Miracle-dz/UniFAR-FTUs huggingface dataset:
A First-Principles Study of Mechanical and Electronic Properties of Cr0.5-xAl0.5TMxN Hard Coatings (TM = Ti, V, Y, Zr, Hf, and Ta)

The structural, mechanical, and electronic properties of cubic Cr0.5-xAl0.5TMxN, doped with TM (transition metal) elements (TM = Ti, V, Y, Zr, Hf, and Ta) at low concentrations (x = 0.03 and 0.06), was investigated by first-principles calculations. The results of the structural properties calculations reveal that the addition of Ti, Y, Hf, Zr, and Ta expand the volume, while V has the opposite effect. All doped compounds are thermodynamically stable, and Cr0.5-xAl0.5TMxN with TM = Ti is energetically more favorable than other doped compounds. At the same doping concentration, Cr0.5-xAl0.5VxN possesses the highest stiffness, hardness, and resistance to external forces due to its greatest mechanical properties, and Cr0.5-xAl0.5TaxN possesses the highest elastic anisotropy and the lowest Young’s modulus. Substituting Cr atoms with TM atoms in a stepwise manner results in a decrease in the bulk modulus, shear modulus, Young’s modulus, and theoretical hardness of Cr0.5-xAl0.5TMxN, while increasing its toughness. Based on the calculation results of the total and partial density of states of Cr0.5Al0. 5N and Cr0.47Al0. 5TM0.03N, all compounds exhibit metallic behavior as indicated by the finite density of states at the Fermi level. The contribution of Ti-3d, V-3d, and Ta-3d orbitals at Fermi level is significantly higher than that of other TM atoms, resulting in a more pronounced metallic character for Cr0.47Al0. 5Ti0.03N, Cr0.47Al0. 5V0.03N, and Cr0.47Al0. 5Ta0.03N.
Is Silver Addition to Scaffolds Based on Polycaprolactone Blended with Calcium Phosphates Able to Inhibit Candida albicans and Candida auris Adhesion and Biofilm Formation?

Candida spp. periprosthetic joint infections are rare but difficult-to-treat events, with a slow onset, unspecific symptoms or signs, and a significant relapse risk. Treatment with antifungals meets with little success, whereas prosthesis removal improves the outcome. In fact, Candida spp. adhere to orthopedic devices and grow forming biofilms that contribute to the persistence of this infection and relapse, and there is insufficient evidence that the use of antifungals has additional benefits for anti-biofilm activity. To date, studies on the direct antifungal activity of silver against Candida spp. are still scanty. Additionally, polycaprolactone (PCL), either pure or blended with calcium phosphate, could be a good candidate for the design of 3D scaffolds as engineered bone graft substitutes. Thus, the present research aimed to assess the antifungal and anti-biofilm activity of PCL-based constructs by the addition of antimicrobials, for instance, silver, against C. albicans and C. auris. The appearance of an inhibition halo around silver-functionalized PCL scaffolds for both C. albicans and C. auris was revealed, and a significant decrease in both adherent and planktonic yeasts further demonstrated the release of Ag+ from the 3D constructs. Due to the combined antifungal, osteoproliferative, and biodegradable properties, PCL-based 3D scaffolds enriched with silver showed good potential for bone tissue engineering and offer a promising strategy as an ideal anti-adhesive and anti-biofilm tool for the reduction in prosthetic joints of infections caused by Candida spp. by using antimicrobial molecule-targeted delivery.
Slow Impacts on Strong Targets Bring on the Heat

An important new paper by Kurosawa and Genda (2017, https://doi.org/10.1002/2017GL076285) reports a previously overlooked source of heating in low velocity meteorite impacts. Plastic deformation of the pressure‐strengthened rocks behind the shock front dissipates energy, which appears as heat in addition to that generated across the shock wave itself. This heat source has surprisingly escaped explicit attention for decades: First, because it is minimized in the geometry typically chosen for laboratory experiments; and second because it is most important in rocks, and less so for the metals usually used in experiments. Nevertheless, modern numerical computer codes that include strength do compute this heating correctly. This raises the philosophical question of whether we can claim to understand some process just because our computer codes compute the results correctly.
Electromigration of Power Devices Part 2: Power Device in Applications in Controlled Environment

In the power semiconductor industry, there is a continuous development toward a higher current capability of devices while device’s dimensions shrink. Although higher current is preferred, the reliability risk of the electromigration (EM) remains challenging. In the present study, a special EM test vehicle is used to investigate the pure EM behavior of power device, following our previous work. The selection of the test condition is discussed in detail, which is defined based on the temperature and current profiles of the device in the actual application, focusing on the applications of the power devices in a controlled environment, i.e., data centers or telecommunication servers. The current and temperature profiles of the device are discussed based on the device’s maximum power dissipation and its respective thermal design. The results show that the different combinations of currents and temperatures can lead to different ratios of acceleration factor between the temperature and the current. Consequently, an inappropriate selection of the testing condition can result in the over-domination of temperature over the current, leading to wrong conclusions. The effects of the PCB surface finish (both Ni and Sn plating) as well as different electron flow directions on the EM were studied. It is concluded that the maximum current that can be applied to a power device in the application within a controlled environment is rather limited by the cooling capability of the system than the EM induced device failure.
Spatial and depth variability of streambed vertical hydraulic conductivity under the regional flow regimes

This study investigated the influence of the regional flow on the streambed vertical hydraulic conductivity (Kv) within the hyporheic zone in three stream reaches of the Weihe River in July 2016. The streambed Kv with two connected depths was investigated at each test reach. Based on the sediment characteristics, the three test reaches could be divided into three categories: a sandy streambed without continuous silt and clay layer, a sandy streambed with continuous silt and clay layer, and a silt–clay streambed. The results demonstrate that the streambed Kv mainly decreases with the depth at the sandy streambed (without continuous silt and clay layer) and increases with the depth at the other two test reaches. At the sandy streambed (with continuous silt and clay layer) where streambed Kv mainly decreases with the depth, the regional upward flux can suspend fine particles and enhance the pore spacing, resulting in the elevated Kv in the upper sediment layers. At another sandy streambed, the continuous silt and clay layer is the main factor that influences the vertical distribution of fine particles and streambed Kv. An increase in streambed Kv with the depth at the silt/clay streambed is attributed to the regional downward movement of water within the sediments that may lead to more fine particles deposited in the pores in the upper sediment layers. The streambed Kv is very close to the bank in the sandy streambed without continuous silt and clay layer and the channel centre in the other two test reaches. Differences in grain size distribution of the sediments at each test reach exercise a strong controlling influence on the streambed Kv. This study promotes the understanding of dynamics influencing the interactions between groundwater and surface water and provides guidelines to scientific water resources management for rivers.
Measuring and modeling northeastern forest ecosystem response to environmental stresses

Because forest responses to CO$\sb2$ fertilization and associated climate change are likely to be extremely complex, numerical models representing forest response to an integrated set of future conditions can be useful predictive tools. I compared predictions of forest net primary productivity (NPP) made by two ecosystem process models (PnET-II and TEM 4.0) using different climate scenarios, spatial resolutions, and methods of representing land cover and soils. Decreasing spatial resolution did not appreciably change NPP estimates. Input datasets, particularly climate, land cover, and soil water holding capacity, were important sources of variability in NPP estimates. These datasets interacted with model structures to produce significant variability in NPP predictions, but a comparison with predictions made by other models suggested that model-to-model differences might be even more important than input datasets. Both models predicted a substantial increase in regional forest NPP under climate change, with PnET-II predicting an average increase of 38% and TEM 4.0 predicting an average increase of 30%. The parameterization of the CO$\sb2$ response, the inclusion/ exclusion of N cycling rates, and the accuracy of input datasets were identified as important items for future attention in modeling efforts. The hemlock woolly adelgid (Adelges tsugae), an aphid-like insect thought native to Japan, is causing significant mortality of eastern hemlock (Tsuga canadensis) trees in forests of the northeastern United States. I measured the impact of hemlock mortality caused by adelgid infestation on forest structure and N cycling rates in Connecticut hemlock forests. With hemlock mortality, light availability, soil temperature, and seedling regeneration increased. Annual net N mineralization rates were higher in stands with adelgid-induced mortality, and annual nitrification rates increased thirty-fold. No change was evident in soil organic matter (SOM) content. N turnover rates increased significantly with hemlock mortality. Increased decomposition rates are changing the quality of the organic matter in the forest floor and mineral soil, but the effect of accelerated decomposition on SOM will not be visible for several more years. The short-term impact of adelgid infestation may be more severe in terms of species composition than in terms of decomposition and nutrient cycling rates.
Current progress and prospects for G protein-coupled estrogen receptor in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a biologically and clinically heterogeneous disease. The G protein-coupled estrogen receptor (GPER) plays a crucial role in mediating the effect of estrogen and estrogen-like compounds in TNBC cells. Compared with other subtypes, GPER has a higher expression in TNBC. The GPER mechanisms have been thoroughly characterized and analyzed in estrogen receptor α (ERα) positive breast cancer, but not in TNBC. Our previous work revealed that a higher expression of GPER mRNA indicates a better prognosis for ERα-positive breast cancer; however, its effects in TNBC differ. Whether GPER could serve as a predictive prognostic marker or therapeutic target for TNBC remains unclear. In this review, we provide a detailed introduction to the subcellular localization of GPER, the different effects of various ligands, and the interactions between GPER and closely associated factors in TNBC. We focused on the internal molecular mechanisms specific to TNBC and thoroughly explored the role of GPER in promoting tumor development. We also discussed the interaction of GPER with specific cytokines and chemokines, and the relationship between GPER and immune evasion. Additionally, we discussed the feasibility of using GPER as a therapeutic target in the context of existing studies. This comprehensive review highlights the effects of GPER on TNBC, providing a framework and directions for future research.
Tightening Leakage Resilience of the Suffix Keyed Sponge

Lightweight cryptographic constructions are often optimized on multiple aspects that put the security bounds to the limit. In this respect, it is important to obtain security bounds that are tight and give an accurate and exact indication of the generic security. However, whereas for black-box security bounds it has become common practice to argue tightness of security bounds, for leakage resilience security bounds this is not the case. This is unfortunate, as for leakage resilience results, tightness is even more important as there is already a lossiness incurred in capturing the actual leakage by a theoretical model in the first place.In this work, we consider the SuKS (Suffix Keyed Sponge) PRF construction and investigate tightness of the leakage resilience bound of Dobraunig and Mennink (ToSC 2019). We observe that, although their black-box security result is tight, their leakage resilience bound is not tight in their bounded leakage term λ. We observe that this is caused by the fact that parts of the security bound contain a term covering multicollisions and a term covering leakage, but an adversary is unable to combine both. We next consider improved security of the SuKS for two types of leakage: fixed position leakage, where the adversary directly learns the value of λ bits of a secret state, and Hamming weight leakage, where the Hamming weight of a fixed part of the state is leaked. For fixed position leakage, a very generous form of bounded leakage, we improve the original bound by making wise use of the multicollision limit function of Daemen et al. (ASIACRYPT 2017). For the more realistic setting of Hamming weight leakage, we structurally revisit the multicollision limit function analysis by including Hamming weight in the computation, a problem that is difficult on its own due to the non-uniform character of this type of leakage. In both cases, we improve and tighten the leakage resilience bound of Dobraunig and Mennink. The improved bound for the SuKS has immediate consequences for the leakage resilience of the NIST lightweight cryptography competition finalist ISAP v2, an authenticated encryption scheme that uses the SuKS internally.
Effect of Emotions on Students Learning Strategies

This research studied how emotions impact learning strategies. Components of learning strategies and motivation were investigated against test anxiety and academic performance. The result revealed that emotions do significantly affect students' learning strategy and motivation. It was also revealed that students that employ self-regulation and academic engagement during learning and studying achieve better in academic assessment. The study further shows that there is correlation between test anxiety and academic performance as students that experience high test anxiety perform low in assessment. Finally, it can be concluded that test anxiety has significant impact on academic outcomes.
Utilization of Mobile Phone and the Ethical Implications on University and Government Services: a Case of Post Graduate Students at Kenyatta University, Kenya

This study investigated access and usage of mobile phones by postgraduate students. Mobile phone technology has greatly contributed to the quick access and sharing of information in the contemporary world. However, the issue of integrity in the usage of mobile phone remains a paradox. The data was collected using a questionnaire that was administered to 165 post graduate students in the Schools of Education and Business. The respondents were purposely selected because the researchers were interested in post graduate students working in government institutions and departments. The data was analyzed using SPSS version 18. The results indicated that mobile phone usage for information sharing and research is very high. All the students sampled used mobile phones to access, share, and search information since all of them had smart phones. It was noted that the University lacked a policy on use of mobile phones to access and read educational materials. Interestingly, it was noted that mobile phone is a handy technology for most examination malpractices and dishonesty. The study highly recommends policy issues on usage of mobile technology by students particularly in enhancing learning and information sharing as well as maintaining sanctity of examinations and individual student integrity.
High Performing Explanatory Fake News Classification on Longer Texts

After misinformation became prevalent in 2020, the research community started prioritizing creating state of the art (SOTA) fake news detectors. However, these models did little in changing user attitudes towards misinformation. Therefore, we try to increase trust between users and AI fake news detectors by implementing an explanatory moderator. We started with two research questions: (1) can long texts like normal news articles perform well in current fake news detectors meant for short texts, and (2) can we create a fake news detector that can achieve comparable high performances to SOTA fake news detectors while representing its classifications in explainable visualizations. To fulfill our first research question, we picked WELFake, a dataset containing news articles from four different news platforms. In order to create a comparable, SOTA fake news detector performance, we ran preliminary models of Majority Class Baseline, Random Forest Classifier with bag of words, and the third place model from the AAAI 2021 Shared Task: COVID-19 Fake News Detection in English competition with WELFake. Lastly, we fulfilled our second research question by making a manually fine-tuned BERT model to access attention masks that we could visualize through BertViz. Our manually fine-tuned BERT model outperformed our comparable, SOTA Two-Fold Four-Model ensemble with a 99.99% test accuracy. We made conclusions that current SOTA fake news detectors made for short texts can perform the same level of accuracy with long texts and explanatory fake news detectors can be comparable to current SOTA models.
Decoding the Luxury Brand Attachment: The Power of Social Media in Driving Consumer Action in Kuwait

Enhancing the capacity of a luxury brand to stir a deep connection with customers has always been the focus of practitioners in the luxury industry. This study explores the potential role of social media in converting attachment with a luxury brand into desirable marketing outcomes. The relationships between “luxury brand attachment,” “motivation,” “social media usage,” “word-of-mouth,” and “purchase intention” are examined to understand the drivers and outcomes of luxury brand attachment in social media. A questionnaire was administered to a sample of 341 social media users on various platforms. Data were analyzed using SPSS and Amos. The results revealed that the most important attribute generating luxury attachment in social media is the capacity of a luxury brand to show prestige and status. The direct relationship between luxury brand attachment and purchase intentions is nonsignificant. A stronger attachment to a luxury brand does not entice direct intention to purchase it unless certain motives drive consumers’ luxury engagement in social media. These insights can guide marketing managers in creating effective strategies for their luxury products.
A Benchmark for Vehicle Re-Identification in Mixed Visible and Infrared Domains

We propose a new benchmark for vehicle re-identification in mixed visible and infrared domains. Unlike cross-modal vehicle re-identification, we focus on a more realistic scenario, namely, mixed-modal vehicle re-identification, in which both probe and gallery sets contain visible and infrared images. We provide auto-cropped visible and infrared images, simulating data from actual surveillance systems. We design a mixed triplet loss function for model training. We report both mixed-modal and cross-modal retrieval performance. Our mixed method performs well in cross-modal retrieval, e.g., the Rank-1 identification rate is 79.19% in the visible-to-infrared retrieval mode.
Detect Sarcasm and Humor Jointly by Neural Multi-Task Learning

Sarcasm is a sophisticated speech act that is intended to express contempt or ridicule on social communities such as Twitter. In recent years, the prevalence of sarcasm on the social media has become highly disruptive to sentiment analysis systems due to not only its tendency of polarity flipping but also usage of figurative language. It is observed that sarcastic texts often convey a humorous effect. Thus, determining the humor in texts can be pertinent to the successful detection of sarcasm, and vice versa. However, current works always regard sarcasm detection and humor identification as separate tasks. In this paper, we argue that these tasks should be treated as a joint, collaborative, effort, considering the semantic connections between sarcasm and humor expressed in texts. Enlightened by the multi-task learning strategy, we present a joint architecture that settles two highly pertinent tasks, sarcasm detection and humor identification. As the basic of deep neural networks, we learn both tasks jointly exploring weight sharing to capture the task-specific features for each task and task-cross features between the two tasks. Extensive experiments on real-world datasets demonstrate that our presented model consistently enhances both sarcasm detection and humor identification tasks consistently with the help of the strong semantic relationships, achieving much better performance than state-of-the-art baselines.
Integrative analysis identifies oxidative stress biomarkers in non-alcoholic fatty liver disease via machine learning and weighted gene co-expression network analysis

Background Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease globally, with the potential to progress to non-alcoholic steatohepatitis (NASH), cirrhosis, and even hepatocellular carcinoma. Given the absence of effective treatments to halt its progression, novel molecular approaches to the NAFLD diagnosis and treatment are of paramount importance. Methods Firstly, we downloaded oxidative stress-related genes from the GeneCards database and retrieved NAFLD-related datasets from the GEO database. Using the Limma R package and WGCNA, we identified differentially expressed genes closely associated with NAFLD. In our study, we identified 31 intersection genes by analyzing the intersection among oxidative stress-related genes, NAFLD-related genes, and genes closely associated with NAFLD as identified through Weighted Gene Co-expression Network Analysis (WGCNA). In a study of 31 intersection genes between NAFLD and Oxidative Stress (OS), we identified three hub genes using three machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine - Recursive Feature Elimination (SVM-RFE), and RandomForest. Subsequently, a nomogram was utilized to predict the incidence of NAFLD. The CIBERSORT algorithm was employed for immune infiltration analysis, single sample Gene Set Enrichment Analysis (ssGSEA) for functional enrichment analysis, and Protein-Protein Interaction (PPI) networks to explore the relationships between the three hub genes and other intersecting genes of NAFLD and OS. The distribution of these three hub genes across six cell clusters was determined using single-cell RNA sequencing. Finally, utilizing relevant data from the Attie Lab Diabetes Database, and liver tissues from NASH mouse model, Western Blot (WB) and Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) assays were conducted, this further validated the significant roles of CDKN1B and TFAM in NAFLD. Results In the course of this research, we identified 31 genes with a strong association with oxidative stress in NAFLD. Subsequent machine learning analysis and external validation pinpointed two genes: CDKN1B and TFAM, as demonstrating the closest correlation to oxidative stress in NAFLD. Conclusion This investigation found two hub genes that hold potential as novel targets for the diagnosis and treatment of NAFLD, thereby offering innovative perspectives for its clinical management.