bluuebunny/crossref_metadata_2025 huggingface dataset:
Sustainable Development According to Resource Productivity in the EU Environmental Policy Context
Authors: Taušová Marcela , Tauš Peter , Domaracká Lucia
Published: June 2022

The constant rise in the consumption of resources puts the environment under pressure. Most resources are non-renewable in nature, which is why they must be utilized with great care. For this reason, the European Union devotes increasingly more attention to their efficient use. It deals with these aspects, making an effort to maintain the long-term competitiveness and to secure sustainable development in line with all of the related environmental impacts. In this context, several goals have been set out, to which the individual EU member states are bound. A method for monitoring resource efficiency was developed, consisting of indicators, the aim of which is to assess the efficiency of the use of soil, water, energy, with the most fundamental one being resource productivity. The results of the efficiency of use of the individual resources in the member states greatly differ, even without further investigating the links and correlations between the indicators. Research on the interrelationships of the individual indicators in terms of mutual influence has not yet been completed. The aim of our study was to define the correlation between the main indicator, resource productivity, and the other indicators at the level of the EU and its member states. For this purpose, we prepared a database with data which, for the sake of uniformity, were obtained from the publicly available Eurostat database. Subsequently, the data were analyzed and evaluated using the statistical software JMP 15 by a regression and correlation analysis. By using the multiple regression analysis, we created a model describing the significance of the impact of the observed variables on the resulting resource productivity of the EU member states. Generally, there is a positive correlation between the resource productivity and the Eco-Innovation index, as well as the utilization rate of recycled materials. For the sake of comparison, we developed a regression model at the level of the V4 countries, with the aim of evaluating the impact of the historical background of the countries on their contemporary ability to reach the goals set out by the environmental policy. The V4 countries are lagging far behind in meeting all of the environmental policy objectives, not only in tracking the main indicator (resource productivity) on which the multiple regression analysis is based. It was interesting to find that the multiple regression model at the V4 level does not include the indicators defined by the EU level model, the key ones, in this case, being water productivity, energy dependence, energy productivity, and environmental tax. This finding may also, after further analyses, be the key for other countries joining the EU in the future, in defining the weaknesses of the newly acceding states in terms of the EU’s move towards a circular economy.
Linkages between the Promotion of Renewable Energy Policies and Low-Carbon Transition Trends in South America’s Electricity Sector
Authors: Peyerl Drielli , Barbosa Mariana Oliveira , Ciotta Mariana , Pelissari Maria Rogieri , Moretto Evandro Mateus
Published: June 2022

The decarbonization of the energy sector is among the leading global goals, and the electricity sector plays a crucial role in this low-carbon transition. However, South American countries have been underrepresented in this discussion. Understanding the particularities and the shifts in the electricity sector landscape of these countries over time and how natural resource availability, technology, and energy policies are decisive to a low-carbon transition summarizes the proposed matters in this research. This work aims to fill this gap by investigating past renewability trends in the electricity sector of five South American countries from 1990 to 2020 through five indicators. As a result, we observed a trend of low-carbon reverse transition in Argentina, Brazil, and Chile, despite the efforts and the success of renewable energy auctions, making short-term energy policy measures necessary. In Venezuela, there is a decrease in consumption and an increase in electricity generation using fossil fuels. Uruguay showed a rise in consumption and continued high use of renewables. Finally, energy policies focusing on quantifiable emission reduction should be a target of the electricity sector to achieve net zero emissions by 2050.
Impact of Oil Financialization on Oil Price Fluctuation: A Perspective of Heterogeneity
Authors: Feng Yanhong , Wang Xiaolei , Chen Shuanglian , Liu Yanqiong
Published: June 2022

A large number of studies have confirmed that oil speculation has played a vital role in oil price fluctuation in recent years. However, the heterogeneous impact of oil financialization on oil price fluctuation has not received enough attention. Based on time series data from January 1990 to October 2021, this paper adopts the Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model and the Ensemble Empirical Mode Decomposition (EEMD) method to study the heterogeneous impact of oil financialization on oil price fluctuation from three perspectives: different periods, different frequencies, and different time points of major events. The research results are as follows. First, the impact of oil financialization on oil price fluctuation in different periods is heterogeneous in terms of fluctuation amplitude and intensity. During major events such as the financial crisis or the COVID pandemic, the impact of oil financialization on oil price fluctuation is volatile and intense. Second, the impact of oil financialization on the oil price fluctuation of different frequencies is mainly reflected in the direction and duration. Oil financialization mainly promotes high-frequency oil price fluctuation in the short term, and it mainly suppresses low-frequency oil price fluctuation in the long term. Third, the impact of oil financialization on oil price fluctuation is heterogeneous in terms of duration, intensity, and transmission speed at different time points of major events.
Jet in Accelerating Turbulent Crossflow with Passive Scalar Transport
Authors: Quiñones Carlos , Araya Guillermo
Published: June 2022

The interaction of a turbulent, spatially developing crossflow with a transverse jet possesses several engineering and technological applications such as film cooling of turbine blades, exhaust plumes, thrust vector control, fuel injection, etc. Direct Numerical Simulation (DNS) of a jet in a crossflow under different streamwise pressure gradients (zero and favorable pressure gradient) is carried out. The purpose is to study the physics behind the transport phenomena and coherent structure dynamics in turbulent crossflow jets at different streamwise pressure gradients and low/high-velocity ratios (0.5 and 1). The temperature was regarded as a passive scalar with a molecular Prandtl number of 0.71. The analysis is performed by prescribing accurate turbulent information (instantaneous velocity and temperature) at the inlet of a computational domain. The upward motion of low-momentum fluid created by the “legs” of the counter-rotating vortex pair (CVP) encounters the downward inviscid flow coming from outside of the turbulent boundary layer, inducing a stagnation point and a shear layer. This layer is characterized by high levels of turbulent mixing, turbulence production, turbulent kinetic energy (TKE) and thermal fluctuations. The formation and development of the above-mentioned shear layer are more evident at higher velocity ratios.
A Thermodynamic Model for Carbon Dioxide Storage in Underground Salt Caverns
Authors: Zhang Yi , Li Wenjing , Chen Guodong
Published: June 2022

In the context of green energy and decarbonization, carbon dioxide storage in underground facilities, such as salt caverns, is one promising technical solution that has aroused attention. However, the thermodynamic behavior of CO2 and the geomechanical response of salt cavities have not been studied comprehensively. In this study, we proposed a thermomechanical model that integrated a salt cavity and wellbore and implemented a series of simulations for carbon dioxide storage in a salt cavern. The model was verified by gas capacity calculations using field testing data. The thermodynamic behaviors of CO2 were determined and compared to methane. The results showed that the critical point coordinates of carbon dioxide were within the storage operation conditions, a phase transition could occur, and the thermodynamic properties around the critical point varied dramatically. For a short CO2 withdrawal operation, the salt cavity remained stable, while the near-wellbore area (NWA) was prone to fracture due to tensile stress concentration. Thus, we concluded that the proposed thermomechanical coupling numerical simulation method provided a comprehensive and quantitative tool for the feasibility analysis of CO2 storage in underground salt caverns.
Properties of Lightweight Controlled Low-Strength Materials Using Construction Waste and EPS for Oil and Gas Pipelines
Authors: Liu Hao , Liu Kaizhi , Xiao Yiqi , Zhang Peng , Zhang Meixia , Zhu Youzeng , Liu Kaixin , Xu Tianshuo , Huang Rui
Published: June 2022

Due to its particularity and importance, long-distance oil and gas pipelines need to be well protected from damage by backfill materials. In this study, construction waste and expanded polystyrene (EPS) were used to replace conventional fine aggregate, and ethylene vinyl acetate-resin (EVA) was used to modify the surface of EPS to prepare lightweight controlled low strength materials (CLSM). Lightweight CLSM was tested in mechanics and physics and its microstructure was studied using microscopic analysis methods. The results revealed that the surface modification of EPS by EVA could greatly improve the compatibility of EPS with inorganic cementitious materials and prepare CLSM with a fluidity greater than 200 mm. EPS and cement content in cementitious materials play an important role in the development of material strength. When the volume ratio of EPS to construction waste was 2, and the content of cement in the cementitious materials was 35%, CLSM’s unconfined compressive strength at 28 days was only 0.48 MPa. In order to obtain the lightweight CLSM that meets the mechanical properties, the EPS content should not be too large. It can be concluded from the microscopic analysis that the increase of EPS content will lead to poor pore uniformity of the specimen, forming a loose mesh structure of defects, which is not conducive to the development of strength. In this study, EPS and construction waste are used to provide a green idea for preparing lightweight controlled low strength materials, which provides a reference for the backfill protection of the material in oil and gas pipelines in the future.
Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review
Authors: Duarte André , Borralho Nuno , Cabral Pedro , Caetano Mário
Published: June 2022

Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented.
Modeling Primary Branch Diameter and Length for Planted Pinus koraiensis by Incorporating Neighbor Competition in Northeast China
Authors: Gao Huilin , Liu Qifeng , Song Ying , Jiang Mengzhu , Yin You
Published: June 2022

Korean pine (Pinus koraiensis Sieb. et Zucc.) is the most important forest vegetation in northeast China. The timber quality of this tree species is largely driven by branch growth and distribution within the crown. Thus, developing branch diameter and length models, especially those that include competition indices, is essential. A total of 48 Korean pine trees were selected to conduct destructive measurements of branch characteristics. This was carried out on all live branches, and a branch diameter and length model was developed. Various indices, including the absolute depth into the branch base (DINC) from tree tip, were used. The equation with the largest Radj2 and smallest root mean square error (RMSE) values was selected as the best model. Each parameter from the best model was reparameterized to the tree variables and competition indices. Finally, the branch diameter model that included diameter at the breast height (DBH), tree height (HT), and the crown length index (CLI), and the branch length model that included DBH and HT exhibited the best performance. The Radj2 and RMSE values were 0.42 and 4 mm, respectively, for the branch diameter model, and 0.77 and 63 cm, respectively, for the branch length model. Branch diameter and length increased as DBH increased and decreased as HT increased. Furthermore, branch diameter decreased as the CLI increased.
Thermal Characteristics and Simulation of Enzymatic Lignin Isolated from Chinese Fir and Birch
Authors: Wang Jinyue , Hou Suyun , Shen Ziling , Wen Jialong , Qi Chusheng
Published: June 2022

Lignin is one of the main components of the plant cell wall, and the thermal properties of in situ biomass lignin are crucial for the multi-scale modeling of biomass properties and the thermodynamic modeling of lignin. In this study, high yields of double enzymatic lignin (DEL) were successfully isolated from softwood Chinese fir (Cunninghamia lanceolate (Lamb.) Hook.) and hardwood white birch (Betula platyphylla Suk.) to represent the in situ wood lignin. Their thermal properties, including specific heat capacity, thermal conductivity, thermal stability, and thermal degradation kinetic parameters, were tested and simulated. The results showed that Chinese fir DEL has different chemical structural units and thermal properties than birch DEL. The specific heat capacities of Chinese fir DEL and birch DEL at 20 °C were 1301 and 1468 J/(kg·K), respectively, and their thermal conductivities were 0.30 and 0.32 W/(m·K). Their specific heat capacity and thermal conductivity showed a positive linear relationship over a temperature range of 20–120 °C. Chinese fir DEL had a better thermal stability and a higher carbon residue than birch DEL. The average activation energy and pre-exponential factor changed with the conversion rate, and their relationships were simulated using linear or quadratic equations in the conversion rate range of 0.02–0.60. A second-order reaction function was found to be the best mechanism function for DEL thermal degradation.
Spray-Dried Nipa Palm Vinegar Powder: Production and Evaluation of Physicochemical, Nutritional, Sensory, and Storage Aspects
Authors: Palachum Wilawan , Klangbud Wiyada Kwanhian , Chisti Yusuf
Published: June 2022

Nipa palm vinegar (NPV) is a naturally fermented vinegar derived from the nipa palm (Nypa fruticans Wurmb) sap. This work optimized production of spray-dried nipa palm vinegar powder. The influence of the various drier air inlet temperatures (150, 170, and 190 °C) and maltodextrin DE10 carrier concentrations (15 and 20% w/v) in the feed, on the characteristics of the product powder was investigated. Nipa palm vinegar powder (NPVp) was evaluated in terms of the following responses: physicochemical and nutritional properties, sensory acceptability, and storage stability. All processing variables affected the responses. Based on product desirability as the optimization criterion, spray-drying with a hot air inlet temperature of 170 °C with a 15% w/v maltodextrin DE10 in the feed was optimal. The nutritional characteristics of the product made under the above identified optimal conditions were (per 100 g dry product): a calorific value of 366.2 kcal; 1.3 g protein; 88.1 g carbohydrate; 0.96 g fat; 883.9 mg potassium; 12.7 mg vitamin C; and 105 mg gallic acid equivalent (GAE) phenolics content. The product, vacuum-packed and heat-sealed in aluminum laminated polyethylene bags, could be stored at 25 °C for at least 180 days without noticeable loss in quality.
A BLE-Connected Piezoresistive and Inertial Chest Band for Remote Monitoring of the Respiratory Activity by an Android Application: Hardware Design and Software Optimization
Authors: De Fazio Roberto , De Vittorio Massimo , Visconti Paolo
Published: June 2022

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated.
Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach
Authors: Fianu Emmanuel Senyo
Published: June 2022

Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model.
Genetic Analysis of ABCB4 Mutations and Variants Related to the Pathogenesis and Pathophysiology of Low Phospholipid-Associated Cholelithiasis
Authors: Wang Helen H. , Portincasa Piero , Liu Min , Wang David Q.-H.
Published: June 2022

Clinical studies have revealed that the ABCB4 gene encodes the phospholipid transporter on the canalicular membrane of hepatocytes, and its mutations and variants are the genetic basis of low phospholipid-associated cholelithiasis (LPAC), a rare type of gallstone disease caused by a single-gene mutation or variation. The main features of LPAC include a reduction or deficiency of phospholipids in bile, symptomatic cholelithiasis at <40 years of age, intrahepatic sludge and microlithiasis, mild chronic cholestasis, a high cholesterol/phospholipid ratio in bile, and recurrence of biliary symptoms after cholecystectomy. Needle-like cholesterol crystals, putatively “anhydrous” cholesterol crystallization at low phospholipid concentrations in model and native bile, are characterized in ABCB4 knockout mice, a unique animal model for LPAC. Gallbladder bile with only trace amounts of phospholipids in these mice is supersaturated with cholesterol, with lipid composition plotting in the left two-phase zone of the ternary phase diagram, consistent with “anhydrous” cholesterol crystallization. In this review, we summarize the molecular biology and physiological functions of ABCB4 and comprehensively discuss the latest advances in the genetic analysis of ABCB4 mutations and variations and their roles in the pathogenesis and pathophysiology of LPAC in humans, based on the results from clinical studies and mouse experiments. To date, approximately 158 distinct LPAC-causing ABCB4 mutations and variants in humans have been reported in the literature, indicating that it is a monogenic risk factor for LPAC. The elucidation of the ABCB4 function in the liver, the identification of ABCB4 mutations and variants in LPAC patients, and the exploration of gene therapy for ABCB4 deficiency in animal models can help us to better understand the cellular, molecular, and genetic mechanisms underlying the onset of the disease, and will pave the way for early diagnosis and prevention of susceptible subjects and effective intervention for LPAC in patients.
scHiCEmbed: Bin-Specific Embeddings of Single-Cell Hi-C Data Using Graph Auto-Encoders
Authors: Liu Tong , Wang Zheng
Published: June 2022

Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.
MarZIC: A Marginal Mediation Model for Zero-Inflated Compositional Mediators with Applications to Microbiome Data
Authors: Wu Quran , O’Malley James , Datta Susmita , Gharaibeh Raad Z. , Jobin Christian , Karagas Margaret R. , Coker Modupe O. , Hoen Anne G. , Christensen Brock C. , Madan Juliette C. , Li Zhigang
Published: June 2022

Background: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). Methods: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. Results: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. Conclusions: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach.
On the Use of MATLAB to Import and Manipulate Geographic Data: A Tool for Landslide Susceptibility Assessment
Authors: Gatto Michele , Misiano Salvatore , Montrasio Lorella
Published: June 2022

Most of the methods for landslide susceptibility assessment are based on mathematical relationships established between factors responsible for the triggering of the phenomenon, named the conditioning factors. These are usually derived from geographic data commonly handled through Geographical Information System (GIS) technology. According to the adopted methodology, after an initial phase conducted on the GIS platform, data need to be transferred to specific software, e.g., MATLAB, for analysis and elaboration. GIS-based risk management platforms are thus sometimes hybrid, requiring relatively complex adaptive procedures before exchanging data among different environments. This paper describes how MATLAB can be used to derive the most common landslide conditioning factors, by managing the geographic data in their typical formats: raster, vector or point data. Specifically, it is discussed how to build matrices of parameters, needed to assess susceptibility, by using grid cell mapping units, and mapping them bypassing GIS. An application of these preliminary operations to a study area affected by shallow landslides in the past is shown; results show how geodata can be managed as easily as in GIS, as well as being displayed in a fashionable way too. Moreover, it is discussed how raster resolution affects the processing time. The paper sets the future development of MATLAB as a fully implemented platform for landslide susceptibility, based on any available methods.
Quality of Informed Consent Practices around the Time of Childbirth: A Cross-Sectional Study in Italy
Authors: Valente Emanuelle Pessa , Mariani Ilaria , Covi Benedetta , Lazzerini Marzia
Published: June 2022

Background: Few studies have explored consent request practices during childbirth. Objective: We explored consent request practices during childbirth in a referral hospital and research centre in Italy, capturing both women and health workers’ perspectives. Methods: Data were collected using self-administrated questionnaires between December 2016 and September 2018. Nine key maternal and newborn procedures were analysed. Associations between consent requests and women characteristics were explored by multiple logistic regression. Results: Among 1244 women, the rate of consent requests varied widely, with caesarean section (CS) showing the highest rate (89.1%) and neonatal conjunctivitis prophylaxis presenting the lowest rate (11.4%). Information provided on “risks/benefits” and “reasons” for procedures by health staff was most often not comprehensive for procedures of interest (range 18.6–87.4%). The lack of informed consent is not specifically linked to any pattern of women characteristics. According to 105 health workers, adequate protocols and standard forms for consent requests were available in 67.6% and 78.1% of cases, respectively, while less than one third (31.4%) reported having received adequate training and supportive supervision on how to deliver informed consent. Conclusions: Study findings align with previous evidence showing that consent request practices during childbirth need to be largely improved. More research is needed to investigate effective strategies for improvement.
Overcoming Barriers to Successful Climate and Health Adaptation Practice: Notes from the Field
Authors: Mallen Evan , Joseph Heather A. , McLaughlin Megan , English Dorette Quintana , Olmedo Carmen , Roach Matt , Tirdea Carmen , Vargo Jason , Wolff Matt , York Emily
Published: June 2022

State and local public health agencies are at the forefront of planning and responding to the health challenges of climate hazards but face substantial barriers to effective climate and health adaptation amidst concurrent environmental and public health crises. To ensure successful adaptation, it is necessary to understand and overcome these barriers. The U.S. Centers for Disease Control and Prevention Climate-Ready States and Cities Initiative (CRSCI) provides funding to state and local health departments to anticipate and respond to health impacts from climate change using the Building Resilience Against Climate Effects (BRACE) framework. This paper explores the barriers to and enablers of successful adaptation projects among BRACE West CRSCI grantees, including Arizona, California, Oregon, and the city and county of San Francisco. The barriers included competing demands such as the COVID-19 pandemic, dependence on partners with similar challenges, staff and leadership turnover, uncertain and complex impacts on at-risk populations, and inadequate resources. The enablers included effective partnerships, leadership support, dedicated and skilled internal staff, and policy windows enabling institutional change and reprioritization. These findings highlight effective strategies in the field that state and local health departments may use to anticipate potential barriers and establish their work in an environment conducive to successful adaptation.
Injury and Poisoning Mortality Trends in Urban and Rural China from 2006 to 2020 Based on Age-Period-Cohort Analysis
Authors: Yuan Xin , Kou Changgui , Zhang Min , Ma Wenyuan , Tang Zhitao , Sun Haiyan , Li Wenjun
Published: June 2022

Injury and poisoning, common public health problems, currently rank fifth among the causes of death in China. In this study, we aimed to analyze the trends and influencing factors of injury and poisoning mortality in urban and rural China using an age-period-cohort model. Crude mortality data for injury and poisoning by sex, age group, and region were obtained from the China Health Statistical Yearbook (2006–2020). Age-standardized mortality rates for injury and poisoning in urban and rural areas were estimated using the Seventh Census of China 2020 population. The trends of injury and poisoning mortality were assessed using Joinpoint analysis. Age–period–cohort models were used to explore the age, period, and birth cohort effects affecting mortality risk. Over a 15-year period, age-standardized mortality rates decreased from 28.81/100,000 in 2006 to 24.78/100,000 in 2020 in urban areas and from 45.49/100,000 to 44.39/100,000 in rural areas. In the male population, the annual change in mortality was −0.4% (95% CI = −1.8%, 1.0%) in urban areas and −1.0% (95% CI = −1.9%, 0.0%) in rural areas. In the female population, the annual change in mortality was −1.2% (95% CI = −2.3%, −0.1%) in urban areas compared with −1.6% (95% CI = −3.1%, −0.1%) in rural areas. The age–period–cohort model showed a significant increase in urban and rural mortality rates starting at ages 49 and 39 years. Both showed a decline followed by an increase in the period. The cohort from 1929 to 2013 showed an overall trend of increasing and then decreasing. From 2006 to 2020, the overall injury and poisoning mortality rates in China showed a decreasing trend, and the mortality rates decreased faster in women than in men and in rural areas than in urban areas. Age effects were the most important risk factors for changes in injury and poisoning mortality. The results of this study will help researchers explore the possible causes of mortality changes in urban and rural areas and provide a scientific basis for injury and poisoning prevention and control priorities and policy formulation in China.
The Cognitive, Behavioral and Interpersonal Impacts of Virtual Practice with Short Health Videos on Chinese Ageing Women: A Discursive Approach
Authors: Liang Hongyan , Pang Huimin
Published: June 2022

In the digital era, the health information presented on virtual platforms plays a pivotal role in supporting people’s active and healthy life. The ageing, especially ageing women, are more likely to seek and accept health information through online media platforms. The study shows that short health videos on social media platforms are extremely popular among ageing women in China for the accessing of virtual coaching. Adopting the qualitative methodology of in-depth interview and discourse analysis, the study investigates virtual coaching with short health video practice among 39 Chinese ageing women in different fields, who are all over sixty years old. Specifically, with the analytical tools of transitivity and generic structure analysis, the study explores the impacts of short health videos on Chinese ageing women’s cognition, behavior and interpersonal relationships. The result shows that virtual practice and coaching via short health videos can build health awareness and a dynamic new lifestyle, and motivate women to positively practice physical activity and maintain positive interpersonal relationships. Factors affecting the effectiveness of short health videos are discussed for future research in the field of modeling and intervention.
The Rough Road: A Single Case Study of Dreamtelling in a Group during the COVID-19 Pandemic and Military Conflict
Authors: Geller Shulamit , Friedman Robi , Levy Sigal , Akerman Yehoshua , Van den Brink Gal , Romach Guy , Shazar Tuli , Goldzweig Gil
Published: June 2022

Sharing dreams is a common practice, and several motives, such as emotional processing, emotional relief, and request for containment, have been identified. An exploratory single case study research design was used to explore the experiences of the COVID-19 pandemic and local military conflict among a group of Israeli students. The group discussed a dream previously shared in social network sites during the first COVID-19 lockdown. A qualitative content analysis of the meeting transcript yielded three meaningful and coherent themes: feeling blocked and helpless in front of a barrier; a sense of intrusion, defense, and psychological coping; belonging to the group as a means of coping with an individual and a collective threat. Each of these themes reflected personal, interpersonal, and social aspects of the participants’ experiences. The results deepen the understanding of people’s dominant experiences and main psychological coping mechanisms during a collective stressful event. Further, they support the positive effect of the dreamtelling approach on individuals’ coping experiences and on enhancing hope by sharing and discussing dreams with others.
Effects of Different Exercise Therapies on Balance Function and Functional Walking Ability in Multiple Sclerosis Disease Patients—A Network Meta-Analysis of Randomized Controlled Trials
Authors: Hao Zikang , Zhang Xiaodan , Chen Ping
Published: June 2022

The objective of this research is to assess the effects of seven different exercise therapies (aquatic exercise, aerobic exercise, yoga, Pilates, virtual reality exercise, whole-body vibration exercise, and resistance exercise) on the balance function and functional walking ability of multiple sclerosis disease patients. Materials and Methods: The effects of different exercise interventions on the balance function and functional walking ability in people with multiple sclerosis were assessed by searching five databases: PubMed, Embase, Cochrane Library, Web of Science, and CNKI; only randomized controlled trials were included. The included studies were assessed for risk of bias using the Cochrane assessment tool. Results: The RCTs were collected between the initial date of the electronic databases’ creation and May 2022. We included 31 RCTs with 904 patients. The results of the collected data analysis showed that yoga can significantly improve patients’ BBS scores (SUCRA = 79.7%) and that aquatic exercise can significantly decrease patients’ TUG scores (SUCRA = 78.8%). Conclusion: Based on the network meta-analysis, we suggest that although each type of exercise is useful, yoga, virtual reality training, and aerobic training are more effective in improving the balance function of people with MS; aquatic exercise, virtual reality training, and aerobic training are more effective in improving the functional walking ability of people with MS.
An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects
Authors: Sharma Manish , Yadav Anuj , Tiwari Jainendra , Karabatak Murat , Yildirim Ozal , Acharya U. Rajendra
Published: June 2022

Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered in sleep laboratories. Manually scoring sleep is a tough, time-intensive, tiresome, and highly subjective activity. Hence, the need of creating automatic sleep-stage classification has risen due to the limitations imposed by manual sleep-stage scoring methods. In this study, a novel machine learning model is developed using dual-channel unipolar electroencephalogram (EEG), chin electromyogram (EMG), and dual-channel electrooculgram (EOG) signals. Using an optimum orthogonal filter bank, sub-bands are obtained by decomposing 30 s epochs of signals. Tsallis entropies are then calculated from the coefficients of these sub-bands. Then, these features are fed an ensemble bagged tree (EBT) classifier for automated sleep classification. We developed our automated sleep classification model using the Sleep Heart Health Study (SHHS) database, which contains two parts, SHHS-1 and SHHS-2, containing more than 8455 subjects with more than 75,000 h of recordings. The proposed model separated three classes if sleep: rapid eye movement (REM), non-REM, and wake, with a classification accuracy of 90.70% and 91.80% using the SHHS-1 and SHHS-2 datasets, respectively. For the five-class problem, the model produces a classification accuracy of 84.3% and 86.3%, corresponding to the SHHS-1 and SHHS-2 databases, respectively, to classify wake, N1, N2, N3, and REM sleep stages. The model acquired Cohen’s kappa (κ) coefficients as 0.838 with SHHS-1 and 0.86 with SHHS-2 for the three-class classification problem. Similarly, the model achieved Cohen’s κ of 0.7746 for SHHS-1 and 0.8007 for SHHS-2 in five-class classification tasks. The model proposed in this study has achieved better performance than the best existing methods. Moreover, the model that has been proposed has been developed to classify sleep stages for both good sleepers as well as patients suffering from sleep disorders. Thus, the proposed wavelet Tsallis entropy-based model is robust and accurate and may help clinicians to comprehend and interpret sleep stages efficiently.
A Cross-Sectional Time Course of COVID-19 Related Worry, Perceived Stress, and General Anxiety in the Context of Post-Traumatic Stress Disorder-like Symptomatology
Authors: Mullins Roger J. , Meeker Timothy J. , Vinch Paige M. , Tulloch Ingrid K. , Saffer Mark I. , Chien Jui-Hong , Bienvenu O. Joseph , Lenz Frederick A.
Published: June 2022

The COVID-19 pandemic within the United States of America resulted in over 800,000 deaths as of February 2022 and has been addressed by social distancing or stay-at-home measures. Collective prolonged multimodal trauma on this scale is likely to elicit symptomatology in the general population consistent with post-traumatic stress disorder (PTSD), somatization, anxiety, and stress. The psychological component of this response contributes substantially to the burden of this disease worldwide. This cross-sectional study examines the relationship between COVID-19-related concern, anxiety, and perceived stress on PTSD-like symptomatology over the course of the COVID-19 pandemic. Participants were recruited via social media within the United States of America between 8th May 2020 and 11th August 2021 to complete an internet questionnaire including mood, personality, and COVID-19-specific scales. General anxiety and PTSD-like symptomatology were above the screening cutoffs for most respondents. These measures increased in severity over the pandemic, with the change point of our Concern scale preceding that of the other significant measures. Measures of COVID-19-related concern, generalized anxiety, and PTSD-like symptomatology were strongly correlated with each other. Anxiety, perceived stress, and PTSD-like symptomatology are strongly interrelated, increase with pandemic length, and are linked to reported levels of concern over COVID-19. These observations may aid future research and policy as the pandemic continues.
Prediction of the Old-Age Dependency Ratio in Chinese Cities Using DMSP/OLS Nighttime Light Data
Authors: Li Yue , Zhang Chengmeng , Tong Yan , Zhang Yalu , Chen Gong
Published: June 2022

The old-age dependency ratio (ODR) is an important indicator reflecting the degree of a regional population’s aging. In the context of aging, this study provides a timely and effective method for predicting the ODR in Chinese cities. Using the provincial ODR from the Seventh National Population Census and Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data, this study aims to predict and analyze the spatial correlation of the municipal ODR in Chinese cities. First, the prediction model of the ODR was established with curve regression. Second, the spatial structure of the municipal ODR was investigated using the Moran’s I method. The experimental results show the following: (1) the correlation between the sum of the nighttime light and ODR is greater than the mean of nighttime light in the study areas; (2) the Sigmoid model fits better than other regression models using the provincial ODR in the past ten years; and (3) there exists an obvious spatial agglomeration and dependence on the municipal ODR. The findings indicate that it is reasonable to use nighttime light data to predict the municipal ODR in large and medium-sized cities. Our approach can provide support for future regional censuses and spatial simulations.
A Case–Control Study Examining the Association of Fiber, Fruit, and Vegetable Intake and the Risk of Colorectal Cancer in a Palestinian Population
Authors: Taha Hania M. , Slade Alexander N. , Schwartz Betty , Arthur Anna E.
Published: June 2022

While there is an association between Western diets and the incidence of colorectal cancer (CRC), this dietary association has remained unexplored in Palestine. The aim of this study was to examine how fiber and fruit and vegetable (FV) intakes are associated with CRC risk among Palestinian adults. We recruited 528 Palestinians between 2014 and 2016. We identified 118 patients who received CRC treatment at Augusta Victoria Hospital, East Jerusalem. We additionally identified 410 controls who consisted of community-based Palestinians without cancer. All participants completed a survey on demographics and a validated dietary intake food screener. Multivariable logistic regression models tested associations between fiber and FV intakes (categorized into quartiles) with CRC risk. After adjusting for significant covariates (age, sex, education, physical activity, smoking status, BMI, IBD, and family history of CRC), as fibers increased across increasing quartiles, the CRC risk significantly decreased (OR = 0.36, 95% CI: 0.15–0.86, p-trend = 0.02). After adjusting for age and sex, as FV intake increased, the CRC risk significantly decreased (OR = 0.34, 95% CI: 0.15–0.75, p-trend = 0.009). Consumption of fiber-rich foods was inversely associated with CRC risk. Understanding this relationship among Palestinians is essential in order to develop targeted, culturally relevant strategies that may potentially alleviate the burden of CRC.