Ho Chi Minh City University of Technology

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

Fused deposition modelling (FDM) is one of the fastest-growing additive manufacturing methods used in printing fibre-reinforced composites (FRC). The performances of the resulting printed parts are limited compared to those by other manufacturing methods due to their inherent defects. Hence, the effort to develop treatment methods to overcome these drawbacks has accelerated during the past few years. The main focus of this study is to review the impact of those defects on the mechanical performance of FRC and therefore to discuss the available treatment methods to eliminate or minimize them in order to enhance the functional properties of the printed parts. As FRC is a combination of polymer matrix material and continuous or short reinforcing fibres, this review will thoroughly discuss both thermoplastic polymers and FRCs printed via FDM technology, including the effect of printing parameters such as layer thickness, infill pattern, raster angle and fibre orientation. The most common defects on printed parts, in particular, the void formation, surface roughness and poor bonding between fibre and matrix, are explored. An inclusive discussion on the effectiveness of chemical, laser, heat and ultrasound treatments to minimize these drawbacks is provided by this review.

A highly porous zeolite imidazolate framework (ZIF-8) was synthesized by a solvothermal method, and used as an efficient heterogeneous catalyst for the Knoevenagel reaction. The solid catalyst was characterized using a variety of different techniques, including X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), dynamic laser light scattering (DLS), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FT-IR), atomic absorption spectrophotometry (AAS), and nitrogen physisorption measurements. Quantitative conversion was achieved under mild conditions. The ZIF-8 catalyst could be facilely separated from the reaction mixture, and could be reused without significant degradation in catalytic activity. Furthermore, no contribution from homogeneous catalysis of active species leaching into reaction solution was detected.

The rapid development of blockchain technology and their numerous emerging applications has received huge attention in recent years. The distributed consensus mechanism is the backbone of a blockchain network. It plays a key role in ensuring the network's security, integrity, and performance. Most current blockchain networks have been deploying the proof-of-work consensus mechanisms, in which the consensus is reached through intensive mining processes. However, this mechanism has several limitations, e.g., energy inefficiency, delay, and vulnerable to security threats. To overcome these problems, a new consensus mechanism has been developed recently, namely proof of stake, which enables to achieve the consensus via proving the stake ownership. This mechanism is expected to become a cutting-edge technology for future blockchain networks. This paper is dedicated to investigating proof-of-stake mechanisms, from fundamental knowledge to advanced proof-of-stake-based protocols along with performance analysis, e.g., energy consumption, delay, and security, as well as their promising applications, particularly in the field of Internet of Vehicles. The formation of stake pools and their effects on the network stake distribution are also analyzed and simulated. The results show that the ratio between the block reward and the total network stake has a significant impact on the decentralization of the network. Technical challenges and potential solutions are also discussed.

Solving the matter of traditional energy consumption and finding the proper alternative resources are vital keys to a sustainable development policy. In recent years, many different thermal insulation materials have been developed for better energy efficiency and less environment damage. These products have confirmed their usefulness in buildings due to their benefits such as low density, high thermal resistance, and cost effectiveness. The efficiency of thermal insulation depends on their thermal conductivity and their ability to maintain their thermal characteristics over a period of time. This study presents factors influencing the thermal conductivity coefficient of three main groups including conventional, alternative, and new advanced materials. The most common factors are moisture content, temperature difference, and bulk density. Other factors are explained in some dependent studies such as airflow velocity, thickness, pressure, and material aging. The relationship between the thermal conductivity values with the mean temperature, moisture content, and density which were obtained from experimental investigation has also been summarized. Finally, uncertainty about the thermal conductivity value of some common insulation materials is also reviewed as the basis of selecting or designing the products used in building envelopes.

Since it was first discovered, thousands of years ago, silkworm silk has been known to be an abundant biopolymer with a vast range of attractive properties. The utilization of silk fibroin (SF), the main protein of silkworm silk, has not been limited to the textile industry but has been further extended to various high-tech application areas, including biomaterials for drug delivery systems and tissue engineering. The outstanding mechanical properties of SF, including its facile processability, superior biocompatibility, controllable biodegradation, and versatile functionalization have allowed its use for innovative applications. In this review, we describe the structure, composition, general properties, and structure-properties relationship of SF. In addition, the methods used for the fabrication and modification of various materials are briefly addressed. Lastly, recent applications of SF-based materials for small molecule drug delivery, biological drug delivery, gene therapy, wound healing, and bone regeneration are reviewed and our perspectives on future development of these favorable materials are also shared.

Large construction projects are inherently complex and dynamic. A comprehensive answer on how to manage such projects successfully is difficult to provide. This paper expounds on the success factors for large construction projects in Vietnam. A survey questionnaire was used to collect data from practitioners. Factor analysis was employed to categorize these success factors perceived by 109 respondents from 42 construction‐related organizations. Factor analysis uncovered that these success factors can be grouped under four categories, here titled the four COMs: comfort, competence, commitment, and communication. The result can be used as a guideline to successfully handle construction projects in Vietnam as well as in other countries, especially in the emerging economies in Asia and the rest of the world.

as well as the clinical trials on their effectiveness. The most popular types commonly used in producing modern dressings are hydrogels, hydrocolloids, alginates, foams, and films. In addition, the review also presents the polymer materials for dressing applications as well as the trend of developing these current modern dressings to maximize their function and create ideal dressings. The last is the discussion about dressing selection in wound treatment and an estimate of the current development tendency of new materials for wound healing dressings.

Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed.

Fossil fuels are currently the most significant energy sources. They are expected to become less available and more expensive, leading to a great demand for energy conservation and alternative energy sources. As a sustainable and renewable energy source, Biomass has piqued interest in generating bioenergy and biofuels over recent years. The thermal conversion of biomass through pyrolysis is an easy, useful, and low-cost process that can be applied to a wide variety of feedstocks. Pyrolysis characteristics of different feedstock samples can be analyzed and examined through thermogravimetric analysis (TGA). TGA has been an essential tool and widely used to investigate the thermal characteristics of a substance under heating environments, such as thermodegradation dynamics and kinetics. Studying the potential of waste biomass for generating sustainable bioenergy carves a pathway into a circular bioeconomy regime, and can help tackle our heavy reliance on nonrenewable energy sources. This study aims to give a deep insight into the wide use of TGA in aiding in the research and development of pyrolysis of different waste biomass sources. The thermal characteristics portrayed by different biomass wastes through TGA are discussed. The effects of significant pyrolysis operating parameters are also illustrated. A more comprehensive understanding of evolved products during the pyrolysis stage can be gained by combining TGA with other analytical methods. The pros and cons of using TGA are also outlined. Overall, an in-depth literature review helps identify current trends and technological improvements (i.e., integrating artificial intelligence) of TGA use with pyrolysis.

The exact connection between Alzheimer's disease (AD) and type 2 diabetes is still in debate. However, poorly controlled blood sugar may increase the risk of developing Alzheimer's. This relationship is so strong that some have called Alzheimer's "diabetes of the brain" or "type 3 diabetes (T3D)". Given more recent studies continue to indicate evidence linking T3D with AD, this review aims to demonstrate the relationship between T3D and AD based on the fact that both the processing of amyloid-β (Aβ) precursor protein toxicity and the clearance of Aβ are attributed to impaired insulin signaling, and that insulin resistance mediates the dysregulation of bioenergetics and progress to AD. Furthermore, insulin-related therapeutic strategies are suggested to succeed in the development of therapies for AD by slowing down their progressive nature or even halting their future complications.

Solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have emerged as potential drug delivery systems for various applications that are produced from physiological, biodegradable, and biocompatible lipids. The methods used to produce SLNs and NLCs have been well investigated and reviewed, but solvent injection method provides an alternative means of preparing these drug carriers. The advantages of solvent injection method include a fast production process, easiness of handling, and applicability in many laboratories without requirement of complicated instruments. The effects of formulations and process parameters of this method on the characteristics of the produced SLNs and NLCs have been investigated in several studies. This review describes the methods currently used to prepare SLNs and NLCs with focus on solvent injection method. We summarize recent development in SLNs and NLCs production using this technique. In addition, the effects of solvent injection process parameters on SLNs and NLCs characteristics are discussed.

Organic compounds are widespread pollutants in wastewater, causing significant risks for living organisms. In terms of advanced oxidation processes, photocatalysis is known as an effective technology for the oxidation and mineralization of numerous non-biodegradable organic contaminants. The underlying mechanisms of photocatalytic degradation can be explored through kinetic studies. In previous works, Langmuir-Hinshelwood and pseudo-first-order models were commonly applied to fit batch-mode experimental data, revealing critical kinetic parameters. However, the application or combination conditions of these models were inconsistent or ignored. This paper briefly reviews kinetic models and various factors influencing the kinetics of photocatalytic degradation. In this review, kinetic models are also systemized by a new approach to establish a general concept of a kinetic model for the photocatalytic degradation of organic compounds in an aqueous solution.

Abstract In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

SUMMARY This paper presents a novel numerical procedure based on the framework of isogeometric analysis for static, free vibration, and buckling analysis of laminated composite plates using the first‐order shear deformation theory. The isogeometric approach utilizes non‐uniform rational B‐splines to implement for the quadratic, cubic, and quartic elements. Shear locking problem still exists in the stiffness formulation, and hence, it can be significantly alleviated by a stabilization technique. Several numerical examples are presented to show the performance of the method, and the results obtained are compared with other available ones. Copyright © 2012 John Wiley & Sons, Ltd.

Streptococcus suis, a bacterium that affects pigs, is a neglected pathogen that causes systemic disease in humans. We conducted a systematic review and meta-analysis to summarize global estimates of the epidemiology, clinical characteristics, and outcomes of this zoonosis. We searched main literature databases for all studies through December 2012 using the search term "streptococcus suis." The prevalence of S. suis infection is highest in Asia; the primary risk factors are occupational exposure and eating of contaminated food. The pooled proportions of case-patients with pig-related occupations and history of eating high-risk food were 38.1% and 37.3%, respectively. The main clinical syndrome was meningitis (pooled rate 68.0%), followed by sepsis, arthritis, endocarditis, and endophthalmitis. The pooled case-fatality rate was 12.8%. Sequelae included hearing loss (39.1%) and vestibular dysfunction (22.7%). Our analysis identified gaps in the literature, particularly in assessing risk factors and sequelae of this infection.

Land-based macroplastic is considered one of the major sources of marine plastic debris. However, estimations of plastic emission from rivers into the oceans remain scarce and uncertain, mainly due to a severe lack of standardized observations. To properly assess global plastic fluxes, detailed information on spatiotemporal variation in river plastic quantities and composition are urgently needed. In this paper, we present a new methodology to characterize riverine macroplastic dynamics. The proposed methodology was applied to estimate the plastic emission from the Saigon River, Vietnam. During a two-week period, hourly cross-sectional profiles of plastic transport were made across the river width. Simultaneously, sub-hourly samples were taken to determine the weight, size and composition of riverine macroplastics (>5cm). Finally, extrapolation of the observations based on available hydrological data yielded new estimates of daily, monthly and annual macroplastic emission into the ocean. Our results suggest that plastic emissions from the Saigon River are up to 4 times higher than previously estimated. Importantly, our flexible methodology can be adapted to local hydrological circumstances and data availability, thus enabling a consistent characterization of macroplastic dynamics in rivers worldwide. Such data will provide crucial knowledge for the optimization of future mediation and recycling efforts.

Abstract This paper presents and exercises a general structure for an object‐oriented‐enriched finite element code. The programming environment provides a robust tool for extended finite element (XFEM) computations and a modular and extensible system. The programme structure has been designed to meet all natural requirements for modularity, extensibility, and robustness. To facilitate mesh–geometry interactions with hundreds of enrichment items, a mesh generator and mesh database are included. The salient features of the programme are: flexibility in the integration schemes (subtriangles, subquadrilaterals, independent near‐tip, and discontinuous quadrature rules); domain integral methods for homogeneous and bi‐material interface cracks arbitrarily oriented with respect to the mesh; geometry is described and updated by level sets, vector level sets or a standard method; standard and enriched approximations are independent; enrichment detection schemes: topological, geometrical, narrow‐band, etc.; multi‐material problem with an arbitrary number of interfaces and slip‐interfaces; non‐linear material models such as J2 plasticity with linear, isotropic and kinematic hardening. To illustrate the possible applications of our paradigm, we present 2D linear elastic fracture mechanics for hundreds of cracks with local near‐tip refinement, and crack propagation in two dimensions as well as complex 3D industrial problems. Copyright © 2007 John Wiley & Sons, Ltd.

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.