data and social networks.
Attenuation of light in different rock types and implications for rock surface luminescence dating

There is growing interest in rock surface burial and exposure luminescence dating for use in Quaternary science and in archaeology. Such methods have enormous potential both in increasing the range of sedimentary contexts that can be dated, and improving the accuracy and the precision of dating within those contexts. Bleaching of the luminescence signal with depth into the rock surface is likely to vary with lithology. However, previous work on rock surface dating has not systematically studied the di ﬀ erences in light attenuation for rocks of di ﬀ erent lithologies, or directly quanti ﬁ ed the attenuation of light in di ﬀ erent rock surfaces. This study investigates the attenuation of light in di ﬀ erent rock types (greywacke, sandstone, two granites and quartzite) using two di ﬀ erent approaches: 1) sunlight bleaching experiments, to assess the residual infrared stimulated luminescence signal measured at 50°C (IRSL 50 ) and the post-IR IRSL signal measured at 225°C (post-IR IRSL 225 ) at di ﬀ erent depths within the rocks after di ﬀ erent durations of exposure to daylight; and, 2) direct measurement of light attenuation in rock slices using a spectrometer. Data from the spectrometer shows that for all rocks, attenuation is greater for shorter wavelengths ( ∼ 400nm) than longer ones. A consistent di ﬀ erence in attenuation coe ﬃ cient is seen when comparing the IRSL 50 and the post-IR IRSL 225 signals; this is thought to re ﬂ ect the di ﬀ erent sensitivity of these two signals to infrared and visible light. Direct measurement using a spectrometer is much more rapid than undertaking a bleaching experiment, and also provides wavelength-resolved attenuation data. Comparison of the numerical values from the two approaches is complex, but they yield consistent results. For the samples analysed here, the rocks that appear lightest in colour show the least attenuation of light and the luminescence signals are bleached to the greatest depths, and are thus the most suitable for dating using luminescence.
Impact of offspring endothelial function from de novo hypertensive disorders during pregnancy: An evidence-based review

De novo hypertensive disorders of pregnancy (HDP) which consist of gestational hypertension and preeclampsia affect maternal and offspring morbidity and mortality, and potentially increase the risk of cardiovascular disease in the offspring. It is well known that de novo HDP causes various maternal complications, including cardiovascular diseases, placental abruption and liver and kidney failure. However, there are studies suggesting that offspring of pregnancies complicated by de novo HDP have an increased risk of long-term cardiovascular disease. The endothelium is an important regulator of vascular function, and its dysfunction is highly associated with the development of cardiovascular diseases. Hence, this review aimed to systematically identify articles related to the effect of de novo HDP on the endothelial function of the offspring. A computerized database search was conducted on PubMed, Scopus, and Medline from 1976 until 2022. A total of 685 articles were obtained. We identified another three additional articles through review articles and Google Scholar. Altogether, we used 13 articles for data extraction. All studies reported that endothelial function was impaired in the offspring of de novo HDP. This is most likely attributed to impaired vasodilation, subclinical atherosclerosis formation, inflammation, and dysregulated epigenetic regulation of endothelial functions.
Seasonal near-surface hypoxia in a temperate fjord in Clayoquot Sound, British Columbia

While fjords often have low oxygen concentrations in their deep waters, this research identified seasonal, near-surface hypoxia (≤ 2 mL L-1 or 2.9 mg L-1) through a year-long monthly time series in Clayoquot Sound, British Columbia. Temperature, salinity, and oxygen data were collected monthly in the upper 50 m at three stations in Herbert Inlet from June 2020 to July 2021, marking the first time series of its kind in a Clayoquot Sound fjord. Hypoxic conditions were shallowest (minimum depth of 12 m) and most intensified in summer; near-surface hypoxia was recorded at one or more stations in all months except in winter. Considering that many local marine species, including wild Pacific salmon, experience adverse effects at oxygen concentrations much higher than the hypoxic threshold, we note that 50 to 100% of the upper 50 m of Herbert Inlet consistently presented low oxygen concentrations (defined here as a guideline as ≤ 4.9 mL L-1 or 6.9 mg L-1) during the 14-month study period. Previous observations collected sporadically since May 1959 confirmed the presence of hypoxic conditions in the past. These findings suggest that long-term, multidisciplinary studies are needed to understand and predict the impact of hypoxia and deoxygenation on wild salmon stocks as climate changes.
1 Ethnic variations in compulsory detention : a Systematic Review and Meta-Analysis of the international data

Background: Evidence suggests Black, Asian and Minority Ethnic (BAME) groups are at increased risk of involuntary psychiatric care. However, there is no published meta-analysis which brings together both international and UK literature and allows for comparison of the two. This study examines compulsory detention in BAME and migrant groups in the UK and internationally. Methods: A systematic review and meta-analysis of quantitative studies comparing involuntary admission, readmission and length of stay in BAME or migrant groups, compared to majority, or native groups was conducted. Explanations offered for the differences were assessed for the strength of evidence supporting them. Findings: Seventy studies were included. Black Caribbean patients had the highest odds ratio of detention (2·53, CI:2·03-3·16). Black African patients had significantly increased odds (2·27, CI:1·62-3·19), as did, to a lesser extent, South Asian patients (1·33, CI:1·07-1·65). Black Caribbean patients had significantly increased odds of readmission (2·30, CI:1·22-4·34). Migrant groups had increased odds of detention compared to native groups (1·54, CI:1·22-1·95). Evidenced explanations included increased perceived risk of violence and police contact with BAME groups. Interpretation: BAME and migrant groups are disproportionately at risk of psychiatric detention, though there is variation across ethnic groups. Attempts to explain elevated rates of detention in ethnic groups should avoid amalgamation and instead conduct culturally specific, hypothesis driven studies examining the numerous contributors to varying rates of detention. Funding: SP was supported by funding from the UCLH National Institute for Health (NIHR) Biomedical Research Centre. SS was supported by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London.
Plural sourcing and returns to intra-firm coordination

Plural sourcing, or the simultaneous use of internal and external sourcing for the same input, is a welldocumented phenomenon. Yet, we know little about how firms decide how much to make and how much to buy. In this study, we put forward a theory that highlights the need to coordinate vertically adjacent activities as an important predictor of the make-and-buy balance. In particular, we explore a change in patent regulation in the US and show that increases in the coordination requirements between a plurally sourced input and other firm activities prompt firms to increase their reliance on insourcing. Moreover, we show that such increases are more pronounced when the activities in question involve teamwork. Finally, we explain that increases in insourcing are attenuated when plural sourcing firms develops strong relationships with their suppliers.
SLA-driven Planning and Optimization of Enterprise Applications

We propose a model-based methodology to size and plan enterprise applications subject to Service Level Agreements (SLAs). Our approach is illustrated using a real-world Enterprise Resource Planning (ERP) application, namely SAP ERP. Firstly, we develop a closed queueing network model with finite capacity regions describing the SAP ERP application performance and show that this model is effective and robust in capturing measured response times and utilizations. Secondly, we propose an analytical cost model that jointly accounts for fixed hardware costs and dynamic operational costs related to power consumption. Based on the developed performance and cost models, we propose to use multi-objective optimization to find the Pareto-optimal solutions that describe the best trade-off solutions between conflicting performance and cost-saving goals. Experimental validation demonstrates the accuracy of the proposed models and shows that the attained Pareto-optimal solutions can be efficiently used by service providers for SLAdriven planning decisions, thus making a strong case in favor of the applicability of our methodology for deployment decisions subject to different SLA requirements.
Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD.
Dynamic Social Network Analysis Using Link Prediction Ms

In the domain of social networking, two important and interesting tasks available for analysis. They are namely, Link prediction and Community detection. Link prediction is useful to predict links over time based changes in social behavior of users. Based on the time duration various graph snapshots can be collected over continues streaming data. Temporal Latent space for each user helps the system to represent single user interaction with social network. Global optimization approach is used to infer temporal latent space of each user. The system proposes a framework to predict links for time t+1 based on incremental BCGD algorithm using generated t snapshots of previous social network analysis and latent spaces of user for previous graph snapshots. The proposed work focuses on a new approach for a new continuous time model that supports continuous inputs rather than discretized graph snapshots.
Quality Control Challenges in Crowdsourcing Medical Labelingand Diagnosis

Crowdsourcing has enabled the collection, aggregation and refinement of human knowledge and judgment, i.e. ground truth, for problem domains with data of increasing complexity and scale. This scale of ground truth data generation, especially towards the development of machine learning based medical applications that require large volumes of consistent diagnoses, poses significant and unique challenges to quality control. Poor quality control in crowdsourced labeling of medical data can result in undesired effects on patients’ health. In this paper, we study medicine-specific quality control problems, including the diversity of grader expertise and diagnosis guidelines’ ambiguity in novel datasets of three eye diseases. We present analytical findings on physicians’ work patterns, evaluate existing quality control methods that rely on task completion time to circumvent the scarcity and cost problems of generating ground truth medical data, and share our experiences with a real-world system that collects medical labels at scale.
Dynamics of Information Revelation in Online Reviews Completed Research

As online reviews provide essential information to guide customers in their prospective purchases. As more such reviews accumulate overtime, one would suspect that complete information becomes available about product and almost no customers should be disappointed in their purchases. Yet, we provide empirical evidence over a large dataset of reviews that negative reviews seem to be arriving at an accelerated rate later on in the lifetime of a product. To better understand this inconsistency, we frame the problem at hand as an information revelation problems. Using a novel approach, we then segment each review as an aggregation of aspects for which the reviewer provides weights, in line with how much she values those aspects, and corresponding experiences vis-à-vis those aspects, which range from positive to negative. We show that this segmentation better explains the review process and better explains the polarity of reviews.
APPLICATIONS OF DEEP LEARNING IN THE ANALYSIS OF MEDICAL IMAGES ( Aplicaciones de deep learning en análisis de imágenes médicas )

There are a lot of diseases that require the analysis of medical images to be diagnosed. Historically this analysis has been done by experts who often don’t even agree with each other, and in some cases a quick and appropriate diagnosis could lead to saving a life. In recent years there has been a development of Machine Learning techniques applied in the field of medical imaging. Automatising diagnosis would reduce wait times, improve effectiveness and detect findings that may have been otherwise overlooked. The purpose of this project is to study the viability of applying Deep Learning methods, concretely Convolutional Neural Networks for image classification, for analysing medical images. The dataset used corresponds to a set of chest x-rays provided by the ​NIH Clinical Center and tagged according to their diagnosis. The goal of this project is determining if this type of computer-aided detection would help with medical imaging findings interpretation, reducing the time diagnosing usually takes and exceeding the effectiveness of radiologists. After describing Deep Learning in depth, and building a model with Convolutional Neural Networks with an accuracy of approximately 60% in classifying healthy and unhealthy lungs, it has been concluded that with the right resources this method can perform medical analysis but there must be awareness of its limitations.
Through-the-wall radar imaging with compressive sensing; theory, practice and future trends-a review

Through-the-Wall Radar Imaging (TWRI) is anemerging technology which enables us to detect behind the wall targets using electromagnetic signals. TWRI has received considerable attention recently due to its diverse applications. This paper presents fundamentals, mathematical foundations and emerging applications of TWRI with special emphasis on Compressive Sensing (CS) and sparse image reconstruction.Multipath propagation stemming from the surrounding walls and nearby targets are among the impinging challenges.Multipath components produce replicas of the genuine target, ghosts, during image reconstruction which may significantly increase the probability of false alarm. The resulting ghost not only creates confusion with genuine targets but may deteriorate the performance of (CS) algorithms as described in this article. The results from a practical scenario show a promising future of the technology which can be adopted in real-life problems including rescue missions and military purposes. A Key words : spect dependence, compressive sensing, multipath ghost, multipath exploitation, through-the-wall-radar imaging.
Social Fund ’ s lifelong learning and regional development : a case study

The a im of th i s paper i s to eva lua te the f i r s t impacts o f the European Soc ia l Fund (hereaf te r ESF) l i f e long lea rn ing in te rvent ions on the reg iona l deve lopment . As i s we l l known, l i f e long lea rn ing i s def ined as the a l l purposefu l l ea rn ing ac t iv i ty , under taken throughout l i f e , on an ongo ing bas i s , w i th the a im of improv ing knowledge , sk i l l s , and competence w i th in a persona l , c iv ic , soc ia l and/or employment re la ted perspec t ive (CEC, 2000) . Beyond the benef i t s , l i f e long lea rn ing represents an advantage for the reg iona l economy tha t cou ld be measured in te rms of both es t imat ion of d i rec t impact on domest ic demand and eva lua t ion of impacts on the per formance of the loca l economies . The combina t ion of these two k inds of e f fec ts genera tes a pos i t ive impact on a w ider sca le : a h igher and sk i l l ed workforce a t t rac t s more inves tment , contr ibut ing to improve the we l l -be ing of a loca l economy. The case s tudy i s the Veneto reg ion . The app l ied methodo log ies used in the case s tudy a re both a survey and an econometr ic mode l . In the f i r s t case , the u t i l i zed method approaches the top ic f rom a microeconomic perspec t ive , wh i le in the second case the approach i s pure ly macroeconomic .
3 Comparison of Aerodynamic Effects Promoted by Mechanical and Fluidic Miniflaps on an Airfoil NACA

Although Gurneys are extensively used in race-car aerodynamics, application on aircraft is rather uncommon. That might be attributed to the fact that it is very intricate to design deployable flaps in the very thin trailing edge region of common airfoils and the effectiveness of Gurney flaps does not justify the effort or additional weight, respectively. Therefore it is of great interest to explore fundamental methods to increase the effectiveness.