Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental study on effect of enhanced surfaces and inclined mode on spray cooling heat transfer performance using HFE-649 coolant

ABSTRACT Experiments have been performed to study the effect of enhanced surfaces including micro-finned, micro-/nano-coated (coating thickness of 100 µm and 200 µm) and hybrid surface on heat transfer coefficient and critical heat flux using spray cooling. Commercial HFE-649 coolant obtained from 3 M Corporation has been used to spray over copper heater of 10 mm2 cross-sectional area using a full cone pressure swirl nozzle with 0.51-mm orifice diameter. The pressure swirl nozzle squeezed the incoming fluid and dispersed into spray droplets in the volumetric flux range between 1.0 and 4.5 cm3/cm2s. Results have been obtained for various surfaces at optimum distance of nozzle-to-surface and comparison is done with the plain surface. Findings revealed that heat transfer performance improved considerably with hybrid surface, which showed at least 360% enhancement in heat transfer coefficient and 72.9% in critical heat flux in contrast to the plain surface. An improvement of 7.1% in critical heat flux is observed for hybrid surface in comparison with other enhanced surfaces. Furthermore, experiments were conducted to examine the effect of inclination angle (0°, 15°, 30°, and 45°) on heat transfer performance. The heat transfer coefficient, critical heat flux, and cooling efficiency all improved dramatically at 15°C inclination angle. In comparison with horizontal spray, an improvement of 18.9% and 20.1% in heat transfer coefficient and critical heat flux, respectively, have been observed. However, heat transfer performance degraded with the further increase of inclination angle. Other experimental parameters remain constant, such as the volumetric flux, spray height, and spray pressure.
Modeling magnetic multipolar phases in density functional theory

Multipolar magnetic phases in correlated insulators represent a great challenge for Density Functional Theory (DFT) due to the coexistence of intermingled interactions, typically spin-orbit coupling, crystal ﬁeld and complex non-collinear and high-rank inter-site exchange, creating a com-plected conﬁgurational space with multiple minima. Though the +U correction to DFT allows, in principle, the modelling of such magnetic ground states, its results strongly depend on the initially symmetry breaking, constraining the nature of order parameter in the converged DFT+U solution. As a rule, DFT+U calculations starting from a set of initial on-site magnetic moments result in a conventional dipolar order. A more sophisticated approach is clearly needed in the case of magnetic multipolar ordering, which is revealed by a null integral of the magnetization density over spheres centered on magnetic atoms, but with non-zero local contributions. Here we show how such phases can be eﬃciently captured using an educated constrained initialisation of the onsite density matrix, which is derived from the multipolar-ordered ground state of an ab initio eﬀective Hamiltonian. Various properties of such exotic ground states, like their one-electron spectra, become therefore accessible by all-electron DFT+U methods. We assess the reliability of this procedure on the Ferro-Octupolar ground state recently predicted in Ba 2 MOsO 6 (M = Ca, Mg, Zn) [Phys. Rev. Lett. 127 , 237201 (2021)].
“The Right Kind of Crazy”: How Patrol Officers Police the Boundaries of Mental Illness through Hybridized Strategies

How to better police mental illness is an evergreen component of criminal justice reform agendas. The Los Angeles Police Department (LAPD) has, like many departments, adopted specialized strategies designed to improve these encounters by tasking officers with both care and control responsibilities. These hybridized policing strategies are illustrative of a larger trend of managing social marginality through institutions that increasingly destabilize the penal/welfare state binary. This article draws from fieldwork with the LAPD to analyze how patrol officers construct the category of “mental illness” and deploy hybridized strategies. The analysis focuses on the inflection points that shape how a subject is categorized and the call’s disposal to understand how policing from the “murky middle” of state governance unfolds on the ground. Findings show how officers strategically invoke the pressure of time and the power of place to construct this category and deploy specialized resources when resolving trouble case, or “5149 and a half,” calls. Here, hybridized strategies function to manage social marginality through a governance of problem solving that appears uninterested in doing either care or control. The article concludes by reflecting on the project of hybridizing care and control to police mental illness specifically and social marginality more broadly.
Identification of Ubiquitin-Related Gene-Pair Signatures for Predicting Tumor Microenvironment Infiltration and Drug Sensitivity of Lung Adenocarcinoma

Simple Summary Lung adenocarcinoma (LUAD) has a high mortality and incidence rate. The therapeutic efficacy of LUAD varies with the individual heterogeneity of the tumor microenvironment (TME). It is necessary to explore more biomarkers and targets to improve the prognosis of patients. Ubiquitination pathways are involved in the biological process of regulating the anti-tumor immunity of immune cells and immunosuppression of tumor cells in the TME of patients. In this study, we clarified the characteristics of ubiquitin-related gene pairs (UbRGPs) and identified the relationship between the status of the TME and UbRGPs of patients with LUAD. A prognostic signature based on six UbRGPs was established, which performed well in predicting the immune infiltration and tumor mutation burden (TMB) in the TME and the response of LUAD to immuno-, chemo-, and targeted therapy. In conclusion, the UbRGPs signature is an independent prognostic indicator and has great potential in assisting the clinical therapy for patients with LUAD. Abstract Lung adenocarcinoma (LUAD) is a common pathological type of lung cancer worldwide, and new biomarkers are urgently required to guide more effective individualized therapy for patients. Ubiquitin-related genes (UbRGs) partially participate in the initiation and progression of lung cancer. In this study, we used ubiquitin-related gene pairs (UbRGPs) in tumor tissues to access the function of UbRGs in overall survival, immunocyte infiltration, and tumor mutation burden (TMB) of patients with LUAD from The Cancer Genome Atlas (TCGA) database. In addition, we constructed a prognostic signature based on six UbRGPs and evaluated its performance in an internal (TCGA testing set) and an external validation set (GSE13213). The prognostic signature revealed that risk scores were negatively correlated with the overall survival, immunocyte infiltration, and expression of immune checkpoint inhibitor-related genes and positively correlated with the TMB. Patients in the high-risk group showed higher sensitivity to partially targeted and chemotherapeutic drugs than those in the low-risk group. This study contributes to the understanding of the characteristics of UbRGPs in LUAD and provides guidance for effective immuno-, chemo-, and targeted therapy.
River discharge estimation under uncertainty from synthetic SWOT-type observations using variational data assimilation

Variational data assimilation is used for discharge estimation for the Garonne River downstream reach, using the 1D full Saint-Venant hydraulic model SIC². Based on water surface elevation observations with the level of noise expected for the forthcoming Surface Water and Ocean Topography (SWOT) mission, discharge estimation problem under uncertainty in the bathymetry and the friction has been considered. Two observation sampling periods has been tested, with the purpose of investigating the influence of the temporal frequency of observations. We emphasize that the temporal sampling frequency should be consistent with the characteristic (travel) time of the dynamical system, in which case the discharge is successfully estimated (rRMSE = 4.6%). Larger temporal sampling intervals require complementary information given by a-priori information on discharge or combined in-situ and remote sensing data assimilation. Simultaneous estimation of discharge and the spatially distributed functions may lead to the equifinality issue. In this case, the estimates of the bathymetry and the friction only help to achieve a better estimation quality of discharge, but may not be usable for subsequent experiments.
Recommendation System using Clustering and Comparing Clustering and Topic Modelling Techniques

In this paper, we have used a technique called clustering to recommend the products to the customer and also tried to compare clustering and Topic modelling to find out which technique is better for our purpose. From all the papers that have been reviewed, we observed that the greater part of the proposal approaches applied content-based filtering (55%). Collaborative-based filtering was applied by just 18% of the looked into approaches, and hybrid based by 16%. Other suggestion ideas included generalizing, thing driven proposals, and crossover suggestions. The content-based filtering approaches overwhelmingly utilized papers that the clients had made, marked, examined, or downloaded [1]. To begin with, it stays muddled which suggestion ideas and approaches are the most encouraging. For instance, analysts demonstrated different results on the presentation of content based and collaborative filtering. A portion of the time content-based filtering performed better contrasted with collaborative filtering sand a portion of the time it performed all the more regrettable.
Proteomic Response of Aedes aegypti Larvae to Silver/Silver Chloride Nanoparticles Synthesized Using Bacillus thuringiensis subsp. israelensis Metabolites

Simple Summary Aedes aegypti is a vector of important mosquito-borne diseases. Green synthesized nanoparticles (NPs) have been used to control the larvae of mosquitoes including A. aegypti. However, the molecular responses of A. aegypti larvae to green synthesized NPs remain unexplored. This work analyzed protein expression in A. aegypti larvae in response to treatment using green synthesized silver/silver chloride nanoparticles (Ag/AgCl NPs) based on two-dimensional polyacrylamide gel electrophoresis. Fifteen differentially expressed protein spots were selected for identification using mass spectrometry. The results showed that the six upregulated proteins in A. aegypti larvae responsible for green synthesized Ag/AgCl NP treatment were involved in mitochondrial dysfunction, DNA and protein damage, inhibition of cell proliferation, and cell apoptosis, implying the modes of actions of Ag/AgCl NPs. This finding has provided greater insight into the possible mechanisms of green synthesized Ag/AgCl NPs on the control of A. aegypti larvae. Abstract Silver/silver chloride nanoparticles (Ag/AgCl NPs) are an alternative approach to control the larvae of Aedes aegypti, a vector of mosquito-borne diseases. However, the molecular mechanisms of Ag/AgCl NPs to A. aegypti have not been reported. In this work, Ag/AgCl NPs were synthesized using supernatant, mixed toxins from Bacillus thuringiensis subsp. israelensis (Bti), and heterologously expressed Cry4Aa and Cry4Ba toxins. The images from scanning electron microscopy revealed that the Ag/AgCl NPs were spherical in shape with a size range of 25–100 nm. The larvicidal activity against A. aegypti larvae revealed that the Ag/AgCl NPs synthesized using the supernatant of Bti exhibited higher toxicity (LC50 = 0.133 μg/mL) than the Ag/AgCl NPs synthesized using insecticidal proteins (LC50 = 0.148–0.217 μg/mL). The proteomic response to Ag/AgCl NPs synthesized using the supernatant of Bti in A. aegypti larvae was compared to the ddH2O-treated control. Two-dimensional gel electrophoresis analysis revealed 110 differentially expressed proteins, of which 15 were selected for identification using mass spectrometry. Six upregulated proteins (myosin I heavy chain, heat shock protein 70, the F0F1-type ATP synthase beta subunit, methyltransferase, protein kinase, and condensin complex subunit 3) that responded to Ag/AgCl NP treatment in A. aegypti were reported for NP treatments in different organisms. These results suggested that possible mechanisms of action of Ag/AgCl NPs on A. aegypti larvae are: mitochondrial dysfunction, DNA and protein damage, inhibition of cell proliferation, and cell apoptosis. The findings from this work provide greater insight into the action of green synthesized Ag/AgCl NPs on the control of A. aegypti larvae.
Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications

Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose.
Visibility Prediction Based on Machine Learning Algorithms

In this study, ground observation data were selected from January 2016 to January 2020. First, six machine learning methods were used to predict visibility. We verified the accuracy of the method with and without principal components analysis (PCA) by combining actual examples with the European Centre for Medium-Range Weather Forecast (ECMWF) data and National Centers for Environmental Prediction (NECP) data. The results show that PCA can improve visibility prediction. Neural networks have high accuracy in machine learning algorithms. The initial visibility data plays an important role in the visibility forecast and can effectively improve forecast accuracy.
Ehrenfest Scheme of Higher Dimensional Topological AdS Black Holes in The Third Order Lovelock-Born-Infeld Gravity

Interpreting the cosmological constant as a thermodynamic pressure and its conjugate quantity as a thermodynamic volume, we reconsider the investigation of P-V critical behaviors of (1+n)-dimensional topological AdS black holes in Lovelock-Born-Infeld gravity. In particular, we give an explicit expression of the universal number \chi=\frac{P_c v_c}{T_c} in terms of the space dimension $n$. Then, we examine the phase transitions at the critical points of such topological black holes for 6 \leq n \leq 11 as required by the physical condition of the thermodynamical quantities. More precisely, the Ehrenfest equations have been checked revealing that the black hole system undergoes a second phase transition at the critical points.
Differences and Similarities in Adaptive Functioning between Children with Autism Spectrum Disorder and Williams–Beuren Syndrome: A Longitudinal Study

Background: The last decade has seen a growing number of comparative studies on adaptive profiles between individuals with autism spectrum disorder (ASD) and Williams–Beuren syndrome (WBS), showing shared and syndrome-specific adaptive trajectories. Studies have revealed similarities in global adaptive profiles across conditions, while some differences have been found in preschoolers on the specific sub-domains of communication and socialization. However, the majority of studies that have focused on the differences in adaptive functioning across these two conditions used a cross-sectional design. To the best of our knowledge, there are no studies exploring the differences and similarities of adaptive functioning over time. Methods: We compared longitudinal data of adaptive functioning measured by Vineland Adaptive Behavior Scales (VABS) between two samples of children and adolescents with ASD and WBS, matched for chronological age and cognitive/developmental level at the time of the first evaluation. Results and Conclusions: We did not find any difference on the global adaptive level, both at the first evaluation and over time. However, significant differences emerged on the socialization and communication levels at the time of recruitment. Longitudinal data show that only the socialization domain remains different over time, with individuals with WBS having better functioning than those with ASD. The results on shared and distinct patterns of adaptive functioning between disorders are discussed from a developmental perspective, thus contributing to the implementation of age-specific interventions.
Power Added Efficiency Model for Mesfet Class E Power Amplifier Using Jackknife Resampling

There are several types of amplifier classes, and this includes the class E amplifier. The class E can achieve its efficiency up to 100%. This paper thus aims on getting the best model in estimating the power added efficiency of Class E power amplifier circuit using Silicon Carbide MESFET. Twelve models are obtained from three independent variables; DC current (Idc), drain voltage (Vdc), and power out (Pout). The original data set of 7 is generated to become 105 data samples (21 sets x 5 observations where each set with two missing observations) using the Jackknife sampling technique at the first stage (7C2). The power added efficiency model employs the Multiple Regression (MR) technique up to the second order of interactions. The best model is based on the eight selection criteria (8SC). The best model is found to be model M12.5. 0, chosen from the six selected model). Efficiency factors affect the power added efficiency estimation are found to be X3(IDC) and X12(interaction between Pout and VDS).
Testing the Functional and Phylogenetic Assembly of Plant Communities in Gobi Deserts of Northern Qinghai–Tibet Plateau

The mechanism governing plant community assembly across large-scale Gobi deserts remains unclear. Here, we inferred the roles of different assembly processes in structuring plant communities in the Gobi deserts of the Qinghai–Tibet Plateau by using a phylogenetic tree, and leaf and root traits. The functional and phylogenetic structures of 183 plant communities were assessed, and their distributions were linked with environmental gradients. Our results demonstrated that functional convergence was prevalent in most functional traits (75% of the traits) and accentuated when all traits were combined. The phylogenetic structure exhibited significant divergence. We observed the contrasting response of functional and phylogenetic assembly structures to environmental gradients. More importantly, we found that the shifts in the functional assembly along environmental gradients were trait-specific, with dominant roles of local factors, such as gravel coverage and soil attributes, in determining the distribution patterns of most traits. However, the distribution patterns of leaf P concentration (LPC), root N concentration (RNC), and root P concentration (RPC) were mainly driven by climatic factors. These results reveal that niche-based processes, such as abiotic filtering and weaker competitive exclusion, are the major drivers of species co-occurrence, which results in the widespread coexistence of phylogenetically distinct but functionally similar species within the Gobi plant community. Our findings could improve the understanding of plant community assembly processes and biodiversity maintenance in extremely harsh drylands.
Implementasi Metode Fp-Growth Dalam Menganalisa Pola Penjualan Obat Pada Apotek

Apotek merupakan salah satu jenis usaha perdagangan dibidang kesehatan yang menyediakan beragam jenis obat-obatan. sistem pengolahan data sangat diperlukan dalam sebuah apotek untuk dapat mempermudah dan memperlancar kinerjanya. Untuk saat ini proses penjualan di Apotek Pelita 3 masih bersifat manual yaitu dengan cara tulis tangan, sehingga pihak apotek masih sulit dalam mendapatkan informasi data pola penjualan obat. Hal ini yang mendasari mengapa peneliti melakukan penelitian di Apotek Pelita 3 terkait dengan analisa penentuan pola penjualan obat pada Apotek. Dengan demikian Apotek memerlukan adanya sebuah sistem pengolahan data berbasis komputer dengan konsep data mining dalam proses penentuan pola penjualan guna untuk mempermudah dan memperlancar kinerja apotek. Salah satu teknik dalam data mining yang dapat dilakukan untuk mengetahui pola penjualan yaitu dengan aturan assosiasi metode Fp-Growth. Hasil dari penelitian ini yaitu berupa sistem yang dapat mempermudah pihak Apotek Pelita 3 dalam menentukan pola penjualan obat dengan cepat dan tepat menggunakan Fp-Growth.Kata Kunci: Data Mining, Metode Fp-Growth, Assosiasi, Penjualan Obat, Apotek.
Optimisation multi-objectif pour la sélection de modèles SVM

Dans cet article, nous proposons une methode d'optimisation multi-objectif pour la selection de modele SVM, en utilisant l'algorithme NSGA-II. Le faux rejet et la fausse acceptation sont les deux criteres employes pour trouver les hyperparameters optimaux d'un ensemble de classifieurs SVM. La strategie proposee est appliquee a une tâche de discrimination chiffre/rejet embarquee dans un systeme plus global d'extraction d'information dans des documents manuscrits. Nos experimentations sur une base reelle de chiffres/rejet montrent que notre methode fournit de meilleurs resultats que les techniques d'optimisation mono-objectif plus classiques telle que l'apprentissage base sour un critere d'aire sous la courbe ROC. Ces resultats sont dans un second temps valides sur des bases de l'UCI.
Design of Fog Based Remote Health Monitoring System

As a result of the Covid-19 outbreak, a trustworthy health care system for remote surveillance was required, particularly in care facilitieas for the elderly. Many studies have been done in this subject, however they still have security, latency, extended time of execution and response delay. An intelligent Healthcare infrastructure termed Remote Health Monitoring (RHM) is introduced in this study to overcome these constraints. The framework uses high-level fog layer services including locally storage, native real-time data processing with combined mining of information in handling certain cloud and sensor network loads and transformed in a decision taker entity. This systems uses a body and camera sensors to diagnose, increasing accuracy and efficiency while protecting privacy. The suggested framework was tested using the iFogSim toolbox. It may minimise latency, energy usage, network connectivity and total reaction time. This work will assist develop a high performing, secure, and dependable intelligent Medical infrastructure.