Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust anthropogenic signal identified in the seasonal cycle of tropospheric temperature

Previous work identified an anthropogenic fingerprint pattern in TAC(x,t), the amplitude of the seasonal cycle of mid- to upper tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite TAC(x,t) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their TAC(x,t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3-4) inter-model and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal TAC(x,t) variability associated with the Atlantic Multidecadal Oscillation and the El Niño~Southern Oscillation. The robustness of the seasonal cycle D&A results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced TAC(x,t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.
Constraining α-attractor models from reheating

We eliminate the parameters originally present in models of inflation of the $\alpha$-attractor type in favor of the scalar spectral index $n_s$ and the tensor-to-scalar ratio $r$. We then write expressions for the number of $e$-folds during reheating $ N_ {re} $. By imposing reasonable conditions on $N_{re}$ we can restrict $n_s$ and $r$ and in turn, we use these constraints in order to find bounds for cosmological quantities of interest such as the number of $e$-folds during inflation and the radiation dominated eras, as well as for the reheating temperature and the running index. The minimum condition that $N_ {re}$ must satisfy is $N_ {re}\geq 0$ which we use to constrain the cosmological quantities mentioned above. In particular, we find that the tensor-to-scalar ratio $r$ (and as a consequence the energy scale of inflation) is bounded from below. We provide figures illustrating the behavior of these quantities as functions of $r$ for several values of $n_s$ and tables containing the bounds so obtained.
Creep Mechanical Properties of Granite under Combined Axial Compression and Water Pressure and Support Characteristics of Surrounding Rock

Granite is a common brittle rock material in underground surrounding rock, such as Yebatan Hydropower Station. The underground cavern of Yebatan Hydropower Station has characteristics of large span, high ground stress, fault development, and abundant groundwater. Long-term stability of surrounding rock is a key factor for the project safe operation. Especially for the surrounding rock of large underground cavern, its long-term rheological stability must be considered. In this paper, taking the surrounding rock of a large underground cavern in Yebatan Hydropower Station as research object, creep mechanical test of the combined axial pressure and water pressure under normal water level and dead water level is carried out, and long-term damage mechanism of rock under different water confining pressures is revealed. Based on rock mechanical properties, calculation and analysis of surrounding rock stability under different support time are carried out, and optimal support time is also discussed.
Investigating the Legacies of Violence and Conflict in Transitional Justice and Peace

Violations of law, human rights abuse, and socio-economic and political grievances are legacies of violence and conflict-affected politics. In recent times, the aggravation of violence and conflict has hindered political development and instigated socio-economic grievances. Transitional justice and peace deal with human rights abuses, violation of rights, violence, and other grievances in societies in transition. One of the main focuses of transitional justice discourse is to engage human rights law for political stability, accountability, and peace in changes, as developed in the strict law practice. This research is descriptive and relies on secondary data. Thus, transitional justice and the rule of law are considered a framework for analysis in violence and conflict-affected politics as intertwined to promote post-violence and conflict or socio-economic and political stability. Therefore, various political actors have engaged in promoting the rule of law and the promotion of peace through security sector reform. With the focus on transitional justice, various processes have been considered through the promotion of rule of law, security, and accountability in the Niger Delta and the northeast of Nigeria that have been ravaged by insurgency, terrorism, violence, and conflict, and these processes have been supported and developed by an increasing number of actors at the national and international levels. Therefore, this paper investigates the socio-economic and political legacies of violence and conflict in transitional justice and peace and examines the interaction of transitional justice and the values of the rule of law in dealing with political tensions as shown in the cases of Boko Haram and Niger Delta. This paper recommends that transitional justice and the rule of law contribute to peace and post-conflict recovery for socio-economic and political grievances resulting from violence and conflict.
Implementing cleaner production in an automotive company: an application of material input per unit of service tool to measure environmental and economic advantages

Environmental impact reduction has been a growing concern of the automotive industry. One of the main challenges is to implement Cleaner Production as a tool to reduce environmental impacts and provide financial profits. The purpose of this article was to evaluate the environmental impact and economic gains by implementing Cleaner Production, using an unexplored approach, in a company of the automotive sector. The research approach used was a case study, carried out by observation and semi-structured surveys. The production process chosen was updated, so technologies were changed to reduce biotic and abiotic disposal. For the analysis of environmental impacts, the ‘Material Input Per Unit of Service’ tool (MIPS) was used, in which data was used to calculate: annual net profit; return on investment (%) and return on investment for a period. The results showed that Cleaner Production implementation focusing on changing technology in the production process caused a 75% reduction in environmental impact, and a 62.7% decrease in production costs. Although of this study considers only one case to test the procedure applicability, it can be easily repeated in other more complex systems, by merely making the necessary adjustments due to the complexity of the processes involved.
The Relationship Between Happiness and Smart Phone Addiction in Regular Physical Exercises

The purpose of this study is to determine the happiness and smartphone addiction levels of individuals who do regular physical exercise, and to evaluate the relationship between the level of happiness with smartphone addiction and sociodemographic variables. This study has been planned as a descriptive relational. The average age of the participants is 25.57 ± 7,033, 62.7% of them are male, 39.7% are high school graduates, 81.7% are single, 42.1% perceive their income as good,% It was determined that 39.7% perceived their health status as good, 54.0% did not have any chronic disease and 61.9% had children. In the study, it was observed that women were happier than men, married women were single, university graduates than primary and high school graduates, and those who perceived good income and health status were happier than those who perceived poor and medium health, and the difference was statistically significant.
A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.
Analyzing education based on metaverse technology

The Metaverse is a post-reality universe, a perpetual multi-user environment that combines physical reality and digital virtuality. It is based on the convergence of technologies that enable multisensory interactions with virtual environments, digital objects, and people, such as virtual reality (VR) and augmented reality (AR). As a result, Metaverse is a web of social, networked immersive environments on persistent multi-user platforms. It allows for real-time, embodied user communication and dynamic interactions with digital artifacts. Its first incarnation was a web of virtual worlds between which avatars could teleport. The modern Metaverse includes social, immersive VR platforms that are compatible with massive multiplayer online video games, open game worlds, and AR collaborative spaces. The aim of the research is to thoroughly examine the perceptions and educational requirements of Metaverse-based education and to investigate its educational applicability. Using a mixed research method, the study investigated instructors' and students' perceptions of the use of the Metaverse in education, as well as the support system for classes that use the Metaverse. And determining the implications for action. plans and directions for its application in education.
Impact of information security threats on small businesses during the Covid-19 pandemic

Information is a significant asset of any organization. The increased information demand by all parties has gained attention and raised security concerns – especially in this digital era where everyone depends heavily on the Internet. The Internet and online platforms expose valuable information to various information threats. These pervasive threats compromise information privacy, safety, and security. Legitimate people and criminals compete to access information. Criminals use innovative ways to gradually increase information security threats, especially in the small business sector with only a minimal budget for proactive security measures. Due to the scarcity of academic research on information security threats for small businesses, this study presents the impact of security threats on businesses during the global Covid-19 pandemic. A qualitative survey within the interpretive approach was used to gather data from 20 small businesses in Western Cape, South Africa, to fill this gap. The study used judgmental sampling to select research participants who are business owners. Data were analyzed using thematic analysis. The results indicated the knowledge gap relating to information threats, even though most businesses are familiar with the costly and negative impact of threats on business operations, resulting in business discontinuity. However, some small business sectors showed minimal awareness a6nd understanding of information security threats, their impact, and proactive mitigation strategies. The study concluded with recommendations to protect against information security threats.
On Secrecy Analysis of Underlay Cognitive UAV-Aided NOMA Systems With TAS/MRC

This article investigates the secrecy performance for an unmanned aerial vehicle (UAV)-aided nonorthogonal multiple access (NOMA) network in the context of an underlay cognitive radio. Specifically, a multiantenna secondary source (<inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>) transmits a mixed signal incorporated with NOMA to two multiantenna secondary destinations (<inline-formula> <tex-math notation="LaTeX">$D_{i}$ </tex-math></inline-formula>) via a UAV-enabled decode-and-forward relay in the presence of an eavesdropper (<inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>). In addition, both a transmit antenna selection scheme at <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> and a maximal ratio combining protocol at <inline-formula> <tex-math notation="LaTeX">${D_{i}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> are taken into account. Considering all the links undergo Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading with path loss, we analyze the secrecy performance in terms of secrecy outage probability for both users. Moreover, we also use Monte-Carlo simulations to verify the accuracy of those derived expressions.
Document clustering

Nowadays, the explosive growth in text data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large‐scale data. Given the vast amount of this kind of unstructured data, the majority of it is not classified, hence unsupervised learning techniques show to be useful in this field. Document clustering has proven to be an efficient tool in organizing textual documents and it has been widely applied in different areas from information retrieval to topic modeling. Before introducing the proposals of document clustering algorithms, the principal steps of the whole process, including the mathematical representation of documents and the preprocessing phase, are discussed. Then, the main clustering algorithms used for text data are critically analyzed, considering prototype‐based, graph‐based, hierarchical, and model‐based approaches.
A homozygous variant in G6PC in a Vietnamese patient with glycogen storage disease type Ia

Glycogen storage diseases (GSDs) are rare inherited metabolic disorders characterized by the absence of required enzymes for the glycogen degradation metabolism. GSD can be divided into more than 12 types based on enzyme deficiency and affected tissues, in which glycogen storage disease type Ia (GSD1a or von Gierke disease) is a liver-affecting form. GSD1a is an autosomal recessive inherited disease caused by mutations in the G6PC gene on chromosome 17q21. The present study reports a Vietnamese family with a 6-month-old male patient diagnosed with type Ia glycogen storage disease. A homozygous variant in the G6PC gene (NM_000151.3: c.518T>C; p.L173P) was detected in the proband using a comprehensive glycogen storage disease panel. This variant has been previously reported in ClinVar (Accession ClinVar: VCV000640818.3). The segregation of the variant was confirmed in ten people of a 3-generation family using Sanger sequencing. The results showed both parents were heterozygous for the variant. In addition, the variant c.518T>C in the G6PC gene was predicted to be deleterious using in silico prediction tools (SIFT, PolyPhen-2, Proven, REVEL, and MutPred2). Our results could help doctors decide on appropriate treatment and diet for the disease. Moreover, the study is also a contribution to molecular studies on GSD1a.
Community-Centered School Leadership: Radical Care and Aperturas During COVID-19

We share school leaders’ perspectives on Zoom videos concerning the needs of immigrant and refugee families in Title I schools. In these videos, participants crafted and shared personal narratives about their leadership experiences during the COVID-19 era of education. Rooted in participatory design research methods, the process of designing these videos were both a research project and an intervention to assist families and school leaders to better understand each other. We present a close analysis of administrators’ perspectives and describe how our codesigned video methodology enabled participants to coconstruct new meanings of school-community relationships during the pandemic through a radical care framework. We conceptualize these reimaginings as aperturas—cracks in the dominant family engagement paradigm that allow us to collectively work towards transformative ends which we term community-centered school leadership. We conclude the article with recommendations for how both school leadership and research can approach and reimagine family engagement postpandemic.
Dynamic Recognition and Analysis of Gait Contour of Dance Movements Based on Generative Adversarial Networks

With the generation of images, videos, and other data, how to identify the gait of the action in the video has gradually become the focus of research. Aiming at the problems of complex and changeable movements, strong coherence, and serious occlusion in dance video images, this paper proposes a dynamic recognition model of gait contour of dance movements based on GAN (generative adversarial networks). GAN method is used to convert the gait diagrams in any state into a group of gait diagrams in normal state with multiple angles, which are arranged in turn. In order to retain as much original feature information as possible, multiple loss strategy is adopted to optimize the network, increase the distance between classes, and reduce the distance within classes. Experimental results show that the average recognition rates of this model at 50°, 90°, and 120°are 93.24, 98.24, and 97.93, respectively, which shows that the recognition accuracy of dance movement recognition method is high. And this method can effectively improve the dynamic recognition of gait contour of dance movements.
A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment

Background: Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC). Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard–Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively. Next, these characteristics were selected with a minimal redundancy maximal relevance algorithm and a sequential feature collection method in single or multi-modalities, and only the optimal features were retained after this procedure. Lastly, the retained characteristics were served as the input features for the support vector machine (SVM)-based method to classify MCI patients, and the performance was estimated with a leave-one-out cross-validation (LOOCV). Results: Our proposed method obtained the best 92.00% accuracy, 94.92% specificity and 89.39% sensitivity with the sMRI in AAL-90 and the fMRI in HOA-112 atlas, which was much better than using the single-modal or single-atlas features. Conclusion: The results demonstrated that the multi-modal and multi-atlas integrated method could effectively recognize MCI patients, which could be extended into various neurological and neuropsychiatric diseases.
Comparison of the prognostic effect of sarcopenia on atezolizumab plus bevacizumab and lenvatinib therapy in hepatocellular carcinoma patients

Sarcopenia has received much attention as a poor prognostic factor in various fields, and has also been reported to worsen prognosis in patients with hepatocellular carcinoma (HCC) treated with sorafenib or lenvatinib (LEN). Atezolizumab/bevacizumab (ATZ/BEV) is recommended as first‐line drug therapy for unresectable‐HCC, but the effect of sarcopenia on patients treated with ATZ/BEV is unknown.
Diffuse Myocardial Fibrosis at Cardiac MRI in Young Adults Born Prematurely: A Cross-sectional Cohort Study

Purpose To measure native T1 values, a marker of diffuse fibrosis, by using cardiac MRI (CMR) in young adults born prematurely. Materials and Methods This secondary analysis of a prospective cohort study included young adults born moderately to extremely preterm and age-matched, term-born participants. CMR was performed with a 3.0-T imager that included cine imaging for the quantification of left ventricular (LV) and right ventricular (RV) volumes and function and native saturation recovery T1 mapping for the assessment of diffuse myocardial fibrosis. Values between preterm and term were compared by using the Student t test. Associations between T1 values and other variables were analyzed by using linear regression and multivariate regression. Results Of the 50 young-adult participants, 32 were born preterm (mean age, 25.8 years ± 4.2 [SD]; 23 women) and 18 were born at term (mean age, 26.2 years ± 5.4; 10 women). Native T1 values were significantly higher in participants born preterm than in participants born at term (1477 msec ± 77 vs 1423 msec ± 71, respectively; unadjusted P = .0019). Native T1 values appeared to be positively associated with indexed LV end-diastolic and end-systolic volumes (β = 2.1, standard error = 0.7 and β = 3.8, standard error = 1.2, respectively), the RV end-diastolic volume index (β = 1.3, standard error = 0.6), and the LV mass index (β = 2.5, standard error = 0.9). Higher T1 values may be associated with reduced cardiac systolic strain measures and diastolic strain measures. Five-minute Apgar scores were inversely associated with native T1 values. Conclusion Young adults born moderately to extremely preterm exhibited significantly higher native T1 values than age-matched, term-born young adults. Keywords: MRI, Cardiac, Heart, Left Ventricle, Cardiomyopathies Clinical trial registration no. NCT03245723 Published under a CC BY 4.0 license Supplemental material is available for this article.