Range Of Applications Research Articles

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.

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.

Evidence and Prospects of Lycopene as Powerful Red Superfood: Modern Approach to Food Science

Abstract: Lycopene is a plant-derived important nutrient belonging to a group of pigments identified as carotenoids. Lycopene has distinctive chemical and structural features contributing to definite biological properties. Lycopene is a powerful antioxidant and prevents cells and tissues from damage caused by singlet oxygen or free radicals. Research studies have shown the healthpromoting potential of lycopene or its metabolites in the prevention of chronic diseases like diabetes, cancer, asthma, metabolic syndrome, arthritis, and cardiovascular diseases. Lycopene is stable for processing like other natural compounds when present in the plant tissue matrix. Over the last few years, the global lycopene market has increased in different sectors like pharmaceuticals, cosmetics, food and dairy products, and other dietary supplements. The most important aspect of lycopene as a superfood is its synergistic nature with other nutrients. The main aim of this review article is to study chemistry, biosynthetic pathways, biotransformation, sources, and acceptable daily intake. Its characterization as a superfood, therapeutic potential, and wide range of applications in the food industry are also studied.

Cobalt based bimetallic catalysts for heterogeneous electro-Fenton adapting to vary pH for HEDP and MIT degradation

ABSTRACT Most of the materials studied as catalysts in the electro-Fenton system are variants of iron oxide or iron hydroxide. However, iron-based catalysts often exhibit weak catalytic capabilities under neutral and alkaline conditions. In this work, we synthesized three cobalt based bimetallic oxides, Co2CuOx, Co2AlOx, and Co2NiOx, using hydrothermal method and evaluated them as catalysts for the heterogeneous electro-Fenton system to remove 1-hydroxyethylidene-1,1-diphosphonic acid (HEDP) and Methylisothiazolinone [2-methyl-4-isothiazolin-3-one] (MIT). Co2NiOx has the highest catalytic degradation activity for HEDP, and Co2CuOx has the best catalytic degradation effect for MIT. Based on characterization results of the catalysts, the reasons for the differences in the pollutant removal efficiency were analysed, and the optimal pH for the three cobalt based oxides to remove HEDP and MIT was investigated. The results showed that the optimal pH values of the three cobalt based bimetallic oxides are not only influenced by the second metal type, but also by the properties of pollutants. Therefore, suitable cobalt based catalysts can be selected based on the different properties of pollutants, or the composition of cobalt based catalysts can be adjusted to meet the different pH requirements of target wastewater. The three cobalt based bimetallic oxides exhibited good degradation of HEDP and MIT under neutral conditions, which to some extent solved the problem of narrow pH range in the practical application of the electro-Fenton process.

In-Domain GAN Inversion for Faithful Reconstruction and Editability.

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution.

Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers.

Nondestructive detection methods, based on vibrational spectroscopy, are vitally important in a wide range of applications including industrial chemistry, pharmacy and national defense. Recently, deep learning has been introduced into vibrational spectroscopy showing great potential. Different from images, text, etc. that offer large labeled data sets, vibrational spectroscopic data is very limited, which requires novel concepts beyond transfer and meta learning. To tackle this, we propose a task-enhanced augmentation network (TeaNet). The key component of TeaNet is a reconstruction module that inputs randomly masked spectra and outputs reconstructed samples that are similar to the original ones, but include additional variations learned from the domain. These augmented samples are used to train the classification model. The reconstruction and prediction parts are trained simultaneously, end-to-end with back-propagation. Results on both synthetic and real-world datasets verified the superiority of the proposed method. In the most difficult synthetic scenarios TeaNet outperformed CNN by 17%. We visualized and analysed the neuron responses of TeaNet and CNN, and found that TeaNet's ability to identify discriminant wavenumbers was excellent compared to CNN. Our approach is general and can be easily adapted to other domains, offering a solution to more accurate and interpretable few-shot learning.

Growing Like a Tree: Finding Trunks From Graph Skeleton Trees.

The message-passing paradigm has served as the foundation of graph neural networks (GNNs) for years, making them achieve great success in a wide range of applications. Despite its elegance, this paradigm presents several unexpected challenges for graph-level tasks, such as the long-range problem, information bottleneck, over-squashing phenomenon, and limited expressivity. In this study, we aim to overcome these major challenges and break the conventional "node- and edge-centric" mindset in graph-level tasks. To this end, we provide an in-depth theoretical analysis of the causes of the information bottleneck from the perspective of information influence. Building on the theoretical results, we offer unique insights to break this bottleneck and suggest extracting a skeleton tree from the original graph, followed by propagating information in a distinctive manner on this tree. Drawing inspiration from natural trees, we further propose to find trunks from graph skeleton trees to create powerful graph representations and develop the corresponding framework for graph-level tasks. Extensive experiments on multiple real-world datasets demonstrate the superiority of our model. Comprehensive experimental analyses further highlight its capability of capturing long-range dependencies and alleviating the over-squashing problem, thereby providing novel insights into graph-level tasks.

Privacy-Preserving Graph Matching Query Supporting Quick Subgraph Extraction

Graph matching, as one of the most fundamental problems in graph database, has a wide range of applications. Due to the large scale of graph database and the hardness of graph matching, it is an attractive alternative to make use of the cloud to store massive data graphs and conduct the complex graph matching. To protect the privacy, data graphs are usually encrypted before being outsourced to the cloud. A few schemes have been proposed to support graph matching query over encrypted graphs. However, none of them can realize efficient subgraph extraction when the matched subgraph needs to be exactly located at the data graph. The graph user has to perform the complex subgraph isomorphism operation to extract the isomorphic subgraph from the matched data graph in state-of-the-art schemes. The time complexity of judging subgraph isomorphism is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n!n^{2})$</tex-math></inline-formula> at most, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of vertices in the data graph. In order to solve this problem, we propose a privacy-preserving graph matching query scheme supporting quick subgraph extraction in this paper. In our design, two non-colluding cloud servers are adopted to accomplish the matching operation jointly. Neither of them can infer the plaintexts of graphs. The first cloud server can prune some impossible matched vertices from the data graph, and get a possible matched matrix to represent which vertices in the data graph might match with the vertices in the query graph. The second cloud server performs the related matrix operations based on its stored information and the information sent by the first cloud server. The two cloud servers jointly verify the correctness of the matched matrix. The graph user can directly and quickly extract the matrix of the subgraph isomorphic to the query graph from the data graph matrix based on the matched matrix, and further recover this subgraph. No subgraph isomorphism operation is involved in the procedure of subgraph extraction for the graph user. The time complexity of subgraph extraction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(m^{2})$</tex-math></inline-formula> in our scheme, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of vertices in the query graph. The extensive experiments with real-world database demonstrate the efficiency of the proposed privacy-preserving graph matching scheme.

Manufacturing and Applications of Cellulosic Films in Packaging: An Alternative for Plastic Films

Background: Different synthetic materials are constantly being utilized as raw materials for packaging, which might have several drawbacks, like plastic, some of which have poor lifespans. Plastic can get damaged, scratched, or fractured over time, easily take in tastes and fragrances, and significantly affect the environment and health. Excessive plastic waste may lead to environmental pollution and related consequences. Plastics that contaminate food have also been linked to health problems like high blood pressure, heart palpitations, headaches, nausea, diarrhea, and flushing. Objective: The present review entails the hazards associated with the usage of plastic as a packaging material and highlights the necessity to reduce its use and promote alternatives and sustainable production. Thus, the present review portrays the need for biopolymers, types of plant polymers, methods of cellulose extraction and derivatization, the manufacturing process of cellulosic films and thereby use as film and coatings for food preservation. The current review also summarizes different quality control tests for effective utilization as an alternative to synthetic polymers. Conclusion: Different polymers derived from agricultural products are excellent candidates for creating packaging materials. Similar to polysaccharides, these polymers, including nanocomposites, films, composites, etc., are helpful in a wide range of applications. Cellulose, one of the most widespread polysaccharides in the world, offers a variety of appealing special qualities. It appears reasonable and must be considered that cellulose can be used effectively as a raw material for packaging. In this review, we explore how cellulose is used in the packaging industry to make composites, coating materials, and edible and non-edible films.

A research on an improved fuzzy approximate entropy algorithm for EMG-based shoulder and neck muscle fatigue detection

A robust muscle fatigue algorithm plays a pivotal role in depicting the degree of muscle fatigue in both time-series EMG signal graphs and spectral graphs, aligning with human perception. While the fuzzy approximate entropy (fApEn) algorithm has been enhanced from the foundation of approximate entropy (ApEn) through the incorporation of fuzzy affiliation, concerns persist regarding the threshold value and the algorithm’s application range. This study extracts EMG signals across varied time durations and head-down angles, employing enhanced signal preprocessing techniques and optimizing the fApEn algorithm. Furthermore, real-time fatigue perceptions of subjects were recorded using the rating of perceived exertion. Experimental outcomes reveal that the EMG signal, post-wavelet analysis preprocessing, demonstrates promising noise reduction capabilities. Notably, the fApEn algorithm exhibits considerable enhancements through the identification of an optimal threshold using the gradient descent algorithm and a machine learning strategy.

Few-shot time series forecasting in a meta-learning framework

Time series forecasting has a wide range of applications in various fields. To eliminate the need for time series data volume, a meta-learning-based few-shot time series forecasting method is proposed. This method uses a residual stack module as its backbone and connects the residuals forward and backward through a multilayer fully connected network so that the model and the meta-learning framework can be seamlessly combined. The Empirical knowledge of different time-sequence tasks is obtained through meta-training. To enable fast adaptation to new prediction tasks, a small meta-network is introduced to adaptively and dynamically generate the learning rate and weight decay coefficient of each step in the network. This method can use sequences of different data distribution characteristics for cross-task learning, and each training task only needs a small number of time series to achieve sequence prediction for the target task. The results show that compared with the two baselines, the proposed method has improved performance on 67.07% and 58.53% of the evaluated tasks. Thus, this method can effectively alleviate the problems caused by insufficient data during training and has broad application prospects in the field of time series.

Clustering analysis for Pythagorean fuzzy sets and its application in multiple attribute decision making

Pythagorean fuzzy sets, as a generalization of intuitionistic fuzzy sets, have a wide range of applications in many fields including image recognition, data mining, decision making, etc. However, there is little research on clustering algorithms of Pythagorean fuzzy sets. In this paper, a novel clustering idea under Pythagorean fuzzy environment is presented. Firstly, the concept of feature vector of Pythagorean fuzzy number (PFN) is presented by taking into account five parameters of PFN, and some new methods to compute the similarity measure of PFNs by applying the feature vector are proposed. Furthermore, a fuzzy similarity matrix by utilizing similarity measure of PFNs is established. Later, the fuzzy similarity matrix is transformed into a fuzzy equivalent matrix which is utilized to establish a novel Pythagorean fuzzy clustering algorithm. Based on the proposed clustering algorithm, a novel multiple attribute decision making (MADM) method under Pythagorean fuzzy environment is presented. To illustrate the effectiveness and feasibility of the proposed technique, an application example is offered.

Engineering Quantum Light Sources with Flat Optics.

Quantum light sources are essential building blocks for many quantum technologies, enabling secure communication, powerful computing, and precise sensing and imaging. Recent advancements have witnessed a significant shift toward the utilization of "flat" optics with thickness at subwavelength scales for the development of quantum light sources. This approach offers notable advantages over conventional bulky counterparts, including compactness, scalability, and improved efficiency, along with added functionalities. This review focuses on the recent advances in leveraging flat optics to generate quantum light sources. Specifically, the generation of entangled photon pairs through spontaneous parametric down-conversion in nonlinear metasurfaces, and single photon emission from quantum emitters including quantum dots and color centers in 3D and 2D materials are explored. The review covers theoretical principles, fabrication techniques, and properties of these sources, with particular emphasis on the enhanced generation and engineering of quantum light sources using optical resonances supported by nanostructures. The diverse application range of these sources is discussed and the current challenges and perspectives in the field arehighlighted.