Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Hot Deformation Process of A100 Steel Based on High-Temperature Rheological Behavior and Microstructure

To obtain the optimal hot deformation process, the rheological and dynamic recrystallization behaviors of A100 steel were researched through isothermal compression tests. Firstly, a Hensel-Spittel constitutive model was established based on the stress–strain curves. Secondly, dynamic recrystallization percentage and grain size models were established to identify the necessary conditions for complete dynamic recrystallization. Finally, microstructural analysis was employed to validate the accuracy of the recrystallization model. The results indicate that the flow stress is highly sensitive to both the strain rate and the temperature, and the HS model demonstrates a high predictive accuracy, with a correlation coefficient of 0.9914. There exists a contradictory relationship between decreasing the average grain size and increasing the recrystallization percentage. The higher the percentage of dynamic recrystallization, the larger the average grain size tends to be. This situation should be avoided when devising the actual processing procedures. The optimal hot working processes for achieving complete dynamic recrystallization and a smaller average grain size are as follows: a strain equal to or greater than 0.6, a temperature between 1193 and 1353 K, and a strain rate between 0.1 and 1 s−1.
The Volatility and Risk-Return Trade-Off of Malaysian Islamic and Conventional Indexes During the Global Financial Crisis and COVID-19 Pandemic

Manuscript type: Research paper Research aims: This study aims to investigate the volatility characteristics and risk-return trade-off of Islamic and conventional indexes in the Malaysian market. Design/Methodology/Approach: The research covers the daily data of the period from August 2007 to December 2022, divided into four distinct periods: the full sample, the period during and after the 2007- 08 financial crisis, and the period during the COVID-19 pandemic. The research employs a hybrid model that combines ARIMA with GARCH family models. Research findings: In this research, both Islamic and conventional indexes in the Malaysian market demonstrate a memory effect, emphasising the persistence of market volatility through the influence of past volatility. Additionally, historical data, represented by lagged values, significantly shape volatility, while negative shocks have an immediate and pronounced impact compared to positive shocks, providing valuable insights for investors and risk management. Lastly, during the COVID-19 crisis, the conventional index showed no leverage effect, and the Islamic index lacked safe haven characteristics, making this crisis unique in the Malaysian financial context. Theoretical contribution/Originality: This research contributes to the understanding of market volatility dynamics in the Malaysian context by utilising a hybrid ARIMA-GARCH model. The identification of memory effects, asymmetric responses, and the unique characteristics observed during the COVID-19 crisis adds to the body of knowledge on financial market behaviour. Practitioner/Policy implication: The findings of this study have practical implications for investors and supervisory entities operating in the Malaysian market. Understanding the persistence of volatility and the differential impact of positive and negative shocks can help investors make more informed decisions, while regulators can use this information to quantify and manage market volatility effectively. Research limitation/Implications: The limitation of the present study is that the results may be influenced by the selection of the sample period, potentially yielding different outcomes depending on market conditions, such as the presence of bull or bear markets, periods of high or low volatility, or other contextual factors.
Pengaruh Promosi Dan Ulasan Konsumen Di Platform Instagram Dan Tiktok Terhadap Minat Beli

The purpose of this research is to identify how social media marketing and user reviews on Instagram and TikTok influence consumers' propensity to make a purchase. In this quantitative study, a total of 149 participants filled out questionnaires. To examine the impact of the independent variables social media, promotions, and customer reviews on the dependent variable intent to buy multiple linear regression analytic approaches are used. The findings of the tests showed that although social media reviews had no influence on consumers' willingness to make a purchase, social media marketing had a favorable and statistically significant effect on consumers' willingness to do so.
Gene expression profiles of skin from cyclin dependent kinases 5-knockdown mice

Objective This study aimed to identify genes regulated by cyclin dependent kinases 5 (CDK5) that participate in hair pigmentation in mice. Methods The mRNA expression profiles of skin samples from CDK5-knockdown mice were constructed using high-throughput RNA sequencing and compared with those of wild-type mice. Results In total, 8,002 known genes were differentially expressed between CDK5-knockdown and wild-type mice. Of these, 3,658 were upregulated and 4,344 were downregulated in the skin of CDK5-knockdown mice. An additional 318 previously unknown genes were also differentially expressed, with 171 downregulated and 147 upregulated genes in the skin of CDK5-knockdown mice. Of the known genes expressed in mouse skin, 80 were associated with hair color, with 61 showing lower expression and 19 exhibiting higher expression in skin of CDK5-knockdown mice. Importantly, the expression of the tyrosinase-related protein 1 (TYRP1) and the calcium signaling pathway were also found to be regulated by CDK5, suggesting that pigmentation is regulated by CDK5 via the calcium signaling pathway and TYRP1. Conclusion The transcriptome profiles obtained from the skin of CDK5-knockdown mice compared to wild-type mice provide a valuable resource to help understand the mechanism by which CDK5 regulates melanogenesis in mice and other animals.
Mozgó személyek követése és 4D vizualizációja Lidar-alapú járáselemzéssel

Absztrakt. A következő tanulmányban Lidar szenzor méréseire alapú járáselemző módszert mutatunk be. A kifejlesztett algoritmus egy 4D vizualizációs keretrendszer része, ahol kültéri környezetben játszódó többszereplős valós jeleneteket elemezhetünk és jelenı́thetünk meg interaktı́van. A követett személyek trajektóriáinak meghatározása után a rendszer úgynevezett szabad nézőpontú videót hoz létre, amely megjelenı́ti a geometriailag rekonstruált és textúrázott jelenetet. A megjelenı́tés során biztosı́tjuk, hogy az előzőleg egy 4D rekonstrukcióban felvett mozgó avatárjaink kövessék a valóságban megfigyelt szereplők mért útpályáit. A rendszer két különböző moduljában támaszkodunk az emberi járás elemzésével kapható jellemzőkre is. Az egyik feladat a követés során kinyert egyedi járáslenyomatok, mint biometrikus jellemzők felhasználása a járókelők újra-azonosı́tására, amennyiben időlegesen elhagyták a megfigyelt területet, majd újra feltűntek a szı́ntéren. A másik feladat, hogy a vizuális megjelenı́tést valósághűbbé téve szinkronizáljuk a megfigyelt szereplők és a mozgatott avatárok lépéseit. Eljárásunkat valós kültéri környezetekben rögzı́tett jeleneteken teszteljük.
A Performance Enhancement Method for Redundant IMU Based on Neural Network and Geometric Constraint

The predicament stems from the fact that low-cost inertial measurement unit (IMU), operating within a context of global satellite navigation system (GNSS) denial, faces difficulties in autonomously providing continuous and high-precision navigational information to unmanned ground vehicles (UGVs). This challenge is primarily attributed to the accumulation of IMU errors over time. In this article, a hybrid approach integrating improved propagation neural network based on random forest regression (RFR-IBPNN) with geometrical information for redundant IMU (RIMU) is proposed to provide a reliable and high-precision navigation solution in GNSS-denied scenarios. Specifically, the dynamic function is designed to adjust the learning rate adaptively, which can avert deficiencies of slow convergence speed and randomness of initial value in the backpropagation neural network (BPNN). Then, random forest regression based on ensemble learning is adopted to optimize single BPNN and improve the reliability and prediction accuracy of the neural network trained with a small number of samples. Furthermore, the geometric model of RIMU system is derived and introduced as dynamic information to further improve the measurement accuracy. The feasibility and effectiveness of the proposed method in this article were proved via experiment. In contrast to the single IMU, multiple-IMUs based on least square method fusion, and single BP neural networks, the proposed method in this article achieved a substantial 40% enhancement in navigation accuracy.
Quality of Life and Safety Outcomes after First-Line Treatment of Symptomatic AF with Cryoablation or Drug Therapy: A Meta-Analysis of Randomized Controlled Trials

Background : Cryoablation has emerged as a recognized interventional strategy for the treatment of atrial fibrillation (AF). Numerous trials have investigated cryoablation as a first-line therapy for AF. This meta-analysis aimed to evaluate the impact of cryoablation on quality of life (QoL) and safety outcomes compared to antiarrhythmic drugs (AADs) in patients with symptomatic AF. Methods : A comprehensive search of the PubMed, EMBASE, and Cochrane Library databases was conducted for randomized controlled trials (RCTs) comparing cryoablation and AADs as first-line treatments for AF until May 2023. Continuous outcome data were analyzed using mean differences (MDs) with 95% confidence intervals (CIs), and dichotomous outcome data were analyzed using relative risks (RRs) with 95% CIs. The primary outcomes assessed were QoL and serious adverse events. Results : Our analysis included four RCTs involving 928 patients. Cryoablation was associated with a significant improvement in the AF Effect on Quality of Life (AFEQT) score (3 trials; MD 7.46, 95% CI 2.50 to 12.42; p = 0.003; I 2 = 79%) and EQ-VAS score (2 trials; MD 1.49, 95% CI 1.13 to 1.86; p < 0.001; I 2 = 0%) compared to AAD therapy. Additionally, cryoablation demonstrated a modest increase in EQ-5D score from baseline compared to AAD therapy, with no statistically significance (2 trials; MD 0.03, 95% CI –0.01 to 0.07; p = 0.07; I 2 = 79%). Furthermore, the rate of serious adverse events was significantly lower with cryoablation compared to AAD therapy (4 trials; 11.8% vs. 16.3%; RR, 0.73; 95% CI, 0.54–1.00; p = 0.05; I 2 = 0%). Cryoablation was also associated with a reduction in overall adverse events, incidence of persistent AF, hospitalizations, and additional ablation. However, there was no significant difference in major adverse cardiovascular events and emergency department visits between the two treatment groups. Conclusions : Cryoablation, as a first-line treatment for symptomatic AF patients, significantly improved AF-specific quality of life and reduced serious adverse events, as well as overall adverse events, persistent AF, hospitalizations, and additional ablation compared to AADs.
A decade of racial and ethnic stroke disparities in the United States

Stroke is the fourth leading cause of death and the leading cause of long-term disability in the United States. Stroke incidence and prevalence is not uniform. It occurs more frequently in some geographical areas of the United States and the rates are higher in minority groups.1,2 Some proposed causes for these disparities include increased rate of vascular risk factors that disproportionally affect some minority groups, differential effect of hypertension on stroke risk across racial groups, lack of access to health care and other social determinants of health, suboptimal control of risk factors, and, although lacking substantive data to date, an inherited predisposition to stroke associated with genetic differences. The Reasons for Geographic and Racial Differences in Stroke (REGARDS) investigators found that the rate of suboptimal control of vascular risk factors and unhealthy lifestyles was significantly greater in African Americans compared to non-Hispanic whites (NHW). 2 Moreover, regarding high blood pressure (BP), a triple threat has been described: blacks are not only more likely to have high BP, they are less likely, once diagnosed, to have their high BP controlled, and this suboptimal control has been shown to confer a stroke risk that is 3 times higher among blacks compared to whites for every 10-mm Hg increase in systolic BP (SBP). 3
GGE biplot analysis for yield performance of grain Amaranth genotypes across different environments in western India

Stable performance of grain amaranths genotypes at specific environment is critical for obtaining high and consistent yield. This study was conducted to assess grain yield stability of thirteen genotypes in varied environments in three successive growing seasons and effects of genotype (G) and genotype×environment (GE) interaction were illustrate graphically based on “what-won-where” to advocate genotypes for a specific environment, using GGE biplot. Result of study revealed that GGE biplot was effective to recognize stable and highest yield (1648.92 kg/ha) genotype as G5 followed by G6 (1457.24 kg/ha) and G3 (1268.62 kg/ha) genotypes while the genotype G7 had the lowest grain yield and was the least stable across varying environments. This study could be aid tool for endorsement of amaranths genotypes for specific environment, taking into elucidation the peculiarities of genotypes and growing stipulation. KEYWORDS
Foundations and relevance of delimiting local tourism destinations

There is an extensive literature on delimiting functional areas for management purposes in the social sciences. Nevertheless, in the field of tourism research there is no established set of criteria for delimiting tourism destinations. However, destinations are considered a main object of analysis and decision-taking for tourism management. This paper discusses the most relevant literature on delimiting functional zones and proposes a methodology for an application in the field of tourism. The research is illustrated with the results on zonification and production of statistical information already obtained by the Canary Islands System of Statistics. The results highlight the relevance of zoning in tourism with the support of the foundations and criteria for delimiting functional areas in the social sciences. Subjectivity is considered to play a crucial role among criteria for delimitation both in tourism and in social sciences in general.
Socio-economic conditions influencing the decision of communities to retain gum producing stands in the gum belt of the Sudan

Gum arabic, a product from Acacia Senegal, is a major product of rain fed agricultural sector in Sudan. It contributes considerably to a household income of gum farmers and foreign exchange earnings for the country. The objective of study was to identify socio-economic conditions of farmers that influence their decision to retain gum arabic stands. The study covered selected parts of Kordofan and the Blue Nile areas. Target groups were identified along with economic performances that determined their livelihoods. Primary and secondary data were collected through field survey. The survey has used semistructured questionnaires by involving randomly selected gum arabic farmers and key-informants besides visiting the surrounding markets. In the survey, 173 respondents representing a total population of 2081 households were selected with an overall average sampling percentage of 8.3%. The results of the study revealed that the probability of farmers’ decision to retain gum arabic stands as a component of their farming system was based on independent explanatory factors that included age of the respondents, type of land ownership, total land area and reliability of gum arabic as a source of income to a household. Covariance and regression mathematical models have shown that Acacia senegal area and gum production to be functions of decision making and have verified additional influential variables such as total land area per household, size of a family and farm income. The results obtained have also revealed that the farmers retained the old practices of land use, which included rearing animals, production of cash and food crops. Such land use pattern has contributed to risk reduction in addition to conservation of natural environment as a consequence of having forest as part of the cropping pattern in the study areas.
Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017: a case in Yunnan Province of China

ABSTRACT Sun-induced chlorophyll fluorescence (SIF) is widely used to monitor vegetation physiological conditions. In this paper, SIF derived from the Global Ozone Monitoring Experiment–2 (GOME-2), and gross primary productivity (GPP), normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture and precipitation are used to investigate vegetation stress in six areas of Yunnan Province in China. The results illustrate that SIF, NDVI, and GPP all decrease in the condition of water stress and increase when recovering towards a healthy condition; LST increases under stress conditions and decreases gradually during recovery conditions. Then, analyses of long time-series for SIF, GPP, NDVI, and LST from 2010 to 2017 are conducted. These performances demonstrate SIF is much more sensitive than GPP, the NDVI, and LST during drought conditions. When vegetation is in the condition of water stress, SIF has earlier responses than other parameters. Last, the relationships between SIF and soil moisture in 2010–2016 yield significant results, with the correlation coefficient r = 0.56, 0.71, 0.75, 0.74, 0.62, 0.52 and 0.47, sequentially. The correlation coefficients are used to measure how strong a relationship is between two variables. This work demonstrates that SIF is a good way to monitor water condition change, and may play a great role in drought monitoring in the future.
Recycling PVC Waste into CO2 Adsorbents: Optimizing Pyrolysis Valorization with Neuro-Fuzzy Models

Nowadays, the environmental challenges associated with plastics are becoming increasingly prominent, making the exploitation of alternatives to landfill disposal a pressing concern. Particularly, polyvinyl chloride (PVC), characterized by its high chlorine content, poses a major environmental risk during degradation. Furthermore, PVC recycling and recovery present considerable challenges. This study aims to optimize the PVC pyrolysis valorization process to produce effective adsorbents for removing contaminants from gaseous effluents, especially CO2. For this purpose, PVC waste was pyrolyzed under varied conditions, and the resulting solid fraction was subjected to a series of chemical and physical activations by means of hydroxides (NaOH and KOH) and nitrogen. Characterization of the PVC-based activated carbons was carried out using surface morphology (SEM), N2 adsorption/desorption, elemental analysis, and FTIR, and their capacity to capture CO2 was assessed. Finally, neuro-fuzzy models were developed for the optimization of the valorization technique. The resulting activated carbons exhibited excellent CO2 adsorption capabilities, particularly those activated with KOH. Optimal activation conditions include activations at 840 °C with NaOH at a ratio of 0.66 and at 760 °C using either NaOH or KOH with ratios below 0.4. Activations under these experimental conditions resulted in a significant increase in the adsorption capacity, of up to 25%, in the resulting samples.
Improving Energy-Efficiency of Capsule Networks on Modern GPUs

Convolutional neural networks (CNNs) have become the compelling solution in machine learning applications as they surpass human-level accuracy in a certain set of tasks. Despite the success of CNNs, they classify images based on the identification of specific features, ignoring the spatial relationships between different features due to the pooling layer. The capsule network (CapsNet) architecture proposed by Google Brain's team is an attempt to address this drawback by grouping several neurons into a single capsule and learning the spatial correlations between different input features. Thus, the CapsNet identifies not only the presence of a feature but also its relationship with other features. However, the success of the CapsNet comes at the cost of underutilization of resources when it is run on a modern GPU equipped with tensor cores (TCs). Due to the structure of capsules in the CapsNet, quite often, functional units in a TC are underutilized which prolong the execution of capsule layers and increase energy consumption. In this work, we propose an architecture to eliminate ineffectual operations and improve energy-efficiency of GPUs. Experimental measurements over a set of state-of-the-art datasets show that the proposed approach improves energy-efficiency by 15% while maintaining the accuracy of CapsNets.
Impact of Probabilistic Modeling Alternatives on the Seismic Fragility Analysis of Reinforced Concrete Dual Wall–Frame Buildings towards Resilient Designs

Demands to advance toward more resilient and sustainable cities in terms of reducing casualties, economic losses, downtime, and environmental impacts derived from earthquake-induced damage are becoming more frequent. Indeed, accurate evaluations of the seismic performance of buildings via numerical simulations are crucial for the sustainable development of the built environment. Nevertheless, performance estimations could be influenced by alternative probabilistic methods that can be chosen throughout the procedure of building-specific risk assessment, specifically in the construction and validation of fragility functions. This study evaluates the numerical impacts of selecting different probabilistic models on seismic risk metrics for reinforced concrete dual wall–frame buildings. Specifically, alternative probabilistic models are implemented and evaluated for (i) the identification and elimination of unusual observations within the simulated data (i.e., outliers); (ii) the selection and implementation of different Probability Distribution Functions (PDFs) to estimate fragility functions at different limit states (LSs); and (iii) the application of goodness-of-fit tests and information criteria to assess the validity of proposed PDFs. According to the results, the risk measures showed large variability at the extreme building LS (collapse). On the other hand, for a lower LS (service level), the measures remain similar in all the cases despite the methods selected. Further, the variability observed in the collapse response is up to two times that after eliminating data outliers. Finally, the large variability obtained with the evaluated alternative probabilistic modeling methods suggests re-opening the technical discussion over the state of the practice often used in earthquake engineering to improve the decision-making process, mitigating earthquake-induced consequences in an environmentally, economically, and socially beneficial manner.