Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Process-Mechanical Stress Analysis of 2.5D/3D ICs with Two Types of Interconnections in Advanced Packaging

Soldering and Cu/Cu direct bonding are the two main interconnection approaches in 2.5D/3D ICs advance packaging. The manufacture process of these joints determines the joint quality and has impacts on chip/device performance and reliability. For soldering, this paper proposed a multiscale framework to introduce the influence of solder ball reflow process to further shock reliability simulation. For Cu/Cu direct bonding, a simulation workflow is introduced which considers the dish effect from Chemical mechanical polishing (CMP) and uses thermo-mechanical coupling solver and a novel pseudo-bonding model to model the bonding process. Subsequently, the bonding defects and strength could be predicted by stress analysis.
College Student Ratings of Student Academic Support: Frequency, Importance, and Modes of Communication

Research suggests that student academic support plays a vital role at the college level as students often view communication with peers as their primary source of academic support (Thompson, 2008). This research advances the Student Academic Support Scale (SASS) as a method of assessing the frequency, importance, and mode of communicating academic support among college students. Study one details scale development procedures, presents the results of factor analysis, and advances the SASS. Study two examines the frequency and importance of specific supportive behaviors and explores the modes commonly used by students to communicate academic support. Implications for teachers, scholars, and university administrators are discussed.
VI-Store: Towards Optimizing Blockchain-Oriented Verifiable Ledger Database

This paper addresses the challenges associated with storing large amounts of state data on a blockchain by proposing a scalable verifiable ledger database. The rise of digital cryptocurrencies has drawn attention to blockchain technology and its potential benefits in terms of data security, system stability, and trust facilitation. However, existing blockchain-oriented systems face limitations in terms of performance and verifiability. Similarly, traditional ledger databases lack the ability to ensure verifiability of state data. To overcome these challenges, the proposed system introduces a scalable verifiable independent architecture named VI-Store, an improved Merkle tree structure called MMB-tree for state data at the billion level. The evaluation results demonstrate the effectiveness of the proposed system, MMB-tree outperforms traditional MBT and MPT index structures, and VI-Store is able to provide stable storage of billion-level state data over a 7*24 period. The findings suggest VI-Store can be used in real blockchain environments.
Improved existence bounds on IPP codes using the Clique Lovász Local Lemma

Codes with the Identifying Parent Property constitute a powerful type of codes with many uses in fingerprinting. Thus, it is of great interest to find sharp existence bounds for that class of codes. By applying a specific variation of the Lovasz Local Lemma, we get existence bounds on q-ary IPP codes that improve previously stated ones.
Simplivariate Models: Ideas and First Examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework ‘simplivariate models’ which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of ‘divide and conquer’, we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli.
Camptothecin targets WRN protein: mechanism and relevance in clinical breast cancer

Werner syndrome protein (WRN) is a RecQ helicase that participates in DNA repair, genome stability and cellular senescence. The five human RecQ helicases, RECQL1, Bloom, WRN, RECQL4 and RECQL5 play critical roles in DNA repair and cell survival after treatment with the anticancer drug camptothecin (CPT). CPT derivatives are widely used in cancer chemotherapy to inhibit topoisomerase I and generate DNA double-strand breaks during replication. Here we studied the effects of CPT on the stability and expression dynamics of human RecQ helicases. In the cells treated with CPT, we observed distinct effects on WRN compared to other human RecQ helicases. CPT altered the cellular localization of WRN and induced its degradation by a ubiquitin-mediated proteasome pathway. WRN knockdown cells as well as CPT treated cells became senescent and stained positive for senescence-associated β-galactosidase at a higher frequency compared to control cells. However, the senescent phenotype was attenuated by ectopic expression of WRN suggesting functional implication of WRN degradation in CPT treated cells. Approximately 5-23% of breast cancer tumors are known to respond to CPT-based chemotherapy. Interestingly, we found that the extent of CPT-induced WRN degradation correlates with increasing sensitivity of breast cancer cells to CPT. The abundance of WRN decreased in CPT-treated sensitive cells; however, WRN remained relatively stable in CPT-resistant breast cancer cells. In a large clinical cohort of breast cancer patients, we find that WRN and topoisomerase I expression correlate with an aggressive tumor phenotype and poor prognosis. Our novel observations suggest that WRN abundance along with CPT-induced degradation could be a promising strategy for personalizing CPT-based cancer chemotherapeutic regimens.
Building Sport Countries’ Overseas Identity and Reputation: A Case Study of Public Paradiplomacy

The aim of this article is to analyze public diplomacy efforts of noncentral governments through sport. The object of the study is Catalonia, a Spanish autonomous region with its own government but without power over national foreign affairs. Public diplomacy links up with relationship building between governments of nation-states and other nation-states and its citizens. However, noncentral governments, as Catalonia, and other organizations with powerful national identity symbols, such as sports organizations, also develop public relations activities to build an international reputation of regions. In the field of sports, some of these territories were given the name of sport countries in the 2003 Barcelona Conference. This article concludes by considering the notion of soft power as coined by Nye to frame the role of sports diplomacy efforts of regional actors.
‘Tell them who i was’[1]: the social construction of aphasia

This article addresses people with aphasia. Talking (communication) is linked to the medical ideology and the social ideology. The medical ideology complements capitalism and promotes the individual. The medical ideology has the power for requiring ‘normal’ language. One speech therapist says, ‘…take away hope but it is a rare person who can go back to talking normally as they did before’. The medical ideology takes people with aphasia and constructs the incompetence which marginalises them from the work force and sometimes from their families. The medical ideology encourages dependency, which is disempowering. The social ideology under a different notion of talking encourages people to engage in two-way communication. I look at sharing information with a sense of humour, which is empowering. I illustrate that people with aphasia have to operate between the two ideologies.
Mitr - Face Emotion Recognition Based Adaptive E- Learning Platform

Assistive Technology (AT) for improving the learning experience for differently abled students has received greater focus after COVID as it was revealed that traditional e-learning applications cannot be effectively used by them. Children with learning difficulties usually suffer from a lower attention span. In the physical scenario, a therapist actively engages the student by changing the activities so as to help them focus. In this paper, we propose an e-learning platform that personalizes the learning experience for children with difficulties by regulating their emotions. The tool uses facial emotion recognition to gauge the interest of the student and plans the learning pathway so as to help him relax and be more engaged. A proof of concept, Mitr meaning friend in Hindi, was developed to check the feasibility of the idea. Mitr is a baseline version, and it proves that the use of Artificial Intelligence (AI) in Assistive Technology can improve personalization and effectiveness for people.
Detection of Mobile LoRa Jammers

Low Power Wide Area Networks (LPWANs) plat-forms (LoRa, NB-IoT, Sigfox) came to add a missing piece to the Internet of Things (IoT) ecosystem, namely long range communication in low power. LPWAN platforms are characterized by low data rate and long transmission time. LoRa specifically, has a data rate from 27 to 0.3 kbps and the transmission time of a single packet might be more than 1.5 seconds for some cases. These characteristics render LPWANs vulnerable to jamming attacks as a malicious user can perform a jamming attack from long range and the long transmission time is allowing a large time window to perform the attack. Moreover, if the jamming node is mobile (i.e., attached on a vehicle or an Unmanned Aerial Vehicle (UAV)), the current countermeasures proposed by the literature will not be relevant anymore. In this paper we investigate if it is possible to detect a mobile LoRa jammer based on the impact of the Doppler effect and based on a combination of signal strength and the entropy of the transmitted data. The results, obtained by utilizing commercial hardware, reveal that we could detect a potential mobile jammer that follows a deceptive jamming strategy with random payload, but further investigation is required for more sophisticated jamming attacks.
On the efficiency of pseudo-marginal random walk Metropolis algorithms

We examine the behaviour of the pseudo-marginal random walk Metropolis algorithm, where evaluations of the target density for the accept/reject probability are estimated rather than computed precisely. Under relatively general conditions on the target distribution, we obtain limiting formulae for the acceptance rate and for the expected squared jump distance, as the dimension of the target approaches infinity, under the assumption that the noise in the estimate of the log-target is additive and is independent of the position. For targets with independent and identically distributed components, we also obtain a limiting diffusion for the first component. We then consider the overall efficiency of the algorithm, in terms of both speed of mixing and computational time. Assuming the additive noise is Gaussian and is inversely proportional to the number of unbiased estimates that are used, we prove that the algorithm is optimally efficient when the variance of the noise is approximately 3.3 and the acceptance rate is approximately 7.0%. We also find that the optimal scaling is insensitive to the noise and that the optimal variance of the noise is insensitive to the scaling. The theory is illustrated with a simulation study using the particle random walk Metropolis.
Generation reliability evaluation of wind energy penetrated power system

This paper presents the generation adequacy evaluation of a power system with high wind energy penetration. First, the wind turbine generator unit models are developed by considering the component failure rates and uncertain nature of wind energy. The Data Synthesizer Software is used to extract the hourly wind speed values from the average seasonal data. The Fuzzy C-means Clustering Method is used to obtain the required number of generation states from the hourly data. Then the Markov process is used to obtain the probability of each generation state. Then, these wind energy models are added to the Roy Billinton Test System (RBTS) by Recursive Algorithm approach and different reliability indices are calculated.
Implementation of Cloth Estimation in 2D-3D Human Body Regression Model

Human body reconstruction is required to perform virtual try-on in cloth estimation. The reconstruction of 3D human body can provide the body shape and poses of the user. A fully expressive 3D human body regression model (ExPose) is used to reconstruct 3D human bodies from 2D images for the point-based modeling of the human clothing model. Subsequently, this model parameters is converted into SMPL-X and SMPL parameters from the given RGB image and inputs to the point-based modeling model along with the corresponding RGB image and clothing segmentation mask. A set of testing models in various poses is collected to evaluate the performance of point-based clothing estimation. A fitness measure is proposed to determine the fitness of the 3D body and outfit generated using SMPL-X and SMPL parameters. Intersection over Union and overlap percentage are also used to determine the set of parameter representing a 3D body in the point-based modeling model. This paper demonstrates that SMPL has better appearance retargeting performance and garment alignment capabilities for the point-based modeling model.
DSMS in ubiquitous-healthcare: A Borealis-based heart rate variability monitor

With tremendous research progress in biomedical sensors and sensor networks, there is an increasingly need for employing new data processing technologies that are capable of online analysis of the streaming medical sensor data, which is a typical health monitoring scenario. This paper describes using the data stream management system (DSMS) in developing ubiquitous health monitoring application. We propose the system architecture suitable for simultaneously processing streaming data from heterogeneous sources and performing different monitoring tasks planned by clinicians. A pilot heart rate variability (HRV) monitor with a graphical user interface is developed based on Borealis and several tasks are implemented, including monitoring Rmssd, MeanHR (mean heart rate), and pNN50. Results of the test runs in the lab environment show the usefulness and potential of DSMS in performing intelligent realtime medical data processing.
Sixteen years of Terra MODIS on-orbit operation, calibration, and performance

Terra MODIS has successfully operated for more than 16 years since its launch in December 1999. From its observations, many science data products have been generated in support of a broad range of research activities and remote sensing applications. Terra MODIS has operated in a number of configurations and experienced a few anomalies, including spacecraft and instrument related events. MODIS collects data in 36 spectral bands that are calibrated regularly by a set of on-board calibrators for their radiometric, spectral, and spatial performance. Periodic lunar observations and long-term radiometric trending over well-characterized ground targets are also used to support sensor on-orbit calibration. Dedicated efforts made by the MODIS Characterization Support Team (MCST) and continuing support from the MODIS Science Team have contributed to the mission success, enabling well-calibrated data products to be continuously generated and routinely delivered to users worldwide. This paper presents an overview of Terra MODIS mission operations, calibration activities, and instrument performance of the past 16 years. It illustrates and describes the results of key sensor performance parameters derived from on-orbit calibration and characterization, such as signal-to-noise ratio (SNR), noise equivalent temperature difference (NEdT), solar diffuser (SD) degradation, changes in sensor responses, center wavelengths, and band-to-band registration (BBR). Also discussed in this paper are the calibration approaches and strategies developed and implemented in support of MODIS Level 1B data production and re-processing, major challenging issues, and lessons learned.
Recognition of Urban Buildings by SIFT Matching with Google Street View Images

Modern buildings have repetitive features such as windows. Hence, SIFT keypoint matching of similar-looking building images produces inherent ambiguity. Relaxation based matching of clusters of keypoints has been proposed to relax the ambiguity problem. We add two improvements to the past works. The first is that the dominant orientation in the SIFT descriptor has its weight both in clustering and in relaxation based matching. As a result, rotationally symmetric keypoints are divided into separate clusters and this leads to finer matching. The second is multi-frame verification on matching with Google Street View images. Accumulated confidence of correspondence is used to suppress false positives. Inter-frame homography is used to measure the discrepancy of the position of the predicted clusters from the actually matched ones. Experiments on Street View images show that the proposed method achieves significant improvement when more than four consecutive frames are used for verification.
Do Scientific Advancements Lean on the Shoulders of Giants? A Bibliometric Investigation of the Ortega Hypothesis

Background In contrast to Newton's well-known aphorism that he had been able “to see further only by standing on the shoulders of giants,” one attributes to the Spanish philosopher Ortega y Gasset the hypothesis saying that top-level research cannot be successful without a mass of medium researchers on which the top rests comparable to an iceberg. Methodology/Principal Findings The Ortega hypothesis predicts that highly-cited papers and medium-cited (or lowly-cited) papers would equally refer to papers with a medium impact. The Newton hypothesis would be supported if the top-level research more frequently cites previously highly-cited work than that medium-level research cites highly-cited work. Our analysis is based on (i) all articles and proceedings papers which were published in 2003 in the life sciences, health sciences, physical sciences, and social sciences, and (ii) all articles and proceeding papers which were cited within these publications. The results show that highly-cited work in all scientific fields more frequently cites previously highly-cited papers than that medium-cited work cites highly-cited work. Conclusions/Significance We demonstrate that papers contributing to the scientific progress in a field lean to a larger extent on previously important contributions than papers contributing little. These findings support the Newton hypothesis and call into question the Ortega hypothesis (given our usage of citation counts as a proxy for impact).
Using emotional intelligence in coaching high-performance athletes: a randomised controlled trial

Emotional intelligence is an important and popular concept within coaching. This randomised controlled trial investigated the short-term impact of coaching using emotional intelligence on three factors related to performance in athletes: anxiety, self-efficacy and team identification. Twenty high-performance netball players were divided into coaching and control groups. The coaching group completed the Bar-On EQ-i to produce emotional intelligence profiles that formed the basis of the solution-focused coaching session. Coaching improved self-efficacy and anxiety but not team identification. There was no change in the control group. Self-efficacy and anxiety are directly linked to scales on the EQ-i whereas team identification is not directly linked. The findings indicate that solution-focused coaching using emotional intelligence is effective, but only when a direct link is identified between a particular component of emotional intelligence and a particular outcome.
Document clustering using gravitational ensemble clustering

Text Mining is a field that is considered as an extension of data mining. In the context of text mining, document clustering is used to set apart likewise documents of a collection into the identical category, called cluster, and divergent documents to distinctive groups. Since every dataset has its own characteristics, finding an appropriate clustering algorithm that can manage all kinds of clusters, is a big challenge. Clustering algorithms has theirs unique approaches for computing the number of clusters, imposing a structure on the data, and attesting the out coming clusters. The idea of combining different clustering is an effort to overwhelm the faults of single algorithms and further enhance their executions. On the other hand, inspired by the gravitational law, different clustering algorithms have been introduced that each one attempted to cluster complex datasets. Gravitational Ensemble Clustering (GEC) is an ensemble method that employs both the concepts of gravitational clustering and ensemble clustering to reach a better clustering result. This paper represents an application of GEC to the problem of document clustering. The proposed method uses a modification of the original GEC algorithm. This modification tries to produce a more varied clustering ensemble using new parameter setting. The GEC algorithm is assessed using document datasets. Promising results of the presented method were obtained in comparison with competing algorithms.