Exploring the therapeutic potential of layered double hydroxides and transition metal dichalcogenides through the convergence of rheumatology and nanotechnology using generative adversarial network.

Two-dimensional Layered double hydroxides (LDHs) are highly used in the biomedical domain due to their biocompatibility, biodegradability, controlled drug loading and release capabilities, and improved cellular permeability. The interaction of LDHs with biological systems could facilitate targeted drug delivery and make them an attractive option for various biomedical applications. Rheumatoid Arthritis (RA) requires targeted drug delivery for optimum therapeutic outcomes. In this study, stacked double hydroxide nanocomposites with dextran sulphate modification (LDH-DS) were developed while exhibiting both targeting and pH-sensitivity for rheumatological conditions. This research examines the loading, release kinetics, and efficiency of the therapeutics of interest in the LDH-based drug delivery system. The mean size of LDH-DS particles (300.1 ± 8.12 nm) is -12.11 ± 0.4 mV. The encapsulation efficiency was 48.52%, and the loading efficacy was 16.81%. In vitro release tests indicate that the drug's discharge is modified more rapidly in PBS at pH 5.4 compared to pH 5.6, which later reached 7.3, showing the case sensitivity to pH. A generative adversarial network (GAN) is used to analyze the drug delivery system in rheumatology. The GAN model achieved high accuracy and classification rates of 99.3% and 99.0%, respectively, and a validity of 99.5%. The second and third administrations resulted in a significant change with p-values of 0.001 and 0.05, respectively. This investigation unequivocally demonstrated that LDH functions as a biocompatible drug delivery matrix, significantly improving delivery effectiveness.

Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies.

Based on deep learning, pancreatic cancer pathology image segmentation technology effectively assists pathologists in achieving improved treatment outcomes. However, compared to traditional image segmentation tasks, the large size of tissues in pathology images requires a larger receptive field. While methods based on dilated convolutions or attention mechanisms can enhance the receptive field, they cannot capture long-range feature dependencies. Directly applying self-attention mechanisms to capture long-range dependencies results in intolerable computational complexity. To address these challenges, we introduce a channel and spatial self-attention (CS) Module designed for efficiently capturing both channel and spatial long-range feature dependencies in pancreatic cancer pathological images. Specifically, the channel and spatial self-attention module consists of an adaptive channel self-attention module and a window-shift spatial self-attention module. The adaptive channel self-attention module adaptively pools features to a fixed size to capture long-range feature dependencies. While the window-shift spatial self-attention module captures spatial long-range dependencies in a window-based manner. Additionally, we propose a re-weighted cross-entropy loss to mitigate the impact of long-tail distribution on performance. Our proposed method surpasses state-of-the-art on both our Pancreatic Cancer Pathology Image (PCPI) dataset and the GlaS challenge dataset. The mDice and mIoU have achieved 73.93% and 59.42% in our PCPI dataset.

High-Matching and Low-Cost Realization of the FHN Neuron Model on Reconfigurable FPGA Board.

The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple 2V term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.

A systematic and comprehensive review and investigation of intelligent IoT-based healthcare systems in rural societies and governments.

Healthcare needs in rural areas differ significantly from those in urban areas. Addressing the healthcare challenges in rural communities is of paramount importance, as these regions often lack access to adequate healthcare facilities. Moreover, technological advancements, particularly in the realm of the Internet of Things (IoT), have brought about significant changes in the healthcare industry. IoT involves connecting real-world objects to digital devices, opening up various possibilities for improving healthcare delivery. One promising application of IoT is its use in monitoring the spread of diseases in remote villages through interconnected sensors and devices. Surprisingly, there has been a noticeable absence of comprehensive research on this topic. Therefore, the primary objective of this study is to conduct a thorough and systematic review of intelligent IoT-based healthcare systems in rural communities and their governance. The analysis covers research papers published until December 2022 to provide valuable insights for future researchers. The selected articles have been categorized into three main groups: monitoring, intelligent services, and body sensor networks. The findings indicate that IoT research has garnered significant attention within the healthcare community. Furthermore, the results illustrate the potential benefits of IoT for governments, especially in rural areas, in improving public health and strengthening economic ties. It is worth noting that establishing a robust security infrastructure is essential for implementing IoT effectively, given its innovative operational principles. In summary, this review enhances scholars' understanding of the current state of IoT research in rural healthcare settings while highlighting areas that warrant further investigation. Additionally, it keeps healthcare professionals informed about the latest advancements and applications of IoT in rural healthcare.

Cost-effective one-spot hydrothermal synthesis of graphene oxide nanoparticles for wastewater remediation: AI-enhanced approach for transition metal oxides.

Outer ear infections (OEs) affect millions of people each year and are associated with significant medical costs.The usage of multiple antibiotics to treat ear contamination is a concern because it can have an environmental impact, especially on soil and water.The increased use of antibiotics has exposed bacterial ecosystems to high concentrations of antibiotic residues.Although there have been efforts to minimize the impact of antibiotics, adsorption methods have yielded better and more viable results, and carbon-based materials are effective for environmental remediation.Graphene oxide (GO) is a versatile material used in various applications such as nanocomposites, antibacterial agents, photocatalysis, electronics, and biomedicine.GO can act as an antibiotic carrier and affect the antibacterial efficacy of antibiotics.However, the processes responsible for the antibacterial activity of GO and antibiotics in treating ear infections are unknown.This study investigates the effect of GO on the antibacterial activity of tetracycline (TT) against Escherichia coli (E.coli)-negative bacteria.Artificial Neural Network-Genetic Algorithm (ANN-GA) was applied to analyze data on the effectiveness of different doses and combinations of graphene oxide and antibiotics in treating ear infections.This study could help identify the most effective treatment protocols and potentially reduce the risk of antibiotic resistance.The R-squared (R2) value, RMSE, and MSE all fall within the proper levels for fitting criteria, with R2 ≥ 0.97 (97%), RMSE ≤ 0.036064, and MSE ≤ 0.00199 (6% variance).The outcomes showed high antimicrobial activity, resulting in a 5-log decline of E.coli.In experiments, GO was shown to coat the bacteria, interfere with their cell membranes, and aid in the prevention of bacterial growth, although this effect was somewhat weaker for E.coli.The concentration and duration at which bare GO can kill E.coli are both important factors.The antibacterial activity of antibiotics can be either boosted or reduced by the presence of GO, depending on the GO's interaction with the antibiotic, the GO's contact with the microbe, and the sensitivity of the bacteria to the antibiotic.The antibacterial efficiency of the combination of GO and antibiotics varies depending on the specific antibiotic and microorganism being targeted.

Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model.

A significant portion of the solid waste filling landfills worldwide is debris from construction and demolition projects. Across the world, a significant portion of the solid waste filling landfills is made up of construction and demolition waste. Recycling construction waste may help cut down on the quantity of waste sent to landfills and the requirement for energy and other natural resources. To help with construction waste reduction, a management hierarchy that begins with rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycle, and eventually disposal is likely to be effective. The objective of this research is to investigate the viability of the Analytic Hierarchy Process (AHP) as a data gathering instrument for the development of a solid waste management assessment tool, followed by an examination of an artificial neural network (ANN). Using a standardized questionnaire, all data was gathered from waste management practitioners in three industry sectors. The survey data was subsequently analyzed using ANN and later AHP. The suggested framework consisted of four components: (1) the development of different level structures for fluffy AHP, (2) the calculation of weights, (3) the collection of data, and (4) the making of decisions. An ANN feedforward with error back propagation (EBP) learning computation is coupled to identify the association between the items and the store execution. It was found that the combination of AHP and ANN has emerged as a key decision support tool for landfilling, incineration, and composting waste management strategies, taking into account the environmental profile and economic and social characteristics of each choice. Composting has the highest sustainable performance when a balanced weight distribution of criteria is assumed, especially if the environmental component is considered in comparison to the other criteria. However, if social and economic features are addressed, incineration or landfilling have more favorable characteristics, respectively.

Investigation on the Wilson Neuronal Model: Optimized Approximation and Digital Multiplierless Implementation.

Neuromorphic engineering is an essential science field which incorporates the basic aspects of issues together such as: physics, mathematics, electronics, etc. The primary block in the Central Nervous System (CNS) is neurons that have functional roles such as: receiving, processing, and transmitting data in the brain. This paper presents Wilson Multiplierless Neuron (WMN) model which is a modified version of the original model. This model uses power-2 based functions, Look-Up Table (LUT) approach and shifters to apply a multiplierless digital realization leads to overhead costs reduction and increases in the final system frequency. The proposed model specifically follows the original neuron model in case of spiking patterns and also dynamical pathways. To validate the proposed model in digital hardware implementation, the FPGA board (Xilinx Virtex II XC2VP30) can be used. Hardware results show the increasing in the system frequency compared with the original model and other similar papers. Numerical results demonstrate that the proposed system speed-up is 210 MHz that is higher than the original one, 85 MHz. Additionally, the overall saving in FPGA resources for the proposed model is 96.86 % that is more than the original model, 95.13 %. From case study viewpoint for CNS consideration, a network consisting of Wilson neurons, synapses, and astrocytes have been considered to test the controlling effects on LTP and LTD processes for investigating the neuronal diseases (medical approaches) such as Epilepsy.

An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm.

Foot & ankle deformity is a chronic disease with high incidence and is best treated in childhood. However, the current diagnostic procedures rely on doctor's consultation and empirical judgment, and lack objective and quantitative evaluation methods, resulting in low screening rates. To solve this problem, this paper aims to construct an evaluation model for children's foot & ankle deformity through data mining and machine learning technologies. Firstly, it proposes the grading rules for children's foot & ankle deformity severity based on analyzing the existing quantitative indexes and expert experience. Then the 3D foot scanner is used to collect the sample data including 30 foot structure indexes. Finally, an advanced sparse multi-objective evolutionary algorithm (sparse MO-FS) is present for feature selection. The effectiveness of the proposed sparse MO-FS and its search efficiency are proved by comparing 8 feature selection methods and 7 search strategies. Using sparse MO-FS, foot length, arch index, ankle index, and hallux valgus index are selected, which not only simplifies the evaluation model but also improves the average classification accuracy of random forest to more than 98%.

Small Network for Lightweight Task in Computer Vision: A Pruning Method Based on Feature Representation.

Many current convolutional neural networks are hard to meet the practical application requirement because of the enormous network parameters. For accelerating the inference speed of networks, more and more attention has been paid to network compression. Network pruning is one of the most efficient and simplest ways to compress and speed up the networks. In this paper, a pruning algorithm for the lightweight task is proposed, and a pruning strategy based on feature representation is investigated. Different from other pruning approaches, the proposed strategy is guided by the practical task and eliminates the irrelevant filters in the network. After pruning, the network is compacted to a smaller size and is easy to recover accuracy with fine-tuning. The performance of the proposed pruning algorithm is validated on the acknowledged image datasets, and the experimental results prove that the proposed algorithm is more suitable to prune the irrelevant filters for the fine-tuning dataset.