Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave based Non-Invasive Localized Anomaly Detection in Wood as a Biological Tissue Phantom

Localized anomalies in biological tissues, such as tumors, wounds, and edema, possess microwave properties starkly different from the healthy tissues. Microwave imaging (MWI) utilizes the changes in the electric properties of the tissues to detect anomalies in these tissues by forming an image of the test region. We present a novel, non-invasive technique to detect the localized anomalies in biological tissues using microwaves for in-vivo applications. Here, the scattering measurements of the test specimen are carried out in the microwave frequency range using a Vector Network Analyzer (VNA), and a modified calibration technique is utilized for de-embedding the calibration plane to the surface of the sample. Wood is used as a biological tissue phantom in the present situation, where a localized anomaly is introduced. The transmission characteristics of the wood sample measured using the VNA are utilized to estimate the change in attenuation and phase constant due to the introduction of an anomaly in the specimen. These changes are subsequently used to compute the dielectric constant (ϵr) and loss tangent (tan δ) of the test specimen. The results suggest that the proposed technique can efficiently differentiate between healthy tissues and tissues with anomalies, with a variation of nearly 50 % in the complex permittivity between the two.
Research on Distributed Temperature Monitoring System for Gasifier Based on Optical Fiber Sensors

Monitoring the wall temperature of gasifiers in the chemical industry can not only timely detect hot spots and avoid accidents, but also predict the thinning degree of refractory bricks and optimize maintenance cycles. In order to achieve distributed temperature measurement, this paper studies a distributed temperature monitoring system for gasifiers based on optical fiber sensors. The system includes four parts: on-site high-temperature resistant temperature sensors, network transmission system, temperature monitoring host, and temperature monitoring analysis software platform. The experimental results indicate that this system can achieve real-time measurement of the temperature in different areas of the gasifier wall.
EDARAVONE, A NOVEL FREE RADICAL SCAVENGER, REDUCES HIGH-MOBILITY GROUP BOX 1 AND PROLONGS SURVIVAL IN A NEONATAL SEPSIS MODEL

Free radicals play an important role in the inflammatory process of sepsis. We hypothesized that edaravone, a novel free radical scavenger, can suppress pathophysiological events and prolong survival in a neonatal sepsis cecal ligation and perforation (CLP) model. Of 32 3-day-old anesthetized and mechanically ventilated piglets, 11 received CLP only, 10 received CLP and edaravone treatment starting 30 min after CLP, and 11 constituted a sham (control) group. Mean arterial pressure (MAP), heart rate, cardiac output, arterial blood gas, serum total hydroperoxide, nitrite and nitrate, TNF-&agr;, and high-mobility group box 1 (HMGB1) were measured before CLP and at 1, 3, and 6 h after CLP. Compared with the CLP group, the edaravone group showed higher MAP at 6 h, lower heart rate at 1 and 3 h, lower total hydroperoxide at 1 h, lower nitrite and nitrate at 3 and 6 h, and higher (although not significantly so) mean cardiac output at 1, 3, and 6 h. TNF-&agr; elevation was delayed from 1 h in the CLP group to 3 h in the edaravone group. In the edaravone group, HMGB1 did not change significantly at any time, whereas in the CLP group, it increased at 6 h. Survival times were longer in the edaravone group than in the CLP group (15.4 ± 1.4 vs. 10.2 ± 1 h; P < 0.005). In addition, each of the serial dilutions of edaravone had a higher biological antioxidant potential than tempol does. In conclusion, edaravone suppressed free radicals, delayed the TNF-&agr; surge, and prevented HMGB1 elevation, thereby maintaining MAP and prolonging survival time in a neonatal sepsis CLP model.
A desk supporting computer-based interaction with paper documents

Before the advent of the personal workstation, office work practice revolved around the paper document. Today the electronic medium offers a number of advantages over paper, but it has not eradicated paper from the office. A growing problem for those who work primarily with paper is lack of direct access to the wide variety of interactive functions available on personal workstations. This paper describes a desk with a computer-controlled projector and camera above it. The result is a system that enables people to interact with ordinary paper documents in ways normally possible only with electronic documents on workstation screens. After discussing the motivation for this work, this paper describes the system and two sample applications that can benefit from this style of interaction: a desk calculator and a French to English translation system. We describe the design and implementation of the system, report on some user tests, and conclude with some general reflections on interacting with computers in this way.
Variation in Antioxidant Enzyme Activities of Two Strawberry Cultivars with Short-term Low Temperature Stress

Changes of superoxide(O ) and hydrogen peroxide(H O ), malondialdehyde(MDA) contents and 2 22 activities of enzymes involving cell defense in leaves of strawberry (Fragaria×ananassa Duch) plantlets under different time of low temperature stress were studied. With the increase of stress times, the rates of O2 generation and contents of H O increased to a certain degree and then decreased. The MDA contents and the 22 relative conductivity fluctuated were increased during the treatment. The activities of antioxidant enzymes, such as superoxide dismutase(SOD), catalase(CAT), peroxidase(POD) and ascorbate peroxidase(APX) were gradually increased to a certain degree and then decreased. The results clearly suggested that low temperature stress triggered an increase of reactive oxygen species (ROS) and the early accumulation of ROS in plants might lead to the production of antioxidant defense system. If the stress were too strong, the defense system of plants could not remove the more production of ROS effectively and result in severely damage to plants or even death.
Determining Risk Factors for Long COVID Using Positive Unlabeled Learning on Electronic Health Records Data from NIH N3C

Post-acute sequelae of SARS-Co V-2 infection (PASC), also known as Long COVID, is an emerging medical condition in the aftermath of the COVID-19 pandemic. Research on this disease is limited by its newness and the lack of reliable controls, which can hinder model development. The National COVID Cohort Collaborative (N3C)11https://ncats.nih. gov/n3c contains Electronic Health Record (EHR) data for 7 million COVID positive patients from 76 sites across the United States, of which there are fifty thousand Long COVID patients. For this study, we model our risk factor analysis as Positive Unlabeled (PU) problem, where we treat Long COVID patients as the positive sample and rest of the COVID positive patients as unlabeled data. We first curate reliable controls using a PU modeling technique called bagging. We then use this cohort of positive and the curated negative samples to model risk factors for Long COVID. We utilize an attention-based deep learning approach using Long Short Term Memory (LSTM) networks on historical diagnosis data prior to COVID-19 infection, to first predict for Long COVID and then extract the model attention values to score input diagnoses for each patient. Using this process, we achieve an Area Under the Receiver Operating Characteristic (AUROC) of 0.93 (0.88 F1 Score) for the prediction task, significantly outperforming the same model trained on randomly selected controls. We then use a scoring process to rank different input diagnoses for each correctly classified patient with attention values extracted from the trained model and find the temporal distribution of top diagnosis codes which, when represented graphically, becomes a helpful tool to for physicians to investigate diagnosis patterns that effect Long COVID and also evaluate model trustworthiness.
Optimal and Competitive Assortments with Endogenous Pricing Under Hierarchical Consumer Choice Models

This paper studies assortment planning and pricing for a product category with heterogeneous product types from two brands. We model consumer choice using the nested multinomial logit framework with two different hierarchical structures: a brand-primary model in which consumers choose a brand first, then a product type in the chosen brand, and a type-primary model in which consumers choose a product type first, then a brand within that product type. We consider a centralized regime that finds the optimal solution for the whole category and a decentralized regime that finds a competitive equilibrium between two brands. We find that optimal and competitive assortments and prices have quite distinctive properties across different models. Specifically, with the brand-primary model, both the optimal and the competitive assortments for each brand consist of the most popular product types from the brand. With the type-primary choice model, the optimal and the competitive assortments for each brand may not always consist of the most popular product types of the brand. Instead, the overall assortment in the category consists of a set of most popular product types. The price of a product under the centralized regime can be characterized by a sum of a markup that is constant across all products and brands, its procurement cost, and its marginal operational cost, implying a lower price for more popular products. The markup may be different for each brand and product type under the decentralized regime, implying a higher price for brands with a larger market share. These properties of the assortments and prices can be used as effective guidelines for managers to identify and price the best assortments and to rule out nonoptimal assortments. Our results suggest that to offer the right set of products and prices, category and/or brand managers should create an assortment planning process that is aligned with the hierarchical choice process consumers commonly follow to make purchasing decisions. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.
Slow covariations in neuronal resting potentials can lead to artefactually fast cross-correlations in their spike trains.

Slow covariations in neuronal resting potentials can lead to artefactually fast cross-correlations in their spike trains. J. Neurophysiol. 80: 3345-3351, 1998. A model of two lateral geniculate nucleus (LGN) cells, which interact only through slow (tens of seconds) covariations in their resting membrane potentials, is used here to investigate the effect of such covariations on cross-correlograms taken during stimulus-driven conditions. Despite the slow timescale of the interactions, the model generates cross-correlograms with peak widths in the range of 25-200 ms. These bear a striking resemblance to those reported in studies of LGN cells by Sillito et al., which were taken at the time as evidence of a fast spike timing synchronization interaction; the model highlights the possibility that those correlogram peaks may have been caused by a mechanism other than spike synchronization. Slow resting potential covariations are suggested instead as the dominant generating mechanism. How can a slow interaction generate covariogram peaks with a width 100-1,000 times thinner than its timescale? Broad peaks caused by slow interactions are modulated by the cells' poststimulus time histograms (PSTHs). When the PSTHs have thin peaks (e.g., tens of milliseconds), the cross-correlogram peaks generated by slow interactions will also be thin; such peaks are easily misinterpretable as being caused by fast interactions. Although this point is explored here in the context of LGN recordings, it is a general point and applies elsewhere. When cross-correlogram peak widths are of the same order of magnitude as PSTH peak widths, experiments designed to reveal short-timescale interactions must be interpreted with the issue of possible contributions from slower interactions in mind.
Cross-site scripting (XSS) worms in Online Social Network (OSN): Taxonomy and defensive mechanisms

The propagation of XSS worms on the social networking sites like Twitter, LinkedIn, Facebook, etc. has observed exponential growth in modern era of Web 2.0 technology. According to recent survey, 43% of web applications are vulnerable to XSS worms. Such unbearable growth of XSS worms has raised some serious security and privacy concerns in OSN. This article discusses a detailed classification of XSS attacks and presents the recent occurrences of XSS attacks on numerous platforms of OSN-based web applications. Numerous existing XSS defensive solutions on OSN have been discussed in order to identify their main contributions and existing performance issues. We present the unique security challenges and issues that exist in the recent state-of-art techniques and based on this, we recommend the further scope.
Auto Annotation using Object Tracking with Multiple in-vehicle Cameras for Federated Learning

In recent years, significant advancements have been made in automated driving technology. To achieve fully automated driving, a vehicle must accurately acquire information regarding objects in its surroundings by gathering large amounts of data for machine learning. In many cases, data collected from each vehicle is transmitted to a central server, which is undesirable because of privacy concerns and traffic generation to retrieve the data. Federated Learning (FL) is a promising technology that addresses these concerns by enabling learning while maintaining the data with the user. However, since the local labeling accuracy for distant objects with existing models is still low, quality of the model training is affected. To address this issue, this paper proposes the use of Multiple object Tracking (MoT) with multiple cameras to improve the labeling accuracy for FL. Our method enables the application of the labeling results of nearby objects to identify distant objects by tracking time-series data from multiple cameras. The evaluation results demonstrate that the proposed method effectively labels distant objects without sending data to the central server.
Content Based Movie Recommendation System Using Machine Learning Algorithm

A recommendation system grounded in user preferences can be applied to suggest various types of content such as web runners, books, pictures, television shows, and songs. The objective of a movie recommendation system is to recommend movies to individuals based on their interests, helping them save time searching the internet for movies amidst the overwhelming options available. A content-grounded recommendation system utilizes specific characteristics to determine suitable recommendations for users. By analyzing attributes present in previously liked movies, the system predicts which movies a user is likely to enjoy. Recommendation systems can utilize one or a combination of multiple attributes to generate recommendations. When designing a movie recommendation system, factors such as genre, ID, keywords, title, director, and actors are taken into account. This paper presents a movie recommender system based on cast, keywords, crew, and genres. A unified tag is created by combining these seven attributes, serving as the primary tag for the movie recommendation system, which guides the selection of recommended movies. This system aids users in saving time by providing personalized movie recommendations, eliminating the need to sift through countless options online. A content-based recommendation system is employed to identify items that are likely to be of interest to users. By leveraging a dataset, the system predicts user preferences based on various attributes.
Local Adaptive Clustering Based Image Matching for Automatic Visual Identification

Monitoring cameras are extensively utilized in industrial production to monitor equipment running. With advancements in computer vision, device recognition using image features is viable. This paper presents a vision-assisted identification system that implements real-time automatic equipment labeling through image matching in surveillance videos. The system deploys the ORB algorithm to extract image features and the GMS algorithm to remove incorrect matching points. According to the principles of clustering and template locality, a method known as Local Adaptive Clustering (LAC) has been established to enhance label positioning. This method segments matching templates using the cluster center, which improves the efficiency and stability of labels. The experimental results demonstrate that LAC effectively curtails the label drift.
Exploring Class B/J Continuum for Doherty Power Amplifier Design at n78 5G Band

This article presents a simplified high peak power Doherty Power Amplifier (DPA) for n78 5G applications. This study implements Foster Loads at the Extrinsic Reference Plane (ERP) at both main and auxiliary PAs for better output power and efficiency. Here, the main PA is operated in a continuous class B/J mode at output power back-off (OPBO) and in saturation at the current reference plane (CRP) for better efficiency over the band. At the output side, using Foster load with less dispersive reactive part removes additional matching network’s requirement since the load-combiner performs the matching condition required for the efficient DPA operation. The proposed DPA results in high peak power of 53.5-55.1dBm with 57-66% Drain efficiency (DE) over 3.3-3.8 GHz (and 45-49.4% DE at 48. 5dBm of output power). For a 5 MHz spacing two-tone signal, better than -22.5dBcC/IMD3 at 6dB OPBO is observed for the proposed DPA design, demonstrating better device linearity.
Aging and the Perception of Temporally Interleaved Words

Objective: The purpose of this study was to examine differences between older and younger listeners in the ability to sequentially attend to and ignore words. Design: Participants (n = 13 older adults and 13 younger adults) completed a temporally interleaved word recognition task. On each trial, 10 words were presented, and participants were instructed to repeat back every other word while ignoring the intervening words. Three variables were examined: (1) whether the word strings that were to be attended and to be ignored created syntactically correct sentences; (2) whether the to-be-attended and to-be-ignored words were presented from the same or from different spatial locations; and (3) whether the five target words in each trial (and the five distractor words in each trial) were spoken by a single talker or by five different talkers. In addition, digit-span forward and digit-span backward were measured and used as variables in correlation analyses. Results: As a group, the younger participants outperformed the older listeners, particularly when the to-be-attended and to-be-ignored words were presented from the same spatial location (versus when they were presented with spatial separation). Compared with the younger participants, older listeners also made more error responses that were to-be-ignored words, although the proportion of errors that were not responses involving masking words did not significantly differ between groups. Scores on the digit-span–forward test (but not digit-span–backward scores or the degree of hearing loss) were associated with older individuals’ performance on this temporally interleaved speech-recognition task. Conclusions: The overall pattern of results suggests that factors other than threshold elevation contribute to speech-understanding problems experienced by older listeners. However, although younger adults outperformed older listeners on this interleaved sentence task, older and younger adults benefited, to a similar extent, from spatial separation of the to-be-attended and to-be-ignored words, and from having a consistent target talker within a trial.
Artificial Neural Network-Driven Healthcare Resource Management for Improved Outcomes in Infectious Disease Control

The efficient administration of healthcare resources is crucial for successful infectious disease control. The merging of technology and medicine has opened the path for revolutionary changes in today's healthcare system. Artificial Neural Networks (ANNs) are one of them that has the potential to radically alter the way healthcare is administered. Using ANNs, this paper presents a novel method dubbed “Dynamic Patient Risk Stratification (DyPRoN). ” DyPRoN performs real-time risk assessments of individual patients, allowing for flexible distribution of medical resources. Adaptability to the dynamic nature of infectious illnesses is hindered by the prevalence of static models in conventional approaches. However, DyPRoN uses ANNs to do continuous analysis of patient data, allowing for risk classification in real time. This adaptive method improves healthcare resource management, which in turn helps with infectious disease control.
Why Won't Aliens Talk to Us? Content and Community Dynamics in Online Citizen Science

We conducted a quantitative analysis of ten citizen science projects hosted on the Zooniverse platform, using a data set of over 50 million activity records and more than 250,000 users, collected between December 2010 and July 2013. We examined the level of participation of users in Zooniverse discussion forums in relation to their contributions toward the completion of scientific (micro-)tasks. As Zooniverse is home to a multitude of projects, we were also interested in the emergence of cross-projects effects, and identified those project characteristics, most importantly the subject domain and the duration of a project. We also looked into the adoption of expert terminology, showing that this phenomenon is dependent on the scientific domain which a project addresses but also affected by how the communication features are actually used by a community. This is the first study of this kind in this increasingly important class of online community, and its insights will inform the design and further development of the Zooniverse platform, and of citizen science systems as a whole.
Cardiac neural crest cells contribute to the dormant multipotent stem cell in the mammalian heart

Arodent cardiac side population cell fraction formed clonal spheroids in serum-free medium, which expressed nestin, Musashi-1, and multi-drug resistance transporter gene 1, markers of undifferentiated neural precursor cells. These markers were lost following differentiation, and were replaced by the expression of neuron-, glial-, smooth muscle cell–, or cardiomyocyte-specific proteins. Cardiosphere-derived cells transplanted into chick embryos migrated to the truncus arteriosus and cardiac outflow tract and contributed to dorsal root ganglia, spinal nerves, and aortic smooth muscle cells. Lineage studies using double transgenic mice encoding protein 0–Cre/Floxed-EGFP revealed undifferentiated and differentiated neural crest-derived cells in the fetal myocardium. Undifferentiated cells expressed GATA-binding protein 4 and nestin, but not actinin, whereas the differentiated cells were identified as cardiomyocytes. These results suggest that cardiac neural crest-derived cells migrate into the heart, remain there as dormant multipotent stem cells—and under the right conditions—differentiate into cardiomyocytes and typical neural crest-derived cells, including neurons, glia, and smooth muscle.