This paper presents the design of a miniaturized polyimide-based antenna for Ultra-Wideband (UWB) communication. Miniaturization is achieved through the utilization of adhesive material with a high dielectric constant (high-Dk). The goal of this work is to investigate the impact of such material on the antenna performance and to optimize its design for UWB operation. The manufacturing of the antenna using the proposed structure was developed and the prototype of antenna for UWB high-band (6 - 9 GHz) is measured and analyzed. By leveraging the high Dk of the adhesive material, the simulation and measurement results showed that the proposed antenna with high Dk adhesive film can achieve the compact dimension with good performance in gain, and time domain characteristics. The results of this study show a potential of exhibiting a reduction in the size of the antenna and will contribute to the advancement of miniaturized UWB antenna technology.

Enhancing the effectiveness of search and rescue operation at sea is always a duty of utmost importance of the coastal states. The search area for distressed objects can be determined by using Monte Carlo simulation, combined with the Median-Filter. Once the search area has been identified, the success of search and rescue operations depends on the sweeping ability of search and rescue vessel at the probability area of the distress object with the minimum time. This is the important element to the success of the search and rescue operation as it minimizes the risk and cost for Search and rescue team. In this article, the authors study and propose the use of Bacterial Foraging Optimization Algorithm (BFOA) to calculate the optimal search and co-ordination route for many search and rescue vessels in Vietnam sea. The simulation results show that it is quite consistent with reality and BFOA can be effectively applied to determine a quick search.

In recent years, nonalcoholic fatty liver disease/steatohepatitis (NAFLD/NASH) has become a leading worldwide disease, and its therapies are facing many complexities. Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors that belong to the nuclear receptor (NR) superfamily and have three subtypes: PPARα (NR1C1), PPARβ/δ (NR1C2), and PPARγ (NR1C3). They have been identified to be essential in the regulation of lipid metabolism by controlling the transcription of genes related to fatty acids, bile acids, and cholesterol metabolism. Many PPAR agonists have been reported, such as natural agonists (fatty acids, eicosanoids, and phospholipids) and synthetic ligands (fibrates, thiazolidinediones, glitazars, and elafibranor). PPAR-based chemicals have a breakthrough in the innovation of therapy for fatty liver disease. This review will provide an overview of how PPARα and PPARγ play roles in lipid homeostasis, discussing the consideration of their agonists as potential therapeutic agents for NAFLD/NASH.

A simple and sensitive liquid chromatography-tandem mass spectrometry (LC– MS/MS) method has been developed for the simultaneous determination of atorvastatin (ATOR), ezetimibe (EZM) and their three metabolites including o-hydroxyl atorvastatin (o-OH ATOR), p-hydroxyl atorvastatin (p-OH ATOR), and ezetimibe-glucuronide (EZM-G) in human plasma using benzyl paraben (BP) as internal standard (IS). The analytes and IS were ionized using ESI positive ion mode (ATOR, o-OH ATOR, and p-OH ATOR), ESI negative ion mode (EZM, EZM-G, and BP), and operated in multiple reaction monitoring (MRM) mode. They were next extracted by salting-out assisted liquid–liquid extraction with acetonitrile, and then analyzed by liquid chromatography on a reversed-phase chromatographic column (50 mm × 4.6 mm; 3.5 µm), using a mixture of ac-etonitrile and an acetic acid solution (0.5%) as the mobile phase, showing high extraction effi-ciency (>70%), and a minimized matrix effect. The method was satisfactorily validated, and showed excellent linearity over wide concentration ranges of 0.06–15 ng/mL, 0.6–150 ng/mL, 0.4–100 ng/mL, 0.12–30 ng/mL, and 0.05–3 ng/mL for EZM, EZM-G, ATOR, o-OH ATOR, and p-OH ATOR, respectively.

Between 11 to 15 February 2019, a dust storm originating from Central Australia with persistent westerly and south westerly winds caused high particles concentration at many sites in the state of New South Wales (NSW), both inland and along the coast. The dust continued on to New Zealand and to Antarctica in the south east. This study uses observed data from air quality monitoring stations in NSW and New Zealand, MODIS 3km AOD (Aerosol Optical Depth) product from Terra/Aqua and lidar aerosol profile from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite data, and the Weather Research Forecast WRF-Chem model based on GOCART-AFWA (Goddard Chemistry Aerosol Radiation and Transport – Air Force and Weather Agency) dust scheme and GOCART aerosol and gas-phase MOZART (Model for Ozone And Related chemical Tracers) chemistry model to study the long-range transport of aerosols for the period 11 to 15 February 2019 across eastern Australia and onto New Zealand and Antarctica. Wild fires also happened in northern NSW at the same time and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. Modelling results by the WRF-Chem model show that for the Canterbury region of South Island of New Zealand, peak concentration of PM10 (and PM2.5) as measured on 14 February 2019 at 05:00 UTC at the monitoring stations of Geraldine, Ashburton, Timaru and Woolston (Christchurch), which are more than 100km from each other and at Rangiora, Kaiapoi about 2 hours later, correspond to the prediction of high PM10 due to intrusion of dust to ground level from transported dust layer above. The Aerosol Optical Depth (AOD) observation data from MODIS Terra/Aqua and CALIOP lidar measurements on board CALIPSO satellite also indicate that high altitude of dust, originated from this dust storm event in Australia, was located above Antarctica. This study suggests that at present dust storms in Australia can transport dust from sources in Central Australia to the Tasman sea, New Zealand and Antarctica. This process has been going on for at least the last 170k years as indicated by dust found in ice cores from Antarctica and sediment records in the Tasman Sea.

Atomic force microscopy (AFM) has been extensively used for the nanoscale characterization of polymeric materials. The coupling of AFM with infrared spectroscope (AFM-IR) provides another advantage to the chemical analyses and thus helps to shed light upon the study of polymers. In this perspective paper, we review recent progress in the use of AFM-IR in polymer science. We describe first the principle of AFM-IR and the recent improvements to enhance its resolution. We discuss then the last progress in the use of AFM-IR as a super-resolution correlated scanned-probe IR spectroscopy for chemical characterization of polymer materials dealing with polymer composites, polymer blends, multilayers and biopolymers. To highlight the advantages of AFM-IR, we report here several results in studying crystallization of both miscible and immiscible blends as well as polymer aging. Then, we demonstrate how this novel technique can be used to determine phase separation, spherulitic structure and crystallization mechanisms at the nanoscale, which have never been achieved before. The review also discusses future trends in the use of AFM-IR in polymer materials, especially in polymer thin film investigation.

Alum sludge is a byproduct of water treatment plants and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this paper explores the use of artificial intelligence (AI) methods to predict the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR based on a database with nine input parameters. The results showed that all three AI models were able to predict CBR with good accuracy, with coefficient of determination (R2) values ranging from 0.94 to 0.99 and mean absolute error (MAE) values ranging from 0.30 to 0.51. In a novel approach, the genetic programming method was used to produce an equation to estimate CBR, which included seven inputs and accurately predicted CBR. The analysis of sensitivity and importance of parameters showed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study suggests that AI methods can effectively predict the performance of alum sludge as a soil stabilizer, and the proposed equation using genetic programming can be a useful tool for predicting CBR.

Background: Radiotherapy is an important part of the medical therapy concept treating tumors in the head and neck region; however, the impact on the field of dentistry could be im-mense. The objective of this study was to evaluate the microtensile bond strength (µTBS) of uni-versal adhesives and to compare the results of different application modes (etch-and-rinse and self-etch) on irradiated and sound human dentin. Methods: Dentin specimen of 180 extracted human wisdom teeth were divided in two superordinate groups. Ninety teeth received either no irradiation (control group) or the high-level therapeutic dose of 60 Gy (test group) applied frac-tionally (2 Gy/day, 5 days/week, 6 weeks). Samples from each group were randomly divided in-to six subgroups assigned to three established universal adhesives (Futurabond® U, VOCO; AdheSE® Universal, Ivoclar Vivadent; Xeno® Select, Dentsply) in both conceivable application modes: etch-and-rinse and self-etch (n = 15). Bond strength was assessed with a universal testing machine immediately after application and light curing of the adhesive materials. Statistical analysis was performed using two-way ANOVA (p<0.01) and Tukey's test (p<0.05). Results: The effect of dentin irradiation and application mode for the adhesive restoration was found to be significant (p<0.01, ANOVA). In all groups, a decrease of µTBS could be observed when used on irradiated dentin. However, no significant influence of high level radiation on the bond strength of Futurabond® U and AdheSE® Universal in both modes could be evaluated (p>0.05, Tukey´s test). Solely in the case of the adhesive system Xeno® Select, significantly reduced bond strength could be determined in self-etch mode after irradiation (group 60 Xse) compared to the control group (0 Xse) (p<0.05, Tukey´s test). Conclusions: Within the limitations of an in vitro study, some effects of high-level tumor irradiation on the bond strength of universal adhesives can be detected. However, these effects were strongly depending on the used material and application mode.

The Cauto River Basin (CRB), the heartland of Cuban agriculture, has been hit hard by drought and water shortage. In response to this pressing issue, this study provides a comprehensive as-sessment of water supply, demand and balance within the Cauto River Basin, considering the baseline and projected socio-economic and climatic conditions by coupling of SWAT and WEAP models. The obtained results reveal that the annual flow in the CRB is projected to slightly de-crease (2.5%) in which the reduction in the rainy season (3.1%) is higher than that in the dry sea-son (1.3%). Total water demand in the baseline scenario is around 1.194 billion m3, dominated by agriculture (96%), with rice crop requiring nearly half. For the future scenario of 2050, the study shows a 16.6% surge in demand to 1.394 billion m3, driven by climate change and agricultural expansion. However, domestic use decreases by 10% due to population reduction. Water deficit in the future is projected to increase by 52% from 172.4 to 262.7 million m3 due to rising water demand and declining water supply. This study shows that integrating a hydrological model into a water allocation model is a promising approach to estimate the water supply, demand and balance, which is a crucial component of water resources management.

In this study, effects of molecular structure transformation of canna starch (CS) in oxidation condition on physicochemical characteristics of thermoplastic starch were examined. Thermoplastic starch (TPS) samples of CS and respective oxidized starch (in 1, 3 and 5% of sodium hypochlorite, w/w) were prepared by using an internal mixer with the presence of 30 wt% of glycerol as plasticizer. The physicochemical, morphological, thermal, and mechanical properties of native starch-based TPS (NTPS) and oxidized starch-based TPS (OTPS) samples were evaluated. The content of carboxyl and carbonyl groups in oxidized canna starch (OCS) increased with the increase in the content of oxidant, leading to an improvement in the plasticization of CS. FTIR spectra of OTPS samples showed the stronger interaction between oxidized starch and plasticizer. SEM images of fracture surface indicated that the morphology of OTPS samples were smoother than that of NTPS samples. XRD patterns confirmed that the crystallite type of starches was also clearly changed after extrusion process and the decrease in crystallinity of OTPS samples is attributed to effects of both oxidation and plasticization. DSC results indicated that Tg of OTPS was greatly influenced by the carbonyl and carboxyl content and the higher content of these groups in oxidized starched could help the plasticization becomes easier. The better plasticization in OTPS samples resulted in the better mechanical properties and thermal stability of OTPS. The outcome suggested that the use of oxidized starch is an effective method to improve the plasticization in preparing thermoplastic starch.

Damage assessment is one of the most crucial issues for bridge engineers, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge. In this regard, the artificial intelligence (AI)-based approach seems a potential way to accomplish those obstacles. This study deploys a comprehensive campaign to determine all dynamic parameters of a pre-damage steel truss bridge structure. Based on the results of mode shape, natural frequency, and damping ratio, a finite element model (FEM) is created and keeps updating. The artificial intelligence network's input data will be analyzed and evaluation from damage cases. The trained artificial neural network model will be curated and evaluated to confirm the approach's feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system is showing good performance in terms of monitoring the structural behavior of the bridge under some unexpected accidents.

The 2019-2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive extend of the wildfires has caused smoke pollutants transported not only to New Zealand but across the Pacific Ocean to South America. At the height of the wildfires, smoke plumes were injected into the stratosphere at height up to 25km and hence transported across the globe. Based on meteorological and air quality simulation using WRF-Chem model, air quality monitoring data collected during the bushfire period and remote sensing data from MODIS and CALIPSO satellites, the extend of the wildfires and the pollutant transport, and their impacts on air quality and health on exposed population in NSW can be analysed. The results showed that WRF-Chem model using Fire Emission Inventory from NCAR (FINN) predicts the dispersion and transport of pollutants and the predicted concentration of PM2.5 and other pollutants from wildfires reasonably well when compared with ground-based and satellite data. The impact on health endpoints such as mortality, respiratory and cardiovascular diseases hospitalisation across the modelling domain is then estimated. The estimated health impact is comparable with previous study based only on observation data, but the results in this study provide much more detailed spatially and temporally with regards to the health impact from the 2019-2020 wildfire.

Modern experimental astroparticle physics features large-scale setups measuring different messengers, namely high-energy particles generated by cosmic accelerators (e.g. supernova remnants, active galactic nuclei, etc): cosmic and gamma rays, neutrinos and recently discovered gravitational waves. Ongoing and future experiments are distributed over the Earth including ground, underground/underwater setups as well as balloon payloads and spacecrafts. The data acquired by these experiments have different formats, storage concepts and publication policies. Such differences are a crucial issue in the era of big data and of multi-messenger analysis strategies in astroparticle physics. We propose a service ASTROPARTICLE.ONLINE in the frame of which we develop an open science system which enables to publish, store, search, select and analyse astroparticle physics data. The cosmic-ray experiments KASCADE-Grande and TAIGA were chosen as pilot experiments to be included in this framework. In the first step of our initiative we will develop and test the following components of the full data life cycle concept: (i) describing, storing and reusing of astroparticle data; (ii) software for performing multi-experiment and multi-messenger analyses like deep-learning methods; (iii) outreach including example applications and tutorial for students and scientists outside the specific research field. In the present paper we describe the concepts of our initiative, and in particular the plans toward a common, federated astroparticle data storage.

The aim of this study is to describe a research protocol of a multi-country randomized controlled trial to investigate the effect of a smartphone CBT stress management program on improving depression and other health- and work-related outcomes among hospital nurses in Vietnam and Thailand during the COVID-19 epidemic as a part of the COCONATS project. The study is a two-arm, parallel randomized control trial. Hospital nurses (N = 1,500) will be recruited in Vietnam and Thailand, and those who meet the criteria will be randomly allocated to the intervention or control (treatment as usual) group. A seven-week, seven-module smartphone-based stress management program will be developed to teach CBT skills. The intervention group will participate in the program for 10 weeks. Depression as the primary outcome will be measured using the DASS21 at baseline and in 3- and 6-month follow-up surveys. A mixed model repeated measures analysis will be used to test the intervention effect in the total combined sample, on an intention-to-treat basis. This is the first study to investigate the effectiveness of a self-guided smartphone CBT program on improving depression among hospital nurses using a RCT design in South-East Asian middle-income countries during the COVID-19 pandemic.