Miracle-dz/UniFAR-FTUs huggingface dataset:
Price Dispersion in Wines

We investigate price dispersion in restaurant sales of wine. We measure empirically the change in price dispersion as competition varies, finding that price dispersion increases with competition. We then propose a spatial model capturing the unique features of restaurant dining to explain this empirical regularity. The model reveals an indirect effect flowing through the price of food, which is announced and known by customers, to the price of wine, which is only revealed once the customer enters the restaurant and receives the wine list. The results add to the literature on “add ons” and price dispersion in bundled good markets. JEL Classification: L13, L90, L11
Automated Diabetic Retinopathy Detection Using Transfer Learning Models

Diabetic retinopathy (DR) is one of the most leading symptoms of vision-loss globally. Early detection and screening can halt its progression. Until date, ophthalmologists have manually screened DR, however, DR detection can be difficult in low-resource areas when there are few ophthalmologists available. Deep learning has recently been one of the most popular strategies for improving performance in a variety of fields, particularly medical image analysis and classification. It can be used to more effectively detect DR and so maintain vision. Transfer learning models are becoming increasingly commonly employed as a deep learning method, and they are quite effective. Two public datasets which contain 1115 retinal fundus images are used in this research. Our research proposed a binary classification of DR, which is done with five Transfer learning models Xception, InceptionResNetV2, MobileNetV2, DenseNet121, and NASNetMobile which achieved the highest validation accuracy of 86.25%, 96.25%, 93.75%, 81.25%, and 80.00% respectively.
Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.
CNN Based COVID-19 Prediction from Chest X-ray Images

Coronavirus disease COVID-19 is an infectious disease caused by a newly discovered coronavirus. COVID-19 virus affects the respiratory system of healthy individuals. Chest X-ray is one of the important imaging methods to identify the coronavirus. In deep learning, a convolutional neural network (CNN), is a class of deep learning models, most commonly applied for better outcomes to analyzing visual imagery. Automated covid-19 using Deep Learning techniques could, therefore, serve as an effective diagnostic aid. In this study, we used a convolutional neural network (CNN) for detecting COVID-19 from chest X-ray images. The overall project comprises various convolutional layers. The Max-pooling layers diminish the size of the picture significantly and by joining convolutional and pooling layers, the net is able to combine its features to learn more global features of the Image. Eventually, we utilize the highlights in two completely associated (Dense) layers. Dropout is a regularization strategy, where the layer arbitrarily replaces an extent of its weights to zero for each training sample. This forces the net to learn features in an appropriate way, not depending a lot on specific weight, and thus improves speculation and 'relu' is the activation function. Applying convolutional neural network which is a Deep Learning algorithm that can take in an input image, relegate significance to different perspectives in the images and have the option to separate one from the other.
An experimental study on the influence of tool path, tool diameter and pitch in single point incremental forming (SPIF)

Abstract Incremental forming process has become more and more popular because of its ease of forming and capability to produce different part shapes using same machine set-up. This article describes an experimental study on the influence of process parameters in single point incremental forming (SPIF) on the surface roughness and thickness of formed part. Experiments are performed on aluminium alloy (Al-1050) sheet on a 3-axis CNC milling machine. Hemispherical-headed tool of stainless steel (SS-304) is used to study the effect of tool path, tool diameter and pitch on the surface roughness (Ra) and thickness (t) of formed part. Design of experiment (DOE) full factorial plan is used, and results are plotted using ANOVA. Considerable effect of tool diameter is found on the surface quality of formed part.
MODIFIED SPOKES WHEEL SHAPED MIMO ANTENNA SYSTEM FOR MULTIBAND AND FUTURE 5G APPLICATIONS: DESIGN AND MEASUREMENT

In this manuscript, a modified spokes wheel shaped two port MIMO (Multi-Input-MultiOutput) antenna with stub loaded ground plane has been presented and experimentally analysed for multiband and EU (European Union) 5900 to 6400MHz for future 5G mobile terminal applications. The proposed MIMO antenna consists of two radiating patches, and its ground plane is modified to achieve the multiband characteristics as well as enhanced isolation. Initially, a rectangular notch, at the center of ground plane (Ground-1), is employed and reveals four resonant points. Further, the ground plane is modified again by employing two inverted L-shaped stubs along with a series of horizontal rectangular stubs (Ground-2) for enhancing the isolation and reducing the mutual coupling between the elements of proposed MIMO antenna. The antenna with ground-2 exhibits seven frequency bands (S11 ≤ −10 dB) 2.2, 6.0, 7.9, 9.6, 11.1, 12.7, and 15.6GHz with corresponding isolation (S12/21) −19.47, −31.22, −34.63, −30.05, −27.16, −39.08, and −22.28 dB. Diversity performance parameters of the proposed MIMO antenna such as ECC, DG, CCL, TARC, and MEG are in acceptable limits at each operational frequency band. The proposed MIMO antenna is designed and fabricated on a low cost FR4 glass epoxy substrate, and the simulations are carried out by using FEM based Ansys HFSS V13 simulator. Simulated and measured results are compared and found in good agreement with each other.
An examination of the heterogenous nature of literacy development in Spanish-speaking preschool children

ABSTRACT This study evaluated the emergent literacy profiles of preschool Spanish-speaking children. Participants included 440 children in dual-language classrooms who were assessed in English and Spanish at the beginning and end of their preschool year. Research Findings: Through latent profile analysis, conducted with fall data, four empirically derived emergent literacy profiles emerged. Further analyses were conducted to determine if there were differential relations between the literacy profiles and spring early reading measures of alphabet knowledge and oral language, measures that are typically used to evaluate kindergarten readiness for literacy instruction. Practice or Policy: Results highlight the heterogeneous nature of emergent literacy development in Spanish-speaking children and demonstrate the need for individualized literacy instruction during the preschool years.
Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective

(1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990–2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21–25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis.
Use of Open Source DIC Tools for Analysis of Multiple Cracking in Fiber-Reinforced Concrete

Digital image correlation (DIC) became indispensable when monitoring and analyzing adevelopment of displacement or strain fields. The method is capable of capturing strain localization, itis not limited to a relative measurement of discrete points as conventional methods and appears to bemore accurate than measurements by means of extensometers or strain-gauges that often suffer fromimperfect attachment to the measured surface. As open source DIC tools appear, the method becomesfeasible and the development is supported by the growing computational power of modern computers.The presented paper introduces open source 2D DIC tools and simple rules to follow when employingthe method. The presented case study on behavior of fiber reinforced high-performance concretedemonstrates a computational feasibility, accuracy and sensitivity of the method for a relatively lowfinancial cost.
COVID-19 and the Grey Zone of Rule-Following

The paper deals with the phenomenon of the grey zone of rule-following — actions that may be perceived as both corresponding to some rule and as breaking this rule. The pandemic of COVID-19 brought the grey zone into relief because a significant part of the responses to imposed anti-COVID measures consists in following new rules less than completely, with the typical example being a lowered mask that covers only the mouth and not the nose. It is argued here that grey-zone actions, if viewed as public activities, have specific spatial and temporal social organization: they are designed to be flexible and oriented toward the possibility of completing them if necessary. At the same time, they are produced to be observably accountable as actions-according-to-the-rule, to prevent an attribution to the actor rule-breaking. The paper also describes some properties of situations where grey-zone actions produce tension, forcing the actor and other participants to initiate an argument or a conflict. The main point of the paper is that performing actions belonging to the grey zone of rule-following does not testify to the actor’s non-observance of the rule. It is better to describe grey-zone actions as rule-oriented and not rule-following or not-following. This suggests that social scientists should abandon dichotomic approach when analyzing rule-following activities, and pay more attention to the participants’ own practices of making sense and order of rules.
5G/B5G Internet of Things MIMO Antenna Design

The current and future wireless communication systems, WiFi, fourth generation (4G), fifth generation (5G), Beyond5G, and sixth generation (6G), are mixtures of many frequency spectrums. Thus, multi-functional common or shared aperture antenna modules, which operate at multiband frequency spectrums, are very desirable. This paper presents a multiple-input and multiple-output (MIMO) antenna design for the 5G/B5G Internet of Things (IoT). The proposed MIMO antenna is designed to operate at multiple bands, i.e., at 3.5 GHz, 3.6 GHz, and 3.7 GHz microwave Sub-6 GHz and 28 GHz mm-wave bands, by employing a single radiating aperture, which is based on a tapered slot antenna. As a proof of concept, multiple tapered slots are placed on the corner of the proposed prototype. With this configuration, multiple directive beams pointing in different directions have been achieved at both bands, which in turn provide uncorrelated channels in MIMO communication. A 3.5 dBi realized gain at 3.6 GHz and an 8 dBi realized gain at 28 GHz are achieved, showing that the proposed design is a suitable candidate for multiple wireless communication standards at Sub-6 GHz and mm-wave bands. The final MIMO structure is printed using PCB technology with an overall size of 120 × 60 × 10 mm3, which matches the dimensions of a modern mobile phone.
2-D Intermittent Sampling Repeater Jamming to SAR Based on Convolution Modulation

2-D intermittent sampling repeater jamming to SAR can form multi-false targets in range and azimuth,so it can get good deceptiveness jamming effection,but it exists the common defect of intermittent sampling repeater jamming. In this paper a new method of 2-D intermittent sampling repeater jamming to SAR based on convolution modulation is proposed. The jammer receives the SAR signal and intermittent sample the singnal in 2-D,then convolves it in 2-D with the expected video frequency signal. By choosing the type of convolution modulation signal,it can flexible and controled jamming effection,and it can overcome the defect of intermittent sampling repeater jamming.Theoretical analysis and computer simulation justify the validity and efficiency of this method.
The Periplasmic Oxidoreductase DsbA Is Required for Virulence of the Phytopathogen Dickeya solani

In bacteria, the DsbA oxidoreductase is a crucial factor responsible for the introduction of disulfide bonds to extracytoplasmic proteins, which include important virulence factors. A lack of proper disulfide bonds frequently leads to instability and/or loss of protein function; therefore, improper disulfide bonding may lead to avirulent phenotypes. The importance of the DsbA function in phytopathogens has not been extensively studied yet. Dickeya solani is a bacterium from the Soft Rot Pectobacteriaceae family which is responsible for very high economic losses mainly in potato. In this work, we constructed a D. solani dsbA mutant and demonstrated that a lack of DsbA caused a loss of virulence. The mutant bacteria showed lower activities of secreted virulence determinants and were unable to develop disease symptoms in a potato plant. The SWATH-MS-based proteomic analysis revealed that the dsbA mutation led to multifaceted effects in the D. solani cells, including not only lower levels of secreted virulence factors, but also the induction of stress responses. Finally, the outer membrane barrier seemed to be disturbed by the mutation. Our results clearly demonstrate that the function played by the DsbA oxidoreductase is crucial for D. solani virulence, and a lack of DsbA significantly disturbs cellular physiology.
Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica

Convolutional neural networks (CNNs) are becoming an increasingly popular approach for classification mapping of large complex regions where manual data collection is too time consuming. Stream boundaries in hyper-arid polar regions such as the McMurdo Dry Valleys (MDVs) in Antarctica are difficult to locate because they have little hydraulic flow throughout the short summer months. This paper utilizes a U-Net CNN to map stream boundaries from lidar derived rasters in Taylor Valley located within the MDVs, covering ∼770 km2. The training dataset consists of 217 (300 × 300 m2) well-distributed tiles of manually classified stream boundaries with diverse geometries (straight, sinuous, meandering, and braided) throughout the valley. The U-Net CNN is trained on elevation, slope, lidar intensity returns, and flow accumulation rasters. These features were used for detection of stream boundaries by providing potential topographic cues such as inflection points at stream boundaries and reflective properties of streams such as linear patterns of wetted soil, water, or ice. Various combinations of these features were analyzed based on performance. The test set performance revealed that elevation and slope had the highest performance of the feature combinations. The test set performance analysis revealed that the CNN model trained with elevation independently received a precision, recall, and F1 score of 0.94±0.05, 0.95±0.04, and 0.94±0.04 respectively, while slope received 0.96±0.03, 0.93±0.04, and 0.94±0.04, respectively. The performance of the test set revealed higher stream boundary prediction accuracies along the coast, while inland performance varied. Meandering streams had the highest stream boundary prediction performance on the test set compared to the other stream geometries tested here because meandering streams are further evolved and have more distinguishable breaks in slope, indicating stream boundaries. These methods provide a novel approach for mapping stream boundaries semi-automatically in complex regions such as hyper-arid environments over larger scales than is possible for current methods.
Robust control of heterogeneous vehicular platoon with uncertain dynamics and communication delay

Platoon formation of highway vehicles has the potential to significantly enhance road safety, improve highway utility, and increase traffic efficiency. However, various uncertainties and disturbances that are present in real-world driving conditions make the implementation of vehicular platoon a challenging problem. This study presents an H-infinity control method for a platoon of heterogeneous vehicles with uncertain vehicle dynamics and uniform communication delay. The requirements of string stability, robustness and tracking performance are systematically measured by the H-infinity norm, and explicitly satisfied by casting into the linear fractional transformation format. A delay-dependent linear matrix inequality is derived to numerically solve the distributed controllers for each vehicle. The performances of the controlled platoon are theoretically analysed by using a delay-dependent Lyapunov function which includes a linear quadratic function of states during the delay period. Simulations with a platoon of heterogeneous vehicles are conducted to demonstrate the effectiveness of the proposed method under random parameters and external disturbances.
Classroom Assessment Practices in Malaysian Primary Schools: A Meta-Analysis

One of the assessment processes used by teachers to help students improve is classroom assessment. Teachers will determine the extent to which students comprehend the topic of learning through classroom assessment. However, issuing implementation instructions in a short period might lead to a range of problems. As a result, the challenges, methodology, and conclusions of prior research on classroom assessment practise in Malaysian primary schools are examined in this paper. This study uses a meta-analysis design and a secondary study form to identify, explore, and interpret all relevant studies related to a topic domain. The review will only include studies conducted between 2020 and 2021 due to a recent change in assessment practices in Malaysian primary schools. A single database, Google Scholar, was used to find relevant literature and keywords were used to find the article. The three criteria used to select articles for analysis were researched on classroom assessment, data collection, and research completed within the last two years. Finally, 12 articles were chosen for metaanalysis based on a review of the literature. There are five qualitative studies and seven quantitative studies included. The findings revealed inconsistencies in Malaysian primary school teachers' reports about their readiness to understand classroom assessment. Previous research on Malaysian classroom assessment practices, on the other hand, used credible methodologies because each study examined a different aspect of the subject. In the future, researchers should concentrate their efforts on developing an instrument that meets validity and reliability criteria, resulting in more reliable data.
4G LTE-Advanced Network Planning and Simulation Study in an Urban Region Utilizing the 700 MHz Frequency

Spectrum scarcity is one of the challenges faced by operators in Indonesia. As a result, research needs to be conducted using 700 MHz. However, only a tiny number of research has been conducted on the 700 MHz band's performance in urban areas. The purpose of this study is to determine the feasibility of implementing the 700 MHz spectrum in an urban region, specifically North Jakarta, by analyzing capacity and coverage planning and simulating network deployment using Forsk Atoll software. According to the planned calculations, North Jakarta requires 179 eNodeB sites. Through simulation, the Reference Signal Received Power (RSRP) value is −40.41 dBm, the throughput is 11.11 Mbps, the radio bearer is 4.78 with a 1.48 efficiency, and the Signal Interference Noise Ratio (SINR) is 1.61 dB. These findings show that deploying 700 MHz for a 4G LTE-Advanced network in the North Jakarta region can relieve the frequency spectrum scarcity issue.