Serum 25-hydroxyvitamin D3 is associated with advanced glycation end products (AGEs) measured as skin autofluorescence: The Rotterdam Study

Advanced glycation end products (AGEs) accumulate in tissues with aging and may influence age-related diseases. They can be estimated non-invasively by skin autofluorescence (SAF) using the AGE Reader™. Serum 25-hydroxyvitamin D3 (25(OH)D3) may inhibit AGEs accumulation through anti-oxidative and anti-inflammatory properties but evidence in humans is scarce. The objective was to investigate the association between serum 25(OH)D3 and SAF in the population-based cohort study. Serum 25(OH)D3 and other covariates were measured at baseline. SAF was measured on average 11.5 years later. Known risk factors for AGE accumulation such as higher age, BMI, and coffee intake, male sex, smoking, diabetes, and decreased renal function were measured at baseline. Linear regression models were adopted to explore the association between 25(OH)D3 and SAF with adjustment for confounders. Interaction terms were tested to identify effect modification. The study was conducted in the general community. 2746 community-dwelling participants (age ≥ 45 years) from the Rotterdam Study were included. Serum 25(OH)D3 inversely associated with SAF and explained 1.5% of the variance (unstandardized B = − 0.002 (95% CI[− 0.003, − 0.002]), standardized β = − 0.125), independently of known risk factors and medication intake. The association was present in both diabetics (B = − 0.004 (95% CI[− 0.008, − 0.001]), β = − 0.192) and non-diabetics (B = − 0.002 (95% CI[− 0.003, − 0.002]), β = − 0.122), both sexes, both smokers and non-smokers and in each RS subcohort. Serum 25(OH)D3 concentration was significantly and inversely associated with SAF measured prospectively, also after adjustment for known risk factors for high SAF and the number of medication used, but the causal chain is yet to be explored in future studies. Clinical Trial Registry (1) Netherlands National Trial Register: Trial ID: NTR6831 (http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=6831). (2) WHO International Clinical Trials Registry Platform: under shared catalogue number NTR6831 (www.who.int/ictrp/network/primary/en/)

Novel nickel foam with multiple microchannels as combustion reaction support for the self-heating methanol steam reforming microreactor

To improve hydrogen production performance of self-heating methanol steam reforming (MSR) microreactor, novel nickel foam with multiple microchannels was proposed as combustion reaction support. A wall temperature comparison of the methanol combustion microreactors with nickel foam catalyst support and particles catalyst support in the combustion reaction process was performed. According to the numerical simulation result of combustion reaction of nickel foam, the shape and size of multiple microchannels of nickel foam were determined. The laser processing was then used to fabricate the multiple microchannels of nickel foam. The experimental results show that the methanol combustion microreactor with nickel foam loaded with Pt catalyst exhibits similar wall temperature distribution with the methanol combustion microreactor with Pt/γ-Al2O3 particles reaction support. Compared with the nickel foam without a microchannel, the maximum temperature difference (ΔTmax) and the maximum temperature of nickel foam with multiple microchannels were decreased, respectively, by 57.8% and 33.8 °C when 1.1 mL/min methanol flow rate was used. Hydrogen production performance of the self-heating MSR microreactor using the nickel foam with multiple microchannels increased by about 21% when 430 °C reforming temperature and 4 mL/h methanol–water mixture flow rate were performed

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Structural design of self-thermal methanol steam reforming microreactor with porous combustion reaction support for hydrogen production

To replace the traditional electric heating mode and increase methanol steam reforming reaction performance in hydrogen production, methanol catalytic combustion was proposed as heat-supply mode for methanol steam reforming microreactor. In this study, the methanol catalytic combustion microreactor and self-thermal methanol steam reforming microreactor for hydrogen production were developed. Furthermore, the catalytic combustion reaction supports with different structures were designed. It was found that the developed self-thermal methanol steam reforming microreactor had better reaction performance. Compared with A-type, the △Tmax of C-type porous reaction support was decreased by 24.4 °C under 1.3 mL/min methanol injection rate. Moreover, methanol conversion and H2 flow rate of the self-thermal methanol steam reforming microreactor with C-type porous reaction support were increased by 15.2% under 10 mL/h methanol-water mixture injection rate and 340 °C self-thermal temperature. Meanwhile, the CO selectivity was decreased by 4.1%. This work provides a new structural design of the self-thermal methanol steam reforming microreactor for hydrogen production for the fuel cell

A survey on data fusion in internet of things

Internet of Things (IoT) aims to create a world that enables the interconnection and integration of things in physical world and cyber space. With the involvement of a great number of wireless sensor devices, IoT generates a diversity of datasets that are massive, multi-sourcing, heterogeneous, and sparse. By taking advantage of these data to further improve IoT services and offer intelligent services, data fusion is always employed first to reduce the size and dimension of data, optimize the amount of data traffic and extract useful information from raw data. Although there exist some surveys on IoT data fusion, the literature still lacks comprehensive insight and discussion on it with regard to different IoT application domains by paying special attention to security and privacy. In this paper, we investigate the properties of IoT data, propose a number of IoT data fusion requirements including the ones about security and privacy, classify the IoT applications into several domains and then provide a thorough review on the state-of-the-art of data fusion in main IoT application domains. In particular, we employ the requirements of IoT data fusion as a measure to evaluate and compare the performance of existing data fusion methods. Based on the thorough survey, we summarize open research issues, highlight promising future research directions and specify research challenges.Peer reviewe

A Survey on Data Fusion in Internet of Things: Towards Secure and Privacy-Preserving Fusion

Lisää embargo, kun artikkeli julkaistaan.Internet of Things (IoT) aims to create a world that enables the interconnection and integration of things in physical world and cyber space. With the involvement of a great number of wireless sensor devices, IoT generates a diversity of datasets that are massive, multi-sourcing, heterogeneous, and sparse. By taking advantage of these data to further improve IoT services and offer intelligent services, data fusion is always employed first to reduce the size and dimension of data, optimize the amount of data traffic and extract useful information from raw data. Although there exist some surveys on IoT data fusion, the literature still lacks comprehensive insight and discussion on it with regard to different IoT application domains by paying special attention to security and privacy. In this paper, we investigate the properties of IoT data, propose a number of IoT data fusion requirements including the ones about security and privacy, classify the IoT applications into several domains and then provide a thorough review on the state-of-the-art of data fusion in main IoT application domains. In particular, we employ the requirements of IoT data fusion as a measure to evaluate and compare the performance of existing data fusion methods. Based on the thorough survey, we summarize open research issues, highlight promising future research directions and specify research challenges.Peer reviewe

Surfactant-mediated synthesis of single-crystalline Bi3O4Br nanorings with enhanced photocatalytic activity

We present a surfactant-mediated approach to the production of single-crystal Bi 3 O 4 Br nanorings via a simple solvothermal method. The ring-like morphology is rare among bismuth oxyhalides, and the reaction pathways are superior to traditional chemical transformations. In detail, Bi 3 O 4 Br nanorings are prepared in three stages: (1) the formation of precursors, (2) selective etching and (3) Ostwald ripening. During these steps, the extra usage of templates is avoided and a series of useful intermediates are obtained. Besides, this method can be extended to fabricate other bismuth oxyhalide nanorings. Under visible-light irradiation, all of our samples are photo-activated. The Bi 3 O 4 Br nanorings exhibit an efficient oxygen-evolution rate (72.54 μmol h -1 ) and pollutant degradation rate (4.71 x 10 -2 g min -1 m -2 ), which can be attributed to their unique ring structures and band potentials. Thus, the surfactant-mediated chemical conversion strategy not only paves a new way to enhance the photocatalytic activity of bismuth oxyhalides, but also provides an important large-scale route for designing other nanomaterials with ring-like structures