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Plane detection and segmentation is one of the most crucial tasks in point cloud processing. The output from this process can be used as input for further processing steps, such as modelling, registration and calibration. However, the sparseness and heterogeneity of Mobile Laser Scanning (MLS) point clouds may lead to problems for existing planar surfaces detection and segmentation methods. This paper proposes a new method that can be applicable to detect and segment planar features in sparse and heterogeneous MLS point clouds. This method utilises the scan profile patterns and the planarity values between different neighbouring scan profiles to detect and segment planar surfaces from MLS point clouds. The proposed method is compared to the three most state-of-the-art segmentation methods (e.g. RANSAC, a robust segmentation method based on robust statistics and diagnostic principal component analysis – RDCPA as well as the plane detection method based on line arrangement). Three datasets are used for the validation of the results. The results show that our proposed method outperforms the existing methods in detecting and segmenting planar surfaces in sparse and heterogeneous MLS point clouds. In some instances, the state-of-the-art methods produce incorrect segmentation results for facade details which have a similar orientation, such as for windows and doors within a facade. While RDCPA produces up to 50% of outliers depending on the neighbourhood threshold, another method could not detect such features at all. When dealing with small features such as a target, some algorithms (including RANSAC) were unable to perform segmentation. However, the propose algorithm was demonstrated to detect all planes in the test data sets correctly. The paper shows that these mis-segmentations in other algorithms may lead to significant errors in the registration process of between 1.047 and 1.614 degrees in the angular parameters, whereas the propose method had only resulted in 0.462 degree angular bias. Furthermore, it is not sensitive to the required method parameters as well as the point density of the point clouds.

Aim To understand the impact of COVID‐19 on isolation bed capacity requirements, nursing workforce requirements and nurse:patient ratios. Background COVID‐19 created an increased demand for isolation beds and nursing workforce globally. Methods This was a retrospective review of bed capacity, bed occupancy and nursing workforce data from the isolation units of a tertiary hospital in Singapore from 23 January 2020 to 31 May 2020. R v4.0.1 and Tidyverse 1.3.0 library were used for data cleaning and plotly 4.9.2.1 library for data visualization. Results In January to March 2020, isolation bed capacity was low (=

All-solid-state batteries have gained significant attention as promising candidates to replace liquid electrolytes in lithium-ion batteries for high safety, energy storage performance, and stability under elevated temperature conditions. However, the low ionic conductivity and unsuitability of lithium metal in solid polymer electrolytes is a critical problem. To resolve this, we used a cubic garnet oxide electrolyte (Li7La3Zr2O12 – LLZO) and ionic liquid in combination with a polymer electrolyte to produce a composite electrolyte membrane. By applying a solid polymer electrolyte on symmetric stainless steel, the composite electrolyte membrane shows high ionic conductivity at elevated temperatures. The effect of LLZO in suppressing lithium dendrite growth within the composite electrolyte was confirmed through symmetric lithium stripping/plating tests under various current densities showing small polarization voltages. The full cell with lithium iron phosphate as the cathode active material achieved a highest specific capacity of 137.4 mAh g−1 and a high capacity retention of 98.47% after 100 cycles at a current density of 50 mA g−1 and a temperature of 60°C. Moreover, the specific discharge capacities were 137 and 100.8 mAh g−1 at current densities of 100 and 200 mA g−1, respectively. This research highlights the capability of solid polymer electrolytes to suppress the evolution of lithium dendrites and enhance the performance of all-solid-state batteries.

Intradural lumbar disc herniation is a rare complication of disc disease. The mechanism by which a herniated disc tears the dura matter remains unknown. The preoperative diagnosis of an intradural lumbar disc herniation is still difficult. We report our experience dealing with a case of intradural lumbar disc herniation at level L3/4 in a 34-year-old man. Based on current experience, we found that attachment of the ventral dura to the posterior longitudinal ligament can be a favorable factor indicating intradural lumbar disc herniation. One should pay attention to those with cauda equina syndrome, as it can thereby promptly suggest a preoperative diagnosis of intradural lumbar disc herniation. Surgeons need to avoid omitting intraoperative lesions by palpating the dura mater during surgery for suspected tumor cases.

All-solid-state batteries have gained significant attention as promising candidates to replace liquid electrolytes in lithium-ion batteries for high safety, energy storage performance, and stability under elevated temperature conditions. However, the low ionic conductivity and unsuitability of lithium metal in solid polymer electrolytes is a critical problem. To resolve this, we used a cubic garnet oxide electrolyte (Li7La3Zr2O12 – LLZO) and ionic liquid in combination with a polymer electrolyte to produce a composite electrolyte membrane. By applying a solid polymer electrolyte on symmetric stainless steel, the composite electrolyte membrane shows high ionic conductivity at elevated temperatures. The effect of LLZO in suppressing lithium dendrite growth within the composite electrolyte was confirmed through symmetric lithium stripping/plating tests under various current densities showing small polarization voltages. The full cell with lithium iron phosphate as the cathode active material achieved a highest specific capacity of 137.4 mAh g−1 and a high capacity retention of 98.47% after 100 cycles at a current density of 50 mA g−1 and a temperature of 60°C. Moreover, the specific discharge capacities were 137 and 100.8 mAh g−1 at current densities of 100 and 200 mA g−1, respectively. This research highlights the capability of solid polymer electrolytes to suppress the evolution of lithium dendrites and enhance the performance of all-solid-state batteries.

Background: We describe the development of a dynamic simulation modelling framework to support agile resource planning during the COVID-19 pandemic. The framework takes into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes to deal with the ever-changing outbreak scenarios.Methods: A specific use case based on short-term bed resource planning is described within the proposed framework. The simulation model was calibrated against historical data for the Singapore COVID-19 situation. The time period for model calibration was from 1st April till 30th April 2020. The model was used to project for bed resource needs over the period from 1st May 2020 till 31st May 2020. Multivariate sensitivity analysis was also conducted for ICU and general isolation bed demand, length-of-stay (LOS), and age-adjusted conversion rates across different care needs. The unmet needs under various scenarios were also evaluated for planning purposes.Results: Several variants of the agile resource planning model were developed to adapt to the fast-changing COVID-19 situation in Singapore. The use case demonstrated an agile adaptation of the model to account for previously unexpected scenarios. The rapid evolution of the pandemic locally revealed streams of new infections that arose from two distinct sources. The model projections were calibrated with the latest data for short-term projections. The agility in flexing plans and collaborative management structures to rapidly deploy human and capital resources to surge the level of care during the COVID-19 pandemic have proven utility in guiding the allocation of scarce healthcare resources and helped system resiliency.Conclusions: The rapidly evolving COVID-19 pandemic in Singapore has necessitated the development of an agile and adaptable modelling framework that can be quickly calibrated to changes both from demand and supply. The modelling framework is able to deploy systems modelling concepts in a holistic manner. This facilitates the evaluation of complex cause-and-effect relationships. A robust collaborative framework, coupled with the availability of in-depth domain knowledge and accurate and updated data availability ensures a model is realistic, timely and useful.

BACKGROUND Pre-emptive pharmacogenomic (PGx) testing is potentially an efficient approach to improve drug safety and efficacy but the target population to test is unclear. OBJECTIVES We aim to describe the prescription pattern of PGx drugs among adult medical outpatients. METHODS We estimated the 5-year cumulative incidence (CI) for receiving three groups of PGx drugs using competing risks analysis: (i) all PGx drugs, (ii) PGx drugs with guidelines and (iii) PGx drugs with serious clinical effects. Comparisons of CIs were also done by patient characteristics using Gray's test. RESULTS The 5-year CIs of receiving any new PGx drug, PGx drug with guidelines and serious clinical effects were 42.6%, 37.3% and 13.7%, respectively. The 5-year CI of receiving any new PGx drug was higher for patients >40 years old (43.6% vs ≤40 years old 36.0%, P < 2.2 × 10-22 ), Malays and Indians (50.3% and 49.8% vs Chinese 31.1%, P < 2.2 × 10-22 ), those who attended one of the following four specialties at the index visit compared to other specialties (infectious diseases [46.2% vs 42.6%, P = 2.9 × 10-4 ], psychiatry [48.3% vs 42.3%, P = 7.4 × 10-13 ], renal [49.8% vs 40.9%, P < 2.2 × 10-22 ], and rheumatology and immunology [54.8% vs 41.7%, P < 2.2 × 10-22 ]) and those prescribed ≥5 drugs at index visit (51.7% vs 0-4 drugs 41.7%, P < 2.2 × 10-22 ). CONCLUSIONS Medical outpatients have a substantial probability of benefiting from pre-emptive PGx testing and this is higher in certain subgroups of patients.

We simulated two-dimensional electronic spectra (2DES) of the chlorophyll a manifold of light-harvesting complex II (LHCII) at various temperatures (77, 110, 150, 190, 230, 273, and 293 K) using the hierarchical equations of the motion-phase matching approach. We confirm the main excitation energy transfer pathways assignments within the chlorophyll a manifold of LHCII measured in a recent work (J. Phys. Chem. B 2019, 123, 6765-6775). The calculated transfer rates are also in general agreement with the measured rates. We also provided theoretical confirmation for the experimental assignments, as uphill and downhill energy transfer processes, of 2D spectral features that were reported in recent experimental reports. These temperature-dependent features were also ascertained to follow the detailed-balance principle.

We can leverage big emergency data as the standards and guidelines for emergency management, some of which are smart response systems, early event anticipation, and coordination between end-users (e.g., the connection between authorities, first responders, and local citizens). Emergency data is available in various sources (e.g., social media, mass media, sensor, and linked open data) or local knowledge. In terms of the 6 Vs of big emergency data (i.e., value, volume, velocity, variety, veracity, and variability), they are challenging actually and consume our research efforts in analysing and capturing essential knowledge, especially in urgent circumstances. With manual methods, it may take years for a person to read, process, and understand massive amounts of big emergency data. We, therefore, need to come up with a digital solution, which can deal with the complexity of emergency data, to get meaningful results in a fast response time...

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

Social data is one of important material for representing social activities. However, efficiently collecting social data from multiple sources is extremely hard due its big-data attributes. Moreover, discovering hidden patterns between social data to understand our society is actually a big challenge. Especially, knowledge around us is not stable but dynamically change over time, therefore, we need to refresh it promptly. SocioScope is created as an automatically tool to overcome aforementioned problems. In addition, other researchers can utilize SocioScope as a framework for reducing their effort time with essential tasks (i.e., collecting data, pre-processing data, and analyzing data).

In this paper, we estimates the effective conductivity of heterogeneous materials constituted aggregates of different polygonal shapes mixed with a matrix material. The problem is considered in the context of periodic homogenization theory. The local problem is formulated using the Lippmann–Schwinger equation of polarization with optimal operator norm. The equation can be used to derive a Fast Fourier Transform (FFT) based iteration scheme and theoretical estimates of the overall properties. When applied to microstructure with polygonal inclusion, the results show a very good agreement between those approaches.

Since trust among entities can change according to various conditions, it is necessary for ambient services to determine when and how the trust has to be updated. Therefore, our contribution in this paper is to present: 1) a new definition of trust that can be extended to various domains; 2) a novel method based on social events and patterns to trigger trust refreshment in ambient services; and 3) a web application framework (called SocioScope) for collecting and analyzing data from multiple data sources. Finally, the case study suggests that this proposal could be applied to trust-aware ambient and recommendation systems.

Steel corrosion is one of major problems that affect the durability of reinforced concrete structures. Chloride concentration is the key parameter for the steel corrosion risk of reinforced concrete exposed to seawater. Therefore, the durability of reinforced concrete can be evaluated by the prediction of chloride concentration into the reinforced concrete. Numerous numerical models have been developed to predict the chloride concentration in concrete however these numerical models have not yet fully simulated the nature of the chemo-physical processes taking place between the concrete and seawater. In this study, the prediction of chloride concentration is carried out by using the geochemical model including chemo-physical process. The geochemical model can improve the accuracy of the durability prediction of reinforced concrete. The accuracy of durability prediction is proved by the comparisons between modelled results and experiments results found in the literature.

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and other predictors such as surgical team, patient, temporal and operational factors in a tree-based model for predicting surgical durations. Experimental results show that the proposed two-step model is more parsimonious and outperforms existing moving averages method used by the hospital. Our model bridges the research-to-practice gap by combining data analytics with expert’s inputs to develop a deployable surgical duration prediction model for a real-world public hospital.

Background Patients with chronic kidney disease (CKD) are at high risk of end-stage kidney disease (ESKD). The Kidney Failure Risk Equation (KFRE), which predicts ESKD risk among patients with CKD, has not been validated in primary care clinics in Southeast Asia (SEA). Therefore, we aimed to (1) evaluate the performance of existing KFRE equations, (2) recalibrate KFRE for better predictive precision, and (3) identify optimally feasible KFRE thresholds for nephrologist referral and dialysis planning in SEA. Methods All patients with CKD visiting nine primary care clinics from 2010 to 2013 in Singapore were included and applied 4-variable KFRE equations incorporating age, sex, estimated glomerular filtration rate (eGFR), and albumin-to-creatinine ratio (ACR). ESKD onset within two and five years were acquired via linkage to the Singapore Renal Registry. A weighted Brier score (the squared difference between observed vs predicted ESKD risks), bias (the median difference between observed vs predicted ESKD risks) and precision (the interquartile range of the bias) were used to select the best-calibrated KFRE equation. Results The recalibrated KFRE (named Recalibrated Pooled KFRE SEA) performed better than existing and other recalibrated KFRE equations in terms of having a smaller Brier score (square root: 2.8% vs. 4.0–9.3% at 5 years; 2.0% vs. 6.1–9.1% at 2 years), less bias (2.5% vs. 3.3–5.2% at 5 years; 1.8% vs. 3.2–3.6% at 2 years), and improved precision (0.5% vs. 1.7–5.2% at 5 years; 0.5% vs. 3.8–4.2% at 2 years). Area under ROC curve for the Recalibrated Pooled KFRE SEA equations were 0.94 (95% confidence interval [CI]: 0.93 to 0.95) at 5 years and 0.96 (95% CI: 0.95 to 0.97) at 2 years. The optimally feasible KFRE thresholds were > 10–16% for 5-year nephrologist referral and > 45% for 2-year dialysis planning. Using the Recalibrated Pooled KFRE SEA, an estimated 82 and 89% ESKD events were included among 10% of subjects at highest estimated risk of ESKD at 5-year and 2-year, respectively. Conclusions The Recalibrated Pooled KFRE SEA performs better than existing KFREs and warrants implementation in primary care settings in SEA.

Light-harvesting complex II (LHCII) – the light-harvesting antenna of Photosystem II – is a naturally abundant system that plays an important role in photosynthesis. In this study, we present a phenomenological analysis of the excitonic energy transfer in LHCII using ultrafast two-dimensional electronic spectroscopy, that we find compares well with previous theoretical and experimental results.

Das Konstrukt der Nachhaltigkeit wird in erster Linie oft mit der Okologie oder der Wirtschaft assoziiert, allerdings kann sich Nachhaltigkeit auch auf das individuelle Lernen des Menschen beziehen. Nachhaltigkeit kann in diesem Zusammenhang bedeuten, Kompetenzen aufzubauen, damit diese fur die Bewaltigung von unterschiedlichen Herausforderungen verfugbar sind. Im Rahmen dieses Forschungsprojekts erfolgte eine empirische Untersuchung der Studyblr-Community. Ziel war es herauszufinden, welchen Einfluss die Interaktionen in der Online-Community auf die Lernmotivation und auf die Kompetenzen zur Lebensbewaltigung ihrer Mitglieder haben.

Social data is only useful unless it is processed to achieve knowledge. Due to this reason, we extended SocioScope framework for generating knowledge graph from social data. With this extended version, we obtained a completed framework for generating, understanding, and connecting social knowledge. Many applications can take advance of our research for supporting decision makings (e.g., recommendation systems and monitoring systems). Besides, connecting knowledge can bring us to the new era: Internet of Knowledge.

Environmental degradation is one of the most serious issues impacting our lives and those of future generations. In this respect, several studies on pro-environmental behaviour have investigated the attitude–behaviour relationship of consumers. This study builds on previous models and examines the influence of four relevant determinants, i.e. consumers' perceptions associated with environmental concern, ecological effect, perceived consumer effectiveness (PCE) and moral obligation on their pro-environmental behaviour. The data were collected from consumers in Vietnam, where there are serious environmental issues. The findings suggest that all the four determinants influenced consumers' conservation behaviour, albeit each in varying strengths. The most influential determinant was PCE, followed by ecological effect, environmental concern and finally moral obligation. The findings of this study have important academic and practical implications. Government and private environmental and sustainability campaigners, especially in Vietnam, would be well advised to use these findings in developing and designing new policies and initiatives aimed at enhancing consumers' pro-environmental behaviour. Copyright © 2016 John Wiley & Sons, Ltd.

Storification is a theoretical technique which aims to construct the underlying relationships from discrete information for packaging them into a logical structure. In this paper, we focus on proposing the definition of serendipity-based storification in the personal history which is the combination of two-step processes: i) discovering hidden stories in the personal history and ii) representing stories using visualization techniques for easily grasping the information. MyMovieHistory Hong & Jung (Cybern Syst 46 (1-2), 69---83 ??) is used as the case study to demonstrate the effect of storification through detecting and presenting patterns in real personal data. The results can be utilized in helping people easily memorize and comprehend their histories. Moreover, additional benefits (e.g., reminding the pass, predicting the future, and communicating who you are) can be gained through the use of storification in the personal history.

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

This paper presents a system that analyzes the sentiment of figurative language contained in short texts collected from Social Networking Services (SNS). This case study sources information from tweets on Twitter and calculates the polarity of the figurative language with three different categories (i.e., sarcastic, ironic, and metaphorical tweets). As in Medhat et al. (Ain Shams Eng J 5(4):1093---1113, 2014), Nguyen and Jung (Mob Netw Appl 20(4):475---486, 2015), many related works have used a lexical-based approach (e.g., dictionary and corpus), and machine learning-based approach (e.g., decision tree, rule discovery, and probabilistic methods) to extract sentiment in a given text. This statistical approach makes use of two main features: i) Content-based, and ii) Emotion Pattern-based. We believe that this combination offers a general method to solve the current problem and easily extends for analyzing other types of figurative languages. The proposed algorithm is evaluated by using Cosine similarity to conduct an experiment over a Data set that contains about 5,000 tweets. The results show that the FIS Model (Figurative language Identification using Statistical-based Model) works well with figurative tweets with a highest achievement of 0.7813.

Registration is an essential procedure for merging point clouds defined in different coordinate systems associated to different scanner positions and orientations. It is usually the first step before the point clouds are further processed to provide spatial information of a scene to support engineering applications. In this paper, a new automatic registration method based on a novel geometric model of a polygonal object is presented. Since the cross section of the shaft of many lamp poles is octagonal, registration based on an octagonal pyramid model is proposed. The presented method only requires a single, common octagonal lamp pole observed in both point clouds, though actual overlap of the point clouds is not strictly required. It can be performed as long as the model parameters can be estimated by fitting the point observations to the model. Moreover, no user interaction is needed to derive approximate values, so the proposed registration can be completely automated. Three independent datasets captured by two scanners were used to verify the method. The registration accuracies in the horizontal and vertical directions were up to 11.7 mm and 4.4 mm at approximately 62 m and 17 m away from the scanner, respectively. With such high accuracies, the estimated registration parameters can serve as a set of initial parameters for fine registration using algorithm such as the iterative closest point ( icp ).

Stone Mastic Asphalt (SMA) is a tough, stable, rut-resistant mixture that takes advantage of the stone-to-stone contact to provide strength and durability for the material. Besides, the warm mix asphalt (WMA) technology allows reducing emissions and energy consumption by reducing the production temperature by 30–50 °C, compared to conventional hot mix asphalt technology (HMA). The dynamic modulus |E*| has been acknowledged as a vital material property in the mechanistic-empirical design and analysis and further reflects the strains and displacements of such layered pavement structures. The objective of this study is twofold, aiming at favoring the potential use of SMA with WMA technique. To this aim, first, laboratory tests were conducted to compare the performance of SMA and HMA through the dynamic modulus. Second, an advanced hybrid artificial intelligence technique to accurately predict the dynamic modulus of asphalt mixtures was developed. This hybrid model (ANN-TLBO) was based on an Artificial Neural Network (ANN) algorithm and Teaching Learning Based Optimization (TLBO) technique. A database containing the as-obtained experimental tests (96 data) was used for the development and assessment of the ANN-TLBO model. The experimental results showed that SMA mixtures exhibited higher values of the dynamic modulus |E*| than HMA, and the WMA technology increased the dynamic modulus values compared with the hot technology. Furthermore, the proposed hybrid algorithm could successfully predict the dynamic modulus with remarkable values of R2 of 0.989 and 0.985 for the training and testing datasets, respectively. Lastly, the effects of temperature and frequency on the dynamic modulus were evaluated and discussed.

Huyen Thi Thanh Vu,1,2 Thanh Xuan Nguyen,2,3 Huong Thi Thu Nguyen,1,2 Tu Anh Le,4 Tam Ngoc Nguyen,1,2 Anh Trung Nguyen,2 Thu Thi Hoai Nguyen,1,2 Hoang Long Nguyen,5 Cuong Tat Nguyen,6 Bach Xuan Tran,7,8 Carl A Latkin,8 Thang Pham,2 Melvyn WB Zhang,9 Roger CM Ho10 1Department of Gerontology, Hanoi Medical University, Hanoi, Vietnam; 2National Geriatric Hospital, Hanoi, Vietnam; 3Dinh Tien Hoang Institute of Medicine, Hanoi, Vietnam; 4Department of Endocrinology, Nghe An Endocrinology Hospital, Nghe An, Vietnam; 5Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 6Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam; 7Department of Health Economics, Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam; 8Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; 9Biomedical Global Institute of Healthcare Research & Technology (BIGHEART), National University of Singapore, Singapore; 10Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Background and aims: Depression and diabetes are becoming increasingly prevalent within the Vietnamese elderly population. However, the linkage between these health conditions in the Vietnamese elderly has not yet been fully investigated. This study aimed to assess the level of depressive symptoms and associated factors among elderly diabetic patients.Methods: A cross-sectional study was conducted at National Geriatric Hospital in the elder patients aged ≥60 years with type 2 diabetes mellitus (T2DM). Depressive symptoms were assessed using the Geriatric Depression Scale, with three categories: normal (0–4 points), mild (5–9 points), and moderate/severe depressive symptoms (≥10 points). We obtained information on the patient’s sociodemographic, medical history, glycemic control (fasting plasma glucose and HbA1c), daily activities (activities of daily living [ADLs] and instruments activities of daily living [IADLs] scale), and fall risks (Time Up and Go test). Logistic regression was used to analyze the factors associated with the presence of depressive symptoms.Results: Among 412 patients, 236 (57.3%) had HbA1c level at 7.0% or higher. There were 327 (79.4%) patients having depressive symptoms. The level of HbA1c was significantly different between the depressive symptom group and the non-depressive symptom group (7.74% and 6.61%, P

Energy equilibration in light-harvesting antenna systems normally occurs before energy is transferred to a reaction center. The equilibration mechanism is a characteristic of the excitation energy transfer (EET) network of the antenna. Characterizing this network is crucial in understanding the first step of photosynthesis. We present our phenomenology-based analysis procedure and results in obtaining the excitonic energy levels, spectral linewidths, and transfer-rate matrix of Light-Harvesting Complex II directly from its 2D electronic spectra recorded at 77 K with waiting times between 100 fs to 100 ps. Due to the restriction of the models and complexity of the system, a unique EET network cannot be constructed. Nevertheless, a recurring pattern of energy transfer with very similar overall time scales between spectral components (excitons) is consistently obtained. The models identify a "bottleneck" state in the 664-668 nm region although with a relatively shorter lifetime (∼4-6 ps) of this state compared to previous studies. The model also determines three terminal exciton states at 675, 677-678, and 680-681 nm that are weakly coupled to each other. The excitation energy equilibration between the three termini is found to be independent of the initial excitation conditions, which is a crucial design for the light-harvesting complexes to ensure the energy flow under different light conditions and avoid excitation trapping. We proposed two EET schemes with tentative pigment assignments based on the interpretation of the modeling results together with previous structure-based calculations and spectroscopic observables. Published version The authors acknowledge support from the Hungarian Ministry of Innovation and Technology, National Research, Development and Innovation Office (Grant Nos. NN-124904 and 2018-1.2.1-NKP-2018-00009 to P.H.L.) and the Singapore Ministry of Education, Academic Research Fund (Tier 2 Grant No. MOE2015-T2-1-039 and Tier 1 Grant No. RG15/18 to H.-S.T.). The ELI-ALPS Project (No. GINOP-2.3.6-15-2015-00001) is supported by the European Union and co-financed by the European Regional Development Fund.

Chlorophyll a (Chl a) and Chlorophyll b (Chl b) are major pigments in the photosynthetic machinery in green plants. Despite their similarities in chemical structures, the relaxation dynamics as observed in the ultrafast methods, such as pump probe and multidimensional spectroscopy, demonstrate a distinct kinetics. Here, we employ the ultrafast two-dimensional electronic spectroscopy (2DES) method to characterize the frequency fluctuation correlation function (FFCF) for Chl a and Chl b in various solvent environments, through the Centre Line Slope (CLS) method. We observe that the FFCF decay for both Chl a and Chl b in the methanol solvent environment has a ~40 ps component that is absent in aprotic solvents, tetrahydrofuran and diethyl ether. We observe also that Chl b exhibit higher values of inhomogeneous broadening than Chl a.

Part 1: Administration; International audience; Public key server is a simple yet effective way of key management in secure end-to-end communication. To ensure the trustworthiness of a public key server, CONIKS employs a tamper-evident data structure on the server and a gossiping protocol among clients in order to detect compromised servers. However, due to lack of incentive and vulnerability to malicious clients, a gossiping protocol is hard to implement in practice. Meanwhile, alternative solutions such as EthIKS are too costly. This paper presents Trusternity, an auditing scheme relying on Ethereum blockchain that is easy to implement, inexpensive to operate and resilient to malicious clients. We also conduct an empirical study of system behaviour in face of attacks and propose a lightweight anomaly detection algorithm to protect clients against such attacks.

International audience; Public key server is a simple yet effective way of key management in secure end-to-end communication. To ensure the trustworthiness of a public key server, transparent log systems such as CONIKS employ a tamper-evident data structure on the server and a gossip-ing protocol among clients in order to detect compromised servers. However, due to lack of incentive and vulnerability to malicious clients, a gossiping protocol is hard to implement in practice. Meanwhile , alternative solutions such as EthIKS are not scalable. This paper presents Trusternity, an auditing scheme relying on Ethereum blockchain that is easy to implement, scalable and inexpensive to operate.

BackgroundContinuing medical education (CME) is indispensable, but costs are a barrier. We tested the effectiveness of a novel mHealth intervention (mCME V.2.0) promoting CME among Vietnamese HIV clinicians.MethodsWe enrolled HIV clinicians from three provinces near Hanoi. The 6-month intervention consisted of (1) daily short message service multiple-choice quiz questions, (2) daily linked readings, (3) links to online CME courses and (4) feedback messages describing the performance of the participant relative to the group. Control participants had equal access to the online CME courses. Our primary endpoint was utilisation of the online CME courses; secondary endpoints were self-study behaviour, performance on a standardised medical exam and job satisfaction.ResultsFrom 121 total HIV clinicians in the three provinces, 106 (87.6%) enrolled, and 48/53 intervention (90%) and 47/53 control (89%) participants completed the endline evaluations. Compared with controls, intervention participants were more likely to use the CME courses (risk ratio (RR) 2.3, 95% CI 1.4 to 3.8, accounting for 83% of course use (PConclusionThe mCME V.2.0 intervention improved self-study behaviour, medical knowledge and job satisfaction. This approach has potential for expansion in Vietnam and similar settings.Trial registration numberNCT02381743.

Health sector decentralization has created an urgent need to strengthen public health management capacity in many countries throughout the developing world. This article describes the establishment of a national management training network in Vietnam that used Project-Based Learning to strengthen management competencies of HIV program workers and linked training to measurable improvement in HIV/AIDS public health program outcomes. Skills were taught using a combination of classroom learning and mentored fieldwork. From 2005 to 2015, 827 HIV/AIDS program managers were trained with this method throughout Vietnam by trainers in 3 regional training centers. A total of 218 applied learning projects were carried out by trainees during this period; 132 resulted in measurable improvements in HIV/AIDS program outputs, and 86 produced well-organized plans for implementing, monitoring, and evaluating HIV/AIDS intervention strategies. Vietnam's management training network represents an important advancement in public health workforce development that helps prepare workers for new roles and responsibilities in a decentralized health system.

Two attractive features of cloud storage services are (1) the automatic synchronization of files between multiple devices and (2) the possibility of sharing files with other users. However, many users are concerned about the security and privacy of data stored in the cloud. Client-side encryption is an effective safeguard, but it requires all client devices to have the decryption key. Current solutions derive these keys from user-chosen passwords, which are easily guessed. We present OmniShare, the first scheme to combine strong client-side encryption with intuitive key distribution mechanisms to enable access from multiple client devices and sharing between users. OmniShare uses a novel combination of out-of-band channels (including QR codes and ultrasonic communication), as well as the cloud storage service itself, to authenticate new devices. We describe the design and implementation of OmniShare and explain how we evaluated its security (using formal methods), its performance (benchmarks), and its usability (cognitive walkthrough). Two attractive features of cloud storage services are (1) the automatic synchronization of files between multiple devices and (2) the possibility of sharing files with other users. However, many users are concerned about the security and privacy of data stored in the cloud. Client-side encryption is an effective safeguard, but it requires all client devices to have the decryption key. Current solutions derive these keys from user-chosen passwords, which are easily guessed. We present OmniShare, the first scheme to combine strong client-side encryption with intuitive key distribution mechanisms to enable access from multiple client devices and sharing between users. OmniShare uses a novel combination of out-of-band channels (including QR codes and ultrasonic communication), as well as the cloud storage service itself, to authenticate new devices. We describe the design and implementation of OmniShare and explain how we evaluated its security (using formal methods), its performance (benchmarks), and its usability (cognitive walkthrough).

We all know that the ratio of social data noise is pretty significant. Therefore, tackling with noise problem is always obtained attention from data scientists. In this paper, we present a research of using reconstruction independent component analysis algorithm for blind separation social event signals from their mixtures (i.e., mixture is the combination of source signal and noise). This issue can be categorized as cocktail party problem. Despite cocktail party problem is a classical topic, however, dealing with social media data can be considered as a new research trend. From the case study with two events on Twitter, we demonstrate that our approach is quite promising. Further, our work can be applied for recommendation systems, or is used as a pre-processing step for other studies (e.g., focus search and event detection).

In this paper, we propose a new approach for filtering noises from social event signals by using independent component analysis technique. With a case study about two practical events, we prove that our approach is feasible. Besides, this idea is also to acquire high adaptability and extensibility property because we use only temporal signals of social events as inputs without requiring any more information. Moreover, our approach can be used as pre-processing step for other signal-based event discovering algorithms.

Objectives Time to definitive care is important for trauma outcomes, thus many emergency medical services (EMS) systems in the world adopt response times of ambulances as a key performance indicator. The objective of this study is to examine the underlying risk factors that can affect ambulance response times (ART) for trauma incidents, so as to derive interventional measures that can improve the ART. Material and methods This was a retrospective study based on two years of trauma data obtained from the national EMS operations centre of Singapore. Trauma patients served by the national EMS provider over the period from 1 January 2011 till 31 December 2012 were included. ART was categorized into “Short” ( 8 min) response times. A modelling framework which leveraged on both multinomial logistic (MNL) regression models and Bayesian networks was proposed for the identification of main and interaction effects. Results Amongst the process-related risk factors, weather, traffic and place of incident were found to be significant. The traffic conditions on the roads were found to have the largest effect—the odds ratio (OR) of “Long” ART in heavy traffic condition was 12.98 (95% CI: 10.66–15.79) times higher than that under light traffic conditions. In addition, the ORs of “Long ART” under “Heavy Rain” condition were significantly higher (OR 1.58, 95% CI: 1.26–1.97) than calls responded under “Fine” weather. After accounting for confounders, the ORs of “Long” ART for trauma incidents at “Home” or “Commercial” locations were also significantly higher than that for “Road” incidents. Conclusion Traffic, weather and the place of incident were found to be significant in affecting the ART. The evaluation of factors affecting the ART enables the development of effective interventions for reducing the ART.

With various emerging Social Networking Services (SNS), it is possible for users to join multiple SNS for social relationships with other users and to collect a large amount of information (e.g., statuses on Facebook and tweets on Twitter). However, these users have been facing difficulties in managing all the data collected from the multiple SNS. It is important to match social identities from the multiple SNS. In this study, we propose a privacy-aware framework for a social identity matching (SIM) method across these multiple SNS. It means that the proposed approach can protect user privacy, because only the public information (e.g., username and the social relationships of the users) is employed to find the best matches between social identities. As a result, we have shown by evaluation that the F-measure of the proposed SIM method is about 60%.