Miracle-dz/UniFAR-FTUs huggingface dataset:
Conceptual study of 3.5-meter segmented mirror for geostationary Earth observation satellite

A feasibility study was conducted for an optical imager system assumed to be mounted on a geostationary orbit satellite for Earth observation. The targeted spatial resolution was less than 10 meters for panchromatic mode at nadir observation conditions, and the observation area was assumed to 100 × 100 square kilometers. The optical system was designed based on a Korsch three mirror anastigmat; the primary mirror was 3.5 meters in diameter, and the focal length was approximately 45 meters. The worst wavefront error was estimated at less than 0.017 λrms in the field of view. As the next step, the primary mirror was segmented, and a trade-off study was conducted on two types of segmented mirror configurations. The optical performance of each configuration was compared in terms of PSF and MTF. Moreover, the deterioration of optical performance due to the misalignment and distortion of the segmented mirror was discussed and numerically estimated by using the Monte Carlo method. The sensitivity of the wavefront error was consequently estimated for the segmented mirror assembly.
Theoretical and numerical analysis on choked multiphase flows of gas and solid particle through a convergent–divergent nozzle:

Particle–gas two-phase flows show significantly different behaviours compared to single-gas flow through a convergent–divergent nozzle. Non-equilibrium effects, thermal and velocity lags lead to the inefficiency of nozzle performance. In the present studies, theoretical analysis and numerical simulations were carried out to study particle–gas flows through a convergent–divergent nozzle. Homogeneous and equilibrium model that no slip in velocity and temperature occurs between particle phase and gas phase was considered to derive mass flow rate and sound speed of multiphase flows. Two-phase flows are regarded as isentropic flows that isentropic relations can be used for homogeneous equilibrium model. Particle volume fraction, specific heat ratio and gas constant parameter were also rewritten. Discrete phase model that includes Lagrangian-Eulerian tracking method was used to calculate particle–gas flows in computational fluid dynamics (CFD) studies. Particle mass loading was varied to investigate its effect .
Multiple Machine Learning Classifiers for Student’s Admission to University Prediction

Data is the most important asset for any organization which is further processed to produce useful information. Machine Learning and Big Data techniques are widely used for industrial sectors to generate useful patterns helpful for earning more profits and expand businesses. From the past few years, a lot of research works have been done by using Big Data techniques on educational data for improvement in Education System. Machine Learning and Big Data can be useful for predicting the students’ admission, performance of teaching, performance of a student, identifying the group of students of similar behavior. However, the manual process of record checking is time consuming, tedious, and error prone; due to the inherent volume and complexity of data. In this study, the combination of linear and non-linear machine learning algorithms; Logistic Regression, Decision Tree, k-NN, and Naïve Bayes have been chosen to perform prediction of the target class for an unseen observation by polling. As the models built in this work are predicting the likelihood of a student taking up the admission into any university based on the student data collected by any marketing agency, the combined models are collectively called as the Admission Predictor. The administrative officials of any academic institution can use this kind of an application to explore and analyze the patterns that are affecting the student admission and come up with enhanced strategies to improve admission. Such an application not only plays a vital role in administration, but also help the management in reformulating the marketing criteria for overall development of academic institution.
Control of tunneling in a triple-well system: an atomtronic switching device

We study a model of bosons confined to three coupled wells. The model describes interactions between bosons, tunneling of bosons between adjacent wells, and the effect of an external field. We consider both integrable and non-integrable regimes within the model. These regimes are first analyzed by means of the energy level spacing distribution. We then conduct a study of the quantum dynamics of the system to probe the conditions under which switching behavior can occur. Through variation of an external field on one well, we demonstrate how the system can be controlled between various switched-on and switched-off configurations.
Socially Aware Network Clustering for Throughput Maximization in Mobile Wireless Sensor Networks

Mobile sensors, such as smart wearables, autonomous cars, cognitive healthcare devices, and intelligent drones, draw great attention due to their ability to provide a wide-range of services. Hence, a consistent connection of each sensor node to the access point is a crucial requirement to obtain high data throughput in such mobile wireless sensor networks (MWSN). Data interference among such densely populated mobile sensor nodes (MSN) must also be minimized to enhance the data gathering reliability at individual MSN. In this context, a novel socially aware network clustering and interference management technique for MWSN is proposed in this work. The proposed method considers the current and prediction of future encounters among MSN to compute the social relationship index (SRI)-factor in developing a novel clustering algorithm. Moreover, a frequency-separation (FS) distance between clusters is utilized to form the non-overlapping clusters. The FS distance aids in reducing the network interference. For further interference management and reliable data transfer, beamforming is also utilized over the clustered MWSN. Subsequently, an optimization problem is formulated to maximize the data throughput over the clustered MWSN with respect to antenna downtilt angle while accounting for the high-density mobile behavior of MSN. Finally, experiments are conducted to evaluate the performance of the proposed method over a time-varying MWSN. The obtained results demonstrate the effectiveness of the proposed when compared to the benchmark methods. The results also validate the utilization of the proposed method over medium and large-scale network applications.
WIDEBAND TRANSMITTING ADAPTIVE DIGITAL BEAMFORMING BASED ON SUB-BAND MULTIPLE LINEAR CONSTRAINED MINIMUM VARIANCE METHOD

This paper describes wideband transmitting adaptive digital beamforming (ADBF) scheme with nulls in the direction of interference. The scheme partitions the wideband transmit signal into independent sub-bands using an analysis filter bank behind each array element. In each channel, sub-band ADBF weight vector is computed based on the minimum variance criterion with multiple linear constraints to form the sub-band transmit beam. Finally, a wideband transmit adaptive beam is reconstructed through the synthesis filters. Theoretical analysis and simulation experiments show that this algorithm can form a wideband transmit beam with deep nulls, and the pointing direction of the null keeps unchanged regardless of frequency. The algorithm proposed in this paper has little computation load and is efficient to implement in engineering applications.
Scientific Methods for Evaluation of Organophosphorus and Carbamate Pesticide Residues in Fruits and Vegetables: A Review

: At present time uses of pesticides and insecticides are increased day by day and leads to contamination in food web and food chain. Excessive use of pesticides causes severe health problems in animals including microorganisms to human being. As a result of the use of too many pesticides, all the basic necessities of life such as air, water and food had become contaminated. Among various insecticides, organophosphorus and carbamate insecticides are derivatives of phosphoric acid and amino carboxylic acid, respectively. These are most commonly used in various fruits and vegetables. The ceaseless utilization of pesticides has made the malicious impacts on environment. Accordingly, various methods have been created by various administrative organizations and research centers and are applied regularly for the measurement and checking of pesticides residues in fruits and vegetables. The current study relates to different extraction and quantification methods used around the world to evaluate organophosphorus and carbamate residues in different fruit and vegetables
Age estimation algorithm based on deep learning and its application in fall detection

With the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimation of the human body, a complete priority detection of the key population, and a phased single aggregation backbone network VoVNetv4 was proposed for feature extraction. At the same time, the regional single aggregation module ROSA module was constructed to encapsulate the feature module regionally. The adaptive stage module was used for feature smoothing. Consistent predictions for each task were made using the CORAL framework as a classifier and tasks were divided in binary. At the same time, a gradient two-node fall detection framework combined with age estimation was designed. The detection was divided into a primary node and a secondary node. In the first-level node, the age estimation algorithm based on VoVNetv4 was used to classify the population of different age groups. A face tracking algorithm was constructed by combining the key point matrices of humans, and the body processed by OpenPose with the central coordinates of the human face. In the secondary node, human age gradient information was used to detect human falls based on the AT-MLP model. The experimental results show that compared with Resnet-34, the MAE value of the proposed method decreased by 0.41. Compared with curriculum learning and the CORAL-CNN method, MAE value decreased by 0.17 relative to the RMSE value. Compared with other methods, the method in this paper was significantly lower, with a biggest drop of 0.51.
Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges

Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The Scopus database was used to evaluate the relevant scientific literature in this field, followed by a bibliometric analysis to extract additional information, such as architecture type, country collaboration, and most cited papers. The findings highlight significant advancements in oil detection at sea, with a strong correlation between technological evolution in detection methods and improved remote sensing data acquisition. Multilayer perceptrons (MLP) emerged as the most prominent neural network architecture in 11 studies, followed by a convolutional neural network (CNN) in 5 studies. U-Net, DeepLabv3+, and fully convolutional network (FCN) were each used in three studies, demonstrating their relative significance too. The analysis provides insights into collaboration, interdisciplinarity, and research methodology and contributes to the development of more effective policies, strategies, and technologies for mitigating the environmental impact of oil spills in OSDMDL.
Hypoglycemic agents and incidence of pancreatic cancer in diabetic patients: a meta-analysis

Background and aims: Hypoglycemic agents are the primary therapeutic approach for the treatment of diabetes and have been postulated to impact pancreatic cancer (PC) incidence in diabetic patients. We conducted a meta-analysis to further evaluate and establish the associations between four common types of hypoglycemic agents [metformin, sulfonylureas, thiazolidinediones (TZDs), and insulin] and PC incidence in individuals with diabetes mellitus (DM). Methods: A comprehensive literature search of PubMed, Web of Science, Embase, and the Cochrane Library identified studies that analyzed the relationship between hypoglycemic agents and PC published between January 2012 and September 2022. Randomized control trials (RCTs), cohorts, and case–control studies were included if there was clear and evaluated defined exposure to the involved hypoglycemic agents and reported PC outcomes in patients with DM. Furthermore, reported relative risks or odds ratios (ORs) or other provided data were required for the calculation of odds ratios. Summary odds ratio estimates with a 95% confidence interval (CI) were estimated using the random-effects model. Additionally, subgroup analysis was performed to figure out the source of heterogeneity. Sensitivity analysis and publication bias detection were also performed. Results: A total of 11 studies were identified that evaluated one or more of the hypoglycemic agents, including three case–control studies and eight cohort studies. Among these, nine focused on metformin, six on sulfonylureas, seven on TZDs, and seven on insulin. Meta-analysis of the 11 observational studies reported no significant association between metformin (OR = 1.04, 95% CI 0.73–1.46) or TZDs (OR = 1.13, 95% CI 0.73–1.75) and PC incidence, while the risk of PC increased by 79% and 185% with sulfonylureas (OR = 1.79, 95% CI 1.29–2.49) and insulin (OR = 2.85, 95% CI 1.75–4.64), respectively. Considerable heterogeneity was observed among the studies and could not be fully accounted for by study design, region, or adjustment for other hypoglycemic agents. Conclusion: Sulfonylureas and insulin may increase the incidence of pancreatic cancer in diabetic patients, with varying effects observed among different ethnicities (Asian and Western). Due to significant heterogeneity across studies, further interpretation of the relationship between hypoglycemic agents and pancreatic cancer incidence in diabetic patients requires well-adjusted data and better-organized clinical trials.
The Neutron Electric Dipole Moment Experiment: Research and Development for the New Spectrometer

This work describes the search for novel storage materials towards an improved neutron electric dipole moment experiment (nEDM) and the measurement of neutron mirror neutron oscillations. A measurement of the nEDM, using Ramsey’s method of separated oscillatory fields, is in preparation at the new high intensity source of ultracold neutrons (UCN) at the Paul Scherrer Institute, Villigen, Switzerland (PSI). The existence of a non-zero nEDM result would violate both parity and time reversal symmetry and, given the CPT theorem, might lead to the discovery of new CP violating mechanisms. Already the current upper limit for the nEDM (|dn| < 2.9× 10−26 e cm) constrains some extensions of the Standard Model. Experiments using UCN stored in traps also provide the possibility of searching the neutron mirror neutron oscillations and, thus, testing the mirror matter hypothesis. The first direct measurement investigating that phenomena was performed at the Institut LaueLangevin (ILL) and a lower limit for the neutron mirror neutron oscillation time was obtained, τnn′ > 103 s (95% C.L.). Presently, both type of experiments are limited mostly by statistics, therefore increasing the number of neutrons available, becomes of much interest. The nEDM experiment at PSI aims at a two orders of magnitude reduction of the experimental uncertainty, to be achieved mainly by (1) the higher UCN flux provided by the new PSI source and better storage materials, (2) better control over systematic effects. The Monte Carlo simulations performed in this work show that moving the existing nEDM apparatus equipped with a quartz UCN trap from the ILL, where it is located, to the new PSI source will result in a factor of ∼20 gain in UCN counts and another factor of 1.5, when a material with a Fermi potential similar to deuterated polystyrene (DPS, VF ≈ 162 neV) is used instead of quartz (VF ≈ 90 neV). High electrical resistivity and dielectric strength are additionally required for the storage chamber wall material, in order to reach maximum HV operation. After an extensive experimental study including several other materials (e.g. Si3N4 and diamond), polystyrene was selected as the best insulator material, while DPS and deuterated polyethylene (DPE) turned out to be suitable for its UCN-reflecting inner coating. The measured values for the Fermi potentials are 161 ± 10 neV for DPS and 214 ± 10 neV for DPE. The new chamber was produced and successfully tested in the nEDM apparatus at ILL, yielding about 50% gain in the number of UCN, as compared to the quartz chamber, which had been used so far.
Relationship between serum biochemical parameters and kidney lesions in broiler chickens with acute tubular necrosis

To evaluate the effectiveness of biochemical factors such as blood urea nitrogen (BUN), creatinine, and serum uric acid in identifying and diagnosing acute tubular necrosis lesion in broilers, kidney tissue and blood samples from 80 broiler chickens at the slaughter stage were collected. Tissue samples were collected in 10% formalin to detect pathological lesions. The components of BUN, creatinine, and serum uric acid were measured with commercially available kits. After preparing the histopathological sections, based on the percentage of necrosis of the kidney tubules in each tissue sample, the pathological lesions were divided into 3 degrees: mild, moderate, and severe. The results showed that 36 samples out of 80 samples (45%) had acute tubular necrosis (ATN) lesions; 14 (17.5%), 12 (15%), and 10 (12.5%) samples had severe, moderate, and mild tubular necrosis lesions, respectively. The amount of uric acid in chickens with moderate and severe lesions showed a significant difference with the chickens without lesions and the mild lesion. However, BUN and creatinine increased significantly only in severe ATN lesions. In general, the results of this study state that severe lesions of renal tubules are associated with an increase in serum BUN, creatinine, and uric acid, but in moderate lesions, only uric acid may increase. Therefore, according to the results of this study, uric acid can be considered as a diagnostic biomarker in moderate to severe ATN.
Surface Modification of Gallium Arsenide by Electrochemical Methods in Different Electrolyte Compositions

We present the study of the n-GaAs surface modification by the electrochemical etching in different electrolyte compositions. The possibility of forming the different micromorphology types on the identical GaAs samples, in particular the possibility of forming the crystallographic, defective-dislocation, and isotope interfaces, was investigated.
An innovative method for offshore wind farm site selection based on the interval number with probability distribution

ABSTRACT There is insufficient research relating to offshore wind farm site selection in China. The current methods for site selection have some defects. First, information loss is caused by two aspects: the implicit assumption that the probability distribution on the interval number is uniform; and ignoring the value of decision makers’ (DMs’) common opinion on the criteria information evaluation. Secondly, the difference in DMs’ utility function has failed to receive attention. An innovative method is proposed in this article to solve these drawbacks. First, a new form of interval number and its weighted operator are proposed to reflect the uncertainty and reduce information loss. Secondly, a new stochastic dominance degree is proposed to quantify the interval number with a probability distribution. Thirdly, a two-stage method integrating the weighted operator with stochastic dominance degree is proposed to evaluate the alternatives. Finally, a case from China proves the effectiveness of this method.
How do online travel reviews facilitate informed purchase decisions?

ABSTRACT We analysed the attributes of online travel reviews that facilitate online travel purchasing decisions. In Study 1, we developed and validated a scale for measuring the perceived value of online travel reviews (PVOTR). The PVOTR scale has six dimentions and was based on a literature review and Fuzzy Delphi Method. It was validated with data from 69 undergraduate students and 485 travelers in Taiwan. In Study 2, we examined the impact of PVOTR on the online travel purchasing attitudes of another 469 travelers in Taiwan. PVOTR significantly affected online travel purchasing attitudes through the mediation of reduced perceived complexity.
Phytofabrication of Selenium Nanoparticles with Moringa oleifera (MO-SeNPs) and Exploring Its Antioxidant and Antidiabetic Potential

The advancement in nanotechnology is the trigger for exploring the synthesis of selenium nanoparticles and their use in biomedicine. Therefore, this study aims to synthesize selenium nanoparticles using M. oleifera as a reducing agent and evaluate their antioxidant and antidiabetic potential. Our result demonstrated a change in the color of the mixture from yellow to red, and UV-Vis spectrometry of the suspension solution confirmed the formation of MO-SeNPs with a single absorbance peak in the range of 240–560 nm wavelength. FTIR analysis revealed several bioactive compounds, such as phenols and amines, that could possibly be responsible for the reduction and stabilization of the MO-SeNPs. FESEM + EDX analysis revealed that the amorphous MO-SeNPs are of high purity, have a spherical shape, and have a size of 20–250 nm in diameter, as determined by HRTEM. MO-SeNPs also exhibit the highest DPPH scavenging activity of 84% at 1000 μg/mL with an IC50 of 454.1 μg/mL and noteworthy reducing ability by reducing power assay. Furthermore, MO-SeNPs showed promising antidiabetic properties with dose-dependent inhibition of α-amylase (26.7% to 44.53%) and α-glucosidase enzyme (4.73% to 19.26%). Hence, these results demonstrated that M. oleifera plant extract possesses the potential to reduce selenium ions to SeNPs under optimized conditions with notable antioxidant and antidiabetic activities.
Revisiting Classifier Two-Sample Tests for GAN Evaluation and Causal Discovery

The goal of two-sample tests is to assess whether two samples, $S_P \sim P^n$ and $S_Q \sim Q^m$, are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the $n$ examples in $S_P$ with a positive label, and by pairing the $m$ examples in $S_Q$ with a negative label. If the null hypothesis "$P = Q$" is true, then the classification accuracy of a binary classifier on a held-out subset of this dataset should remain near chance-level. As we will show, such Classifier Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly, return test statistics in interpretable units, have a simple null distribution, and their predictive uncertainty allow to interpret where $P$ and $Q$ differ. The goal of this paper is to establish the properties, performance, and uses of C2ST. First, we analyze their main theoretical properties. Second, we compare their performance against a variety of state-of-the-art alternatives. Third, we propose their use to evaluate the sample quality of generative models with intractable likelihoods, such as Generative Adversarial Networks (GANs). Fourth, we showcase the novel application of GANs together with C2ST for causal discovery.