King Mongkut's University of Technology Thonburi

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Graphene oxide (GO) has recently attracted great attention due to its unique chemical and physical properties. In this work, the GO nanosheets were prepared by a chemical exfoliation technique. The structural and optical properties of the as-prepared GO nanosheets were characterized by Raman, FTIR, UV-vis and photoluminescence spectroscopy. The FTIR results confirmed the existence of oxygen-containing groups on the GO nanosheets and the photoluminescence spectra of GO nanosheets showed the emission peak in the visible regions. These results indicate that the GO nanosheets could be used as a promising new material for biological applications such as biofunctionalization and fluorescence biosensors.

In 2015, fly ash utilization rates were 70% for China, 62% for India, and 50% for the US. This leaves substantial potential for increased utilization. This article summarizes available literature concerning physical and chemical and geotechnical properties of fly ash which affect its options for re-use. Fly ashes are broadly classified worldwide into two chemical types for their industrial applications, mostly in cement industries, namely class C and class F. Class C fly ash, with its higher levels of calcium oxide, generally has self-cementing properties. In terms of global fly ash composition, fly ash from India on average contains higher levels of silicon dioxide than that from the US and China. In terms of particle size, studies report that fly ash more often is poorly graded than well-graded; fly ash from India in particular tends to be poorly graded. Optimum moisture content (OMC) values for fly ashes vary from 11 to 53%, and maximum dry density values range from 1.01 to 1.78 g/cm3. Country-specific trends in terms of fly ash OMC and maximum dry density values are not readily apparent. Fly ash tends to be non-plastic, meaning it will not swell if used as a foundation material for structures. Reported fly ash shrinkage limits range from 38 to 65. Permeability of pure fly ash generally varies from 10−4 to 10-7 cm/sec, and angle of friction varies from 25° to 40°. Keywords: Fly ash, Geotechnical, Chemical, Physical, Coal combustion byproducts, Coal combustion residuals

How do trees support their upright massive bodies? The support comes from the incredibly strong and stiff, and highly crystalline nanoscale fibrils of extended cellulose chains, called cellulose nanofibers. Cellulose nanofibers and their crystalline parts-cellulose nanocrystals, collectively nanocelluloses, are therefore the recent hot materials to incorporate in man-made sustainable, environmentally sound, and mechanically strong materials. Nanocelluloses are generally obtained through a top-down process, during or after which the original surface chemistry and interface interactions can be dramatically changed. Therefore, surface and interface engineering are extremely important when nanocellulosic materials with a bottom-up process are fabricated. Herein, the main focus is on promising chemical modification and nonmodification approaches, aiming to prospect this hot topic from novel aspects, including nanocellulose-, chemistry-, and process-oriented surface and interface engineering for advanced nanocellulosic materials. The reinforcement of nanocelluloses in some functional materials, such as structural materials, films, filaments, aerogels, and foams, is discussed, relating to tailored surface and/or interface engineering. Although some of the nanocellulosic products have already reached the industrial arena, it is hoped that more and more nanocellulose-based products will become available in everyday life in the next few years.

Language Teaching Young Lear ners', the author CarolineT.Linsementionsthat"teachingESLorEFL to young learners is an evolving field, and many efforts are being made around the world to improve the process for both teachers and students."Cameron ( 2001) also supports that "teaching languages to children need (s) all skills of the good primary teachers in managing children and keeping them on task, plus knowledge of the language, of language teaching, and of language learning."From this, it can be said that teachers of young learners require both practical and theoretical knowledge for teaching their students and increasing the quality of learning.To seek for that knowledge,thisbookmayprovidewhatteachersarelookingfor.

The Long Term Evolution-Advanced (LTEAdvanced) networks are being developed to provide mobile broadband services for the fourth generation (4G) cellular wireless systems. Deviceto- device (D2D) communications is a promising technique to provide wireless peer-to-peer services and enhance spectrum utilization in the LTE-Advanced networks. In D2D communications, the user equipments (UEs) are allowed to directly communicate between each other by reusing the cellular resources rather than using uplink and downlink resources in the cellular mode when communicating via the base station. However, enabling D2D communications in a cellular network poses two major challenges. First, the interference caused to the cellular users by D2D devices could critically affect the performances of the cellular devices. Second, the minimum quality-of-service (QoS) requirements of D2D communications need to be guaranteed. In this article, we introduce a novel resource allocation scheme (i.e. joint resource block scheduling and power control) for D2D communications in LTE-Advanced networks to maximize the spectrum utilization while addressing the above challenges. First, an overview of LTE-Advanced networks, and architecture and signaling support for provisioning of D2D communications in these networks are described. Furthermore, research issues and the current state-of-the-art of D2D communications are discussed. Then, a resource allocation scheme based on a column generation method is proposed for D2D communications. The objective is to maximize the spectrum utilization by finding the minimum transmission length in terms of time slots for D2D links while protecting the cellular users from harmful interference and guaranteeing the QoS of D2D links. The performance of this scheme is evaluated through simulations.

A commercial blend of essential oil (EO) compounds was added to a grass, maize silage, and concentrate diet fed to dairy cattle in order to determine their influence on protein metabolism by ruminal microorganisms. EO inhibited (P < 0.05) the rate of deamination of amino acids. Pure-culture studies indicated that the species most sensitive to EO were ammonia-hyperproducing bacteria and anaerobic fungi.

Coronavirus disease 2019 (COVID-19) is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diagnosis of COVID-19 depends on quantitative reverse transcription PCR (qRT-PCR), which is time-consuming and requires expensive instrumentation. Here, we report an ultrasensitive electrochemical biosensor based on isothermal rolling circle amplification (RCA) for rapid detection of SARS-CoV-2. The assay involves the hybridization of the RCA amplicons with probes that were functionalized with redox active labels that are detectable by an electrochemical biosensor. The one-step sandwich hybridization assay could detect as low as 1 copy/μL of N and S genes, in less than 2 h. Sensor evaluation with 106 clinical samples, including 41 SARS-CoV-2 positive and 9 samples positive for other respiratory viruses, gave a 100% concordance result with qRT-PCR, with complete correlation between the biosensor current signals and quantitation cycle (Cq) values. In summary, this biosensor could be used as an on-site, real-time diagnostic test for COVID-19.

Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stochastic variation of the financial market. Stock market prices are dynamic, highly sensitive, nonlinear and chaotic. There are different techniques for forecast prices in the time-variant domain and due to variability and uncertain behavior in stock prices, traditional methods, such as data mining, statistical approaches, and non-deep neural networks models are not suited for prediction and generalized forecasting stock prices. While autoregressive fractional integrated moving average (ARFIMA) model provides a flexible tool for classes of long-memory models. The advancement of machine learning-based deep non-linear modelling confirms that the hybrid model efficiently extracts profound features and model non-linear functions. LSTM networks are a special kind of recurrent neural network (RNN) that map sequences of input observations to output observations with capabilities of long-term dependencies. A novel ARFIMA-LSTM hybrid recurrent network is presented in which ARFIMA model-based filters having the linear tendencies better than ARIMA model in the data and passes the residual to the LSTM model that captures nonlinearity in the residual values with the help of exogenous dependent variables. The model not only minimizes the volatility problem but also overcome the over fitting problem of neural networks. The model is evaluated using PSX company data of the stock market based on RMSE, MSE and MAPE along with a comparison of ARIMA, LSTM model and generalized regression radial basis neural network (GRNN) ensemble method independently. The forecasting performance indicates the effectiveness of the proposed AFRIMA-LSTM hybrid model to improve around 80% accuracy on RMSE as compared to traditional forecasting counterparts.

The release of metals and metal-containing compounds into the environment is a growing concern in developed and developing countries, as human exposure to metals is associated with adverse health effects in virtually every organ system. Unfortunately, quantifying metals in the environment is expensive; analysis costs using certified laboratories typically exceed $100/sample, making the routine analysis of toxic metals cost-prohibitive for applications such as occupational exposure or environmental protection. Here, we report on a simple, inexpensive technology with the potential to render toxic metals detection accessible for both the developing and developed world that combines colorimetric and electrochemical microfluidic paper-based analytical devices (mPAD) in a three-dimensional configuration. Unlike previous mPADs designed for measuring metals, the device reported here separates colorimetric detection on one layer from electrochemical detection on a different layer. Separate detection layers allows different chemistries to be applied to a single sample on the same device. To demonstrate the effectiveness of this approach, colorimetric detection is shown for Ni, Fe, Cu, and Cr and electrochemical detection for Pb and Cd. Detection limits as low as 0.12 μg (Cr) were achieved on the colorimetric layer while detection limits as low as 0.25 ng (Cd and Pb) were achieved on the electrochemical layer. Selectivity for the target analytes was demonstrated for common interferences. As an example of the device utility, particulate metals collected on air sampling filters were analyzed. Levels measured with the mPAD matched known values for the certified reference samples of collected particulate matter.

Paper-based analytical devices (PADs) represent a growing class of elegant, yet inexpensive chemical sensor technologies designed for point-of-use applications. Most PADs, however, still utilize some form of instrumentation such as a camera for quantitative detection. We describe here a simple technique to render PAD measurements more quantitative and straightforward using the distance of colour development as a detection motif. The so-called distance-based detection enables PAD chemistries that are more portable and less resource intensive compared to classical approaches that rely on the use of peripheral equipment for quantitative measurement. We demonstrate the utility and broad applicability of this technique with measurements of glucose, nickel, and glutathione using three different detection chemistries: enzymatic reactions, metal complexation, and nanoparticle aggregation, respectively. The results show excellent quantitative agreement with certified standards in complex sample matrices. This work provides the first demonstration of distance-based PAD detection with broad application as a class of new, inexpensive sensor technologies designed for point-of-use applications.

Although an Internet-of-Things-based smart home solution can provide an improved and better approach to healthcare management, yet its end user adoption is very low. With elderly people as the main target, these conservative users pose a serious challenge to the successful implementation of smart home healthcare services. The objective of this research was to develop and test a theoretical framework empirically for determining the core factors that can affect the elderly users' acceptance of smart home services for healthcare. Accordingly, an online survey was conducted with 254 elderly people aged 55 years and above across four Asian countries. Partial least square structural equation modeling was applied to analyze the effect of eight hypothesized predicting constructs. The user perceptions were measured on a conceptual level rather than the actual usage intention toward a specific service. Performance expectancy, effort expectancy, expert advice, and perceived trust have a positive impact on the behavioral intention. The same association is negative for technology anxiety and perceived cost. Facilitating conditions and social influence do not have any effect on the behavioral intention. The model could explain 81.4% of the total variance in the dependent variable i.e., behavioral intention. Effort expectancy is the leading predictor of smart homes for healthcare acceptance among the elderly. Together with expert advice, perceived trust, and perceived cost, these four factors represent the key influence of the elderly peoples' acceptance behavior. This paper provides the groundwork to explore the process of the actual adoption of smart home services for healthcare by the elderly people with potential future research areas.

A microfluidic paper-based analytical device (μPAD) for the separation of blood plasma from whole blood is described. The device can separate plasma from whole blood and quantify plasma proteins in a single step. The μPAD was fabricated using the wax dipping method, and the final device was composed of a blood separation membrane combined with patterned Whatman No.1 paper. Blood separation membranes, LF1, MF1, VF1 and VF2 were tested for blood separation on the μPAD. The LF1 membrane was found to be the most suitable for blood separations when fabricating the μPAD by wax dipping. For blood separation, the blood cells (both red and white) were trapped on blood separation membrane allowing pure plasma to flow to the detection zone by capillary force. The LF1-μPAD was shown to be functional with human whole blood of 24-55% hematocrit without dilution, and effectively separated blood cells from plasma within 2 min when blood volumes of between 15-22 μL were added to the device. Microscopy was used to confirm that the device isolated plasma with high purity with no blood cells or cell hemolysis in the detection zone. The efficiency of blood separation on the μPAD was studied by plasma protein detection using the bromocresol green (BCG) colorimetric assay. The results revealed that protein detection on the μPAD was not significantly different from the conventional method (p > 0.05, pair t-test). The colorimetric measurement reproducibility on the μPAD was 2.62% (n = 10) and 5.84% (n = 30) for within-day and between day precision, respectively. Our proposed blood separation on μPAD has the potential for reducing turnaround time, sample volume, sample preparation and detection processes for clinical diagnosis and point-of care testing.

Abstract This article presents an experimental investigation where the thermal conductivity and viscosity of silver-deionized water nanofluid is measured and studied. The mixture consists of silver nanoparticles of 0.3, 0.6, and 0.9% of volume concentrations and studied for temperatures between 50°C and 90°C. The transient hot-wire apparatus and Cannon-Fenske viscometer are used to measure the thermal conductivity and kinematic viscosity of nanofluid, respectively. The thermal conductivity increases with the increase in temperature and particle concentrations. A minimum and maximum enhancement of 27% at 0.3 vol% and 80% at 0.9 vol% are observed at an average temperature of 70°C. The viscosity decreases with the increase in temperature and increases with the increase in particle concentrations. The effect of Brownian motion and thermophoresis on the thermo-physical properties is discussed. Thus, an experimental correlation for thermal conductivity and viscosity, which relates the volume concentration and temperature, is developed, and the proposed correlation is found to be in good agreement with the experimental results. Keywords: thermal conductivityviscositysilvernanofluidtemperaturenanoparticle enhancement

Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples.

The rapid advancement in Artificial Intelligence (AI) and big data has developed significance in the water sector, particularly in hydrological time-series predictions. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have become research focal points due to their effectiveness in modeling non-linear, time-variant hydrological systems. This review explores the different architectures of RNNs, LSTMs, and Gated Recurrent Units (GRUs) and their efficacy in predicting hydrological time-series data.•RNNs are foundational but face limitations such as vanishing gradients, which impede their ability to model long-term dependencies. LSTMs and GRUs have been developed to overcome these limitations, with LSTMs using memory cells and gating mechanisms, while GRUs provide a more streamlined architecture with similar benefits.•The integration of attention mechanisms and hybrid models that combine RNNs, LSTMs, and GRUs with other Machine learning (ML) and Deep Learning (DL) has improved prediction accuracy by capturing both temporal and spatial dependencies.•Despite their effectiveness, practical implementations of these models in hydrological time series prediction require extensive datasets and substantial computational resources. Future research should develop interpretable architectures, enhance data quality, incorporate domain knowledge, and utilize transfer learning to improve model generalization and scalability across diverse hydrological contexts.

In this paper, we extend the result of Wardowski (Fixed Point Theory Appl. 2012:94, 2012) by applying some weaker conditions on the self map of a complete metric space and on the mapping F, concerning the contractions defined by Wardowski. With these weaker conditions, we prove a fixed point result for F-Suzuki contractions which generalizes the result of Wardowski. MSC:74H10, 54H25.

Natural colorants from plant-based materials have gained increasing popularity due to health consciousness of consumers. Among the many steps involved in the production of natural colorants, pigment extraction is one of the most important. Soxhlet extraction, maceration, and hydrodistillation are conventional methods that have been widely used in industry and laboratory for such a purpose. Recently, various non-conventional methods, such as supercritical fluid extraction, pressurized liquid extraction, microwave-assisted extraction, ultrasound-assisted extraction, pulsed-electric field extraction, and enzyme-assisted extraction have emerged as alternatives to conventional methods due to the advantages of the former in terms of smaller solvent consumption, shorter extraction time, and more environment-friendliness. Prior to the extraction step, pretreatment of plant materials to enhance the stability of natural pigments is another important step that must be carefully taken care of. In this paper, a comprehensive review of appropriate pretreatment and extraction methods for chlorophylls, carotenoids, betalains, and anthocyanins, which are major classes of plant pigments, is provided by using pigment stability and extraction yield as assessment criteria.

Since the launch of the first consumer grade EEG measuring sensors `NeuroSky Mindset' in 2007, the market has witnessed an introduction of at least one new product every year by competing manufacturers, which include NeuroSky, Emotiv, interaXon and OpenBCI. There are numerous variations in the make and versions, but these products clearly share the key selling points of affordability, portability, and ease of use. These features are patently well placed provided one of the main objectives for their development is to attract a new target group of commercial users. Nevertheless, with several decades of traditional EEG usage in clinical and experimental settings, the shift toward commercial and engineering sides has not been achieved without skepticism. With this in mind, researchers in related fields have been tirelessly working to ensure that these putatively novel features were not introduced at the expense of efficiency and accuracy by conducting validation studies to compare the performance of data derived from consumer grade EEG devices with ones from standard research grade counterparts. In this review, we seek to provide the detail of the products supplied by the major players, summarize studies that evaluate consumer product's performance against research grade devices, the key areas of applications that these consumer grade devices have been employed in over the past five years or so, and finally give our perspectives on the limitations and what these innovative tools could offer going forward in terms of research and commercial applications.

Smart grid technology is the key for an efficient use of distributed energy resources. Noting the climate change becomes an important issue the whole world is currently facing, the ever increasing price of petroleum products and the reduction in cost of renewable energy power systems, opportunities for renewable energy systems to address electricity generation seems to be increasing. However, to achieve commercialization and widespread use, an efficient energy management strategy of system needs to be addressed. Recently, the concept of smart grid has been successfully applied to the electric power systems. This paper presents the study of integrating renewable energy in smart grid system. The introductory sections provide the role of renewable energy and distributed generation in smart grid system. Subsequent sections cover the concept of smart grid as well as benefits and barrier of smart grid renewable energy system. Pricing is a significant variable in success of renewable energy promotion. Thus, it is important to gain insight to renewable energy pricing by considering unique characteristics associated with renewable energy alternatives. A review of work done in renewable smart grid systems in recent years indicates the promising potential of such research characteristics in the future. This would be useful to developers and practitioners of renewable energy systems and to policy makers.