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Highly ordered TiO2 nanorods are considered promising for photoanodes in dye-sensitized solar cells due to their high charge transfer rate of photo-generated electrons, but they often suffer from low specific area, which may lead to a low conversion efficiency. Here, we produce long vertical single-crystalline rutile TiO2-nanorod arrays on fluorine-doped tin oxide conductive glass substrates(FTO)by the hydrothermal method. To further increase the specific area, the TiO2-nanorod arrays are etched in a secondary hydrothermal process by hydrochloric acid. The etching time have a major effect on the nanorod microstructure and on the dye-sensitized solar cells efficiency. The best result is reached with 8 h of etching, which results in a record high conversion efficiency of 11.14 ± 0.12% (certified efficiency 10.3%) under full sunlight illumination (AM 1.5 G, 100 mW/cm2). The record cell has an open voltage of 0.79 V and a short current density of 21.59 mA/cm2. The proposed manufacturing approach of TiO2 nanorods is highly potential for producing high-efficiency dye-sensitized solar cells.
Landsat ETM/TM data and an artificial neural network (ANN) were applied to analyse the expanding of the city of XiŽan and land use/cover changing of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index (NDBI) were used respectively to retrieve its urban boundary. Results showed that the urban area had increased by the rate of 12.3% per year, with area expansion from 253.37 km2 in 2000 to 358.60 km2 in 2003. Large areas of farmland in the north and southwest were converted into urban construction land. The land use/cover changes of XiŽan were mainly caused by: fast development of urban economic, population immigration from countryside, great development of infrastructure such as transportation, and huge demands of urban market. In addition, affected by the government policy of "returning farmland to woodland ", some farmland was converted to economic woodland, such as Chinese gooseberry garden and vineyard etc.
Among different types of changes, a specific type named long-tailed change (LTC), induced by wide-spectrum and sporadic events (hereafter long-tailed business events (LBEs), poses fresh challenges to available change management solutions in business process management. The disorder in economic and social life caused by the competition of COVID-19 epidemics and countermeasures all over the world fully demonstrates the impact of this new change management problem. Based on the principle of separation of concerns, this paper proposes a systematic framework to solve the above problem. The solution consists of a low-code mechanism for process adaptation and business policy conformance. As a result, front-line practitioners can quickly react to changes by using a domain-specific language (DSL) while a corresponding verification of functional and non-functional attributes maintains compliance with business constraints. We validate the solution through a case study of an e-commerce scenario during the COVID-19 pandemic.
In order to create a safe and secure, technology supported living environment for elderly people, a Wuhan based company, Wuxin Ltd, and plans to develop a platform for ICT enabled elderly care services. The platform would provide services both for home based care and for caring institutions. One of the main objectives is to collect and combine data from different devices that can be used for health, wellbeing and assisted living solutions. The purpose of this thesis is to provide requirements and architecture design for the Wuxin platform. The background includes discussion on market's needs, and specifically on the needs in the target environment in Wuhan, and a small study of products available on the market in China. These are followed by the requirements analysis and software architecture design. The methods are described in a literature review on requirements engineering (RE) and software architecture. In RE section, requirements process models, requirements activities such as requirements elicitation, requirements analysis and validation, and requirements management are studied. In software architecture section, architectural design process is discussed first and then architectural design styles are presented. The requirements process for Wuxin Platform is then described, resulting to the agreed requirements based on which the architecture of Wuxin Platform has been designed. Wuxin Platform is decomposed into subsystems and every subsystem is designed following the object oriented style.
The accuracy of the state of health (SoH) estimation and prediction is of great importance to the operational effectiveness and safety of electric vehicles. Present approaches mostly employ data-driven analysis with laboratory measurements to determine these parameters. Here a novel method is proposed using discrete incremental capacity analysis based on real-life driving data, which enables to estimate the battery SoH without any prior detailed knowledge of battery internal specifics such as current capacity/resistance information. The method accounts for the battery characteristics. It is robust, highly compatible, and has a short computing time and low memory requirement. It's capable to evaluate the SoH of various type of electric vehicles under different charging strategies. The short computing time and low memory needed for the SoH estimation also demonstrates its potential for practical use. Moreover, the clustering analysis is presented, which provides SoH comparison information of certain EV to that of EVs belonging to same type.
Accurate estimation of the state of charge (SOC) and state of health (SOH) is a fundamental requirement for the management system of a lithium-ion battery, but also important to the safety and operational effectiveness of electric vehicles and energy storage systems. Here a model-based method is implemented to assess the SOC and SOH simultaneously. An equivalent circuit model is employed to describe the battery dynamics with recursive least squares online identifying model parameters and unscented Kalman filter estimating battery state. A minimalist electrochemical model is proposed to describe the distribution of the lithium content inside the battery relating the SOH to the capacity fading due to irreversible loss of Li. Based on the real-time capacity value, the state of charge could further be estimated. Comparing the experimental results shows that the battery capacity, i.e., SOH could be predicted timely with a mean error around 2%, which confirms the validity of the proposed co-estimation method for SOC and SOH.
Purpose: Intratympanic (IT) drug delivery receives attention due to its effectivity in treatment for Menière’s disease (MD). Due to the release of the consensuses and new evidence on IT drug delivery for MD have been published, the review with a view to supplementing the details of IT treatment of MD is indispensable. Methods: The literatures on IT injection for MD treatment over the last two decades are retrieved, International consensus (ICON) on treatment of Menière’s disease (2018), Clinical Practice Guideline (2020) and European Position statement on Diagnosis and Treatment of Meniere’s Disease (2018) are taken into account for reference, and follow advice from experts from Europe, USA and China. Results: Experts agree on the following: (1) The effectiveness of IT methylprednisolone (ITM) on vertigo control seems to be somewhat better than that of IT dexamethasone (ITD), and ITM can restore hearing in some cases. (2) Due to the ototoxicity of aminoglycosides, the application of intratympanic gentamicin (ITG) in MD patients with good hearing is conservative. However, some studies suggest that ITG with low doses has no significant effect on hearing, which needs to be further proved by clinical studies with high levels of evidence. (3) Currently, generally accepted treatment endpoint of ITG is no vertigo attack in a 12-month period or a vestibular loss in objective tests in the affected ear. Conclusion: More studies with high level of evidence are needed to evaluate the drug type, efficacy, and therapeutic endpoint of IT therapy for MD.
The development of high-performance yet cost-effective catalysts for electrochemical synthesis of H2O2 is a great challenge. Here, the amorphous nickel oxide NiOx supported on carbon nanosheets was prepared by the photochemical metal organic deposition method. The evolution of the crystalline structure, microstructure, and 2-electron oxygen reduction reaction (2e-ORR) activity in 0.1 M KOH was systematically investigated. The results reveal that the amorphous NiOx is highly efficient and selective toward 2e-ORR with an onset potential of 0.76 V versus reversible hydrogen electrode (RHE), 91% selectivity, and an electron transfer number of ∼2.2 over a wide potential range of 0.15-0.60 V versus RHE, which is outstanding among the metal oxide-based catalysts for 2e-ORR. Such a performance is closely associated with the mesoporous structure of the carbon nanosheets. Furthermore, the appropriate bonding strength of Ni-OH derived from the amorphous nature is crucial for the high selectivity. The theoretical calculation reveals that the *OOH intermediate prefers to adsorb on the amorphous NiOx-C by the end-on mode, facilitating the 2e-ORR process. The present amorphous NiOx loaded on carbon nanosheets can be promising electrocatalysts for synthesizing H2O2 after the stability issues are well addressed.
Both the electronic and surface structures of metal nanomaterials play critical roles in determining their chemical properties. However, the non-molecular nature of conventional nanoparticles makes it extremely challenging to understand the molecular mechanism behind many of their unique electronic and surface properties. In this work, we report the synthesis, molecular and electronic structures of an atomically precise nanoparticle, [Ag206L72]q (L = thiolate, halide; q = charge). With a four-shell Ag7@Ag32@Ag77@Ag90 Ino-decahedral structure having a nearly perfect D5h symmetry, the metal core of the nanoparticle is co-stabilized by 68 thiolate and 4 halide ligands. Both electrochemistry and plasmonic absorption reveal the metallic nature of the nanoparticles, which is explained by density functional theory calculations. Electronically, the nanoparticle can be considered as a superatom, just short of a major electron shell closing of 138 electrons (q = –4). More importantly, many of ligands capping on the nanoparticle are labile due to their low-coordination modes, leading to high surface reactivity for catalysing the synthesis of indoles from 2-ethynylaniline derivatives. The results exemplify the power of the atomic-precision nanocluster approach to catalysis in probing reaction mechanisms and in revealing the interplay of heterogeneous reactivities, electronic and surface structural dynamics, thereby providing ways for optimization.
China is the world leader in several areas of clean energy, but not in Concentrating Solar Power (CSP). Our analysis provides an interesting viewpoint to China's possible role in helping with the market breakthrough of CSP. We present a short overview of the state-of-the-art of CSP including the status in China. A blueprint for China's CSP development is elaborated based on China's 13th 5-year program, but also on China's previous success factors in PV and wind power. The results of this study suggest that China could play a more prominent global role in CSP, but this would require stronger efforts in several areas ranging from innovation to policies.
Here we report a self-powered photodetector based on a ZnO/CH3NH3PbI3 heterojunction and a MoO3 hole-transport layer. The organolead iodide perovskite photodetector is sensitive to broadband wavelengths from the ultraviolet light to the entire visible light region (250-800 nm), showing a high photo-responsivity of 24.3 A W-1 and a high detectivity value of 3.56 × 1014 cm Hz1/2 W-1 at 500 nm without external bias voltage. Meanwhile, we found that the photodetective performances are closely related to the thickness of the MoO3 layer, which acts as a hole-transport layer and an electron-blocking layer and can effectively decrease the recombination of holes and electrons. Additionally, the as-fabricated photodetector exhibits good stability and only 9.3% photoelectric response current decay after a 3-month illumination test. The high detectivity and responsivity of such a ZnO nanorod/perovskite heterojunction are clearly demonstrated and should be widely applicable to other photoelectric detection devices.
Contemporary challenges related to sustainability are shared across the globe. Their materializations, prioritizations and emphases, however, vary from one region to another. This chapter shares the experiences from LeNSin project seminars and pilot courses and discusses the potential of design education as a transdisciplinary matchmaker between various actors and networks.
Most Web search diversity approaches can be categorized as Document Level Diversification (DocLD), Topic Level Diversification (TopicLD) or Term Level Diversification (TermLD). DocLD selects the relevant documents with minimal content overlap to each other. It does not take the coverage of query subtopics into account. TopicLD solves this by modeling query subtopics explicitly. However, the automatic mining of query subtopics is difficult. TermLD tries to cover as many query topic terms as possible, which reduces the task of finding a query's subtopics into finding a set of representative topic terms. In this paper, we propose a novel User Session Level Diversification (UserLD) approach based on the observation that a query's subtopics are implicitly reflected by the search intents in different user sessions. Our approach consists of two phases: (I) Session Graph Construction and (II) Diversity Reranking. For a given query, phase (I) builds a Session Graph which considers relevant user sessions and preliminary retrieval results as nodes and the nodes' pairwise similarities as edge weights. Phase (II) reranks the preliminary retrieval results by minimizing a Session Graph based diversity loss function. Extensive experiments on two standard datasets of NACSIS Test Collections for IR (NTCIR) demonstrate the effectiveness of our approach. The advantage of our approach lies in its ability of avoiding mining the query subtopics in advance while achieving almost the same or better performances compared with previous approaches.
Carbon nanotubes (CNTs) are promising candidates for smart electronic devices. However, it is challenging to mediate their bandgap or chirality from a vapor-liquid-solid growth process. Here, we demonstrate rate-selected semiconducting CNT arrays based on interlocking between the atomic assembly rate and bandgap of CNTs. Rate analysis confirms the Schulz-Flory distribution which leads to various decay rates as length increases in metallic and semiconducting CNTs. Quantitatively, a nearly ten-fold faster decay rate of metallic CNTs leads to a spontaneous purification of the predicted 99.9999% semiconducting CNTs at a length of 154 mm, and the longest CNT can be 650 mm through an optimized reactor. Transistors fabricated on them deliver a high current of 14 μA μm-1 with on/off ratio around 108 and mobility over 4000 cm2 V-1 s-1. Our rate-selected strategy offers more freedom to control the CNT purity in-situ and offers a robust methodology to synthesize perfectly assembled nanotubes over a long scale.
Abstract Recently, machine learning-based channel estimation has attracted much attention. The performance of machine learning-based estimation has been validated by simulation experiments. However, little attention has been paid to the theoretical performance analysis. In this paper, we investigate the mean square error (MSE) performance of machine learning-based estimation. Hypothesis testing is employed to analyze its MSE upper bound. Furthermore, we build a statistical model for hypothesis testing, which holds when the linear learning module with a low input dimension is used in machine learning-based channel estimation, and derive a clear analytical relation between the size of the training data and performance. Then, we simulate the machine learning-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems to verify our analysis results. Finally, the design considerations for the situation where only limited training data is available are discussed. In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.
Tailor-designed cathode materials are essential for Li-ion batteries with both high energy density and outstanding capacity retention. Here we have designed and fabricated coral-shaped hierarchical porous LiFePO4/graphene hybrids for lithium-ion batteries. These novel hybrid materials exhibit excellent electrochemical performance over a wide temperature range from −40 °C to +60 °C. Even at −40 °C, the hybrid cathode can deliver a high initial capacity of 120 mAhg−1 and still maintain a discharge capacity of 80 mAhg−1 after 500 cycles with a very low capacity loss of 0.066% per cycle. The excellent wide-temperature performance can be ascribed to the porous structure and fast ion/electronic transport kinetics of the high conductive framework.
The compound parabolic concentrator (CPC) is a highly interesting solar collector technology for different low-concentration applications due to no tracking requirement. The CPC with a tubular absorber is the most common type of CPC. Here, a comprehensive state-of-the-art review of this CPC type is presented, including design features, structure, applications, etc. Key design guidelines, structural improvements, and recent developments are also presented.
Abstract The objective of this paper is to deeply understand and analytically characterize the dynamic strength enhancement of rock. Inspired by the incubation time criterion, a modified dynamic fracture criterion considering whole stress history, called the incubation characteristic time criterion, is proposed. The physical meaning of the modified criterion is interpreted by the hysteresis effect in dynamic fracture revealed by the experimental observation of microcrack kinematics. The dynamic tensile strength determined with the modified criterion is validated by comparing it with published experimental data and the determined result from the original incubation time criterion. Factors in the modified criterion that affect the ultimate dynamic fracture include the quasi-static tensile strength, incubation characteristic time and stress history. The sensitivity of dynamic tensile strength to fundamental material properties is analysed through the combination of the quasi-static tensile strength and the incubation characteristic time. In addition, three types of waveforms are used to investigate the effects of the stress history on the dynamic tensile fracture. Finally, the simple numerical implementation of the modified criterion combined with the classical linear elastic model visually reproduces the analytical calculation of dynamic fracture under different stress histories. In conclusion, the modified criterion provides a novel approach to determine the dynamic fracture of rock under arbitrary loading conditions by considering the whole stress history in comparison with the rate-dependent models.
High-nickel ternary cathode materials such as LiNi0.8Co0.1Mn0.1O2 (NCM811) are among the most promising cathode materials due to their high capacity and low cost. However, irreversible phase transition and interfacial side reactions over cycles remain critical concerns. In this paper, a two-step carbon coating process is developed to suppress the adverse phase transition and excessive side reactions at the electrode-electrolyte interface. Sucrose is melted firstly at mild temperature to be coated onto the particle surface, which is converted into carbon in situ at elevated temperature under oxygen atmosphere. Both the cyclic (96.7% capacity retention after 100 cycles at 1 C) and rate performance (130 mAh g−1 at 5 C) of the NCM811 cathode are effectively improved by the carbon coating treatment.
Gas kick is generally difficult to discover in time using traditional surface detection methods, which results in a significant wastage of time and money. Owing to the restriction of the low data-transmission speed of measurement-while-drilling system, downhole measured data is usually ignored in gas-kick detection. Furthermore, surface detection methods comprising the use of pressure and flow-rate sensors require professional knowledge and many input parameters, some of which are required to be assumed. In this study, we used downhole dual measurement points for detecting gas kick without the use of other surface input parameters. Firstly, we developed an end-to-end supervised neural network to determine the still and circulation working conditions, which were used for calculating the drilling fluid density and viscosity. Secondly, an unscented Kalman filter was applied to perform a backward gas fraction calculation dynamically. However, this downhole calculation method cannot be used in highly deviated and horizontal wells. Because there is a downhole fluctuating pressure generated during the rock breaking, we proposed an auxiliary gas-kick detection method based on the theory of pressure wave attenuation. This method can be applied to all well types. To evaluate the proposed gas-kick detection method, we used a gas-liquid flow simulation model combined with a pump rate model, screw-drilling-tool pressure-consumption model, rock-breaking model, and formation permeability model to generate transient data with the highest possible accuracy. The advection upstream splitting model was used as the numerical scheme. The accuracy of the simulation model was successfully validated using two field experimental data sets. Finally, we generated a set of vertical and horizontal well data each with the simulation model to test the gas-kick detection method. The experiment results showed that the proposed gas-kick detection model was successful in detecting gas kick and obtaining the accurate gas fraction.
Abstract In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.
Exercise and diet are treatments for nonalcoholic fatty liver disease (NAFLD) and prediabetes, however, how exercise and diet interventions impact gut microbiota in patients is incompletely understood. We previously reported a 8.6-month, four-arm (Aerobic exercise, n = 29; Diet, n = 28; Aerobic exercise + Diet, n = 29; No intervention, n = 29) randomized, singe blinded (for researchers), and controlled intervention in patients with NAFLD and prediabetes to assess the effect of interventions on the primary outcomes of liver fat content and glucose metabolism. Here we report the third primary outcome of the trial—gut microbiota composition—in participants who completed the trial (22 in Aerobic exercise, 22 in Diet, 23 in Aerobic exercise + Diet, 18 in No Intervention). We show that combined aerobic exercise and diet intervention are associated with diversified and stabilized keystone taxa, while exercise and diet interventions alone increase network connectivity and robustness between taxa. No adverse effects were observed with the interventions. In addition, in exploratory ad-hoc analyses we find that not all subjects responded to the intervention in a similar manner, when using differentially altered gut microbe amplicon sequence variants abundance to classify the responders and low/non-responders. A personalized gut microbial network at baseline could predict the individual responses in liver fat to exercise intervention. Our findings suggest an avenue for developing personalized intervention strategies for treatment of NAFLD based on host-gut microbiome ecosystem interactions, however, future studies with large sample size are needed to validate these discoveries. The Trial Registration Number is ISRCTN 42622771.
Although one of the most promising aqueous batteries, all Zn-Mn systems suffer from Zn dendrites and the low-capacity Mn4+/Mn3+ process (readily leading to the occurrence of Jahn–Teller distortion, which in turn causes structural collapse and voltage/capacity fading). Here, the Mn3+ reconstruction and disproportionation are exploited to prepare the stable, Mn2+-rich manganese oxides on carbon-cloth (CMOs) in a discharged state through an inverted design, which promotes reversible Mn2+/Mn4+ kinetics and mitigates oxygen-related redox activity. Such a 1.65 V Mn2+-rich cathode enable constructing a 2.2 V Zn-Mn battery, providing a high area capacity of 4.16 mA h cm–2 (25 mA h cm–2 for 10 mL electrolyte) and superior 4000-cycle stability. Moreover, a flexible hybrid 2.7 V Zn-Mn battery is constructed using 2-pH hydrogel electrolytes to demonstrate excellent practicality and stability. A further insight has been gained to the commercial application of aqueous energy storage devices toward low-cost, high safety, and excellent energy density.
Abstract Revealing how aquatic organisms respond to dam impacts is essential for river biomonitoring and management. Traditional examinations of dam impacts on macroinvertebrate assemblages were frequently conducted within single rivers (i.e., between upstream vs. downstream locations) and based on taxonomic identities but have rarely been expanded to level of entire basins (i.e., between dammed vs. undammed rivers) and from a functional trait perspective. Here, we evaluated the effects of dams on macroinvertebrate assemblages at both the within-river and basin scales using functional traits in two comparable tropical tributaries of the Lancang-Mekong River. At different scales, maximum body size, functional feeding groups (FFG), voltinism and occurrence in drift respond significantly to dam impact. Armoring categories varied significantly between downstream sites and upstream sites, and oviposition behavior, habits and adult life span significantly differed between rivers. The key traits at the within-river scale resembled to those at the between-river scale, suggesting that within-river trait variation could further shape functional trait structure at the basin scale in dammed rivers. Furthermore, water nutrients and habitat quality induced by dams showed the most important role in shaping trait structure, although trait-environment relationships varied between the two different scales. In addition, the trait-environment relationships were stronger in the dry season than in the wet season, suggesting a more important role of environmental filtering processes in the dry season compared with the wet season. This study highlights the utility of the trait-based approach to diagnose the effects of damming and emphasizes the importance of spatial scale to examine dam impacts in riverine systems.
Abstract The objective of this study was to investigate granite responses to blasting. The focus was on the pressure and attenuation of shock waves in granite. Tests are reported on ten cylinders subjected to explosions from central pressed trinitrotoluene (TNT) charges with approximate density of 1.6 g/cm3. Three cylinders had dimensions Ø150 mm × 200 mm; seven, Ø240 mm × 300 mm. Specimens had concentric holes drilled from both ends: one 20-mm hole to position the explosive charge and one 50-mm hole to insert a granite plug equipped with Manganin gauges, which were applied to monitor the pressures of the shock waves. The configuration of the gauges was analyzed before testing to investigate how precisely they could measure shock waves in the granite. One or two gauges were used in each cylinder at distances of 7, 15, 22 or 35 mm from the explosive charge in the cylinder axis. At detonation of the charge, the measured peak pressure values ranged from 15.9–4.4 GPa depending on distance from the explosive, with pressure rise times of ∼0.5 μs. In one specimen, deflagration occurred, resulting in a low peak pressure of 1.35 GPa 11 mm from the explosive and a 16-μs pressure rise time. For specimens with two gauges, shock-wave velocities were found to depend strongly on the distance from the explosive. Fitting a curve to the experimental data, an exponential relation for the shock-wave peak pressure and its attenuation was obtained, expressing pressure (GPa) as a function of increasing distance (mm) from the explosive: p = 19.4exp(−0.04x). The findings, especially regarding the damping term, may for instance be useful for verification of numerical models for blasting simulation.
The application of Global Navigation Satellite System (GNSS) on the railway greatly reduces the cost on train localization. However, the railway environment is complex and changes with the train movement, buildings, trees, railroad cuts and mountains will block and reflect the GNSS signals, which will bring errors to the GNSS-based train position estimation. This paper proposes a railway scenario identification method based on historical GNSS receiver observation data to identify scenarios along the railway. Firstly, a railway environment scenario parameter model library is established according to Feature of Sky Occlusion (FSO) of typical scenarios, apply historical GNSS observation data along the railway to establish the FSO models of scenario segments, and generate FSO feature sequences. The dynamic time warping algorithm (DTW) is used to match the FSO parameter model of the scenario segment with the FSO model library. This paper collected data from field experiments at Beijing Sanjiadian station to verifythe algorithm. The scenario identification results showed that the scenario identification method based on DTW can effectively identify the railway scenarios.
Background. Pre-diabetes and non-alcoholic fatty liver disease (NAFLD) are associated with an unhealthy lifestyle and pose extremely high costs to the healthcare system. In this study, we aim to explore whether individualized aerobic exercise (AEx) and low carbohydrate diet (LCh) intervention affect hepatic fat content (HFC) in pre-diabetes via modification of gut microbiota composition and other post-interventional effects. Methods/design. A 6-month randomized intervention with 6-month follow-up is conducted from January 2013 to December 2015. The target sample size for intervention is 200 postmenopausal women and middle-aged men aged 50–65 year-old with pre-diabetes and NAFLD. The qualified subjects are randomized into 4 groups with 50 subjects in each group: 1 = AEx, 2 = LCh, 3 = AEx + LCh, and 4 = control. In addition, two age-matched reference groups (5 = pre-diabetes without NAFLD (n = 50) and 6 = Healthy without pre-diabetes or NAFLD (n = 50)) are included. The exercise program consists of progressive and variable aerobic exercise (intensity of 60 to 75% of initial fitness level, 3–5 times/week and 30–60 min/time). The diet program includes dietary consultation plus supplementation with a special lunch meal (40% of total energy intake/day) which aims to reduce the amount of carbohydrate consumption (30%). The control and reference groups are advised to maintain their habitual habits during the intervention. The primary outcome measures are HFC, serum metabolomics and gut microbiota composition. The secondary outcome measures include body composition and cytokines. In addition, socio-psychological aspects, social support, physical activity and diet will be performed by means of questionnaire and interview. Discussion. Specific individualized exercise and diet intervention in this study offers a more efficient approach for liver fat reduction and diabetes prevention via modification of gut microbiota composition. Besides, the study explores the importance of incorporating fitness assessment and exercise in the management of patients with pre-diabetes and fatty liver disorders. If our program is shown to be effective, it will open new strategies to combat these chronic diseases.
The study aimed to assess whether aerobic exercise (AEx) training and a fbre-enriched diet can reduce hepatic fat content (HFC) and increase glycaemic control in pre-diabetic patients with non-alcoholic fatty liver disease (NAFLD). Six-hundred-and-three patients from seven clinics in Yangpu district, Shanghai, China were recruited. Of them 115 individuals aged 50–65-year fulflled the inclusion criteria (NAFLD with impaired fasting glucose or impaired glucose tolerance) and were randomly assigned into exercise (AEx n=29), diet (Diet n=28), exercise plus diet (AED n=29), or no-intervention (NI n=29) groups. Progressive supervised AEx training (60–75% VO2max intensity) was given 2-3 times/week in 30–60min/sessions, and the diet intervention was provided as lunch with 38% carbohydrate and diet fbre of 12g/day for 8.6-month. HFC was assessed by 1H MRS. We found that HFC was signifcantly reduced in the AEx (−24.4%), diet (−23.2%), and AED (−47.9%) groups by contrast to the 20.9% increase in the NI group (p=0.001 for all) after intervention. However, only AED group signifcantly decreased HbA1c (−4.4%, p=0.01) compared with the NI group (−0.6%). Aerobic exercise training combined with fbre-enriched diet can reduce HFC more efectively than either exercise or increased fbre-intake alone in pre-diabetic patients with NAFLD.
Nucleation of neutral iodine particles has recently been found to involve both iodic acid (HIO3) and iodous acid (HIO2). However, the precise role of HIO2in iodine oxoacid nucleation remains unclear. Herein, we probe such a role by investigating the cluster formation mechanisms and kinetics of (HIO3)m(HIO2)n(m = 0-4, n = 0-4) clusters with quantum chemical calculations and atmospheric cluster dynamics modeling. When compared with HIO3, we find that HIO2binds more strongly with HIO3and also more strongly with HIO2. After accounting for ambient vapor concentrations, the fastest nucleation rate is predicted for mixed HIO3-HIO2clusters rather than for pure HIO3or HIO2ones. Our calculations reveal that the strong binding results from HIO2exhibiting a base behavior (accepting a proton from HIO3) and forming stronger halogen bonds. Moreover, the binding energies of (HIO3)m(HIO2)nclusters show a far more tolerant choice of growth paths when compared with the strict stoichiometry required for sulfuric acid-base nucleation. Our predicted cluster formation rates and dimer concentrations are acceptably consistent with those measured by the Cosmic Leaving Outdoor Droplets (CLOUD) experiment. This study suggests that HIO2could facilitate the nucleation of other acids beyond HIO3in regions where base vapors such as ammonia or amines are scarce.