Range Of Applications Research Articles

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.

Uncertainty budget for detector-based absolute radiometric calibration with GLAMR

The accuracy of the absolute radiometric calibration (RadCal) for remote sensing instruments is essential to their wide range of applications. The uncertainty associated with the traditional source-based RadCal method is assessed at a 2% (k=1) or higher level for radiance measurement. To further improve the accuracy to meet the demands of climate studies, a detector-based approach using tunable lasers as a light source has been devised. The Goddard Laser for Absolute Measurement of Radiance, known as the GLAMR system, is a notable example of the incorporation of such technology. Using transfer radiometers calibrated at the National Institute of Standards and Technology as calibration standards, the absolute spectral response function of a remote sensing instrument is measured with its uncertainty traceable to the International System of Units. This paper presents a comprehensive uncertainty analysis of the detector-based absolute RadCal using the GLAMR system. It identifies and examines uncertainty sources during the GLAMR RadCal test, including those from the GLAMR system, the testing configuration, and data processing methodologies. Analysis is carried out to quantify the contribution of each source and emphasize the most influential factors. It is shown that the calibration uncertainty of GLAMR RadCal can be better than 0.3% (k=1) in the wavelength range of 350–950 nm and 0.6% (k=1) between 950 and 2300 nm, with the exception of regions with strong water absorption. In addition, recommendations are made to refine the calibration process to further reduce the uncertainty.

Endowing Metal-Organic Coordination Materials with Chiroptical Activity by a Chiral Anion Strategy.

Recently, chiral metal-organic coordination materials have emerged as promising candidates for a wide range of applications in chiroptoelectronics, chiral catalysis, and information encryption, etc. Notably, the chiroptical effect of coordination chromophores makes them appealing for applications such as photodetectors, OLEDs, 3D displays, and bioimaging. The direct synthesis of chiral coordination materials using chiral organic ligands or complexes with metal-centered chirality is very often tedious and costly. In the case of ionic coordination materials, the combination of chiral anions with cationic, achiral coordination compounds through noncovalent interactions may endow molecular materials with desirable chiroptical properties. The use of such a simple chiral strategy has been proven effective in inducing promising circular dichroism and/or circularly polarized luminescence signals. This concept article mainly delves into the latest advances in exploring the efficacy of such a chiral anion strategy for transforming achiral coordination materials into chromophores with superb photo- or electro-chiroptical properties. In particular, ionic small-molecular metal complexes, metal clusters, coordination supramolecular assemblies, and metal-organic frameworks containing chiral anions are discussed. A perspective on the future opportunities on the preparation of chiroptical materials with the chiral anion strategy is also presented.

3D Monte Carlo geometry inversion using gravity data

Diverse Monte Carlo methods have gained widespread use across a broad range of applications. However, the challenge of 3D Monte Carlo sampling remains due to the curse of dimensionality. To date, only a few works have been published regarding 3D Monte Carlo sampling. This study aims to develop an efficient 3D transdimensional Monte Carlo framework for reconstructing the spatial geometry of an anomalous body using gravity data. Our framework also can quantify the uncertainty of the shape of an anomalous body recovered from geophysical measurements. To improve the computational efficiency of 3D Monte Carlo sampling, we develop a sparse geometry parameterization strategy. This approach adequately approximates the shape of a complex 3D anomalous body using a set of simple geometries, such as ellipsoids. Each ellipsoid can be characterized by a few parameters, such as the centroid, axes, and orientations, significantly reducing the number of parameters to be sampled. During the sampling, we randomly perturb the number, locations, sizes, and orientations of the ellipsoids. To impose prior structural constraints from other geophysical methods, such as seismic imaging, we design a new method by placing a fixed layer oriented along the top boundary of the anomalous body. The fixed layer is then connected to the randomly sampled ellipsoids using an alpha shape, allowing us to estimate the geometry of the anomalous source body. The current work focuses on the reconstruction of salt bodies. We start with a synthetic spherical salt model and then conduct a more realistic study using a simplified 3D SEG/EAGE salt model, which has a much more complex geometry than the synthetic spherical model. Finally, we apply our method to the Galveston Island salt dome, offshore Texas. The numerical results demonstrate that our framework can effectively recover the shape of an anomalous body and quantify the uncertainty of the reconstructed geometry.

A Comprehensive Study on Model Simplification and Parameter Estimation of Brushless Doubly Fed Standalone Generation System

Brushless doubly-fed induction generator (BDFIG) has great potential in stand-alone generation applications. Currently, lots of control strategies have been proposed based on the BDFIG d-q model with different machine parameters ignored for simplification, but the simplification influences on model accuracy have not been discussed systematically, and the application ranges are not clear. Therefore, aiming at controller design, this paper provides a comprehensive study on model simplification and parameter estimation of brushless doubly-fed stand- alone generation. Based on the intuitive DFIG-like BDFIG model and the typical electrical quantity relations widely used in stand-alone controller design, steady-state and dynamic parameter sensitivities of BDFIG are analyzed with operation condition fully considered. Based on the analysis, the universal BDFIG simplified d-q model aiming at stand-alone control is evaluated sufficiently, and the application range is clarified. The simplification of control analysis is illustrated, and a highly-targeted and simple parameter estimation method is proposed for simplified modeling and control. Finally, simulations and experiments verify the analysis accuracy of the simplified DFIG-like BDFIG model and the feasibility of the proposed parameter estimation method.

Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype

E-health is being adapted in modern hospitals as a significant addition to the existing healthcare services. To this end, modern hospitals urgently require a mobile, high-capacity, secure, and cost-effective communication infrastructure. In this paper, we explore potential applications of a hybrid broadband power line communication (PLC) and Wi-Fi in an indoor hospital scenario. It utilizes the existing power line cables and Wi-Fi plug-and-play devices for indoor broadband communication. Broadband power line (BPL) adaptors with Wi-Fi outputs are used to build an access network in hospitals, particularly in areas where the wireless router signal is poor. The Tenda PH10 AV1,000 AC Wi-Fi power line adapter is a set of BPL adapters that offer operational bandwidth of up to 1,000 Mbps. These adapters are based on the HomePlug AV2 protocol and can provide a data rate up to 200 Mbps on the physical layer. An experiment using the PLC Wi-Fi kit is carried out to show that a Wi-Fi and PLC hybrid network is the best candidate to provide wide range of practical applications in a hospital including, but not limited to, telemedicine, electronic medical records, early-stage disease diagnosis, health management, real-time monitoring, and remote surgeries.