Range Of Applications Research Articles

The Processing of a Novel Health Beverage Based on Extracts from Green Tea and Chios Mastiha

In the present study, the processing, characterization, and assessment of novel non-alcoholic and sugar-free drinks based on bioactive extracts from valuable natural sources, such as green tea enriched with Chios mastiha, are considered. Currently, the transition towards the consumption of healthy and sustainable food and beverages promoting human health and well-being is strongly encouraged and biologically active compounds from natural resources have a broad range of ap-plications in this sector. In this context, three beverages (all non-alcoholic, non-carbonated, and sugar-free) were created, including extracts of green tea with Chios mastiha, matcha green tea with Chios mastiha and louisa green tea with Chios mastiha, and an evaluation of their biological potential was performed. Specifically, an analysis of water, extracts, and additives for the beverage production was carried out. Microbiological and nutritional value determination was also conducted in samples of the three products. According to the experimental results, the novel health beverage produced from green tea enriched with Chios Mastiha extracts was found to have improved organoleptic characteristics and was microbiologically stable and safe for a period of 180 days from the production date at 25 °C. It is also considered stable and safe for 3 days after production, even if it remains open at 25 °C. In view of a possible scale-up of this application, safety, and preservation control should continue for at least 540 days from the date of production. In conclusion, the current research findings support the development of a novel non-alcoholic sugar-free health drink based on bioactive extracts from green tea enriched with Chios mastiha, to contribute to maintaining human health and also to strengthen the economy.

Numerical studies on structure optimization and flow characteristics of a hydrogen recirculation ejector under multiple load conditions

Ejectors are ideal hydrogen recirculation devices for PEMFC systems but typically exhibit poor recirculation performance at low loads. For purpose of extending ejector's operating range in vehicular applications, the entraining performance across multiple loads should be focused. In this study, A 40 kW ejector was designed with a 1D theory and optimized using a 2D CFD model. Simulations were conducted to study the influence of various diameter and axial dimensions on the ejector's performance and flow characteristics. The results reveal that as the load increases from 20% to 100%, the suboptimal diffuser exit diameter decreases from 2.5 to 1.5 times the mixing chamber diameter, the optimal nozzle exit position increases from 0.59 to 0.63 times, and the optimal mixing chamber length increases from 2.2 to 4.3 times. A convergent nozzle with a short throat significantly improves low-load entraining performance and achieves the highest performance growth in the variable-nozzle design. Additionally, an optimization method for axial dimensions based on the proposed parameter flow uniformity was established, allowing for precise determination of the optimal dimensions through a few simulations.

MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction.

Research on graph representation learning has received great attention in recent years. However, most of the studies so far have focused on the embedding of single-layer graphs. The few studies dealing with the problem of representation learning of multilayer structures rely on the strong hypothesis that the inter-layer links are known, and this limits the range of possible applications. Here we propose MultiplexSAGE, a generalization of the GraphSAGE algorithm that allows embedding multiplex networks. We show that MultiplexSAGE is capable to reconstruct both the intra-layer and the inter-layer connectivity, outperforming competing methods. Next, through a comprehensive experimental analysis, we shed light also on the performance of the embedding, both in simple and multiplex networks, showing that both the density of the graph and the randomness of the links strongly influences the quality of the embedding.

Overview of Path Planning Algorithms

Background: Path-planning algorithms are widely used in robotics, vehicles, UAVs, carrier-based aircraft towing vehicles, etc. Using these algorithms, an optimal path with safe obstacle avoidance and high efficiency can be planned. In recent years, path-planning algorithms have received more and more attention from scholars at home and abroad. Objective: In order to promote the application and development of path-planning algorithms, an in-depth analysis of the current development status of path-planning algorithms was presented. The most widely used and representative algorithms in several representative results were extracted, and the characteristics and advantages and disadvantages of each algorithm were analyzed and elaborated in detail for the readers' reference. Methods: The existing path-planning algorithms were classified, and a brief overview of each traditional algorithm was given, followed by an in-depth study of the improved algorithms to summarize the advantages and disadvantages of each type of algorithm. Finally, based on these research results, the future development trend of path-planning algorithms was projected. Results: Through the research and analysis of the path-planning algorithm, it was found that the path- planning algorithm before the improvement had the problems of an unsmooth path, low computational efficiency, a slow response time, the inability to safely avoid obstacles, etc. After the improvement and optimization of the path-planning algorithm, the performance was greatly improved. Conclusion: After research and analysis, it was found that multi-algorithm fusion had great potential for development in path-planning compared to the application and optimization of a single algorithm. In addition, further research is needed in the areas of algorithm adaptability, fusion, intelligent algorithms, and extending the range of algorithmic applications.

Research on Image Encryption Method based on the Chaotic Iteration of a Ternary Nonlinear Function

Objective: Considering that some image encryption algorithms have the disadvantages of complex structure and high computational cost, and there are not many commonly used chaotic systems, which are easy to crack by attacks, to solve these problems, this paper proposes an image encryption algorithm based on three-dimensional nonlinear functions to solve these problems. Methods: The algorithm mainly combines the sinusoidal chaotic map with the ternary nonlinear function system to encrypt the image. Firstly, multiple ternary nonlinear function chaotic systems are designed. Then, the function iteration system is changed to invoke the computation of a specific expression under a random number; it is a chaotic sequence generated according to a chaotic mapping such as sine, and then the value of this chaotic sequence is used to select a ternary nonlinear function for iteration to obtain a chaotic sequence. Finally, the chaotic sequence performs the XOR and scrambling operations on the grey image Results: The algorithm has a simple structure, a better encryption effect, and more incredible difficulty deciphering. Moreover, through the phase diagram and bifurcation diagram, it can be seen that the system has good chaotic characteristics Conclusion: The method in this paper is novel; this method is a random variable order composite operation, which can not only be applied to image encryption but also can be used for fractal map generation and so on, and in some other chaotic fields will have a wide range of applications. It has essential research value.

Monte Carlo dosimetry study of newly designed shielded applicators for intensity modulated brachytherapy of cervical and vaginal cancers

IntroductionThe utilization of metal shields in intensity-modulated brachytherapy (IMBT) enables the modulation of the dose, resulting in improved conformance to the tumor while simultaneously reducing the doses to organs at risk (OARs). Utilizing higher-energy sources like 60Co in IMBT for cervical and vaginal cancers has consistently posed challenges. This study evaluates the dosimetric aspects of modified applicators designed for IMBT using 60Co and 192Ir sources.Materials and MethodsGATE, a Geant4-based simulation code, was utilized to model and simulate four distinct applicators. The clinical applicators were redesigned to place the structure of the source tube and the shield while keeping the general characteristics unchanged. These shields were evaluated by calculating transmission factors (TFs) and the dose homogeneities were also determined.ResultTransmission factors for the IMBT technique in redesigned intrauterine applicators and tungsten shields for iridium and cobalt sources were at least 12.8 and 65.4%, and these values were obtained for the intravaginal applicator at 0.2 and 7.0%, respectively. The dose homogeneities for all combinations of radionuclide-shield were within a 15% range of the non-IMBT applicators.ConclusionThis study has quantitatively evaluated the dosimetric effect of tungsten shields in the IMBT technique for cervical and vaginal cancer using cobalt sources. 192Ir compared to 60Co resulted in higher effectiveness for the designed intrauterine and intravaginal shields. while implementing tungsten shields in the redesigned applicators against the 60Co source may not offer complete protection, it does show promising results in reducing the dose to organs at risk.

Study on Attenuation Mechanism and Durability Improvement of Platinum-Carbon Catalysts for Proton Exchange Membrane Fuel Cells

The relationship between Pt particle size and activity and durability of Pt/C electrocatalysts on rotating disk electrodes is established and the results show that Pt/C electrocatalysts with particle size over 3.5 nm have excellent resistance to Pt particle decay and carbon corrosion. Further, the research results on the mechanism of Pt particle decay and carbon corrosion show that the Pt particle attenuation is composed of 80% Ostwald ripening and 20% particle agglomeration, and the carbon corrosion is affected by the catalytic action of Pt particles. Therefore, the above results show that regulating the Pt particle size to 3.5–4.0 nm can improve the durability of Pt/C electrocatalysts on RDE. To verify the accuracy of this conclusion and determine the optimal particle size range in practical application, single cells with 5 × 5 cm2 is assembled to evaluate the performance and durability of cathode Pt/C electrocatalysts under H2/air. The results show that cathode Pt/C electrocatalysts with particle size between 3.5 nm and 3.8 nm have high single cell performance (2.3 A cm−2@0.65 V) and durability (A loss of 15 mV@0.8 A cm−2 after 30000 cycles). These findings reveal the attenuation mechanism of Pt/C electrocatalysts and provide ideas for the development of high-durability Pt-based electrocatalysts for practical applications.

Vapor-Liquid Equilibrium of System Comprising Green Solvents: A Holistic Review

Abstract: The design and operation of distillation columns is based on vapor-liquid equilibrium data, which is a necessity for the chemical industry. In recent years, chemical industry has embraced green chemistry and sustainable development. Furthermore, green solvents are more environmentfriendly and cleaner than conventional solvents and thus offer a good alternative. Very limited work has been reported in the literature that focuses on the generation of isobaric/isothermal vapor-liquid equilibrium (VLE) data of systems comprising green solvents. In this paper, reported VLE data are explored for three emerging green solvents, such as cyclopentyl methyl ether (CPME), γ- valerolactone (GVL), and 2-methyltetrahydrofuran (2-MeTHF). Emerging green solvents have favorable environmental, health, and safety characteristics, making them attractive alternatives for a wide range of applications. The study focuses on two critical separations; the extraction of formic acid from Power-to-X chemical processes and purification of acetic acid from chemical synthesis or fermentation processes. Both processes are integral parts of the chemical industry's sustainable development. To facilitate these separations, accurate VLE data for these green solvents with acetic acid/formic acid systems are essential. The paper reviews literature related to VLE data for systems involving these green solvents. It provides insights into the experimental conditions, equipment, analysis methods, thermodynamic models, and error-minimizing functions used in the previous studies. The researchers can refer to this information as a useful reference prior to the VLE experimentation and modeling of systems comprising these three green solvents. Moreover, the paper presents an overview of recent research on green solvents and their applications, illustrating their versatility and potential for various industrial processes. Research efforts are needed to generate VLE data for green solvent systems and support the chemical industry in transitions towards more sustainable practices.

Diphenyl Ethers: Isolation, Bioactivities and Biosynthesis

Abstract: Diphenyl ethers (DPEs) are mainly produced by microorganisms and plants. The chemical structure of DPEs is relatively simple, and all of these compounds have the structure of biphenyl ethers in their chemical structure, the difference being the difference in the substituents on the benzene ring. Several recent studies have shown antibacterial, antioxidant, antitumor, antitumor, antihemolytic, neuroprotective, and anti-Alzheimer effects. DPEs could be candidate compounds for the treatment of Alzheimer's disease, by inhibiting the aggregation of Aβ42. Many DPEs from natural products exhibit good biological activity and they play an important role in the control of microbial infections. Diphenyl ethers have a wide range of applications and research value in the fields of pesticides, pharmaceuticals, textiles, household products, and public health. In this paper, we review the research progress of diphenyl ethers isolated from marine and plant endophytic sources in recent years, including their biosynthesis, and lay the foundation for further utilization and development.

Miniaturization of Nucleic Acid Assemblies in Nanodevice: Nano-Oddities

Abstract: In the past decades, it has been evident that nano miniaturization technology plays a vital role in innovations, biomedical and industrial applications. Most importantly, the use of Lab on chip (LOC) is revolutionizing and highly replacing the use of conventional technologies due to its advantages that include reliability, biocompatibility, tunability, portability, controllability, cost-effective, low time, and energy consumption with more accurate results. The different nucleic acid structures formed by non-classical ways of pairing can result in highly stable structures, known as nano-oddities. These nucleic acid nano-oddities could be fabricated for a wide range of applications with unique properties. This review encompasses the major findings, advances, fabrication, miniaturization, applications, and the future prospects of nucleic acid assemblies in different kinds of nanodevices.

A Review on Design and Mechanical Properties of Additively Manufactured NiTi Implants for Orthopedic Applications.

NiTi alloy has a wide range of applications as a biomaterial due to its high ductility, low corrosion rate, and favorable biocompatibility. Although Young’s modulus of NiTi is relatively low, it still needs to be reduced; one of the promising ways is by introducing porous structure. Traditional manufacturing processes, such as casting, can hardly produce complex porous structures. Additive manufacturing (AM) is one of the most advanced manufacturing technologies that can solve impurity issues, and selective laser melting (SLM) is one of the well-known methods. This paper reviews the developments of AM-NiTi with a particular focus on SLM-NiTi utilization in biomedical applications. Correspondingly, this paper aims to describe the three key factors, including powder preparation, processing parameters, and gas atmosphere during the overall process of porous NiTi. The porous structure design is of vital importance, so the unit cell and pore parameters are discussed. The mechanical properties of SLM-NiTi, such as hardness, compressive strength, tensile strength, fatigue behavior, and damping properties and their relationship with design parameters are summarized. In the end, it points out the current challenges. Considering the increasing application of NiTi implants, this review paper may open new frontiers for advanced and modern designs.

A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems.

With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment. Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets. Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics. In this work, we contribute with a unifying taxonomy to classify offline RL methods. Furthermore, we provide a comprehensive review of the latest algorithmic breakthroughs in the field using a unified notation as well as a review of existing benchmarks' properties and shortcomings. Additionally, we provide a figure that summarizes the performance of each method and class of methods on different dataset properties, equipping researchers with the tools to decide which type of algorithm is best suited for the problem at hand and identify which classes of algorithms look the most promising. Finally, we provide our perspective on open problems and propose future research directions for this rapidly growing field.

New Ureas and Amides - An Account of Recent Trends and Developments in Low Molecular Weight Gelators

Abstract: The last 20 years have witnessed major advancements in the field of supramolecular chemistry and have brought us closer to the designing of low molecular weight gelators with desired properties and applications. In that regard, amide- and ureabased gelators comprise a unique class as they are extremely versatile in terms of molecular design and offer a wide range of applications, like anion responsive materials, selective sensing of heavy metal ions, environmental remediation and many more. Both sets of compounds have similar molecular scaffolds, making them an excellent tool to determine the relative importance of the supramolecular interactions involved in the gelation process. Besides, the concept of crystal engineering can also be employed to understand the underlying mechanism of gelation by scrutinizing the interactions and supramolecular assemblies formed by these systems. In this article, we focus on various supramolecular assemblies formed by various amide and urea derivatives and their recently reported applications to establish structure-property correlation and their futuristic aspects.

Lightweight Deep Learning: An Overview

With the recent success of the deep neural networks (DNNs) in the field of artificial intelligence, the urge of deploying DNNs has drawn tremendous attention because it can benefit a wide range of applications on edge or embedded devices. Lightweight deep learning (DL) indicates the procedures of compressing DNN models into more compact ones, which are suitable to be executed on edge devices due to their limited resources and computational capabilities while maintaining comparable performance as the original. Currently, the approaches of model compression include but not limited to network pruning, quantization, knowledge distillation, neural architecture search. In this work, we present a fresh overview to summarize recent development and challenges for model compression.

Fabrication and characterization of unique sustain modified chitosan nanoparticles for biomedical applications

Chitosan (CS) is a biopolymer that offers a wide range in biomedical applications due to its biocompatibility, biodegradability, low toxicity and antimicrobial activity. Syringaldehyde (1) is a naturally occurring organic compound characterized by its use in multiple fields such as pharmaceuticals, food, cosmetics, textiles and biological applications. Herein, development of chitosan derivative with physicochemical and anticancer properties via Schiff base formation from the reaction of chitosan with sustainable eco-friendly syringaldehyde yielded the (CS-1) derivative. Moreover, in the presence of polyethylene glycol diglycidyl ether (PEGDGE) or sodium tripolyphosphate (TPP) as crosslinkers gave chitosan derivatives (CS-2) and (CS-3NPs) respectively. The chemical structures of the new chitosan derivatives were confirmed using different tools. (CS-3NPs) nanoparticle showed improvement in crystallinity, and (CS-2) derivative revealed the highest thermal stability compared to virgin chitosan. The cytotoxicity activity of chitosan and its derivatives were evaluated against HeLa (human cervical carcinoma) and HEp-2 (Human Larynx carcinoma) cell lines. The highest cytotoxicity activity was exhibited by (CS-3NPs) compared to virgin chitosan against HeLa cell growth inhibition and apoptosis of 90.38 ± 1.46% and 30.3% respectively and IC50 of 108.01 ± 3.94 µg/ml. From the above results, it can be concluded that chitosan nanoparticle (CS-3NPs) has good therapeutic value as a potential antitumor agent against the HeLa cancer cell line.

A Closed Formalism for Anatomy-Independent Projection and Optimization of Magnetic Stimulation Coils on Arbitrarily Shaped Surfaces.

Transcranial magnetic stimulation (TMS) is a popular method for the noninvasive stimulation of neurons in the brain. It has become a standard instrument in experimental brain research and has been approved for a range of diagnostic and therapeutic applications. These applications require appropriately shaped coils. Various applications have been established or approved for specific coil designs with their corresponding spatial electric field distributions. However, the specific coil implementation may no longer be appropriate from the perspective of available material and manufacturing opportunities or considering the latest understanding of how to achieve induced electric fields in the head most efficiently. Furthermore, in some cases, field measurements of coils with unknown winding or a user-defined field are available and require an actual implementation. Similar applications exist for magnetic resonance imaging coils. This work aims at introducing a complete formalism free from heuristics, iterative optimization, and ad-hoc or manual steps to form practical stimulation coils with individual turns to either equivalently match an existing coil or produce a given field. The target coil can reside on practically any sufficiently large or closed surface adjacent to or around the head. The method derives an equivalent field through vector projection exploiting the well-known Huygens' and Love's equivalence principle. In contrast to other coil design or optimization approaches recently presented, the procedure is an explicit forward Hilbert-space vector projection or basis change. For demonstration, we map a commercial figure-of-eight coil as one of the most widely used devices and a more intricate coil recently approved clinically for addiction treatment (H4) onto a bent surface close to the head for highest efficiency and lowest field energy. The resulting projections are within ≤4% of the target field and reduce the necessary pulse energy by more than 40%.

An Exceptional Valorization of CuO Nanoparticles in Ionic Liquids as an Efficient Medium for the Electrophilic Substitution of Indole Towards the Formation of Bis(indolyl)methanes

Aim: Ionic liquids are promising green solvents with simple but unique structure-related physical properties such as negligible vapour pressure, exceptional thermal conductivity, remarkable thermal stability and their suitability and inertness towards a broad range of catalytic applications. CuO NPs have been addressed as a cost-effective and a reagent of a choice that necessitates only mild reaction conditions to offer a high yield of the desired products with exceptional selectivity in a short duration of time. Therefore, in the present work, attempts have been made to explore the catalytic potentials of CuO NPs in an ionic liquid medium to synthesize biologically important bis(indolyl)methanes. Background: Catalytic explorations of metal oxide nanoparticles in ionic liquids offers a cooperative effect that has a significant impact on the kinetics as well as on the outcome of the reaction. Therefore, such catalytic systems in the present times have seized the scientific community's interest from the perspectives of sustainable development in synthetic organic chemistry. The combination of metal oxide nanoparticles with highly tunable ionic liquids is not only used to synthesize simple organic molecules but also explored in the synthesis of complex organic molecules of high commercial and biological relevance. Objectives: The current work offers a rapid and robust protocol for synthesizing bis(indolyl)methanes via electrophilic substitution reaction between indole and various aldehydes in the presence of a CuO nanoparticles-ionic liquid system. The discussion focuses on the high tolerance of different functionalities by the catalytic system leading to the synthesis of bis(indolyl)methanes. Methods: CuO NPs have been synthesized via the co-precipitation method using ionic liquid. The applicability of metal oxide nanoparticles-IL matrix was further investigated in synthesizing bis(indolyl)methanes. Results: The FT-IR absorption below 600 cm-1 and the XRD pattern showing all the peaks in the diffraction diagram revealed the formation of CuO NPs. FESEM images show the flake-shaped morphology of CuO NPs and are found to be separated from the agglomerated clusters Conclusion: Ionic liquid-CuO NPs matrix reveals good to exceptional catalytic properties, and their advancements as a catalytic system at room temperature open new avenues for synthetic organic chemists.

Design of Dual Lower Bound Hysteresis Control in Switched-Capacitor DC–DC Converter for Optimum Efficiency and Transient Speed in Wide Loading Range for IoT Application

Design of Dual Lower Bound Hysteresis Control in Switched-Capacitor DC–DC Converter for Optimum Efficiency and Transient Speed in Wide Loading Range for IoT Application

Schiff Bases: Versatile Mediators of Medicinal and Multifunctional Advancements

Abstract: This review aims to shed light on the profound implications of Schiff Bases in combating a spectrum of pathogens by delving into their complex classification, synthesis, and reactions. The investigation also covers the varied molecular properties of Schiff bases, highlighting their potential use as chelating agents in coordination chemistry. Moreover, the investigation explores the discerning nature of Schiff Bases about metal ions and their adeptness in establishing intricate associations, highlighting their significance in metal coordination chemistry and specialized pharmaceutical transport mechanisms. Moreover, the review delves into the synthetic capacity of Schiff Bases, highlighting their importance in synthetic methodologies due to their exceptional adaptability, selectivity, and structural similarity to organic compounds. The methodology employs a rigorous systematic literature review to understand Schiff Bases comprehensively. This involves a meticulous analysis of various research articles and publications, allowing for a comprehensive exploration of the topic. The assessment of experimental investigations contributes to comprehending their molecular attributes, specificity for metal ions, and capacity for synthesis. The presented analysis amalgamates a multitude of sources to provide a nuanced and comprehensive viewpoint on the subject matter of Schiff Bases. The findings underscore the multifaceted utility of Schiff Bases in the fight against pathogens, their adaptability as chelating compounds, and their discerning affinity for metal ions. The examination of synthesis highlights their profound importance in synthetic methodologies and their striking resemblance to compounds found in living organisms. In conclusion, this analysis reveals Schiff Bases as highly adaptable compounds with potential in antimicrobial therapy, coordination chemistry, and precision drug delivery. The distinctive molecular attributes of these substances, functioning as chelators, contribute to their notable importance. The ability of Schiff bases to form complexes and their preference for metal ions highlight the wide range of applications for these molecules. Schiff Bases have a transformative effect on chemistry and medicine as we investigate their synthetic potential, driven by their versatility and structural similarity to biological compounds.

Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling.

In this paper, we propose FGPR: a Federated Gaussian process ( GP) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local computations. Notably, the resulting global model excels in personalization as FGPR jointly learns a shared prior across all devices. The predictive posterior is then obtained by exploiting this shared prior and conditioning on local data, which encodes personalized features from a specific dataset. Theoretically, we show that FGPR converges to a critical point of the full log-marginal likelihood function, subject to statistical errors. This result offers standalone value as it brings federated learning theoretical results to correlated paradigms. Through extensive case studies on several regression tasks, we show that FGPR excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.