Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockchain for Health IoT: A privacy‐preserving data sharing system

Health Internet of Things (Health IoT) has been limited by isolated information and a lack of security. As the combination of blockchain and Health IoT could potentially address these two limitations, it has attracted significant interest. However, blockchain‐based systems often fail to balance data sharing and privacy protection. Therefore, we proposed a Health IoT‐based privacy‐preserving data sharing blockchain system. We designed a privacy‐preserving method based on the content extraction signature scheme to enable patients to establish fine‐grained privacy protection. We designed a Byzantine fault‐tolerant leader election mechanism that enhances the security of the Raft algorithm while providing efficiency in the data sharing. Furthermore, we designed a summary contract to ensure efficient data retrieval. The proposed mechanism was evaluated in terms of the efficiency and security. The simulation and analysis results demonstrate that our scheme offers a secure and effective technique for achieving privacy‐preserving and efficient sharing of IoT medical data.
A Comparative and Extensive Study of Covid 19 Diagnosis using Lung Ultrasound Images

The COVID-19 pandemic has resulted in a worldwide health crisis that has affected all facets of human existence and has brought the world to a halt. The most important pre-requisite for COVID-19 diagnosis is early detection. Machine learning algorithms can help in speeding up the process while saving money and effort. Following a comprehensive background study on the various medical imaging options available, it was discovered that there are few surveys focusing on COVID-19 identification based on Lung Ultrasound. The feasibility of lung ultrasound is visible from the survey. In this paper, huge efforts have been undertaken to study the road-map of lung ultrasound markers for detecting COVID-19. The detection of abnormal A lines, B lines and pleural lines or traces in ultrasound images will aid in the rapid identification and control of the ongoing COVID- 19 epidemic. The numerous deep learning models will make diagnosis easier and more accurate, assisting doctors and front-line employees in this pandemic emergency.
Biomass Turnover Rates in Metabolically Active and Inactive Marine Calanoid Copepods

Lipid-storing copepods are fundamental to the functioning of marine ecosystems, transferring energy from primary producers to higher trophic levels and sequestering atmospheric carbon (C) in the deep ocean. Quantifying trophic transfer and biogeochemical cycling by copepods requires improved understanding of copepod metabolic rates in both surface waters and during lipid-fueled metabolism over winter. Here we present new biomass turnover rates of C and nitrogen (N) in Calanoides acutus, Calanoides natalis, Calanus glacialis and Calanus hyperboreus alongside published data for Calanus finmarchicus and Calanus pacificus. Turnover rates in metabolically active animals, normalised to 10°C, ranged between 0.007 – 0.105 d-1 and 0.004 – 0.065 d-1 for C and N, respectively. Turnover rates of C were typically faster than those for N, supporting the understanding that non-protein C, e.g. lipid, is catabolised faster than protein. Re-analysis of published data indicates that inactive, overwintering C. finmarchicus turn over wax ester lipids at a rate of 0.0016 d-1. These and other basal rate data will facilitate the mechanistic representation of copepod physiology in global biogeochemical models, thereby reducing uncertainties in our predictions of future ocean ecosystem functioning and C sequestration.
Cognitive and Autonomic Dysfunction in Multiple System Atrophy Type P and C: A Comparative Study

Multiple System Atrophy (MSA) is a rare neurodegenerative disease, clinically defined by a combination of autonomic dysfunction and motor involvement, that may be predominantly extrapyramidal (MSA-P) or cerebellar (MSA-C). Although dementia is generally considered a red flag against the clinical diagnosis of MSA, in the last decade the evidence of cognitive impairment in MSA patients has been growing. Cognitive dysfunction appears to involve mainly, but not exclusively, executive functions, and may have different characteristics and progression in the two subtypes of the disease (i.e., MSA-P and MSA-C). Despite continued efforts, combining in-vivo imaging studies as well as pathological studies, the physiopathological bases of cognitive involvement in MSA are still unclear. In this view, the possible link between cardiovascular autonomic impairment and decreased cognitive performance, extensively investigated in PD, needs to be clarified as well. In the present study, we evaluated a cohort of 20 MSA patients (9 MSA-P, 11 MSA-C) by means of a neuropsychological battery, hemodynamic assessment (heart rate and arterial blood pressure) during rest and active standing and bedside autonomic function tests assessed by heart rate variability (HRV) parameters and sympathetic skin response (SSR) in the same experimental session. Overall, global cognitive functioning, as indicated by the MoCA score, was preserved in most patients. However, short- and long-term memory and attentional and frontal-executive functions were moderately impaired. When comparing MSA-P and MSA-C, the latter obtained lower scores in tests of executive functions and verbal memory. Conversely, no statistically significant difference in cardiovascular autonomic parameters was identified between MSA-P and MSA-C patients. In conclusion, moderate cognitive deficits, involving executive functions and memory, are present in MSA, particularly in MSA-C patients. In addition, our findings do not support the role of dysautonomia as a major driver of cognitive differences between MSA-P and MSA-C.
Clutter Suppression Algorithm of Ultrasonic Color Doppler Imaging Based on BP Neural Network

Aiming at the time complexity of singular value spectrum weighted Hankel SVD filtering algorithm, a clutter suppression algorithm for ultrasonic color Doppler imaging based on BP neural network model is proposed in this paper. Firstly, using the PRF data collected by portable ultrasound instrument, we verify the singular value weighted Hankel SVD filtering algorithm, and the results show that the algorithm has high accuracy; Then, the BP neural network model is established based on the input and output data of singular value weighted Hankel-SVD filtering algorithm; Finally, the clutter suppression algorithm of ultrasonic color Doppler imaging based on BP neural network model is established. The experimental results show that compared with Hankel SVD filtering algorithm, the clutter suppression algorithm proposed in this paper greatly shortens the operation time without reducing the accuracy, so as to improve the real-time performance of the filtering algorithm.
Generating a Future Land Use Change Scenario with a Modified Population-Coupled Markov Cellular Automata Model

With the population increasing and land use patterns changing, there will be environmental consequences. To solve these impending problems, information on the future land use pattern is needed. This study attempted to develop an enhanced land use model, capable of predicting future conditions. The traditional Markov model was modified by incorporating a Cellular Automata (CA) and a population variable to depict the neighboring effects and the impacts of population growth on urbanization. The performance of this new model was quantitatively assessed by generating the 2001 land use patterns of the East Fork Little Miami River watershed in southwest Ohio with and without the CA and the population variable and compared with the actual 2001 land use imagery. From the comparison, it was apparent that the land use map generated with the CA and population variable was more accurate. To further ascertain its applicability in a larger watershed, the same procedure was used to model the entire Little Miami River watershed. The validation results demonstrated that the performance of the modified CA-Markov model at both watershed scales was acceptable, and the inclusion of the CA and population variable could markedly improve model predictability. Based on these findings, the 2030 land use scenario for the LMR watershed was postulated. The resultant map showed much urban expansion in the western and southern portions of the basin. This information can be useful to planners and resource managers, enhancing their efforts in generating more sustainable future development strategies.
Investigation of the tack force of poultices using the probe tack test

Abstract Only a few systematic tack force studies have been conducted on poultices although they are widely used in therapeutic fields. In this study, we evaluated the factors influencing the tack force of poultices using the design of experiments (DoE) approach. The effects of detachment speed, pressuring time, pressuring force, coating thickness, carbomer content, and active pharmaceutical integrant (API) content on adhesion performance were evaluated using 46 response surface designs, after which the maximum value of the stress curve (σ max) was recorded. The results revealed that all factors except pressuring time significantly affected the response (at the 5% level), and two-factor interactions were identified using the DoE approach to evaluate carbomer content, pressuring force, coating thickness, and API content.
Identification of Drug–Cancer Associations: A Nationwide Screening Study

The main tool in drug safety monitoring, spontaneous reporting of adverse effects, is unlikely to detect delayed adverse drug effects including cancer. Hypothesis-free screening studies based on administrative data could improve ongoing drug safety monitoring. Using Danish health registries, we conducted a series of case–control studies by identifying individuals with incident cancer in Denmark from 2001 to 2018, matching each case with 10 population controls on age, sex, and calendar time. ORs were estimated using conditional logistic regression accounting for matching factors, educational level, and selected comorbidities. A total of 13,577 drug–cancer associations were examined for individual drugs and 8,996 for drug classes. We reviewed 274 drug–cancer pairs where an association with high use and a cumulative dose–response pattern was present. We classified 65 associations as not readily attributable to bias of which 20 were established as carcinogens by the International Agency for Research on Cancer and the remaining 45 associations may warrant further study. The screening program identified drugs with known carcinogenic effects and highlighted a number of drugs that were not established as carcinogens and warrant further study. The effect estimates in this study should be interpreted cautiously and will need confirmation targeted epidemiologic and translational studies. Significance: This study provides a screening tool for drug carcinogenicity aimed at hypothesis generation and explorative purposes. As such, the study may help to identify drugs with unknown carcinogenic effects and, ultimately, improve drug safety as part of the ongoing safety monitoring of drugs.
A machine learning approach to extracting spatial information from geological texts in Chinese

Abstract Texts have become an important spatial data resource. Interpretation of unstructured geoscience texts using natural language processing methods can effectively facilitate the discovery and retrieval of geographic information. Yet studies on the extraction of spatial information from textual geoscience data are limited compared to digital geoscience data. In this work, a machine learning approach is proposed for mining spatial relations in Chinese geological texts. The approach views spatial relation extraction as a sequence labeling problem, avoids the division of relation categories, and enables mining fine-grained spatial relations. The extracted geological texts commonly describe three-dimensional spatial relations among regions, strata, and lithologies. The extracted spatial relations are classified into three major categories (topological relations, absolute directional relations and relative directional relations) and 14 subcategories. We validated the proposed model with a test dataset, constructed visual displays of the extracted spatial relations on different topics, and quantified the uncertainty in the process from spatial entity recognition to spatial relation extraction. With the detailed portrayal of these spatial relations, this study provides support for solving theoretical and practical problems of cognition, prediction, decision-making, and evaluation in geoscience.
AUTOMATED PREPARATION GRADE JUDGEMENT SYSTEM FOR WEATHERING STEEL BRIDGES BY DEEP LEARNING

The quality and durability of repair coatings for steel bridges are significantly affected by the surface preparation prior to repair coating. Surface preparation by blasting stipulated by ISO Sa2 1/2 or higher is required as surface treatment before repair coating. The weathering steel bridge have unevenly corroded surface (rust grade D), and it is difficult to evaluate the surface condition by comparing the photographic example. Therefore, there is a high possibility that the difference in evaluation occurs by the engineer. In this paper, the authors attempted to develop a support system to determine preparation grade from the image of blasting. The purpose of this paper is to discuss the accuracy of the trained CNN classifier by the difference in the size of the teaching image data, and to evaluate the performance of the classifier by test data. It was considered that the high value of 83% or more was able to be obtained by the result of all evaluation indices, and the classifier with high performance was constructed by this method.
Distribution Network Restoration in a Multiagent Framework Using a Convex OPF Model

The ever-increasing requirement for reliability and quality of supply suggests to enable the self-healing features of modern distribution networks employing the intelligent measurement, communication, and control facilities of smart grids. In this paper, the concept of multiagent automation in smart grids is applied to build a self-healing framework to be used for restoration service. In this regard, an agent interaction mechanism is designed to build a reduced model of only those parts of the network that could participate in the restoration process. This reduced model is subject to a global optimization method, aiming at restoring a maximum of loads with minimum switching operations. This optimization problem, including power flow constraints is formulated as a convex second-order cone programming and solved using GUROBI solver. The proposed multi-agent systems-based strategy is completely scalable and leads to a global optimum solution (up to the desired accuracy) in a short time, without the need for powerful processors. The simulation studies are carried out on a 70-bus distribution network in case of multiple fault scenarios, using MATLAB/Yalmip toolbox.
Research on the Impact of the Strength of Channel Conflict on Cross-Channel Integration: The Mediating Effect of Channel Fluency and Stability

Based on event system theory, we explore the effect of the strength of channel conflict on cross-channel integration from the perspective of manufacturers, and then analyzes the mediating effect of channel fluency and channel stability. Taking manufactures who implement cross-channel integration as research samples, and basing the data collected from 229 respondents, the study uses multiple regression analysis and Bootstrap method to test the research hypotheses. The empirical research findings show that: the strength of channel conflict plays a negative role on channel fluency and channel stability; the strength of channel conflict has a double-edged sword effect on cross-channel integration: it can reduce cross-channel integration by destroying channel fluency, and at the same time can improve cross-channel integration by destroying and reducing channel stability.
The Borg scale is a sustainable method for prescribing and monitoring self-administered aerobic endurance exercise in patients with chronic kidney disease

Abstract Aim To examine adherence, performance, and safety of self-administered aerobic endurance exercise when exercise intensity was prescribed and self-monitored with the Borg RPE scale in patients with chronic kidney disease (CKD), and the relationship between performed exercise and change in walking distance. Materials and Methods 97 men and 50 women (age 66 ± 14 years, measured GFR 22 ± 8 mL/min/1.73m2) were prescribed 60 min aerobic endurance exercise/week at RPE 13–15. The 6-minute walk test was measured at 0, 4, 8 and 12 months of exercise. Results 100 patients completed the study, 80% reported exercise intensity at 12 months, 74% performed exercise within the prescribed RPE. Median RPE was 13 (13–15). Median duration was 56 (33–109) minutes/week. Patients with a short walking distance at baseline performed significantly less minutes of exercise/week (p = 0.039). There was no correlation between weekly duration and change in walking distance. No exercise-related incidents were recorded. Walking distance improved significantly by 30 ± 56 metres (p < 0.001). Conclusions The Borg RPE scale is useful, acceptable, simple and safe for prescribing and monitoring intensity of self-administered aerobic endurance exercise in patients with CKD. A RPE of 13–15 improved walking distance in well-functioning and deconditioned patients, within a wide range of weekly duration of exercise.
The Activation of Protamine 1 Using Epigenome Editing Decreases the Proliferation of Tumorigenic Cells

DNA methyltransferases (DNMT) and histone deacetylases (HDAC) inhibitors are used as cancer epigenome drugs. However, these epigenetic drugs lack targeting specificity and could risk inducing genome instability and the expression of oncogenes. Therefore, there is a need to develop new therapeutic strategies where specific cancer genes can be targeted for silencing or activation. The CRISPR/dCas9 system represents a promising, powerful therapeutic tool because of its simplicity and specificity. Protamine 1 (PRM1) is exclusively expressed in sperm and has a vital role in the tight packaging of DNA, thus inducing transcriptional silencing in sperm cells. We hypothesized that the activation of the PRM1 gene in tumorigenic cells would lead to DNA condensation and reduce the proliferation of these cells. To test our hypothesis, we transfected human embryonic kidney cells 293T with a dCas9-P300 plasmid that adds acetyl groups to the promoter region of PRM1 via specific gRNAs plasmids. RNA-Seq analysis of transfected cells revealed high specificity of targeted gene activation. PRM1 expression resulted in a significant decrease in cell proliferation as measured by the BrdU ELISA assay. To confirm that the activation of PRM1 was due to acetyl groups deposited to H3K27, a ChIP-qPCR was performed. The acetylation of the PRM1 promoter region targeted by dCas9-p300 in transfected cells was higher than that of the control cells. Interestingly, the targeted promoter region for acetylation showed reduced DNA methylation. These findings demonstrate the efficacy of epigenome editing in activating PRM1 in non-expressing tumorigenic cells, which could be used as a promising therapeutic strategy in cancer treatment.
Bifidobacterium adolescentis Is Effective in Relieving Type 2 Diabetes and May Be Related to Its Dominant Core Genome and Gut Microbiota Modulation Capacity

The prevalence of diabetes mellitus is increasing globally. Probiotics have been shown to be an effective intervention for diabetes. This study focused on the relieving effects and possible mechanisms of 16 strains of two dominant Bifidobacterium species (B. bifidum and B. adolescentis, which exist in the human gut at different life stages) on type 2 diabetes (T2D). The results indicated that more B. adolescentis strains appeared to be superior in alleviating T2D symptoms than B. bifidum strains. This effect was closely related to the ability of B. adolescentis to restore the homeostasis of the gut microbiota, increase the abundance of short-chain fatty acid-producing flora, and alleviate inflammation in mice with T2D. In addition, compared with B. bifidum, B. adolescentis had a higher number of core genes, and these genes were more evolutionarily stable, including unique environmental tolerance, carbon and nitrogen utilization genes, and a blood sugar regulation gene, glgP. This may be one of the reasons why B. adolescentis is more likely to colonize in the adult gut and show a superior ability to relieve T2D. This study provides insights into future studies aimed at investigating probiotics for the treatment of metabolic diseases.
Research on electromagnetic transient response of photovoltaic system when lightning strikes the earth

In this paper, a mathematical model of photovoltaic power station is established in the EMTP environment to present the distribution of induced voltage on photovoltaic panels caused by lightning strikes on high buildings. The transient calculation results proves that, when lightning strikes the lightning rod of a photovoltaic power station or other towering buildings, the lightning current passes through the soil to the grounding system of the photovoltaic panel frame, which can generate a higher overvoltage on the photovoltaic panel and its frame, on the order of kilovolts. Therefore, to limit this overvoltage, this work proposes feasible lightning protection measures for the overvoltage problem. The installation of lightning arresters is one of the most common measures to protect electrical equipment from overvoltage hazards. In addition, another measure is to reduce the grounding resistance and artificially reduce the soil resistivity, thereby reducing the damage of the ground potential to the PV equipment insulation. From the simulation results and the VI characteristics of lightning arrester, the transient voltage is limited to reach the BS 60950 standard. The results show that the correct lightning protection measures will help reduce the damage of lightning strikes to the PV system.
Assessing Interpersonal Proximity Evaluation in the COVID-19 Era: Evidence From the Affective Priming Task

Social proximity has since ever been evaluated as positive. However, the outbreak of the COVID-19 pandemic has dramatically reduced our social relations to avoid spreading the contagion. The present study aims to investigate people's current assessment of social proximity by using an affective priming paradigm (APP). We hypothesized that if our evaluation of social proximity is positive, then words with positive valence (e.g., relaxed) should be processed faster when preceded by images of social proximity than social distancing. On the contrary, if our evaluation of social proximity is turning negative, then words with a negative valence (e.g., sad) should be processed faster when preceded by images of social proximity than social distancing. To this end, we presented participants with prime images showing line drawings representing humans in situations of proximity or distancing and asked them to evaluate the valence (i.e., positive or negative) of a subsequent target word. In a follow-up session, the same participants evaluated the prime images as being positively or negatively valenced. Results showed that a large subset of participants who rated the prime images of social proximity as positive also processed positive words faster when these were preceded by images of social proximity than social distancing. Conversely, a smaller subset of participants who rated the prime images of social proximity as less positive processed negative words faster when these were preceded by images of social proximity than social distancing. These results suggest individual differences in the assessment of social proximity likely driven by the pandemic.