Conectar
by Longgang Zhao, Yuan Wang, Eric Mishio Bawa, Zichun Meng, Jingkai Wei, Sarah Newman-Norlund, Tushar Trivedi, Hatice Hasturk, Roger D. Newman-Norlund, Julius Fridriksson, Anwar T. Merchant
BackgroundCognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters.
MethodsWe used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011–2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI).
ResultsUsing the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36–12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62–7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race.
ConclusionThe model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.
by YanXiao Zhao, Basyarah Hamat, Tao Wang, SongEn Wang, Leah Ling Li Pang
AimsIn order to explore new consumer demands for Chinese tea set products, propose an innovative tea set product design and evaluation method to improve the user experience and satisfaction of the produced tea sets, thereby promoting the development of the tea set market and the promotion of tea culture.
MethodsFirstly, grounded theory (GT) was used to analyze interview data to extract consumer demand indicators and construct a design evaluation hierarchical model. Secondly, the Analytical Hierarchy Process (AHP) was used to calculate the weights of the indicators, determine their priority of importance, and obtain several indicators that have a greater impact on the tea set design to guide innovative design practice. Lastly, the tea set design schemes were evaluated using the fuzzy comprehensive evaluation method to select the optimal design scheme and also to act as a guideline for further design optimization.
ConclusionThis study explores the innovative design and evaluation method for tea set products based on GT-AHP-FCE and validates the feasibility of this approach through a practical example of tea set design inspired by “The Classic of Mountains and Seas.”. It provides innovative theoretical and practical guidance for designers of subsequent tea set products and also provides a new path for the inheritance and innovation of traditional culture.
by Chi Su, Yilong Hua, Yi Liu, Shu Tao, Fei Jia, Wenhui Zhao, Wangyang Lin
Mining wastewater with heavy metals poses a serious threat to the ecological environment. However, the acute single and combined ecological effects of heavy metals, such as chromium (Cr) and nickel (Ni), on freshwater ostracods, and the development of relevant prediction models, remain poorly understood. In this study, Heterocypris sp. was chosen to investigate the single and combined acute toxicity of Cr and Ni. Then, the quantitative structure-activity relationship (QSAR) model was used to predict the combined toxicity of Cr and Ni. The single acute toxicity experiments revealed high toxicity for both Cr and Ni. In addition, Cr exhibited greater toxicity compared to Ni, as evidenced by its lower 96-hour half-lethal concentration (LC50) of 1.07 mg/L compared to 4.7 mg/L for Ni. Furthermore, the combined acute toxicity experiments showed that the toxicity of Cr-Ni was higher than Ni but lower than Cr. Compared with the concentration addition (CA) and independent action (IA) models, the predicted results of the QSAR model were more consistent with the experimental results for the Cr-Ni combined acute toxicity. So, the high accuracy of QSAR model identified its feasibility to predict the toxicity of heavy metal pollutants in mining wastewater.by Stephanie E. Martinez, Amit V. Pandey, Tania E. Perez Jimenez, Zhaohui Zhu, Michael H. Court
Greyhounds metabolize cytochrome P450 (CYP) 2B11 substrates more slowly than other dog breeds. However, CYP2B11 gene variants associated with decreased CYP2B11 expression do not fully explain reduced CYP2B11 activity in this breed. P450 oxidoreductase (POR) is an essential redox partner for all CYPs. POR protein variants can enhance or repress CYP enzyme function in a CYP isoform and substrate dependent manner. The study objectives were to identify POR protein variants in greyhounds and determine their effect on coexpressed CYP2B11 and CYP2D15 enzyme function. Gene sequencing identified two missense variants (Glu315Gln and Asp570Glu) forming four alleles, POR-H1 (reference), POR-H2 (570Glu), POR-H3 (315Gln, 570Glu) and POR-H4 (315Gln). Out of 68 dog breeds surveyed, POR-H2 was widely distributed across multiple breeds, while POR-H3 was largely restricted to greyhounds and Scottish deerhounds (35% allele frequencies), and POR-H4 was rare. Three-dimensional protein structure modelling indicated significant effects of Glu315Gln (but not Asp570Glu) on protein flexibility through loss of a salt bridge between Glu315 and Arg519. Recombinant POR-H1 (reference) and each POR variant (H2-H4) were expressed alone or with CYP2B11 or CYP2D15 in insect cells. No substantial effects on POR protein expression or enzyme activity (cytochrome c reduction) were observed for any POR variant (versus POR-H1) when expressed alone or with CYP2B11 or CYP2D15. Furthermore, there were no effects on CYP2B11 or CYP2D15 protein expression, or on CYP2D15 enzyme kinetics by any POR variant (versus POR-H1). However, Vmax values for 7-benzyloxyresorufin, propofol and bupropion oxidation by CYP2B11 were significantly reduced by coexpression with POR-H3 (by 34–37%) and POR-H4 (by 65–72%) compared with POR-H1. Km values were unaffected. Our results indicate that the Glu315Gln mutation (common to POR-H3 and POR-H4) reduces CYP2B11 enzyme function without affecting at least one other major canine hepatic P450 (CYP2D15). Additional in vivo studies are warranted to confirm these findings.by Yi Li, Hongyu Gao, Jinghui Zhao, Liqing Ma, Dan Hu
ObjectiveBempedoic acid, an innovative oral medication, has garnered significant interest in recent times due to its potential as a therapeutic intervention for hypercholesterolemia. Nonetheless, the outcomes of the initial investigations might have been more definitive and coherent. Our objective was to perform a quantitative meta-analysis in order to evaluate bempedoic acid’s safety and effectiveness.
MethodsA search was conducted on ClinicalTrials.gov, and PubMed from the time of inception until September 28, 2023. Randomized controlled trials comparing the safety and efficacy of bempedoic acid among patients with statin intolerance and those without were included in our analysis. The trial outcomes were summarized using a random effects model and were provided as mean differences or odds ratios (ORs) with a confidence interval of 95%. Additionally, trial heterogeneity and the possibility of bias were evaluated and investigated.
ResultsBempedoic acid treatment reduced low-density lipoprotein cholesterol levels more than placebo (mean difference -2.97%, 95% CI -5.89% to -0.05%), according to a pooled analysis of 16 eligible trials. The risk of death (OR 1.18, 95% CI 0.70 to 1.98) and muscle-associated occurrences (OR 1.00, 95% CI 0.77 to 1.31) was not impacted by bempedoic acid. In contrast, discontinuation of treatment was more frequently caused by adverse events in the bempedoic acid group (OR 1.13, 95% CI 1.01 to 1.27).
ConclusionsIn patients with statin intolerance as well as those without, bempedoic acid is a safe and efficacious lipid-lowering agent, according to findings from randomized controlled trials.
by Xiaoquan Zhao, Aayushi Hingle, Cameron C. Shaw, Amy Murphy, Breonna R. Riddick, Rochelle R. Davidson Mhonde, Bruce G. Taylor, Phoebe A. Lamuda, Harold A. Pollack, John A. Schneider, Faye S. Taxman
Criminal legal system involvement (CLI) is a critical social determinant of health that lies at the intersection of multiple sources of health disparities. The COVID-19 pandemic exacerbates many of these disparities, and specific vulnerabilities faced by the CLI population. This study investigated the prevalence of COVID-19-related misinformation, as well as its relationship with COVID-19 information sources used among Americans experiencing CLI. A nationally representative sample of American adults aged 18+ (N = 1,161), including a subsample of CLI individuals (n = 168), were surveyed in February-March 2021. On a 10-item test, CLI participants endorsed a greater number of misinformation statements (M = 1.88 vs. 1.27) than non-CLI participants, pby Zhen Zhao, Owen Lou, Yiyang Wang, Raymond Yin, Carrie Gong, Florence Deng, Ethan C. Wu, Jing Yi Xie, Jerry Wu, Avery Ma, Yongzhi Guo, Wei Ting Xiong
While systemic corticosteroids quicken patient recovery during acute exacerbations of COPD, they also have many adverse effects. The optimal duration of corticosteroid administration remains uncertain. We performed a systematic review and meta-analysis to compare patient outcomes between short- (≤7 days) and long- (>7 days) corticosteroid regimens in adults with acute exacerbations of COPD. MEDLINE, EMBASE, CENTRAL, and hand searches were used to identify eligible studies. Risk of bias was assessed using the Cochrane RoB 2.0 tool and ROBINS-I. Data were summarized as ORs (odds ratios) or MDs (mean differences) whenever possible and qualitatively described otherwise. A total of 11532 participants from eight RCTs and three retrospective cohort studies were included, with 1296 from seven RCTs and two cohort studies eligible for meta-analyses. Heterogeneity was present in the methodology and settings of the studies. The OR (using short duration as the treatment arm) for mortality was 0.76 (95% CI = 0.40–1.44, n = 1055). The MD for hospital length-of-stay was -0.91 days (95% CI = -1.81–-0.02 days, n = 421). The OR for re-exacerbations was 1.31 (95% CI = 0.90–1.90, n = 552). The OR for hyperglycemia was 0.90 (95% CI = 0.60–1.33, n = 423). The OR for infection incidence was 0.96 (95% CI = 0.59–1.156, n = 389). The MD for one-second forced expiratory volume change was -18.40 mL (95% CI = -111.80–75.01 mL, n = 161). The RCTs generally had low or unclear risks of bias, while the cohort studies had serious or moderate risks of bias. Our meta-analyses were affected by imprecision due to insufficient data. Some heterogeneity was present in the results, suggesting population, setting, and treatment details are potential prognostic factors. Our evidence suggests that short-duration treatments are not worse than long-duration treatments in moderate/severe exacerbations and may lead to considerably better outcomes in milder exacerbations. This supports the current GOLD guidelines. Trial registration: Our protocol is registered in PROSPERO: CRD42023374410.by Hui Zhao, Guikun Yu, Xian Cheng
Renewable hydrogen energy has received growing attention due to the energy shortage and increasing CO2 emissions. With these issues in mind, renewable hydrogen has become an important component of future energy systems in many countries, especially in the transportation sector. However, the shortage of hydrogenation station and the risks associated with their construction have become an urgent issue for the development of hydrogen energy transportation. To better implement the hydrogenation station project, a risk management framework is proposed for risk control. First, a comprehensive risk index system is developed, using a weighting method based on the G1 method and the C-OWA operator. Second, a grey fuzzy synthetic assessment method is applied to evaluate the risk based on the 3D risk assessment framework. Finally, risk is assigned to different participants and actionable measures are proposed. This paper summarizes the obstacles to the development of hydrogen energy transportation, highlights the potential of hydrogen energy development, and suggests workable solutions for the use of hydrogen energy in the future transportation industry.by Xing Zhao, Xiangqian Li, Changman Ren
PurposeThe purpose of this paper is to study the impact of enterprises’ digital transformation on the risk of stock price crashes, but also to study the mediating role of enterprises’ financialization and accounting conservatism in the enterprises’ digital transformation on stock price crash risk.
Design/methodology/approachBased on the data of 2,599 listed companies in China from 2010 to 2019, this paper constructs indicators of enterprise digital transformation through word frequency analysis method, and uses fixed-effects model and mediated-effects model to explore the impact and mechanism of enterprise digital transformation on the stock price crash risk.
FindingsThis study shows that firms’ digital transformation reduces the risk of stock price crashes and that financialization of firms and accounting conservatism play a significant mediating effect between enterprises’ digital transformation and the risk of stock price crashes.
Originality/valueThis study enriches the study of stock price crash risk by including digital transformation in the field of stock price crash research, and it examines the mediating roles of financialization of enterprises and accounting conservatism, which provides a new explanatory mechanism to the study of the correlation between digital transformation of enterprises and the risk of stock price crash.
by Duong Thi Thuy Doan, Colin Binns, Andy Lee, Yun Zhao, Minh Ngoc Pham, Hoa Thi Phuong Dinh, Chuong Canh Nguyen, Ha Thi Thu Bui
IntroductionBreastfeeding has many benefits for mothers, children, and the environment over both the short and longr-term. Prenatal intention to breastfeed is a powerful predictor of short-term breastfeeding outcomes.
ObjectiveThis study aims to analyze breastfeeding intentions, including the intention to feed infants with breastmilk only and to continue exclusive breastfeeding to 6 months among pregnant mothers in Hanoi, Vietnam.
MethodsThe analysis included 1230 singleton mothers, between 24- and 36-weeks’ gestation, who attended antenatal clinics in two hospitals in Hanoi in 2020.
ResultsThe proportion of mothers with an “breastfeeding intention” (i.e., intention to feed an infant with breastmilk only) and “exclusive breastfeeding intention” to 6 months was 59.9% and 41.7%, respectively. Mothers who were 25 years or older (aOR = 1.35, 95%CI:1.00–1.81), had an undergraduate educational degree or higher (aOR = 1.38, 95%CI: 1.08–1.76), had observed another woman breastfeeding (aOR = 1.43, 95%CI: 1.03–2.00), were not living with parents-in-law (aOR = 1.34, CI: 1.05–1.70), and were multiparous (aOR = 1.60, 95%CI: 1.16–2.19) had higher odds of “exclusive breastfeeding intention” to 6 months. Among primiparous women, those who thought their husbands support breastfeeding were more likely to intend to feed an infant with breastmilk only. Among multiparous women, feeding the previous child with breastmilk exclusively before the introduction of complementary foods and not giving solid foods together with water until 6 months, were significant predictors for both breastfeeding intentions.
ConclusionMothers without exclusive breastfeeding experience should be provided with greater support to promote exclusive breastfeeding intention and outcomes.
by Zhitao Guo, Xiaojie Zhao, Li Yao, Zhiying Long
The human brain can be regarded as a complex network with interacting connections between brain regions. Complex brain network analyses have been widely applied to functional magnetic resonance imaging (fMRI) data and have revealed the existence of community structures in brain networks. The identification of communities may provide insight into understanding the topological functions of brain networks. Among various community detection methods, the modularity maximization (MM) method has the advantages of model conciseness, fast convergence and strong adaptability to large-scale networks and has been extended from single-layer networks to multilayer networks to investigate the community structure changes of brain networks. However, the problems of MM, suffering from instability and failing to detect hierarchical community structure in networks, largely limit the application of MM in the community detection of brain networks. In this study, we proposed the weighted modularity maximization (WMM) method by using the weight matrix to weight the adjacency matrix and improve the performance of MM. Moreover, we further proposed the two-step WMM method to detect the hierarchical community structures of networks by utilizing node attributes. The results of the synthetic networks without node attributes demonstrated that WMM showed better partition accuracy than both MM and robust MM and better stability than MM. The two-step WMM method showed better accuracy of community partitioning than WMM for synthetic networks with node attributes. Moreover, the results of resting state fMRI (rs-fMRI) data showed that two-step WMM had the advantage of detecting the hierarchical communities over WMM and was more insensitive to the density of the rs-fMRI networks than WMM.by Xinzhi Wang, Kim Geok Soh, Shamsulariffin Samsudin, Nuannuan Deng, Xutao Liu, Yue Zhao, Saddam Akbar
ObjectiveThis study aims to meta-analyze the impact of high-intensity functional training on athletes’ physical fitness and sport-specific performance.
MethodsA systematic search was conducted in five well-known academic databases (PubMed, Scopus, Web of Science, EBSCOhost, and the Cochrane Library) up to July 1, 2023. The literature screening criteria included: (1) studies involving healthy athletes, (2) a HIFT program, (3) an assessment of outcomes related to athletes’ physical fitness or sport-specific performance, and (4) the inclusion of randomized controlled trials. The Physical Therapy Evidence Database (PEDro) scale was used to evaluate the quality of studies included in the meta-analysis.
Results13 medium- and high-quality studies met the inclusion criteria for the systematic review, involving 478 athletes aged between 10 and 24.5 years. The training showed a small to large effect size (ES = 0.414–3.351; all p Conclusion
High-intensity functional training effectively improves athletes’ muscle strength, power, flexibility, and sport-specific performance but has no significant impact on endurance and agility. Future research is needed to explore the impact of high-intensity functional training on athletes’ speed, balance, and technical and tactical performance parameters.
by Tomoko Fujitani, Mariko Harada Sassa, Zhaoqing Lyu, Yukiko Fujii, Kouji H. Harada
Lignan polyphenols derived from plants are metabolized by bacteria in the gut to mammalian lignans, such as enterolactone (ENL) and enterodiol (END). Mammalian lignan intake has been reported to be associated with obesity and low blood glucose levels. However, the factors that are responsible for individual differences in the metabolic capacity for ENL and END are not well understood. In the present study, the effects of enterotypes of isoflavone metabolism, equol producers (EQP) and O-desmethylangolensin producers (O-DMAP), on lignan metabolism were examined. EQP was defined by urinary daidzein (DAI) and equol concentrations as log(equol/DAI) ≥ –1.42. O-DMAP was defined by urinary DAI and O-DMA concentrations as O-DMA/DAI > 0.018. Isoflavone and lignan concentrations in urine samples from 440 Japanese women were measured by gas chromatography-mass spectrometry. Metabolic enterotypes were determined from the urinary equol and O-DMA concentrations. Urinary END and ENL concentrations were compared in four groups, combinations of EQP (+/–) and O-DMAP (+/–). The urinary lignan concentration was significantly higher in the O-DMAP/EQP group (ENL: Pby Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Saurav Mallik, Zhongming Zhao
Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.by Ting Zou, Xiaohui Zhou, Qinwen Wang, Yongjie Zhao, Meisheng Zhu, Lei Zhang, Wei Chen, Pari Abuliz, Haijun Miao, Keyimu Kabinur, Kader Alimu
ObjectiveTo investigate the associations of serum DNA methylation levels of chemokine signaling pathway genes with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) in elderly people in Xinjiang, China, and to screen out genes whose DNA methylation could distinguish AD and MCI.
Materials and methods37 AD, 40 MCI and 80 controls were included in the present study. DNA methylation assay was done using quantitative methylation-specific polymerase chain reaction (qMSP). Genotyping was done using Sanger sequencing.
ResultsDNA methylation levels of ADCY2, MAP2K1 and AKT1 were significantly different among AD, MCI and controls. In the comparisons of each two groups, AKT1 and MAP2K1’s methylation was both significantly different between AD and MCI (p MAP2K1’s methylation was also significantly different between MCI and controls. Therefore, AKT1’s methylation was considered as the candidate serum marker to distinguish AD from MCI, and its association with AD was independent of APOE ε4 allele (p AKT1 hypermethylation was an independent risk factor for AD and MAP2K1 hypomethylation was an independent risk factor for MCI in logistic regression analysis (p Conclusion
This study found that the serum of AKT1 hypermethylation is related to AD independently of APOE ε4, which was differentially expressed in the Entorhinal Cortex of the brain and was an independent risk factor for AD. It could be used as one of the candidate serum markers to distinguish AD and MCI. Serum of MAP2K1 hypomethylation is an independent risk factor for MCI.
by Chao Li, Zhong Liu, Justin Anderson, Zhongyu Liu, Liping Tang, Yao Li, Ning Peng, Jianguo Chen, Xueming Liu, Lianwu Fu, Tim M. Townes, Steven M. Rowe, David M. Bedwell, Jennifer Guimbellot, Rui Zhao
A major unmet need in the cystic fibrosis (CF) therapeutic landscape is the lack of effective treatments for nonsense CFTR mutations, which affect approximately 10% of CF patients. Correction of nonsense CFTR mutations via genomic editing represents a promising therapeutic approach. In this study, we tested whether prime editing, a novel CRISPR-based genomic editing method, can be a potential therapeutic modality to correct nonsense CFTR mutations. We generated iPSCs from a CF patient homozygous for the CFTR W1282X mutation. We demonstrated that prime editing corrected one mutant allele in iPSCs, which effectively restored CFTR function in iPSC-derived airway epithelial cells and organoids. We further demonstrated that prime editing may directly repair mutations in iPSC-derived airway epithelial cells when the prime editing machinery is efficiently delivered by helper-dependent adenovirus (HDAd). Together, our data demonstrated that prime editing may potentially be applied to correct CFTR mutations such as W1282X.by Xiaoxuan Zhao, Yijie Hu, Wenjun Xiao, Yiming Ma, Dan Shen, Yuepeng Jiang, Yi Shen, Suxia Wang, Jing Ma
ObjectivesUnexplained recurrent spontaneous abortion (URSA) remains an intractable reproductive dilemma due to the lack of understanding of the pathogenesis. This study aimed to evaluate the preclinical evidence for the mesenchymal stromal cell (MSC) treatment for URSA.
MethodsA meticulous literature search was independently performed by two authors across the Cochrane Library, EMBASE, and PubMed databases from inception to April 9, 2023. Each study incorporated was assessed using the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) risk of bias tool. The amalgamated standardized mean difference (SMD) accompanied by 95% confidence interval (CI) were deduced through a fixed-effects or random-effects model analysis.
ResultsA total of ten studies incorporating 140 mice were subjected to data analysis. The MSC treatment yielded a significant reduction in the abortion rate within the URSA model (OR = 0.23, 95%CI [0.17, 0.3], PP = 0.01), IL10 (SMD 1.60, 95% CI [0.58, 2.61], P = 0.002), IFN-γ (SMD -1.66, 95%CI [-2.79, -0.52], P = 0.004), and TNF-α (SMD -1.98, 95% CI [-2.93, -1.04], PPP>0.05).
ConclusionsThe findings underscore the considerable potential of MSCs in URSA therapy. Nonetheless, the demand for enhanced transparency in research design and direct comparisons between various MSC sources and administration routes in URSA is paramount to engendering robust evidence that could pave the way for successful clinical translation.
by Youcai Ma, Zhaobing Cui
Innovation is the first driving force to lead development, how to improve manufacturing innovation performance has become a hot topic. Based on 47 listed companies in the computer, communication and other electronic equipment manufacturing industry in the A-share market, this paper adopted the Fuzzy set qualitative comparative analysis (fsQCA) to explore the influencing factors of technology, organization and environment on the innovation performance of manufacturing industry and the improvement path. The findings are as follows: (1) A single condition is not a necessary condition for high innovation performance in manufacturing industry, but government support plays a key role in improving innovation performance in manufacturing industry. (2) There are two improvement paths for high innovation performance in manufacturing industry, which are specifically explained as “technology-environment dual improvement path” and “technology-organization-environment collaborative improvement path”. (3) The improvement of innovation performance in the manufacturing industry is the result of multiple factors, showing the characteristics of “all paths lead to the same destination”. Different manufacturing enterprises have different paths to improve innovation performance based on their actual conditions. Based on these findings, this study may provide some implications for the effective improvement of manufacturing innovation performance.by Jiawei Wu, Peng Ren, Boming Song, Ran Zhang, Chen Zhao, Xiao Zhang
As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.by Huanhuan Wang, Xiaoli Fan, Qilan Zhao, Pengfei Cui
Agrifood systems account for 31% of global greenhouse gas emissions. Substantial emissions reduction in agrifood systems is critical to achieving the temperature goal set by the Paris Agreement. A key challenge in reducing GHG emissions in the agrifood value chain is the imbalanced allocation of benefits and costs associated with emissions reduction among agrifood value chain participants. However, only a few studies have examined agrifood emissions reduction from a value chain perspective, especially using dynamic methods to investigate participants’ long-term emissions reduction strategies. This paper helps fill this gap in the existing literature by examining the impact of collaborations among agrifood value chain participants on correcting those misallocations and reducing emissions in agrifood systems. We develop a dynamic differential game model to examine participants’ long-term emissions reduction strategies in a three-stage agrifood value chain. We use the Hamilton-Jacobi-Bellman equation to derive the Nash equilibrium emissions reduction strategies under non-cooperative, cost-sharing, and cooperative mechanisms. We then conduct numerical analysis and sensitivity analysis to validate our model. Our results show that collaboration among value chain participants leads to higher emissions reduction efforts and profits for the entire value chain. Specifically, based on our numerical results, the cooperative mechanism results in the greatest emissions reduction effort by the three participants, which leads to a total that is nearly three times higher than that of the non-cooperative mechanism and close to two times higher than the cost-sharing mechanism. The cooperative mechanism also recorded the highest profits for the entire value chain, surpassing the non-cooperative and cost-sharing mechanisms by around 37% and 16%, respectively. Our results provide valuable insights for policymakers and agrifood industry stakeholders to develop strategies and policies encouraging emissions reduction collaborations in the agrifood value chain and reduce emissions in the agrifood systems.
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