Miracle-dz/UniFAR-FTUs huggingface dataset:
Chipless RFID Humidity Sensor Based on Rectangular-Stub-Loaded SRR

A chipless RFID humidity sensor based on a rectangular-stub-loaded SRR is proposed to solve the problems of considerable size, complex computing circuit, high cost, dependence on the power supply, and limited lifetime of conventional humidity sensors. The resonant unit of this sensor is improved by loading the rectangular stub at the opening of the open rectangular ring, which has a high-quality factor and good resonance characteristics. Changes in ambient humidity affect the polyvinyl alcohol (PV A) material layer coated on the surface of the sensing unit, which in turn changes the resonant frequency of the sensing unit and ultimately realizes humidity sensing. The hybrid coding technology of frequency position and amplitude modulation is used in the coding, and the coding unit consisting of five resonant units is capable of coding 10 bits of identity information, which significantly improves the coding capacity of the sensor and further miniaturizes the sensor. The dimensions of the sensor are 19x52x0.762mm3. Under the experimental environment of 50%-80% relative humidity, the operating band of the sensor is 3.54~4.95GHz, and the average moisture sensitivity reaches 6.6MHz/%RH.
Query by Image and Video Content: The QBIC System

Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. >
Person Identification using Periocular Region

The world has been affected by the corona virus epidemic, which soon turned into a pandemic. The need for face masks and social isolation highlight the need for contact-free biometric authentication for all future systems. Periocular biometric, which does not require physical contact is a solution in such scenarios because it can detect people wearing face masks. The term "periocular region" describes the region around the eye, which includes a variety of elements such the sclera, eyelids, lashes, brows, and skin. Our method involves extraction of the region of interest by cropping the periocular region of every facial image. CLAHE is then performed on each periocular image extracted. For feature extraction, VGG16 and ResNet50 CNN models are employed. KNN classifiers are used for classification of the image. Our experiments demonstrate that the proposed approach achieves an accuracy of 80% with ResNet50 and 75% with VGG16 on a challenging dataset consisting of 24 subjects with significant variations in pose, illumination, and expression. The proposed approach offers a promising solution for noninvasive and contactless biometric identification, which can have broad applications in security and surveillance systems.
Foxp3+ Regulatory T Cells Are Required for Recovery from Severe Sepsis

The role of regulatory T cells (Tregs) in bacterial sepsis remains controversial because antibody-mediated depletion experiments gave conflicting results. We employed DEREG mice (DEpletion of REGulatory T cells) and a caecal ligation and puncture model to elucidate the role of CD4+Foxp3+ Tregs in sepsis. In DEREG mice natural Tregs can be visualized easily and selectively depleted by diphtheria toxin because the animals express the diphtheria toxin receptor and enhanced green fluorescent protein as a fusion protein under the control of the foxp3 locus. We confirmed rapid Treg-activation and an increased ratio of Tregs to Teffs in sepsis. Nevertheless, 24 h after sepsis induction, Treg-depleted and control mice showed equally strong inflammation, immune cell immigration into the peritoneum and bacterial dissemination. During the first 36 h of disease survival was not influenced by Treg-depletion. Later, however, only Treg-competent animals recovered from the insult. We conclude that the suppressive capacity of Tregs is not sufficient to control overwhelming inflammation and early mortality, but is a prerequisite for the recovery from severe sepsis.
Distributed Association Rules Mining Based on Pruned Concept Lattices

The common association rules mining methods in multiple databases were inefficient due either to larger amount of candidate itemsets for communication overhead or higher times of database scan. Based on discussing the relation between the concept of pruned concept lattice and the representation of frequent itemsets, the closed frequent itemsets of pruned concept lattice was defined. UMPCL, an approximately association rules mining method in horizontally partitioned databases based on multiple PCLs, was proposed. The main ideas of this method are using a frequent concept to represent some few of frequent itemsets to decrease the number of frequent itemsets and rules, and using a slightly lower support for pruning concept lattices before been merged to decrease the size of exchanged messages. The theoretic analysis and experiment show that such method is efficient.
The elusive goal of workload characterization

The study and design of computer systems requires good models of the workload to which these systems are subjected. Until recently, the data necessary to build these models---observations from production installations---were not available, especially for parallel computers. Instead, most models were based on assumptions and mathematical attributes that facilitate analysis. Recently a number of supercomputer sites have made accounting data available that make it possible to build realistic workload models. It is not clear, however, how to generalize from specific observations to an abstract model of the workload. This paper presents observations of workloads from several parallel supercomputers and discusses modeling issues that have caused problems for researchers in this area.
Research on Transductive Few-Shot Image Classification Methods Based on Metric Learning

In fields such as medical image analysis, autonomous driving, and military reconnaissance, obtaining a large number of shots is difficult or costly, resulting in a limited amount of data. Therefore, researching and addressing the challenge of classifying images with limited training shots is of significant practical importance. Metric learning approaches have demonstrated their effectiveness. Recently, the Ensemble Augmented-Shot Y -shaped Learning (EASY) algorithm has achieved promising classification results, consisting of a feature extraction segment and a classifier segment. Backbone of the feature extraction segment is ResNet-12, a lightweight general image classification network suitable for few-shot image classification. However, its low parameter count leads to limited global perception capability, and it lacks task-specific sensitivity, making it less effective in discriminating fine-grained details. To overcome these constraints, this study introduces an innovative feature extraction network called SEYNet, which incorporates SE attention mechanism into the ResNet-12 backbone. This integration brings global context information into the feature extraction process, aiding in a better understanding of semantic information across the entire image. By enhancing the weights of specific channels, SEYNet captures subtle differences between shots and extracts more discriminative features. Additionally, the paper adopts an unsupervised discriminant subspace learning method to learn the similarity among features of the support set and unlabeled query set, and then performs linear projection to improve the performance of transductive few-shot image classification. Compared to EASY, the proposed method achieves improved classification accuracy of 2.36% and 1.65% in scenarios of 5-way 1-shot and 5-way 5-shot on FC100 dataset. The outcomes of the experiments demonstrate that the proposed method effectively enhances classification accuracy.
Consistently Estimating Internal Climate Variability from Climate Model Simulations

AbstractThis paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multi-model ensemble. The method regresses each model’s estimate of internal variability from the preindustrial control simulation on the variability derived from a model’s ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multi-model consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2 forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics and polar regions and extremely likely decreases in mid-latitudes. Applying the method to sea-ice volume and area shows tha.
Does it work everywhere? Group Model Building as participative method in intercultural perspective

Group Model Building (GMB) is a type of facilitated modeling, in which the input of the participants to structuring a complex problem is crucial. There is a high level of participant interaction and involvement. The method focuses on open communication between participants to gain insight in complex problem and to foster consensus. Herewith the facilitators aim to create commitment to the results and proposed leverages for change. However, GMB was developed in the Netherlands and the United States, and therefore also mainly implemented there. These countries are characterized by a small power distance and a large acceptance of participative ways of working. The question is whether the GMB method also works in contexts with a large power distance and where participative ways of working are less common. This paper aims to contribute to knowledge about the role of power distance as cultural context in the efficiency of GMB, by comparing the results of GMB interventions in various countries which differ in terms of national power distance. Our results show that the GMB effects on communication, learning and consensus are comparable in different cultural contexts. Participants from countries characterized by high power distance experience strongest contributions of the intervention to commitment.
Risk and resilience management in cultural heritage

Objectives. To verify the organisational strategies for the risk and resilience management after catastrophic events. The question is: which strategical approaches can be applied to cultural heritage for disasters risk management and for the resilience of damaged or destroyed heritage ? Methodology. Analysis of main existing approaches and verification of their adequacy to the specific field that from the recovering of the previous situation to catastrophic events like earthquakes, tsunamis or floods, today it is extending to historical buildings, monuments and museums destruction as the consequence of iconoclastic positions and movements. Findings. The paper presents and discusses two strategic organisational approaches, of Clustering and Networking, addressed to face catastrophic events in the field of cultural heritage relating the strategy to the evolution grade of institutions dedicated to cultural heritage. Research limits. The fast evolution of the context and of the cases number in which verifying the proposed approaches. Practical implications. Adjustment of intervention strategies to the dimensions of the phenomenon and to the evolution of institutions operating in the field of cultural heritage in case of catastrophes intentionally produced by mankind. Originality of the study. As there still are no models of risk and resilience management in cultural heritage with regard to disasters intentionally produced by mankind, the paper provides a theoretical frame for the creation and verification of possible models applicable to cultural heritage.
EpiMap: towards quantifying contact networks and modelling the spread of infections in developing countries

We describe the EpiMap project, in which mobile phones and sensors record the proximity of other devices, to gather information on human interactions within the rural communities of developing countries. Collected information will be used to develop improved mathematical models of the spread of infectious diseases, such as measles, tuberculosis and pneumococcal diseases. Modelling will be complemented by the use of surveys to aid in the understanding of living conditions in these villages. EpiMap is an extension of the FluPhone project, which we carried out in 2010. FluPhone collected data on human interaction, by using mobile phones to record information such as locality and user symptoms. Delay tolerant opportunistic networks such as the Haggle framework [5] were used as a basis for communication. We introduce the EpiMap vision for a system of opportunistic networks combined with satellite communication, designed to face the challenges posed by weak power and communications infrastructure in the rural regions of developing countries in Asia, Africa and South America. We aim to use a delay-tolerant small satellite for data transfer between developing countries and Europe and North America. Data collected through EpiMap can be used to help design more efficient vaccination strategies and equitable control programmes.
Cavity Backed Cross Dipole Antenna With Wide Axial Ratio Beamwidth and High Efficiency

This paper presents the design and realization of broadband and wide-beam right hand circularly polarized (RHCP) cross dipole antenna. The crossed dipole is backed by a conical shaped cavity to achieve improved axial ratio beam-width over a wide frequency band with enhanced gain. The design incorporates a conventional feeding technique using a coaxial probe. A sleeve is designed which is soldered with the cable to ensure strength for the cable. The fabricated prototype of the proposed antenna has an overall size of $0.87 \lambda \mathrm{X} 0.87 \lambda \mathrm{X} 0.38\lambda$ ($\lambda$ is the free-space wavelength at the lowest CP operating frequency). The obtained impedance bandwidth (RL >15dB) is 18% (2-2.4GHz) and axial ratio bandwidth (AR <3dB) is 9%. The axial ratio (AR <3dB) beam-width achieved overall phi planes is 120° for 5.5% bandwidth. Efficiency achieved is above 90% for complete bandwidth.
An Effective Method for the Compression of Images Used in Virtual Environments

This research paper introduces a concept for photo compression that utilises the spatial redundancy approach. The proposed method use the discrete cosine transform technique with various block sizes, specifically 8x8, 16x16, 32x32, and 64x64. The process of quantization can be achieved by establishing a set of threshold values at various distances. Due to these aforementioned considerations, the implementation of this strategy is straightforward, requiring less memory usage and may be completed in a shorter duration. The utilization of the Discrete Cosine Transform (DCT) methodology was employed to develop and execute a compression approach for virtual graphics through the utilization of MATLAB. The calculation of the compression ratio and peak signal-to-noise ratio was performed for each compressed block to get the ideal results. In order to enhance the analysis of the collected data, utilized the metrics of Compression Ratio (CR) and Peak Signal-to-Noise Ratio (PSNR), in conjunction with software tools that are applicable across several sectors.
Using a Machine Learning Algorithm and Fundus Images to Treat Diabetic Retinopathy

Diabetes-related complications include diabetic retinopathy (DR), a condition that damages the eyes. Pictures of the retinal fundus can be used to find it. Traditional methods for processing photos have several limitations when it comes to analyzing data and hiding patterns. In recent years, deep neural network have to be effectively used in a variety of computer vision tasks. On the openly avatlable retinal fundus picture available on Kaggle dataset, we have to apply the pre-trained images in Convolutional Neural Network (CNN), MobileNetV1 and VGG16 in this research. In favor of to get larger and clear the of feature extraction, we have used pre-processing and augmentation approaches. 35,126 retinal eye funds images from the Kaggle dataset with five labelsof class are used in the experiment. OnEary trained MobileNet and V1VGG16 with learning are transferred and Tuning clearly, training accuracy was 91.42% and 91.84%, respectively. VGG16 and MobileNetV1 both achieved test accuracy of 91.51% and 91.77%, respectively.
Quality of Piped and Stored Water in Households with Children Under Five Years of Age Enrolled in the Mali Site of the Global Enteric Multi-Center Study (GEMS)

Water, sanitation, and hygiene information was collected during a matched case-control study of moderate and severe diarrhea (MSD) among 4,096 children < 5 years of age in Bamako, Mali. Primary use of piped water (conditional odds ratio [cOR] = 0.45; 0.34–0.62), continuous water access (cOR = 0.30; 0.20–0.43), fetching water daily (cOR = 0.77; 0.63–0.96), and breastfeeding (cOR = 0.65; 0.49–0.88) significantly reduced the likelihood of MSD. Fetching water in > 30 minutes (cOR = 2.56; 1.55–4.23) was associated with MSD. Piped tap water and courier-delivered water contained high (> 2 mg/L) concentrations of free residual chlorine and no detectable Escherichia coli. However, many households stored water overnight, resulting in inadequate free residual chlorine (< 0.2 mg/L) for preventing microbial contamination. Coliforms and E. coli were detected in 48% and 8% of stored household water samples, respectively. Although most of Bamako's population enjoys access to an improved water source, water quality is often compromised during household storage.
Demystifying Fake News Detection Methods

The developing subject of study known as fake news has piqued the curiosity of both academics and media practitioners. Fake news has become more prevalent in the digital age, highlighting how long-standing institutional safeguards against false information are undermined. Governmental organizations use social media to distribute critical information through their verified Facebook profiles, and official Twitter handles, as the information reaches many people quickly. However, a variety of deceptive practices, such as propaganda and rumors, have the potential to trick consumers on a regular basis. During COVID, fake news and rumors were particularly prevalent and easily disseminated, which caused confusion in those trying times. Therefore, given the current times, there is a dire need for applications to detect fake news. This paper reviews recent studies on various methods for spotting fake news on the Internet. It covers traditional techniques and cutting-edge innovations employing machine learning methods, mobile apps and websites for identifying bogus news. This review paper provides a comprehensive study by giving directions towards future research prospects to the researchers studying in this field of study.
AwareLESS authentication: insensible input based authentication

To increase the security of handheld devices, we propose awareLESS authentication. Since insensible input prevents the leakage of the key information, it can provide more secure authentication scheme. Experiments that used a pressure sensor show that users can input a preset rhythm by insensible finger motion, and the boundary between insensible and sensible is extended by adding vibration while input.
Concentration and Viability of Airborne Bacteria Over the Kuroshio Extension Region in the Northwestern Pacific Ocean: Data From Three Cruises

Airborne bacteria have been shown to act as condensation and ice nuclei in mixed‐phase clouds and are consequently hypothesized to have significant effects on atmospheric processes and even the global climate. However, few data are available regarding their concentration and variation in the air over the open ocean. Aerosol samples were collected during three cruises in the early summers of 2013, 2014, and 2016 over the Kuroshio Extension region of the northwest Pacific Ocean. The concentrations of viable and nonviable bacterial cells in the marine surface air were quantified using epifluorescence enumeration with the LIVE/DEAD BacLight stain. The concentrations of total bacteria varied between 1.0 × 104 and 2.5 × 105 cells m−3 and averaged 5.2 × 104, 1.0 × 105, and 7.5 × 104 cells m−3 in the three respective cruises. The viabilities, i.e., the ratios of the concentration of viable bacterial cells to that of total bacterial cells, ranged from 80% to 100% (average 93%), and the respective means were 93%, 89%, and 96% in the cruises. The total bacterial concentration had a close correlation with the wind speed near the sea surface, and the bacterial viability correlated negatively with the air temperature, sea surface temperature, and concentration of coarse particles (size > 1 μm). The deposition and sea spray fluxes of bacteria were roughly estimated as hundreds of cells m−2 s−1 on average. The limited data on bacterial concentration and viability from the three cruises indicate the rapid air‐sea exchange of bacteria over the Kuroshio Extension region of the northwest Pacific Ocean.
Association of breast cancer outcome with status of p53 and MDM2 SNP309.

BACKGROUND A common single-nucleotide polymorphism (SNP) in the promoter region of the MDM2 gene, known as T-309G and referred to as SNP309 for this study, leads to increased expression of Mdm2 protein and attenuated function of the p53 tumor suppressor protein. We investigated whether genetic variants in MDM2 were associated with breast cancer incidence and survival and whether the variant status could interact with the tumor p53 status to modify breast cancer survival. METHODS We used multivariable logistic and Cox regression analyses to study the relationship of SNP309 status and the status of a second MDM2 SNP in exon 12 at codon 354 (SNP354) with breast cancer incidence and with disease-specific survival among 293 case patients and 317 cancer-free control subjects. Survival analysis included 248 of the 293 case patients who had known tumor p53 status. All statistical tests were two-sided. RESULTS We did not observe an association between SNP309 status and breast cancer incidence in the unstratified analysis, but we did find a statistically significant association between SNP354 status and breast cancer incidence (odds ratio = 3.34, 95% confidence interval [CI] = 1.88 to 5.93). We also discovered a statistically significant interaction between SNP309 status and tumor p53 expression for breast cancer survival (P(interaction) = . 002). Among homozygous carriers of the common MDM2 SNP309 allele (T/T), a mutant p53 status (risk ratio [RR] of death = 2.33, 95% CI = 1.08 to 5.03) and aberrant p53 protein expression (RR = 2.61, 95% CI = 1.22 to 5.57) in breast tumors were associated with poor survival. Tumor p53 status was not associated with breast cancer survival among carriers of the variant MDM2 SNP309 allele (G/T or G/G), which is consistent with a dominant effect of the variant allele. CONCLUSION A strong interaction between SNP309 status and tumor p53 status appears to modify the association between p53 status and breast cancer survival.