The Effectiveness of an eHealth Family-Based Intervention Program in Patients With Uncontrolled Type 2 Diabetes Mellitus (T2DM) in the Community Via WeChat: Randomized Controlled Trial

Background: Intervention based on family support and risk perception can enhance type 2 diabetes mellitus (T2DM) patients’ self-care activities. In addition, eHealth education is considered to improve family members’ support for patients with T2DM. However, there is little evidence from rigorously designed studies on the effectiveness of an intervention combining these approaches. Objective: This randomized controlled trial (RCT) aimed to assess the effectiveness of an eHealth family-based health education intervention for patients with T2DM to improve their glucose control, risk perception, and self-care behaviors. Methods: This single-center, 2-parallel-group RCT was conducted between 2019 and 2020. Overall, 228 patients were recruited from Jiading District, Shanghai, and randomly divided into intervention and control groups. The intervention group received an eHealth family intervention based on community management via WeChat, whereas the control group received usual care. The primary outcome was the glycated hemoglobin (HbA1c) level of the patients with T2DM, and the secondary outcomes were self-management behavior (general and specific diet, exercise, blood sugar testing, foot care, and smoking), risk perception (risk knowledge, personal control, worry, optimism bias, and personal risk), and family support (supportive and nonsupportive behaviors). A 2-tailed paired-sample t test was used to compare the participants at baseline and follow-up within the control and intervention groups. An analysis of covariance was used to measure the intervention effect. Results: In total, 225 patients with T2DM were followed up for 1 year. After intervention, they had significantly lower HbA1c values (β=–.69, 95% CI –0.99 to –0.39; PP=.003), special diet (β=.71, 95% CI 0.34 to 1.09; PP=.04), foot care (β=1.82, 95% CI 1.23 to 2.42; PPPP=.001), optimism bias (β=.26, 95% CI 0.09 to 0.43; P=.003), and supportive behaviors (β=5.52, 95% CI 4.03 to 7.01; P\u3c.001). Conclusions: The eHealth family-based intervention improved glucose control and self-care activities among patients with T2DM by aiding the implementation of interventions to improve T2DM risk perceptions among family members. The intervention is generalizable for patients with T2DM using health management systems in community health centers. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900020736; https://www.chictr.org.cn/showprojen.aspx?proj=3121

Machine building Gearbox Fault Diagnosis Based on EEMD-SVD

Abstract Gearbox is an important mechanical device to transmit power. In order to ensure the normal operation of gearbox under the condition of top load, high efficiency and high precision, it's necessary to extract fault feature information using signal processing method and to further analyze and research gearbox fault. In the paper an improved de-noising method based on de-noising of singular value decomposition (SVD) is proposed, which sets threshold on the basis of standard derivation of the difference between adjacent singular values, and simulation is made. Through further research, combined it with ensemble empirical mode decomposition (EEMD), a new de-noising method based on EEMD-SVD (ensemble empirical mode decomposition and singular value decomposition) is derived, which is proved to be an effective de-noising method through simulation experiment. EEMD-SVD method is applied to fault diagnosis of gearbox and good results are achieved

A POLYMER-BASED MICROFLUIDIC RESISTIVE SENSOR FOR DETECTING DISTRIBUTED LOADS

ABSTRACT This paper reports on a polymer-based microfluidic resistive sensor for detecting distributed loads. The sensor is comprised of a polymer rectangular microstructure with an embedded electrolyte-filled microchannel and an array of electrodes aligned along the microchannel length. Electrolyte solution in the microchannel serves as impedance transduction. Distributed loads acting on the polymer microstructure give rise to different deflection along the microstructure length, which is recorded as the resistance change in electrolyte solution. This sensor can detect distributed loads by monitoring the resistance change at each pair of electrodes. A sensor with an in-plane dimension of ~20mm10mm and five pairs of electrodes is fabricated using a CNC machine. 1M KCl solution is used as the electrolyte. Using a custom built electronic circuit on breadboard and a custom LabVIEW program, the static and dynamic performance of the sensor is characterized, demonstrating the feasibility of employing this sensor to detect distributed loads

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096

Design of bio-nanosystems for oral delivery of functional compounds

Nanotechnology has been referred to as one of the most interesting topics in food technology due to the potentialities of its use by food industry. This calls for studying the behavior of nanosystems as carriers of biological and functional compounds aiming at their utilization for delivery, controlled release and protection of such compounds during food processing and oral ingestion. This review highlights the principles of design and production of bio-nanosystems for oral delivery and their behavior within the human gastrointestinal (GI) tract, while providing an insight into the application of reverse engineering approach to the design of those bio-nanosystems. Nanocapsules, nanohydrogels, lipid-based and multilayer nanosystems are discussed (in terms of their main ingredients, production techniques, predominant forces and properties) and some examples of possible food applications are given. Phenomena occurring in in vitro digestion models are presented, mainly using examples related to the utilization of lipid-based nanosystems and their physicochemical behavior throughout the GI tract. Furthermore, it is shown how a reverse engineering approach, through two main steps, can be used to design bio-nanosystems for food applications, and finally a last section is presented to discuss future trends and consumer perception on food nanotechnology.Miguel A. Cerqueira, Ana C. Pinheiro, Helder D. Silva, Philippe E. Ramos, Ana I. Bourbon, Oscar L. Ramos (SFRH/BPD/72753/2010, SFRH/BD/48120/2008, SFRH/BD/81288/2011, SFRH/BD/80800/2011, SFRH/BD/73178/2010 and SFRH/BPD/80766/2011, respectively) are the recipients of a fellowship from the Fundacao para a Ciencia e Tecnologia (FCT, POPH-QREN and FSE Portugal). Maria L. Flores-Lopez thanks Mexican Science and Technology Council (CONACYT, Mexico) for PhD fellowship support (CONACYT Grant number: 215499/310847). The support of EU Cost Actions FA0904 and FA1001 is gratefully acknowledged

Measurement of forward charged hadron flow harmonics in peripheral PbPb collisions at √sNN = 5.02 TeV with the LHCb detector

Flow harmonic coefficients, v n , which are the key to studying the hydrodynamics of the quark-gluon plasma (QGP) created in heavy-ion collisions, have been measured in various collision systems and kinematic regions and using various particle species. The study of flow harmonics in a wide pseudorapidity range is particularly valuable to understand the temperature dependence of the shear viscosity to entropy density ratio of the QGP. This paper presents the first LHCb results of the second- and the third-order flow harmonic coefficients of charged hadrons as a function of transverse momentum in the forward region, corresponding to pseudorapidities between 2.0 and 4.9, using the data collected from PbPb collisions in 2018 at a center-of-mass energy of 5.02 TeV . The coefficients measured using the two-particle angular correlation analysis method are smaller than the central-pseudorapidity measurements at ALICE and ATLAS from the same collision system but share similar features

The Design of a Permanent Magnet In-Wheel Motor with Dual-Stator and Dual-Field-Excitation Used in Electric Vehicles

The in-wheel motor has received more attention owing to its simple structure, high transmission efficiency, flexible control, and easy integration design. It is difficult to achieve high performance with conventional motors due to their dimensions and structure. This paper presents a new dual-stator and dual-field-excitation permanent-magnet in-wheel motor (DDPMIM) that is based on the structure of the conventional in-wheel motor and the structure of both the radial and axial magnetic field motor. The finite element analysis (FEA) model of the DDPMIM is established and compared with that of the conventional in-wheel motor. The results show that the DDPMIM achieves a higher output torque at low speeds and that the flux-weakening control strategy is not needed in the full speed range

Effect of Storage Time on Main Quality Indicators of Cotton

The quality indicators of cotton will change during storage. Taking the 5.89 million t of Xinjiang cotton from 2016 to 2021 as a sample, this paper analyzed the main fiber quality indicator data of warehouse-in and warehouse-out cotton for storage of 1.5, 3.0, 4.0, 5.0, 6.0, and 7.0 years. It was found that the color grade of cotton decreased with the extension of storage time. The cotton with storage time of 5.0 years mainly changed from white cotton grade 2 and white cotton grade 3 to light yellow stained cotton grade 1 and yellow stained cotton grade 1. Among them, the increase of light yellow stained cotton grade 1 was the largest, and the change to yellow stained cotton grade 1 was the largest at the storage 6.0-7.0 years. In addition, there were no significant changes in moisture regain, Micronaire value, upper half mean length, length uniformity index and fiber strength