bluuebunny/crossref_metadata_2025_split huggingface dataset:
IoT based Agriculture (IoTA): Architecture, Cyber Attack, Cyber Crime and Digital Forensics Challenges
Authors: Rudrakar Santoshi , Rughani Parag 
Published: September 2022

Abstract
The Internet of Things (IoT) is a thriving technology in almost all fields of human life to make their lives easy. Smart Agriculture is one of the IoT applications on which farmers are widely switching to improve productivity and reduce labor costs. As IoT involves the Internet, data transmission technology creates the possibility of privacy/security breaches concerning users’ data. The cybercrime rate may also increase in IoT-based agriculture (IoTA). Thus there must be ready concerning digital forensics in IoTA. Various review articles are available regarding the emerging cyber attack, cybercrime, and forensics challenges in IoT applications like smart home, smart city, smart healthcare, smart grid, smart retail, smart supply chain management, and smart agriculture, while a few review articles on IoTA security and forensic challenges. This review paper is focused on IoTA solutions and their security and forensics challenges. The systematic literature review method has been used for this study which includes articles published from 2011 - 2021. This article has brought the limelight on IoT architecture and its application in agricluture (IoTA) to emphasize on its introduction, architecture, challenges and issues related emerging cybercrimes and digital forensics. At the end, this review paper discusses various challenges in securing IoTA systems and presents future directions for research.
Solid-phase extraction of organic dyes on mixed-ligand Zr(IV) metal-organic framework
Authors: Kharisov Boris I. , Kharissova Oxana V. , Zhinzhilo Vladimir А. , Bryantseva Julia D. , Uflyand Igor E. 
Published: September 2022

Abstract
Effective removal of organic dyes from aqueous solution is an important task for targeted wastewater treatment. In this work, a mixed-ligand Zr(IV) metal organic framework based on terephthalic acid and 1,10-phenanthroline was used for the solid-phase extraction of dyes. The maximum adsorption capacity of the sorbent for Congo red and methylene blue dyes reached 40 mg/g. The adsorption behavior was studied, including the effect of time, temperature, adsorbent dosage, pH and coexisting ions. The reliability of the sorbent for removing artificial dyes from food samples and in artificial seawater was tested. The possibility of using the sorbent as a filler for column chromatography for dye separation is shown.
Extended Multi-Stream Adaptive Graph Convolutional Networks (EMS-AAGCN) for Skeleton-Based Human Action Recognition
Authors: Mehmood Faisal , Zhao He , Chen Enqing , Guo Xin , Albinali Ali Ahmad , Razzaq Abdul 
Published: September 2022

Abstract
Skeleton-based action recognition using graph convolutional networks (GCNs), which specify CNNs to a more flexible non-Euclidean frame, has shown outstanding results. However, many problems remain the same in the earlier GCN-based models. (I) All model layers and input data have the same graph structure. Given the GCN model’s hierarchy and the variety of action recognition input, this may not be appropriate. (II) Bone length and orientation are rarely studied because they are too helpful and different for human action recognition. This paper Article Title presents an extended multi-stream adaptive graph convolutional neu-ral network (EMS-AAGCN) for skeleton-based action recognition. The proposed model’s network topology can be trained uniformly or individually based on the input data. This approach is based on data, allowing the model to make graphs more flexible and quickly adapt to various datasets. In addition, a spatial-temporal channel attention module in the suggested adaptive graph convolutional layer allows it to focus more on joints, frames, and features. Furthermore, an enhanced multi-stream framework models joints,bones and their motion, improving recognition accuracy. Our method significances the state-of-the-art on the two massive datasets, NTU-RGBD and Kinetics-Skeleton.
The Possibilities of Using Virtual Environments in Research on Wayfinding
Authors: Stachoň Zdeněk , Jochecová Kateřina , Kvarda Ondřej , Snopková Dajana , Ugwitz Pavel , Šašinková Alžběta , Ježek Stanislav , Kubíček Petr , Juřík Vojtěch , Švedová Hana , Sasinka Cenek 
Published: September 2022

Abstract
The study evaluated the degree of comparability of human behaviour exhibited during an evacuation scenario conducted in two environments, one real and the other virtual. For this purpose, we created a precise 3D model (digital twin) of a real campus building. In both the experimental (virtual environment) and control (real environment) groups, the participants were all first-time visitors to the building. The groups were instructed to locate a target room where they would subsequently be required to complete a simple task, at which point an evacuation alarm would be set off. We evaluated three types of data collected during their quest to find a way out of the building: an eye-tracker logged gaze hits on various objects (e.g., navigation signs), recorded the locomotion and trajectory of the participants, and logged a combination of these parameters (e.g., points along the path where participants sighted certain objects). The sample was composed of 73 participants (35 for the real environment, 38 for the virtual environment). We analysed and confirmed the results and concluded that despite certain aspects of human behaviour differing during the simulated evacuation, the locomotion behaviour exhibited by the participants in both environments was generally comparable. To improve the potential use of these findings in practice, we selected easily accessible hardware and excluded expensive devices such as treadmills, etc.
PeriFast/Corrosion: a 3D pseudo-spectral peridynamic Matlab code for corrosion
Authors: Wang Longzhen , Jafarzadeh Siavash , Bobaru Florin 
Published: September 2022

Abstract
We introduce PeriFast/Corrosion, a MATLAB code that uses the fast convolution-based method (FCBM) for peridynamic (PD) models of corrosion damage. The FCBM uses the convolutional structure of PD equations and employs the Fast Fourier transform (FFT) to achieve a computational complexity of 𝑂(𝑁𝑙𝑜𝑔𝑁). PeriFast/Corrosion has significantly lower memory allocation needs, 𝑂(𝑁), compared with, for example, the meshfree method with direct summation for PD models that requires 𝑂(𝑁2). The PD corrosion model and the fast convolution-based method are briefly reviewed and the detailed structure of the code is presented. The code efficiently solves 3D uniform corrosion (in copper) and pitting corrosion (in stainless steel) problems with multiple growing and merging pits, set in a complicated shape sample. Discussions on possible immediate extensions of the code to other corrosion damage problems are provided. PeriFast/Corrosion is a branch of PeriFast codes and is freely available on GitHub [1].
Quantitative prediction of toxin-producingAphanizomenoncyanobacteria in freshwaters using Sentinel-2 satellite imagery
Authors: Gunawardana Menik Hitihami M. A. S. V. , Sanjaya Kelum , Atapaththu Keerthi S. S. , Yapa Mudiyanselage Ajith L. W. Y. , Masakorala Kanaji , Widana Gamage Shirani M. K. 
Published: September 2022

AbstractThis study aimed to develop an empirical model to predict the spatial distribution of Aphanizomenon using the Ridiyagama reservoir in Sri Lanka with a dual-model strategy. In December 2020, a bloom was detected with a high density of Aphanizomenon and chlorophyll-a concentration. We generated a set of algorithms using in situ chlorophyll-a data with surface reflectance of Sentinel-2 bands on the same day using linear regression analysis. The in situ chlorophyll-a concentration was better regressed to the reflectance ratio of (1 + R665)/(1–R705) derived from B4 and B5 bands of Sentinel-2 with high reliability (R2 = 0.81, p &lt; 0.001). The second regression model was developed to predict Aphanizomenon cell density using chlorophyll-a as the proxy and the relationship was strong and significant (R2 = 0.75, p&lt;0.001). Coupling the former regression models, an empirical model was derived to predict Aphanizomenon cell density in the same reservoir with high reliability (R2 = 0.71, p&lt;0.001). Furthermore, the predicted and observed spatial distribution of Aphanizomenon was fairly agreed. Our results highlight that the present empirical model has a high capability for an accurate prediction of Aphanizomenon cell density and their spatial distribution in freshwaters, which helps in the management of toxic algal blooms and associated health impacts.
Risk-based treatment targets for onsite non-potable water systems using new pathogen data
Authors: Pecson Brian , Kaufmann Anya , Sharvelle Sybil , Post Brie , Leverenz Harold , Ashbolt Nicholas , Olivieri Adam 
Published: September 2022

Abstract
Using local sources (roof runoff, stormwater, graywater, and onsite wastewater) to meet non-potable water demands can minimize potable water use in buildings and increase supply reliability. In 2017, an Independent Advisory Panel developed a risk-based framework to identify pathogen log reduction targets (LRTs) for onsite non-potable water systems (ONWSs). Subsequently, California's legislature mandated the development and adoption of regulations—including risk-based LRTs—for use in multifamily residential, commercial, and mixed-use buildings. A California Expert Panel was convened in 2021 to (1) update the LRT requirements using new, quantitative pathogen data and (2) propose treatment trains capable of meeting the updated LRTs. This paper presents the updated risk-based LRTs for multiple pathogens (viruses, protozoa, and bacteria) and an expanded set of end-uses including toilet flushing, clothes washing, irrigation, dust and fire suppression, car washing, and decorative fountains. The updated 95th percentile LRTs required for each source water, pathogen, and end-use were typically within 1-log10 of the 2017 LRTs regardless of the approach used to estimate pathogen concentrations. LRT requirements decreased with influent pathogen concentrations from wastewater to graywater to stormwater to roof runoff. Cost and footprint estimates provide details on the capital, operations and maintenance, and siting requirements for ONWS implementation.
Study on key technology of identification of mine water inrush source by PSO-LightGBM
Authors: Ji Yuan , Dong Donglin , Mei Aoshuang , Wei Zhonglin 
Published: September 2022

Abstract
Mine water inrush is a major type of disaster in coal mine production in China. It causes heavy casualties and serious economic losses and threatens coal mine safety. To quickly and accurately identify mine water inrush source, according to the hydrochemical characteristics of different aquifers in the Donghuantuo mining area, this paper systematically analyzes the hydraulic connection of the aquifers in main coal mining areas before and after mining activities. Four types of hydrochemical data were collected: No. 5 coal seam roof water, No. 8 coal seam roof water, No. 122 coal seam floor water, and No. 1214 coal seam aquifer water in the Donghuantuo mining area. In addition, based on the hydrochemical data, the parameter selection of LightGBM was optimized by Particle Swarm Optimization (PSO) and constructed the PSO-LightGBM water inrush source identification model. The recognition accuracy of PSO-LightGBM model was compared with LightGBM model, classification regression tree (CART) model, and random forest (RF) model. The results showed that coal mining activities would have a significant impact on the water quality characteristics of the roof sandstone fissure water of No. 5 coal mine. Mining activities had a certain impact on the accuracy of the identification model. In addition, compared with the four recognition models, PSO-LightGBM model had the highest recognition accuracy of 97.22%. It showed that the model had high accuracy, stability, generalization ability, and important reference value for the identification of mine water inrush source.
Production of Soluble Murine TRAILs in Escherichia coli with Zn2+
Supplementation
Authors: Wang Xupu , Wang Lizheng , Liu Wenmo , Feng Xinyao , Wu Hui , Zhang Haihong , Wu Jiaxin , Kong Wei , Yu Xianghui , Yu Bin 
Published: September 2022

Background:
Accumulating evidence has demonstrated the immunomodulatory effects of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) in rheumatoid arthritis and the tumor microenvironment, besides its known capacity of specifically inducing the apoptosis of cancer cells. Mice are common available animal models for studying the roles of TRAIL. However, mice express only a single TRAIL receptor (mTRAILR) with an intracellular death domain, in contrast to the two TRAIL receptors (TRAILR1 and TRAILR2) in humans. Moreover, human TRAIL binds weakly to mTRAILR, whereas mouse TRAIL has high affinity for the human TRAIL-Rs. Therefore, we considered that murine TRAIL would be more suitable than human TRAIL for exploring the immunoregulatory effect of TRAIL in immunocompetent mice or when using mouse cells as the target. To our knowledge, the detailed method for production of recombinant murine TRAIL has not been reported.

Objective:
In this study, we aimed to design and express two soluble forms of murine TRAIL and verify the properties of the protein.

Methods:
Recombinant murine TRAILs were expressed in Escherichia coli BL21 (DE3, and Ni-chelating affinity chromatography was used for protein purification. SDS-PAGE, GDS-PAGE and HPLC were applied to analyze the protein structure. The cytotoxicity of our purified murine TRAILs was evaluated in the TRAIL-sensitive human breast cancer ZR-75-30 cells and murine breast cancer 4T1 cells. Finally, validation of the tumor-killing ability of the murine protein in vivo.

Results:
Two soluble forms of murine TRAILs (mT_N99 and mT_N188) were purified and demonstrated with high purity and trimeric structure. In addition, Zn2+ supplement was essential to produce soluble murine TRAILs in E.coli BL21 (DE3). The two purified soluble mTRAILs showed similar cytotoxicity to cancer cells, moreover, mT_N99 also showed a good anti-tumor effect in vivo and is more suitable for the treatment of murine tumor models.

Conclusion:
A production approach for recombinant murine TRAIL was determined, which covered the design of shortened forms, expression, purification and characterization.
COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach
Authors: Ke Si Yang , Neeley-Tass E Shannon , Barnes Michael , Hanson Carl L , Giraud-Carrier Christophe , Snell Quinn 
Published: September 2022

Background
Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19–related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19.

Objective
The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action.

Methods
A total of 646,885,238 COVID-19–related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets.

Results
In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action.

Conclusions
During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals.
Effect of SARS-CoV-2 Infection and Infection Severity on Longer-Term Glycemic Control and Weight in People With Type 2 Diabetes
Authors: Wong Rachel , Vaddavalli Rohith , Hall Margaret A. , Patel Monil V. , Bramante Carolyn T. , Casarighi Elena , Johnson Steven G. , Lingam Veena , Miller Joshua D. , Reusch Jane , Saltz Mary , Stürmer Til , Tronieri Jena S. , Wilkins Kenneth J. , Buse John B. , Saltz Joel , Huling Jared D. , Moffitt Richard ,   on behalf of the N3C Consortium, Anand Adit , Bennett Tellen , Chute Christopher G. , DeWitt Peter , Evans Michael , Garcia Victor , Gersing Kenneth , Girvin Andrew , Haendel Melissa , Harper Jeremy R. , Hajagos Janos , Hong Stephanie , Pfaff Emily , Wooldridge Jacob , Yoo Yun Jae 
Published: September 2022

OBJECTIVE
To evaluate the association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severity of infection with longer-term glycemic control and weight in people with type 2 diabetes (T2D) in the U.S.

RESEARCH DESIGN AND METHODS
We conducted a retrospective cohort study using longitudinal electronic health record data of patients with SARS-CoV-2 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least one HbA1c and weight measurement prior to and after an index date of their first coronavirus disease 2019 (COVID-19) diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the postindex average HbA1c and postindex average weight over a 1 year time period beginning 90 days after the index date among patients who did and did not have SARS-CoV-2 infection. Secondary outcomes were postindex average HbA1c and weight in patients who required hospitalization or mechanical ventilation.

RESULTS
There was no significant difference in the postindex average HbA1c or weight in patients who had SARS-CoV-2 infection compared with control subjects. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19.

CONCLUSIONS
In a multicenter cohort of patients in the U.S. with preexisting T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19. Mechanical ventilation was associated with a decrease in HbA1c after COVID-19.
The Impact of Area-Limited Skin Burns in the Severity of the Burn Disease and Outcome of Inhalation Injury
Authors: Zhirkova E. A. , Spiridonova T. G. , Eliseenkova E. I. , Sachkov A. V. , Borisov V. S. , Brygin P. A. , Gasanov A. M. 
Published: September 2022

IntroductionInhalation injury (ini) makes burn disease more severe. However, it remains unexplored what is the minimum area of the burn that can significantly aggravate of the course of ini.The objectivewas to compare the incidence of acute respiratory failure (arf), pneumonia, and mortality between patients with ini and ini with superficial skin burns up to 3% total body surface area (tbsa).Material and methods125 patients with ini and i–ii degree skin burns up to 3% tbsa were allocated into 4 groups: ini without skin burns, ini with burns up to 1% tbsa, up to 2%, and up to 3% tbsa.ResultsIn the group with ini and skin burns up to 2%, the number of arf, pneumonia cases, and deaths did not statistically significantly differ from the ini group, however in group of ini and burns of more than 2% tbsa, there were more of those complications.ConclusionSkin burns of i–ii degree over 2% tbsa increase the incidence of acute respiratory failure, pneumonia, and death in patients with ini.
Acute Mesenteric Ischemia in Critically Ill Patients. Possibilities of Laboratory Diagnostics. Systematic Literature Review and Meta-Analysis
Authors: Ponomaryova A. D. , Leyderman I. N. , Kasherininov I. Yu. 
Published: September 2022

Background Acute mesenteric ischemia (ami) is diagnosed in 1 case per 5,000–15,000 operations associated with elective abdominal surgical intervention and in 1 case of 1,000 emergency laparotomies associated with the acute abdomen clinical features. Non-occlusive disorders account for 20-30% of all cases of ami. The absence of pathognomonic symptoms and syndromes, as well as specific laboratory markers of ami, determines significant difficulties in the early diagnosis of this disease, especially in critically ill patients.Aim of study To identify the main laboratory markers of acute mesenteric ischemia applicable in the resuscitation and intensive care unit (ricu) based on a systematic literature review.Material and methods A systematic search and selection of publications was carried out in march-april 2021 in accordance with the prisma system checklist (prisma, preferred reporting items for systematic reviews and meta-analyzes). The search used two databases: the cochrane library of systematic reviews (https://www.cochranelibrary.com) and the pubmed bibliographic database (https://pubmed.ncbi.nlm.nih.gov). Key terms in the search parameters were: acute mesenteric ischemia; bowel necrosis predictors; laboratory diagnostics of mesenteric ischemia. Search parameters were limited to the year of publication (not later than 2015). The main results of the review included patients with an established diagnosis of acute mesenteric ischemia, indicating the level of laboratory parameters (ph of mixed venous blood, lactate, d-dimer, white blood count, mpv, i-fabp, serum marker and alpha-glutathione-s-transferase, indicators of the coagulation and hemostasis). Case reports and editorial letters were excluded from the search.Results The threshold value for serum l-lactate ranged from 1.05 mmol/l to 5.6 mmol/l. The sensitivity of serum lactate for the diagnosis of ami ranged from 34.7% (95% ci: 0.82–91.64%) to 89.51% (95.4% ci: 75.12–94.28%), and the specificity ranged from 47.3% (94.7% ci: 26.54–67.98%) to 94.5% (93.2% ci: 75.44–96.85%). The threshold level of d-dimer varied from 0.58 nmol/l to 7.84 nmol/l. The sensitivity of blood d-dimer for the diagnosis of ami was 72.6% (94% ci: 56.1–92.4%) to 99.7% (94% ci: 69.15–100%); the specificity ranged from 69% (95% ci: 7.41–33.63%) to 97.89% (95% ci: 90.42–98.1%). Parameters such as transaminases (ast, alt), mpv, ldh, d-lactate, serum amylase were not included in the review due to the lack of a sufficient number of studies.Conclusion Today, there is no highly specific laboratory marker that could be considered the “gold standard” in the laboratory diagnosis of acute mesenteric ischemia. The most promising in predicting ami are serum levels of l-lactate and d-dimer.