Successful Applications In Various Fields Research Articles

A Simple and Sensitive Approach for Real‐Time Sensing of Enzymatically Catalyzed Hydrogelation

AbstractDespite the critical role of hydrogels in material science and biotechnology, current methods for analyzing their formation lack real‐time monitoring and require complex sample preparation and instrumentation. In this work, an innovative methodology is introduced for the real‐time analysis of enzymatically catalyzed hydrogelation. Electrochemical impedance spectroscopy (EIS) coupled with interdigitated electrodes (IDEs) to sense and transduce the gelation reaction of model precursor carboxymethyl cellulose‐tyramine (CMC‐TA) conjugates that undergoes enzymatic cross‐linking by horseradish peroxidase (HRP) and hydrogen peroxide (H2O2). Real‐time monitoring involves single‐frequency analyses at 3 × 105 Hz, where the measured impedance consists solely of a resistive component, and the admittance equates to solution conductance. The gelation trajectories for all tested enzymatically cross‐linked hydrogel component combinations are determined by substituting the conductance data in the modified Michaelis–Menten kinetic model. Specifically, for CMC‐TA cross‐linked by HRP, the authors calculate apparent KM and kcat values of 82.1 µM and 95.5 s−1, respectively. These findings are further validated through rheological characterization, including oscillatory shear measurements and microrheology. Overall, this research paves the way for a streamlined, accurate, and cost‐effective approach to controllable enzymatically initiated hydrogel synthesis, enhancing their successful application in various fields ranging from material science to biotechnology.

Deep reinforcement learning for financial trading using multi-modal features

Deep Reinforcement Learning (DRL) has several successful applications in various fields. One of these fields is financial trading, in which an agent interacts with its environment in order to maximize profit by purchasing and selling financial assets. Besides price-related features, sentiment-related features have recently been used for this task. Sentiment information reflects the public emotional reaction to these assets. This type of information has been shown to have a beneficial impact on the performance of the agents in recent works. However, directly incorporating such information into trading agents is not always trivial, often leading to sub-optimal results, e.g., due to overfitting. In this work, we propose a novel multi-modal financial embedding-based approach, as an easy and efficient way of combining various modalities from multiple sources of information, such as price and sentiment data. In addition to effectively combining multi-modal information, the proposed method allows for (a) quantifying the impact of different modalities in the decisions of the model, as well as (b) altering the importance of them on-the-fly without performing retraining. We evaluate the agents’ performance using the Profit and Loss (PnL) metric and figure that the impact of sentiment utilization in combination with embeddings is advantageous for DRL training.

From fiber Bragg gratings to coaxial cable Bragg gratings: One-dimensional microwave quasi-periodic photonic crystals

Coaxial cables and optical fibers are two types of cylindrical waveguides used in telecommunications. Fiber Bragg gratings (FBGs) have found successful applications in various fields, such as optical communications, fiber lasers, and fiber-optic sensing. In this paper, we propose and numerically investigate the implementations of various fiber Bragg configurations, including uniform, chirped, apodized, and phase-shifted configurations, on coaxial cables to generate the corresponding special types of coaxial cable Bragg gratings (CCBGs). The simulation results of different CCBGs match well with the well-known FBG theories. It is demonstrated that the reflection spectrum of a CCBG can be flexibly tailored by introducing various quasi-periodic perturbations in the permittivity of the dielectric layer along the coaxial cable. The proposed special types of CCBGs with unique characteristics could find potential applications in radio frequency signal processing, communication, and sensing fields.

Short-term water demand forecasting using data-centric machine learning approaches

Abstract Accurate water demand forecasting is the key to urban water management and can alleviate system pressure brought by urbanisation, water scarcity and climate change. However, existing research on water demand forecasting using machine learning is focused on model-centric approaches, where various forecasting models are tested to improve accuracy. The study undertakes a data-centric machine learning approach by analysing the impact of training data length, temporal resolution and data uncertainty on forecasting model results. The models evaluated are Autoregressive (AR) Integrated Moving Average (ARIMA), Neural Network (NN), Random Forest (RF) and Prophet. The first two are commonly used forecasting models. RF has shown similar forecast accuracy to NN but has received less attention. Prophet is a new model that has not been applied to short-term water demand forecasting, though it has had successful applications in various fields. The results obtained from four case studies show that (1) data-centric machine learning approaches offer promise for improving forecast accuracy of short-term water demands; (2) accurate forecasts are possible with short training data; (3) RF and NN models are superior at forecasting high-temporal resolution data; and (4) data quality improvement can achieve a level of accuracy increase comparable to model-centric machine learning approaches.

Systematic Review of the Early Detection and Classification of Plant Diseases Using Deep Learning

Deep learning (DL) addresses the brilliant period of Artificial intelligence (AI) and is slowly developing into the main technique in numerous fields. Currently it assumes a significant part in the early location and order of plant diseases. Plant diseases have long been one of the main threats to food security, significantly reducing crop yields and quality. Therefore accurate disease diagnosis is the main goal. The utilization of machine learning (ML) innovation in this space is accepted to have prompted a huge expansion in usefulness in the hydroponics area, particularly in the new rise of ML which appears to expand the degree of precision. As the latest modern technology in image processing and successful application in various fields, deep learning has great potential and broad prospects in agriculture. This paper surveys 40 studies using deep learning methods applied to agriculture and food production. In this study, deep learning is compared to other popular image processing techniques. The findings show that deep learning provides better performance. Future directions may additionally consist of the usage of drones and agricultural robots to automate photo seize and then zooming in on plant sickness image datasets, using newly posted fashions that describe more efficient architectures with fewer parameters, as well as the use of new techniques for photograph enlargement inclusive of generative adversarial networks (GANs).

Fabrication of biogenic silver nanoparticles from Ficus religiosa bark extract and their application for chromium removal.

Ficus religiosa bark extract was chosen as a reducing agent to fabricate biogenic silver nanoparticles (AgNPs), which were used to treat chromium in synthetic wastewater. The AgNPs formed were characterized using ultraviolet-visible absorption spectroscopy (UV-vis), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The results of AgNPs characterization showed that the UV-vis spectrophotometer gave an absorbance peak for AgNPs synthesized from bark extract at 448 nm. The peaks in the XRD pattern of AgNPs synthesized from the bark extract of Ficus religiosa are associated with that of the face-centered-cubic form of metallic silver. The SEM analysis showed clear spherical morphology of AgNPs. These synthesized AgNPs were applied for the removal of chromium from synthetic wastewater. It was seen from the treatment results that above 74.8% removal efficiency was achieved. HIGHLIGHTS: Green synthesis of nanoparticles using plant extracts and their successful application in various fields has been previously studied by various researchers. Ficus religiosa bark extract was used for the synthesis of silver nanoparticles, which after characterization using ultraviolet-visible absorption spectroscopy, scanning electron microscopy, and X-ray diffraction were used to treat chromium in synthetic wastewater prepared in lab. The use of silver nanoparticles for the treatment of heavy metals in wastewater can be considered a good replacement option for the conventional treatment techniques.

Ni(ii) immobilized on poly(guanidine-triazine-sulfonamide) (PGTSA/Ni): a mesoporous nanocatalyst for synthesis of imines.

Mesoporous materials have been the subject of intense research regarding their unique structural and textural properties and successful applications in various fields. This study reports a novel approach for synthesizing a novel porous polymer stabilizer through condensation polymerization in which Fe3O4 magnetic nanoparticles (Fe3O4 MNPs) are used as hard templates. Using this method allowed the facile and fast removal of the template and mesopores formation following the Fe3O4 MNPs. Different techniques were performed to characterize the structure of the polymer. Based on the obtained results, the obtained mesoporous polymeric network was multi-layered and consisted of repeating units of sulfonamide, triazine, and guanidine as a novel heterogeneous multifunctional support. Afterward, the new nickel organometallic complex was supported on its inner surface using the porous poly sulfonamide triazine guanidine (PGTSA/Ni). In this process, the obtained PGTSA/Ni nanocomposite was used as a heterogeneous catalyst in the synthesis of imines from amines. Since this reaction has an acceptorless dehydrogenation pathway, the hydrogen gas is released as its by-product. The synthesized nanocatalyst was structurally confirmed using different characterization modalities, including FT-IR, SEM, XRD, EDX, TEM, elemental mapping, ICP-AES, BET, and TGA. In addition, all products were obtained in high turnover frequency (TOF) and turnover number (TON). The corresponding results revealed the high selectivity and activity of the prepared catalyst through these coupling reactions. Overall, the synthesized nanocatalyst is useable for eight cycles with no considerable catalytic efficiency loss.

Classification and detection of insects from field images using deep learning for smart pest management: A systematic review

Insect pest is one of the main causes affecting agricultural crop yield and quality all over the world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction and control actions. The great breakthrough of deep learning (DL) technology has resulted in its successful applications in various fields, including automatic insect pest monitoring. DL creates both new strengths and a series of challenges for data processing in smart pest monitoring (SPM). This review outlines the technical methods of DL frameworks and their applications in SPM with emphasis on insect pest classification and detection using field images. The methodologies and technical details evolved in insect pest classification and detection using DL are summarized and distilled during different processing stages: image acquisition, data preprocessing and modeling techniques. Finally, a general framework is provided to facilitate the smart insect monitoring and future challenges and trends are highlighted. In a word, our purpose is to provide researchers and technicians with a better understanding of DL techniques and their state-of-art achievements in SPM, which can promote the implement of various SPM applications.

Multi-agent modeling and simulation in the AI age

With the rapid development of artificial intelligence (AI) technology and its successful application in various fields, modeling and simulation technology, especially multi-agent modeling and simulation (MAMS), of complex systems has rapidly advanced. In this study, we first describe the concept, technical advantages, research steps, and research status of MAMS. Then we review the development status of the hybrid modeling and simulation combining multi-agent and system dynamics, the modeling and simulation of multi-agent reinforcement learning, and the modeling and simulation of large-scale multi-agent. Lastly, we introduce existing MAMS platforms and their comparative studies. This work summarizes the current research situation of MAMS, thus helping scholars understand the systematic technology development of MAMS in the AI era. It also paves the way for further research on MAMS technology.

Nudgeathons to control HIV: designing strategies using behavioural economics.

Nudgeathons to control HIV: designing strategies using behavioural economics.

Structural modulation and band gap optimisation of electrochemically anodised TiO2 nanotubes

This analytical study highlights the effects of dimensional modulations on the characteristic properties of anodically grown Titania nanotubes. Titania nanotubes were grown using electrochemical anodization method employing a two-electrode system. The as-grown nanotubes with different dimensions were characterized in order to study their structural, electrical and optical properties. The investigation reveals that the impact of dimension was pivotal in tuning the band gap of these anodically grown Titania nanotubes which asserts its successful application in various fields. The desired dimensional modulation of the nanotubes was achieved by changing the corresponding anodising voltage. This study also employs a remedial two step anodization technique in addition to the conventional one step method and presents its distinctive edge over the other as established by various characterisation techniques. The comparative variations in the structural and crystallite dimensions of the Titania nanotubes anodised by two different methods were investigated by Field Emission Scanning Electron Microscopy and X-Ray Diffraction spectroscopy respectively, while the band gap tuning ability as well as absorption characteristics were determined by UV-Diffuse Reflectance spectroscopy. Finally, the effective changes in the band gap were corroborated by analysing the field effect dark and photo-conductive nature of the two materials.

VHF radio signal modulation classification based on convolution neural networks

Deep learning architecture has been attracting increasing attention due to the successful applications in various fields. However, its application in radio system has not been well explored. In this paper, we consider the very high frequency (VHF) radio signal modulation classification based on convolution neural networks (CNN). The main principle of CNN is analysed and a five-layer CNN model is built. The proposed CNN-based modulation classification method is proved useful for simulated radio signals generated by MATLAB, that the overall classification accuracy is high even in low SNR. In addition, the proposed CNN-based method is used for real VHF radio signals, and the key factors effecting the classification accuracy are analysed.

Crack risk in Nd: YAG laser welding of Ti-6Al-4V alloy

The excellent properties of titanium and titanium alloys, including corrosion resistance, high strength to weight ratio and high operating temperature, have led to their successful application in various fields. However, titanium and its alloys are destroyed by the conventional welding process if welding parameters are not controlled. Laser welding is one of the most reliable methods for use with titanium alloys because of its precision and rapid processing capability. In addition, it enables control of the welding parameters and their effects. In this study, Ti-6Al-4V alloy sheets were welded by Nd: YAG pulsed laser and the potential crack risk on the weld joints was investigated. The optimum results obtained in 3.9kW peak power, better ductility was obtained from the Grade 1 titanium alloy filling material.

Selecting and estimating regular vine copulae and application to financial returns

Regular vine distributions which constitute a flexible class of multivariate dependence models are discussed. Since multivariate copulae constructed through pair-copula decompositions were introduced to the statistical community, interest in these models has been growing steadily and they are finding successful applications in various fields. Research so far has however been concentrating on so-called canonical and D-vine copulae, which are more restrictive cases of regular vine copulae. It is shown how to evaluate the density of arbitrary regular vine specifications. This opens the vine copula methodology to the flexible modeling of complex dependencies even in larger dimensions. In this regard, a new automated model selection and estimation technique based on graph theoretical considerations is presented. This comprehensive search strategy is evaluated in a large simulation study and applied to a 16-dimensional financial data set of international equity, fixed income and commodity indices which were observed over the last decade, in particular during the recent financial crisis. The analysis provides economically well interpretable results and interesting insights into the dependence structure among these indices.

A ESTIMATING MALAYSIAN ECOTOURISM DEMAND USING FUZZY MODEL (CASE STUDY OF STATE OF MALACCA)

Fuzzy regression model (FRM) is an alternative to evaluate the relation between variables among the forecasting models, when the data are not sufficient to identify the relation and we have uncertainty in data. Behavior and thinking of the people, with their differences on emotions and intuitions, produce the ambiguity in social studies. Instead of using point estimation in conventional probability theory, fuzzy set theory can be used to granulate a concept into a set with membership function and thus decreases the amount of required data. In the last two decades, Fuzzy Sets Theory that was introduced in 1965 by Prof. Lotfi A. Zadeh has received wide attention particularly with successful applications in various fields ranging from engineering to social science studies. Besides being an alternative to Conventional Regression Model (CRM), the Fuzzy Regression Model (FRM) has the ability to evaluate relationships between variables when the data series are limited and uncertain. Moreover, the fuzzy regression is able to address the ambiguity in the variables, such as human behavior, thinking and subjectivity. In this paper, we estimate the demand model for ecotourism in Malacca state by using FRM as an alternative approach. Results show that trip cost, opportunity cost, time and income are the significant variables that could explain variation in number of trip as dependent variable.

Micro-powder injection molding

Micro-system technology and related products will be used more widely in the new millennium. For their successful applications in various fields, e.g. in fluidic, medical, optical and telecommunications, economical mass production of the micro-components will be of great importance. Micro-powder injection molding (μPIM) reported in this work is being developed for possible mass production of metallic or ceramic micro-components. In the initial findings presented here, μPIM was processed using silicon mold inserts with square or round cavities in dimensions of 100 μm, with an aspect ratio of 2.5. Alumina, PZT and 316L stainless steel powders were tested with different binder systems such as PVA+ H 2 O, EVA+ PW and PAN250+EVA+HDPE. Results show that all the powders can be used for the molding of micro-components: the finer the powder, the better is the surface finish. PVA+H 2O binder system can be used for room-temperature molding, but with difficulties during molding and de-molding. Micro-components were successfully molded with EVA+PW binder and PZT powders. However, the green parts slumped during thermal de-binding. 316L stainless steel micro-components were successfully molded, de-bound and sintered using PAN250+EVA+HDPE binder system.

Injection moulding of tungsten carbide-cobalt: J.Bruhn, B.Terselius. (Hogskolan Kristianstad, Sweden.)

Micro-system technology and related products will be used more widely in the new millennium. For their successful applications in various fields, e.g. in fluidic, medical, optical and telecommunications, economical mass production of the micro-components will be of great importance. Micro-powder injection molding (μPIM) reported in this work is being developed for possible mass production of metallic or ceramic micro-components. In the initial findings presented here, μPIM was processed using silicon mold inserts with square or round cavities in dimensions of 100 μm, with an aspect ratio of 2.5. Alumina, PZT and 316L stainless steel powders were tested with different binder systems such as PVA+H2O,EVA+PW and PAN250+EVA+HDPE. Results show that all the powders can be used for the molding of micro-components: the finer the powder, the better is the surface finish. PVA+H2O binder system can be used for room-temperature molding, but with difficulties during molding and de-molding. Micro-components were successfully molded with EVA+PW binder and PZT powders. However, the green parts slumped during thermal de-binding. 316L stainless steel micro-components were successfully molded, de-bound and sintered using PAN250+EVA+HDPE binder system.