bluuebunny/crossref_metadata_2025 huggingface dataset:
Scoping review to evaluate the effects of peer support on the mental health of young adults
Authors: Richard Jérémie , Rebinsky Reid , Suresh Rahul , Kubic Serena , Carter Adam , Cunningham Jasmyn E A , Ker Amy , Williams Kayla , Sorin Mark
Published: August 2022

Objectives

Young adults report disproportionality greater mental health problems compared with the rest of the population with numerous barriers preventing them from seeking help. Peer support, defined as a form of social-emotional support offered by an individual with a shared lived experience, has been reported as being effective in improving a variety of mental health outcomes in differing populations. The objective of this scoping review is to provide an overview of the literature investigating the impact of peer support on the mental health of young adults.

Design

A scoping review methodology was used to identify relevant peer-reviewed articles in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines across six databases and Google/Google Scholar. Overall, 17 eligible studies met the inclusion criteria and were included in the review.

Results

Overall, studies suggest that peer support is associated with improvements in mental health including greater happiness, self-esteem and effective coping, and reductions in depression, loneliness and anxiety. This effect appears to be present among university students, non-student young adults and ethnic/sexual minorities. Both individual and group peer support appear to be beneficial for mental health with positive effects also being present for those providing the support.

Conclusions

Peer support appears to be a promising avenue towards improving the mental health of young adults, with lower barriers to accessing these services when compared with traditional mental health services. The importance of training peer supporters and the differential impact of peer support based on the method of delivery should be investigated in future research.
Effects of remote learning during the COVID-19 lockdown on children’s visual health: a systematic review
Authors: Cortés-Albornoz María Camila , Ramírez-Guerrero Sofía , Rojas-Carabali William , de-la-Torre Alejandra , Talero-Gutiérrez Claudia
Published: August 2022

Objectives

Increased exposure to digital devices as part of online classes increases susceptibility to visual impairments, particularly among school students taught using e-learning strategies. This study aimed to identify the impact of remote learning during the COVID-19 lockdown on children’s visual health.

Design

Systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Data sources

Scopus, PubMed and ScienceDirect databases from the year 2020 onwards.

Eligibility criteria

We included cross-sectional, case–control, cohort studies, case series and case reports, published in English, Spanish or French, that approached the effects of remote learning during the COVID-19 lockdown on visual health in neurotypical children.

Data extraction and synthesis

We included a total of 21 articles with previous quality assessments using the Joanna Briggs checklist. Risk of bias assessment was applied using the National Institutes of Health quality assessment tool for before-and-after studies with no control group; the tool developed by Hoyet alto assess cross-sectional studies; the Muradet altool to evaluate the methodological quality of case reports and case series; and the Newcastle-Ottawa Scale for cohort studies.

Results

All but one study reported a deleterious impact of the COVID-19 lockdown on visual health in children. Overall, the most frequently identified ocular effects were refractive errors, accommodation disturbances and visual symptoms such as dry eye and asthenopia.

Conclusions

Increased dependence on digital devices for online classes has either induced or exacerbated visual disturbances, such as rapid progression of myopia, dry eye and visual fatigue symptoms, and vergence and accommodation disturbances, in children who engaged in remote learning during the COVID-19 lockdown.

PROSPERO registration number

CRD42022307107.
Working from home during COVID-19 in a Danish hospital research setting: experiences of researchers and healthcare providers, explored by Group Concept Mapping
Authors: Specht Ina Olmer , Winckler Karoline , Christensen Robin , Bomhoff Claus , Raffing Rie , Wæhrens Eva Ejlersen
Published: August 2022

Objectives

The COVID-19 pandemic has changed the working environment, how we think of it and how it stands to develop into the future. Knowledge about how people have continued to work on-site and adjusted to working from home during the COVID-19 lockdown will be vital for planning work arrangements in the post-pandemic period. Our primary objective was to investigate experiences of working from home or having colleagues working from home during a late stage of the COVID-19 lockdown among researchers and healthcare providers in a hospital research setting. Second, we aimed to investigate researchers’ productivity through changes in various proxy measures during lockdown as compared with pre-lockdown.

Design

Mixed-method participatory Group Concept Mapping (GCM).

Setting and participants

GCM, based on a mixed-method participatory approach, was applied involving researchers’ and healthcare providers’ online sorting and rating experiences working from home during the COVID-19 pandemic. At a face-to-face meeting, participants achieved consensus on the number and labelling of domains—the basis for developing a conceptual model.

Results

Through the GCM approach, 47 participants generated 125 unique statements of experiences related to working from home, which were organised into seven clusters. Using these clusters, we developed a conceptual model that illustrated the pros and cons of working from home.

Conclusion

The future work setting, the role of the office and the overall work environment need to respond to workers’ increased wish for flexible work arrangements and co-decision.
Quality improvement initiative to improve infant safe sleep practices in the newborn nursery
Authors: Shaikh Sophie Kay , Chamberlain Lauren , Nazareth-Pidgeon Kristina Marie , Boggan Joel C
Published: August 2022

The American Academy of Pediatrics recommends that healthcare professionals model their safe infant sleeping environment recommendations, yet adherence to safe sleep practices within our community hospital mother–baby unit was low. We used quality improvement (QI) methodology to increase adherence to infant safe sleep practices, with a goal to improve the proportion of infants sleeping in an environment that would be considered ‘perfect sleep’ to 70% within a 1-year period. The project occurred while the hospital was preparing for Baby Friendly certification, with increased emphasis on rooming in and skin to skin at the same time.

Multiple Plan–Do–Study–Act cycles were performed. Initial cycles targeted nurse and parental education, while later cycles focused on providing sleep sacks/wearable blankets for the infants.

While we did not meet our goal, the percentage of infants with ‘perfect sleep’ increased from a baseline of 41.9% to 67.3%, and we also saw improvement in each of the individual components that contribute to this composite measure. Improvements were sustained over 12 months later, suggesting that QI interventions targeting infant safe sleep in this inpatient setting can have long-lasting results. This project also suggests that infant safe sleep QI initiatives and preparation towards Baby Friendly Hospital Certification can be complementary.
National estimates of emergency department visits for medication-related self-harm: United States, 2016–2019
Authors: Geller Andrew I , Ehlman Daniel C , Lovegrove Maribeth C , Budnitz Daniel S
Published: August 2022

Background

Medication poisoning is a common form of self-harm injury, and increases in injuries due to self-harm, including suicide attempts, have been reported over the last two decades.

Methods

Cross-sectional (2016–2019) data from 60 emergency departments (EDs) participating in an active, nationally representative public health surveillance system were analysed and US national estimates of ED visits for medication-related self-harm injuries were calculated.

Results

Based on 18 074 surveillance cases, there were an estimated 269 198 (95% CI 222 059 to 316 337) ED visits for medication-related self-harm injuries annually in 2016–2019 compared with 1 404 090 visits annually from therapeutic use of medications. Population rates of medication-related self-harm ED visits were highest among persons aged 11–19 years (58.5 (95% CI 45.0 to 72.0) per 10 000) and lowest among those aged ≥65 years (6.6 (95% CI 4.4 to 8.8) per 10 000). Among persons aged 11–19 years, the ED visit rate for females was four times that for males (95.4 (95% CI 74.2 to 116.7) vs 23.0 (95% CI 16.4 to 29.6) per 10 000). Medical or psychiatric admission was required for three-quarters (75.1%; 95% CI 70.0% to 80.2%) of visits. Concurrent use of alcohol or illicit substances was documented in 40.2% (95% CI 36.8% to 43.7%) of visits, and multiple medication products were implicated in 38.6% (95% CI 36.8% to 40.4%). The most frequently implicated medication categories varied by patient age.

Conclusions

Medication-related self-harm injuries are an important contributor to the overall burden of ED visits and hospitalisations for medication-related harm, with the highest rates among adolescent and young adult females. These findings support continued prevention efforts targeting patients at risk of self-harm.
Asthma exacerbations are associated with a decline in lung function: a longitudinal population-based study
Authors: Soremekun Seyi , Heaney Liam G , Skinner Derek , Bulathsinhala Lakmini , Carter Victoria , Chaudhry Isha , Hosseini Naeimeh , Eleangovan Neva , Murray Ruth , Tran Trung N , Emmanuel Benjamin , Garcia Gil Esther , Menzies-Gow Andrew , Peters Matthew , Lugogo Njira , Jones Rupert , Price David B
Published: August 2022

Rationale

Progressive lung function (LF) decline in patients with asthma contributes to worse outcomes. Asthma exacerbations are thought to contribute to this decline; however, evidence is limited with mixed results.

Methods

This historical cohort study of a broad asthma patient population in the Optimum Patient Care Research Database, examined asthma patients with 3+eligible post-18th birthday peak expiratory flow rate (PEF) records (primary analysis) or records of forced expiratory flow in 1 s (FEV1) (sensitivity analysis). Adjusted linear growth models tested the association between mean annual exacerbation rate (AER) and LF trajectory.

Results

We studied 1 09 182 patients with follow-up ranging from 5 to 50 years, of which 75 280 had data for all variables included in the adjusted analyses. For each additional exacerbation, an estimated additional −1.34 L/min PEF per year (95% CI −1.23 to –1.50) were lost. Patients with AERs >2/year and aged 18–24 years at baseline lost an additional −5.95 L/min PEF/year (95% CI −8.63 to –3.28) compared with those with AER 0. These differences in the rate of LF decline between AER groups became progressively smaller as age at baseline increased. The results using FEV1were consistent with the above.

Conclusion

To our knowledge, this study is the largest nationwide cohort of its kind and demonstrates that asthma exacerbations are associated with faster LF decline. This was more prominent in younger patients but was evident in older patients when it was related to lower starting LF, suggesting a persistent deteriorating phenotype that develops in adulthood over time. Earlier intervention with appropriate management in younger patients with asthma could be of value to prevent excessive LF decline.
Exchange Rate Volatility and Trade Flows of Pakistan with Major Trading Partners: Is There an Asymmetric Third-Country Effect?
Authors: Akhtar Parveen , Ullah Sana , Majeed Muhammad Tariq , Usman Ahmed
Published: August 2022

In this study, we explored the effect of third-country exchange rate volatility on Pakistan’s trade with the major trading partner e.g., USA, Germany, UK, China, Italy, Spain, France, Japan, and Malaysia. To capture the effect of third-country exchange rate volatility, we used monthly data of exports and imports of Pakistan covering the period from January 1981 to January 2018. We used the NARDL technique to obtain the asymmetric results, which are more applicable in reality due to the difference in traders’ response to increased volatility as compared to when they are dealing with decreased volatility. This technique captured the cointegration in the models. The findings of this study suggest that the volatility of the exchange rate has importance in Pakistan trade. The results revealed that in both short-run and long-run, the third-country effect is found significant in eight export and eight import models. The results of both models revealed that the effects of exchange rate volatility are asymmetric in nature on Pakistan’s trade with major trading partners.
Data Sharing and Information Platforms in Crisis Response and Preparedness: Exploring the Role of Open Data Sharing Platforms and Collective Intelligence in COVID-19 Response Efforts, and Preparedness for Future Pandemics
Authors: Lungati Angela Oduor
Published: August 2022

COVID-19 has wreaked unprecedented havoc in the world. Response efforts have also made huge evident gaps in preparedness and governments around the world's capacity to respond to a health crisis of this magnitude adequately. As a result, local communities have taken matters into their own hands and turned to technology platforms to coordinate mutual aid efforts, shed light on response gaps, and hold governments accountable. This paper explores the role of open data sharing platforms and collective intelligence in COVID-19 response efforts by studying two examples of community-led initiatives from Spain and Japan. Frena La Curva (Spain) and Safecast (Japan) utilized the Ushahidi platform, an open-source technology tool born out of Kenya's post-election violence that has been widely used in over 160 countries for crisis response since its inception in 2008. Research reports have been warning of pandemic breakouts for decades. However, the response to COVID-19 was inadequate, with healthcare systems buckling under the pressure of the spread of the disease.

Moreover, existing social protection programs could not shield citizens despite having experienced similar economic impacts in the years that have passed. Data hugging and suppression of information regarding the pandemic outbreak led to significant delays in measures being put in place to curb the spread of COVID-19. This paper proposes that governments would benefit from leveraging open data and technology platforms to engage with ordinary citizens and eliminate data blind spots in the design of social protection programs. It also posits that we need to invest in interoperable data exchange systems to increase the speed of response and learning. Finally, it also proposes the need for internet freedom and access as a critical tool for preparedness by enabling the free flow of information.
Digital Trust and the Reconstruction of Trust in the Digital Society: An Integrated Model based on Trust Theory and Expectation Confirmation Theory
Authors: Guo Yuanyuan
Published: August 2022

Digital trust is born with the evolution of digital society. It is an inescapable topic in the digital society and it is developed from traditional interpersonal trust and institutional trust and has been extensively used in the Internet space. At present, the research on digital trust is rare. Based on the Trust Theory and the Expectation Confirmation Theory, this paper puts forward an integration model with user satisfaction as the intermediary variable. Besides, this paper develops a set of scales for evaluating digital trust combined with maturity scales and points out that digital trust consists of digital cognitive trust and emotional trust. This paper assumed that user perception and user expectation indirectly affect digital trust through user satisfaction and used SPSS 23.0 to do reliability, validity test, and exploratory factor analysis. The results found that user satisfaction plays a mediating role by fitting, evaluating, and optimizing the structural equation model with AMOS23.0. User satisfaction is a partial intermediary between user perception and digital trust, and it is the complete intermediary between user expectation and digital trust. These results demonstrate two things. Firstly, in the digital society, the construction of users' digital trust is based on users' satisfaction. The government should provide diversified and high-quality e-government services as far as possible. Secondly, digital trust is directly or indirectly affected by user perception and user expectation. The government should build a safe, green, and harmonious digital environment for users and make e-government services consistent with users' expectation.
Nash Social Welfare in Selfish and Online Load Balancing
Authors: Bilò Vittorio , Monaco Gianpiero , Moscardelli Luca , Vinci Cosimo
Published: August 2022

In load-balancing problems there is a set of clients, each wishing to select a resource from a set of permissible ones to execute a certain task. Each resource has a latency function, which depends on its workload, and a client’s cost is the completion time of her chosen resource. Two fundamental variants of load-balancing problems are
selfish load balancing
(a.k.a.
load-balancing games
), where clients are non-cooperative selfish players aimed at minimizing their own cost solely, and
online load balancing
, where clients appear online and have to be irrevocably assigned to a resource without any knowledge about future requests. We revisit both problems under the objective of minimizing the
Nash Social Welfare
, i.e., the geometric mean of the clients’ costs. To the best of our knowledge, despite being a celebrated welfare estimator in many social contexts, the Nash Social Welfare has not been considered so far as a benchmarking quality measure in load-balancing problems. We provide tight bounds on the price of anarchy of pure Nash equilibria and on the competitive ratio of the greedy algorithm under very general latency functions, including polynomial ones. For this particular class, we also prove that the greedy strategy is optimal, as it matches the performance of any possible online algorithm.
Learning Strong Substitutes Demand via Queries
Authors: Lock Edwin , Goldberg Paul W. , Marmolejo-Cossío Francisco
Published: August 2022

This article addresses the computational challenges of learning strong substitutes demand when given access to a demand (or valuation) oracle. Strong substitutes demand generalises the well-studied gross substitutes demand to a multi-unit setting. Recent work by Baldwin and Klemperer shows that any such demand can be expressed in a natural way as a finite list of weighted bid vectors. A simplified version of this bidding language has been used by the Bank of England.

Assuming access to a demand oracle, we provide an algorithm that computes the unique list of weighted bid vectors corresponding to a bidder’s demand preferences. In the special case where their demand can be expressed using positive bids only, we have an efficient algorithm that learns this list in linear time. We also show super-polynomial lower bounds on the query complexity of computing the list of bids in the general case where bids may be positive and negative. Our algorithms constitute the first systematic approach for bidders to construct a bid list corresponding to non-trivial demand, allowing them to participate in “product-mix” auctions.
LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator
Authors: Tao Zhuofu , Wu Chen , Liang Yuan , Wang Kun , He Lei
Published: August 2022

Graph convolutional networks (GCNs) have been introduced to effectively process non-Euclidean graph data. However, GCNs incur large amounts of irregularity in computation and memory access, which prevents efficient use of traditional neural network accelerators. Moreover, existing dedicated GCN accelerators demand high memory volumes and are difficult to implement onto resource limited edge devices.

In this work, we propose LW-GCN, a lightweight FPGA-based accelerator with a software-hardware co-designed process to tackle irregularity in computation and memory access in GCN inference. LW-GCN decomposes the main GCN operations into Sparse Matrix-Matrix Multiplication (SpMM) and Matrix-Matrix Multiplication (MM). We propose a novel compression format to balance workload across PEs and prevent data hazards. Moreover, we apply data quantization and workload tiling, and map both SpMM and MM of GCN inference onto a uniform architecture on resource limited hardware. Evaluation on GCN and GraphSAGE are performed on Xilinx Kintex-7 FPGA with three popular datasets. Compared to existing CPU, GPU, and state-of-the-art FPGA-based accelerator, LW-GCN reduces latency by up to 60×, 12×, and 1.7× and increases power efficiency by up to 912×, 511×, and 3.87×, respectively. Furthermore, compared with NVIDIA’s latest edge GPU Jetson Xavier NX, LW-GCN achieves speedup and energy savings of 32× and 84×, respectively.
Improved Random Grid-based Cheating Prevention Visual Cryptography Using Latin Square
Authors: Sun Sophie C. C. , Zhao Yongkang , Fu Fang-Wei , Ren Yawei
Published: August 2022

Visual cryptography scheme is a method of encrypting secret image into
n
noiselike shares. The secret image can be reconstructed by stacking adequate shares. In the past two decades, many schemes have been proposed to realize the cheating prevention visual cryptography scheme (CPVCS). Significantly, Ren et al. [
9
] first introduced the idea of CPVCS with the help of Latin square. Inspired by their work, in this article, a new reliable scheme is proposed. More precisely, to facilitate the certification process, we embed meaningful characters into the randomly chosen authentication patterns in each divided blocks. Furthermore, we fix the security vulnerability in the stacked results of share

S
g

and verification

Ver
g

, where 1≤
g
≤
n
. Since the improved scheme encrypts the secret image by utilizing random grids, the generated shares have no pixel expansion. Finally, theoretical analysis and experimental results are conducted to evaluate the efficiency and security of the proposed scheme.
Comparison of the Wearable Mobile Laser Scanner (WMLS) with Other Point Cloud Data Collection Methods in Cultural Heritage: A Case Study of Diokaisareia
Authors: Ulvi Ali , Yiğit Abdurahman Yasin
Published: August 2022

For the protection of cultural heritage, modern techniques have been used alongside traditional methods in recent years. In addition to two modern measurement techniques (Unmanned aerial vehicle photogrammetry and terrestrial laser scanner) that have been the subject of much research as a three-dimensional (3D) data collection technique for the documentation of cultural heritage, theWearable Mobile Laser Scanner (WMLS)technique has also recently been included in the research. Especially in cultural heritage documentation, it is essential to obtain accurate and precise data as well as fast and high-quality data. This study includes the visual and statistical comparison of the WMLS measurement method, which enables fast data collection, working with thesimultaneous localization and mapping (SLAM)algorithm, in terms of accuracy and precision. To assess the accuracy of the three measurement approaches, eighteen (18)checkpoints (ChP)considered absolute values were measured using total-station techniques. With these data, theroot means square error (RMSE)of each point were determined according to all three measurement techniques, and the directional and statistical errors were calculated. As a result of this research, while the terrestrial laser scanner method with an RMSE of 0.8 cm provides the best value, the RMSE of 2.64 cm and 4.92 cm was calculated in unmanned aerial vehicle photogrammetry and WMLS methods, respectively. At the end of the study, theories and limitations were taken into consideration for all three approaches. It was observed that the obtained accuracy of all three provide the measurement principles of cultural heritage and that a modern measurement tool such as WMLS was a significant innovation.
Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis
Authors: Dash Deba Prasad , Kolekar Maheshkumar , Chakraborty Chinmay , Khosravi Mohammad R.
Published: August 2022

Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as
support vector machine (SVM)
classifiers,
artificial neural network (ANN)
classifier, and deep learning techniques such as a
convolutional neural network (CNN)
classifier, and
long-short term memory (LSTM)
network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizures.

Impact Statement-
This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detection is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, and sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summarizing it will give a new prospective to the reader.
A Survey of Natural Language Generation
Authors: Dong Chenhe , Li Yinghui , Gong Haifan , Chen Miaoxin , Li Junxin , Shen Ying , Yang Min
Published: August 2022

This article offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text, and computational creativity.
Integrating distributed energy resources
Authors: Alahmed Ahmed S. , Tong Lang
Published: August 2022

The rapid growth of the behind-the-meter (BTM) distributed generation has led to initiatives to reform the net energy metering (NEM) policies to address pressing concerns of rising electricity bills, fairness of cost allocation, and the long-term growth of distributed energy resources. This article presents an analytical framework for the optimal prosumer consumption decision using an inclusive NEM X tariff model that covers existing and proposed NEM tariff designs. The structure of the optimal consumption policy lends itself to near closed-form optimal solutions suitable for practical energy management systems that are responsive to stochastic BTM generation and dynamic pricing. The short and long-run performance of NEM and feed-in tariffs (FiT) are considered under a sequential rate-setting decision process. Also presented are numerical results that characterize social welfare distributions, cross-subsidies, and long-run solar adoption performance for selected NEM and FiT policy designs.
The Michigan O’Brien Kidney Research Center: transforming translational kidney research through systems biology
Authors: Bitzer Markus , Ju Wenjun , Subramanian Lalita , Troost Jonathan P. , Tychewicz Joseph , Steck Becky , Wiggins Roger C. , Gipson Debbie S. , Gadegbeku Crystal A. , Brosius Frank C. , Kretzler Matthias , Pennathur Subramaniam
Published: August 2022

Research on kidney diseases is being transformed by the rapid expansion and innovations in omics technologies. The analysis, integration, and interpretation of big data, however, have been an impediment to the growing interest in applying these technologies to understand kidney function and failure. Targeting this urgent need, the University of Michigan O’Brien Kidney Translational Core Center (MKTC) and its Administrative Core established the Applied Systems Biology Core. The Core provides need-based support for the global kidney community centered on enabling incorporation of systems biology approaches by creating web-based, user-friendly analytic and visualization tools, like Nephroseq and Nephrocell, guiding with experimental design, and processing, analysis, and integration of large data sets. The enrichment core supports systems biology education and dissemination through workshops, seminars, and individualized training sessions. Meanwhile, the Pilot and Feasibility Program of the MKTC provides pilot funding to both early-career and established investigators new to the field, to integrate a systems biology approach into their research projects. The relevance and value of the portfolio of training and services offered by MKTC are reflected in the expanding community of young investigators, collaborators, and users accessing resources and engaging in systems biology-based kidney research, thereby motivating MKTC to persevere in its mission to serve the kidney research community by enabling access to state-of-the-art data sets, tools, technologies, expertise, and learning opportunities for transformative basic, translational, and clinical studies that will usher in solutions to improve the lives of people impacted by kidney disease.
Wireless Communication Network Security System Based on Big Data Information Transmission Technology
Authors: Zhao Yichao , Ouyang Wenjun
Published: August 2022

This paper establishes a unified perception information transmission model for the difference in the number of cognitive radio users in wireless communication network systems. The model can effectively improve the utilization of spectrum resources. At the same time, this paper introduces a cyclic delay full diversity sensing information coding transmission scheme for the security risks of wireless communication networks. We established a system simulation model to compare performance under different correlation coefficients. The study found that the total interference intensity in the cyclic delay diversity system is lower than that of the frequency division duplex and time division duplex. Its antijamming attack capability also becomes stronger accordingly.
A Lazy Learning-Based Self-Interference Cancellation Approach for In-Band Full-Duplex Wireless Communication Systems
Authors: Zhao Ou , Liao Wei-Shun , Li Keren , Matsumura Takeshi , Kojima Fumihide , Harada Hiroshi
Published: August 2022

We propose a new lazy learning-based cancellation approach to improve spectral efficiency for current wireless communication systems, suppress self-interference (SI) sent from base stations, and enable in-band full-duplex (IBFD) transmissions in cellular networks. Our proposed approach consists of two phases based on traditional IBFD systems: an offline phase for database generation and an online phase for data transmission. In the offline phase, the output before a 0/1 decision is premeasured without the desired signal input and recorded in a database with self-defined feature vectors (FVs). In the online phase, a suitable result is sought from the generated database with the help of a learning method and FV for the same system architecture with the desired signal input. The result is then assigned an SI cancellation value. Regular and eager learning-based cancellation approaches are employed to evaluate the proposed method and simulate the transmission output. Computer simulation results indicated that the proposed cancellation methods could achieve about 134 dB SI suppression and achieve nearly the same transmission levels as methods with no SI effect, enabling the IBFD operations in wireless communication systems better than the regular and eager learning-based techniques.
[Retracted] Construction of Sports Nutrition Dynamic Intervention Mechanism Based on the Improvement of College Students’ Physical Health
Authors: Wang Dongsheng , Chen Baoping
Published: August 2022

Exercise can improve people’s physical health and strengthen their body bones. But the physique of college students is generally too poor. The enhancement of College Students’ physique cannot be completed in a day. This study aims to explore the best sports nutrition intervention scheme for strengthening college students’ physique from the construction of sports nutrition dynamic intervention mechanism, so as to provide theoretical reference for public physical education teaching and students’ independent exercise. In this study, 100 full‐time associate students in our university were selected as the research objects. Before the experiment, the subjects’ body shape, body function, and physical quality were tested. Based on the random mathematical method, the full‐time associate students were randomly divided into four groups: control group, exercise intervention group, nutrition intervention group, and sports nutrition intervention group, with 25 people in each group. After the experiment, the body shape, body function, and physical quality of the four groups were tested. The results show that exercise and nutrition intervention have little effect on college students’ height, but have great influence on body mass index, vital capacity and 800 meter running. Especially after the experiment, the vital capacity of the exercise group and the sports nutrition group increased by nearly 250 ml and 500 ml, respectively, and the 800 m running time increased by nearly 10 s and 18 s, respectively. Strengthening exercise and increasing nutritional intake can effectively improve the institutional health of college students.
The Neutrophil-to-Lymphocyte Ratio and the Platelet-to-Lymphocyte Ratio as Predictors of Mortality in Older Adults Hospitalized with COVID-19 in Peru
Authors: Ortega-Rojas Solangel , Salazar-Talla Leslie , Romero-Cerdán Anthony , Soto-Becerra Percy , Díaz-Vélez Cristian , Urrunaga-Pastor Diego , Maguiña Jorge L.
Published: August 2022

Background. The prognostic value of the neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) in patients with COVID-19 is rarely described in older adults. We aimed to estimate the prognostic value of NLR and PLR, determining the mortality of adults over 60 years of age hospitalized for COVID-19 in three hospitals in Peru from March to May 2020. Methods. We performed a secondary analysis of data from a retrospective cohort carried out in Lambayeque, Peru, from March 18 to May 13, 2020. Older adults hospitalized for COVID-19 were included. The outcome variable was in-hospital mortality by all causes, while the exposure variable was the NLR and PLR (categorized in tertiles and numerically, performing a logarithmic transformation). We included sociodemographic variables, comorbidities, vital functions, laboratory markers, and treatment received during hospital stay. We evaluated the association between NLR and PLR using the hazard ratio (HR) in a Cox regression model. We estimated HR with their respective 95% confidence intervals (95% CI). We estimated cumulative/dynamic time-dependent ROC curves and reported area under the curve ROC (AUC-ROC) for 15-, 30-, and 60-day mortality with their respective simultaneous confidence intervals (confidence bands (CB)). Also, we estimated an optimal cut-off point based on the maximally selected rank statistics. Results. A total of 262 hospitalized older adults were analyzed, 71.8% (

n
=
188

) of whom were male with a median age of 70 years (interquartile range: 65-78). The mean NLR and PLR were 16.8 (95% CI: 14.9-18.7; SD: 15.5) and 50.3 (95% CI: 44.6-55.9; SD: 46.3), respectively. The mortality rate was 68.7% (95% CI: 62.7-74.3). The adjusted Cox regression analysis showed that the high NLR (

adjusted
 
HR
 

aHR

=
2.12

; 95% CI: 1.43-3.14) and PLR (

aHR
=
1.90

; 95% CI: 1.30-2.79) tertiles were associated with a higher risk of mortality. The maximum AUC-ROC values at 60 days of follow-up for NLR and PLR were 0.713 (95%CB: 0.627-0.800) and 0.697 (95%CB: 0.583-0.754), respectively. Conclusions. The NLR and PLR are predictors of higher risk of mortality, and these results suggest that both could be reliable and practical markers for the identification of older adults at high risk of mortality by COVID-19. NLR and PLR have prognostic value, with an AUC greater than 0.5; however, by themselves, they are weak prognostic markers. It is important to carry out future studies incorporating these two markers into preexisting models or designing new ones considering them.
Grape Seed Proanthocyanidins Exert a Neuroprotective Effect by Regulating Microglial M1/M2 Polarisation in Rats with Spinal Cord Injury
Authors: Liu Wen-zhao , Ma Zhan-jun , Kang Ji-he , Lin Ai-xin , Wang Zhao-heng , Chen Hai-wei , Guo Xu-dong , He Xue-gang , Kang Xue-wen
Published: August 2022

Spinal cord injury (SCI) is a highly disabling disorder for which few effective treatments are available. Grape seed proanthocyanidins (GSPs) are polyphenolic compounds with various biological activities. In our preliminary experiment, GSP promoted functional recovery in rats with SCI, but the mechanism remains unclear. Therefore, we explored the protective effects of GSP on SCI and its possible underlying mechanisms. We found that GSP promoted locomotor recovery, reduced neuronal apoptosis, increased neuronal preservation, and regulated microglial polarisation in vivo. We also performed in vitro studies to verify the effects of GSP on neuronal protection and microglial polarisation and their potential mechanisms. We found that GSP regulated microglial polarisation and inhibited apoptosis in PC12 cells induced by M1-BV2 cells through the Toll-like receptor 4- (TLR4-) mediated nuclear factor kappa B (NF-κB) and phosphatidylinositol 3-kinase/serine threonine kinase (PI3K/AKT) signaling pathways. This suggests that GSP regulates microglial polarisation and prevents neuronal apoptosis, possibly by the TLR4-mediated NF-κB and PI3K/AKT signaling pathways.
Yolo-Based Traffic Sign Recognition Algorithm
Authors: Li Ming , Zhang Li , Li Linlin , Song Wenlong
Published: August 2022

With the rapid development of intelligent transportation, more and more vehicles are equipped with intelligent traffic sign recognition systems, which can reduce the potential safety hazards caused by human cognitive errors. Therefore, a more safe and reliable traffic sign recognition system is the demand of drivers, and it is also the research hotspot of current automobile manufacturers. However, the pictures taken by the actual driving car are inevitably distorted and blurred. In addition, there are external uncontrollable factors, such as the impact of bad weather, which make the research of traffic sign recognition system face many difficulties, and the practical application is far from mature. In order to solve the above challenges, this paper proposes a Yolo model for traffic sign recognition. Firstly, the traffic signs are roughly divided into several categories and then preprocessed according to the characteristics of various types of signs. The processed pictures are input into the optimized convolutional neural network to subdivide the categories to obtain the specific categories. Finally, the proposed recognition algorithm is tested with the data set based on the German traffic sign recognition standard and compared with other baseline algorithms. The results show that the algorithm greatly improves the running speed on the basis of ensuring a high classification accuracy and is more suitable for traffic sign recognition system.
Predictors for Intravenous Immunoglobulin Resistance in Patients with Kawasaki Disease
Authors: Li Wei , Zhang Li , Wang Zhouping , He Xiufang , Lin Huimei , Wang Yanfei , Yuan Jia , Xie Xiaofei , Zhang Xu , Qin Youzhen , Huang Ping
Published: August 2022

Background. Between 10 and 20% of Kawasaki disease (KD) patients are resistant to treatment with initial intravenous immunoglobulin (IVIG) and have a high risk of developing coronary artery lesions. Some studies have been conducted to identify predictive factors. However, the results are controversial. This study aims to identify the risk factors for IVIG-resistant KD patients in a Chinese population. Methods. We performed a retrospective analysis of medical records of consecutive KD patients from two medical centers in South China from January 2015 to December 2017. A total of 1281 KD patients were eligible for inclusion in this study and maintained follow-up for over 12 months. The KD patients were divided into two groups based on IVIG response. Clinical characteristics and laboratory variables were compared between the two groups. Multivariate logistic regression analysis was performed to identify the risk factors of IVIG resistance in KD patients. Results. Of the 1281 KD patients, 141 (11.0%) cases who were IVIG resistant to adjunctive therapies for primary treatment were classified as group 1. The remaining patients were in group 2 (n = 1140), classified as the control group. There was a signiﬁcant difference in male to female ratio and the length of hospital stay between the two groups (

P
<
0.05

). Group 1 had a higher white blood cell count (

P
=
0.01

) and C-reactive protein level (

P
<
0.01

) before IVIG treatment than in group 2. Group 1 had a significantly higher white blood cell count and percentage of neutrophils after the IVIG infusion than in group 2 (

P
<
0.001

). In addition, the mean values of C-reactive protein level and neutrophil percentage before and after treatment difference comparison were significantly different. Multivariate analysis showed that patients presenting with coronary artery lesions in the acute phase and a C-reactive protein level >100 mg/L at diagnosis were associated with IVIG resistance in KD. During the 12-month follow-up period, group 1 had an obviously higher incidence of coronary artery lesions than group 2, and the difference between the groups was statistically significant (

P
<
0.001

). Conclusions. Patients presenting with coronary artery lesions in the acute phase and elevated C-reactive protein levels before IVIG treatment might be a useful and important value for predicting IVIG resistance in KD. Risk assessment based on coronary artery lesions and C-reactive protein levels prior to the treatment may improve the outcome of IVIG resistance.
Socioeconomic Development Trend Prediction Model and Research Which is Based on Deep Neural Network
Authors: Zhang Hongming , Wei Wenmiao , Wang Kai
Published: August 2022

In recent years, my country’s economic development has entered a new stage, and economic development has entered a new height. Gross Domestic Product (GDP) is the most important and commonly used measure to evaluate social and economic development. It can comprehensively reflect the operating conditions of the economy. At the 2014 APEC summit, Xi Jinping focused on the characteristics of the new normal of China’s economy. The economic growth has undergone major changes, and the economic structure has become more and more complete and has been continuously optimized. Therefore, the driving force of innovation plays an important role in the future social and economic development. The high-tech demonstration zone is the main hub of my country’s economic innovation and scientific and technological progress, and the development of the high-tech demonstration zone is particularly important in the development of the innovative economy. By predicting the economic growth, the development structure can be adjusted in time, and the coordinated allocation of resources can be achieved, which is conducive to the stable and rapid development of the economy. Therefore, it is very necessary to predict my country’s economic growth. At present, more and more scholars have invested more energy in the establishment of economic forecasting models. Previously, the prediction effect of traditional models was not satisfactory. At present, the field of target camera machine learning has been transferred. The application of the powerful nonlinear fitting ability of the machine learning model can improve the prediction effect. Based on this, this paper studies the use of artificial neural network with improved BP algorithm to predict social and economic development, and analyzes social and economic development trends and changing laws. The results show that the forecasting model constructed in this paper has a good forecasting effect, and the research results can provide a theoretical basis for scientifically formulating macro-control policies.
Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm
Authors: Yun Laiyan , Jiao Huihua , Lu Kai
Published: August 2022

As China’s economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user’s attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user’s satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform.