Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential effects of migration networks: The case of China

The crucial role of migration networks in the process of migration has been well established. The main goal of our paper is to examine the role of migration networks in the case of China, a country that has had the largest migrant population in human history. Specifically, we focus on issues that have received relatively little attention in the migration literature. We first examined how the use of migration networks differs for individuals with different characteristics such as education, gender and household registration (hukou) status. Based on the migration literature, we generated a set of hypotheses. We then examined the use of migration networks by people with different characteristics both in the context of migration departure (initiation of migration) and destination choice, using the micro-data from the 2000 China Population Census. Our results show that female migrants, migrants without local hukou, and younger migrants are more likely to rely on well-developed migration networks, whereas the most educated migrants are less likely to depend on them. We also found that migration networks are more important for floating migrants than for permanent migrants.
Edinburgh Research Explorer 256×256, 100kfps, 61% Fill-factor time-resolved SPAD image sensor for microscopy applications

— A 256×256 Single Photon Avalanche Diode (SPAD) image sensor operating at 100kfps with fill factor of 61% and pixel pitch of 16µm is reported. An all-NMOS 7T pixel allows high uniformity gated operation down to 4ns and ~600ps fall time with on-chip delay generation. The sensor operates with 0.996 temporal aperture ratio (TAR) in rolling shutter and has a parasitic light sensitivity (PLS) in excess of -160dB when operated in global shutter. Gating and cooling allow the suppression of dark noise, which, in combination with the high fill factor, enables competitive low-light performance with electron multiplying CCDs (EMCCDs) whilst offering time-resolved imaging modes.
Sources and Delivery of Nutrients to the Northwestern Gulf of Mexico from Streams in the South-Central United States1

Rebich, Richard A. , Natalie A. Houston, Scott V. Mize, Daniel K. Pearson, Patricia B. Ging, and C. Evan Hornig, 2011. Sources and Delivery of Nutrients to the Northwestern Gulf of Mexico From Streams in the South‐Central United States. Journal of the American Water Resources Association (JAWRA) 47(5):1061‐1086. DOI: 10.1111/j.1752‐1688.2011. 00583.x
Resveratrol Prevents Cognitive Impairment in Type 2 Diabetic Mice by Upregulating Nrf2 Expression and Transcriptional Level Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy

Purpose: This study aimed to determine whether the natural antioxidant resveratrol (RSV) prevents type 2 diabetes mellitus (T2DM)-induced cognitive impairment and to explore whether redox-associated factor nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in the neuroprotective effect of RSV. Materials and Methods: We established a T2DM model with 8-week-old male ICR mice by administration of a high-fat diet for 2 months and low-dose streptozotocin for 3 days. Then, diabetic and age-matched control mice were treated with or without RSV for 4 months every other day and subjected to the Morris water maze test. After the mice were euthanized, whole brains were sectioned for Nissl staining and immuno ﬂ uorescence labeling. Hippocampal sections were observed by transmission electron microscopy to evaluate the ultrastructure of synapses. In ﬂ ammatory factors, oxidative stress-related indexes, and Nrf2 and downstream target gene expression were analyzed in hippocampal tissues by quantitative real-time PCR, Western blotting, and associated quantitative kits. Results: In the Morris water maze test, compared to control mice, T2DM mice showed learning and memory impairments, but RSV treatment prevented the learning and memory decline in T2DM mice. Similarly, RSV prevented T2DM-induced hippocampal neuron destruction and synaptic ultrastructural damage. The expression levels of in ﬂ ammatory factors and oxidative stress-related indicators were increased in the T2DM group compared with the control group but were decreased signi ﬁ cantly by RSV treatment in the T2DM group. Additionally, the expression of Nrf2 and its downstream target genes was decreased in the T2DM group compared with the control group and was signi ﬁ cantly increased by RSV treatment in the T2DM group. Conclusion: RSV prevented T2DM-induced cognitive impairment through anti-in ﬂ ammatory and antioxidant activities. This effect was accompanied by the upregulation of Nrf2 transcriptional activity and the increased expression of downstream antioxidant genes.
Edinburgh Research Explorer Learning Fast Sparsifying Transforms

—Given a dataset, the task of learning a transform that allows sparse representations of the data bears the name of dictionary learning. In many applications, these learned dictionaries represent the data much better than the static well-known transforms (Fourier, Hadamard etc.). The main downside of learned transforms is that they lack structure and therefore they are not computationally efﬁcient, unlike their classical counterparts. These posse several difﬁculties especially when using power limited hardware such as mobile devices, therefore discouraging the application of sparsity techniques in such scenarios. In this paper we construct orthogonal and non-orthogonal dictionaries that are factorized as a product of a few basic transformations. In the orthogonal case, we solve exactly the dictionary update problem for one basic transformation, which can be viewed as a generalized Givens rotation, and then propose to construct orthogonal dictionaries that are a product of these transformations, guaranteeing their fast manipulation. We also propose a method to construct fast square but non-orthogonal dictionaries that are factorized as a product of few transforms that can be viewed as a further generalization of Givens rotations to the non-orthogonal setting. We show how the proposed transforms can balance very well data representation performance and computational complexity. We also compare with classical fast and learned general and orthogonal transforms.
Cognitive Systems Research

Interactive software agents, such as chatbots, are progressively being used in the area of health and well-being. In such applications, where agents engage with users in interpersonal conversations for, e.g., coaching, comfort or behavior-change interventions, there is an increased need for understanding agents’ empathic capabilities. In the current state-of-the-art, there are no tools to do that. In order to understand empathic capabilities in interactive software agents, we need a precise notion of empathy. The literature discusses a variety of definitions of empathy, but there is no consensus of a formal definition. Based on a systematic literature review and a qualitative analysis of recent approaches to empathy in interactive agents for health and well-being, a formal definition—an ontology—of empathy is developed. We present the potential of the formal definition in a controlled user-study by applying it as a tool for assessing empathy in two state-of-the-art health and well-being chatbots; Replika and Wysa. Our findings suggest that our definition captures necessary conditions for assessing empathy in interactive agents, and how it can uncover and explain trends in changing perceptions of empathy over time. The definition, implemented in Web Ontology Language (OWL), may serve as an automated tool, enabling systems to recognize empathy in interactions—be it an interactive agent evaluating its own empathic performance or an intelligent system assessing the empathic capability of its interlocutors.
Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus

Abstract. The purpose of this article is to examine the prediction accuracy of the Random Forests, a machine learning method, when it is applied for residential mass appraisals in the city of Nicosia, Cyprus. The analysis is performed using transaction sales data from the Cyprus Department of Lands and Surveys, the Consumer Price Index of Cyprus from the Cyprus Statistical Service and the Central Bank of Cyprus' Residential Index (Price index for apartments). The Consumer Price Index and the price index for apartments record quarterly price changes, while the dependent variables for the computational models were the Declared and the Accepted Prices that were conditional on observed values of a variety of independent variables. The Random Forests method exhibited enhanced prediction accuracy, especially for the models that comprised of a sufficient number of independent variables, indicating the method as prominent, although it has not yet been utilized adequately for mass appraisals.
Uncovering Customer Issues in E-Commerce: Sentiment Analysis and Topic Modeling Approach

The rise of digital technology has revolutionized consumer behavior, leading to a notable transition toward online shopping. Ensuring the provision of advantages and satisfaction to customers is crucial, along with the competitive nature of the e-commerce industry. The emergence of digital media, particularly social media platforms, provides a channel for customers to express their experiences, encompassing expressions of praise and complaints regarding perceived dissatisfaction with the services offered. Parties are presented with valuable opportunities to actively delve into issues that contribute to user dissatisfaction through this open media and support the targeted data-driven considerations for decision-making. In this study, we use text mining methods to uncover user issues based on textual customer traces on Twitter. We use IndoBERT sentiment classification to define the satisfaction polarity. This research study centers around the examination of negative expressions. Subsequently, a two-stage topic modeling methodology is employed to investigate and identify the primary issues underlying customer dissatisfaction. We uncover five primary issues in customer dissatisfaction, including application issues (25%), delivery issues (21%), express courier issues (18%), cost-related issues (17%), and customer service issues (17%). We comprehensively examine sub-topics on each primary issue to understand the underlying problems better. This method offers a more profound approach to uncovering customer satisfaction issues and supporting targeted strategies in enhancing customer experience by responding to emerging customer satisfaction issues.
Identification of Different Arabic Dialects Using Randomly Multimodal Deep Learning (RMDL) Approach on AOC Database

Like other languages, the Arabic dialect has both spoken and written forms. The first form is the true mother tongue of Arabic speakers explored on a daily basis. As for the second form, it represents the dialect of Modern Standard Arabic (MSA) which mainly constitutes the content of most Arabic databases due to its predominance in written form. However, the large size of Arabic text databases requires robust and accurate machine learning methods. Random Multimodal Deep Learning (R MDL) is one such deep learning approach that is explored in this article to address the issue of finding the best deep learning architecture and structure while simultaneously maintaining improvement accuracy and robustness for the classification and identification of Arabic dialects (MSA, Gulf (GLF), Egypt (EGY) and Levantine (Lev)) via a set of deep learning architectures. Experimental tests were carried out on the Arabic Online Commentary (AOC) database and showed good results in terms of performance evaluated using the RMDL approach compared to those obtained with other deep learning algorithms.
Skin Cancer Classification Using EfficientNetV2 and ViT B16

Skin cancer is a significant global health issue. This condition occurs when skin cells grow uncontrollably and become malignant, often triggered by prolonged exposure to ultraviolet (UV) radiation from the sun or other sources. Technological advancements and machine learning algorithms have provided new hope in skin cancer detection in recent years. This research utilized Convolutional Neural Networks (CNN), EfficientNet, and Vision Transformer (VIT) methods to analyze skin lesion images and detect skin cancer automatically. The study employed the ISIC dataset, consisting of images of skin lesions classified as benign and malignant. In the evaluation of the models, it was observed that with 150 epochs, the EfficientNet model achieved the highest performance, reaching an accuracy of 88%. The Accuracy indicates the model’s ability to accurately classify skin lesion images and detect potential cases of skin cancer. However, it is worth noting that the EfficientNet model also displayed stable handling of overfitting, maintaining an accuracy of 87%. This implies that the model effectively generalizes its learned features without compromising performance. These results demonstrate the effectiveness of the EfficientNet model in achieving a high level of Accuracy and addressing the challenges of overfitting, making it a promising approach for skin cancer detection. The findings of this research indicate that all three models could detect skin cancer with relatively high Accuracy. This study highlights the potential of these deep learning models in improving the diagnosis and treatment of skin cancer, with the primary goal of supporting early detection and appropriate interventions for patients. Through this technology, it is hoped that efforts in the prevention, treatment, and management of skin cancer can be enhanced overall.
Impact of New Technologies on Soybean at Farmers’ Field

A total 75 front line demonstration of soybean organized on the farmer’s fields of Sehore district under Vindhyan plateau agro climatic zone of Madhya Pradesh during rainy season of 1999-2000 to 2003-04. The results of these demonstrations revealed that the varieties JS-335, JS-93-05, JS-90-41 and NRC-37 yielded 84.35, 159.06, 133.95 and 42.10 per cent higher than local variety sown under farmer’s practices. The highest mean yield of 22.02 q ha-1 under recommended technologies, productivity increase of 49.86 to 72.20 per cent in seed yield over farmers practices, while additional income of Rs.6725 to 11201 ha-1 with 1:2.40 to 1:3.68 incremental benefit: cost ratio.
Mall Customer Clustering Using Gaussian Mixture Model, K-Means, and BIRCH Algorithm

Nowadays, online shopping has become the norm of customers around the world, therefore businesses need ways to identify their customer demographics, one of the way available is by using machine learning clustering algorithms to help in clustering customer demographic. This research paper aims to study machine learning algorithms which can be used to cluster customers based on the features which define them, those being: Gender, Age, Annual Income, Spending Score (1-100). The study employs clustering algorithms such as GMM (Gaussian Mixture Model), K-Means Clustering, and BIRCH. The metric Davies Bouldin Score is used to measure the quality of clusters produced by these 3 different algorithms. The findings show that K-Means clustering is the best clustering algorithm to be used in clustering the customers because in this case, the dataset which was used is more suitable to be clustered in this algorithm, however the other algorithms also perform good when the dataset suits them, overall, the findings can be used to better understand the business’ customer demographic. The implications of this study extend to various sectors of marketing, including e-commerce, retail, digital marketing, where marketing strategies are employed to gain a competitive edge.
Campus Placement using Machine Learning: An Extensive Review and Comparative Study of Machine Learning Methods

Unemployment has been one of the most socio-economic issues. Many countries are facing challenges related to both open unemployment where individuals are actively seeking jobs and are unable to find them. In the present era of education and workforce dynamics is characterized by diversity, demanding many innovative ideas, tools and strategies for talent matching and resource allocation methods. The traditional methods of placement process are often reliant on manual evaluations and are quite inadequate in meeting the needs for individuals and organizations. This study highlights the reasons behind the growing need for Machine Learning based placement assessment. It ensures efficient talent matching, reducing costs, and saving time. The paper highlights the advantages of remote and flexible assessments, displaying how technology can replace the old methods of hiring.
Robust Decentralized Secondary Control Validation in Inverter-based Microgrid Testbed

Grid-forming inverter-based microgrids are increasingly becoming popular. They provide reliability and resilience, especially, when operating in islanded mode. Although microgrid operations rely on centralized controllers, communication failures due to cyber-physical attacks or natural disasters can make autonomous operation of islanded microgrids with multiple grid-forming inverters challenging. This paper examines a decentralized secondary control scheme that is robust to inverter clock synchronization errors and can be seamlessly applied to grid-forming control architectures. The proposed scheme over-comes the well-known stability problem that arises from parallel operation of local integral controllers and provides theoretical guarantees for stability. Moreover, valid conditions are provided for appropriately choosing the secondary control gains. The efficacy of our approach in restoring the frequency while ensuring fair power sharing is demonstrated in our microgrid hardware testbed comprising of six grid-forming inverters.
An Analytical Framework for Assessing Equity Investor Preferences in Green Technology Shares

This research paper investigates the preferences of equity investors for green technology shares, exploring the intricate interplay of personal attitude, subjective norms, and perceived behavioral control in shaping individuals' intentions to invest in companies at the forefront of green technology. Grounded in the Theory of Planned Behavior, the study employs a comprehensive theoretical framework and empirical analysis to delineate the influences of these psychological constructs. The research emphasizes the significance of the green economy and the role of green technology in fostering sustainable development, highlighting the challenges and opportunities associated with its adoption. The study not only contributes to academic understanding but also holds practical implications for policymakers, businesses, and investors seeking to navigate the evolving landscape of sustainable finance. Through a robust methodology and structural equation modeling, the research confirms positive relationships between personal attitude, subjective norm, perceived behavioral control, trust, and the intention to invest in companies with green technologies. The findings offer valuable insights for fostering a more resilient and environmentally responsible investment landscape, contributing to the broader goal of sustainable development.
The increase of rainfall erosivity and initial soil erosion processes due to rainfall acidification

The drastic growth of population in highly industrialized urban areas, as well as fossil fuel use, is increasing levels of airborne pollutants and enhancing acid rain. In rapidly developing countries such as Iran, the occurrence of acid rain has also increased. Acid rain is a driving factor of erosion due to the destructive effects on biota and aggregate stability; however, little is known about its impact on specific rates of erosion at the pedon scale. Thus, the present study aimed to investigate the effect of acid rain at pH levels of 5.25, 4.25, and 3.75 for rainfall intensities of 40, 60, and 80 mm h−1 on initial soil erosion processes under dry and saturated soil conditions using rainfall simulations. The results were compared using a two‐way ANOVA and Duncan tests and showed that initial soil erosion rates with acidic rain and non‐acidic rain under dry soil conditions were significantly different. The highest levels of soil particle loss due to splash effects in all rainfall intensities were observed with the most acidic rain (pH = 3.75), reaching maximum values of 16 g m−2 min−1. The lowest levels of particle losses were observed in the control plot where non‐acidic rain was used, with values ranging from 3.8 to 8.1 g m−2 min−1. Similarly, under saturated soil conditions, the lowest level of soil particle loss was observed in the control plot, and the highest peaks of soil loss were observed for the most acidic rains (pH = 3.75 and pH = 4.25), reaching maximum average values of 40 g m−2 min−1. However, for saturated soils with acidic water but with non‐acidic rain, the highest soil particle loss was observed for the control plot for all the rainfall intensities. In conclusion, acidic rain has a negative impact on soils, which can be more intense with a concomitant increase in rainfall intensity. Rapid solutions, therefore, need to be found to reduce the emission of pollutants into the air, otherwise, rainfall erosivity may drastically increase.
Shifting powers, prospects and perspectives? A critical reading of the European Union’s geopolitical reasoning on critical raw materials

Global energy transitions and rises in demand for critical raw materials (CRMs) are predicted to reshape global politics in yet uncertain but profound ways. With CRMs being vital for Europe’s decarbonization process, the present study sets out to examine the EU’s geopolitical discourse by taking an inquisitive yet critical stance focusing particularly on the new “geopolitical” Commission’s geopolitical reasoning on CRMs. Building upon critical geopolitics , a discourse analysis was conducted on 9 European Commission communications (2019 –2020). Three key observations were made: first, a ‘ geopolitical’ and ‘ assertive ’ EU is crucial in an increasingly ‘ fragile’ , ‘ polarized ’ and ‘ competitive ’ world. Second, securing CRMs is a ‘ security question ’, requiring ‘ strate gic approaches and partnerships’ , notably with ‘ resource –rich regions’ and particularly with Africa. Third, the EU’s narrative is ambiguous. While classical geopolitical assumptions are distinguishable, it remains questionable whether the EU will depart from its familiar path of liberal cooperation, multilateralism and trade when scouting for CRMs, although its role as a “benign ally” can be questioned. Further studies on the EU’s geopolitical reasoning, along with its actual practice in the area of CRMs, are warranted. Likewise, a critical reading of reports and foresight preceding EU policymaking is encouraged, to better comprehend how the EU’s dominating geopolitical discourse on CRMs and subsequent practice is produced in the first place.