Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical geostatistics : modeling and spatial analysis

About Practical Geostatistics. - Introduction. Why Geostatistics? - Data Types and Structures for 3D Geostatistics. - Basic Concepts of Geostatistics. - Integration of Geology with Geostatistics. - Practical Application of Geostatistics. - Geostatistical Uncertainty and Probability. - Visualization and Spatial Analysis. - Practical Data Management for Geostatistics. - Worked Examples.
Beyond Motivation and Engagement: Students’ Voices on the Use of Game-based Learning in a Bachelor Computer Science Online Degree

Video games have many potential uses beyond pure entertainment, including their use in educational contexts. Yet, it remains really challenging to put together guidelines to design effective game-like interventions in educational contexts. This study examines existing work relating to gamification, game-based learning, and serious games, and finds there is still limited qualitative work concerning the student perspective and limited work developing pedagogical guidelines for developers wishing to develop effective game-based learning experiences. The study focuses on the perception of students in regard to game-based learning activities in the context of a BSc Computer Science online degree. Students enrolled in the online degree were invited to fill in an online survey after their experience with a selection of game-based learning activities in the online degree. Reflexive Thematic Analysis was used to evaluate the open-ended responses from 55 participants. First, quantitative and qualitative results revealed insightful information along with four overarching themes (“Complementary to lectures on topics that are usually hard or too abstract to teach”, “Allow students to take on identities and learn from different angles and perspectives”, “Balanced challenge and context relevance to minimise students wasting their time”, and “Reward players for their effort with meaningful rewards and provide a safe space for failure”), suggesting that game-based learning interventions offer more than just motivation and engagement. Second, technical and pedagogical principles emerged from the data analysis, proposing guidelines for future designers of game-based learning activities in similar educational contexts. Finally, the study provides a selection of twelve open-source and browser-based game-based learning activities, the ones students encountered in the BSc Computer Science online degree.
Healthy River Ecosystems: vision or reality?

Ecosystem health is a relatively new field that brings together our biophysical understanding of how natural systems function with societal goals and human values (Rapport et al., 1998). Its central theme is the essential services provided by ecosystems that are vital to human welfare. There are obvious tensions that arise from this mix of medical, social and ecological disciplines and the emerging field of ecosystem health is often a topic of considerable debate (Fairweather, 1999). Despite these tensions, there are several good reasons why this broader ecosystem approach has merit. There is no doubt that the human health analogy strikes a chord with the wider public and helps to promote awareness of what are serious environmental issues. There are also obvious direct relationships between river health and human health and it is now well recognised that rivers and wetlands provide essential ecosystem services that also benefit humans. Furthermore, many other human uses of rivers and associated wetlands are dependent on them being in a healthy state. Throughout this paper, I use the term ‘healthy’ to describe ‘in good condition’, acknowledging that in the case of ecosystems desired endpoints can be set from either an ecocentric or anthropocentric perspective (Karr, 1999).
Nudging Users or Redesigning Interfaces? Evaluating Novel Strategies for Digital Wellbeing Through inControl

As web designers may deliberately adopt design patterns to hook users’ attention, researchers and practitioners have innovated several tools for supporting users’ digital self-control, hoping to help users self-regulate technology use – especially social networks and video streaming platforms – and achieve digital wellbeing. Unfortunately, these tools often restrict usage, e.g., through self-imposed timers and blockers, limiting interaction possibilities. This paper describes the design, development, and evaluation of two alternative strategies for digital self-control targeting the Facebook and YouTube websites. Specifically, we implemented a Chrome extension that a) highlights when the user is scrolling infinitely by progressively darkening the background (nudging strategy), and b) redesigns the homepages isolating guilty pleasure recommendations and proposing a minimalistic interface (redesign strategy). We compared the two strategies in a three-week field study with 14 participants, finding that both strategies promoted intentional use and allowed participants to decrease time spent and passive scrolling. In particular, participants liked the nudging strategy more as it supported conscious use without changing the overall user experience. We conclude with design implications for moving from traditional digital self-control tools to diverse approaches that may better support digital wellbeing in the long term.
Strong coupling and local control of dimensionality across brain areas

The dimensionality of a network’s collective activity is the number of modes into which it is organized. This quantity is of great interest in neural coding: small dimensionality suggests a compressed neural code and possibly high robustness and generalizability, while high dimensionality suggests expansion of input features to enable ﬂexible downstream computation. Here, for recurrent neural circuits operating in the ubiquitous balanced regime, we show how dimensionality arises mechanistically via perhaps the most basic property of neural circuits: a single number characterizing the net strength of their connectivity. Our results combine novel theoretical approaches with new analyses of high-density neuropixels recordings and high-throughput synaptic physiology datasets. The analysis of electrophysiological recordings identiﬁes bounds on the dimensionality of neural responses across brain regions, showing that it is on the order of hundreds – striking a balance between high and low-dimensional codes. Furthermore, focusing on the visual stream, we show that dimensionality expands from primary to deeper visual areas and similarly within an area from layer 2/3 to layer 5. We interpret these results via a novel theoretical result which links dimensionality to a single measure of net connectivity strength. This requires calculations that extend beyond traditional mean-ﬁeld approaches to neural networks. Our result suggests that areas across the brain operate in a strongly coupled regime where dimensionality is under sensitive control by net connectivity strength; moreover, we show how this net connectivity strength is regulated by local connectivity features, or synaptic motifs. This enables us to interpret changes in dimensionality in terms of changes in coupling among pairs and triplets of neurons. Analysis of large-scale synaptic physiology datasets from both mouse and human cortex then reveal the presence of synaptic coupling motifs capable of substantially regulating this dimensionality.
Eff-AQI: An Efficient CNN-Based Model for Air Pollution Estimation: A Study Case in India

One of the most dangerous problems facing humanity is air pollution. According to GBD estimates, poor air quality and indoor air pollution cause nearly 2 million premature deaths in India. Air quality monitoring stations are expensive to install. These issues require a cost-effective resolution. India’s energy infrastructure requires a low-power solution in order to prevent new issues while resolving old ones. Image-based air pollution detection using artificial intelligence has become a popular option. Nevertheless, two issues remain: There are few image-based air pollution data sets. Existing methods utilize a model with numerous parameters, which requires a great deal of processing power. Based on that, we developed Eff-AQI, a reliable artificial intelligence model with 1.9 million parameters. The proposed model could obtain the following results: 9.56 RMSE, 0.99 R2, 89.92% balanced accuracy, and 89.38% accuracy for AQI estimation; 14.62 RMSE, 0.99 R2, 90.56% balanced accuracy, and 91.83% accuracy for PM2.5 estimation; and 14.40 RMSE, 0.98 R2, 96.25% balanced accuracy, and 95.42% accuracy for PM10 estimation. The proposed model outperformed DOViT, the model with the highest accuracy among all surveyed SoTAs, by 2.64 points, and it has 46.32 times smaller parameters compared to DOViT. The proposed model can achieve the same R2 score with 54.16 times smaller parameters than Ensemble DNN. We also make available on Kaggle the novel air pollution image data with the corresponding labels: AQI, PM2.5, PM10, O3, CO, SO2, and NO2. The investigation revealed that the majority of SoTAs could utilize the dataset to enhance performance. The proposed model is more accurate and has fewer parameters than SoTAs. Environmental sustainability and reduced pollution are also involved. Increasing society’s or stakeholders’ high-confidence understanding of air pollution situations in order to develop effective and efficient mitigation solutions. This initiative is beneficial to AI for the social good and the Sustainable Development Goals, especially SDG 3, "Good Health and Well-Being," and SDG 11, "Sustainable Cities and Communities. "
Litter segmentation with LOTS dataset

The marine ecosystem faces a significant threat due to the release of human waste into the sea. One of the most challenging issues is identifying and removing small particles that settle on the sand. These particles can be ingested by local fauna or cause harm to the marine ecosystem. Distinguishing these particles from natural materials like shells and stones is difficult, as they blend in with the surroundings. To address this problem, we utilized the Litter On The Sand (LOTS) dataset, which comprises images of clean, dirty, and wavy sand from three different beaches. We established an initial benchmark on this dataset by employing state-of-the-art Deep Learning segmentation techniques. The evaluated models included MultiResU-Net, Half MultiResU-Net, and Quarter MultiResU-Net. The results revealed that the Half MultiResU-Net model outperformed the others for most types of sand analyzed, providing valuable insights for future efforts in combating marine litter and preserving the health of our marine ecosystems.
EDGELESS Project: On the Road to Serverless Edge AI

The EDGELESS project is set to efficiently operate serverless computing in extremely diverse computing environments, from resource-constrained edge devices to highly-virtualized cloud platforms. Automatic deployment and reconfiguration will leverage AI/ML techniques, resulting in a flexible horizontally-scalable computation solution able to fully use heterogeneous edge resources while preserving vertical integration with the cloud and the benefits of serverless and its companion programming model, i.e., Function-as-a-Service (FaaS). The system under design will be environmentally sustainable, as it will dynamically concentrate resources physically (e.g., by temporarily switching off far-edge devices) or logically (e.g., by dispatching tasks towards a specific set of nodes) at the expense of performance-tolerant applications.
Intelligent and Sustainable Transportation through Multi-Objective Model for the Logistic Route-Order Dispatching System

Solution of multi-objective optimization in the logistics sector have become an integral important part of the Intelligent Transportation System (ITS). In this work we focus on the intelligent and sustainable transportation processes through the design of the multi-objective model for the logistic route-order dispatching system. We consider transportation costs, emissions, order importance and risks for failures, for the logistic route-order dispatching system. We present an Integer Linear Programming (ILP) optimization model and apply state-of-the-art techniques as a part of SCIP framework to solve pilot problem instances and evaluate the performance of the model. We obtain results of solving the model on a single monolithic Google Cloud Compute (GCP) to estimate the time complexity of the solving process in relation to the various problem sizes. The results from the experiments show low complexity of the problems of various sizes. Therefore scalability of the model looks promising for the applicability in various industry-related scenarios and computing environments. In particular, using hybrid-cloud systems and state-of-the-art optimization frameworks such as IBM CPLEX or Gurobi.
RNA binding limits the ligand induced transcriptional potential of estrogen receptor-alpha (ERα)

Transcription factors (TFs) primarily regulate gene expression by binding to DNA through their DNA binding domains (DBD). Additionally, approximately half of these TFs also interact with RNA. However, the role of RNA in enabling TF binding on chromatin and subsequent transcription is poorly understood. Estrogen receptor-α (ERα) is one such TF that activates genes in response to estrogen stimulation. Here, we report that ERα interacts with various types of RNAs in ligand dependent manner via its RNA binding motif in hinge region. RNA binding defects lead to a global loss of ERα binding in the genome, particularly at weaker ERα motifs. In the absence of RNA binding, the ERα exhibits dynamic behavior in the nucleus and unexpectedly, the dynamic binding coincides with robust polymerase loading on ERα bound chromatin regions. The higher occupancy of PolII was recapitulated by robust ligand induced transcription of ERα-regulated genes. Collectively, our results suggest that RNA interactions strengthen ERα binding to chromatin limiting the ligand-dependent transcriptional upregulation of estrogen-induced genes.
Cyclin B3 is a dominant fast-acting cyclin that drives rapid early embryonic mitoses

In many species, early embryonic mitoses proceed at a very rapid pace, but how this pace is achieved is not understood. Here we show that in the early C. elegans embryo, cyclin B3 is the dominant driver of rapid embryonic mitoses. Metazoans typically have three cyclin B isoforms that associate with and activate Cdk1 kinase to orchestrate mitotic events: the related cyclins B1 and B2 and the more divergent cyclin B3. We show that whereas embryos expressing cyclins B1 and B2 support slow mitosis (NEBD to Anaphase ∼ 600s), the presence of cyclin B3 dominantly drives the ∼3-fold faster mitosis observed in wildtype embryos. CYB-1/2-driven mitosis is longer than CYB-3-driven mitosis primarily because the progression of mitotic events itself is slower, rather than delayed anaphase onset due to activation of the spindle checkpoint or inhibitory phosphorylation of the anaphase activator CDC-20. Addition of cyclin B1 to cyclin B3-only mitosis introduces an ∼60s delay between the completion of chromosome alignment and anaphase onset, which likely ensures segregation fidelity; this delay is mediated by inhibitory phosphorylation on CDC-20. Thus, the dominance of cyclin B3 in driving mitotic events, coupled to introduction of a short cyclin B1-dependent delay in anaphase onset, sets the rapid pace and ensures fidelity of mitoses in the early C. elegans embryo.
Culture substrate stiffness impacts human myoblast contractility-dependent proliferation and nuclear envelope wrinkling

The remarkable self-repair ability of skeletal muscle tissues is driven by muscle stem cells, whose activities are orchestrated by a variety of transient cues present in their microenvironment. Understanding how the cross-interactions of these biophysical and biochemical microenvironmental cues influences muscle stem cells and their progeny is crucial in strategizing remedies for pathological dysregulation of these cues in aging and disease. In this study, we investigated the cell-level influences of extracellular matrix ligands in the context of mechanically tuned culture substrates on primary human myoblast contractility and proliferation within 16 hours of plating. We found that fibronectin led to stronger stiffness-dependent responses in myoblasts compared to laminin and collagen as revealed by cell spreading area, focal adhesion morphology, traction stresses, and proliferation. While we did not observe stiffness-dependent changes in cell fate signatures at this early time-point, an analysis of the stiffness-sensitive myoblast proteome uncovered an enrichment for differentiation associated signatures on 17 kPa culture substrates, in addition to distinct profiles with relation to metabolism, cytoskeletal regulation, ECM interactions, and nuclear component enrichment. Interestingly, we found that softer substrates increased the incidence of myoblasts with a wrinkled nucleus, and that the extent of wrinkling could predict Ki67 expression. While nuclear wrinkling and Ki67 expression could be controlled by pharmacological manipulation of cellular contractility, cellular traction stresses did not correlate with nuclear envelope wrinkling level. These results provide new insights into the regulation of human myoblast stiffness-dependent contractility response by ECM ligands and also highlight a link between myoblast contractility and proliferation.
Leave no Place Behind: Improved Geolocation in Humanitarian Documents

Geographical location is a crucial element of humanitarian response, outlining vulnerable populations, ongoing events, and available resources. Latest developments in Natural Language Processing may help in extracting vital information from the deluge of reports and documents produced by the humanitarian sector. However, the performance and biases of existing state-of-the-art information extraction tools are unknown. In this work, we develop annotated resources to fine-tune the popular Named Entity Recognition (NER) tools Spacy and roBERTa to perform geotagging of humanitarian texts. We then propose a geocoding method FeatureRank which links the candidate locations to the GeoNames database. We find that not only does the humanitarian-domain data improves the performance of the classifiers (up to F1 = 0.92), but it also alleviates some of the bias of the existing tools, which erroneously favor locations in the Western countries. Thus, we conclude that more resources from non-Western documents are necessary to ensure that off-the-shelf NER systems are suitable for the deployment in the humanitarian sector.
Atlas of DES (desmin) variants: Impact of variants located within the head domain on filament assembly

Desmin is a muscle-specific intermediate filament protein, which plays a significant role in providing structural integrity of cardiomyocytes by connecting different cell organelles and multi-protein complexes. DES mutations cause cardiomyopathies and skeletal myopathies. Most of these pathogenic mutations are localized in the highly conserved rod domain and affect the filament assembly. However, the impact of DES variants within the N-terminal head domain on the filament assembly process is widely unknown. Therefore, we inserted a set of 85 different head domain variants with unknown significance from human genetic databases in expression constructs and investigated their impact on filament formation in cell culture in combination with confocal microscopy. The majority of these desmin variants do not affect the filament assembly. However, the desmin variants -p.S13P, -p.N107D, -p.E108G and -p.K109E significantly inhibit the filament assembly. Additionally, we expressed and purified recombinant desmin and investigated the filament assembly defects by atomic force microscopy verifying these findings at the single molecular level. Furthermore, we truncated systematically the head domain to investigate which general parts of this domain are necessary for filament assembly. In summary, our functional investigations might be relevant for the classification of novel DES variants and the genetic counselling of patients carrying desmin head variants.
Quantitative evaluation of internal clustering validation indices using binary datasets

Different clustering methods often classify the same dataset differently. Selecting the ‘best’ clustering solution out of a multitude of alternatives is possible with cluster validation indices. The behavior of validity indices changes with the structure of the sample and the properties of the clustering algorithm. Unique properties of each index cause increasing or decreasing performance in some conditions. Due to the large variety of cluster validation indices, choosing the most suitable index concerning the dataset and clustering algorithms is challenging. We aim to assess different internal clustering validation indices. In the present paper, the validity indices consist of geometric and non-geometric methods. For this purpose, we applied simulated datasets with different noise levels. Each dataset was repeated 20 times. Three clustering algorithms with Jaccard dissimilarity are used, and 27 clustering validation indices are evaluated. The results provide a reliability guideline for the selection cluster validity indices.
Quad Element Reconfigurable Radiation Pattern MIMO Antenna for Indoor Wireless Communication

|A quad-element recon(cid:12)gurable radiation pattern Multiple Input Multiple Output (MIMO) antenna is designed for WLAN and 5G applications suitable for indoor wireless communications. It consists of four radiating elements that operate over triband frequencies 2.4, 3.5, and 5.5 GHz. Moreover, the pattern diversity is obtained by introducing two diagonally crossed slots in the radiator to steer the main beams of the antenna in 8 diﬀerent angular directions using eight PIN diodes. The overall physical dimension of the proposed antenna is about 0 : 55 (cid:2) 0 : 55 (cid:21) 0 . In addition, an Acrylonitrile Butadiene Styrene (ABS) enclosure is designed, and the performance of the proposed antenna is evaluated. The measurement results show that the proposed antenna has an impedance bandwidth of 4.18%, 14.13%, and 28.5% at the said frequencies, respectively.
Persistent HIV-related Stigma in Rural Uganda Persistent HIV-related Stigma in Rural Uganda During a Period of Increasing HIV Incidence Despite During a Period of Increasing HIV Incidence Despite Treatment Expansion Treatment Expansion

Objective— Program implementers have argued that the increasing availability of anti-retroviral therapy (ART) will reduce the stigma of HIV. We analyzed data from Uganda to assess how HIV-related stigma has changed during a period of ART expansion. Design— Serial cross-sectional surveys. Methods— We analyzed data from the Uganda AIDS Rural Treatment Outcomes (UARTO) study during 2007-2012 to estimate trends in internalized stigma among people living with HIV (PLHIV) at the time of treatment initiation. We analyzed data from the Uganda Demographic and Health Surveys (DHS) from 2006 and 2011 to estimate trends in stigmatizing attitudes and anticipated stigma in the general population. We fitted regression models adjusted for socio-demographic characteristics, with year of data collection as the primary explanatory variable. Results— We estimated an upward trend in internalized stigma among PLHIV presenting for treatment initiation