Association of hypertension burden with stroke risk in patients with heart failure with preserved ejection fraction.

Introduction: Whether the hypertension burden is associated with stroke incidence is inconclusive. In this study, we aimed to investigate the relationship between hypertension burden and stroke risk in patients with heart failure with preserved ejection fraction (HFpEF). Methods: HFpEF patients from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial were divided into three groups (low, medium, and high risk) according to their hypertension burden values. Higher hypertension burden risk represented the longer duration of hypertension. We evaluated the association of hypertension burden with stroke risk using Fine and Gray's competing risk models. Results: A total of 3431 HFpEF patients (mean age: 68.5 ± 9.58 years, 51.6% females) were enrolled. During a median follow-up of 3.3 years, per 10-point increase in hypertension burden was associated with any stroke (hazard ratio [HR] 1.15, 95% confidence interval [CI] 1.08-1.21), new-onset stroke (HR 1.14, 95% CI 1.07-1.21), and ischemic stroke (HR 1.10, 95% CI 1.02-1.17). When hypertension burden was analyzed as a categorical variable, any stroke risk was increased in the medium- (HR 1.59, 95% CI 1.01-2.40) and high-risk (HR 3.19, 95% CI 2.05-4.97) groups when compared with the low-risk group. For the outcomes of new-onset (HR 2.92, 95% CI 1.80-4.74) and ischemic stroke (HR 2.46, 95% CI 1.41-4.29), similar results were observed in patients with high-versus low-risk hypertension burden. Conclusions: Increasing hypertension burden was associated with an increased risk of stroke, suggesting that shortening hypertension duration might appropriately minimize the stroke incidence in HFpEF patients.

Safety and immunogenicity of CoronaVac in healthy adults: A prospective observational multicenter real-world study in Henan Province, China.

Vaccination has emerged as the primar approach for managing the COVID-19 pandemic. Despite certain clinical trials reporting the safety and immunogenicity of CoronaVac, additional multicenter real-world studies are still necessary. In this study, we recruited 506 healthy volunteers who were not infected with COVID-19 or vaccinated. Each participant provided peripheral blood samples three times: prior to the first dose of vaccine, prior to the second dose, and 8 weeks following the second dose. Ultimately, 388 participants completed the entire follow-up process. No serious adverse events were observed among any of the participants. Within 1 week of vaccination, 13.4% of participants experienced systemic adverse reactions, with fatigue (5.93%) and dizziness (3.35%) being the most frequent. Although some clinical indicators, including creatinine, significantly changed after vaccination (p < 0.05), the mean of all altered indicators remained within the normal range. The positive rates of neutralizing antibodies (NAb), IgG, and IgM were 12.3%, 18.85%, and 5.24% prior to the second dose, respectively; and 57.99%, 86.34%, and 2.32% at 8 weeks following the second dose, respectively. Additionally, seven indicators, such as sex, age, and BMI, were significantly correlated with NAb (p < 0.05). Finally, a prediction model was developed based on age, monocytes, and alanine aminotransferase (ALT) with an AUC value of 87.56% in the train set and 80.71% in the test set. This study demonstrated that safety and immunogenicity of CoronaVac were good. The prediction model based on the baseline clinical characteristics prior to vaccination can help to develop more suitable vaccination strategies.

An Improved Theory for Designing and Numerically Calibrating Circular Touch Mode Capacitive Pressure Sensors.

The design, especially the numerical calibration, of a circular touch mode capacitive pressure sensor is highly dependent on the accuracy of the analytical solution of the contact problem between the circular conductive membrane and the rigid plate of the sensor. In this paper, the plate/membrane contact problem is reformulated using a more accurate in-plane equilibrium equation, and a new and more accurate analytical solution is presented. On this basis, the design and numerical calibration theory for circular touch mode capacitive pressure sensors has been greatly improved and perfected. The analytical relationships of pressure and capacitance are numerically calculated using the new and previous analytical solutions, and the gradually increasing difference between the two numerical calculation results with the gradual increase in the applied pressure is graphically shown. How to use analytical solutions and analytical relationships to design and numerically calibrate a circular touch mode capacitive pressure sensor with a specified pressure detecting range is illustrated in detail. The effect of changing design parameters on capacitance-pressure analytical relationships is comprehensively investigated; thus, the direction of changing design parameters to meet the required or desired range of pressure or capacitance is clarified.

S2 I2 N0-3 score predicts short- and long-term mortality and morbidity in HFrEF: a post-hoc analysis of the GUIDE-IT trial.

AIMS: This study investigated the S2 I2 N0-3 score, a simple tool comprising stroke history, insulin-treated diabetes, and N-terminal pro-brain natriuretic peptide, for forecasting mortality and morbidity in heart failure (HF) with reduced ejection fraction (HFrEF). METHODS AND RESULTS: Analysing 890 GUIDE-IT HFrEF trial participants, we stratified them by baseline S2 I2 N0-3 risk score into three risk groups. We examined the score's association with five adverse outcomes over short (90 days) and extended periods (median follow-up of 15 months) using Cox and competing risk models. Our analysis revealed significant positive associations between the S2 I2 N0-3 strata and adverse outcomes. When analysed as a continuous variable, each point increment of the S2 I2 N0-3 score was associated with a higher risk of short- and long-term cardiovascular death [short term: hazard ratio (HR) 1.43, 95% confidence interval (CI) 1.03-1.98; long term: HR 1.18, 95% CI 1.02-1.38], all-cause death (HR 1.52, 95% CI 1.12-2.07; HR 1.18, 95% CI 1.03-1.36), HF hospitalization (HR 1.39, 95% CI 1.20-1.62; HR 1.18, 95% CI 1.06-1.31), any hospitalization (HR 1.19, 95% CI 1.06-1.34; HR 1.09, 95% CI 1.00-1.19), and the composite outcome of cardiovascular death and HF hospitalization (HR 1.39, 95% CI 1.21-1.60; HR 1.17, 95% CI 1.06-1.30). The S2 I2 N0-3 demonstrated reliable prognostic value, with C-indices ranging from 0.619 to 0.753 across outcomes and time points. When compared with the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score using Z-statistics, net reclassification index, and integrated discrimination improvement, the S2 I2 N0-3 showed comparable predictive power for all outcomes during both short- and long-term follow-ups. CONCLUSIONS: The S2 I2 N0-3 risk score had modest predictive values for both short- and long-term clinical outcomes in HFrEF patients, offering equivalent performance to the established MAGGIC score.

Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm.

BACKGROUND: Patients with colorectal cancer (CRC) are prone to stress ulcer after laparoscopic surgery. The analysis of risk factors for stress ulcer (SU) in patients with CRC is important to reduce mortality and improve patient prognosis. AIM: To identify risk factors for SU after laparoscopic surgery for CRC, and develop a nomogram model to predict the risk of SU in these patients.METHODSThe clinical data of 135 patients with CRC who underwent laparoscopic surgery between November 2021 and June 2022 were reviewed retrospectively. They were divided into two categories depending on the presence of SUs: The SU group (n = 23) and the non-SU group (n = 112). Univariate analysis and multivariate logistic regression analysis were used to screen for factors associated with postoperative SU in patients undergoing laparoscopic surgery, and a risk factor-based nomogram model was built based on these risk factors. By plotting the model's receiver operating characteristic (ROC) curve and calibration curve, a Hosmer-Lemeshow goodness of fit test was performed. RESULTS: Among the 135 patients with CRC, 23 patients had postoperative SU, with an incidence of 17.04%. The SU group had higher levels of heat shock protein (HSP) 70, HSP90, and gastrin (GAS) than the non-SU group. Age, lymph node metastasis, HSP70, HSP90, and GAS levels were statistically different between the two groups, but other indicators were not statistically different. Logistic regression analysis showed that age ≥ 65 years, lymph node metastasis, and increased levels of HSP70, HSP90 and GAS were all risk factors for postoperative SU in patients with CRC (P < 0.05). According to these five risk factors, the area under the ROC curve for the nomogram model was 0.988 (95%CI: 0.971-1.0); the calibration curve demonstrated excellent agreement between predicted and actual probabilities, and the Hosmer-Lemeshow goodness of fit test revealed that the difference was not statistically significant (χ2 = 0.753, P = 0.999), suggesting that the nomogram model had good discrimination, calibration, and stability. CONCLUSION: Patients with CRC aged ≥ 65 years, with lymph node metastasis and elevated HSP70, HSP90, GAS levels, are prone to post-laparoscopic surgery SU. Our nomogram model shows good predictive value.

The Thermal Stress Problem of Bimodular Curved Beams under the Action of End-Side Concentrated Shear Force.

A bimodular material is a kind of material that presents two elastic moduli in tension and compression. In classical thermoelasticity, however, the bimodular material is rarely considered due to its complexity in analysis. In fact, almost all materials will present, more or less, bimodular characteristics, and in some cases, the mechanical properties of materials cannot be fully utilized simply by ignoring the bimodular characteristics. In this study, the thermal stress problem of bimodular curved beams under the action of end-side concentrated shear force is analytically and numerically investigated, in which the temperature rise modes in a thermal environment are considered arbitrary. Using the stress function method based on compatibility conditions, a two-dimensional solution of thermoelasticity of the bimodular curved beam subjected to end-side concentrated shear force was obtained. The results show that the solution for a bimodular curved beam with a thermal effect can be reduced to that of a bimodular curved beam without a thermal effect. At the same time, the numerical simulation for the problem verifies the correctness of the theoretical solution. The results may serve as a theoretical reference for the refined analysis and optimization of curved beams in a thermal environment.

Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map.

OBJECTIVE: To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. METHODS: A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance. RESULTS: Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802). CONCLUSION: The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. CRITICAL RELEVANCE STATEMENT: The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.

Association of the Triglyceride-Glucose Index With Risk of Alzheimer's Disease: A Prospective Cohort Study.

INTRODUCTION: Triglyceride-glucose index (TyG) is a reliable surrogate marker of insulin resistance, and insulin resistance has been implicated in Alzheimer's disease pathophysiology. However, the relationship between the TyG index and Alzheimer's disease remains unclear. This study aimed to evaluate the association of the TyG index with the risk of Alzheimer's disease. METHODS: This prospective study included 2,170 participants free of Alzheimer's disease from the Framingham Heart Study Offspring Cohort Exam 7 (1998-2001), whose follow-up data were collected until 2018. The TyG index was calculated as Ln(fasting triglyceride [mg/dL] × fasting glucose [mg/dL]/2). The association of the TyG index with Alzheimer's disease was evaluated by competing risk regression model. Statistical analyses were performed in 2023. RESULTS: During a median follow-up of 13.8 years, 163 (7.5%) participants developed Alzheimer's disease. When compared with the reference (TyG index ≤8.28), a significantly elevated risk of Alzheimer's disease was seen in the group with a triglyceride-glucose index of 8.68-9.09 (adjusted hazard ratio=1.69, 95% CI=1.02, 2.81). When the TyG index was considered as a continuous variable, each unit increment in the TyG index was not significantly associated with the risk of Alzheimer's disease (adjusted hazard ratio=1.32, 95% CI=0.98, 1.77). CONCLUSIONS: This study showed that moderately elevated TyG index was independently associated with a higher incidence of Alzheimer's disease. TheTyG index might be used to define a high-risk population of Alzheimer's disease.

A simple ATTR-CM score to identify transthyretin amyloid cardiomyopathy burden in HFpEF patients.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is often found in patients with heart failure with preserved ejection fraction (HFpEF). However, the evidence regarding ATTR-CM and prognosis in HFpEF remains scarce. This study sought to determine whether the ATTR-CM burden was associated with clinical outcomes in HFpEF patients. METHODS: We evaluated the associations of baseline ATTR-CM score with adverse outcomes in HFpEF patients from the TOPCAT trial using the Cox proportional hazards model or the competing risk regression model. The discriminatory ability of the ATTR-CM score was assessed using the area under the time-dependent receiver operating characteristic curve (AUC). RESULTS: We included 870 HFpEF patients, 18.9% of which had an ATTR-CM score ≥6. Per 1 increment in the ATTR-CM score was significantly associated with an increased risk of the primary outcome (adjusted hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.12-1.27) with an AUC of 0.652 (0.594-0.711), whereas patients with ATTR-CM score ≥6 presented higher risks of the primary outcome (adjusted HR 2.20, 95% CI 1.65-2.95). Similar results were observed toward the secondary outcomes. CONCLUSIONS: The simple ATTR-CM score identified an 18.9% ATTR-CM burden in HFpEF patients, and a higher ATTR-CM burden might predict adverse outcomes with moderate discriminatory abilities in HFpEF.

Hybrid Domain Consistency Constraints-Based Deep Neural Network for Facial Expression Recognition.

Facial expression recognition (FER) has received increasing attention. However, multiple factors (e.g., uneven illumination, facial deflection, occlusion, and subjectivity of annotations in image datasets) probably reduce the performance of traditional FER methods. Thus, we propose a novel Hybrid Domain Consistency Network (HDCNet) based on a feature constraint method that combines both spatial domain consistency and channel domain consistency. Specifically, first, the proposed HDCNet mines the potential attention consistency feature expression (different from manual features, e.g., HOG and SIFT) as effective supervision information by comparing the original sample image with the augmented facial expression image. Second, HDCNet extracts facial expression-related features in the spatial and channel domains, and then it constrains the consistent expression of features through the mixed domain consistency loss function. In addition, the loss function based on the attention-consistency constraints does not require additional labels. Third, the network weights are learned to optimize the classification network through the loss function of the mixed domain consistency constraints. Finally, experiments conducted on the public RAF-DB and AffectNet benchmark datasets verify that the proposed HDCNet improved classification accuracy by 0.3-3.84% compared to the existing methods.

Insights into Advanced Neurological Dysfunction Mechanisms Following DBS Surgery in Parkinson's Patients: Neuroinflammation and Pyroptosis.

Parkinson's disease is a severe neurodegenerative disorder. Currently, deep brain electrical stimulation (DBS) is the first line of surgical treatment. However, serious neurological impairments such as speech disorders, disturbances of consciousness, and depression after surgery limit the efficacy of treatment. In this review, we summarize the recent experimental and clinical studies that have explored the possible causes of neurological deficits after DBS. Furthermore, we tried to identify clues from oxidative stress and pathological changes in patients that could lead to the activation of microglia and astrocytes in DBS surgical injury. Notably, reliable evidence supports the idea that neuroinflammation is caused by microglia and astrocytes, which may contribute to caspase-1 pathway-mediated neuronal pyroptosis. Finally, existing drugs and treatments may partially ameliorate the loss of neurological function in patients following DBS surgery by exerting neuroprotective effects.

Oral Fungal Alterations in Patients with COVID-19 and Recovered Patients.

The oral bacteriome, gut bacteriome, and gut mycobiome are associated with coronavirus disease 2019 (COVID-19). However, the oral fungal microbiota in COVID-19 remains unclear. This article aims to characterize the oral mycobiome in COVID-19 and recovered patients. Tongue coating specimens of 71 COVID-19 patients, 36 suspected cases (SCs), 22 recovered COVID-19 patients, 36 SCs who recovered, and 132 controls from Henan are collected and analyzed using internal transcribed spacer sequencing. The richness of oral fungi is increased in COVID-19 versus controls, and beta diversity analysis reveals separate fungal communities for COVID-19 and control. The ratio of Ascomycota and Basidiomycota is higher in COVID-19, and the opportunistic pathogens, including the genera Candida, Saccharomyces, and Simplicillium, are increased in COVID-19. The classifier based on two fungal biomarkers is constructed and can distinguish COVID-19 patients from controls in the training, testing, and independent cohorts. Importantly, the classifier successfully diagnoses SCs with positive specific severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies as COVID-19 patients. The correlation between distinct fungi and bacteria in COVID-19 and control groups is depicted. These data suggest that the oral mycobiome may play a role in COVID-19.

Progress of polymer-based strategies in fungal disease management: Designed for different roles.

Fungal diseases have posed a great challenge to global health, but have fewer solutions compared to bacterial and viral infections. Development and application of new treatment modalities for fungi are limited by their inherent essential properties as eukaryotes. The microorganism identification and drug sensitivity analyze are limited by their proliferation rates. Moreover, there are currently no vaccines for prevention. Polymer science and related interdisciplinary technologies have revolutionized the field of fungal disease management. To date, numerous advanced polymer-based systems have been developed for management of fungal diseases, including prevention, diagnosis, treatment and monitoring. In this review, we provide an overview of current needs and advances in polymer-based strategies against fungal diseases. We high light various treatment modalities. Delivery systems of antifungal drugs, systems based on polymers' innate antifungal activities, and photodynamic therapies each follow their own mechanisms and unique design clues. We also discuss various prevention strategies including immunization and antifungal medical devices, and further describe point-of-care testing platforms as futuristic diagnostic and monitoring tools. The broad application of polymer-based strategies for both public and personal health management is prospected and integrated systems have become a promising direction. However, there is a gap between experimental studies and clinical translation. In future, well-designed in vivo trials should be conducted to reveal the underlying mechanisms and explore the efficacy as well as biosafety of polymer-based products.

Nocardia rubra cell-wall skeleton activates an immune response in cervical tissue via stimulating FPR3 to enhance dendritic cell-mediated Th1 differentiation.

Nocardia rubra cell wall skeleton (Nr-CWS) has proven to be a successful medicine for therapy of cervical human papillomavirus infection. The mechanism of action of Nr-CWS is unclear but may involve a stimulatory effect on the host immune system. We previously found that CD4+ T cells were increased in cervical tissue after Nr-CWS treatment. Microarray data from these cervical tissues revealed the significant upregulation of formylated peptide receptor 3 (FPR3). This study aimed to explore the role of Nr-CWS in immunomodulatory based on these findings. Examination of CD4+ T cell subsets in cervical tissue from patients who received Nr-CWS revealed substantial increases in Th1 cytokines and transcription factors. The regulatory effects of Nr-CWS on the function and phenotype of dendritic cells (DCs) were assessed in comparison with the traditional DC maturation inducer lipopolysaccharide (LPS). Similar to LPS, Nr-CWS potently induced DC maturation and interleukin-12 (IL-12) secretion. Differentiation of T cells induced by Nr-CWS stimulated DCs was assessed using the mixed lymphocyte reaction assay. Significant differentiation towards Th1 was evident. Finally, FPR3 expression in DCs in response to Nr-CWS and LPS was measured. Nr-CWS potently upregulated FPR3 expression, while the LPS did not. Silencing FPR3 in DCs reduced Nr-CWS-induced IL-12 production and Th1 cell polarization in co-cultured T cells. The collective findings indicate that Nr-CWS may target FPR3 on the surface of DC cells and activate a Th1-type immune response. The findings clarify the basis of the antiviral immune effects of Nr-CWS on human papillomavirus.

Application of the Variational Method to the Large Deformation Problem of Thin Cylindrical Shells with Different Moduli in Tension and Compression.

In this study, the variational method concerning displacement components is applied to solve the large deformation problem of a thin cylindrical shell with its four sides fully fixed and under uniformly distributed loads, in which the material that constitutes the shell has a bimodular effect, in comparison to traditional materials, that is, the material will present different moduli of elasticity when it is in tension and compression. For the purpose of the use of the displacement variational method, the physical equations on the bimodular material model and the geometrical equation under large deformation are derived first. Thereafter, the total strain potential energy is expressed in terms of the displacement component, thus bringing the possibilities for the classical Ritz method. Finally, the relationship between load and central deflection is obtained, which is validated with the numerical simulation, and the jumping phenomenon of thin cylindrical shell with a bimodular effect is analyzed. The results indicate that the bimodular effect will change the stiffness of the shell, thus resulting in the corresponding change in the deformation magnitude. When the shell is relatively thin, the bimodular effect will influence the occurrence of the jumping phenomenon of the cylindrical shell.

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module.

Because of its ability to objectively reflect people's emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, ß, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51-15.43% compared with existing methods.

Influence of calcium-based desensitizing toothpastes on the bonding performance of universal adhesive.

The study evaluated the effect of calcium-based desensitizing toothpastes on the dentinal tubule occlusion and its influence on the dentin bond strength of universal adhesive. Mid-coronal dentin samples were prepared for hypersensitivity model and treated by the following calcium-based desensitizing toothpastes: no treatment (Control), Clinpro (fTCP), Pro-Relief (Pro-Argin), and Repair & Protect (Novamin). Single Bond Universal adhesive was applied in self-etch or etch-and-rinse mode. The dentinal tubule occlusion and adhesion interface were evaluated under scanning electron microscope (SEM). A double-fluorescence technique was used to examine interfacial permeability under confocal laser scanning microscopy (CLSM). The micro-tensile bond strength (µTBS) was employed, followed by the fracture interface observation. SEM showed the toothpastes occluded dentinal tubules, and the occlusion exhibited stability against acid and abrasion. Hindered resin infiltration was observed in the adhesion interface after desensitization. CLSM showed more water permeation within or under the adhesion interface in etch-and-rinse mode than self-etch mode. Desensitization decreased the µTBS in self-etch mode. When using etch-and-rinse mode, the desensitized samples presented similar µTBS to the control group. No difference in µTBS was found between the two bonding modes, except for the control group. Calcium-based desensitizing toothpastes can effectively occlude the exposed dentinal tubules with acid-resistant and abrasion-resistant stability. The desensitization reduced the dentin bond strength of the universal adhesive system in self-etch mode but did not affect the bond strength of etch-and-rinse mode.

The Influence of Technostress, Learning Goal Orientation, and Perceived Team Learning Climate on Intra-Team Knowledge Sharing and Innovative Practices Among ICT-Enabled Team Members.

In ICT-enabled teams, innovation involves intensive adoption of ICTs and knowledge sharing among all members rather than a few experts. However, ICTs bring not only efficiency but also technostress, which hinders knowledge sharing and innovative practices among team members. To investigate this paradox, we drew on the job demand-control (JDC) model derived from the control theory of occupational stress to construct a theoretical framework regarding the collective influence of technostress, learning goal orientation, perceived team learning climate, and intra-team knowledge sharing on the innovative practices of ICT-enabled team members. Our multiple regression analyses of 481 ICT consultants' responses show that intra-team knowledge sharing positively influenced innovative practices; perceived team learning climate positively moderated this relationship. Further, technostress negatively influenced intra-team knowledge sharing; learning goal orientation positively influenced intra-team knowledge sharing, although the relationship demonstrated an inverted U-shape. Finally, learning goal orientation negatively moderated the relationship between technostress and intra-team knowledge sharing. Our results shed light on the paradox regarding ICT adoption, with theoretical implications for employee-driven innovation, team learning climate, intra-team knowledge sharing, learning goal orientation, and managerial practices about the design and adoption of ICT-enabled jobs.

Polymer Conductive Membrane-Based Circular Capacitive Pressure Sensors from Non-Touch Mode of Operation to Touch Mode of Operation: An Analytical Solution-Based Method for Design and Numerical Calibration.

Polymer-based conductive membranes play an important role in the development of elastic deflection-based pressure sensors. In this paper, an analytical solution-based method is presented for the design and numerical calibration of polymer conductive membrane-based circular capacitive pressure sensors from non-touch mode of operation to touch mode of operation. The contact problem of a circular membrane in frictionless contact with a rigid flat plate under pressure is analytically solved, and its analytical solution is used for the design of touch mode circular capacitive pressure sensors for the first time. The analytical relationship with input pressure as independent variable and output capacitance as dependent variable is precisely derived and is used for the numerical calibrations of the analytical relationships with input capacitance as the independent variable and output pressure as the dependent variable in order to meet the capacitive pressure sensor mechanism of detecting pressure by measuring capacitance. For the first time, an example showing the design and numerical calibration of a given (given design parameters) polymer conductive membrane-based circular capacitive pressure sensor from non-touch mode of operation to touch mode of operation is provided. Then, the influence of changing several important design parameters on input capacitance-output pressure relationships is comprehensively investigated in order to clarify the desired input-output relationships when changing design parameters.