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Reversible data hiding in encrypted images (RDHEI) is a technology that embeds data directly in the encrypted images without decryption or knowledge of the content of the images. However, it is dif...

In this paper, a novel electromagnetic negative-stiffness spring (ENSS) is constructed by coaxial permanent annular magnets and coils with rectangular cross section. The stiffness of the ENSS is negative when the coils carry exciting current. In the stiffness characterization study, the analytical model of the ENSS based on filament method is established and validated experimentally. With the stiffness model, the factors affecting the electromagnetic force and stiffness characteristics are investigated in a quantitative way. The effectiveness of a vibration isolator based on the ENSS is tested on a prototype isolator. The relationships between the transmissibility and the exciting frequency are measured under diverse amount of exciting currents. The experiment results demonstrate that the performance of the isolator can be easily tuned online.

With the extensive use of cloud services in different applications, it's a problem for the cloud service provider to manage or process the privacy data that are encrypted by the content owner. Therefore, signal processing technology in the encrypted domain has attracted the attention of researchers. In this paper, we propose a new reversible data hiding method for encrypted images based on two-phase histogram shifting. In the proposed method, the original image is encrypted by using special image division and additive homomorphic encryption. After image encryption, the encrypted image can partially maintain spatial correlation for data embedding while the content security of the encrypted image is ensured. Due to the spatial correlation, the data hider can generate two difference histograms from the encrypted image, which provide high embedding capacity. A two-phase histogram shift scheme is used to embed the secret data into the two difference histograms. At the receiver side, the secret data can be extracted from the encrypted image or the decrypted image, and the image can be recovered to its original version without any error. The experimental results demonstrated that the proposed method can efficiently improve the capacity of data embedding and outperform other related methods, while the visual quality of the marked image can be maintained.

Pulse localization is the basic task of the pulse diagnosis with robot. More accurate location can reduce the misdiagnosis caused by different types of pulse. Traditional works usually use a collection surface with a certain area for contact detection, and move the collection surface to collect changes of power for pulse localization. These methods often require the subjects place their wrist in a given position. In this paper, we propose a novel pulse localization method which uses the infrared camera as the input sensor, and locates the pulse on wrist with the neural network. This method can not only reduce the contact between the machine and the subject, reduce the discomfort of the process, but also reduce the preparation time for the test, which can improve the detection efficiency. The experiments show that our proposed method can locate the pulse with high accuracy. And we have applied this method to pulse diagnosis robot for pulse data collection.

PurposeStandardised Training of Paediatric Resident (STPR) plays an essential role in training qualified paediatricians. Until now, China had no paediatric resident competency index system to effectively guide and evaluate the competence of paediatric residents. This study aimed to establish a competency index system for paediatric residents in China to provide a reference for improving the training system and quality of STPR.Study design and settingThis study conducted two rounds of Delphi expert consultation survey among paediatric medical experts (n=16), followed by screening, revising and supplementing indicators using the boundary value method. Next, the analytic hierarchy process was used to determine the weight of indicators and finally establish a competency index system for paediatric residents.ResultsThe results of the statistical analysis revealed a positive coefficient of 100% for both rounds of expert consultation. The expert authority coefficient values were 0.82 and 0.83, and the expert coordination coefficient test was pConclusionsIn this study, a competency index system for paediatric residents was constructed following the characteristics and quality requirements for paediatric residents in China and is expected to significantly improve the overall level of paediatricians’ medical service quality and supply.

ObjectiveSince China launched its nationwide systemic healthcare reform in 2009, policies such as the elimination of drug markups and the reform of medical insurance payments have forced some hospitals into a crisis threatening their survival. Both public and private hospitals have been building and strengthening their capacity to achieve sustainable development. However, the existing research has not provided a comprehensive evaluation tool required to support this effort. Therefore, this study develops an organisational capability evaluation index system to help public and private hospitals assess their current conditions.DesignThe Delphi method was used to construct a hospital organisational capability evaluation index system in conjunction with the boundary value method and an analytic hierarchical process. Then, a questionnaire survey was administered in 55 hospitals (32 non-profit and 23 for-profit hospitals), and Cronbach’s α and a factor analysis were used to verify the index system’s reliability and validity.Setting and participantsA literature review and semistructured interviews with 23 hospital managers and scholars clarified the definition of hospital organisational capability and formed an indicator pool. Additionally, 20 hospital directors were selected from public and private hospitals to participate in two rounds of the Delphi consultation.ResultsThe Delphi consultation resulted in an index system including 12 primary and 40 secondary indicators demonstrated to be reliable and valid. The three indicators with the largest weights were ‘regulation capability’ (0.251), ‘decision-making capability’ (0.121) and ‘executive capability’ (0.105).ConclusionThis study constructed an index system based on theoretical and practical considerations, and is expected to be applied to quantitatively evaluate the organisational capability of both public and private hospitals in China, and support their adaptation to external environmental changes.

Background The coronavirus disease 2019 (COVID‐19) has developed into a global outbreak. Patients with cardiovascular disease (CVD) with COVID‐19 have different clinical characteristics and prognostic outcomes. This study aimed to summarize the clinical characteristics and laboratory indicators of patients with COVID‐19 with CVD, especially the critically ill patients. Methods and Results This study included 244 patients diagnosed with COVID‐19 and CVD (hypertension, coronary heart disease, or heart failure). The patients were categorized into critical (n=36) and noncritical (n=208) groups according to the interim guidance of China’s National Health Commission. Clinical, laboratory, and outcome data were collected from the patients’ medical records and compared between the 2 groups. The average body mass index of patients was significantly higher in the critical group than in the noncritical group. Neutrophil/lymphocyte ratio, and C‐reactive protein, procalcitonin, and fibrinogen, and d ‐dimer levels at admission were significantly increased in the critical group. The all‐cause mortality rate among cases of COVID‐19 combined with CVD was 19.26%; the proportion of coronary heart disease and heart failure was significantly higher in deceased patients than in recovered patients. High body mass index, previous history of coronary heart disease, lactic acid accumulation, and a decrease in the partial pressure of oxygen were associated with death. Conclusions All‐cause mortality in patients with COVID‐19 with CVD in hospitals is high. The high neutrophil/lymphocyte ratio may be a predictor of critical patients. Overweight/obesity combined with coronary heart disease, severe hypoxia, and lactic acid accumulation resulting from respiratory failure are related to poor outcomes. Registration URL: https://www.chictr.org.cn ; Unique identifier: ChiCTR2000029865.

We present the design of a power management integrated circuit that processes harvested energy from radio frequency waves and piezoelectric vibrations. The rectification of piezoelectric and RF sources has a power conversion efficiency (PCE) of 87.73% and 74.70%, respectively. The asynchronous and microcontroller-less integrated circuit (IC) is designed to be low power, so the bulk of the harvested energy goes to three loads. The output peak powers of 111 &mu, W, 156 &mu, W, and 128 &mu, W will be sufficient to run small devices for RF communication systems.

In this paper, we proposed a novel reversible data hiding method in encrypted image (RDHEI), which is based on the compression of pixel differences. In the proposed method, at the content owner&rsquo, side the image is divided into non-overlapping blocks, and a block-level image encryption scheme is used to generate the encrypted image, which partially retains spatial correlation in the blocks. Due to the spatial correlation, in each block the pixels are highly likely to be similar. Therefore, the pixel differences in all blocks are concentrated in a small range and can be compressed. By the compression of pixel differences, the data hider can vacate the room to accommodate secret data in the encrypted image without losing information. At the receiver&rsquo, s side, the receiver can obtain secret data or retrieve the original image using different keys with no error. The experimental results demonstrate that, compared with existing methods, the proposed method can achieve a higher capacity and visual quality.

Highlights • Symptoms of anxiety were more prevalent among farmer, people staying out of Beijing during the epidemic, poor sleep, and those who did not exercise. • The status of anxiety symptoms were significantly higher than usually, but it were lower than that during Ebola, influenza A and SARS outbreak. • Knowledge and perceptions of COVID-19, physical activity, and sleeping time are the main factors affecting anxiety., Background The outbreak of COVID-19 poses a challenge to psychological resilience. The aim of this study was to evaluate the prevalence of anxiety and identify risk and protective factors associated with the presence of anxiety symptoms in the face of COVID-19 among adults. Methods A cross-sectional online survey was conducted in adults from March 2nd to March 16th 2020. The self-rating anxiety scale (SAS) was used to measure the status of anxiety. Unconditional multivariate logistic regression was performed to identify the factors associated with anxiety. Results Among the 7144 respondents, 9.3% met the criteria for anxiety risk based on the SAS. Symptoms of anxiety were more prevalent among farmer (OR=1.43, 95%CI: 1.03-1.99), respondents lived in urban out of Beijing during the COVID-19 outbreak (OR=1.73, 95%CI: 1.14-2.63), and slept less than six hours per day (OR=2.64, 95%CI: 1.96-3.57). Compared to participants who didn’t exercise, a lower risk of anxiety was observed in those exercised 30-60 minutes/day (OR=0.62, 95%CI: 0.41-0.94) and more than 60 minutes/day (OR=0.57, 95%CI: 0.37-0.88). And compared with participants whose knowledge and perceptions of COVID-9 scores in lower quartile, the OR (95%CI) for the second, third and upper quartile were 0.58 (0.46, 0.73), 0.48 (0.37, 0.61) and 0.42(0.33, 0.52), respectively. Limitations No diagnostic interview for mental disorders was administered in the original studies limiting analysis of sensitivity and specificity of the Swahili PHQ-9. Conclusion There was a high level of anxiety in the face of COVID-19 among adults. The results point to characteristics of adults in particular need for attention to anxiety and suggest possible targets for intervention such as strengthening of physical activity and knowledge and perceptions of COVID-19.

PurposeThis study investigated the KAP towards COVID-19 and their influencing factors among primary and middle school students during the self-quarantine period in Beijing.MethodsThis was a cross-sectional study among students from 18 primary and middle schools in Beijing during March 2020. Stratified cluster sampling was conducted. Demographic and KAP-related COVID-19 information was collected through an online questionnaire. The influencing factors were analyzed by multivariable logistic regression.ResultsA total of 7,377 students were included. The overall correct rate for COVID-19 knowledge was 74.1%, while only 31.5% and 40.5% could identify the high-risk places of cross-infection and warning body temperature. Although 94.5% of respondents believed the epidemic could be controlled, over 50% expressed various concerns about the epidemic. The compliance rates for basic preventing behaviors were all over 80%, while those for “rational and effective ventilation” (39.2%) and “dinning separately” (38.6%) were low. The KAP levels were significantly differed according to various school categories of students. The COVID-19 knowledge (OR= 3.309, 95% CI: 2.921, 3.748) and attitude (OR=1.145, 95% CI: 1.003, 1.308) were associated with preventive practices. Besides, female, urban students, those with a healthy lifestyle, and those with the willingness to engage in healthcare tended to have better preventive practices.ConclusionMost students in Beijing hold a high level of knowledge, optimistic attitudes and have appropriate practices towards COVID-19. However, targeted interventions are still necessary, especially for students with high-risk characteristics.Implications and contributionsThe performance and the potential factors of COVID-19-related knowledge, attitudes and practices (KAP) among students in primary and middle schools is still unclear.This study investigates the characteristics and the level of KAP among students. The results of the study may contribute to the targeted education and interventions for students.

Background To promote the integration of medical resources, Beijing has built medical alliances since 2012, but this reform has not changed the state of disordered medical treatment. Patients are still willing to go to high-level hospitals for medical treatment. What causes patients to prefer high-level hospitals? To explore the reasons behind this preference for high-level medical treatment among patients and to guide patients to make visits in an orderly manner, we conducted this study and compared patients’ satisfaction with hospitals of different levels in the context of medical resource integration. Methods This study conducted a questionnaire survey among 1250 patients who were selected in 18 medical alliances in Beijing from October to December 2016. The study type is a comparative study based on cross-sectional data. Patient satisfaction was the main outcome, and descriptive analysis, chi-square tests, nonparametric tests and binary logistic regression analysis were used. The level of statistical significance was set at p Results The overall satisfaction score of the medical alliances was 3.375, and the satisfaction scores of core hospitals and cooperative hospitals were 2.77 and 3.07, respectively. The overall patient satisfaction rate was 44.62%, and the satisfaction rates of core hospitals and cooperative hospitals were 34.34 and 50.43%, respectively. The type of hospital and understanding of medical alliance policy were the factors associated with patient satisfaction with the medical alliance. Conclusions The patients’ satisfaction with cooperative hospitals was higher than their satisfaction with core hospitals. Although the patients were more satisfied with the service attitude of the cooperative hospitals, they still preferred core hospitals due to their higher expectations for their medical treatment and greater trust in their medical technology. It is necessary to explore the establishment of closed medical alliances under the unified management of human and financial resources to enable medical alliances to become a community of common interests and provide integrated medical services for patients.

This study aims to evaluate the safety and analgesic efficacy of pre-mixed nitrous oxide/oxygen mixture treatment of pain induced by dressing change for perianal abscess.This protocol is a randomized, double-blind, placebo-controlled trial.This study will be implemented in the Hospital of Traditional Chinese Medicine. Subjects enrolled in this study are hospitalized patients who suffered from moderate to severe pain due to dressing change after incision and drainage. Two hundred patients will be selected and randomly assigned to either an intervention or a control group. The intervention group will get routine pain treatment plus pre-mixed nitrous oxide/oxygen mixture treatment and the control group will be treated with routine pain management plus medical air treatment. All these patients, medical staff and investigators are blind to the nature of the gas in each cylinder, which is randomized. Data will be collected at baseline (T0), 5 min (T1) after the starting of intervention and 5 min post intervention (T2) for each group. The primary outcome is the level of pain relief at T1 and T2. The secondary outcomes cover physiological parameters, adverse events, satisfaction of patients and health professionals and the acceptance from patients.Results of this study will be discussed and the safety and effect of nitrous oxide/oxygen treatment of pain induced by dressing change will be proven.When the finding of this study has an active effect on the treatment of pain caused by dressing change, it may provide more options for nursing staff to choose nurse-led analgesia techniques and then improving the level and quality of pain care as well as patients' overall satisfaction with the Anorectal Department in China.目的: 本研究旨在评估肛周脓肿术后换药引起疼痛时的预混合一氧化二氮/氧气混合物治疗的安全性和镇痛效果。 设计: 该方案是一项随机、双盲、安慰剂对照试验。 方法: 本研究将在中医医院实施。本研究的受试者是因切开引流后换药而引起中度至重度疼痛的住院患者。共计200名患者,这些患者将被随机分配到干预组或对照组。干预组接受常规疼痛治疗加上预混合一氧化二氮/氧气混合物治疗,而对照组主要接受常规疼痛治疗和医用气体治疗。所有患者、医务人员和调查人员都不清楚气瓶中气体的性质,并且,这些气瓶都是随机分配。应分别在基线(T0)、干预开始后5分钟(T1)和干预结束后5分钟(T2)收集数据。主要疼痛缓解结果见T1和T2。次要结果包括生理参数、不良事件、患者和卫生专业人员的满意度以及患者的接受程度等。 讨论: 将讨论这项研究的结果,并对一氧化二氮/氧气治疗换药引起的疼痛的安全性和效果进行证明。 影响: 如果本研究的调查结果显示,治疗过程中,换药引起的疼痛可带来积极影响,则可为护理人员选择镇痛技术,并且所选择的阵痛技术将由护士主导,从而提高我国肛肠科疼痛护理的水平和质量,提高患者的整体满意度,并可为患者提供更多选择。.

The Beijing-Tianjin-Hebei (BTH) region in China suffers from heavy air pollution, especially in heating period. SO2 and NO2 are two of the key primary gaseous pollutants emitted by coal burning. The increase in air pollution caused by heating in the south-central part of the BTH region is higher than that in the northern part. And the distribution of SO2 and NO2 increment has significant differences. In this work, SO2 and NO2 emissions over the BTH region are determined using an adaptive “nudging” constrained method and a variational processing technique based on Ozone Monitoring Instrument (OMI) satellite data and surface measurement data collected in 2015. The application of the method can provide reliable, up-to-date and high-resolution mapping of sources of SO2 and NO2 emissions. These SO2 and NO2 emissions reflect the spatial differences in point and area sources in urban agglomerations and rural areas under different meteorological conditions during the non-heating and heating seasons. The intensity and influence of SO2 and NO2 emissions, particularly those of SO2, are significantly greater during the heating season than those during the non-heating season. Winter increases in SO2 emissions in the northern areas of the BTH region are larger than those in the southern part. In addition, significant increases in SO2 emissions occur mainly in suburban and rural areas, while those of NO2 emissions mainly occur in urban agglomerations. In the major urban areas, where coal has been replaced by natural gas or electric power for heating, winter heating causes much smaller increases in SO2 emissions than in other areas. The large amounts of bulk coal consumption in the suburban and rural areas could cause significant regional air pollution. Clear increases in SO2 and NO2 emissions in winter occur along a belt from southern Beijing to Langfang, Baoding, Shijiazhuang and Xingtai, which is consistent with a special “quasi-steady” air pollutant transport belt in the region. All above results show that the adaptive “nudging” constrained emission method could be an effective tool for air pollution control during certain seasons.

Images with resolution beyond the diffraction limit (superresolution) were obtained by combining conventional microscopy with microscale microsphere, which provides a new possibility for real-time optical superresolution imaging. Here, we propose a method to manipulate the microsphere by attaching it to a probe with optical glue. The optical simulation has been performed to demonstrate the influence of probe on superresolution image. The probe was fixed on a translation stage which was regarded as a robotic manipulator with four degrees of freedom. The kinematic analysis of the robotic manipulator was studied for manipulation strategy of microsphere. The force analysis has been carried out to analyze the possibility of microsphere detach from probe. To improve imaging quality, the microsphere was immersed in liquid medium and the resolution of 130 nm (∼λ/4.5) was achieved. In addition, by scanning the microsphere at the speed of 5 μm/s over the sample surface and stitching the recorded images, the superresolution image of large continuous area was achieved. This work had important practical significance for manipulating microsphere and optical superresolution imaging.

Concerns over air quality reduction resulting from burning fossil fuels have driven the development of clean and renewable energy sources. Supercapacitors, batteries and solar cells serve as eco-friendly energy storage and conversion systems vitally important for the sustainable development of human society. However, many diverse elements influence the performance of energy storage and conversion systems. The overall efficiency of systems depends on the specific structure and properties of incorporated functional materials. Carbon materials, such as graphene, are especially promising for materials development in the energy storage and conversion fields. Graphene, a two-dimensional (2D) carbon material only a single atom thick, has massless Dirac fermions (electron transport is governed by Dirac's equation), displays outstanding electrical conductivity, superior thermal conductivity and excellent mechanical properties. 2D free-standing graphene films and powders have paved the way for promising energy applications. Recently, much effort has been spent trying to improve the number of active sites in electrode materials within 3D network/aerogel structures derived from graphene. This is because graphene aerogels are promising materials for energy systems due to their porous hierarchical structure which affords rapid electron/ion transport, superior chemical and physical stability, and good cycle performance. This review aims to summarize the synthetic methods, mechanistic aspects, and energy storage and conversion applications of novel 3D network graphene, graphene derivatives and graphene-based materials. Areas of application include supercapacitors, Li-batteries, H2 and thermal energy storage, fuel cells and solar cells.

Rainfed lowland rice is the most popular choice for rice cultivation in Sarawak, Borneo. In general, rice production in Sarawak consists of seven phases, namely, preparing land, establishing crop, transplanting, managing crop, harvesting, post-harvesting, and milling. Most farmers in Sarawak depend on indigenous knowledge and experience for rice cultivation. In this paper, an improved fuzzy failure mode and effect analysis (FMEA) with genetic algorithm-based design of fuzzy membership functions and monotone fuzzy rules relabeling is employed as a knowledge-based tool for risk analysis and assessment pertaining to rice production in Sarawak. The specific focus is on issues related to the environment as well as health and safety of farmers and consumers. With the support from the Sarawak Government, we analyze useful data and information pertaining to various rice fields from experienced farmers to develop the fuzzy FMEA model. Specifically, we develop fuzzy FMEA to inculcate the best practices for farmers to improve yield and enhance food safety. Through this paper, we identify that musculoskeletal disorders due to bad postures is the most noticeable occupational health hazard; as a result, new techniques and tools are invented and introduced to mitigate this risk. In summary, this is a new attempt to implement a quality and risk assessment tool that contributes toward enhancing rice productivity in Sarawak, and modernizing the local agricultural sector.

Ellipsometry is an important metrology tool in a plethora of industries. The measurement accuracy can be significantly affected by the existence of Poisson-Gaussian mixed noise. This paper quantifies the induced error on normalized Mueller matrix measurements through statistical analysis. A method is then proposed to mitigate the effects of Poisson-Gaussian noise in spectroscopic ellipsometry signal demodulation, based on maximum likelihood estimation. The noise is characterized through experiments on an in-house setup. The improved performance of dimension reconstruction from the proposed method is demonstrated through simulations.

// Xin-Lin Wu 1 , Zhi-Wen Yang 1 , Li He 1 , Pei-De Dong 1 , Ming-Xing Hou 1 , Xing-Kai Meng 2 , Hai-Ping Zhao 2 , Zhao-Yang Wang 1 , Feng Wang 1 , Baoluri 3, 4 , Wurenqimuge 3, 4 , Agudamu 1 , Yong-Feng Jia 3, 4 and Lin Shi 3, 4 1 Department of Gastrointestinal Surgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, Inner Mongolian Autonomous Region, China 2 Department of Hepatobiliary Surgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, Inner Mongolian Autonomous Region, China 3 Department of Pathology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, Inner Mongolian Autonomous Region, China 4 Institute of Pathology and Pathophysiology, Inner Mongolia Medical University, Hohhot 010059, Inner Mongolian Autonomous Region, China Correspondence to: Lin Shi, email: wuxinlin@126.com Keywords: colorectal cancer, RRS1, angiogenesis, cell cycle arrest, apoptosis Received: March 06, 2017 Accepted: August 26, 2017 Published: September 15, 2017 ABSTRACT Colorectal cancer (CRC) is one of the most common malignancies worldwide. Ribosome biogenesis regulatory protein homolog (RRS1) is an essential factor involved in ribosome biogenesis, while its role in CRC remains largely unclear. Here, we found that RRS1 expression was significantly higher in CRC tissues compared with adjacent normal tissues. RRS1 High expression also predicted poor overall survival of CRC patients. Knockdown of RRS1 induced the G2/M cell cycle arrest, apoptosis and suppressed the proliferation of RKO and HCT-116 CRC cells. Furthermore, angiogenesis was also reduced in CRC cells after RRS1 knockdown. In addition, suppression of RRS1 blunted the tumor formation of CRC cells in nude mice. At the molecular level, silencing of RRS1 decreased the expression of M-phase inducer phosphatase 3 (CDC25C), Cyclin-dependent kinase 1 (CDK1), antigen KI-67 (KI67) and increased the protein level of cyclin-dependent kinase inhibitor 1 (CDKN1A) and tumor suppressor p53 (p53). Taken together, our findings provide evidence that RRS1 may promote the development of colon cancer. Therefore, targeting RRS1 may be a promising therapeutic strategy for CRC patients.

The fuzzy inference system (FIS) is useful for developing an improved Risk Priority Number (RPN) model for risk evaluation in failure mode and effect analysis (FMEA). A general FIS_RPN model considers three risk factors, i.e., severity, occurrence, and detection, as the inputs and produces an FIS_RPN score as the output. At present, there are two issues pertaining to practical implementation of classical FIS_RPN models as follows: 1) the fulfillment of the monotonicity property between the FIS_RPN score (output) and the risk factors (inputs); and 2) difficulty in obtaining a complete and monotone fuzzy rule base. The aim of this paper is to propose a new analytical interval FIS_RPN model to solve the aforementioned issues. Specifically, the incomplete and potentially nonmonotone fuzzy rules provided by FMEA users are transformed into a set of interval-valued fuzzy rules in order to produce an interval FIS_RPN model. The interval FIS_RPN model aggregates a set of risk ratings and produces a risk interval, which is useful for risk evaluation and prioritization. Properties of the proposed interval FIS_RPN model are analyzed mathematically. An FMEA procedure that incorporates the proposed interval FIS_RPN model is devised. A case study with real information from a semiconductor company is conducted to evaluate the usefulness of the proposed model. The experimental results indicate that the interval FIS_RPN model is able to appropriately rank the failure modes, even when the fuzzy rules provided by FMEA users are incomplete and nonmonotone.

In this paper, a self-organizing map (SOM) neural network is used to visualize corrective actions of failure modes and effects analysis (FMEA). SOM is a popular unsupervised neural network model that aims to produce a low-dimensional map (typically a two-dimensional map) for visualizing high-dimensional data. With regards to FMEA, it is a popular methodology to identify potential failure modes for a product or a process, to assess the risk associated with those failure modes, also, to identify and carry out corrective actions to address the most serious concerns. Despite the popularity of FMEA in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. The use of SOM in FMEA is new. In this paper, corrective actions in FMEA are described in their severity, occurrence and detect scores. SOM is then used as a visualization aid for FMEA users to see the relationship among corrective actions via a map. Color information from the SOM map is then included to the FMEA worksheet for better visualization. In addition, a Risk Priority Number Interval is used to allow corrective actions to be evaluated and ordered in groups. Such approach provides a quick and easily understandable framework to elucidate important information from a complex FMEA worksheet; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is two-fold, viz., the use of SOM as an effective neural network learning paradigm to facilitate FMEA implementations, and the use of a computational visualization approach to tackle the two well-known shortcomings of FMEA.

A new approach that adopts the use of fuzzy adaptive resonance theory (ART) neural network in estimating high-performance concrete (HPC) mix proportion from experimental data is devised. The proposed model receives a set of desired concrete performances, searches for a set of mix proportions that is near to the desired concrete performances, classifies the mix proportions into clusters, measures the similarity between performances of deduced clusters with desired performances, and deduces a mix proportion. The proposed model was used to estimate the mix proportions of five batches of concrete based on the performance criteria of 7th and 28th day compressive strengths. The generated mix proportions were used in an experimental work and the errors were within 13% for 7th compressive strength; and 7% for the 28th day compressive strength, signifying the reliability of the fuzzy ART-based model in estimating the mix proportion of HPC. This article contributes to an alternative method of mix proportion estimation of HPC by avoiding the use of complicated function approximation techniques.

The increasing external pressure to establish sustainable operations has forced firms to embrace approaches such as sustainable manufacturing through product recovery. To improve the performance of product recovery, we studied a decision-making problem involving the selection between end-of-life product remanufacturing and dismantling. A quality-dependent multi-objective optimization model was developed and validated to identify the optimal or near optimal product recovery solution that best balances the economic, environmental and societal performances of product recovery for sustainability. We also investigated how the quality level, recovery cost and retail price impact the product recovery decision under different decision makers’ preferences. Experiments on a case of an automobile engine model demonstrated the multi-objective optimization model's effectiveness to achieve satisfactory recovery solution. The results also provide insights for product recovery practices, which can assist firms in adapting their practices to meet the challenges of sustainability.

Purpose The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model. Design/methodology/approach The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month’s variables) and forecasting (based on the previous three months’ variables) phytoplankton dynamics in a Macau freshwater reservoir, MSR, which has experienced cyanobacterial blooms in recent years. There were 15 environmental parameters, including pH, SiO2, alkalinity, bicarbonate (HCO3−), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate (NO3−), orthophosphate (PO43−), total phosphorus (TP), suspended solids (SS) and total organic carbon (TOC) selected from the correlation analysis, with eight years (2001-2008) of data for training, and the most recent three years (2009-2011) for testing. Findings For both accuracy performance and generalized performance, the ANN, GA-SVM and GA-RVM had similar predictive powers of R2 of 0.73-0.75. However, whereas ANN and GA-RVM models showed very similar forecast performances, GA-SVM models had better forecast performances of R2 (0.862), RMSE (0.266) and MAE (0.0710) with the respective parameters of 0.987, 0.161 and 0.032 optimized using GA. Originality/value This is the first application of GA-SVM and GA-RVM models for predicting and forecasting algal bloom in freshwater reservoirs. GA-SVM was shown to be an effective new way for monitoring algal bloom problem in water resources.

Reverse logistics has emerged as a promising strategy for enhancing environmental sustainability through remanufacturing, reusing, or recycling used components. It is crucial to pursue quality-driven decision-making for component recovery because quality is a dominant factor for component salvage value and its recoverability. To maximise the profit from component recovery, a quality-driven decision model was proposed in this study. Remaining useful life (RUL) was utilised as a measure of quality in the proposed model, where conditional RUL distribution was predicted by utilising both the failure data and condition monitoring data based on a proportional hazard model. Under RUL uncertainty, an interval decision-making approach was developed to suggest recovery strategies for the decision-makers to identify a satisfactory solution according to their risk preferences. Compared to the existing approaches for quality-driven recovery decision-making based on RUL prediction, this work provides a more accurate an...

In this study, a novel vibration isolator is presented. The presented isolator possesses the controllable stiffness and can be employed in vibration isolation at a low-resonance frequency. The controllable stiffness of the isolator is obtained by manipulating the negative stiffness-based current in a system with a positive and a negative stiffness in parallel. By using an electromagnetic device consisting of permanent magnetic rings and coils, the designed isolator shows that the stiffness can be manipulated as needed and the operational stiffness range is large in vibration isolation. We experimentally demonstrate that the modeling of controllable stiffness and the approximation of the negative stiffness expressions are effective for controlling the resonance frequency and the transmissibility of the vibration isolation system, enhancing applications such as warship stealth technology, vehicles suspension system, and active vibration isolator.

With rapid economic growth, road transport is contributing substantial adverse effects on urban air quality, especially in densely populated cities with high growth rate of GDP per capita, such as Macau. A high spatial–temporal resolution road traffic emission inventory is essential for assessment of environmental stresses imposed by local vehicle movements. To improve the accuracy and temporal–spatial resolution for emission inventory, through a bottom-up approach, link-based road traffic emission inventory with a spatial resolution of 0.1 km ∗ 0.1 km and a temporal resolution of 1 h for Macau in 2014 was developed by using a traffic model (VISUM), a road traffic emission model (TREM), the Geographic Information System (GIS), and the most up-to-date information available. Results show that the total annual emissions of CO, CO2, PM, NOX, and VOC in 2014 were 14,770, 413,099, 69, 1151, and 2945 tons, respectively. The estimated fuel consumption agreed well also with the statistical fuel consumption in Macau. Meanwhile, analysis of 3 scenarios on changes of road traffic emissions due to the operation of a light railway transit (LRT) system, variation on share of diesel, electric, and gasoline within the vehicle fleet, and replacement of vehicles with ones of Euro 5 and Euro 6 emission standards was carried out. This study provides a solid framework for developing high spatial–temporal resolution emission inventories for other densely populated cities of small area.

Glucagon-like peptide 2 (GLP-2), a member of Glucagon peptide family involved in regulating energy metabolism, can be produced and secreted by preproglucagonergic (PPG) neurons in the brain. GLP-2 reduces food intake but at which brain sites GLP-2 exerts its feeding-suppress effects are still unclear. In this study, we used the stereological microinjection technique and behavioral test to examine the functions of locally delivered GLP-2 into DMH on feeding behavior. We compared effects of different concentration of GLP-2 on the food intake behavior in free-feeding rats and fasted-refeeding rats. We found that GLP-2 inhibited food intake in fasted rats after a short-term intervention in a dose-dependent manner. Importantly, the effects of locally delivered GLP-2 can be blocked by specific GLP-1 receptor antagonist Exendin(9-39), but not the melanocortin-4 receptor antagonist SHU9119, indicating the involvement of specificity of GLP-2 signaling in regulating the feeding behavior. Taken together, our data revealed that GLP-2 peptide pharmacologically inhibited food intake in DMH and this effect could be blocked functionally by Exendin(9-39).

To maintain the monotonicity property of a fuzzy inference system, a monotonically ordered and complete set of fuzzy rules is necessary. However, monotonically ordered fuzzy rules are not always available, e.g., errors in human judgments lead to nonmonotone fuzzy rules. The focus of this paper is on a new monotone fuzzy rule relabeling (MFRR) method that is able to relabel a set of nonmonotone fuzzy rules to meet the monotonicity property with reduced computation. Unlike the brute-force approach, which is susceptible to the combinatorial explosion problem, the proposed MFRR method explores within a reduced search space to find the solutions, therefore decreasing the computational requirements. The usefulness of the proposed method in undertaking failure mode and effect analysis problems is demonstrated using publicly available information. The results indicate that the MFRR method can produce optimal solutions with reduced computational time.

Display Omitted A new RPN model based on Perceptual Computing is proposed.Linguistic uncertainties pertaining to group decision making in FMEA are considered.Providing RPN scores in both numerals and linguistic words.A better insight pertaining to the risk of a failure mode is provided.A case study related to edible bird nest farming in Borneo Island is reported. A failure mode and effect analysis (FMEA) procedure that incorporates a novel Perceptual Computing (Per-C)based Risk Priority Number (RPN) model is proposed in this paper. The proposed model considers linguistic uncertainties and vagueness of words, because it is more natural to use words, instead of numerals, for an FMEA user to express his/her knowledge when he/she provides an assessment. Therefore, it is important to consider the inherited uncertainties in words used by humans for assessment as an additional risk factor in the entire FMEA reasoning process. As such, we propose to use Per-C to analyze the uncertainties in words provided by different FMEA users. There are three potential sources of risks. Firstly, the risk factors of Severity (S), Occurrence (O), and Detection (D) are graded using words by each FMEA user, and indicated as interval type-2 fuzzy sets (IT2FSs). Secondly, the relative importance of S, O, and D are reflected by the weights given by each FMEA user in words, which are indicated as IT2FSs. Thirdly, the expertise level of each FMEA user is reflected by words, which are expressed as IT2FSs too. The proposed Per-C-RPN model allows these three sources of risks from each FMEA user to be considered and combined in terms of IT2FSs. A case study related to edible bird nest farming in Borneo Island is reported. The results indicate the effectiveness of the proposed model. In summary, this paper contributes to a new Per-C-RPN model that utilizes imprecise assessment grades pertaining to group decision making in FMEA.

Background Our previous study indicates that IL-37 plays a critical role in both atherosclerosis and arterial calcification. However, whether IL-37 concentrations are significantly changed in patients with arterial calcification has not yet been investigated. Methods Anterior tibial arterial wall specimens were obtained from 8 patients with type 2 diabetes mellitus and 8 patients who experienced a traffic accident. IL-37 expression was measured by immunohistochemistry in the calcified and the normal samples. In addition, plasma IL-37 concentrations were measured in 75 patients with coronary artery calcification (CAC) and 50 patients without coronary artery calcification (NCAC). Results High concentrations of IL-37 were detected in calcified samples, whereas low concentrations of IL-37 were detected in the normal arteries. Macrophages and vascular smooth muscle cells were the main source of IL-37. P lasma IL-37 concentrations were significantly increased in CAC patients compared with NCAC patients. A correlation analysis showed that IL-37 was positively correlated with age, fasting glucose, alkaline phosphatase, IL-6, TNF-α, C-reactive protein and Agatston scores. Binary logistic regression analyses demonstrated that fasting glucose and IL-37 were independently associated with the presence of CAC. Conclusions Increased IL-37 concentrations are associated with the onset of arterial calcification.

Propose a novel hybrid approach that involves three phases.Consider quantitative and qualitative criteria to evaluate EOL product performance.Establish an optimization model with conflicting objectives and constraints.Estimate general quality condition and potential recovery value of EOL product.The numerical example illustrates effectiveness and superiority of the approach. Performance evaluation and selection of end-of-life products have emerged as crucial issues for firms looking to adopt a product recovery strategy to achieve environmental responsibility while still meeting profit goals within the reverse supply chain. However, there is still a lack of a comprehensive methodology to address the issues because of the variety of decision factors involved and the inherent uncertainties associated with them. This research proposes and investigates a multiphase hybrid approach to identify the recoverable products that best meet the criteria set (such as technical feasibility, economic benefit, and environmental effect). The proposed method simultaneously considers multiple and conflicting goals, practical constraints, and information uncertainty. First, fuzzy logic and probability theory are utilized to estimate the quality condition of used products and subsequently conduct the cost-benefit analysis based on the product life-cycle information. Then, the modified preference ranking organization method for enrichment evaluations is used as a multi-criteria decision-making tool to quantifyand aggregate the linguistic evaluation items into recovery preference factors. Further, all the qualitative and quantitative data are incorporated in a goal programming model to achieve a compromise and satisfied solution. We present a numerical example to illustrate the effectiveness and superiority of the proposed approach. The results indicate that the proposed approach can provide strong and flexible support for product recovery decision-making within the reverse supply chain.

This paper deals with the construction of the foundation pit of the East Station of Meixi Lake, belonging to Reme Area underground supporting transport system of Changsha, China. A description is given of the deformation monitoring of the open excavation. During site operations, the construction process monitoring data are continuously analyzed. It has been found that the deformations induced by the excavation of the foundation pit have some influence on the surrounding buildings. Monitoring of both horizontal pile-top displacement and ground surface settlements are extremely important to ensure safety of the neighboring constructions. Based on the monitoring information gathered during construction, engineers should adjust construction parameters to optimize the design at any moment, and adopt effective treatment measures to ensure the follow-up constructions can safely and successfully complete.

Fuzzy numbers are ranked using Dempster-Shafer theory (DST) with fuzzy targets.Fuzzy targets are used to reflect human viewpoints and avoid biases in fuzzy ranking.Fuzzy numbers are ranked with different viewpoints and with a combined viewpoint.Transferable Belief Model is adopted to elaborate DST.Benchmark examples are adopted for experimental studies and positive results are obtained. In this paper, an extended ranking method for fuzzy numbers, which is a synthesis of fuzzy targets and the Dempster-Shafer Theory (DST) of evidence, is devised. The use of fuzzy targets to reflect human viewpoints in fuzzy ranking is not new. However, different fuzzy targets can lead to contradictory fuzzy ranking results; making it difficult to reach a final decision. In this paper, the results from different viewpoints are treated as different sources of evidence, and Murphy's combination rule is used to aggregate the fuzzy ranking results. DST allows fuzzy numbers to be compared and ranked while preserving their uncertain and imprecise characteristics. In addition, a hybrid method consisting of fuzzy targets and DST with the Transferable Belief Model is formulated, which fulfils a number of important ordering properties. A series of empirical experiments with benchmark examples has been conducted and the experimental results clearly indicate the usefulness of the proposed method.

There is a strong need for recovery decision-making for end-of-life (EOL) products to satisfy sustainable manufacturing requirements. This paper develops and tests a profit maximisation model by simultaneously integrating recovery option selection and disassembly planning. The proposed model considers the quality of EOL components. This paper utilises an integrated method of multi-target reverse recursion and partial topological sorting to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling. In order to determine recovery options, disassembly level and disassembly sequence simultaneously, this paper develops an improved co-evolutionary algorithm (ICA) to search for an optimal EOL solution. The proposed algorithm adopts the evolutionary mechanism of localised interaction and endosymbiotic competition. Further, an advanced local search operator is introduced to improve convergence performance, and a global disturbance strategy is also suggested to prevent premat...

High-static-low-dynamic stiffness (HSLDS) or quasi-zero stiffness (QZS) has attracted a great deal of attention in vibration isolation research. However, most existing HSLDS isolators have a nonlinear stiffness, which may lead to nonlinear behavior under a large excitation. To improve the isolation performance under violent vibration conditions, a tunable QZS isolator with a long working stroke is proposed in this paper. The isolator is developed by combining a linear electromagnetic spring (LES) in parallel with a conventional linear isolation system. The LES contains three toroidal coils arranged coaxially with a ring magnet. By controlling the current excited to the coils, the LES can generate a linear negative stiffness that balances the positive stiffness of the conventional system, thereby achieving a quasi-zero stiffness over the long stroke. The influence of the LES parameters on its stiffness is studied using the derived stiffness analysis model, and based on the results, a design procedure to increase the tunable stiffness range and extend the linear stroke is proposed. The dynamic response of the isolator is analyzed and experimentally verified. Finally, it turns out that the QZS isolator is suitable for large excitations and can be tuned online.

Background To improve the efficiency of the use of medical resources, China has implemented medical alliances (MAs) to implement a hierarchical diagnosis and treatment system. The willingness to undertake a first visit to primary care institutions (PCIs) is an important indicator of the effect of this system. Beijing has also built MAs since 2013, but to date, there have been few studies on the first visit to PCIs in Beijing. The purpose of this study is to analyze patients’ willingness to make their first visit to PCIs and its influencing factors to provide references for the realization of a hierarchical diagnosis and treatment system. Methods Two relatively different districts with large differences in resources in Beijing, D and F, were selected, and a self-reported questionnaire and convenience sampling method were applied. A cross-sectional survey was administered to 1221 patients of MAs. The chi-square test and binary logistic regression were used to analyze the influencing factors of patients’ willingness to undertake a first visit to a PCI. Results Fewer patients in District D received medical alliance services (44.42%) than those in District F (59.25%), but patients in District D had a higher degree of satisfaction with the services they received (72.04%) than those in District F (28.96%). Patients in District D had a higher willingness to undertake a first visit (64.00%) than those in District F (58.18%). Patients of an older age, low medical expenses, participation in urban employees’ basic medical insurance, a high understanding of MAs and high satisfaction with medical services were indicators of being more willing to choose primary care institutions for their first visit. Conclusions The different medical resources and MA constructions in the two districts have resulted in a difference between the two districts in terms of the willingness of individuals to make their first visit to PCIs. Strengthening the service capabilities of PCIs remains a priority. The government should propose solutions to solve the problems encountered in practice and actively promote the realization of MAs and hierarchical diagnosis and treatment. Electronic supplementary material The online version of this article (10.1186/s12913-019-4184-0) contains supplementary material, which is available to authorized users.

The circular RNA ciRS-7 has been reported to be involved in the pathogenesis of various tumors, including gastric and colorectal cancer. However, the role of ciRS-7 in esophageal squamous cell carcinoma (ESCC) remains unsolved. In this study, we found that the ciRS-7 expression was significantly upregulated in ESCC cancer tissues compared with matched normal tissues and associated with poor patient survival. Overexpression of ciRS-7 abrogated the tumor-suppressive roles of miR-7 including cell proliferation, migration and invasion in vitro as well as tumor growth and lung metastasis in vivo. Mechanistically, ciRS-7 functioned as the sponge of miR-7 and reactivated its downstream HOXB13-mediated NF-κB/p65 pathway. Conclusively, our findings demonstrate how ciRS-7 induces malignant progression of ESCC and that ciRS-7 may act as a novel prognostic marker and therapeutic target for this lethal disease.

Super-resolution images beyond classic diffraction limit can be generated by microspheres with appropriate refractive index. This paper presented a method of controlling liquid-immersed microsphere for achieving images with a resolution beyond the diffraction limit. The microsphere was attached to the probe using optical glue. The optical simulation has been performed to demonstrate the influence of probe on super-resolution image. To improve imaging quality, the microsphere was immersed in liquid medium. The probe was fixed in a translation stage which was regarded as a robotic manipulator with four degrees of freedoms (DOF). By scanning the submerged microsphere over the sample surface at the speed of $5\text{x}10^{-6}$ m/s, the super-resolution image of large continuous region was obtained. This work has developed a new alternative for real-time optical super-resolution imaging, and can be practically applied in multifarious fields of scientific research.

This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA.

The proposed Fuzzy peer assessment methodology. A new fuzzy peer assessment methodology is proposed.A synthesis of perceptual computing and a fuzzy ranking algorithm.Providing both crisp scores and recommendations.A case study in Universiti Malaysia Sarawak is reported.An illustrative example is adopted to detect possible free riders. In this paper, a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed. Instead of numerals, words are used in the evaluation process, in order to provide greater flexibility. The proposed methodology is a synthesis of perceptual computing (Per-C) and a fuzzy ranking algorithm. Per-C is adopted because it allows uncertainties of words to be considered in the evaluation process. Meanwhile, the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student's contribution in a group, and subsequently rank the student accordingly. A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students.

Biodiesel's composition has a significant effect on biodiesel’s main physical and chemical properties. Through the test and analysis of the composition and the physical & chemical properties of 5 types of biodiesel, this paper uses linear regression model and least square method to conduct a research on the correlation between biodiesel's physical and chemical properties such as density, cold filter plugging point (CFPP), cetane number and kinematic viscosity, biodiesel's FAME content and the number of double bonds in its UFAME, and establishes correlation functions for relevant indexes, providing a theoretical basis for rapid test of biodiesel’s physico-chemical performance index.

This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data of the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the stress increase in externally prestressed tendon, Δfps, is then reported. The contributions of this paper is two folds; (i) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (ii) it provides a novel solution for estimating the Δfps even without a complete physical knowledge of unbonded tendons. Copyright © 2015 Techno-Press, Ltd.

Background/Aims: Hypoxia is a basic pathological challenge that is associated with numerous cardiovascular disorders including aberrant cardiac remodeling. Transforming growth factor beta (TGF-β) signaling pathway plays a pivotal role in mediating cardiac fibroblast (CF) function and cardiac fibrosis. Recent data suggested that microRNA-101a (miR-101a) exerted anti-fibrotic effects in post-infarct cardiac remodeling and improved cardiac function. This study aimed to investigate the potential relationship between hypoxia, miR-101a and TGF-β signaling pathway in CFs. Methods and Results: Two weeks following coronary artery occlusion in rats, the expression levels of both TGFβ1 and TGFβRI were increased, but the expression of miR-101a was decreased at the site of the infarct and along its border. Cultured rat neonatal CFs treated with hypoxia were characterized by the up-regulation of TGFβ1 and TGFβRI and the down-regulation of miR-101a. Delivery of miR-101a mimics significantly suppressed the expression of TGFβRI and p-Smad 3, CF differentiation and collagen content of CFs. These anti-fibrotic effects were abrogated by co-transfection with AMO-miR-101a, an antisense inhibitor of miR-101a. The repression of TGFβRI, a target of miR-101a, was validated by luciferase reporter assays targeting the 3'UTR of TGFβRI. Additionally, we found that overexpression of miR-101a reversed the improved migration ability of CFs and further reduced CF proliferation caused by hypoxia. Conclusion: Our study illustrates that miR-101a exerts anti-fibrotic effects by targeting TGFβRI, suggesting that miR-101a plays a multi-faceted role in modulating TGF-β signaling pathway and cardiac fibrosis.

Principle component regression (PCR), artificial neural network (ANN), and their combination used as data-driven models were selected to apply in this study to predict (based on the current-month variables) and forecast (based on the last 3-month-ahead variables) the phytoplankton dynamics in Macau Main Storage Reservoir (MSR) that is experiencing algal bloom in recent years. The models used the comprehensive 8 years’ monthly water quality data for training and the most recent 3 years’ monthly data for testing. Twenty-four water quality variables including physical, chemical, and biological parameters were involved, and comparisons were made to select the best models that can be applied to MSR. Simulation results revealed that ANN has better accuracy and generalization performance in comparison with PCR both for the prediction and the forecasted model. Using principal component analysis (PCA) for the data, inputs did not show better performance for the ANN, implying that eliminating the uncorrelated variables do not increase the prediction capability for the adopted model. Globally, in contrast with previous studies showing that the hybrid model can handle both linear and nonlinear components of the problems well, the PCR-ANN in this study obtain no better improvement.

This paper proposed a set of measurement system based on the method of stick vibration mode and LabVIEW virtual instrument technology, which is used for measuring the young's modulus of loudspeaker spider. The inherent frequency and young's modulus of a cotton spider are measured by this system. After the young’s modulus is obtained, modal analysis is conducted on the sample by using finite element simulation method. The measured results and the simulation results are compared. The difference between two results is 0.4% which verify the correctness of the system.

Background: Atherosclerosis (AS) is an inflammatory and immune disease. Regulatory T cells (Tregs) suppress the activation of T cells and have been shown to play a protective role during the pathogenesis of AS. However, specific markers for Tregs are lacking. Recently, glycoprotein A repetitions predominant (GARP) was discovered as a specific marker of activated Tregs, and we therefore utilized GARP as a specific surface marker for Tregs in the current study. Methods: To assess whether GARP+ Tregs are downregulated in patients with acute coronary syndrome (ACS), we examined CD4+CD25+GARP+ T cell frequencies as well as their associated cytokines and suppressive function. Additionally, we compared GARP expression to that of FOXP3, which may be more sensitive as a marker of activated Tregs in patients with ACS. Results: Patients with ACS demonstrated a significant decrease in circulating CD4+CD25+GARP+ Tregs. Moreover, the suppressive function of Tregs and levels of related cytokines were also impaired in ACS patients compared to those with stable angina (SA) or normal coronary artery (NCA). Additionally, after TCR stimulation, peripheral blood mononuclear cells (PBMCs) from patients with ACS exhibited a decrease in CD4+CD25+GARP+ Tregs. Conclusions: These fnding indicate that circulating CD4+CD25+GARP+ Tregs are impaired in patients withACS. Thus, targeting GARP may promote the protective function of Tregs in ACS.

In order to efficiently extract the sample Mueller matrix by dual rotating-retarder ellipsometry, it is critical for the data reduction technique to achieve a minimal data processing burden while considering the ease of retarder control. In this paper, we propose an unevenly spaced sampling strategy to reach a globally optimal measurement matrix with minimum sampling points for continuous measurements. Taking into account the robustness to both systematic errors and detection noise, we develop multi-objective optimization models to identify the optimal unevenly spaced sampling points. A combined global search algorithm based on the multi-objective genetic algorithm is subsequently designed to solve our model. Finally, simulations and experiments are conducted to validate our approach as well as to provide near-optimal schemes for different design scenarios. The results demonstrate that significant improvement on error immunity performance can be achieved by applying an unevenly sampled measurement strategy compared to an evenly sampled one for our ellipsometer scenario.

TiO2/bentonite composites were prepared by a sol–gel method. X-ray diffraction measurements of the samples revealed that TiO2 had intercalated into the layers of bentonite and existed in anatase phase of TiO2. Acid red 3R was used as model compounds of textile dyes in this study. The effects of operational parameters, including TiO2 content, pH, as well as catalyst dosage, on photocatalytic degradation performance were examined. The experimental results showed that the removal rate of acid red 3R can be up to 96.7% under the following conditions: the dosage of TiO2/bentonite was 2 g/L, the content of TiO2 was 30%, the pH value was 3. And the photocatalytic reaction followed pseudo-first-order kinetics models.