bluuebunny/crossref_metadata_2025 huggingface dataset:
A Systematic Review of User Studies as a Basis for the Design of Systems for Automatic Sign Language Processing
Authors: Prietch Soraia , Sánchez J. Alfredo , Guerrero Josefina
Published: October 2022

Deaf persons, whether or not they are sign language users, make up one of various existing marginalized populations that historically have been socially and politically underrepresented. Unfortunately, this also happens in technology design. Conducting user studies in which marginalized populations are represented is a step towards guaranteeing their right to participate in choices and decisions that are made for, with, and by them. This article presents and discusses results from a Systematic Literature Review (SLR) of user studies in the design of systems for Automatic Sign Language Processing (ASLP). Following our SLR protocol, from 2,486 papers initially found, we applied inclusion and exclusion criteria to finally select 37 papers in our review. We excluded publications that were not full papers, were not related to our main topic of interest, or that reported results that had been updated by more recent papers. All the selected papers focus on user studies as a basis for the design of three major aspects of ASLP: generation (ASLG), recognition (ASLR), and translation (ASLT). With regard to our specific area of interest, we analyzed four areas related to our research questions: goals and research methods, types of user involvement in the interaction design life cycle, cultural and collaborative aspects, and other lessons learned from the primary studies under review. Salient findings from our analysis show that numerical scale questionnaires are the most frequently used research instruments, co-designing ASLP systems with sign language users is not a common practice (as potential users are included mostly in the evaluation phase), and only seldom are Deaf persons who are sign language users included as members of research teams. These findings point to the need of conducting more inclusive and qualitative research for, with and by Deaf persons who are sign language users.
Designing Human-centered AI for Mental Health: Developing Clinically Relevant Applications for Online CBT Treatment
Authors: Thieme Anja , Hanratty Maryann , Lyons Maria , Palacios Jorge , Marques Rita Faia , Morrison Cecily , Doherty Gavin
Published: October 2022

Recent advances in AI and machine learning (ML) promise significant transformations in the future delivery of healthcare. Despite a surge in research and development, few works have moved beyond demonstrations of technical feasibility and algorithmic performance. However, to realize many of the ambitious visions for how AI can contribute to clinical impact requires the closer design and study of AI tools or interventions within specific health and care contexts. This article outlines our collaborative, human-centered approach to developing an AI application that predicts treatment outcomes for patients who are receiving human-supported, internet-delivered Cognitive Behavioral Therapy (iCBT) for symptoms of depression and anxiety. Intersecting the fields of HCI, AI, and healthcare, we describe how we addressed the specific challenges of (1)
identifying clinically relevant AI applications
; and (2)
designing AI applications for sensitive use contexts
like mental health. Aiming to better assist the work practices of iCBT supporters, we share how learnings from an interview study with 15 iCBT supporters surfaced their practices and information needs and revealed new opportunities for the use of AI. Combined with insights from the clinical literature and technical feasibility constraints, this led to the development of two clinical outcome prediction models. To clarify their potential utility for use in practice, we conducted 13 design sessions with iCBT supporters that utilized interface mock-ups to concretize the AI output and derive additional design requirements. Our findings demonstrate how design choices can impact interpretations of the AI predictions as well as supporter motivation and sense of agency. We detail how this analysis and the design principles derived from it enabled the integration of the prediction models into a production interface. Reporting on identified risks of over-reliance on AI outputs and needs for balanced information assessment and preservation of a focus on individualized care, we discuss and reflect on what constitutes a responsible, human-centered approach to AI design in this healthcare context.
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach
Authors: Lan Xiaohan , Yuan Yitian , Wang Xin , Chen Long , Wang Zhi , Ma Lin , Zhu Wenwu
Published: October 2022

Temporal Sentence Grounding in Videos (TSGV)
, which aims to ground a natural language sentence that indicates complex human activities in an untrimmed video, has drawn widespread attention over the past few years. However, recent studies have found that current benchmark datasets may have obvious moment annotation biases, enabling several simple baselines even without training to achieve
state-of-the-art (SOTA)
performance. In this paper, we take a closer look at existing evaluation protocols for TSGV, and find that both the prevailing dataset splits and evaluation metrics are the devils that lead to untrustworthy benchmarking. Therefore, we propose to re-organize the two widely-used datasets, making the ground-truth moment distributions different in the training and test splits, i.e.,
out-of-distribution (OOD)
test. Meanwhile, we introduce a new evaluation metric “dR@
n
,IoU=
m
” that discounts the basic recall scores especially with small IoU thresholds, so as to alleviate the inflating evaluation caused by biased datasets with a large proportion of long ground-truth moments. New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress in TSGV. Furthermore, we propose a novel causality-based
Multi-branch Deconfounding Debiasing (MDD)
framework for unbiased moment prediction. Specifically, we design a multi-branch deconfounder to eliminate the effects caused by multiple confounders with causal intervention. In order to help the model better align the semantics between sentence queries and video moments, we enhance the representations during feature encoding. Specifically, for textual information, the query is parsed into several verb-centered phrases to obtain a more fine-grained textual feature. For visual information, the positional information has been decomposed from the moment features to enhance the representations of moments with diverse locations. Extensive experiments demonstrate that our proposed approach can achieve competitive results among existing SOTA approaches and outperform the base model with great gains.
Graph Neural Networks in IoT: A Survey
Authors: Dong Guimin , Tang Mingyue , Wang Zhiyuan , Gao Jiechao , Guo Sikun , Cai Lihua , Gutierrez Robert , Campbel Bradford , Barnes Laura E. , Boukhechba Mehdi
Published: October 2022

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT.
Beyond Erectile Dysfunction: cGMP-Specific Phosphodiesterase 5 Inhibitors for Other Clinical Disorders
Authors: Samidurai Arun , Xi Lei , Das Anindita , Kukreja Rakesh C.
Published: October 2022

Cyclic guanosine monophosphate (cGMP), an important intracellular second messenger, mediates cellular functional responses in all vital organs. Phosphodiesterase 5 (PDE5) is one of the 11 members of the cyclic nucleotide phosphodiesterase (PDE) family that specifically targets cGMP generated by nitric oxide–driven activation of the soluble guanylyl cyclase. PDE5 inhibitors, including sildenafil and tadalafil, are widely used for the treatment of erectile dysfunction, pulmonary arterial hypertension, and certain urological disorders. Preclinical studies have shown promising effects of PDE5 inhibitors in the treatment of myocardial infarction, cardiac hypertrophy, heart failure, cancer and anticancer-drug-associated cardiotoxicity, diabetes, Duchenne muscular dystrophy, Alzheimer's disease, and other aging-related conditions. Many clinical trials with PDE5 inhibitors have focused on the potential cardiovascular, anticancer, and neurological benefits. In this review, we provide an overview of the current state of knowledge on PDE5 inhibitors and their potential therapeutic indications for various clinical disorders beyond erectile dysfunction.
Fair Risk Algorithms
Authors: Berk Richard A. , Kuchibhotla Arun Kumar , Tchetgen Tchetgen Eric
Published: October 2022

Machine learning algorithms are becoming ubiquitous in modern life. When used to help inform human decision making, they have been criticized by some for insufficient accuracy, an absence of transparency, and unfairness. Many of these concerns can be legitimate, although they are less convincing when compared with the uneven quality of human decisions. There is now a large literature in statistics and computer science offering a range of proposed improvements. In this article, we focus on machine learning algorithms used to forecast risk, such as those employed by judges to anticipate a convicted offender's future dangerousness and by physicians to help formulate a medical prognosis or ration scarce medical care. We review a variety of conceptual, technical, and practical features common to risk algorithms and offer suggestions for how their development and use might be meaningfully advanced. Fairness concerns are emphasized.
Ovarian hormones do not mediate protection against pulmonary hypertension and right ventricular remodeling in female mice exposed to chronic, inhaled nicotine
Authors: Fried Nicholas D. , Whitehead Anna , Lazartigues Eric , Yue Xinping , Gardner Jason D.
Published: October 2022

The emergence of electronic cigarettes poses a threat to cardiovascular and pulmonary health, but the direct contribution of nicotine to these disease processes is largely unknown. Our laboratory has previously shown that chronic, inhaled nicotine induces pulmonary hypertension and right ventricular remodeling in male mice, but not female mice. This study using a bilateral ovariectomy model suggests that the cardiopulmonary protection observed in nicotine-exposed female mice may be independent of ovarian hormones.
The Mathematical Modeling Approach for the Wastewater Treatment Process in Saudi Arabia during COVID‐19 Pandemic
Authors: Ahmadini Abdullah , Msmali Ahmed , Mutum Zico , Raghav Yashpal Singh
Published: October 2022

The novel coronavirus disease (COVID‐19) pandemic has had devastating effects on healthcare systems and the global economy. Moreover, coronavirus has been found in human feces, sewage, and in wastewater treatment plants. In this paper, we highlight the transmission behavior, occurrence, and persistence of the virus in sewage and wastewater treatment plants. Our approach follows the process of identifying a coronavirus hotspot through existing wastewater plants in major cities of Saudi Arabia. The mathematical distributions, including the log‐normal distribution, Gaussian model, and susceptible‐exposed‐infected‐recovered (SEIR) model, are adopted to predict the coronavirus load in wastewater plants. We highlight not only the potential virus removal techniques from wastewater treatment plants but also methods of tracing SARS‐CoV‐2 in humans through wastewater treatment plants. The results indicate that our modeling approach may facilitate the analysis of SARS‐CoV‐2 loads in wastewater for early prediction of the epidemic outbreak and provide significant implications to the public health system.
Spatiotemporal Relationship between Ecosystem Service Value and Ecological Risk in Disaster‐Prone Mountainous Areas: Taking the Upper Reaches of the Minjiang River as an Example
Authors: Duan Linsen , Shao Huaiyong , Xiang Mingshun , Wang Hao , Wang Chunjian , Mei Hao , Tan Yuxiang , Yang Xiaofeng
Published: October 2022

Mountainous areas are susceptible to disasters; the frequent occurrence of disasters drives the changes in ecosystem service value (ESV) and also brings certain ecological risk, which further increases the incidence of disasters. However, few scholars have investigated the spatiotemporal correlation between the ESV of disaster‐prone mountainous areas and ecological risk index (ERI) with basin as the unit. This paper aims to clarify the spatial relationship between ESV and ERI under the changes of land use. Taking the upper reaches of the Minjiang River as the study area, the authors collected the land use data of 2000–2020, estimated ESV by the value equivalent factor per unit area method, and constructed the ERI. On this basis, the relationship between ESV and ERI was investigated in details. The results show the following: (1) From 2000 to 2020, the total ESV exhibited a fluctuating upward trend. The spatial distribution of ESV was greatly affected by slope and altitude; an important reason for the rising ESV in the study area is the increase of forest area and water area. (2) The upper reaches of the Minjiang River had a generally low ERI and relatively good overall ecoenvironment. After 2010, however, the ecological risk continued to rise. Most of the strongly high risk areas are areas with frequent human activities, such as low‐altitude areas and river banks. (3) There is a spatial correlation and coupling between ESV and ERI in the study area; i.e., the strongly high ESV areas generally had a low ecological risk. The correlation intensified with the elapse of time. The changes in the service value of regional ecosystems driven by unreasonable land use will have a great impact on the ecoenvironment. By clarifying the spatiotemporal relationship between ESV and ERI, this research provides theoretical basis and data support to the formulation of ecoenvironmental restoration and protection plans for the upper reaches of the Minjiang River and to the coordinated development between society, economy, and ecoenvironment in the region.
[Retracted] Personal Information Security Environment Monitoring and Law Protection Using Big Data Analysis
Authors: Zhu Wei
Published: October 2022

This article explores the causes of the issues with personal data privacy and outlines the limitations of China’s current legal framework. This article makes the argument that, in the information age, self‐discipline and legal protection should be combined in order to safeguard personal information safety. It also makes specific recommendations for strengthening legal protection. This research also develops a data processing platform for data safety and privacy protection while studying the technology of data safety environment monitoring and privacy protection. This work develops optimization methodologies, such as dynamic privacy budget allocation, to increase the model’s speed of convergence and the calibre of the generated data. It adjusts to various privacy and timeliness needs under the assumption that the objective of selective matching of private safety will be satisfied. According to the experimental findings, this algorithm’s accuracy can reach 96.27%. This method enhances the model’s speed of convergence and the calibre of the data created, and it addresses the flaw that the present data fusion publishing procedure cannot withstand the attack of background information. The study’s findings can serve as a starting point for future work on data security and the protection of personal information.
Urinary PCA3 a Superior Diagnostic Biomarker for Prostate Cancer among Ghanaian Men
Authors: Opoku Mensah Bismark , Fondjo Linda Ahenkorah , Owiredu W. K. B. A. , Adusei Ben
Published: October 2022

Introduction. Prostate cancer is one of the most commonly diagnosed cancers in men. Prostate-specific antigen (PSA) has been the biomarker of choice for screening and diagnosis of prostate cancer. However, inefficiencies exist with its diagnostic capabilities. This study thus evaluated the diagnostic and prognostic potential of urinary PCA3 as an alternative biomarker for prostate cancer in the Ghanaian population. Methods. A hospital-based cross-sectional study was conducted at the Urology Department of the 37 Military Hospital, Accra, Ghana. A total of 237 participants aged 40 years and above with any form of suspected prostate disorder were recruited into the study after written informed consent was obtained. Total serum PSA levels was measured using the electrochemiluminescence method and transrectal ultrasound-guided systematic core needle biopsies were obtained from each study participant. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic accuracies of serum PSA, DRE, and PCA3 as diagnostic tools for prostate cancer. These three diagnostic tools were also evaluated in various combinations to ascertain the combinations with the best diagnostic accuracy. Results. Prostate cancer was diagnosed in 26.6% of the participants. Benign prostate hyperplasia and prostatitis were diagnosed in 48.5% and 24.9% participants, respectively. DRE had a sensitivity of 93.7% and a specificity of 12.1%. PSA had a sensitivity of 92.1% and a specificity of 16.1%. PCA3 had a sensitivity of 57.1% and a specificity of 85.6% and showed a better accuracy (

AUC
=
83.0

) compared to PSA (

AUC
=
60.0

) and DRE (

AUC
=
65.0

) as individual diagnostic tools. The combination of DRE+PCA3 score had the best diagnostic accuracy (

AUC
=
0.80

) with a sensitivity and specificity of 60.3% and 80.5%, respectively. Conclusion. The urinary PCA3 assay showed a better diagnostic performance compared to serum PSA and DRE. PCA3 as a stand-alone and in combination with DRE could be a suitable complimentary marker in diagnosis and management of prostate cancer.
Comment and Improvement on the Methods for Deriving Structural Resistance Design Value
Authors: Yukun Hu , Jitao Yao , Pengfei Ma , Zhengjie Cheng , Luyang Zhang
Published: October 2022

With the increasing emphasis on sustainability, more and more environmentally friendly new materials and structures are developed, but they are difficult to be applied in engineering practice due to the lack of mature force resistance models. The design assisted by the testing method proposed by ISO2394:2015 and EN1990:2002 can solve this problem effectively. The aim of this paper was to analyze the characteristics of the method from a mathematical and statistical point of view based on the data and to suggest improvements. The resistance of each concrete member was derived by the design assisted by testing methods. The derivation results showed that the

p

values, the number of tests

n

, the coefficient of variation

θ

, and the coefficient of variation of resistance

V

R

known or unknown have a large impact on the derived results. Also, the derivation by the Bayesian procedure or interval estimation methods might be negative. This indicates that the theory of the method is not rigorous and has some disadvantages.
Mechanism Investigation of Wuwei Shexiang Pills on Gouty Arthritis via Network Pharmacology, Molecule Docking, and Pharmacological Verification
Authors: Lang Jirui , Li Li , Chen Shilong , Quan Yunyun , Yi Jing , Zeng Jin , Li Yong , Zhao Junning , Yin Zhujun
Published: October 2022

Background. Gout is a common crystal-related arthritis caused by the deposition of monosodium urates (MSU). Tibetan medicine Wuwei Shexiang Pills (WSP) has been demonstrated to exhibit anti-inflammatory, antihyperuricemia, and antigout activities. However, the underlying mechanism is unknown. Objectives. To explore the mechanisms of Wuwei Shexiang Pills on gouty arthritis via network pharmacology, molecule docking, and pharmacological verification. Methods. The ingredients and targets of WSP were obtained by searching and screening in BATMAN-TCM and SwissADME. The targets involving the gout were acquired from public databases. The shared targets were put onto STRING to construct a PPI network. Furthermore, Metascape was applied for the GO and KEGG enrichment analysis to predict the biological processes and signaling pathways. And molecular docking was performed to validate the binding association between the key ingredients and the relative proteins of TNF signaling. Based on the serum pharmacology, the predicted antigout mechanism of WSP was validated in MSU-induced THP-1 macrophages. The levels of inflammatory cytokines and mRNA were measured by ELISA and qRT-PCR, respectively, and MAPK, NF-κB, and NLRP3 signaling-associated proteins were determined by western blot and immunofluorescence staining. Results. 48 bioactive ingredients and 165 common targets were found in WSP. The data showed that 5-Cis-Cyclopentadecen-1-One, 5-Cis-Cyclotetradecen-1-One, (−)-isoshyobunone, etc. were potential active ingredients. TNF signaling, HIF-1 signaling, and Jak-STAT signaling were predicted to be the potential pathways against gout. The molecule docking analysis found that most ingredients had a high affinity for p65, NLRP3, IL-1β, TNF-α, and p38. The data from in vitro experiment showed that WSP suppressed the production and gene expression of inflammatory cytokines. Furthermore, WSP could inhibit the activation of MAPK, NF-κB, and NLRP3 signaling pathways. Conclusion. Our finding suggested that the antigout effect of WSP could be achieved by inhibiting MAPK, NF-κB, and NLRP3 signaling pathways. WSP might be a candidate drug for gouty treatment.
Robust Adaptive Beamforming via Modified Variable Loading with Subsampling Preprocessing
Authors: Chen Xiangwei , Sheng Weixing
Published: October 2022

For robust adaptive beamforming (RAB), the variable loading (VL) technique can provide a better trade-off between robustness and adaptivity than diagonal loading (DL). Despite its importance, few research efforts have explored the loading factor for VL to ensure robustness in various environments. Moreover, the performance of VL is restricted by the sample covariance matrix in snapshot deficiency situations. This paper proposes a modified variable loading (VL) method for robust adaptive beamforming, considering imprecise steering vector effects and finite sample size impairments. First, a novel subsampling method is used to construct the calibrated covariance matrix to improve the robustness of the VL in sample-starving scenarios. Then, a parameter-free method for the VL factor is proposed to further enhance the insensitivity to the steering vector mismatches of the antenna array. Simulation results verify the effectiveness and robustness of the proposed method as compared to the traditional VL and other widely used robust techniques.
[Retracted] Evaluation Model and Decision Analysis of Digital Firms Ranked in Forbes’ Top Companies
Authors: Kao Fang Chen , Dinh Thanh Nhan , Nguyen Anh Thi Truc
Published: October 2022

This study evaluates the profitability and marketability efficiencies of digital firms ranked in Forbes’ list of top companies by using a two‐stage network data envelopment analysis (DEA) model with multiplicative efficiency aggregation under the second‐order cone programming (SOCP) and examines the respective impacts of the 1995–2001 dot‐com bubble and the 2007–2009 global financial crisis on the companies’ efficiencies by applying impulse response function (IRF) analysis. The data of our 49 sampled companies are derived from the Compustat database. The covered period is 1999–2018. These results present the stable and increasing improvement of profitability and marketability efficiencies; in addition, two crisis events have no significant impact on the performance of digital firms. This research is supposed to offer a reasonable and objective evaluation model to measure the performance of digital firms, providing the managers and investors a reference for making their decision.
A Study on C‐Exponential Mean Labeling of Graphs
Authors: Baskaran Thamaraiselvi , Ganapathy Rajasekaran
Published: October 2022

A function h is mentioned as a C‐exponential mean labeling of a graph G(V, E) that has s vertices and r edges if h : V(G)⟶{1, 2, 3, ⋯, r + 1} is injective and the generated function h∗ : E(G)⟶{2, 3, 4, ⋯, r + 1} defined by for all ab ∈ E(G), is bijective. A graph which recognizes a C‐exponential mean labeling is defined as C‐exponential mean graph. In the following study, we have studied the exponential meanness of the path, the graph triangular tree of Tn, , cartesian product of two paths Pm▫Pn, one‐sided step graph of STn, double‐sided step graph of 2ST2n, one‐sided arrow graph of , double‐sided arrow graph of , and subdivision of ladder graph S(Lt).
Network Communication Protocol Reverse Engineering Based on Auto-Encoder
Authors: Yu Tianxiang , Xin Yang , Tao Yuexin , Hou Bingqing , Zhu Hongliang
Published: October 2022

Network communication protocol reverse engineering is useful for network security, including protocol fuzz testing, botnet command infiltration, and service script generation. Many models have been proposed to generate field boundary, field semantic, state machine, and some other format information from network trace and program execution for text-based protocol and hybrid protocols. However, how to extract format information from network trace data for binary-based protocol still remains a challenging issue. Existing network-trace-based models focus on text-based and hybrid protocols, using tokenization and some other heuristic rules, like field identification, to perform reverse engineering, which makes it hard to apply to binary-based protocol. In this paper, we propose a whole mechanism for binary-based protocol reverse engineering based on auto-encoder models and other clustering algorithms using only network trace data. After evaluation, we set some metrics and compare our model with existing other models, showing its necessity to the field of protocol reverse engineering.
Models in the Construction of Accounting Informatization Transformation Based on Digital Twin
Authors: Liu Jilu
Published: October 2022

The construction of accounting informatization is an effective tool for enterprises to transform from focusing on financial accounting to focusing on group management and decision support. Digital twins make full use of physical models, sensor updates and other data, integrate interdisciplinary and multi-scale modeling processes, and perform complete mapping in virtual space, which can reflect the process of the entire life cycle of the corresponding physical equipment. Based on the technology of digital twin, this paper solved the related problems in the transformation and construction of accounting informatization, and proposed a clustering analysis algorithm. Company A was taken as the experimental object, companies B and C were taken as the experimental control group, combined with data analysis, among the accountants of company A, 179 people have a supportive attitude towards the transformation of accounting information, 16 people have a neutral attitude, and 5 people have an opposing attitude, accounting for 89.5%, 8% and 2.5% respectively. However, among companies B and C, 196 and 199 people hold a favorable attitude, accounting for 98% and 99.5% respectively. The experimental results showed that the acceptance of the accounting informatization transformation is still relatively high among the employees of the enterprise.
Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy
Authors: Sabzalian Mohammad Hosein , Pirouzi Sasan , Aredes Mauricio , Wanderley Franca Bruno , Carolina Cunha Ana
Published: October 2022

With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.
Construction of Intelligent Building Integrated Evaluation System Based on BIM Technology
Authors: Song Mengmeng
Published: October 2022

In order to alleviate the impact of the old bridge by the environment and its own life and the problem of increasing maintenance cost, the intelligent building integrated evaluation system based on BIM technology is proposed. Firstly, based on BIM technology, an all-weather hardware monitoring system is constructed by using multiple sensors laid on the bridge. The software communicates with the monitoring system through 5G public network and applies the unique advantages of deep neural network in classification to the assessment of the health status of old bridges with the help of the multiclassification convolutional neural network embedded in the software. The test results show that the preprocessed data is imported into the health diagnosis module for training, and the training accuracy reaches about 70%, and the loss curve is stable at about 0.5. Conclusion. The system meets the design requirements and solves the problem of difficult health diagnosis caused by long monitoring period and low efficiency of old bridges. The system has advantages of low difficulty in obtaining parameters, high precision, and accurate health assessment.
Stretching Training Rehabilitation Has Potential to Alleviate Ankylosing Spondylitis in Mice by Inactivating the Wnt/β-Catenin Pathway
Authors: Xie Yu , Li Xiang , Zhang Qiuchi , Guo Feng , Wang Yue
Published: October 2022

Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by invasion of the joints of the central axis that involves soft tissues and joints surrounding the spine. Stretching training rehabilitation (STR) has been widely applied for the treatment of AS. The Wnt/β-catenin signalling pathway is closely related to AS. In this study, we aimed to explore the potential molecular mechanisms underlying the protective effect of STR on AS both in vitro and in vivo. Male DBA/1 mice were employed to establish an AS animal model. Hematoxylin-eosin staining showed that STR reversed pathological damages in bone tissues and the total antioxidant capacity of AS mice and increased the antioxidant capacity by upregulating superoxide dismutase and malondialdehyde expression in DBA/1 mice. The MTT, RT-qPCR, and Western blotting results further indicated that STR improved the survival rate of cells by downregulating the expression of target genes in the Wnt/β-catenin pathway and by inhibiting cell inflammation and apoptosis. In conclusion, our findings indicated that STR treatment might be an effective therapeutical strategy for AS.
Analysis of Dynamic Relationship between Energy Consumption and Economic Growth Based on PVAR Model
Authors: Han Junyan
Published: October 2022

In order to analyze the relationship between energy consumption and economic growth dynamics, the author proposes a dynamic analysis technology of energy consumption and economic growth based on the PVAR model. This technology uses the PVAR method to compare and quantitatively describe the relationship between economic growth and energy consumption in developed and developing countries from 1990 to 2009, using impulse response functions and variance decomposition analysis methods; study the similarities and differences between total energy consumption and various fossil energy consumption and the dynamic impact of economic development, and finally establish a PVAR model for analysis. Experimental results show that: in the forecast of variance analysis, the impact explanatory power of the total energy consumption of developing countries on the fluctuation of economic growth reaches 8.85070. However, the contribution of total energy consumption in developed countries to economic growth is insufficient. The explanatory power of economic growth in developed countries to total energy consumption is 15.58070, while that in developing country is 29.28070, both of which are greater than the explanatory power of their respective total energy consumption on economic growth. Conclusion. The technology based on the PVAR model can effectively meet the needs of analyzing the dynamic relationship between energy consumption and economic growth.
Empirical Research for Investment Model Based on VMD-LSTM
Authors: Gu Aihua , Wang Zhengqian , Yin Zuohao , Zhou Mingming , Li Shujun , Xun Qifeng , Dong Jian
Published: October 2022

Investment diversification has become an inevitable trend with the development of the world economy. In this work, we first compare the K-Nearest Neighbor model, the Artificial Neural Network model, the grey prediction model, and the LSTM (Long Short-Term Memory Networks) prediction model for a period of data analysis. The experimental results show that LSTM is superior, and thus LSTM model is selected to forecast the long-term prices in this work. Then, we introduce some indicators, such as convergence divergence ratio and risk coefficient to qualitatively analyze the market price. The five-day moving average method is used to formulate the best trading strategy based on the above-introduced indicators. We apply the commonly used regression indicators (

R

2

and

R
M
S
E

) to verify the reliability of the prediction model. Then we introduce new strategies to compare the performance of different ones with them. We found that the five-day moving average method achieved 20% higher returns than the other strategies we used for comparison. Considering the fact that transaction costs may change, we perform the polynomial fitting based on existing strategies by changing the commission cost. The results show that a 1% increase in the gold commission will reduce the total return by 24% to 25%, while a 1% increase in the bitcoin commission will only reduce the total return by 7% to 8%.
A Digital Mental Health Intervention for Children and Parents Using a User-Centred Design
Authors: Mahlous Ahmed Redha , Okkali Bersan
Published: October 2022

The number of children with mental health problems is ever-growing; as a result, nearly 850,000 children in the UK are believed to have clinically significant problems, and only a quarter show evidence of mental illness. Family members often have a hard time dealing with children with mental health problems. As a result, digital mental health interventions are becoming popular for people seeking professional mental health services. Previous studies in this area have also shown that parents who are divorced or working away from home struggle to maintain contact with their children. This lack of communication between the parents and their children can worsen the children’s mental health conditions and prevent early diagnosis. Human-centred design thinking is applied step by step in this paper to provide an intuitive understanding of the design process. Five stages of the design thinking process were examined to follow a correct path. The results were promising, and the feedback received assured that the product helps parents to better monitor their children’s mental health and provides support when needed. The design thinking process was followed in concordance with the user needs identified from previous studies in this area, which led to a working solution that benefits both parents and children in tackling these problems.
Assessment on Performance and Emission Characteristics of the CRDI Engine Fueled with Ethanol/Diesel Blends in Addition to EGR
Authors: Dineshkumar C. , Jeyakumar P. D. , Arvinda Pandian C. K. , Rajmohan N. , Elumalai P. V. , Kamesh N. , Shaik Saboor , Sharifpur Mohsen , Khalilpoor Nima
Published: October 2022

In this research, the CRDI engine characteristics were analyzed with the aid of exhaust gas recirculation rate (EGR) adoption fueled with ethanol blends. The test fuels were the various blends with ethanol, such as (10% of ethanol + 90% of diesel) E10D90 (20% of ethanol + 80% of diesel), E20D80, and (30% of ethanol + 70% of diesel) E30D70. From the results, it was revealed that performance characteristics were reduced when using a higher concentration of the alcohols mixed with diesel fuel. The blend E30D70 showed that brake thermal efficiency (BTE) without EGR drops by 3.8%, increased by 9.14% of BSFC, a 9.25% decrease in oxides of nitrogen emissions, and slightly decreased CO and HC emissions compared to baseline diesel operation at 60% load condition. The blend E10D90 with 20% EGR shows the highest BTE of 8.87% when compared with base fuel, due to proper fuel mixture taking place in the inlet manifold. The results indicate that the engine runs smoothly, and E30D70 has chosen an optimum blend. A further experiment was performed using E30D70 with different rates of exhaust gas recirculation system. The addition of exhaust gas recirculation with E30D70 in the common rail diesel engine exhibits oxides of nitrogen emission, but in contrast, it was noticed to have inferior performance characteristics and drastically decreased HC and CO emissions. The hydrocarbon emission decreased E10D90, E20D80, and E30D70 at 60% load condition by 21.42%, 37.38%, and 48.76%, respectively. The blends E10D90, E20D80, and E30D70 decreased carbon dioxide by 7.9%, 30.08%, and 31.98%, respectively. The maximum reduction of NOx emission was observed at about 51.06% at an EGR rate of 20% with E30D70.
A Comparison Study of Doctor-Patient Internet Interactions in Traditional and Modern Medicine: Empirical Evidence from Online Healthcare Communities
Authors: Cao Song , Gao Xiang , Niu Shuzhen , Wei Qian
Published: October 2022

Online healthcare platforms serve not just as a medical knowledge-sharing community but also bring about effective interactions between professional physicians and patients. However, it is unclear whether online technology adoption affects such interactions in the same way between traditional Chinese medicine and modern medical departments. By utilizing a large sample of online doctor-patient interaction information from 168,870 doctor-specific interactive webpages recorded in a famous Chinese online healthcare community, this paper studies the differences between 17,513 traditional medicine doctor homepages and 151,357 others from more than 100 different specialty areas. Our chosen platform is representative since it covers about 800,000 physicians working at over 10,000 hospitals across all major provincial regions in China. We document that online medical service users tend to accept and use online health care services. However, patients seeing Chinese medicine doctors exhibit the following unique characteristics. They still prefer choosing doctors according to third-party information and may be reluctant to pay for the current online service price level. This problem is hard to overcome by the platform in the short run. Patients need a long-term process to adapt to the upgraded medical environment gradually. Therefore, establishing a personalized doctor recommendation system has become the most urgent demand presently.
[Retracted] Study on the Efficacy and Safety of Oral Care in ICU Patients Based on Meta‐Analysis
Authors: Gao Yang , Xu Huifen , Yang Yahui , Wu Juan
Published: October 2022

The effectiveness and safety of oral care in Intensive Care Unit (ICU) patients by meta‐analysis are explored. According to the research direction of the effectiveness and safety of oral nursing in ICU patients, the corresponding literature studies are retrieved in literature databases and meta‐analysis is performed. A total of 17 Chinese and English literature studies are included, and the literature has no obvious publication bias. The experimental results show that the improvement effect of dry mouth and halitosis in the improved group is significantly higher than that in the traditional group, and the dry mouth score, plaque index, and complications such as oral mucosa infection, oral mucosa damage, and halitosis are significantly reduced in the improved group, and the differences are statistically significant (P < 0.05). Improved oral care can significantly improve the symptoms of dry mouth and halitosis in ICU patients, quickly remove dental plaque and effectively reduce the incidence of complications such as halitosis, oral mucosal infection, and oral mucosal damage. Improved oral care is an effective and safe ICU nursing program.
Catalytic Hydrothermal Carbonization of Avocado Peel
Authors: Tran Lien Thi , Nguyen Minh Quang , Hoang Ha Trong , Nguyen Hoang Tien , Vu Thu Ha Thi
Published: October 2022

The hydrothermal carbonization (HTC) of avocado peel was investigated by varying the reaction temperature, reaction time, and catalyst/feedstock ratio. After the HTC process, there was a drastic change in both the structure and chemical composition of the hydrochar compared to the feedstock. This modification aids hydrochar’s improved fuel characteristics, as seen by a drop in the H/C and O/C ratios, as well as an increase in fixed carbon content. The higher heating value (HHV) increased in the feedstock to a maximum value of 27.15 MJ·kg−1, corresponding to hydrochar, which is obtained at an optimized condition. Notably, the HHV and carbon content of the noncatalytic sample are only higher than those of the feedstock but lower than those of the samples surveyed. The combustion behavior and thermal characteristics of hydrochars show that the HTC of the avocado peel in the presence of FeCl3 catalyst introduces a possible direction of application in converting agro-industrial by-products into fuel.
Block-Based Multicut Benders Decomposition Algorithm for Transmission and Energy Storage Co-Planning
Authors: de Oliveira Edimar José , de Paula Arthur Neves , de Oliveira Leonardo Willer , Honório Leonardo de Mello
Published: October 2022

This study proposes a block-based multicut Benders decomposition algorithm to solve the co-planning of transmission expansion and energy storage problem in a bi-level approach. The proposal breaks the chronological representative period into multiple subperiods blocks. This division makes it possible to use parallel computation methods to solve each block simultaneously, reducing the simulation time, which allows the use of a more extensive time window to model the variability of random variables of the system, such as wind and load. In the proposed algorithm, the master problem defines the State of Charge (SoC) of the energy storage devices between the blocks and the investment in transmission and energy storage devices. To demonstrate the effectiveness of the proposed method, different sizes of representative periods are evaluated in three test systems: Garver 6-bus, IEEE-RTS 24-bus, and IEEE-118 188-bus. The tests compare the performance of the proposed block-based multicut Benders decomposition algorithm with the usual approach applied in the literature considering Benders decomposition and the complete problem formulated as a Mixed-Integer Linear Programming (MILP) problem.
Characterization and Modelling of Multiscale Natural Fractures in Shale Reservoirs: A Case Study from a Block in the Southern Sichuan Basin
Authors: Gai Shaohua , Jia Ailin , Wei Yunsheng , Liu Ruohan
Published: October 2022

Natural fractures are vital to the efficiencies of drilling and completion operation. The purpose of this paper is to characterize and model multiscale natural fractures in shale reservoirs. Based on the seismic and log responses, as well as outcrop and core observations, we divide natural fractures into Macro, Meso, and Micro three scales. Macroscale fractures are the faults picked directly in seismic profiles. Also, Mesoscale fractures are the natural fracture corridors analyzed by ant tracking technique in 3D seismic data. Furthermore, Microscale fractures are the fractures observed in imaging logs and cores. The fracture intensity is obtained by the correlation between ant tracking attributes and fracture density in borehole. The fracture aperture, dip, and azimuth are three main parameters, which are recognized by the loggings and cores. Stochastic modelling is applied to factures. We find that faults identified by the ant tracking result are excellent in line with Macroscale faults interpreted directly from seismic data. In addition, Mesoscale fractures are indicated from the ant tracking result, which are in accord with breakpoint in the well and in keeping with tectonic history of the area. Such high consistency indicates the ant tracking result is reliable. Moreover, image logs and cores reveal that it mainly develops high angle natural fractures and the fracture aperture is about 1 mm. The fracture strike includes three sets (NNW-SSE, NE-SW, and NNE-SSW). The distribution of the natural fractures in discrete fracture network (DFN) system is distributed controlled by the ant tracking result. Comparing the histograms of DFN results and fracture characterized by seismic and logging responses, as well as outcrop and core observation, it suggests that the major part of the observed natural fractures is retained into our DFN model.