bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial distributions and determinants of anaemia among adolescent girls in Ethiopia: a secondary analysis of EDHS 2016 – a cross-sectional study
Authors: Atalell Kendalem Asmare , Tamir Tadesse Tarik , Alemu Tewodros Getaneh , Techane Masresha Asmare
Published: June 2022

Objective

This study aimed to investigate the spatial distributions and determinants of anaemia among adolescent girls in Ethiopia. Exploring the spatial epidemiology of anaemia and identifying the risk factors would inform policymakers to come up with evidence-based prevention strategies for anaemia, especially in adolescent girls, who are the most affected segment of the population.

Methods

Secondary analysis of the Ethiopian Demographic and Health Survey 2016 was conducted. A total of 3172 adolescents were included in the analysis. The Bernoulli model was fitted using SaTScan V.9.6 to identify hotspot areas and the geospatial pattern and prediction of anaemia were mapped using ArcGIS V.10.8. A multilevel logistic regression model was fitted to identify factors associated with anaemia among adolescent girls. Adjusted OR with 95% CI was calculated and variables having a p value less than 0.05 were statistically significant factors of anaemia.

Result

The overall prevalence of anaemia among adolescent girls in Ethiopia was 23.8 (22.4 to 25.3), with significant spatial variations across the country. The SaTScan analysis identified a primary cluster in the eastern, northeastern and southeastern parts of Ethiopia (loglikelihood ratio=39, p<0.001). High anaemia prevalence was observed in eastern parts of the country. In the multivariable multilevel logistic regression analysis, no formal education (adjusted OR (AOR)=1.49, 95% CI 1.05 to 2.12), Afar (AOR=3.36, 95% CI 1.87 to 6.05), Somali (AOR=4.63, 95% CI 2.61 to 8.23), Harari (AOR=1.90, 95% CI 1.32 to 4.10), Dire Dawa (AOR=2.32, 95% CI 1.32 to 4.10) and high cluster altitude (AOR=1.37, 95% CI 1.03 to 1.82) were significantly associated with anaemia.

Conclusion

The national distributions of anaemia varied substantially across Ethiopia. Educational status, region and cluster altitude were significantly associated with anaemia in the multivariable logistic regression model. Thus, targeted public health interventions for adolescent girls should be implemented in the hotspot areas.
Mental health services in response to the COVID-19 pandemic in high-income countries: protocol for a rapid review
Authors: Yu Ge , Craig Dawn , Fu Yu
Published: June 2022

Introduction

The COVID-19 pandemic has caused disruptions to mental health services, forcing the rapid implementation of alternative ways of delivering services alongside a greater immediate, and continuously growing, demand across those services. The care and level of mental health service provided are felt to be inadequate to respond to the increasing demand for mental health conditions in the time of the pandemic, leading to an urgent need to learn from service change and consequences to inform solutions and plans to support the NHS postpandemic plan in the UK. This rapid review aims to understand the changes in mental health services during the pandemic and summarise the impact of these changes on the health outcomes of people with mental health conditions.

Methods and analysis

Cochrane CENTRAL, MEDLINE, Embase and PsycInfo will be searched for eligible studies with key terms indicating mental health AND COVID-19 AND health services. Peer-reviewed empirical studies aiming to investigate or describe new models of care, services, initiatives or programmes developed or evolved for patients (aged 18 years or over) with mental health in response to COVID-19, published in the English language and undertaken in a high-income country defined by Organisation for Economic Co-operation and Development (OECD) member will be included. Studies reporting views of the general public, letters of opinion to peer-review journals, editorial or commentaries will be excluded. Study selection and data extraction will be undertaken independently by two reviewers. Evidence will be summarised narratively and in a logic model.

Ethics and dissemination

Ethics approval is not required for this review. A list of interventions/services/models of care delivered to people with mental health conditions will be grouped as ‘Do’, ‘Don’t’ and ‘Don’t know’ based on the evidence on effectiveness and acceptability. The results will be written for publication in an open-access peer-reviewed journal and disseminated to the public and patients, clinicians, commissioners, funders and academic conferences.

PROSPERO registration number

CRD42022306923.
Data linkage of German statutory health insurance claims data and care needs assessments preceding a population-based cohort study on nursing home admission
Authors: Domhoff Dominik , Seibert Kathrin , Stiefler Susanne , Wolf-Ostermann Karin , Peschke Dirk
Published: June 2022

Objectives

We perform and evaluate record linkage of German Care Needs Assessment (CNA) data to Statutory Health Insurance (SHI) claims data. The resulting dataset should enable the identification of factors in healthcare predicting the time between the onset of long-term care dependency and the admission to a nursing home in Germany in subsequent analyses.

Design

A deterministic record linkage was conducted using the key variables region, sex, date of birth and care level. In further steps, the underlying cause of care dependency (International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10)) was added for a higher level of distinction. Before linkage, the suitability of the two datasets for these procedures was assessed. After linkage, the results of each stage were analysed and the resulting dataset was evaluated cross-sectionally with respect to bias generated through this process.

Setting

The study comprises data from the German SHI and Statutory Long-Term Care Insurance.

Participants

The study cohort comprised 158 069 individuals who became care dependent in 2006. We obtained CNA data for the year 2006 including 188 935 individuals.

Results

We could link CNAs to 66 310 individuals of the original study cohort, corresponding to 42.0%. Records from two federal states could not be matched due to missing data. Linkage rates were lower where more people shared the same attributes. The resulting dataset showed minor differences regarding age, sex and care level compared to the original cohort.

Conclusions

Data linkage between German SHI claims data and CNA data is feasible. Failure to link was mostly attributable to a lack of distinction between individuals using available identifiers. The resulting dataset contains relevant information from both health services provision and functional status of care dependent people and is suitable for further analyses with critical reflection of representativity.
Autoimmune gastritis: long-term natural history in naïveHelicobacter pylori-negative patients
Authors: Rugge Massimo , Bricca Ludovica , Guzzinati Stefano , Sacchi Diana , Pizzi Marco , Savarino Edoardo , Farinati Fabio , Zorzi Manuel , Fassan Matteo , Dei Tos Angelo Paolo , Malfertheiner Peter , Genta Robert M , Graham David Y
Published: June 2022

Objective

Autoimmune gastritis (AIG) is an immunomediated disease targeting parietal cells, eventually resulting in oxyntic-restricted atrophy. This long-term follow-up study aimed at elucidating the natural history, histological phenotype(s), and associated cancer risk of patients with AIG consistently testedH. pylori-negative (naïveH. pylori-negative subjects).

Design

Two-hundred eleven naïveH. pylori-negative patients (tested by serology, histology, molecular biology) with AIG (F:M=3.15:1; p<0.001) were prospectively followed up with paired biopsies (T1 vs T2; mean follow-up years:7.5 (SD:4.4); median:7). Histology distinguished non-atrophic versus atrophic AIG. Atrophy was further subtyped/scored as non-metaplastic versus metaplastic (pseudopyloric (PPM) and intestinal (IM)). Enterochromaffin-like-cell (ECL) status was categorised as diffuse versus adenomatoid hyperplasia/dysplasia, and type 1 neuroendocrine tumours (Type1-NETs).

Results

Over the long-term histological follow-up, AIG consistently featured oxyntic-predominant-mononuclear inflammation. At T1, PPM-score was greater than IM (200/211 vs 160/211, respectively); IM scores increased from T1 to T2 (160/211 to 179/211), with no changes in the PPM prevalence (T1=200/211; T2=201/211). At both T1/T2, the prevalence of OLGA-III-stage was <5%; no Operative Link on Gastritis Assessment (OLGA)-IV-stage occurred. ECL-cell-status progressed from diffuse to adenomatoid hyperplasia/dysplasia (T1=167/14 vs T2=151/25). Type1-NETs (T1=10; T2=11) always coexisted with extensive oxyntic-atrophy, and ECL adenomatoid-hyperplasia/dysplasia. No excess risk of gastric or other malignancies was found over a cumulative follow-up time of 10 541 person years, except for (marginally significant) thyroid cancer (SIR=3.09; 95% CI 1.001 to 7.20).

Conclusions

Oxyntic-restricted inflammation, PPM (more than IM), and ECL-cell hyperplasia/neoplasia are the histological AIG hallmarks. Compared with the general population, corpus-restricted inflammation/atrophy does not increase the GC risk. The excess of GC risk reported in patients with AIG could plausibly result from unrecognised previous/currentH. pyloricomorbidity.
Fiducial perturbative power corrections within the $$\mathbf{q}_T$$ subtraction formalism
Authors: Camarda Stefano , Cieri Leandro , Ferrera Giancarlo
Published: June 2022

We consider higher-order QCD corrections to the production of high-mass systems in hadron collisions within the transverse-momentum ($$q_T$$

q
T

) subtraction formalism. We present a method to consistently remove the linear power corrections in $$q_T$$

q
T

which appears when fiducial kinematical cuts are applied on the final state system. We consider explicitly the case of fiducial cross sections for Drell–Yan lepton pair production at the Large Hadron Collider up to next-to-next-to-next-to-leading order (N$$^3$$

3

LO) in QCD. We have implemented our method within the numerical program and we have obtained perturbative predictions which are in agreement at the permille level with those obtained with local subtraction formalisms up to the next-to-next-to-leading order (NNLO). At the N$$^3$$

3

LO we are able to provide predictions for fiducial cross sections with numerical accuracy at the permille level.
Classification and Traveling Wave Solutions for the Gilson–Pickering Equation
Authors: Yang Deniu
Published: June 2022

The main purpose of this paper is to form a complete group classification for the Gilson–Pickering equation. Exact traveling wave solutions and bifurcations of the Gilson–Pickering equation are studied by the method of dynamical systems. The study on the wave solutions of the model derives a planar Hamiltonian system. Based on phase portraits, many exact explicit parametric representations of wave solutions are obtained under different parametric conditions.
Indistinguishability-Enhanced Entanglement Recovery by Spatially Localized Operations and Classical Communication
Authors: Piccolini Matteo , Nosrati Farzam , Morandotti Roberto , Lo Franco Rosario
Published: June 2022

We extend a procedure exploiting spatial indistinguishability of identical particles to recover the spoiled entanglement between two qubits interacting with Markovian noisy environments. Here, the spatially localized operations and classical communication (sLOCC) operational framework is used to activate the entanglement restoration from the indistinguishable constituents. We consider the realistic scenario where noise acts for the whole duration of the process. Three standard types of noises are considered: a phase damping, a depolarizing, and an amplitude damping channel. Within this general scenario, we find the entanglement to be restored in an amount proportional to the degree of spatial indistinguishability. These results elevate sLOCC to a practical framework for accessing and utilizing quantum state protection within a quantum network of spatially indistinguishable subsystems.
Hyper-node Relational Graph Attention Network for Multi-modal Knowledge Graph Completion
Authors: Liang Shuang , Zhu Anjie , Zhang Jiasheng , Shao Jie
Published: June 2022

Knowledge graphs often suffer from incompleteness, and
knowledge graph completion (KGC)
aims at inferring the missing triplets through knowledge graph embedding from known factual triplets. However, most existing knowledge graph embedding methods only use the relational information of knowledge graph and treat the entities and relations as IDs with simple embedding layer, ignoring the multi-modal information among triplets, such as text descriptions, images, etc. In this work, we propose a novel network to incorporate different modal information with graph structure information for more precise representation of multi-modal knowledge graph, termed as
hyper-node relational graph attention (HRGAT)
network. In HRGAT, we use low-rank multi-modal fusion to model the intra-modality and inter-modality dynamics, which transforms the original knowledge graph to a hyper-node graph. Then,
relational graph attention (RGAT)
network is used, which contains relation-specific attention and entity-relation fusion operation to capture the graph structure information. Finally, we aggregate the updated multi-modal information and graph structure information to generate the final embeddings of knowledge graph to achieve KGC. By exploring multi-modal information and graph structure information, HRGAT embraces faster convergence speed and achieves the state-of-the-art for KGC on the standard datasets. Implementation code is available at https://github.com/broliang/HRGAT.
PAINT: Photo-realistic Fashion Design Synthesis
Authors: Gu Xiaoling , Huang Jie , Wong Yongkang , Yu Jun , Fan Jianping , Peng Pai , Kankanhalli Mohan S.
Published: June 2022

In this article, we investigate a new problem of generating a variety of multi-view fashion designs conditioned on a human pose and texture examples of arbitrary sizes, which can replace the repetitive and low-level design work for fashion designers. To solve this challenging multi-modal image translation problem, we propose a novel Photo-reAlistic fashIon desigN synThesis (PAINT) framework, which decomposes the framework into three manageable stages. In the first stage, we employ a
Layout Generative Network
(LGN) to transform an input human pose into a series of person semantic layouts. In the second stage, we propose a
Texture Synthesis Network
(TSN) to synthesize textures on all transformed semantic layouts. Specifically, we design a novel attentive texture transfer mechanism for precisely expanding texture patches to the irregular clothing regions of the target fashion designs. In the third stage, we leverage an
Appearance Flow Network
(AFN) to generate the fashion design images of other viewpoints from a single-view observation by learning 2D multi-scale appearance flow fields. Experimental results demonstrate that our method is capable of generating diverse photo-realistic multi-view fashion design images with fine-grained appearance details conditioned on the provided multiple inputs. The source code and trained models are available at
https://github.com/gxl-groups/PAINT
.
MLI: A Multi-level Inference Mechanism for User Attributes in Social Networks
Authors: Zhang Hang , Yang Yajun , Wang Xin , Gao Hong , Hu Qinghua
Published: June 2022

In the social network, each user has attributes for self-description called user attributes, which are semantically hierarchical. Attribute inference has become an essential way for social platforms to realize user classifications and targeted recommendations. Most existing approaches mainly focus on the flat inference problem neglecting the semantic hierarchy of user attributes, which will cause serious inconsistency in multi-level tasks. In this article, we propose a multi-level model MLI, where information propagation part collects attribute information by mining the global graph structure, and the attribute correction part realizes the mutual correction between different levels of attributes. Further, we put forward the concept of generalized semantic tree, a way of representing the hierarchical structure of user attributes, whose nodes are allowed to have multiple parent nodes unlike the regular tree. Both regular and generalized semantic trees are commonly used in practice, and can be handled by our model. Besides, by making the inference start from sub-networks with sufficient attribute information, we design a “Ripple” algorithm to improve the efficiency and effectiveness of our model. For evaluation purposes, we conduct extensive verification experiments on DBLP datasets. The experimental results show the superior effect of MLI, compared with the state-of-the-art methods.
Image Super-Resolution via Lightweight Attention-Directed Feature Aggregation Network
Authors: Wang Li , Li Ke , Tang Jingjing , Liang Yuying
Published: June 2022

The advent of convolutional neural networks (CNNs) has brought substantial progress in image super-resolution (SR) reconstruction. However, most SR methods pursue deep architectures to boost performance, and the resulting large model sizes are impractical for real-world applications. Furthermore, they insufficiently explore the internal structural information of image features, disadvantaging the restoration of fine texture details. To solve these challenges, we propose a lightweight architecture based on a CNN named attention-directed feature aggregation network (AFAN), consisting of chained stacking multi-aware attention modules (MAAMs) and a simple channel attention module (SCAM), for image SR. Specifically, in each MAAM, we construct a space-aware attention block (SAAB) and a dimension-aware attention block (DAAB) that individually yield unique three-dimensional modulation coefficients to adaptively recalibrate structural information from an asymmetric convolution residual block (ACRB). The synergistic strategy captures multiple content features that are both space-aware and dimension-aware to preserve more fine-grained details. In addition, to further enhance the accuracy and robustness of the network, SCAM is embedded in the last MAAM to highlight channels with high activated values at low computational load. Comprehensive experiments verify that our proposed network attains high qualitative accuracy while employing fewer parameters and moderate computational requirements, exceeding most state-of-the-art lightweight approaches.
An Understanding-oriented Robust Machine Reading Comprehension Model
Authors: Ren Feiliang , Liu Yongkang , Li Bochao , Liu Shilei , Wang Bingchao , Wang Jiaqi , Liu Chunchao , Ma Qi
Published: June 2022

Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this article, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over-sensitivity, over-stability, and generalization. Specifically, we first use a natural language inference module to help the model understand the accurate semantic meanings of input questions to address the issues of over-sensitivity and over-stability. Then, in the machine reading comprehension module, we propose a memory-guided multi-head attention method that can further well understand the semantic meanings of input questions and passages. Third, we propose a multi-language learning mechanism to address the issue of generalization. Finally, these modules are integrated with a multi-task learning-based method. We evaluate our model on three benchmark datasets that are designed to measure models’ robustness, including DuReader (robust) and two SQuAD-related datasets. Extensive experiments show that our model can well address the mentioned three kinds of robustness issues. And it achieves much better results than the compared state-of-the-art models on all these datasets under different evaluation metrics, even under some extreme and unfair evaluations. The source code of our work is available at
https://github.com/neukg/RobustMRC
.
Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations
Authors: Hagen Christoph , Weinert Christian , Sendner Christoph , Dmitrienko Alexandra , Schneider Thomas
Published: June 2022

Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigations.

Our study of three popular messengers (WhatsApp, Signal, and Telegram) shows that large-scale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal. For Telegram, we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (cross-messenger) usage statistics, which also reveal that very few users change the default privacy settings.

Furthermore, we demonstrate that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal. Most notably, we show that with the password cracking tool “JTR,” we can iterate through the entire worldwide mobile phone number space in < 150 s on a consumer-grade GPU. We also propose a significantly improved rainbow table construction for non-uniformly distributed input domains that is of independent interest.

Regarding mitigations, we most notably propose two novel rate-limiting schemes: our 
incremental
contact discovery for services without server-side contact storage strictly improves over Signal’s current approach while being compatible with private set intersection, whereas our 
differential
scheme allows even stricter rate limits at the overhead for service providers to store a small constant-size state that does not reveal any contact information.
The Impact of Road Types on the Energy Consumption of Electric Vehicles
Authors: Jonas Tim , Wilde Taris , Hunter Christopher D. , Macht Gretchen A.
Published: June 2022

The widespread adoption of battery electric vehicles (BEVs) is hindered by their limited ranges and long charging times. Optimizing eco-driving strategies and BEV-specific routing through a thorough understanding of the BEV discharge behavior is vital to overcome these barriers in the short term. Therefore, this study investigates the impact of road types on the BEV discharge behavior while accounting for explanatory variables (i.e., ambient temperature, the initial state of charge, and driver). Thirty participants drove a 2017 Volkswagen eGolf along two predefined routes in Rhode Island. The results illustrate that BEVs are the most efficient on-road types with medium speed and low variation (i.e., “major collectors,” “minor arterials,” and “other principal arterials”). Meanwhile, findings confirmed a significantly higher average energy consumption rate on roads with higher average speeds (“interstates” and “other freeways/expressways”). Moreover, “local roads,” associated with a low average travel speed and a high variation in speed, showed a negative effect on BEV efficiency. The study further supported previous findings that BEVs are less efficient in colder temperatures. Thus, adapting eco-driving strategies, including the alteration of route choice to avoid “local roads” and “interstates,” can offer BEV drivers the potential for energy savings and range extensions. We propose a consideration of these findings to mitigate the effects of BEV range limitations and ease BEV adoption and ownership.
Fractional Rectified Linear Unit Activation Function and Its Variants
Authors: Job Megha S. , Bhateja Priyanka H. , Gupta Muskan , Bingi Kishore , Prusty B. Rajanarayan
Published: June 2022

This paper focuses on deriving and validating the fractional-order form of rectified linear unit activation function and its linear and nonlinear variants. The linear variants include the leaky and parametric, whereas the nonlinear variants include the exponential, sigmoid-weighted, and Gaussian error functions. Besides, a standard formula has been created and used while developing the fractional form of linear variants. Moreover, different expansion series such as Maclaurin and Taylor have been used while designing the fractional version of nonlinear variants. A simulation study has been conducted to validate the performance of all the developed fractional activation functions utilizing a single and multilayer neural network model and to compare them with their conventional counterparts. In this simulation study, a neural network model has been created to predict the system-generated power of a Texas wind turbine. The performance has been evaluated by varying the activation function in the hidden and output layers with the developed functions for single and multilayer networks.
Monitoring System for the Construction of Arch Cover Method Subway Station Based on DT and IoT
Authors: Jiang Hongren , Jiang Annan
Published: June 2022

Aiming at the problems of safety and monitoring beyond the existing experience in the construction of the subway stations with arch cover method in Dalian, China, combined with the real-time monitoring requirements of intelligent construction status, a multi-information construction monitoring system based on digital twin(DT) and Internet of Things(IoT) is proposed, and the digital twin 3D architecture is established. First, on the basis of the IoT monitoring experience of tunnel, the installation plan of the automatic monitoring device of the arch cover method subway station is clarified, and the sensor arrangement is carried out. Then, based on the Revit platform, the establishment of the main structure model of the station and the BIM parametric modeling technology of the sensors are realized, and the monitoring properties of the sensors are expanded. Finally, through the design of the BIM database and in the form of secondary development, a BIM-based construction information visualization monitoring system is established to realize the correlation between the sensor model and the monitoring data and achieve the effect of information visualization query and color change early warning. The system was initially applied at Shikui Road Station of Dalian Metro Line 5 and achieved good results. The system can intuitively reproduce and accurately describe the subway station construction status, which provides an advanced technical means for the informatization and visualization management of the construction of arch cover method subway stations.
Floristic Diversity and Evaluation of the Potential of Spontaneous Medicinal Plants in the Bigoudine Watershed (Moroccan Western High Atlas)
Authors: Kahouadji Mohammed Said , Hseini Souâda , Jaafar Brahim , Zaid Younes , Kahouadji Azzeddine , Zidane Lahcen
Published: June 2022

The Bigoudine watershed (Western High Atlas) enjoys a floristic originality and a large number of plant species of therapeutic and aromatic interest widely used by the local population in traditional medicine. This region is subject to very difficult geographical conditions and is occupied by a poor population with a fairly high rate of illiteracy. Ethnobotanical surveys and floristic and phytoecological surveys carried out in two periods, the first in 2003–2004 and the second in 2016–2018, made it possible to identify the spontaneous medicinal flora and assess the potential of medicinal and aromatic plants in the region. The assessment of the biomass potential is carried out on three medicinal species among the most widely used plants in the area, Thymus pallidus Coss. ex Batt, Lavandula dentata L., and Salvia lavandulifolia Vahl. The results obtained made it possible to catalog 57 spontaneous medicinal species divided into 44 genera and 26 families, the most dominant of which are Lamiaceae, Asteraceae, Amaranthaceae, Anacardiaceae, Asparagaceae, Cistaceae, Cupressaceae, Fabaceae, and Oleaceae. The location of these species has been identified, and the plant formations concerned have been delimited. The estimated biomass potential of Thymus pallidus Coss. ex Batt, Lavandula dentata L., and Salvia lavandulifolia Vahl. varies according to species, types of plant formations, their structures, and their biological states. This spontaneous medicinal floristic wealth, within the region studied, is exposed to severe socioeconomic conditions, which are the main causes of non-respectful exploitation of natural resources threatening their balance and sustainable regeneration. Medicinal and aromatic plants in the Bigoudine watershed region require adapted measures allowing the rational development of certain potentially exploitable plants and the conservation of heavily used and threatened species.
SCFFNet: Spatial Context Feature Fusion Network for Understanding the Highly Congested Scenes
Authors: Xiong Liyan , Yi Hu , Huang Xiaohui , Huang Weichun
Published: June 2022

Accurate counting in dense scenes can effectively prevent the occurrence of abnormal events, which is crucial for flow management, traffic control, and urban safety. In recent years, the application of deep learning technology in counting tasks has significantly improved the performance of models, but it still faces many challenges, including the diversity of target distribution between image and background, the drastic change of target scale, and serious occlusion. To solve these problems, this paper proposes a spatial context feature fusion network, abbreviated as SCFFNet, to understand highly congested scenes and perform accurate counts as well as produce high-quality estimated density maps. SCFFNet first uses rich convolutions with different scales to calculate scale-aware features, adaptively encodes the scale of contextual information needed to accurately estimate density maps, and then calibrates and refuses the fused feature maps through a channel spatial attention-aware module, which improves the model’s ability to suppress background and focus on main features. Finally, the final estimated density map is generated by a dilated convolution module. We conduct experiments on five public crowd datasets, UCF_CC_50, WorldExpo’10, ShanghaiTech, Mall, and Beijing BRT, and the results show that our method achieves lower counting errors than existing state-of-the-art methods. In addition, we extend SCFFNet to count other objects, such as vehicles in the vehicle dataset HBR_YD, and the experimental results show that our proposed method significantly improves the output quality with higher accuracy than previous methods.
Green Building Project Cost Budgeting and Cost Control Integrating Interactive VR Genetic Algorithm
Authors: Zhou Li , Song Shijiu
Published: June 2022

The development of buildings in the future will tend to the direction of green buildings. At present, most of the calculations of their costs usually use empirical models, which lack the basis for quantitative prediction. For the purpose of improving the accuracy of budget and control of project cost for green buildings, this paper applies interactive virtual reality (VR) genetic algorithm and analyze project budget accordingly. The prediction accuracy is effectively increased based on the mathematical analysis model. Finally, the test results suggest that the proposed algorithm can effectively improve the cost control efficiency and engineering quality of green buildings and reduce the construction cost of buildings.
Experimental and Analytical Study on the Influence of Saturation Pressure and Surface Roughness on Pool Boiling CHF of HFE-7100
Authors: Fan Xiaoguang , Gu Shiya , Lei Jingsheng , Luo Guihang , Meng Fanbin , Wu Liyan , Gu Shiyan
Published: June 2022

Critical heat flux (CHF) determines the safety and application of pool boiling heat transfer in many industrial fields. The influence of saturation pressure and surface roughness on saturated pool boiling CHF in hydrofluoroether HFE-7100 was experimentally studied in this investigation. Visualization and heat transfer measurements were conducted at the critical and transition boiling state, and further, the accuracy of CHF prediction models and enhancement mechanism had been analyzed. The polished boiling surfaces had various surface roughness values ranging from 0.019 to 0.587 μm and their contact angles varied from 7° to 10°, while the experimental saturation pressure changed from 0.7 to 2.0 bar. The visual images showed that the pool boiling phenomenon at a critical state was composed of different-sized bubbles, vapor column, and large mushroom vapor, whereas the unsteady blanket of vapor continually injected bubbles at a transition state. The saturation pressure and surface roughness had an obvious improvement on pool boiling CHF, which might be ascribed to the effects of bubble momentum owing to evaporation, distribution and recovery period of a heat transfer boundary layer, capillary action of the working liquid, as well as ratio of vapor jets’ area. Compared with the well-known correlations reported in the literature, CHF correlation of Bailey et al. (2006) predicted the current results more accurately. To further improve the prediction accuracy, a new empirical correlation for CHF dimensionless K considering the effects of saturation pressure and surface roughness was developed, and the predicted values were in better agreement with the experimental data.
Identifying Key Relationships between Nation-State Cyberattacks and Geopolitical and Economic Factors: A Model
Authors: González-Manzano Lorena , de Fuentes José M. , Ramos Cristina , Sánchez Ángel , Quispe Florabel
Published: June 2022

Nation-state cyberattacks, and particularly Advanced Persistent Threats (APTs), have rocketed in the last years. Their use may be aligned with nation-state geopolitical and economic (GPE) interests, which are key for the underlying international relations (IRs). However, the interdependency between APTs and GPE (and thus IRs) has not been characterized yet and it could be a steppingstone for an enhanced cyberthreat intelligence (CTI). To address this limitation, a set of analytic models are proposed in this work. They are built considering 234M geopolitical events and 306 malicious software tools linked to 13 groups of 7 countries between 2000 and 2019. Models show a substantial support for launched and received cyberattacks considering GPE factors in most countries. Moreover, strategic issues are the key motivator when launching APTs. Therefore, from the CTI perspective, our results show that there is a likely cause-effect relationship between IRs (particularly GPE relevant indicators) and APTs.
Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification
Authors: Al Duhayyim Mesfer , Mengash Hanan Abdullah , Marzouk Radwa , Nour Mohamed K , Mahgoub Hany , Althukair Fahd , Mohamed Abdullah
Published: June 2022

Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent “semantic” feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network–long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches.
Effect of Ropivacaine Combined with Small Doses of Triamcinolone and Continuous Nerve Block of Unilateral Paravertebral Canal Guided by Ultrasound on Metastasis after Radical Treatment of Lung Cancer
Authors: Xu Dan , Luo Wei
Published: June 2022

Continuous nerve block of unilateral paravertebral canal is one of the most reliable and the most commonly performed techniques to control intra- and postoperative pain. It is the technique of injecting local anaesthetic alongside the thoracic vertebra close to where the spinal nerves emerge from the intervertebral foramen. This produces unilateral, segmental, somatic, and sympathetic nerve blockade, which is effective for anaesthesia and in treating acute and chronic pain of unilateral origin from the chest and abdomen. Ultrasound guidance with or without nerve stimulator has reduced the amount of local anaesthetics (LA) to achieve a successful block, which may minimize complications of continuous nerve block of unilateral paravertebral canal. Previous studies have suggested that continuous nerve block of unilateral paravertebral canal with ropivacaine combined with small doses of triamcinolone was effective to achieve sensory and motor block after lung cancer surgery. Previous studies have used a fixed large dose in different volumes and concentrations for a nerve block, infraclavicular block, axillary block, and humeral canal block. The results of these studies with the respects of onset time, successful rate, and block duration were not consistent. Various factors, including the technique used, the anatomic aspects of the injection, and the pharmacodynamics aspect of drug, may influence the results. We tested the hypothesis that 50,25,10 mg ropivacaine and 50,25,10 mg triamcinolone of three combinations of volumes and concentrations for ultrasound-guided continuous nerve block of unilateral paravertebral canal produce different effects in the aspect of survival rate. Here, the drug effect was analyzed using the cross cluster K-nearest neighbors (KNN). The whole experimentation was carried out under MATLAB environment.
Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization
Authors: Pradhan Arabinda , Bisoy Sukant Kishoro , Sain Mangal
Published: June 2022

Increasing scale of task in cloud network leads to problem in load balancing and its improvement in parameters. In this paper, we proposed a hybrid scheduling policy which is hybrid of both Particle Swarm Optimization (PSO) algorithm and actor-critic algorithm named as Hybrid Particle Swarm Optimization Actor Critic (HPSOAC) to solve this issue. This hybrid scheduling policy helps to each agent to improve an individual learning as well as learning through exchanging information among other agents. An experiment is carried out by the help of Python simulator with TensorFlow. Outcome shows that our proposed scheduling policy reduces 5.16% and 10.86% in energy consumption, reduces 7.13% and 10.04% in makespan time, and has marginally better resource utilization over Deep Q-network (DQN) and Q-learning based on Modified Particle Swarm Optimization (QMPSO) algorithm, respectively.
A Multiagent Memetic Optimization Algorithm Based on Temporal Asymptotic Surprise in Complex Networks to Reveal the Structure of the Dynamic Community
Authors: Ranjkesh Somayeh , Masoumi Behrooz , Hashemi Seyyed Mohsen
Published: June 2022

Complex networks are used in a variety of applications. Revealing the structure of a community is one of the essential features of a network, during which remote communities are discovered in a complex network. In the real world, dynamic networks are evolving, and the problem of tracking and detecting communities at different time intervals is raised. We can use dynamic graphs to model these types of networks. This paper proposes a multiagent optimization memetic algorithm in complex networks to detect dynamic communities and calls it DYNMAMA (dynamic multiagent memetic algorithm). The temporal asymptotic surprise is used as an evaluation function of the algorithm. In the proposed algorithm, work is done on dynamic data. This algorithm does not need to specify the number of communities in advance and meets the time smoothing limit, and this applies to dynamic real-world and synthetic networks. The results of the performance of the evaluation function show that this proposed algorithm can find an optimal and more convergent solution compared to modern approaches.
Development Analysis of the Sports Industry Based on the Data Mining Model
Authors: Huang Qi , Zou Xiaowei , Zhang Tao , Yang Haidong
Published: June 2022

The Chinese sports industry encompasses both the secondary and tertiary sectors of the country’s economy. More prominent are the issues of unbalanced industrial structure development, mismatch between supply and demand, and rapid expansion of the sports goods manufacturing industry. This paper first employs the literature method to investigate the dynamic mechanism and promotion strategy of China’s sports industry’s green development. In order to improve the accuracy of data mining and quantitative analysis in the sports industry, this paper proposes a time series-based model for the analysis of sports industry data. Utilizing the global steady-state feature fusion method, the statistical and quantitative fusion analysis method, and the fuzzy analytical control method, accurate mining of sports industry data is achieved. The simulation results demonstrate that this method has greater precision and a higher degree of feature matching for sports industry data mining, thereby reducing the disturbance error of sports industry data mining.
EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development
Authors: Rocha-Herrera César Alfredo , Díaz-Manríquez Alan , Barron-Zambrano Jose Hugo , Elizondo-Leal Juan Carlos , Saldivar-Alonso Vicente Paul , Martínez-Angulo Jose Ramon , Nuño-Maganda Marco Aurelio , Polanco-Martagon Said
Published: June 2022

Brain–computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain–computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.
A Systematic Review and Meta-Analysis Comparing the Safety and Efficacy of Spinal Anesthesia and Spinal Anesthesia Combined with Obturator Nerve Block in Transurethral Resection of Bladder Tumors
Authors: Deng Wanxin , Zhang Qiang , Yao Hongmei
Published: June 2022

Background. Transurethral resection of bladder tumors (TURBT) is the main surgical treatment for bladder cancer, but during TURBT, it is easy to stimulate the obturator nerve passing close to the lateral side of the bladder wall and induce involuntary contraction of the adductor muscle group of the thigh innervated by it, which will affect the surgical process and lead to adverse reactions. Obturator nerve block (ONB) helps to prevent the obturator nerve reflex. This study systematically evaluated and meta-analyzed the reports on the co-application of ONB and spinal anesthesia (SA) in TURBT in recent years to provide evidence for clinical diagnosis and treatment. Methods. The clinical randomized controlled literature studies of ONB combined with SA in TURBT published in PubMed, EMBASE, the Cochrane Library, CNKI (China National Knowledge Infrastructure), and Wanfang databases from January 2000 to December 2021 were searched. After screening the qualified literature studies, the literature quality was assessed by the Jadad scale. The incidence of obturator nerve reflex, the incidence of bladder perforation, length of hospital stay, and tumor recurrence rate were used as outcome indicators. The meta-analysis was performed with the R language toolkit. Results. A total of 444 articles were initially retrieved, and after the screening, a total of 8 articles were included in the selection, and a total of 635 patients with ureterovesical tumor resection were included. The meta-analysis showed that the use of SA + ONB anesthesia during TURBT was associated with a smaller incidence of bladder perforation (RR = 0.24, 95% CI (0.11, 0.53), Z = −3.48,

P
=
0.0005

), a smaller incidence of obturator nerve reflex (RR = 0.22, 95% CI (0.13, 0.36), Z = −6.11,

P
=
0.0001

), a significantly shorter length of hospital stay (MD = −1.81, 95% CI (−2.65, −0.97), Z = −4.24,

P
=
0.0001

), and a significantly lower tumor recurrence rate (RR = 0.46, 95% CI (0.29, 0.73), Z = −3.30,

P
=
0.001

) compared with SA alone. Conclusion. The application of SA combined with ONB in TURBT can effectively reduce the incidence of obturator nerve reflex, reduce the incidence of bladder perforation, shorten the hospital stay and reduce the tumor recurrence rate.
Analysis of Variable Compression Ratio Engine Using Biodiesel when Incorporated with Metal Oxides from Chemical and Biological Resources
Authors: Anish M. , Arunkumar T. , Jayaprabakar J. , Nivin Joy T. , Al Obaid Sami , Alfarraj Saleh , Raj M. M. , Markos Mebratu
Published: June 2022

Specifically, the current investigation will look at the impacts of nanoemulsions such as titanium and zinc oxides as well as plant extracts and a four‐stroke D.I.VCR engine for biodiesel that is produced both chemically and biologically on the environment. Meanwhile, when a homogeneous catalyst is used, a substantial volume of wastewater is made available for the separation and cleaning of the biodiesel catalyst. Because of this, waste cooking oil (WCO) was used to make biodiesel, which were then transesterified using heterogeneous base catalysts. This was done for environmental reasons as well as to minimize the cost of producing biodiesel. The D.I variable compression engine’s performance and engine parameters, such as brake specific fuel consumption, brake thermal efficiency, and CO, HC, and NOx emissions, were investigated. Nanoparticles have been shown to produce less NOx than pure diesel when combined with zinc oxides that have been biologically generated at the lowest possible concentration. The carbon dioxide emissions from a biologically generated titanium oxide sample are greater than those from the other samples, with Pure Diesel emitting the least amount of CO2. The emission of less oxygen from a Pure Diesel sample and a chemically generated titanium oxide sample showed a pattern that was comparable. A comprehensive study was conducted to examine the performance and emission characteristics of an internal combustion engine with and without the use of nanoadditives, and the findings revealed an astonishing outcome.