Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Work-Family Conflict on The Work Stress of The State Civil Apparatus with Locus of Control as Moderation

This study aimed to examine the role of personality factors, locus of control, as moderation in the relationship between work-family conflict and stress. Researchers hypothesize that the internal locus of control may weaken the effect of work-family conflict on work stress. The sample of this study was taken from 110 employees of the Personnel Bureau of the Ministry of Health. The data taken is primary data collected by distributing questionnaires to respondents who have been selected. The practical implications of this study can provide insight to leaders in the Ministry of Health about the effect of work-family conflict on the stress level of workers so that the results of the study can be considered to reduce the influence.
Generation of empirical correlation for predicting drag reduction of oil-water flows with natural polymers

There is an increasing need to accurately predict the behaviour of fluid in the different flow geometry as applicable in the industries. The prediction of drag reduction phenomenon observed during the two-phase oil-water flow with drag reducing polymers in horizontal pipes was investigated. The Power law model was adopted in this study to empirically correlate the data acquired from our earlier experimental works in a 12-mm ID and 20-mm ID pipes. The model accurately predicts the drag reduction across the horizontal pipes. The agreement between the predicted and experimental drag reductions was better in the 12-mm ID pipe than in the 20-mm ID pipe. More work and data is needed to enhance the predictive accuracy of applicable models. Keywords: Drag reduction; polymers; horizontal pipes, oil-water flow, modelling
Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer

Background Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50%. Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristics in TNBC patients remains obscure. Method Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by single-sample gene set enrichment analysis (ssGSEA). Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes. Result TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3. Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3. The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3. Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response. Conclusion In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtype-specific treatment of TNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression.
Recognition of Patterns of Benthic Diatom Assemblages within a River System to Aid Bioassessment

Benthic algae, especially diatoms, are commonly used to assess water quality in rivers. However, algal-based assessments are challenging at the river system scale because longitudinal variation in physical habitat conditions may obscure algal responses to changes in water quality. In the present study, we surveyed benthic diatoms and environmental variables from a mountainous Chinese river system. Hierarchical clustering, discrimination analysis, and indicator species analysis were used together to explore associations between distribution patterns of diatom assemblages and water quality variables. Study sites were clustered into five groups based on their diatom community composition, with sites grouped by the sampling months. Chemical oxygen demand (COD), elevation, and total nitrogen (TN) were the most important predictors for site classification. Site groups with higher elevations had higher TN concentrations; however, COD concentrations were higher in lower elevation groups. Moreover, COD concentrations significantly differed between temporally separated groups. In total, 49 indicator species were identified for individual groups, with most taxa indicating the eutrophic condition. Additionally, we found that European diatom indices are not closely associated with water quality variables. We conclude that the identification of algal patterns and their driving forces can provide valuable information to aid bioassessment at the river system scale.
The RPUF Composite’s Physical and Mechanical Properties with Ramie Stem Particle Reinforcement

Rigid polyurethane foam (RPUF) is a common insulation material that has low thermal conductivity and good mechanical properties. The qualities of RPUF may be enhanced by adding natural fiber reinforcing. The purpose of this work is to examine how the mechanical and physical characteristics of RPUF composite are affected by the amount of ramie stem particles present. Japanese Industrial Standard (JIS) A 5908-2003 is the basis for the mechanical test (modulus of elasticity, modulus of rupture, compressive strength, internal bond) and the physical test (density, moisture content, water absorption). The study's findings suggest that the physical properties of the RPUF composite were affected by the addition of ramie particles for reinforcement. The density increase in particle content 2.5% – 7.5% range, is not statistically significant. The MC and WA increased statistically significantly. The values of MOE and MOR were not significantly different. In fact, with the addition of 2.5% and 5% ramie particles, the compressive strength value was higher than the RPUF composite without ramie particle filler. Then overall, the IB value was increased by increasing the ramie particles in the RPUF composite. The addition of 2.5-5% ramie particles into the RPUF composite showed optimum results in mechanical properties. Thereby, the addition of ramie particles in the RPUF composite means reducing the use of polyurethane. Therefore, it can reduce production costs. Then the product is more environmentally friendly
OSTRACODS IN THE PLANKTON OF THE SIVASH BAY (THE SEA OF AZOV) DURING ITS TRANSFORMATION FROM BRACKISH TO HYPERSALINE STATE

Bay Sivash (the Sea of Azov), the largest lagoon in Europe, demonstrates a sharp ecosystem change due to anthropogenic impact. A pronounced salinity increase occurs as a result of the decision to stop supplying Dnieper water into the North-Crimean Canal. Salinity has increased from 20–22 g/L to 55–75 g/L in the lagoon. This led to the changes of biotic composition. The authors used data on ostracod species composition and abundance in samples of 2014–2016 samples with comparison with data from samples collected in 2004, before the salinity increase. The identification of ostracod species in the Sivash Bay was first made; five species was found in 2004–2016: Cyprideis torosa (Jones, 1850), Loxoconcha bulgarica Caraion, 1960, Loxoconcha aestuarii Marinov, 1963, Cytherois cepa Klie, 1937, Leptocythere devexa Schornikov, 1966. In June and August 2004, the average ostracod abundance in plankton was 22 ind. /m 3 (CV = 1.506). In October 2014, the ostracod abundance in the plankton reached 210 ind. /m 3 , and in August 2015 – average of 1273 ind. /m 3 (CV = 1.220). In some plankton samples, ostracods dominated in numbers and reached 3850 ind. /m 3 . In the salinity range of 50–80 g/L, a significant positive correlation was observed between total ostracod abundance in plankton and salinity.
Bidirectional encoders to state-of-the-art: a review of BERT and its transformative impact on natural language processing

First developed in 2018 by Google researchers, Bidirectional Encoder Representations from Transformers (BERT) represents a breakthrough in natural language processing (NLP). BERT achieved state-of-the-art results across a range of NLP tasks while using a single transformer-based neural network architecture. This work reviews BERT's technical approach, performance when published, and significant research impact since release. We provide background on BERT's foundations like transformer encoders and transfer learning from universal language models. Core technical innovations include deeply bidirectional conditioning and a masked language modeling objective during BERT's unsupervised pretraining phase. For evaluation, BERT was fine-tuned and tested on eleven NLP tasks ranging from question answering to sentiment analysis via the GLUE benchmark, achieving new state-of-the-art results. Additionally, this work analyzes BERT's immense research influence as an accessible technique surpassing specialized models. BERT catalyzed adoption of pretraining and transfer learning for NLP. Quantitatively, over 10,000 papers have extended BERT and it is integrated widely across industry applications. Future directions based on BERT scale towards billions of parameters and multilingual representations. In summary, this work reviews the method, performance, impact and future outlook for BERT as a foundational NLP technique. We provide background on BERT's foundations like transformer encoders and transfer learning from universal language models. Core technical innovations include deeply bidirectional conditioning and a masked language modeling objective during BERT's unsupervised pretraining phase. For evaluation, BERT was fine-tuned and tested on eleven NLP tasks ranging from question answering to sentiment analysis via the GLUE benchmark, achieving new state-of-the-art results. Additionally, this work analyzes BERT's immense research influence as an accessible technique surpassing specialized models. BERT catalyzed adoption of pretraining and transfer learning for NLP. Quantitatively, over 10,000 papers have extended BERT and it is integrated widely across industry applications. Future directions based on BERT scale towards billions of parameters and multilingual representations. In summary, this work reviews the method, performance, impact and future outlook for BERT as a foundational NLP technique.
Application of Several Artificial Intelligence Models for Forecasting Meteorological Drought Using the Standardized Precipitation Index in the Saïss Plain (Northern Morocco)

Drought is one of the main natural factors influencing different aspects of human life. Over the decades, intelligent techniques have proven to be very capable of modeling and predicting nonlinear and dynamic time series. Therefore, the present study aims to predict drought by using and comparing neuro-fuzzy adaptive inference systems (ANFIS), artificial neural network of multilayered perceptron (ANN-MLP) and the support vector model (SVR). For this purpose, the precipitation data obtained from the Ain Bittit station were used for a statistical period of 34 years. In addition, the short-term (3 and 6 months) and long-term (9 and 12 months) time scales were calculated using the standardized precipitation index (SPI). Then, depending on the results of the calculations, the period 1979-2000 was selected as a control group and the period 2003-2012 was selected as an experimental group. In order to predict the SPI for the (t + 1) period, SPI values, precipitation from previous months were used. The results indicated that, in the majority of time scales, the ANFIS model gives SPI values and predictive dryness more accurately than the SVR,
Climatology of seasonal snowfall accumulation across the Sierra Nevada (USA): Accumulation rates, distributions, and variability

A detailed picture of how snowfall varies across high‐elevation mountain ranges in both space and time remains a knowledge gap in understanding the montane hydrologic cycle. Previous studies generally used point‐scale snow measurements in an attempt to represent the spatial variability of snowfall across a range; however, these traditional approaches provide incomplete insight into the cumulative snowfall (CS) distribution from the basin‐scale to the mountain range‐scale. In this study, a high‐resolution, spatially distributed snow reanalysis was utilized to characterize 31 winters (water years 1985–2015) of snowfall distributions, snowfall accumulation rates, and snowstorms across the Sierra Nevada (USA). The CS data set (quantified in units of snow water equivalent) was verified against over 2600 station years of in situ observations. The seasonal CS was found to have mean and root‐mean‐squared differences of −4 and 12 cm, respectively, and a correlation of 0.96 with snow pillow observations. Using this novel CS information, results indicated that the CS accumulates rapidly across all 20 basins examined with, on average, at least 50% of the integrated CS accumulating in less than or equal to 6 days or three snowstorms over each basin. The largest (or leading) snowstorms each season yield ∼27% of the CS, on average, and most frequently last 4 days. Across the range, over 40% of the leading snowstorms occur in February. This study showed that the hydroclimatology of the Sierra Nevada is driven by hydrological extremes as manifested in the high interannual variability of its seasonally integrated CS, 4.4–41.3 km3, during the 31 years.
Identifying Changes in Trends of Summer Air Temperatures of the USA High Plains

Change in climate variables, especially air temperature, can substantially impact water availability, use, management, allocation, and projections for rural and urban applications. This study presents analyses for detecting summer air temperature change by investigating trends of two separate climate-periods in the USA High Plains. Two trend periods, the reference period (1895–1930) and the warming period (1971–2006), were investigated using parametric and nonparametric methods. During the reference period, minimum air temperature (Tmin) was statistically stationary at a nonsignificant increasing rate of 0.02°C/year. However, from early 1970s, Tmin increased at a significant rate of 0.02°C/ year. The maximum air temperature (Tmax) had a weaker warming signal than Tmin during the reference period. During the warming period, Tmax had a cooling trend at a nonsignificant rate of −0.004°C/year. About 22% of the High Plains had significant warming trends before 1930. Compared to the summers before 1930, the summer temperatures of the High Palins since the 1970s increased, on average, by 0.86°C. Overall, parametric methods lead to the conclusion that 50% of the study area experienced a significant warming trend in Tmin. In comparison, nonparametric methods indicated that 94% of the study area experienced a warming trend. Overall, in recent decades, summer average temperatures in the High Plains have been warming as compared to the early twentieth-century decades, and the warming is most likely driven primarily by increasing nighttime Tmin.
The Interplay between Liver Sinusoidal Endothelial Cells, Platelets, and Neutrophil Extracellular Traps in the Development and Progression of Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a societal burden due to the lack of effective treatment and incomplete pathophysiology understanding. This review explores the intricate connections among liver sinusoidal endothelial cells (LSECs), platelets, neutrophil extracellular traps (NETs), and coagulation disruptions in MASLD pathogenesis. In MASLD’s early stages, LSECs undergo capillarization and dysfunction due to excessive dietary macronutrients and gut-derived products. Capillarization leads to ischemic changes in hepatocytes, triggering pro-inflammatory responses in Kupffer cells (KCs) and activating hepatic stellate cells (HSCs). Capillarized LSECs show a pro-inflammatory phenotype through adhesion molecule overexpression, autophagy loss, and increased cytokines production. Platelet interaction favors leucocyte recruitment, NETs formation, and liver inflammatory foci. Liver fibrosis is facilitated by reduced nitric oxide, HSC activation, profibrogenic mediators, and increased angiogenesis. Moreover, platelet attachment, activation, α-granule cargo release, and NETs formation contribute to MASLD progression. Platelets foster fibrosis and microthrombosis, leading to parenchymal extinction and fibrotic healing. Additionally, platelets promote tumor growth, epithelial–mesenchymal transition, and tumor cell metastasis. MASLD’s prothrombotic features are exacerbated by insulin resistance, diabetes, and obesity, manifesting as increased von Willebrand factor, platelet hyperaggregability, hypo-fibrinolysis, and a prothrombotic fibrin clot structure. Improving LSEC health and using antiplatelet treatment appear promising for preventing MASLD development and progression.
Fruit Disease Classification and Localization Using Region Based Regression

Fruit disease classification using computer vision techniques is completely viral upon machine learning capabilities. The complete analysis is based upon regression using region proposal networks and their optimization using Gradient Clipping methodology. Fruit diseases such as flyspeck, blotch, scab and rot are detected and classified using the Region proposal network regression. The recent works carried out on the fruit disease detection and classification on agricultural crops were gathered and surveyed in this paper. Finally the very recent work carried out using regression and computer vision techniques were identified and applied to the data collected here with 6231 images and 24 classes. The diseased and disinfected were filtered for training purpose into 3110 images as diseases and the rest as disinfected. The hyperparameters tuning optimization was able to fit only the random data images, instead gradient clipping resulted in the proper limit cropping of the diseased portions of the crop footage. To improve the training data stability regression was employed with this optimization to show the results obtained from 95.3% to 97.8%. Here, the RCNN classification using neural networks resulted in the overall accuracy of the fruit disease classification model to 95.83%, where the Gradient Clipping optimization resulted in the improvement of accuracy of model to 97.8%.
Prediksi Penuaan Wajah Manusia Berbasis Generative Adversarial Network

Karena struktur wajah manusia yang berbeda-beda, wajah merupakan salah satu ciri yang digunakan untuk mengidentifikasi seseorang. Wajah sering digunakan sebagai pengenal biometrik. Namun, seiring bertambahnya usia manusia, wajah mereka bisa berubah karena faktor lingkungan dan gaya hidup. Karena efek penuaan pada wajah, komputer tidak dapat mengenali kemiripan antara citra wajah dari orang yang sama pada usia yang berbeda. Penelitian pengenalan wajah biasanya menggunakan data berpasangan (paired data), yang sangat sulit didapat. Di sisi lain, volume data yang tidak berpasangan (unpaired data) sangat besar dan mudah diakses. Sebaliknya, keterbatasan data berpasangan memotivasi para peneliti untuk mengembangkan teknik sintesis citra yang tidak bergantung pada data berpasangan. Tanpa perlu data berpasangan, metode CycleGAN mampu menghasilkan citra sintetik yang lebih realistis dengan resolusi lebih tinggi. Hal itulah yang memotivasi penelitian ini dalam penggunaan data tidak berpasangan untuk memprediksi penuaan wajah manusia menggunakan CycleGAN. Pada penelitian ini, digunakan citra dari dataset UTKFace yang terdiri atas citra wajah berbagai usia. Untuk keperluan eksperimen, citra dari UTKFace dibagi ke dalam dua ranah, yaitu citra wajah usia muda dan citra wajah usia tua, untuk keperluan sistem penuaan wajah yang dibangun. Dengan demikian, citra wajah berusia muda tidak memiliki pasangan pada citra wajah usia tua (unpaired data). Dengan nilai Frechet Inception Distance (FID) = 2,24, hasil percobaan menunjukkan bahwa metode yang digunakan mampu mencapai kinerja yang sangat baik pada sistem penuaan wajah yang dibangun.
Angular Dispersion of Free-Electron-Light Coupling in an Optical Fiber-Integrated Metagrating

Free electrons can couple to optical material excitations on nanometer-length and attosecond-time scales, opening-up unique opportunities for both the generation of radiation and the manipulation of the electron wave function. Here, we exploit the Smith–Purcell effect to experimentally study the coherent coupling of free electrons and light in a circular metallo-dielectric metagrating that is fabricated onto the input facet of a multimode optical fiber. Using hyperspectral angle-resolved (HSAR) far-field imaging inside a scanning electron microscope, we probe the angular dispersion of Smith–Purcell radiation (SPR) that is simultaneously generated in free space and inside the fiber by an electron beam that grazes the metagrating at a nanoscale distance. Furthermore, we analyze the spectral distribution of SPR that is emitted into guided optical modes and correlate it with the numerical aperture of the fiber. By varying the electron energy between 5 and 30 keV, we observe the emission of SPR from the ultraviolet to the near-infrared spectral range, and up to the third emission order. In addition, we detect incoherent cathodoluminescence that is generated by electrons penetrating the input facet of the fiber and scattering inelastically. As a result, our HSAR measurements reveal a Fano resonance that is coupled to a Rayleigh anomaly of the metagrating, and that overlaps with the angular dispersion of second-order SPR at 20 keV. Our findings demonstrate the potential of optical fiber-integrated metasurfaces as a versatile platform to implement novel ultrafast light sources and to synthesize complex free-electron quantum states with light.