Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinetics of Chemical and Microbial Iron Reduction Along an Inundation Gradient in a Tidal Marsh of the Min River Estuary, Southeastern China

Chemical (via buffered ascorbate-citrate) and microbial (via Shewanella decolorationis S12) iron reduction kinetics in the surficial tidal marsh sediments of the Min River Estuary were compared at six locations along the inundation gradient. A reactive continuum model was successfully applied to describe the kinetic results. Along the inundation gradient, an enhanced tidal inundation frequency towards the sea resulted in changes in the pH, Eh, salinity, as well as the abundances and fractions of reactive Fe(II) and Fe(III). Significant differences were observed between the chemical and microbial iron reductions among the six locations. Larger reducible Fe(III) pools, higher initial rates, and lower mineral heterogeneity were observed in chemical assays when compared to microbial assays. The microbial and chemical reducible Fe(III) as well as their initial reduction rates generally decreased along the inundation gradient. The chemical mineral heterogeneity gradually increased along the inundation gradient, while the microbial mineral heterogeneities were not significantly different at the six locations. Consequently, a higher iron reduction potential was observed in the uplands when compared to that in the lowlands. The result highlights the role of tidal hydrology and topography in iron reduction and suggest that both the pool and mineral heterogeneities of highly reactive Fe(III) should be considered in iron biogeochemical investigations.
Process characteristics of heterogeneous fine mud in the coal flotation

Processing objects is a kind of hard-to-float and high-ash fine coal which collected in Kailuan mining.Flotation rate experiment was designed for investigating the process characteristics of fine coal.Size analysis and density analysis of five cleans and fine-grained of the flotation rate experiment were studied.The mineral morphology of flotation rate experiment cleans was observed by scanning electron microscopy(SEM). The results show that fine mud contamination exists throughout the flotation process.The fine mud content in J1,J2,J3,J4 and J5 is similar,but gradually increases in its product.With the conduct of flotation,the flotation rate of high-ash fine mud(ash content 50%) reduces,and the operation recovery of fine mud per unit time also reduce.Flotation rate and cumulative yield of heterogeneity fine mud increase with its size and density reducing.The way of high-ash fine mud into clean coal products is mechanical entrainment and covering the surface of coarse-grained coal by SEM and Stokes number(St) analysis.
Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning

Aluminum alloys are widely used due to their exceptional properties, but the systematic relationship between their grain size and their tensile strength has not been thoroughly explored in the literature. This study aims to fill this gap by compiling a comprehensive dataset and utilizing machine learning models that consider both the alloy composition and the grain size. A pivotal enhancement to this study was the integration of hardness as a feature variable, providing a more robust predictor of the tensile strength. The refined models demonstrated a marked improvement in predictive performance, with XGBoost exhibiting an R2 value of 0.914. Polynomial regression was also applied to derive a mathematical relationship between the tensile strength, alloy composition, and grain size, contributing to a more profound comprehension of these interdependencies. The improved methodology and analytical techniques, validated by the models’ enhanced accuracy, are not only relevant to aluminum alloys, but also hold promise for application to other material systems, potentially revolutionizing the prediction of material properties.
Pembelajaran Berdiferensiasi dalam Meningkatkan Hasil Belajar Matematika Siswa Kelas IX Semester Genap SMP Negeri 1 Wera Tahun Pelajaran 2021/2022

Pembelajaran sosial emosional perlu diterpakan dalam menciptakan suasana belajar yang berbasis pada perkembangan setiap individu, dalam hal ini pembelajaran berdiferensiasi. Penelitian ini merupakan penelitian tindakan kelas dalam penerapan pembelajaran berdiferensiasi yang diadopsi dari Kemmis dan Taggart. Tujuan penelitian adalah mendeskripsikan pembelajaran berdiferensiasi dan meningkatkan hasil belajar siswa dalam pembelajaran matematika. Penelitian ini dilaksanakan di SMP Negeri 1 Wera semester genap tahun pelajaran 2021/2022, dengan subjek penelitian adalah kelas IX. Prosedur penelitian dengan mengikuti alur PTK yaitu mulai dari perencanaan (menyusun RPP, perangkat pembelajaran, lembar observasi, dan intrumen evaluasi), pelaksanaan penelitian (penerapan pembelajaran berdiferensiasi), evaluasi (tes disetiap akhir siklus pembelajaran), dan refleksi (identifikasi kekurangan dan kelebihan pelaksanaan diakhir siklus pembelajaran). Data dikumpulkan dengan lembar observasi yang diperoleh melalui pengamatan proses pembelajaran dan melalui hasil tes evaluasi disetiap akhir siklus pembelajaran dengan menggunakan instrumen tes. Data yang diperoleh dianalisis menggunakan analisis kuantitatif deskriptif. Adapun hasil penelitian yang dilaksanakan pada 29 orang siswa dengan perolehan ketuntasan klasikal siklus I sebesar 62.07%, yang menunjukan bahwa terdapat 18 orang siswa memperoleh nilai di atas 75, dan 11 orang siswa tidak memenuhi standar ketuntasan. Kemudian pada siklus II dengan perolehan ketuntasan sebesar 89.66%, yang menunjukkan bahwa terdapat 26 orang siswa memenuhi standar ketuntasan, dan hanya 3 orang siswa yang tidak memenuhi ketuntasan minimal. Penerapan pembelajaran berdiferensiasi terhadap matapelajaran matematika dapat meningkatkan hasil belajar siswa melalui pengkalsifikasian kemampuan siswa, menggunakan pengembangan materi yang bervariasi sesuai kemampuan siswa, dan melakukan pendekatan secara individu.
Adaptive Interpolation Filter Using Correlation for Inter Prediction

In video coding, interpolation filters play an important role in motion-compensated prediction. In Versatile Video Coding (VVC), adaptive motion vector resolution (AMVR) that makes use of fractional samples by 8-tap Discrete Cosine Transform-based interpolation filter (DCT-IF), 7-tap DCT-IF, or 6-tap Smoothing interpolation filter (SIF) was adopted. This paper proposes an adaptive interpolation filter to improve motion-compensated prediction. A 12-tap DCT-IF is designed to improve the filter response in the high frequency range and applied by a proposed filter selection method using correlation that considers the frequency characteristics of reference block found from motion estimation. Experimental results show that the proposed method achieved the overall Bjøntegaard Delta (BD)-rate gains of -0.27%, -0.12%, and -0.01% for Y, Cb, and Cr components, respectively, under the Random Access (RA) configuration compared with VVC.
The Influence of Personality Traits on Ethical Behavior of Police: The Mediating Effect of Ethical Leadership

Unethical behavior by police officers continues to garner a lot of attention. Engagement in such conduct will tarnish police reputation as law enforcers who are responsible to protect the welfare of society. Hence, fostering ethical behavior among police officers should be encouraged. However, there is a dearth of in-depth knowledge in existing literature, providing a sufficient explanation contributing to the ethical behavior of police officers. The purpose of this study is to assess the influence of the reciprocal relationship between personal factors and environmental factors on behavioral outcomes. By employing the Social Cognitive Theory (SCT) as an underpinning theory, this present study intends to examine the influences of personal factors, namely personality traits, on the ethical behavior of the police. This study also introduces ethical leadership as a mediating variable to fill the knowledge gap in the existing literature.
Comparative Analysis of Capsular and Secreted Polysaccharides Produced by Rhodotorula mucilaginosa and Cryptococcus neoformans

Fungal infections are a global public health challenge, especially among immunocompromised patients. Basidiomycetous yeasts, such as Rhodotorula mucilaginosa, have emerged as opportunistic pathogens, but have received less attention than Cryptococcus neoformans. This study aimed to characterize the polysaccharides of R. mucilaginosa and compare them with those of C. neoformans, analyzing their clinical implications. Comprehensive physicochemical, mechanical, and ultrastructural analyses of polysaccharides from both species were performed, revealing correlations with virulence and pathogenicity. R. mucilaginosa cells are surrounded by a capsule smaller than that produced by C. neoformans, but with similar polysaccharides. Those polysaccharides are also secreted by R. mucilaginosa. Cross-reactivity with R. mucilaginosa was observed in a diagnostic C. neoformans antigen test, using both in vitro and in vivo samples, highlighting the need for more reliable tests. Some R. mucilaginosa strains exhibited virulence comparable to that of C. neoformans in an invertebrate experimental model (Tenebrio molitor). This study contributes to a deeper understanding of yeast pathogenicity and virulence, highlighting the need for more accurate diagnostic tests to improve the differential diagnosis of infections caused by basidiomycetous yeasts.
A Heuristic Evaluation of Partitioning Techniques Considering Early-Type Galaxy Databases

: Galaxies are one of the most interesting and complex astronomical objects statistically due to their continuous diversiﬁcation caused mainly due to incidents such as accretion, action, or mergers. Multivariate studies are one of the most useful tools to analyze these type of data and to understand various components of them. We study a sample of the local universe of Orlando 509 galaxies, imputed with a Predictive Mean Matching (PMM) multiple imputation algorithm, with the aim of classifying the galaxies into distinct clusters through k-medoids and k-mean algorithms and, in turn, performing a heuristic evaluation of the two partitioning algorithms through the percentage of misclassiﬁcation observed. From the clustering algorithms, it was observed that there were four distinct clusters of the galaxies with misclassiﬁcation of about 1.96%. Also, comparing the percentage of misclassiﬁcation heuristically k-means is a superior algorithm to k-medoids under ﬁxed optimal sizes when the said category of galaxy datasets is concerned. By considering that galaxies are continuously evolving complex objects and using appropriate statistical tools, we are able to derive an explanatory classiﬁcation of galaxies, based on the physical diverse properties of galaxies, and also establish a better method of partitioning when working on the galaxies.
Dilution of the Antarctic ozone hole into southern midlatitudes, 1998–2000

[1] The reduction in southern midlatitude ozone is quantified by evaluating the trajectories of ozone-depleted air masses, assuming that photochemical recovery of ozone in advected air parcels can be ignored. This procedure is carried out for the 3 months from 15 October to 15 January for each of the years 1998, 1999, and 2000. Two distinct source regions, the vortex core and the vortex edge, are considered, and for each day, diabatic reverse domain filling calculations are performed for an ensemble of parcels between 30°S and 60°S and 400–700 K in altitude. In 1998, 1999, and 2000 the mean calculated ozone reduction is 16, 18, and 19 DU, respectively. Air parcels from the vortex edge region are significant contributors to the reduction, especially during spring. Results for four longitudinal and three latitudinal midlatitude subregions are also presented. A comparison with the Total Ozone Mapping Spectrometer measurements of total column ozone shows that without the dilution, ozone over Southern Hemisphere midlatitudes would be 5–6% higher during spring and summer. This result is probably an overestimate due to the neglect of photochemical recovery.
Enhancing Diagnostic Decision-Making: Ensemble Learning Techniques for Reliable Stress Level Classification

An intense level of academic pressure causes students to experience stress, and if this stress is not addressed, it can cause adverse mental and physical effects. Since the pandemic situation, students have received more assignments and other tasks due to the shift of classes to an online mode. Students may not realize that they are stressed, but it may be evident from other factors, including sleep deprivation and changes in eating habits. In this context, this paper presents a novel ensemble learning approach that proposes an architecture for stress level classification. It analyzes certain factors such as the sleep hours, productive time periods, screen time, weekly assignments and their submission statuses, and the studying methodology that contribute to stress among the students by collecting a survey from the student community. The survey data are preprocessed to categorize stress levels into three categories: highly stressed, manageable stress, and no stress. For the analysis of the minority class, oversampling methodology is used to remove the imbalance in the dataset, and decision tree, random forest classifier, AdaBoost, gradient boost, and ensemble learning algorithms with various combinations are implemented. To assess the model’s performance, different metrics were used, such as the confusion matrix, accuracy, precision, recall, and F1 score. The results showed that the efficient ensemble learning academic stress classifier gave an accuracy of 93.48% and an F1 score of 93.14%. Fivefold cross-validation was also performed, and an accuracy of 93.45% was achieved. The receiver operating characteristic curve (ROC) value gave an accuracy of 98% for the no-stress category, while providing a 91% true positive rate for manageable and high-stress classes. The proposed ensemble learning with fivefold cross-validation outperformed various state-of-the-art algorithms to predict the stress level accurately. By using these results, students can identify areas for improvement, thereby reducing their stress levels and altering their academic lifestyles, thereby making our stress prediction approach more effective.
Persistent Homology Analysis of the Microstructure of Laser-Powder-Bed-Fused Al–12Si Alloy

The laser powder bed fusion (L-PBF) process provides the cellular microstructure (primary α phase surrounded by a eutectic Si network) inside hypo-eutectic Al–Si alloys. The microstructure changes to the particle-dispersed microstructure with heat treatments at around 500 °C. The microstructural change leads to a significant reduction in the tensile strength. However, the microstructural descriptors representing the cellular and particle-dispersed microstructures have not been established, resulting in difficulty in terms of discussion regarding the structure–property relationship. In this study, an attempt was made to analyze the microstructure in L-PBF-built and subsequently heat-treated Al–12Si (mass%) alloys using the persistent homology, which can analyze the spatial distributions and connections of secondary phases. The zero-dimensional persistent homology revealed that the spacing between adjacent Si particles was independent of Si particle size in the as-built alloy, whereas fewer Si particles existed near large Si particles in the heat-treated alloy. Furthermore, the first principal component of a one-dimensional persistent homology diagram would represent the microstructural characteristics from cellular to particle-dispersed morphology. These microstructural descriptors were strongly correlated with the tensile and yield strengths. This study provides a new insight into the microstructural indices describing unique microstructures in L-PBF-built alloys.
Depth-Resolved Attenuation Mapping of the Vaginal Wall under Prolapse and after Laser Treatment Using Cross-Polarization Optical Coherence Tomography: A Pilot Study

Vaginal wall prolapse is the most common type of pelvic organ prolapse and is mainly associated with collagen bundle changes in the lamina propria. Neodymium (Nd:YAG) laser treatment was used as an innovative, minimally invasive and non-ablative procedure for the treatment of early-stage vaginal wall prolapse. The purpose of this pilot study was to assess connective tissue changes in the vaginal wall under prolapse without treatment and after Nd:YAG laser treatment using cross-polarization optical coherence tomography (CP OCT) with depth-resolved attenuation mapping. A total of 26 freshly excised samples of vaginal wall from 26 patients with age norm (n = 8), stage I–II prolapses without treatment (n = 8) and stage I–II prolapse 1–2 months after Nd:YAG laser treatment (n = 10) were assessed. As a result, for the first time, depth-resolved attenuation maps of the vaginal wall in the B-scan projection in the co- and cross-polarization channels were constructed. Two parameters within the lamina propria were target calculated: the median value and the percentages of high (≥4 mm−1) and low (<4 mm−1) attenuation coefficient values. A significant (p < 0.0001) decrease in the parameters in the case of vaginal wall prolapse compared to the age norm was identified. After laser treatment, a significant (p < 0.0001) increase in the parameters compared to the normal level was also observed. Notably, in the cross-channel, both parameters showed a greater difference between the groups than in the co-channel. Therefore, using the cross-channel achieved more reliable differentiation between the groups. To conclude, attenuation coefficient maps allow visualization and quantification of changes in the condition of the connective tissue of the vaginal wall. In the future, CP OCT could be used for in vivo detection of early-stage vaginal wall prolapse and for monitoring the effectiveness of treatment.
Intelligent Algorithms for Planning and Performing Flight Missions of an Unmanned Aerial Vehicles Based on Neural Networks

Unmanned aerial vehicles are widely used in various industries, such as aerospace, geodesy, cartography, remote environmental monitoring, and for military purposes. However, currently there are a number of factors that have a negative impact on the successful performance of special tasks of unmanned aerial vehicles due to the fact that currently remote-controlled means based on the «natural intellect» of the operator prevail in unmanned aerial vehicles and the lack of transition to the most autonomous actions, such as route planning, bypassing flight blocking zones, forecasting navigation situation, as well as search, recognition and classification of remote monitoring objects. To effectively perform tasks, it is necessary to develop intelligent algorithms to automate the process of planning and executing mission tasks. The article discusses the basic principles of neural networks and their application in the tasks of route planning and flight control for remote monitoring of an unmanned aircraft.
Prediction of LDL in hypertriglyceridemic subjects using an innovative ensemble machine learning technique

Abstract Objectives Determining low-density lipoprotein (LDL) is a costly and time-consuming operation, but triglyceride value above 400 (TG>400) always requires LDL measurement. Obtaining a fast LDL forecast by accurate prediction can be valuable to experts. However, if a high error margin exists, LDL prediction can be critical and unusable. Our objective is LDL value and level prediction with an error less than low total acceptable error rate (% TEa). Methods Our present work used 6392 lab records to predict the patient LDL value using state-of-the-art Artificial Intelligence methods. The designed model, p-LDL-M, predicts LDL value and class with an overall average test score of 98.70 %, using custom, hyper-parameter-tuned Ensemble Machine Learning algorithm. Results The results show that using our innovative p-LDL-M is advisable for subjects with critical TG>400. Analysis proved that our model is positively affected by the Hopkins and Friedewald equations normally used for (TG≤400). The conclusion follows that the test score performance of p-LDL-M using only (TG>400) is 7.72 % inferior to the same p-LDL-M, using Hopkins and Friedewald supported data. In addition, the test score performance of the NIH-Equ-2 for (TG>400) is much inferior to p-LDL-M prediction results. Conclusions In conclusion, obtaining an accurate and fast LDL value and level forecast for people with (TG>400) using our innovative p-LDL-M is highly recommendable.
A model for geotechnical analysis of flow slides and debris flows

Flow slides and debris flows incorporate a broad range of sediment-fluid mixtures that are intermediate between dry rock avalanches and hyperconcentrated flows. Following a comprehensive review of some existing analytical ap- proaches to debris flow runout analysis, a new analytical model based on energy conservation has been formulated. The new analytical model was developed to deepen the understanding of fundamental aspects in modeling of granular flows and to improve the geotechnical mobility analysis of flow slides and debris flows. The Lagrangian finite difference method was used to solve the governing equations. The model and numerical scheme have been tested against analytical solutions and experiments of granular flows with simplified geometries for sliding mass and basal topography. Results of granular flow simulations indicate that the model based on energy conservation performs well and is robust. The model can be used for geotechnical analysis of a wide range of dense granular flows, such as flow slides and debris flows.
Robust Finger Vein Recognition Based on Lightweight Attention Convolutional Neural Networks

In the field of computer vision (CV), convolutional neural networks (CNN) have demonstrated superior capability in feature extraction. However, the translation during the process of capturing finger vein images can potentially degrade the accuracy rate of the model, making it challenging to apply CNN to real-time and high accuracy finger vein recognition. Additionally, while CNN offers high accuracy, they also require a high number of parameters. Previous studies also confirmed the absence of shift-invariant features in CNN. In the light of these issues, this research proposes a lightweight attention convolution neural network (LACNN) for applications in finger vein recognition. The model incorporates diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CAM). To evaluate the performance of the model under finger vein recognition, the finger-vein by university sains malaysia (FV-USM) public database is employed for analysis and comparison with recent research methods. According to the result, the new model achieves an accuracy recognition rate of 99.82% with a parameter count of only 1.23 million. Thus, the LACNN presented in this study surpasses the other recent finger vein recognition methods.