bluuebunny/crossref_metadata_2025 huggingface dataset:
Systematic scoping review of occupational health injuries and illnesses among Indigenous workers
Authors: Shannon Brett , Jennings Warren , Friedman Lee
Published: July 2022

Indigenous populations in the USA, Australia, New Zealand (NZ) and Canada total more than 13 million, but continue to be marginalised in their respective regions. The goal of this comprehensive review of all studies evaluating adverse occupational health outcomes among Indigenous populations in these countries was to identify gaps in the literature and future research directions. A systematic scoping review of research published between 1970 and 2020 was undertaken using the methodological framework initially proposed by Arksey and O’Malley. Country, Indigenous participants, study type, exposure, adverse health outcome, occupation and industry were identified for each paper. Of the 1272 research papers identified, only 51 articles met the inclusion criteria of this scoping review. Almost half of the studies (n=24, 47.1%) were published after 2010. Only 13 (25.5%) studies specifically focused on Indigenous persons at the time of the study design, and less than half of the studies (47.1%) included more than 100 Indigenous participants. Most studies used the following general terms without mention of specific indigenous groups: Indigenous (Australia), Māori (NZ), Aboriginal (Canada) and American Indian or Alaskan Native (USA). Only one study acknowledged asking respondents their preferred terminology. Over the past 50 years, there has been a paucity of research directly or indirectly evaluating occupational health outcomes of Indigenous populations in these four countries. There is a need for better sampling strategies and inclusion of demographic questions that capture Indigenous status in surveys, workers’ compensation data and other commonly used data sources to develop adequate baseline data for targeted future interventions.
Cannabis use and workplace cannabis availability, perceptions and policies among Canadian workers: a comparison before and after the legalisation of non-medical cannabis
Authors: Carnide Nancy , Lee Hyunmi , Landsman Victoria , Frone Michael R , Furlan Andrea D , Smith Peter M
Published: July 2022

Objectives

Little data exist examining the impact of non-medical (recreational) cannabis legalisation among a working population. The objective was to compare cannabis use patterns and workplace risk perceptions, cannabis availability and workplace use policies before and almost 1 year after legalisation in Canadian workers.

Methods

Two overlapping cross-sectional samples of Canadian workers were surveyed 4 months before legalisation (time 1 (T1), n=2011) and 9–11 months after legalisation (time 2 (T2), n=4032), gathering information on cannabis use (overall and workplace use), workers’ perceptions regarding risks of workplace use, availability of cannabis at work and awareness of workplace substance use policies. The marginal distributions of these variables at T1 and T2 were compared, adjusting for sociodemographic, work and health and lifestyle factors.

Results

Cannabis use status changed from prelegalisation to postlegalisation (p<0.0001), with fewer respondents reporting former use (ie, more than 1 year ago; 40.4% at T1, 33.0% at T2) and a greater proportion of workers reporting past-year use (30.4% at T1, 39.3% at T2). Never use remained stable (29.2% at T1, 27.6% at T2). Workplace cannabis use also remained stable (9.4% at T1, 9.1% at T2; p=0.4580). At T1, 62.7% of respondents reported being aware of their workplace having a substance use policy, increasing to 79.0% at T2 (p<0.0001). Small magnitude changes occurred in perceptions of risk and workplace availability.

Conclusions

Results point to a lack of substantive changes in the short-term from prelegalisation to postlegalisation. Longer-term data among workers are needed given the evolving nature of this legislative policy.
Predictors of response to secukinumab in patients with psoriatic arthritis and axial manifestations: a post-hoc analysis of the MAXIMISE trial
Authors: Baraliakos Xenofon , Pournara Effie , Gossec Laure , Mease Philip J , White Roisin , O’Brien Eamonn , Schulz Barbara , Marzo-Ortega Helena , Coates Laura C
Published: July 2022

Objectives

To investigate patient characteristics predictive of response to secukinumab in patients with psoriatic arthritis (PsA) with axial manifestations.

Methods

In a post-hoc analysis from the MAXIMISE trial (NCT02721966) in patients with PsA and axial manifestations, we tested the hypothesis that the OR of the effect of treatment on the primary endpoint of the trial (Assessment of SpondyloArthritis international Society (ASAS) 20 responder status at week 12) would be different depending on 12 prespecified predictor variables. We applied a two-model logistic regression approach, a main effects and an interaction model.

Results

The OR (95% CI) for ASAS20 response for the presence of nail dystrophy was 3.2 (95% CI 0.93 to 10.99) in the secukinumab 150 mg group and 5.0 (95% CI 1.47 to 17.19) in the secukinumab 300 mg group compared with the placebo group (p=0.029). Odds of being a responder were similar in men and women in the secukinumab groups, though men fared worse than women in the placebo group (p=0.039). Current smokers were less likely to be ASAS20 responders compared with never smokers regardless of the treatment group (p=0.589).

Conclusion

Nail dystrophy was identified as a predictor of response to secukinumab in patients with PsA with axial manifestations in the MAXIMISE trial. These findings may be explained by the nail-entheseal concept as part of the axial phenotype in PsA .
The smoking population is not hardening in South Korea: a study using the Korea Community Health Survey from 2010 to 2018
Authors: Jung Boyoung , Lee Jung Ah , Kim Ye-Jee , Cho Hong-Jun
Published: July 2022

Introduction

The hardening hypothesis proposes that the proportion of hardcore smokers increases when smoking prevalence declines. To evaluate whether such hardening occurs in South Korea, we examined the association between quitting behaviours, the number of cigarettes smoked per day and the proportion of hardcore smokers and smoking prevalence among local districts in South Korea.

Methods

This study used the cross-sectional data from the Korea Community Health Survey (2010–2018) to examine local district-level associations between smoking prevalence and quit attempts, quit plans, quit ratios, cigarettes smoked per day and the proportion of hardcore smokers. Panel regression analysis was performed using the indicators of hardcore smoking (quit attempts, quit plans, quit ratios, cigarettes smoked per day and proportion of hardcore smokers) as the outcome variables, and prevalence of smoking, local districts, age and sex as predictor variables.

Results

When the smoking prevalence of the districts decreased by 1%, quit attempts, quit plans and quit ratios increased by 0.24% (95% CI 0.11 to 0.37), 0.37% (95% CI 0.26 to 0.47) and 1.71% (95% CI 1.65 to 1.76), respectively. Cigarette consumption decreased by 0.17 cigarettes per day (95% 0.15 to 0.19), and the prevalence of hardcore smokers decreased by 0.88% (95% CI 0.78 to 0.98) when smoking prevalence decreased by 1%.

Conclusion

Hardening of smoking did not occur in South Korea when smoking prevalence declined, which suggests tobacco control policies in South Korea have been effective in reducing smoking prevalence without increasing the proportion of hardcore smokers.
Dynamics of charged string cloud collapse in rainbow gravity
Authors: Sheikh Umber , Arshad Sana , Arshad Muhammad
Published: July 2022

This work will proceed to examine the dynamics of collapsing charged string cloud in a spherically symmetric rainbow metric. This is the framework that allows new stars and compact objects to emerge. The dynamical variables (mass density, string tension) and their physical existence as energy conditions are computed and visually depicted. We also calculated the duration and radius of development of the collapsing string cloud’s apparent horizons. The mass density, string tension, expected time and radius of apparent horizon creation are dependent on the Rainbow parameter [Formula: see text] At the center of the configuration [Formula: see text] the mass density, string tension and electric field intensity are at their maximum leading to the formation of a compact object (black hole). The fluid’s mass density and string tension are reduced by the electric field. Furthermore, the presence of an electric field delays the formation of a compact object (black hole).
Weakly Supervised Text-based Actor-Action Video Segmentation by Clip-level Multi-instance Learning
Authors: Chen Weidong , Li Guorong , Zhang Xinfeng , Wang Shuhui , Li Liang , Huang Qingming
Published: July 2022

In real-world scenarios, it is common that a video contains multiple actors and their activities. Selectively localizing one specific actor and its action spatially and temporally via a language query becomes a vital and challenging task. Existing fully supervised methods require extensive elaborately annotated data and are sensitive to the class labels, which cannot satisfy real-world applications’ needs. Thus, we introduce the task of weakly supervised actor-action video segmentation from a sentence query (AAVSS) in this work, where only the video-sentence pairs are provided. To the best of our knowledge, our work is the first to perform AAVSS under weakly supervised situations. However, this task is extremely challenging not only because the task aims to learn the complex interactions between two heterogeneous modalities but also because the task needs to learn fine-grained analysis of video content without pixel-level annotations. To overcome the challenges, we propose a two-stage network. The network first follows the sentence guidance to localize the candidate region and then performs segmentation to achieve selective segmentation. Specifically, a novel tracker-based clip-level multiple instance learning paradigm is proposed in this article to learn the matches between regions and sentences, which makes our two-stage network robust to the region proposal network. Furthermore, two intrinsic characteristics of the video, temporal consistency and motion information, are utilized in companion with the weak supervision to facilitate the region-query matching. Through extensive experiments, the proposed method achieves comparable performance to state-of-the-art fully supervised approaches on two large-scale benchmarks, including A2D Sentences and J-HMDB Sentences.
Smart System: Joint Utility and Frequency for Pattern Classification
Authors: Lin Qi , Gan Wensheng , Wu Yongdong , Chen Jiahui , Chen Chien-Ming
Published: July 2022

Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized, which will help manufacturing organizations to finish another round of upgrading. In this article, we propose two new algorithms with respect to big data analysis, namely UFCgenand UFCfast. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFCfastalgorithm outperforms the levelwise-based UFCgenalgorithm in terms of both execution time and memory consumption.
Early DSE and Automatic Generation of Coarse-grained Merged Accelerators
Authors: Brumar Iulian , Zacharopoulos Georgios , Yao Yuan , Rama Saketh , Brooks David , Wei Gu-Yeon
Published: July 2022

Post-Moore’s law area-constrained systems rely on accelerators to deliver performance enhancements. Coarse-grained accelerators can offer substantial domain acceleration, but manual, ad hoc identification of code to accelerate is prohibitively expensive. Because cycle-accurate simulators and high-level synthesis (HLS) flows are so time-consuming, the manual creation of high-utilization accelerators that exploit control and data flow patterns at optimal granularities is rarely successful. To address these challenges, we present AccelMerger, the first automated methodology to create coarse-grained, control- and data-flow-rich merged accelerators. AccelMerger uses sequence alignment matching to recognize similar function call-graphs and loops, and neural networks to quickly evaluate their post-HLS characteristics. It accurately identifies which functions to accelerate, and it merges accelerators to respect an area budget and to accommodate system communication characteristics like latency and bandwidth. Merging two accelerators can save as much as 99% of the area of one. The space saved is used by a globally optimal integer linear program to allocate more accelerators for increased performance. We demonstrate AccelMerger’s effectiveness using HLS flows without any manual effort to fine-tune the resulting designs. On FPGA-based systems, AccelMerger yields application performance improvements of up to 16.7× over software implementations, and 1.91× on average with respect to state-of-the-art early-stage design space exploration tools.
Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity
Authors: Zhou Shuai , Liu Chi , Ye Dayong , Zhu Tianqing , Zhou Wanlei , Yu Philip S.
Published: July 2022

The outstanding performance of deep neural networks has promoted deep learning applications in a broad set of domains. However, the potential risks caused by adversarial samples have hindered the large-scale deployment of deep learning. In these scenarios, adversarial perturbations, imperceptible to human eyes, significantly decrease the model’s final performance. Many papers have been published on adversarial attacks and their countermeasures in the realm of deep learning. Most focus on evasion attacks, where the adversarial examples are found at test time, as opposed to poisoning attacks where poisoned data is inserted into the training data. Further, it is difficult to evaluate the real threat of adversarial attacks or the robustness of a deep learning model, as there are no standard evaluation methods. Hence, with this article, we review the literature to date. Additionally, we attempt to offer the first analysis framework for a systematic understanding of adversarial attacks. The framework is built from the perspective of cybersecurity to provide a lifecycle for adversarial attacks and defenses.
Adversarial Learning for Cross Domain Recommendations
Authors: Li Pan , Brost Brian , Tuzhilin Alexander
Published: July 2022

Existing cross domain recommender systems typically assume homogeneous user preferences across multiple domains to capture similarities of user-item interactions and to provide cross domain recommendations accordingly. Meanwhile, the heterogeneity of user behaviors is usually not well studied and captured during the recommendation process, where users might have vastly different interests in different domains. In addition, previous models focus primarily on recommendation tasks between domain pairs, and cannot be naturally extended to serve for multiple domain recommendation applications. To address these challenges, we propose to utilize the idea of adversarial learning to intelligently incorporate global user preferences and domain-specific user preferences for providing satisfying cross domain recommendations. In particular, our proposed Adversarial Cross Domain Recommendation (ACDR) model first obtains the latent representations of global user preferences from their explicit feature information, and then transforms them into domain-specific user embeddings, where we take into account user behaviors and their heterogeneous preferences among different domains. By doing so, we address the differences among user representations in the domain-specific latent space while also preserving global user preferences, as we effectively segment the distributions of domain-specific user embeddings in the shared latent space. The convergence of our proposed model is theoretically guaranteed. The proposed ACDR model leads to significant and consistent improvements in cross domain recommendation performance over the state-of-the-art baseline models, which we demonstrate through extensive experiments on three real-world datasets. In addition, we show that the improvements are greater on those datasets that are smaller and more sparse, on those users that have fewer interaction records in the dataset, and when user interactions from more product domains are included in the cross domain recommendation model.
Sustainability in Health Care
Authors: Hu Howard , Cohen Gary , Sharma Bhavna , Yin Hao , McConnell Rob
Published: July 2022

The academic public health and biomedical communities have a long history of researching and documenting the adverse impacts of pollution on human health. However, the healthcare industry itself is a major contributor to pollution as well as the greenhouse gas (GHG) emissions responsible for global warming. For example, the health sectors of the United States, Australia, England, and Canada are estimated to emit a combined 748 million metric tons of carbon dioxide equivalents annually, equivalent to a nation that would rank seventh in the world for GHG emissions. Moreover, the healthcare sector is a major consumer of natural resources, thereby contributing to the imbalances characteristic of what is increasingly being referred to as the Anthropocene and a threat to planetary health. In this article, we summarize current information on the healthcare industry's environmental footprint and the potential for markedly reducing that footprint by applying the principles and tools of sustainability science. We discuss some of the industry's special challenges, including those associated with new construction (which have undergone relatively little examination in relation to sustainability, despite predictions of accelerated growth). We examine current ideas and efforts to advance sustainability solutions in the healthcare industry, in high-, middle-, and low-income countries alike, where the healthcare industry can be expected to grow the fastest. Finally, we review case studies and discuss research needs.
The Law Meets Psychological Expertise: Eight Best Practices to Improve Forensic Psychological Assessment
Authors: Neal Tess M.S. , Martire Kristy A. , Johan Jennifer L. , Mathers Elizabeth M. , Otto Randy K.
Published: July 2022

We review the state of forensic mental health assessment. The field is in much better shape than in the past; however, significant problems of quality remain, with much room for improvement. We provide an overview of forensic psychology's history and discuss its possible future, with multiple audiences in mind. We distill decades of scholarship from and about fundamental basic science and forensic science, clinical and forensic psychology, and the law of expert evidence into eight best practices for the validity of a forensic psychological assessment. We argue these best practices should apply when a psychological assessment relies on the norms, values, and esteem of science to inform legal processes. The eight key considerations include ( a) foundational validity of the assessment; ( b) validity of the assessment as applied; ( c) management and mitigation of bias; ( d) attention to quality assurance; ( e) appropriate communication of data, results, and opinions; ( f) explicit consideration of limitations and assumptions; ( g) weighing of alternative views or disagreements; and ( h) adherence with ethical obligations, professional guidelines, codes of conduct, and rules of evidence.
Climate Change Impacts on Eastern Boundary Upwelling Systems
Authors: Bograd Steven J. , Jacox Michael G. , Hazen Elliott L. , Lovecchio Elisa , Montes Ivonne , Pozo Buil Mercedes , Shannon Lynne J. , Sydeman William J. , Rykaczewski Ryan R.
Published: July 2022

The world's eastern boundary upwelling systems (EBUSs) contribute disproportionately to global ocean productivity and provide critical ecosystem services to human society. The impact of climate change on EBUSs and the ecosystems they support is thus a subject of considerable interest. Here, we review hypotheses of climate-driven change in the physics, biogeochemistry, and ecology of EBUSs; describe observed changes over recent decades; and present projected changes over the twenty-first century. Similarities in historical and projected change among EBUSs include a trend toward upwelling intensification in poleward regions, mitigatedwarming in near-coastal regions where upwelling intensifies, and enhanced water-column stratification and a shoaling mixed layer. However, there remains significant uncertainty in how EBUSs will evolve with climate change, particularly in how the sometimes competing changes in upwelling intensity, source-water chemistry, and stratification will affect productivity and ecosystem structure. We summarize the commonalities and differences in historical and projected change in EBUSs and conclude with an assessment of key remaining uncertainties and questions. Future studies will need to address these questions to better understand, project, and adapt to climate-driven changes in EBUSs.
A Numerical Study of Mechanical Degradation of Carbon-Coated Graphite Active Particles in Li-ion Battery Anodes
Authors: Marin-Montin Jorge , Zurita-Gotor Mauricio , Montero-Chacón Francisco
Published: July 2022

During operation, different diffusive and mechanical phenomena take place inside LIBs that result in a loss of performance and, eventually, threaten battery life. One of the main drawbacks affecting anode materials is the significant volumetric expansion (contraction) experienced by active material particles during lithiation (delithiation) processes, which may cause fracture. In this work, we present a novel numerical model to analyze coupled diffusion-mechanical problems accounting for material inhomogeneities. We are able to describe the nucleation of cracks and their propagation during particle cycling, depending on charging and discharging rates. Moreover, our model is able to reproduce complex fracture processes such as branching and change of directions. This description relies on combined use of a randomness parameter and a stochastic characterization of material properties within a lattice model approach. The model is used to analyze the effect of particle coating as a strategy to diminish the effect of transient cracking (that leads to early capacity fade). This is studied in detail at the coating-substrate interface and novel material designs are tested within our simulation framework.
Analysis of Battery Performance and Mass Transfer Behavior for Organic Redox Flow Battery with Different Flow Fields
Authors: Chu Fengming , Xiao Guozhen , Xia Lixing , Yang Yue , Yang Guoan , Tan Zhan’ao
Published: July 2022

The mass transfer performance is unclear to limit the development of organic flow battery, which is regarded as the emerging electrochemical energy storage technology. The mass transfer behaviors in the electrode are influenced by the flow fields, which is indispensable. A three-dimensional and steady numerical model of the organic flow battery is established and the results are verified by the experiments data. The battery performance and mass transfer behaviors are analyzed under different flow field for the charge/discharge processes based on this model. Compared with other flow fields, the interdigital flow field can achieve the best charge-discharge performance, which is mainly due to the improvement distribution uniformity of active species. The average concentration of the interdigital flow field is 45.1% higher that of the conventional flow field. The effects of the flow rates and initial concentration of electrolyte on the battery performance are investigated, and the results indicate that appropriate inlet flow rate can lead to the highest net discharge powers and power-based efficiency of the flow battery. Although the increasing of the initial concentration can improve the battery performance, the improvement is too slight when the concentration is high enough, which can lead to the waste of active species.
High Cycle Stability of Nanoporous Si Composites in All-solid-state Lithium-ion Batteries
Authors: Okuno Ryota , Yamamoto Mari , Kato Atsutaka , Takahashi Masanari
Published: July 2022

Stress relaxation of Si with large structural fluctuations is a critical challenge for its practical application in lithium-ion batteries (LIBs). In this study, nanoporous Si particles, which are prepared by Mg2Si reduction of mesoporous SiO2 spheres, are applied as an anode active material for all-solid-state LIBs (ASSLIBs) with a Li3PS4 solid electrolyte. Nanoporous Si half-cells exhibit an excellent cyclability with a high-capacity retention of about 90% at 50 cycles compared to non-porous Si half-cells below 20%. The cross-sectional characteristics of nanoporous and non-porous Si composite anodes are accurately compared using electrochemical impedance spectroscopy and field emission scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy. Based on these results, we conclude that the expansion/contraction of nanosized Si pores and the elastic deformation of Li3PS4 effectively relieve the structural stress derived from the volume change of Si particles/aggregates during lithiation and delithiation, resulting in high cycle stability. These findings provide valuable information for the rational design of Si-based anodes for high-performance ASSLIBs.
A Method to Determine Fast Charging Procedures by Operando Overvoltage Analysis
Authors: Münster Patrick , Winter Martin , Niehoff Philip
Published: July 2022

A method to determine threshold voltage conditions for Li plating in lithium ion battery cells is presented. Transferring open-circuit values determined in a 3-electrode electrochemical measurement onto a 2-electrode cell setup, the boundary conditions for Li plating can be assessed. In multi-layer pouch cells, these boundary conditions agree perfectly with the exact onset of Li plating as proven by post mortem analysis. By knowledge of the Li plating threshold voltage conditions, plating-free fast-charging procedures can be exercised leading to an increase in charging rate by 84% and 79% for two different cell systems, respectively. Cycling above or below the Li plating threshold voltage, Li plating occurrence can be deliberately controlled. Comparing plating and plating-free conditions, the applied charging voltage properties differ hardly. Hence, the applied analysis of overvoltage proves a more sensitive and specific operando method to predict Li plating.
The Utilization of Iridium Nanoparticles Impregnated on Metal Oxides (Ceria, Titania, and Zirconia) with a Simple and Ecologically Safe Synthesis Approach in Oxygen Evolution Reactions
Authors: Akbayrak Merve , Önal Ahmet M.
Published: July 2022

Oxygen evolution reactions (OERs) have sluggish kinetics, which limits the hydrogen generation from the electrolysis of water. In this regard, various metal oxides based electrocatalysts that contain precious metals such as Pt, Ir, Rh, and Ru have been developed for OERs in the literature In this work, Ir nanoparticles were impregnated on the surface of metal oxides (MO2; M = Ti, Zr, and Ce) using a simple and ecologically acceptable approach (reduction and impregnation in water). The electrocatalytic performance of the prepared catalysts (Ir/MO2) was tested for OER in an alkaline medium. The results show that the onset potentials of Ir/MO2 catalysts have slight variations (1.552 V vs RHE for Ir@CeO2, 1.554 V vs RHE for Ir@ZrO2, and 1.598 V vs RHE for Ir@TiO2) in this reaction. The overpotential and the Tafel slope values of these catalysts were also compared and it was found that the Ir@CeO2 catalyst provided the lowest overpotential value (η
10 = 0.379 V vs RHE) and the lowest Tafel slope value (b = 93.4 mV.dec−1). Herein, the activity and stability of Ir@CeO2 towards OER in alkaline media were investigated in detail with electrochemical impedance spectroscopy, chronoamperometry, and linear sweep voltammograms taken at different temperatures.
The erythroid K-Cl cotransport inhibitor [(dihydroindenyl)oxy]acetic acid blocks erythroid Ca2+-activated K+ channel KCNN4
Authors: Rivera Alicia , Nasburg Joshua A. , Shim Heesung , Shmukler Boris E. , Kitten Jason , Wohlgemuth Jay G. , Dlott Jeffrey S. , Snyder L. Michael , Brugnara Carlo , Wulff Heike , Alper Seth L.
Published: July 2022

Red cell volume is a major determinant of HbS concentration in sickle cell disease. Cellular deoxy-HbS concentration determines the delay time, the interval between HbS deoxygenation and deoxy-HbS polymerization. Major membrane transporter protein determinants of sickle red cell volume include the SLC12/KCC K-Cl cotransporters KCC3/SLC12A6 and KCC1/SLC12A4, and the KCNN4/KCa3.1 Ca2+-activated K+ channel (Gardos channel). Among standard inhibitors of KCC-mediated K-Cl cotransport, only [(dihydroindenyl)oxy]acetic acid (DIOA) has been reported to lack inhibitory activity against the related bumetanide-sensitive erythroid Na-K-2Cl cotransporter NKCC1/SLC12A2. DIOA has been often used to inhibit K-Cl cotransport when studying the expression and regulation of other K+ transporters and K+ channels. We report here that DIOA at concentrations routinely used to inhibit K-Cl cotransport can also abrogate activity of the KCNN4/KCa3.1 Gardos channel in human and mouse red cells and in human sickle red cells. DIOA inhibition of A23187-stimulated erythroid K+ uptake (Gardos channel activity) was chloride-independent and persisted in mouse red cells genetically devoid of the principal K-Cl cotransporters KCC3 and KCC1. DIOA also inhibited YODA1-stimulated, chloride-independent erythroid K+ uptake. In contrast, DIOA exhibited no inhibitory effect on K+ influx into A23187-treated red cells of Kcnn4−/− mice. DIOA inhibition of human KCa3.1 was validated (IC50 42 µM) by whole cell patch clamp in HEK-293 cells. RosettaLigand docking experiments identified a potential binding site for DIOA in the fenestration region of human KCa3.1. We conclude that DIOA at concentrations routinely used to inhibit K-Cl cotransport can also block the KCNN4/KCa3.1 Gardos channel in normal and sickle red cells.
Precise Marketing Classification of Agricultural Products for E-Commerce Live Broadcast Platform Using Clustering
Authors: Shi Yuxian , Li Xinhui , ZhouGong Shuchen , Li Xiaoqian , Wang Hongbing
Published: July 2022

Live delivery of goods is a novel type of marketing model that has injected new vitality into the development of agricultural e-commerce. It is only in the last few years that live streaming has become a popular e-commerce marketing approach. Agriculture e-commerce in China has a new lease on life thanks to the advent of a new marketing model. This article comprehensively surveys the live broadcast delivery models in the e-commerce era. The comprehensive survey includes the significance and current state of the live broadcast delivery models and identifies the factors impeding the integration and development of agricultural products. The review includes the new marketing model of live broadcast delivery and makes pertinent recommendations. The article presents a novel model for precise marketing classification of agricultural products for e-commerce live broadcast platforms using clustering. Moreover, this article uses customer development space to achieve precise marketing for the e-commerce live broadcast platform. The article enhances K-means with local density and information entropy and proposes an improved TFA customer segmentation method. The results demonstrate that the proposed method is capable of efficiently classifying customers, accurately reflecting their value, and optimizing the algorithm’s efficiency by optimizing the clustering technique.
The Protective Effect of Trichosanthes kirilowii Peel Polysaccharide on the Oxidative Damaged HepG2 and HUASMC Cells
Authors: Zhang Jinli , Gao Heren , Zhu Liya , Yuan Xiangyu , Yang Xi , Xu Min , Yang Yang
Published: July 2022

Background. Oxidative stress is an important cause of liver disease and atherosclerosis. Natural substances with antioxidant activity are good drugs for treating liver disease and atherosclerosis. Trichosanthes kirilowii Peel Polysaccharide (TKPP) can remove DPPH (2,2-Diphenyl-1-picrylhydrazyl) free radicals and hydroxyl free radicals in vitro, which shows antioxidant activity. Therefore, it is speculated that it can protect human hepatoma cell line (HepG2) and umbilical artery smooth muscle cell (HUASMC) against oxidative damage by hydrogen peroxide (H2O2). Methods. Oxidative damage cell models of HepG2 and HUASMC were induced by H2O2. HepG2 and HUASMC were divided into blank group, H2O2 injury group, TKPP treatment group, and glutathione (GSH) positive control group. Cell Counting Kit-8 (CCK-8) was used to detect cell viability. The level of total GSH and the amount of Nitric oxide (NO) secreted by cells were detected by specific kits. The gene and protein expressions of catalase (CAT) and superoxide dismutase (SOD) were detected by fluorescence quantitative PCR and Western Blot. Results. In these two kinds of cells, compared with the control group, the survival rate, total GSH level, and NO secretion, CAT and SOD gene and protein expressions were significantly decreased in the H2O2 damaged group. In the TKPP treatment group, the cell survival rate was significantly elevated with the increase of the polysaccharide concentration, and the total GSH level, NO secretion, CAT and SOD gene expression, and protein expression levels were also significantly increased. Conclusion. TKPP can improve the activities of HepG2 and HUASMC cells damaged by H2O2 and protect the cellular antioxidant system.
Performance of Locally Adopted Goats in Sundarbazar Municipality of Lamjung District, Nepal
Authors: Rana Dipendra , Paudel Samyog , Chaudhary Gopal , Pokhrel Abiskar , Bhandari Santosh , Sharma Surya Prasad
Published: July 2022

Goat (Capra hircus) is a small ruminant with hollow horn that is found throughout the world. A survey (N = 60) was carried out to evaluate the performance of locally adopted goats under farmers’ management practices in Sundarbazar municipality of Lamjung district between February and August, 2017. The study focused on two breeds, Khari and Jamunaparias well as their crossbred. Individual goats’ primary data, collected using a convenient sampling technique and pretested questionnaires with open and close ended questions, were analyzed using SPSS 20 and MS Excel 2010. Results revealed significant effects of a Doe’s breed on age at first estrus (p < 0.001), age at first kidding (p < 0.001), gestation length (p < 0.001), postpartum estrus (p < 0.001), and kidding interval (p < 0.05). Age at first estrus and age at first kidding were lowest (252.3 days and 403.9 days, respectively) for Khari. Highly significant lowest kidding interval (214.7 days) and significantly shortest gestation length (148.2 days) were found for Khari. No significant effect of parity was observed on age at first estrus, age at first kidding, kidding interval, and postpartum estrus, but a significant effect (p < 0.05) was observed on gestation length with lowest values in 4th to 6th parity (149.5 days). Doe’s breed showed a significant effect on litter size at birth (p < 0.05). However, no significant effect of the breed was found on litter birth weight and litter size at weaning (p < 0.05). The results demonstrated Khari as the desirable breed for the locality.
Investigation and Analysis of Classroom Teaching Quality in Universities in an Interval Intuitionistic Fuzzy Environment
Authors: Qi Quansong
Published: July 2022

In recent years, China’s economic strength has been developing rapidly. At the same time, people’s daily living standards have also been increasingly rising. In this context, the authorities are paying more and more attention to the training of students as well as talents. As a result, the quality of classroom teaching in universities has become a major concern. As an essential evaluation tool for teaching quality, evaluation of classroom teaching has been widely recognised and implemented. With the rapid development of computer technology and information technology, teaching evaluation based on various algorithms has been widely used, thus becoming an increasingly important teaching evaluation method. Thus, classroom teaching assessment has become one of the hot issues discussed in the field of education both at home and abroad. A great number of universities are now fully aware that the effectiveness of teachers is central to their survival and development and can have a direct impact on the training of students. The criteria for evaluating the effectiveness of teaching are highly subjective, but at the same time, there is a considerable degree of objectivity. As a result, the application of more scientific methods and computational data can make the results of classroom assessment more reliable. However, there is a lack of reliable reference data for evaluating the quality of classroom teaching in higher education. In addition, it is difficult to develop an evaluation data management and evaluation management model based on multiple data sources, which may cause a negative effect on the impartiality and objectivity of evaluation results. What is worse, many teachers do not have enough time to interact with their students. After all, most teachers in universities now have a heavy teaching load, and much of their time is spent on lesson preparation. Hence, they usually lack active interaction with their students. In the current education system, the objectives of higher education are mainly test-taking, and practical application is lacking. Therefore, based on the existing teaching basis, how to improve the effectiveness of university classroom teaching and improve the overall quality of students on the basis of test-taking objectives will be a key issue in teaching research. In order to solve this problem, this paper mainly researches the evaluation of classroom teaching quality in universities based on the interval intuitionistic fuzzy theory. This study is based on a detailed analysis of the needs of teaching quality assessment management, in terms of comprehensiveness of assessment and data sources. Then, the teaching evaluation system is designed and developed based on an analysis of the roles of students and teachers. This system can be applied to speed up the efficiency and fairness of teaching evaluation in universities, thereby greatly improving the management of teaching in universities.
Health‐Related Quality of Life and Adherence to Hydroxyurea and Other Disease‐Modifying Therapies among Individuals with Sickle Cell Disease: A Systematic Review
Authors: Yang Mira , Elmuti Lena , Badawy Sherif M.
Published: July 2022

Background. Sickle cell disease (SCD) is a hemoglobinopathy with increasing global prevalence resulting in pain episodes and multiorgan complications. Complications of SCD have been shown to adversely impact health‐related quality of life (HRQOL) comprised of physical, social, and emotional domains; hence, HRQOL measures can serve as an effective evaluator of disease burden. Hydroxyurea (HU) and other disease‐modifying therapies have demonstrated to significantly improve clinical outcomes in patients with SCD. Medication adherence is an essential mediator of the clinical benefits of these therapies; low adherence has been shown to increase disease burden and healthcare utilization. This systematic literature review intends to determine the association between adherence to disease‐modifying therapies and HRQOL in patients with SCD. Methods. We found a total of 12 articles involving 788 participants, which included both patients with SCD and caregivers/parents. Adherence was measured using self‐report instruments, laboratory markers, such as fetal hemoglobin and mean corpuscular volume, and mHealth medication trackers. HRQOL was measured using self‐report instruments. Results. All studies demonstrated a correlation between higher HU adherence and better HRQOL scores. Higher HU adherence was associated with lower pain impact, less frequent pain episodes, less fatigue, and improved physical function and mobility, reflecting better physical HRQOL outcomes. Higher adherence was also associated with improved emotional response, decreased anxiety and depressive symptoms, and better social functioning and peer relationships. In addition, our findings indicated that having less frequent barriers to HU adherence was associated with better HRQOL scores. No studies evaluated HRQOL outcomes in relation to adherence to l‐glutamine, voxelotor, or crizanlizumab. Conclusions. Optimizing HU adherence has the potential to improve HRQOL in patients with SCD in addition to reducing healthcare utilization and improving treatment satisfaction. Addressing barriers to HU adherence can positively strengthen the relationship between adherence and HRQOL to potentially improve patient outcomes.
[Retracted] International Chinese Education Expert System Based on Artificial Intelligence and Machine Learning Algorithms
Authors: Shen Lin , Latif Faiza
Published: July 2022

This study builds an international Chinese education expert system based on artificial intelligence and machine learning algorithms, introduces interval intuition fuzzy sets to express expert evaluation information, and uses the entropy weight method to determine the weight of evaluation attributes in order to improve the effect of international Chinese teaching and learning. The group utility value, personal regret value, and comprehensive evaluation value of each system are then calculated in this study. At the same time, this study introduces the degree of closeness and satisfaction to improve the decision‐making process and finally determines the optimal solution. In addition, this study constructs an intelligent system based on the improved algorithm. The research shows that the international Chinese education expert system based on artificial intelligence and machine learning algorithm proposed in this study has a very good effect.
Modeling the Thermal Performance for Different Types of Solar Chimney Power Plants
Authors: Smaisim Ghassan F. , Abed Azher M. , Shamel Ali
Published: July 2022

Nowadays, due to restrictions on fossil fuels, the use of renewable energies is increasing day by day. Among renewable energies, solar energy has received more attraction due to its availability in all places. Among solar energy technologies, the solar tower has been welcomed due to its high power generation of electrical energy. For accurate modeling of the studied system, each component of the system has been evaluated and modeling has been done. Therefore, in this research, solar tower modeling has been conducted to achieve high electrical energy production, and to better compare the production rate, 5 cities in Iran with different weather conditions have been considered. According to the results, it can be mentioned that the highest production power by the studied system is related to Shiraz city with an average production of 20 kW/m2, and among the cities studied, the lowest rate is related to Mashhad with a production power of 15 kW/m2.
Socio‐Emotional Wealth, Innovation Environment, and Innovative Investment Path of Family Enterprises: Implications for Environmental Accountability
Authors: Liu Hui
Published: July 2022

Driven by social development, family enterprises continue to grow in terms of scale and number, becoming an important force that promotes China’s economic development, and how to achieve the healthy growth of family enterprises has become an inevitable topic. With the increasingly fierce market competition, more and more family businesses have changed from a single market model to cross‐industry operation, trying to seek new growth points through industry diversification. Multiculturalism is the product of a particular era, and it is closely related to the great changes in society in a specific era, including new changes in the fields of family, marriage, religion, education, and race relations and even to the international context. Multiculturalism is a theoretical response to these changes and represents the current cultural research tendencies of the international academic community. Socio‐emotional wealth and an innovative environment are particularly important for family businesses in a multicultural context. The article first introduces the social emotional wealth and the innovation investment of family enterprises, then focuses on the impact of the innovation environment on the innovation investment of family enterprises, and analyzes the influence mechanism of social emotional wealth and innovation environment on family innovation investment, so as to put forward corresponding countermeasures and suggestions, which also has certain guiding significance for the innovation management and practice of Chinese family enterprises.
Container Shipping Scheduling Method Based on the Evidence Reasoning Approach in Fluctuating CCFI and BDI Cycle
Authors: Xu Yisong , Zhuang Xinyu
Published: July 2022

Due to the COVID-19/Omicron pandemic and the trade war and tariffs between China and America, the supply chain between Asia and America, or Asia and Europe, faced unprecedented challenges. With the outbreak of the Russian-Ukrainian war, the global supply chain has become increasingly unbalanced. In particular, the shortage of container ships and the continuous fluctuation of the BDI and CCFI make shipping scheduling increasingly important for ocean carriers. To solve this problem, in this study, we propose an analytic approach considering the fluctuation of BDI and CCFI based on interval evidence reasoning and the Hungarian algorithm. The proposed approach uses two pairs of nonlinear optimisation models to construct a Nash equilibrium assignment model to compute the shipping company’s scheduling and maximum utility in the CCFI and BDI cycles. Compared to the simulation algorithm, the analytical algorithm can take advantage of stability and efficiency. Finally, a COSCO company’s container shipping scheduling problem was examined to demonstrate the efficiency and effectiveness of the proposed approach.