Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of Building Information Modeling Interoperability into Nonlinear Finite Element Analysis of Bridge Substructures

Acceptance of building information modeling (BIM) throughout the bridge industry is widely recognized as inevitable. However, while adoption of BIM technologies has proliferated throughout the building industry, advances pertaining to BIM for bridges and other structures remain underway. The present study aims to increase the potential for integrating BIM processes into software packages that possess capabilities for nonlinear finite element analysis (FEA) of bridge substructures. Moreover, the present study is intended to galvanize stakeholders within the area of soil–structure interaction (SSI) modeling and analysis to more effectively participate in interoperability workflows as part of bridge design. A proof-of-concept exporter tool was formulated in a modular manner to robustly export finite element model data stored in a selected SSI analysis package into the Industry Foundation Classes (IFC) standard, with the IFC standard chosen as the target schema owing to its widespread usage in the construction industry. The ability of the exporter tool to—with fidelity—convert a model from the selected analysis software to the IFC standard is showcased through an illustrative case study. In carrying out the case study, collections of geotechnical modeling parameters were identified that, while being common to numerous nonlinear SSI analysis packages, are not present in the existing IFC schema. Inclusion of these parameters in the IFC schema is proposed, serving to: 1) lower the barrier for entry of SSI modeling and analysis stakeholders to take part in interoperability processes and 2) encourage adoption of novel BIM processes in the bridge industry.
Towards Finding the Impact of Deep Learning in Educational Time Series Datasets – A Systematic Literature Review

—Besides teaching in the education system, instructors do a bunch of background processes such as preparing study material, question paper setting, managing attendance, log book entry, student assessment, and the result analysis of the class. Moreover, Learning Management System(LMS) is mandatory if the course is online. The Massive Open Online Course (MOOC) is an example of the worldwide online education system. Nowadays, educators are using Google to efficiently formulate study material, question papers, and especially for self-preparation. Also, student assessment and result analysis tools are available to get instant results by feeding student data. Artificial Intelligence (AI) is driving behind these applications to deliver the most precise outcome. To accomplish that, AI requires historical data to train the model, and this sequential (year-wise, month-wise, etc) information is called time series data. This Systematic Literature Review (SLR) is conducted to find the contribution of time series algorithms in Education. There are enormous changes in algorithm architecture analogized to the traditional neural network to endure all kinds of data. Though it significantly raises the performance, it expands the complexity, resources, and execution time as well. Due to this, comprehending the algorithm architecture and the method of the execution process is a challenging phase before creating the model. But it is essential to have enough knowledge to select the suitable technique for the right solution. The first part reviews the time series problems in educational datasets using Deep Learning(DL). The second part describes the architecture of the time series model, such as the Recurrent Neural Network (RNN) and its variants called Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), the differences between each other, and the classification of performance metrics. Finally, the factors affecting the time series model accuracy and the significance of this work are summarized to incite the people who desire to initiate the research in educational time series problems.
A Comprehensive Review on Wireless Power Transfer Systems for Charging Portable Electronics

Wireless power transfer (WPT) for portable electronic applications has been gaining a lot of interest over the past few decades. This study provides a comprehensive review of the recent advancements in WPT technology, along with the challenges faced in its practical implementation. The modeling and design of WPT systems, including the effect of cross-coupling in multiple receivers, have been discussed and the techniques for efficiency improvement have been highlighted. The challenges of coil design, EMI shielding, and foreign object detection have been pointed out and various cutting-edge solutions have been presented. With improvements in wide bandgap technology, there is a push to operate WPT systems at mega-hertz frequencies. The reason for this is twofold: the miniaturization of the system and the ability to achieve a better magnetic link efficiency. However, with higher frequency comes the challenge of operating the power electronic components efficiently by using soft-switching techniques. Hence, an in-depth discussion on soft-switched topologies such as the Class D and Class E converters and their variations has been provided. Finally, the effects of magnetic field exposure on humans along with safety standards have been discussed.
Diverging Ozone Trends Above Western North America: Boundary Layer Decreases Versus Free Tropospheric Increases

This study has produced an improved percentile and seasonal (median) trend estimate of free tropospheric ozone above western North America (WNA), through a data fusion of ozonesonde, lidar, commercial aircraft, and field campaign measurements. Our method combines heterogeneous data sets according to the consensus data characteristics and inherent uncertainty in order to produce our best fused product. In response to different data collection environments (in situ or ground‐based), we investigate the ozone variability based on a wide range of percentiles, which is preferable for trend detection due to tropospheric ozone's high degree of heteroscedasticity (i.e., inconsistent trends and variability between different ozone percentiles). We then compare the ozone trends and variability above the California sub‐domain to the full WNA region for better understanding of the correlations between different regional scales. In California, the 1995–2021 percentile (from the 5th to 95th) and seasonal trends are clearly positive in terms of high signal‐to‐noise ratios. The magnitude of the trends is generally weaker over WNA compared to California, but reliable positive trends can still be found between the 10th and 70th percentiles, as well as winter and summer, whereas autumn shows a negative trend over the same period. In addition, dozens of rural surface sites across the region are selected to represent the boundary layer variability. In contrast to increasing free tropospheric ozone, we find overall strong negative surface trends since 1995, with the greatest divergence found in summer. Throughout the analysis implications of the COVID‐19 economic downturn on ozone variability are discussed.
Clean eco-friendly cooking energy as sustainable approach and mitigation to climate change: A case study of Ankole, Western Uganda

The study investigates how communities in the Ankole region, western Uganda are coping with a shortage of cooking fuels, climate change and what strategies they have set up to counteract its effects using innovative, sustainable, renewable and affordable technological methods. The objectives of the study are: 1) to investigate the type of cookstoves used in cooking that is being used in the area under study. 2) To suggest eco-friendly cook stoves that can be used for cooking to save the environment and reduce health hazards that are related to inhalation of smoke. It was carried out in the districts of Mbarara and Bushenyi which are diverse in their setting. The main objective was to investigate how traditional cooking fuels have become a health hazard to many mothers and children in Ankole, human activities and rural-urban migration, have caused hiking of prices for fuel for cooking; wetland drainage, bush clearing for farming, charcoal burning, brick making associated with social and economic developments have affected the environment. Traditional methods of cooking still dominate in Ankole, where three stones are still used. Charcoal has become a major fuel for cooking in many homes as an alternative to firewood, in addition to briquettes, biogas, LPG and volcanic rocks. A sustainable eco-friendly stove is proposed to mitigate environmental degradation. Keywords: Cooking fuels, climate change, health hazard, black termite mound, beehive briquettes, volcanic rocks, affordable, reliable, sustainable, modern energy.
Analysis of Morphological and Morphometric Changes in a Parenchymal Tissue after the Radiofrequency Ablation Procedure

Background and Objectives: Prostate cancer is on the rise in the European Union, and radiofrequency ablation (RFA) is one of the minimally invasive treatment options used for its treatment. Therefore, the aim of this study was to investigate and analyze the effects of RFA on prostate tissues. Materials and Methods: A standard prostate RFA procedure was performed on 13 non-purebred dogs in three sessions: no cooling (NC), cooling with a 0.1% NaCl solution (C.01), and cooling using a 0.9% NaCl solution (C.09). Microtome-cut 2–3 µm sections of prostate samples were stained with hematoxylin and eosin and further examined. Results: A histopathologic evaluation identified four zones of exposure: direct, application, necrosis, and transitional, as the damage on tissues decreased going further from the ablation site. The areas and perimeters of these zones were calculated, and geometric shapes of ablative lesions were evaluated using the quotient formula. Areas and perimeters of prostate tissue lesions in the NC and C.09 sessions were of similar size, whereas those found in C.01 were statistically significantly smaller. Lesions observed in session C.01 were of the most regular geometric shape, while the most irregular ones were found in session C.09. The shapes of lesions closest to the ablation electrode were the most irregular, becoming more regular the further away from the electrode they were. Conclusions: Prostate RFA leads to tissue damage with distinct morphological zones. Notably, the prostate lesions were the smallest and the most regular in shape after RFA procedures using the 0.1% NaCl cooling solution. It can be argued that smaller ablation sites may result in smaller scars, thus allowing for faster tissue healing if the blood flow and innervation at the ablation site are not compromised.
Analysis of Mouse Brain Sections by Live-cell Time-lapse Confocal Microscopy

The developing cerebral cortex of mammals is generated from nascent pyramidal neurons, which radially migrate from their birthplace in the ventral part of the neural tube to the cortical surface. Subtle aberrations in this process may cause significant changes in cortical structure and lead to developmental neurological disorders. During pyramidal neuron migration, we recently showed that the migrating neuron, which bypasses its last preceding neuron, is critical for its proper positioning and contributes to cerebral cortex thickness. Studying this process requires an imaging system with single-cell resolution and a prolonged observation window. Therefore, we built a system to maintain an organotypic brain slice on the stage of a Leica SP5 confocal microscope, which facilitated high-resolution imaging over a 12-hour time-lapse observation period of cellular events during neuron migration. Here, we share our protocol along with guidelines for overcoming difficulties during the setup. This protocol facilitates the observation of, but is not limited to, neurodevelopmental and pathological processes occurring during neuron migration.
Effects of Voluntary Wheel Running Exercise on Chemotherapy-Impaired Cognitive and Motor Performance in Mice

Chemotherapy-induced cognitive impairment (chemobrain) and muscle wasting (cachexia) are persisting side effects which adversely affect the quality of life of cancer survivors. We therefore investigated the efficacy of physical exercise as a non-pharmacological intervention to reverse the adverse effects of chemotherapy. We examined whether physical exercise in terms of voluntary wheel running could prevent chemotherapy-induced cognitive and motor impairments in mice treated with the multi-kinase inhibitor sorafenib. Adult male BALB/c mice were subdivided into runner and non-runner groups and orally administered with sorafenib (60 mg/kg) or vehicle continuously for four weeks. Mice could freely access the running wheel anytime during sorafenib or vehicle treatment. We found that sorafenib treatment reduced body weight gain (% of change, vehicle: 3.28 ± 3.29, sorafenib: −9.24 ± 1.52, p = 0.0004), impaired hippocampal-dependent spatial memory in the Y maze (exploration index, vehicle: 35.57 ± 11.38%, sorafenib: −29.62 ± 7.90%, p < 0.0001), increased anhedonia-like behaviour in the sucrose preference test (sucrose preference, vehicle: 66.57 ± 3.52%, sorafenib: 44.54 ± 4.25%, p = 0.0005) and impaired motor skill acquisition in rotarod test (latency to fall on day 1: 37.87 ± 8.05 and day 2: 37.22 ± 12.26 s, p > 0.05) but did not induce muscle wasting or reduce grip strength. Concomitant voluntary running reduced anhedonia-like behaviour (sucrose preference, sedentary: 44.54 ± 4.25%, runners: 59.33 ± 4.02%, p = 0.0357), restored impairment in motor skill acquisition (latency to fall on day 1: 50.85 ± 15.45 and day 2: 168.50 ± 37.08 s, p = 0.0004), but failed to rescue spatial memory deficit. Immunostaining results revealed that sorafenib treatment did not affect the number of proliferating cells and immature neurons in the hippocampal dentate gyrus (DG), whereas running significantly increased cell proliferation in both vehicle- (total Ki-67+ cells, sedentary: 16,687.34 ± 72.63, exercise: 3320.03 ± 182.57, p < 0.0001) and sorafenib-treated mice (Ki-67+ cells in the ventral DG, sedentary: 688.82.34 ± 38.16, exercise: 979.53 ± 73.88, p < 0.0400). Our results suggest that spatial memory impairment and anhedonia-like behaviour precede the presence of muscle wasting, and these behavioural deficits are independent of the changes in adult hippocampal neurogenesis. Running effectively prevents body weight loss, improves motor skill acquisition and reduces anhedonia-like behaviour associated with increased proliferating cells and immature neurons in DG. Taken together, they support physical exercise rehabilitation as an effective strategy to prevent chemotherapy side effects in terms of mood dysregulation and motor deficit.
A Hierarchical Neural Abstractive Summarization with Self-Attention Mechanism

Recently, the attentional seq2seq model had made a remarkable progress on the abstractive summarization. But most of these models do not considers the relation between original sentences, which is the important feature in extractive method. In this work, we proposed a Hierarchical Neural model to address problem. First, we use a self-attention to discovers the relation between original sentences. Secondly, we use a copy mechanism to solve the OOV problem. The experiment demonstrates that our model achieves state-of-the-art ROUGE scores on LCSTS dataset.
How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models

In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider using a prespecified decay function with a prespecified rate of decay. A problem then is that the chosen decay function could be misspecified yielding biased results and incorrect conclusions. In this paper, we introduce three parametric weight decay functions (exponential, linear, and one-step) that can be embedded in a relational event model. A statistical method is presented to decide which memory decay function and memory parameter best fit the observed sequence of events. We present simulation studies that show the presence of bias in the estimates of effects of the statistics whenever the decay, as well as the memory parameter, are not properly estimated, and the ability to test different memory models against each other using the Bayes factor. Finally, we apply the methodology to two empirical case studies.
Effects of A 1 adenosine receptor blockade by bamiphylline on ischaemic preconditioning during coronary angioplasty

Objective The role of A, adenosine receptors in preconditioning in humans is unknown. To establish whether bamiphylline, a selective antagonist of A, adenosine receptors, abolishes ischaemic preconditioning in man, 36 consecutive patients undergoing single-vessel coronary angioplasty were randomized to receive intravenous infusion of bamiphylline (5 mg . kg" ') or placebo (09% NaCl) immediately prior to the procedure.
Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case

Our research involves analyzing the latest models used for electricity price forecasting, which include both traditional inferential statistical methods and newer deep learning techniques. Through our analysis of historical data and the use of multiple weekday dummies, we have proposed an innovative solution for forecasting electricity spot prices. This solution involves breaking down the spot price series into two components: a seasonal trend component and a stochastic component. By utilizing this approach, we are able to provide highly accurate predictions for all considered time frames.
THE INFLUENCE OF TELEWORKING, WORK STRESS, AND WORKLOAD TOWARD WORK-LIFE BALANCE DURING WFH (CASE STUDY IN SOUTH JAKARTA)

The COVID-19 outbreak has forced employees to work from home as one of the government's actions to minimize the spread of the coronavirus. However, for some employees, implementing WFH is a challenge for them. In this study, there are Teleworking, Work Stress, Workload, and Work-Life Balance as variables to be examined because researchers want to know the influence of Teleworking on Work-Life Balance, Work Stress on Work-Life Balance, and Workload on Work-Life. Balance, as well as the simultaneous influence of Teleworking, Work Stress, and Workload on Work-Life Balance. The method used in this study is quantitative and statistical analysis was carried out using SPSS 28.1. Data from 179 workers in the South Jakarta area who had or are currently working from home were obtained through questionnaires. The results show that all variables have a significant influence on work-life balance. So, it is important to maintain the work-life balance of all workers and influencing factors for the benefit of all parties. Keywords: teleworking, work stress, workload, work-life balance
Different techniques utilized for characterization of metallic nanoparticles synthesized using biological agents: A review

Nanobiotechnology, an emerging stream, is an amalgamation of nanotechnology and biology. It involves synthesis of metallic nanoparticles mediated by biological materials of both plant and animal origins. The biological process of synthesis of nanoparticles is ecofriendly, requires less labor, and has many unique properties, derived from the biological matter used in their synthesis. The synthesis of metallic nanoparticles has to be followed by the characterization for different properties such as size, shape, capping materials, stability etc, which helps in clearly defining the synthesized nanoparticles on the basis of the observied properties. Keeping our past works, synthesis and characterization of nanoparticles in the background, we have reviewed different techniques utilized for characterization of metallic nanoparticles synthesized using biological agents. This review will serve as a comprehensive guide, assisting in better understanding techniques for characterization of metallic nanoparticles synthesized using biological agents.
Weighted-sparse subspace clustering method for image segmentation

On the basis of sparse subspace clustering algorithm,a novel image segmentation method based on weighted-sparse subspace clustering is presented.By the constraints of weighted-sparsity,each feature data can be linearly represented by a few most similar feature data within the same subspace,and the resulting coefficient matrix sparse inter-class.Experiments show that the proposed weighted-sparse subspace clustering method can obtain higher clustering accuracy than the state of art methods for both clean and noisy data.Segmentation results by using this method on natural color images show good visual consistency.
Recent methods on short text stream clustering: A survey study

The volume and the velocity of data in social media are increasing and the social media has become a very useful environment to detect and track the real‐world events. However, to fulfill this, it is crucial to group‐related texts according to their topics and clustering takes an essential role at this point since we have no prior knowledge about the topics and their evolution in social media. In this survey, we review the current approaches and techniques proposed for short text stream clustering in recent years. The reviewed techniques are grouped according to their methodology and discussed in detail. Also, the datasets utilized to evaluate the performance of the proposed methods and the results are summarized together with the clustering quality measures used for these evaluations. Furthermore, current challenges about short‐text stream clustering are discussed.