bluuebunny/crossref_metadata_2025 huggingface dataset:
The lateral photovoltaic effect in the Ni-SiO2 -Si structure with bias
Authors: Ling Xiang , Zhu Peng Fei , Zhu Kun , Song Pei , Li Xiong
Published: May 2023

We had designed a clamping device to study lateral photovoltaic effect (LPE) in Ni-SiO2-Si structure due to the appropriate barrier height. We studied LPE with different laser wavelengths and power in Ni-SiO2-Si structures, the LPE have a prominent sensitivity and linearity. The most suitable laser wavelength is 532nm for studying the LPE in Ni-SiO2-Si structure, and LPE will increase with the laser power within a certain range and reach the threshold ultimately. The transient response time is 450 μs and the relaxation time is 2250 μs in Ni-SiO2-Si structure without bias. The LPE sensitivity have a significant improvement with bias. However, the LPE have a poor linearity between two electrodes when bias was applied on electrodes position. We change the bias application method, the linearity and sensitivity of LPE obtained a significant progress when bias was applied in middle of electrodes connection from -4V to -7V. The transient response time is 6 μs and the relaxion time is 47μs with -7V bias. Not only improve the LPE sensitivity but also increase response speed with bias. The research can provide a method for obtaining high sensitivity and response speed based on LPE sensors.
Predicting Contraceptive Usage for Married African Women Residing in Rural Areas: A Comparative Study of Deep Learning and Machine Learning Models with XAI Insights
Authors: Nuri Kaleab Wondemu , Kanda Michee Sanza , Justine Elikana Kulwa , Panda Amiya Ranjan , Pradhan Himanshu Sekhar
Published: May 2023

Background
The use of modern contraceptives is a crucial aspect of family planning, especially for women residing in rural areas of Africa. This study seeks to address the issue of contraceptive usage among married African women living in rural areas by exploring the effectiveness of machine learning and deep learning models for predicting this usage.
Methodology
The data used in the study was obtained from the Multiple Indicator Cluster Survey 6 (MICS6) to develop and compare machine learning and deep learning models for predicting contraceptive usage among married African women residing in rural areas. In addition to predictive accuracy, the study also focused on incorporating explainable Artificial Intelligence (XAI) insights to enhance the interpretability of the models.
Result
Artificial Neural Network (ANN) with BFloat16 and Convolutional Neural Network (CNN) + Long Short-Term Memory (LSTM) were the best predictive models with 75% accuracy in both models and F1-score of 73% and 74% respectively. Additionally, XAI techniques provide valuable insights into the factors that influence contraceptive usage in the target population.
Conclusion
The results of this study indicate that deep learning models outperform traditional machine learning models in predicting contraceptive usage among married African women residing in rural areas. The implications of this research are significant, as the findings could inform policy and intervention strategies aimed at improving family planning services in rural areas of Africa.
Multi-classifier fusion base on belief-value for the diagnosis of neuropsychiatric disorders
Authors: Zhao Feng , Ye Shixin , Lv Ke , Wang Qin , Li Yuan , Mao Ning , Ren Yande
Published: May 2023

Neuropsychiatric disorders seriously affect the health of patients, and early diagnosis and treatment are crucial to improve the quality of patients’ life. Machine learning and other related methods can be used for disease diagnosis and prediction, among which multi-classifier fusion method has been widely studied due to its significant performance over single classifiers. In this paper, we propose a multi-classifier fusion classification framework based on belief-valuefor the neuropsychiatric disorders diagnosis. Specifically, the belief-value measures the belief level of different samples by considering information from two perspectives, which are distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples). The proposed belief-value is more representative compared to the belief-value which only uses a single type of information. Further, based on the concept of multi-view learning, we performed the calculation of the belief-values under the sample space with different features, and the complementary relationship between different belief-values was captured by a multilayer perceptual (MLP) network. Compared with majority voting and linear fusion methods, the MLP network can better capture the nonlinear relationship between belief-values, which produces better diagnostic results. Experimental results show that the proposed method outperforms single classifier and multi-classifier linear fusion methods for the diagnosis of neuropsychiatric disorders.
Design of Accurate Multi-class Optimized Lightweight Convolution Neural Network for Rice Varieties Classification
Authors: Selvaraj Deepika , Venkatesan Arunachalam
Published: May 2023

Worldwide, more than 40k rice varieties are existing, each with different nutritional content and quality. Identifying these has to be consistent, automated, and accurate. Considering the feature extraction process, convolution Neural Networks (CNN) are preferred over machine learning (ML) for this classification. Transfer learning approaches help to optimize the CNN model; therefore, it fits in an FPGA. Seven different CNN models were proposed to classify five rice varieties, each model differs based on the: kernel depth; the number of convolution layers (CL); the number of fully connected layers (FCL); and the number of neurons per FCL. These were analyzed considering 70% and 30% for training and testing respectively. A dataset of 15,000 images/variety with each image of resolution. This results in an Optimized Lightweight Convolutional Neural Network (OpLW-CNN) model, having a CL, followed by two FCLs. This model is further analyzed using a random set of images: 500, 5000, and 75000 to fit the model optimally. This model achieves 99%, 98.13%, and 98.14% of specificity, F1-score, and accuracy for a set of 5000 images. These metrics are approximately 1% to 2% lesser than the performance of the benchmark model, and 81.5% fewer computations. Also, this model requires less than a second to classify an image.
The association between ambient temperature and antimicrobial resistance of high-priority bacterial pathogens in China nationwide: A modeling study with difference-in-difference approach
Authors: Shi Lin , Li Weibin , Yang Shifang , Li Jia , Liu Xu , Zeng Yingchao , Xia Haohai , Yang Xinyi , Zhao Manzhi , Yang Lianping
Published: May 2023

Background Climate warming not only affects humans, megafauna and other flora but also the world’s microbial organisms. However, the relationship between ambient temperature and antimicrobial resistance (AMR) of important clinical bacteria is still unclear; therefore, we explored the association between ambient temperature and AMR in China.Methods Six bacteria-antibacterial combination, identified by the World Health Organization as high priority, were collected from the China Antimicrobial Resistance Surveillance System (CARSS) database for 30 provinces from 2014 to 2020. Meteorological factor, socioeconomic and health care factors were obtained from the China Statistical Yearbook. A revised Difference-in-Difference (DID) approach was applied to quantitatively assess the association between ambient temperature and AMR, and exploring the modified effects of socioeconomic and health care factors.Results Ambient temperature had a significant protective effect on Methicillin-Resistant Staphylococcus Aureus (MRSA) (Relative risk (RR) = 0.959, p = 0.042) and Vancomycin Resistant Enterococcus Faecium (VRefm) (RR = 0.798, p < 0.001), and was positively associated with Penicillin Resistant Streptococcus Pneumoniae (PRSP) (RR = 1.556, p < 0.001). Whereas higher socioeconomic levels (higher GDP, higher income per capita, higher consumption per capita) and increased hospitalization rate will eliminate the protective effect of ambient temperature on them. The frequency of medical visit, institution per province did not have an interaction effect on MRSA and VRefm. Interestingly, PRSP was sensitive to the socioeconomic and health care factors, which almost modify its positive association with ambient temperature.Conclusions Ambient temperature was significantly associated with AMR, having varied effects for different bacteria-antibacterial combination. Some certain socioeconomic and health care factors will influence the association between temperature and antimicrobial resistance, thereby increasing the risk of bacterial resistance. The effect of temperature on antimicrobial resistance, and interactions with socioeconomic and hospitalization rate should be seriously considered in antimicrobial stewardship within a changing climate.
Towards an understanding of global brain data governance: perceptions on the ethical and legal principles that influence brain data governance
Authors: Eke Damian , Ochang Paschal , Stahl Bernd Carsten
Published: May 2023

Advances in neuroscience and other disciplines are producing large-scale brain data that consists of datasets from multiple organisms, disciplines, jurisdictions in different formats. However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and legal principles which may influence key stakeholders involved in the generation, collection, processing and sharing of brain data thereby raising ethical and legal challenges. Also, although calls for a culturally informed brain data governance framework have been made, practical steps and studies to understand the global perceptions of key stakeholders using neuroscientists who are influenced by these ethical legal principles is currently lacking. Therefore, using the research question how do ethical and legal principles influence data governance in neuroscience? we attempt to understand the perceptions of key actors on the principles, issues and concerns that can arise from brain data research. We carried out interviews with 21 leading international neuroscientists. The analytical insights revealed key ethical and legal principles, areas of convergence, visibility, and the issues and concerns that arise in brain data research around these principles. These issues and concerns mainly circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. The research answers the call for a cross cultural study of global brain data governance and the results of the study will assist in understanding the issues and concerns that arise in brain data governance.
Linguistic Errors of Indonesian Air Transportation Hoax
Authors: Pinem Yune Andryani
Published: May 2023

The research was to find out linguistics’ indicators of errors found in the body content of false news discourse mingling around air transportation issues that appeared 2-years before the presidential election in 2019. The qualitative research used hoax discourse from the turnbackhoax.com collection. Errors found in discourse were later generated according to linguistics’ domain in Dulay et al., as well as additional spelling errors and surface strategy taxonomy. Toward false news (n=44) found in the data source, errors majorly emerge in newly ungrammatical word formation with omission process equitably distributed in all linguistics’ domains. Errors in phonology portray localization in several dialects in Indonesia, while errors in morphology and syntax appear for language economy purposes. Primarily affected, lexicon and semantics is the domain with the most variant of surface structure errors (n=4). This is relevant to the hoax spread’s intended purpose to deceive the audience by modifying words in originally true news to give different content meanings. Omission resulting in word shortening in the form of blending and clipping with 98% occurrences is popularly used by millennials with a medium level of media literacy to communicate in the digital society environment. To enhance relatedness in readers’ thoughts and feelings, explain errors that deliberately happened in pragmatics and spelling. Being familiar with these indicators can alert the public to question a post’s credibility, thus encouraging the urge for clarification.
Epoxy/Graphite Composite Electrode Modified with Recycled Silver Nanoparticles: An Eco-Friendly Strategy to Improve Lead Detection
Authors: Cabral Taíssa , do Nascimento Suéllen , de Faria Lucas , Lisboa Thalles , Borges Pedro , Nossol Edson , Semaan Felipe , Dornellas Rafael , Pacheco Wagner
Published: May 2023

In this work, silver nanoparticles (AgNPs) obtained from photographic waste were synthesized and electrodeposited via cyclic voltammetry using epoxy-graphite composite as an electrochemical substrate. Both electrodes, unmodified (Epoxy/C) and modified (Epoxy/C/AgNPs), were characterized electrochemically by electrochemical impedance spectroscopy (EIS), charge transfer constant (K0 ), and electroactive area. The modified electrode provided lower charge transfer resistance (275 Ω), more kinetically favored electron transfer (K0 = 1.15 × 10-3 cm s-1), and a 1.7- fold increase in the active area compared to the unmodified electrode. Additional characterizations by scanning electron microscopy (SEM) and Raman spectroscopy confirmed the presence of AgNPs structures on the carbonaceous surface. As a proof of concept, Pb2+ was used as a model analyte, and a square wave anodic stripping voltammetry (SWASV) method was developed to evaluate the analytical performance of both electrodes. A wider linear range (4.0 to 40.0 µg L-1), the appropriate limit of detection (1.2 µg L-1), and a 6-fold increase in sensitivity were found using the modified electrode, suggesting that the AgNPs significantly contributed to the performance of the electrode. The proposed method was applied to three real water samples, where the Pb2+ levels varied from 11.3 to 19.5 µg L-1. The proposed protocol (reuse of silver waste) has proven to be a powerful tool for improving the detection of Pb2+, which can be helpful for other electrochemical sensing applications in locations with minimal infrastructure.
Game Theoretic Approach to Multi-Agent Transportation Problems on Network
Authors:   St. Petersburg State University, Khaled Alkhaled , Petrosyan Leon ,   St. Petersburg State University
Published: May 2023

In this paper, we consider a network game where players are multi-agent systems (we call them in this paper "coalitions") under the condition that the trajectories of players (coalitions) should (have no common arcs, or have no common vertices) i. e. must not intersect. In the same time the trajectories of players inside coalition can intersect (have common arcs,or have common vertices). The last condition complicates the problem, since the sets of strategies turn out to be mutually dependent. A family of Nash equilibrium is constructed and it is also shown that the minimum total time (cost) of players is achieved in a strategy profile that is a Nash equilibrium. A cooperative approach to solving the problem is proposed. Also, another cooperative mini maximal approach to solving the problem is investigated. We also consider the proportional solution and the Shapley value to allocate total minimal costs between players. Two approaches for constructing the characteristic function have been developed.
The τ-value in Multistage Games with Pairwise Interactions
Authors:   St. Petersburg State University, Bulgakova Mariia A.
Published: May 2023

We consider multistage bimatrix games with pairwise interactions. On the first stage players chose their neighbours and formed a network. On the later stages bimatrix games between neighbours by network take places. As a solution consider the τ-value (Tijs, 1987). Earlier we calculated coefficient λ of τ-value in case of two-stage game. Now we consider a general case of one-stage game with any players and any number of links. We assumed followings: N is set of players, N ≥ 2 and any type of network g. It is also assumed, that there are not necessarily paths between every pair of vertices. We will consider conditions for time-consistency of τ-value in two-stage game.
Cooperation in the Multi-Agent System with Different Types of Interactions
Authors:   St. Petersburg State University, Grinikh Aleksandra L. ,   HSE University
Published: May 2023

This paper summarizes the list of our works that contain researches about optimality principles for the "n-person prisoner's dilemma" game. The classic model is considered through the new payoff function for each player that allows to consider it without restrictions for the number of players. The new characteristic function gives an opportunity to introduce the time-consistent subset of the core of the dynamic game. In accordance with this type of game we consider some specific properties of players' payoffs and construct the new way of their interactions. Using the network representation, the classic model is modified to the wider class of games that allows to specify players' influence to each other's payoff function. These investigations can be used for the description of cooperation in the other multi-agent systems.
Owen Value for Dynamic Games on Networks
Authors:   St. Petersburg State University, Pankratova Yaroslavna B. , Petrosyan Leon ,   St. Petersburg State University
Published: May 2023

In the presented paper, we consider dynamic network games with coalition structure in which players cooperate to get the best outcomes. As solution the Owen value is proposed. To simplify the calculations the new characteristic function is introduced based on the possibility of cutting connections by players outside the coalition. For a special case, comparison of the Owen value with other solutions is done.
Comparing the Manipulability of Approval Voting and Borda
Authors:   ITMO University, Teplova Daria , Ianovski Egor ,   HSE University
Published: May 2023

The Gibbard-Satterthwaite theorem established that no non-trivial voting rule is strategy-proof, but that does not mean that all voting rules are equally susceptible to strategic manipulation. Over the past fifty years numerous approaches have been proposed to compare the manipulability of voting rules in terms of the probability of manipulation, the domains on which manipulation is possible, the complexity of finding such a manipulation, and others. In the closely related field of matching, Pathak and Snmez (2013) pioneered a notion of manipulability based on case-by-case comparison of manipulable profiles. The advantage of this approach is that it is independent of the underlying statistical culture or the computational power of the agents, and it has proven fruitful in the matching literature. In this paper, we extend the notion of Pathak and Snmez to voting, studying the families of k-approval and truncated Borda scoring rules. We find that, with one exception, the notion does not allow for a meaningful ordering of the manipulability of these rules.
Solution of the Meeting Time Choice Problem for n Persons
Authors:   Institute of Applied Mathematical Research of Karelian Research Center of the Russian Academy of Sciences, Yashin Vladimir V.
Published: May 2023

We consider a game-theoretic model of negotiations of n persons about a meeting time. The problem is to determine the time of the meeting, with the consensus of all players required to make a final decision. The solution is found by backward induction in the class of stationary strategies. Players' wins are represented by piecewise linear functions having one peak. An subgame perfect equilibrium for the problem in the case of δ ≤ 1/2 is found in analytical form.
Stable Goal-Based Management Strategy for Supply Chain Finance
Authors:   St. Petersburg State University, Zenkevich Nikolay , Ostashova Anastasiia ,   St. Petersburg State University, Ivakina Anastasiia ,   St. Petersburg State University
Published: May 2023

The article is devoted to improving the methodology for managing the total working capital of the supply chain to reduce costs for given ranges of liquidity. The paper analyzes the current state of research on working capital management in supply chain finance and substantiates the multi-objective nature of managing joint working capital in the supply chain. The possibilities of individual financial instruments to reduce the cost of total working capital for given ranges of liquidity have been explored. The necessity of using the technique of dynamic goal programming in the problem of working capital management is substantiated. A model and algorithm of goal management have been developed, which is implemented on cases of real supply chains and has shown its effectiveness
Dynamic Structure in a Four-strategy Game: Theory and Experiment
Authors:   Zhejiang University, Zhijian Wang , Shujie Zhou ,   Zhejiang University, Qinmei Yao ,   Zhejiang University, Yijia Wang ,   Ant Group, Gang Pan ,   Zhejiang University
Published: May 2023

Game dynamics theory, as any field of science, the consistency between theory and experiment is essential. In the past 10 years, important progress has been made in the merging of the theory and experiment in this field, in which dynamics cycle is the presentation. However, the achievement failed to eliminate the constraints of the Euclidean two-dimensional cycle. This paper uses a classic four-strategy game to study the dynamic structure (non-Euclidean superplane cycle). The consistency is in significant between the three ways: (1) analytical results from evolutionary dynamics equations, (2) agent-based simulation results from learning models and (3) laboratory results from human subjects game experiments. The consistency suggests that, the game dynamic structure could be quantitatively predictable, observable, and controllable in general.
Evolution and concept of environmental flows (e-flows): meta-analysis
Authors: Gebreegziabher Gizaw Abera , Degefa Sileshi , Furi Wakgari , Legesse Gudina
Published: May 2023

River ecosystems have become fragile due to river flow alterations in the wake of expending structural controls triggered by the ever-increasing demands of freshwater. The efforts to alleviate the effects of river flow alterations have paved the way for the emergence and expansion of the concept of e-flows. The role of e-flows in ecosystem protection and water resources management is not yet fully understood and practiced. This review summarizes the historical evolution of the concept of e-flows and recaps its assessment methods; analyzes the impacts of river flow alterations; indicates the challenges e-flows have been facing and identifies the gaps in the focus of e-flows research. A total of 303 articles published since 2010 were collected and systematically assessed. Most of the publications are focused on describing the effects and management of river flow alterations. According to the findings of this meta-analysis, less attention is given to basin-scale, social and policy aspects of e-flows. Furthermore, little consideration is given to the part played by groundwater in e-flows along with the roles of e-flows in maintaining the health of floodplains and e-flows of intermittent rivers and ephemeral streams.
Constructed wetland technology for the treatment and reuse of urban household greywater under conditions of Africa's Sahel region
Authors: Compaoré Cheik Omar Tidiane , Maiga Ynoussa , Nikiéma Mahamadi , Mien Oumarou , Nagalo Issa , Panandtigri Hermann Tambila , Mihelcic James R. , Ouattara Aboubakar Sidiki
Published: May 2023

Three pilot systems of a constructed wetland were assessed to advance the urban reuse of greywater for household gardening in areas of the world representative of Africa's Sahel (one unplanted and two planted with local species, namely Andropogon gayanus and Chrysopogon zizanioides). Principal component analysis showed planted systems provided higher removal efficiencies than the unplanted system and A. gayanus performed better for treating most water quality parameters. As expected, removal efficiencies for suspended solids (SS) and chemical oxygen demand (COD) were greater than 90% in all filters. The removal of five-day biochemical oxygen demand (BOD5) was, however, significantly greater in the filter planted with A. gayanus than in the unplanted control. For plant-beneficial parameters such as nutrients (NO3−, NO2−, NH4+, and PO43−), the removal in the planted filters was significantly higher than in the unplanted filter (>90% versus 73%–78%). The reduction of fecal coliforms was significantly greater in the two planted systems and exceeded 2.5 log10 removal. Analysis of the microbial water quality characteristics showed the concentration of fecal microbial indicators would achieve WHO guidelines for restricted irrigation with retention of some embedded nutrients, thus reducing pressures on areas experiencing climate variability, water scarcity, and land degradation.
Synthesis of Ferrocenylimidazo[1,2-a]pyridine-3-amines Using Catalyst
ZrO(NO3)2.2H2O and their Theoretical Studies
Authors: Shajari Nahid , Yahyaei Hooriye , Zafarani Seyyede Sanaz
Published: May 2023

In this study, synthesis of ferrocenylimidazo[1,2-a]pyridine-3-amines was reported from
the condensation reaction of ferrocenecarboxaldehyde, 2-aminopyridine, and isocyanides using
ZrO(NO3)2.2H2O as an efficient, new, and reusable catalyst under reflux conditions in ethanol. The
structures of the products were confirmed by IR, 1H NMR, 13C NMR, and elemental analysis. Quantum
theoretical calculations for the structure of compounds (4a, 4b, and 4c) were done using the Def2
with the TZVPPD basis set. Geometric parameters were obtained from the optimized structures, and
experimental results were analyzed with the calculated data. The structures of the products were
proved by IR, 1H NMR, 13C NMR, and elemental analysis. Computations were carried out for the IR
spectra data and 1H NMR and 13C NMR chemical shifts of the ferrocenylimidazo[1,2-a]pyridine-3-
amine derivatives in the ground state. Ultimately, a great agreement was found between experimental
and theoretical results.
An Overview of Julia-lythgoe Olefination
Authors: Varsha Vijayan , Radhika Sankaran , Anilkumar Gopinathan
Published: May 2023

Julia-Lythgoe olefination (or simply Julia olefination) is an olefination process between phenyl sulfones and aldehydes (or ketones) to give alkenes after alcohol functionalization and re-ductive elimination using sodium amalgam or SmI2. It is mainly used to synthesize E-alkenes and is a key step in numerous total syntheses of many natural products. This review exclusively deals with the Julia-Lythgoe olefination and concentrates mainly on the applications of this reaction in natural product synthesis covering literature up to 2021.
Novel Penicillin Derivatives Against Selected Multiple-drug Resistant Bacterial Strains: Design, Synthesis, Structural Analysis, In Silico and In Vitro Studies
Authors: Hussen Narmin Hamaamin , Hamid Shokhan Jamal , Sabir Mohammed Nawzad , Hasan Aso Hameed , Mohammed Sewara Jalal , Shali Aras Ahmed Kamal
Published: May 2023

Introduction:

The rising numbers of multiple drug-resistant (MDR) pathogens and the
consequent antibacterial therapy failure that resulted in severe medical conditions push to illustrate
new molecules with extended activity against the resistant strains. In this manner, chemical
derivatization of known antibiotics is proposed to save efforts in drug discovery, and penicillins
serve as an ideal in this regard.

Methods:

Seven synthesized 6-aminopenicillanic acid-imine derivatives (2a-g) were structure elucidated
using FT-IR, 1H NMR, 13C NMR, and MS spectroscopy. In silico molecular docking and
ADMET studies were made. The analyzed compounds obeyed Lipinski’s rule of five and showed
promising in vitro bactericidal potential when assayed against E. coli, E. cloacae, P. aeruginosa,
S. aureus, and A. baumannii. MDR strains using disc diffusion and microplate dilution techniques.

Results:

The MIC values were 8 to 32 μg/mL with more potency than ampicillin, explained by
better membrane penetration and more ligand-protein binding capacity. The 2g entity was active
against E. coli. This study was designed to find new active penicillin derivatives against MDR
pathogens.

Conclusion:

The products showed antibacterial activity against selected MDR species and good
PHK, PHD properties, and low predicted toxicity, offering them as future candidates that require
further preclinical assays.
The Effects of Obesity on Bone Turnover Markers in Diabetic Patients with
Diabetic Ketosis or Ketoacidosis
Authors: Gong Min , Xu Chenglin , Wen Song , Yuan Yue , Yang Liling , Zhou Mingyue , Li Yanyan , Zhou Ligang
Published: May 2023

Purpose:

Despite the fact that diabetes individuals are often associated with a higher risk of bone fracture, our previous research demonstrated that Diabetic ketosis (DK) or ketoacidosis (DKA) induced significant alterations in bone biomarkers. It is unknown whether there is a difference in bone metabolism between obese and non-obese diabetic populations while they are in DK or DKA; hence the current study will investigate this further to aid in the prognosis and prediction of bone fracture risk in patients with different BMIs.

Methods:

We categorized patients into four groups based on their BMI utilizing data from our hospital's medical record system from 2018 to 2020 in the Department of Endocrinology: obese DK or DKA patients (OB+DK/DKA, n = 41), non-obese DK or DKA patients (DK/DKA, n = 201), obese type 2 diabetes patients without DK or DKA (OB+T2D, n = 93), and patients with type 2 diabetes only (T2D only, n = 304). The comparisons were made on glycosylated hemoglobin (HbA1c), body mass index (BMI), fasting plasma C-peptide (FPCP), and plasma lipids, in addition to bone metabolism indicators such as total 25-OH-VitD3 (25-OH-VitD3), N-terminal middle molecular fragment of osteocalcin (NMID), -C terminal cross-linking telopeptide of type 1 collagen (-CTX), parathyroid hormone (PTH), and blood calcium (Ca2+).

Results:

The OB+DK/DKA group had a lower average age (p < 0.05) than the DK/DKA group, while the DK/DKA group had a significantly lower FPCP (p < 0.05). The 25-OH-VitD3 levels of DK/DKA patients were considerably lower than those of the T2D-only group (p < 0.05). In contrast, NMID and Ca2+ levels were significantly lower than those of non-ketosis or acidosis patients (p < 0.05), and PTH levels in the DK/DKA group were significantly lower than those of OB+ T2D patients (p < 0.05). In contrast, the β-CTX of the DK or DKA group (OB+DK/DKA and DK+DKA) was significantly greater than that of the non-DK or DKA group (p < 0.05), although there was no significant difference in blood phosphorus between OB+DK/DKA and DK/ DKA (p > 0.05). The levels of thyroid-stimulating hor-mone (TSH) and free T4 (FT4) did not differ significantly among the four groups (p > 0.05); however, the levels of total T3 (TT3), T4 (TT4), and free T3 (FT3) were significantly lower in the DK/DKA group (p < 0.05); the ratio of TT3 to TT4 (TT3/TT4) was significantly decreased in the DK/DKA group, whereas the ratio of FT3/FT4 was significantly lower (p < 0.05).

Conclusion:

Obese patients with DK or DKA have a younger onset age, superior pancreatic function, and better blood glucose management than non-obese patients with DK/DKA. Despite having higher bone absorption signals than non-DK/DKA patients, OB+DK/DKA patients have stronger bone for-mation markers than non-obese DK/DKA patients, according to a recent study. Changes in markers of bone metabolism may be linked to non-thyroidal illness syndrome in cases of DK or DKA.
Intelligent Technique for Moving Object Detection from Problematic Video
Captured through Camera Sensor
Authors: Mishra Sneha , Yadav Dileep Kumar
Published: May 2023

Aim:

The significant aim of the proposed work is to develop an adaptive method to compute
the threshold during run-time and update it adaptively for each pixel in the testing phase. It
classifies motion-oriented pixels from the scene for moving objects using background subtraction
and enhances using post-processing.

Background:

According to the huge demand for surveillance system, society is looking towards an
intelligent video surveillance system that detect and track moving objects from video captured
through a surveillance camera. So, it is very crucial and highly recommended throughout the globe
in numerous domains such as video-based surveillance, healthcare, transportation, and many more.
Practically, this research area faces lots of challenging issues such as illumination variation, cluttered
background, camouflage, etc. So, this paper has developed an adaptive background subtraction
method to handle such challenging problems.

Objective:

To focus and study the problematic video data captured through the camera sensor.
To handle challenging issues available in real-time video scenes.
To develop a background subtraction method and update the background model adaptively for moving
object detection.

Method:

The proposed method has been accomplished using the following sections:
Background model construction
Automatic generation of threshold
Background subtraction
Maintenance of background model

Results:

The qualitative analysis of the proposed work is experimented with publicly available datasets
and compared with considered state-of-the-art methods. In this work, library sequence (thermal
data) of CDNET and other color video frame sequences Foreground aperture, Waving Tree and
Camouflage are considered from Microsoft’s Wallflower. The quantitative values depicted in Table-
1. This work demonstrate the better performance of the proposed method as compared to state-ofthe-
art methods. It also generates better outcomes and handles the problem of a dynamic environment
and illumination variation.

Conclusion:

Currently, the world is demanding computer vision-based security and surveillancebased
applications for society. This work has provided a method for the detection of moving information
using an adaptive method of background subtraction approach for moving object detection
in video scenes. The performance evaluation depicts better average results as compared to considered
peer methods.
Impact of COVID-19 on Public Interest in Breast Cancer Screening and Related Symptoms: Google Trends Analysis
Authors: Tan Si Ying , Tang Matilda Swee Sun , Ong Chin-Ann Johnny , Tan Veronique Kiak Mien , Shannon Nicholas Brian
Published: May 2023

Background

The COVID-19 pandemic has led to a decrease in cancer screening due to the redeployment of health care resources and public avoidance of health care facilities. Breast cancer is the most common cancer diagnosed in female individuals, with improved survival rates from early detection. An avoidance of screening, resulting in late detection, greatly affects survival and increases health care resource burden and costs.

Objective

This study aimed to evaluate if a sustained decrease in public interest in screening occurred and to evaluate other search terms, and hence interest, associated with that.

Methods

This study used Google Trends to analyze public interest in breast cancer screening and symptoms. We queried search data for 4 keyword terms (“mammogram,” “breast pain,” “breast lump,” and “nipple discharge”) from January 1, 2019, to January 1, 2022. The relative search frequency metric was used to assess interest in these terms, and related queries were retrieved for each keyword to evaluate trends in search patterns.

Results

Despite an initial drastic drop in interest in mammography from March to April 2020, this quickly recovered by July 2020. After this period, alongside the recovery of interest in screening, there was a rapid increase in interest for arranging for mammography. Relative search frequencies of perceived breast cancer–related symptoms such as breast lump, nipple discharge, and breast pain remained stable. There was increase public interest in natural and alternative therapy of breast lumps despite the recovery of interest in mammography and breast biopsy. There was a significant correlation between search activity and Breast Cancer Awareness Month in October.

Conclusions

Online search interest in breast cancer screening experienced a sharp decline at the beginning of the COVID-19 pandemic, with a subsequent return to baseline interest in arranging for mammography followed this short period of decreased interest.
Online Mental Health Forums and Rural Resilience: Mixed Methods Study and Logic Model
Authors: Steiner Artur , Farmer Jane , Kamstra Peter , Carlisle Karen , McCosker Anthony , Kilpatrick Sue
Published: May 2023

Background

Rural mental health is a growing area of concern internationally, and online mental health forums offer a potential response to addressing service gaps in rural communities.

Objective

The objective of this study was to explore and identify pathways by which online peer support mental health forums help to build resilience for rural residents experiencing mental ill-health by contributing to overcoming their specific contextual challenges.

Methods

We developed a Theoretical Resilience Framework and applied it to 3000 qualitative posts from 3 Australian online mental health forums and to data from 30 interviews with rural forum users.

Results

Drawing on the findings and an abductive approach, a logic model was developed to illustrate links between the resilience resources built and enabling features of forums that make them spaces that facilitate resilience.

Conclusions

The study demonstrated that online forums make valuable contributions to social well-being and access to a range of timely support services for rural people experiencing mental ill-health, and, while doing so, involve users in the processes of resilience building. The study provides a new way for practitioners to frame the work of and value produced by forums. It gives a logic model that can be used in evaluation and audit as it facilitates a causal framing of how forums, as an intervention, link with resilience outcomes. Ultimately, the study contributes to developing new knowledge about how rural resilience building can be conceptualized and measured while showing how forums are part of contemporary health service provision in rural places.
Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach (Preprint)
Authors: Baba Ayako , Bunji Kyosuke
Published: May 2023

BACKGROUND

One of the reasons why students go to counseling is being called on based on self-reported health survey results. However, there is no concordant standard for such calls.

OBJECTIVE

This study aims to develop a machine learning (ML) model to predict students’ mental health problems in 1 year and the following year using the health survey’s content and answering time (response time, response time stamp, and answer date).

METHODS

Data were obtained from the responses of 3561 (62.58%) of 5690 undergraduate students from University A in Japan (a national university) who completed the health survey in 2020 and 2021. We performed 2 analyses; in analysis 1, a mental health problem in 2020 was predicted from demographics, answers for the health survey, and answering time in the same year, and in analysis 2, a mental health problem in 2021 was predicted from the same input variables as in analysis 1. We compared the results from different ML models, such as logistic regression, elastic net, random forest, XGBoost, and LightGBM. The results with and without answering time conditions were compared using the adopted model.

RESULTS

On the basis of the comparison of the models, we adopted the LightGBM model. In this model, both analyses and conditions achieved adequate performance (eg, Matthews correlation coefficient [MCC] of with answering time condition in analysis 1 was 0.970 and MCC of without answering time condition in analysis 1 was 0.976; MCC of with answering time condition in analysis 2 was 0.986 and that of without answering time condition in analysis 2 was 0.971). In both analyses and in both conditions, the response to the questions about campus life (eg, anxiety and future) had the highest impact (Gain 0.131-0.216; Shapley additive explanations 0.018-0.028). Shapley additive explanations of 5 to 6 input variables from questions about campus life were included in the top 10. In contrast to our expectation, the inclusion of answering time–related variables did not exhibit substantial improvement in the prediction of students’ mental health problems. However, certain variables generated based on the answering time are apparently helpful in improving the prediction and affecting the prediction probability.

CONCLUSIONS

These results demonstrate the possibility of predicting mental health across years using health survey data. Demographic and behavioral data, including answering time, were effective as well as self-rating items. This model demonstrates the possibility of synergistically using the characteristics of health surveys and advantages of ML. These findings can improve health survey items and calling criteria.
Corpus-Based Discourse Analysis of a Reddit Community of Users of Crystal Methamphetamine: Mixed Methods Study (Preprint)
Authors: Lustig Andrew , Brookes Gavin
Published: May 2023

BACKGROUND

Methamphetamine is a highly addictive stimulant that affects the central nervous system. Crystal methamphetamine is a form of the drug resembling glass fragments or shiny bluish-white rocks that can be taken through smoking, swallowing, snorting, or injecting the powder once it has been dissolved in water or alcohol.

OBJECTIVE

The objective of this study is to examine how identities are socially (discursively) constructed by people who use methamphetamine within a subreddit for people who regularly use crystal meth.

METHODS

Using a mixed methods approach, we analyzed 1000 threads (318,422 words) from a subreddit for regular crystal meth users. The qualitative component of the analysis used concordancing and corpus-based discourse analysis to identify discursive themes informed by assemblage theory. The quantitative portion of the analysis used corpus linguistic techniques including keyword analysis to identify words occurring with statistically marked frequency in the corpus and collocation analysis to analyze their discursive context.

RESULTS

Our findings reveal that the subreddit contributors use a rich and varied lexicon to describe crystal meth and other substances, ranging from a neuroscientific register (eg, methamphetamine and dopamine) to informal vernacular (eg, meth, dope, and fent) and commercial appellations (eg, Adderall and Seroquel). They also use linguistic resources to construct symbolic boundaries between different types of methamphetamine users, differentiating between the esteemed category of “functional addicts” and relegating others to the stigmatized category of “tweakers.” In addition, contributors contest the dominant view that methamphetamine use inevitably leads to psychosis, arguing instead for a more nuanced understanding that considers the interplay of factors such as sleep deprivation, poor nutrition, and neglected hygiene.

CONCLUSIONS

The subreddit contributors’ discourse offers a “set and setting” perspective, which provides a fresh viewpoint on drug-induced psychosis and can guide future harm reduction strategies and research. In contrast to this view, many previous studies overlook the real-world complexities of methamphetamine use, perhaps due to the use of controlled experimental settings. Actual drug use, intoxication, and addiction are complex, multifaceted, and elusive phenomena that defy straightforward characterization.

CLINICALTRIAL
Develop a Quality Indicator System for Evaluating the Internet Plus Home Care Nursing Services Based on SERVQUAL Model：a Delphi-Analytic Hierarchy Process Study (Preprint)
Authors: YE Lei , Yang Shulan , Jiang Biyan , Liu Caixia , Jin Xiaoqing , Chang polun
Published: May 2023

BACKGROUND

In the context of global population aging and the rapid development of information technology, the demand for Internet Plus Home Care Nursing (Internet + HCN) services have been on the rise, especially in China. The Internet + HCN services have the potential to maximize existing human resources to counter the shortage of medical healthcare services. However, at present, the Internet + HCN services can hardly spread for the lack of standardized service quality governance. Quality indicators for service evaluation of the Internet + HCN services are under-defined.

OBJECTIVE

To develop a quality indicator system for evaluating the Internet + HCN services, and to shed some theoretical light on assessing mHealth service quality from a user experience perspective.

METHODS

An initial quality indicator system was established adopting the SERVQUAL model, based on scenarios related to the Internet + HCN services. Then, the Delphi Method was applied to modify the indicators according to experts’ opinions. And the Analytic Hierarchy Process (AHP) was applied to calculate the indicator weight. Finally, a quality indicator system for evaluating the Internet + HCN services was developed based on the SERVQUAL Model.

RESULTS

Altogether, 17 experts from relevant fields such as nursing education, clinical nursing, health management, and health informatics were consulted through email surveys. The response rates in both rounds of Delphi and the AHP were 100%. The average expert authority coefficients were 0.912 and 0.925 respectively in two rounds of Delphi. And the Kendall’s W, indicating variation coefficients, ranged from 0.262 to 0.265. Finally, a quality indicator system for evaluating the Internet + HCN services, comprising 5 primary indicators and 15 secondary indicators, was developed. Primary indicators and their AHP generated weights were listed as follows: assurance (0.245), reliability (0.240), tangibles (0.192), responsiveness (0.190), and empathy (0.132).

CONCLUSIONS

By measuring the services quality gap between user expectations and perceptions, the proposed SERVQUAL Model based quality indicator system has the potential in improving the quality of Internet + HCN services through the perspective of user experiences.
Application and challenges of a metaverse in medicine (Preprint)
Authors: Li Congcong , qu lai , Cai Hongfei , Wang Yingshu
Published: May 2023

UNSTRUCTURED

Metaverse has been confirmed as a relatively amorphous concept of innovation, which refers to technological advancement. Metaverse, i.e., a coalition between reality world and virtual world, has created significant significance and convenience in education, communication, economy, etc. The COVID-19 outbreak has stimulated the growth of metaverse applications in medicine. The above-mentioned technology has broad applications while comprising online remote medical treatment, online conferences, medical education, preparation of surgical plans, etc. Moreover, technical, security, and financial challenges should be tackled down by the future widespread use of metaverse. Metaverse is limitlessly promising, and it will exert a certain effect on future scientific and technological advancements in the medical industry. The review article primarily aims to summarize the application of the metaverse in medicine and their challenge in the future of medicine.