Standard Database Research Articles

Prediction of Molar Entropy of Gaseous Molecules for a New Pὃschl‐Teller Potential Model

ABSTRACTThe Pὃschl–Teller potential is a molecular potential energy function that has only been reported for bound state. This Pὃschl–Teller potential is a good representation of many molecules and has not been examined for any thermodynamic property irrespective of its fitness for molecular study. In this study, the molar entropy of four molecules (Pbr, BBr, CsCl, and CsO molecules) is calculated via the molar partition function. The predicted results are compared with the experimental data recorded in the National Institute of Standards and Technology (NIST) database. It is noted that the predicted values for the studied molecules perfectly agree with the experimental results with the following average absolute percentage deviation, PBr is 0.0158%, BBr is 0.0053%, CsCl is 0.0020%, and CsO is 0.0052%. The present model reproduces better results for CsCl and CsO molecules compared to the shifted Tietz–Wei potential and improved Tietz‐oscillator previously reported whose average absolute percentage deviation are 0.361% and 0.284% for CsCl and 0.272% and 0.228% for CsO, respectively.

Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling

Stochastic sampling is performed to reduce hardware energy consumption and prevent overfitting by reducing parameters, because not all data are required for learning. In this study, a new approach, pseudo‐synaptic sampling (PS2) method, which approximates the conventional synaptic sampling machine (S2M) method through a hardware‐friendly implementation while demonstrating superior efficiency, is introduced. By sampling in front of neurons rather than at each synapse, the PS2 method improves hardware energy efficiency and ensures scalability. Furthermore, it improves energy and area efficiency by eliminating the additional circuit required by other techniques, such as the random walk (RW) method previously used which requires an additional circuit to frequently charge/discharge the membrane potential. Herein, the average firing rate equation for the S2M method is modified to suit the experimental conditions of this study. Through this numerical simulations, it is confirmed that the activation function of the PS2 method aligns with that of the S2M method and verified that the PS2 method can implement stochasticity for restricted Boltzmann machine (RBM) neurons. Experimental validation of the PS2 method, compared to the RW method, for Modified National Institute of Standards and Technology database (MNIST) training and inference on field‐programmable‐gate‐array‐implemented spiking RBM chips reveals promising results. In an MNIST 100‐handwritten digit experiment, the PS2 method exhibits on‐chip training accuracy (92%) comparable to that of the RW method (93%). Furthermore, in the energy consumption analysis, it is shown that the PS2 method reduces power consumption by 94.94% compared to the RW method, highlighting its enhanced power efficiency due to a reduced number of circuit elements. In the investigation into the impact of increasing the frequency at which random bits are generated, it is shown that the RW method experiences accuracy degradation even with slight increases, whereas the PS2 method maintains accuracy over significantly longer periods. This enables further power reduction by allowing for a longer period during random bit generation. In this study, a foundation is laid for maximizing the energy efficiency of spiking neural network processors by optimizing internal noise generation mechanisms.

Somatosensory integration in robot-assisted motor restoration post-stroke

Disruption of somatosensorimotor integration (SMI) after stroke is a significant obstacle to achieving precise motor restoration. Integrating somatosensory input into motor relearning to reconstruct SMI is critical during stroke rehabilitation. However, current robotic approaches focus primarily on precise control of repetitive movements and rarely effectively engage and modulate somatosensory responses, which impedes motor rehabilitation that relies on SMI. This article discusses how to effectively regulate somatosensory feedback from target muscles through peripheral and central neuromodulatory stimulations based on quantitatively measured somatosensory responses in real time during robot-assisted rehabilitation after stroke. Further development of standardized recording protocols and diagnostic databases of quantitative neuroimaging features in response to post-stroke somatosensory stimulations for real-time precise detection, and optimized combinations of peripheral somatosensory stimulations with robot assistance and central nervous neuromodulation are needed to enhance the recruitment of targeted ascending neuromuscular pathways in robot-assisted training, aiming to achieve precise muscle control and integrated somatosensorimotor functions, thereby improving long-term neurorehabilitation after stroke.

A practical Approach to Handwritten Digit Generation with Generative Adversarial Networks on MNIST

Implementation of a Generative Adversarial Network (GAN) utilizing the MNIST (Modified National Institute of Standards and Technology database) dataset of handwritten digits, the GAN comprises a generator creating synthetic images from random noise and a discriminator to classifying these images as real or fake. The Generator utilizes leaky ReLU (rectified linear unit) activations, Batch normalization and reshaping to produce 28x28 grayscale images, while the Discriminator uses dense layers, leaky ReLU, and dropout to enhance classification accuracy. Both networks are trained using the Adam optimizer to improve stability and performance. The GAN is trained in an adversarial setup where the Generator seeks to create convincing images and the Discriminator aims to correctly classify them. Results demonstrate the GAN's ability to generate high-quality handwritten digit images, displaying its effectiveness and providing valuable insights into best practices for GAN implementation and training.

Unveiling the origin of XMM-Newton soft proton flares

Context. Low-energy (<300 keV) protons entering the field of view of XMM-Newton can scatter with the X-ray mirror surface and reach the focal plane. They are observed in the form of a sudden increase in the background level, the so-called soft proton flares, affecting up to 40% of the mission observing time. Soft protons can hardly be disentangled from true X-ray events and cannot be rejected on board. Aims. All future high throughput grazing incidence X-ray telescopes operating outside the radiation belts are potentially affected by soft proton-induced contamination that must be foreseen and limited since the design phase. In-flight XMM-Newton’s observations of soft protons represent a unique laboratory to validate and improve our understanding of their interaction with the mirror, optical filters, and X-ray instruments. At the same time, such models would link the observed background flares to the primary proton population encountered by the telescope, converting XMM-Newton into a monitor for soft protons. Methods. We built a Geant4 simulation of XMM-Newton, including a verified mass model of the X-ray mirror, the focal plane assembly, and the EPIC MOS and pn-CCDs. Analytical computations and, when available, laboratory measurements collected from literature were used to verify the correct modelling of the proton scattering and transmission to the detection plane. Similarly to the instrument X-ray response, we encoded the energy redistribution and proton transmission efficiency into a redistribution matrix file (RMF), mapping the probability that a proton from 2 to 300 keV is detected in a certain detector channel, and an auxiliary response file (ARF), storing the grasp towards protons. Both files were formatted according to the standard NASA calibration database and any compliant X-ray data analysis tool can be used to simulate or analyse soft proton-induced background spectra. An overall systematic uncertainty of 30% was assumed on the basis of the estimated accuracy of the mirror geometry and transmission models. Results. For the validation, three averaged soft proton spectra, one for each filter configuration, were extracted from a collection of 13 years of MOS observations of the focused non X-ray background and analysed with Xspec. A similar power-law distribution is found for the three filter configurations, plus black-body-like emission below tens of keV used as a correction factor, based on the dedicated spectral analysis of 55 in-flight proton flares presented in Paper II. The best-fit model is in agreement with the power-law distribution predicted from independent measurements for the XMM-Newton orbit, spent mostly in the magnetosheath and nearby regions. For the first time we are able to link detected soft proton flares with the proton radiation environment in the Earth’s magnetosphere, while proving the validity of the simulation chain in predicting the background of future missions. Benefiting from this work and contributions from the Athena instrument consortia, we also present the response files for the Athena mission and updated estimates for its focused charged background.

A Review of the Challenges of Adaptive Filtering Technology in High-fidelity Audio Signal Processing

Abstract. High-fidelity audio signal processing plays an important role in modern audio technology. With the increasing demand for this technology in various audio application scenarios, the optimization of adaptive filters has emerged as a significant challenge in this field. This paper focuses on improving the performance of adaptive filters in high-fidelity audio signal processing which aims to improve the adaptability and efficiency of filters in complex audio environments by improving algorithms. In this study, the adaptive filtering algorithm based on wavelet transform and particle swarm optimization is used to verify the audio data processing by simulation and real data processing. The research data was collected using the standard audio signal database and the actual collected high-fidelity audio samples. The results show that the improved adaptive filter can significantly improve the performance of complex audio environments, improve the clarity and fidelity of audio signals and reduce the computational complexity. It is also suitable for real-time processing scenarios with limited resources. The conclusion shows that this method provides an efficient solution for high-fidelity audio signal processing and has a wide application prospect.

FERDCNN: an efficient method for facial expression recognition through deep convolutional neural networks

Facial expression recognition (FER) has caught the research community’s attention recently because it can affect many real-life applications. Multiple studies have focused on automatic FER, most of which use a machine learning methodology, FER has continued to be a difficult and exciting issue in computer vision. Deep learning has recently drawn increased attention as a solution to several practical issues, including facial expression recognition. This article introduces an efficient method for FER (FERDCNN) verified on five different pre-trained deep CNN (DCNN) models (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101). In the proposed method, firstly the input image has been pre-processed using face detection, resizing, gamma correction, and histogram equalization techniques. Secondly, the images go through DCNN to extract deep features. Finally, support vector machine (SVM) and transfer learning are used to classify generated features. Recent methods have been employed to evaluate and contrast the performance of the proposed approach on two publicly standard databases namely, CK+ and JAFFE on the seven classes of fundamental emotions, including anger, disgust, fear, happiness, sadness, and surprise beside neutrality for CK+ and contempt for JAFFE. The suggested method tested Four different traditional supervised classifiers with deep features, Experimental found that AlexNet excels as a feature extractor, while SVM demonstrates superiority as a classifier because of this combination achieving the highest accuracy rates of 99.0% and 95.16% for the CK+ database and the JAFFE datasets, respectively.

Association of Intellectual and Developmental Disabilities With Worse Outcomes After Surgical Treatment of Cancer.

Patients with intellectual and developmental disabilities (IDD) face unique challenges resulting in disparities in their health care. We sought to define the effect that IDD had on achievement of a "textbook outcome" (TO) following a cancer operation among a nationally representative cohort of patients. Data on patients who underwent surgery for a malignant indication, including lung, breast, liver, biliary tract, pancreas, and colorectal, between 2014 and 2020 were extracted from the 100% Medicare Standard Analytical Files database. The association of IDD with TO (defined as the absence of postoperative complications, extended length of stay, 90-day readmission, and 90-day mortality), expenditures, and discharge status was assessed using multivariable logistic regression. Among 500,472 Medicare beneficiaries, 4,326 (0.9%) with IDD had a cancer diagnosis (breast, n=481; lung, n=419; hepatobiliary, n=194; pancreas, n=145; colorectal, n=3,087). Although overall incidence of TO was 50.5%, patients with IDD were less likely to achieve a TO than those without (37.1% vs 50.6%, respectively; odds ratio [OR], 0.50; 95% CI, 0.46-0.53; P<.001). On multivariable regression, patients with IDD had higher odds of a postoperative complication (OR, 1.53; 95% CI, 1.43-1.64), extended length of stay (OR, 2.06; 95% CI, 1.93-2.21), 90-day readmission (OR, 1.15; 95% CI, 1.07-1.24), 90-day mortality (OR, 1.90; 95% CI, 1.70-2.13), and discharge to a skilled nursing facility (OR, 4.28; 95% CI, 3.97-4.62) (all P<.001). Patients with IDD had a much lower chance of a postoperative TO, as well as discharge to a nonhome setting. The data highlight the need to improve the care of patients with IDD to assure equitable oncologic surgical care.

Формирование вторичного ареала караганы древовидной в лесопарках г. Екатеринбурга

The article is devoted to the analysis of the distribution and features of the ecological and biological confinement of Caragana arborescens Lam. in the forest parks of Yekaterinburg. Caragana plantations have been studied in 14 out of the 15 city forest parks: Kalinovskij, Zheleznodorozhnyj, Karas’ye-Ozerskij, Moskovskij, Nizhne-Isetskij, Sanatornyj, Uktusskij, Shuvakishskij, Yuzhnyj, Shartashskij, named after the Foresters of Russia, Obroshinskij, Central and South-West – on an area of 12,077 hectares based on a standard electronic database. The ontogenesis of the species and its morphological features have been studied in 10 local habitats by the example of 2 forest parks – named after the Foresters of Russia with the largest number of caragana and Uktussky with the minimum. The formation of the secondary area of caragana in the forest parks of Yekaterinburg depends on the presence of generative individuals nearby. The maximum growth area is established in the forest park named after the Foresters of Russia (98.6 ha), the minimum – in the SouthWest (0.4 ha). The area of distribution is determined by the area of plantings that have been carried out in the 1970s and 1980s, which is confirmed by our research in the forest parks. C. arborescens prefers to settle in mixed-herb (67.8 %) and berry (26.8 %) pine forests with a stand density of 0.6–0.7, being a heliosciophyte. The number of caragana trees in the habitats increases with the increase in the density of the stand from 404 to 1359 plants. When the density of the pine stand is 0.3–0.5, the amount of caragana decreases, and at 0.1–0.2 it disappears. The distribution of C. arborescens depends on the age categories and productivity of forest plantations, it grows mainly in mature pine (68.8 %) trees in high-quality (the 2nd quality class – 62.3 %) plantations. In the ontogeneisis of caragana, 2 periods and 6 agerelated conditions have been established, with the pregenirative fraction predominating. The studied habitats are normal, incomplete, but capable of self-maintenance, since under favourable conditions individuals reproduce by self-seeding. The life forms of the caragana tree are formed in open areas as an aeroxylic shrub, and in pine forests as a small tree. The Caragana arborescens introductions in the forest parks of Yekaterinburg are rare and currently pose no biological hazard to forest ecosystems.

Dual Stream Encoder–Decoder Architecture with Feature Fusion Model for Underwater Object Detection

Underwater surveillance is an imminent and fascinating exploratory domain, particularly in monitoring aquatic ecosystems. This field offers valuable insights into underwater behavior and activities, which have broad applications across various domains. Specifically, underwater surveillance involves detecting and tracking moving objects within aquatic environments. However, the complex properties of water make object detection a challenging task. Background subtraction is a commonly employed technique for detecting local changes in video scenes by segmenting images into the background and foreground to isolate the object of interest. Within this context, we propose an innovative dual-stream encoder–decoder framework based on the VGG-16 and ResNet-50 models for detecting moving objects in underwater frames. The network includes a feature fusion module that effectively extracts multiple-level features. Using a limited set of images and performing training in an end-to-end manner, the proposed framework yields accurate results without post-processing. The efficacy of the proposed technique is confirmed through visual and quantitative comparisons with eight cutting-edge methods using two standard databases. The first one employed in our experiments is the Underwater Change Detection Dataset, which includes five challenges, each challenge comprising approximately 1000 frames. The categories in this dataset were recorded under various underwater conditions. The second dataset used for practical analysis is the Fish4Knowledge dataset, where we considered five challenges. Each category, recorded in different aquatic settings, contains a varying number of frames, typically exceeding 1000 per category. Our proposed method surpasses all methods used for comparison by attaining an average F-measure of 0.98 on the Underwater Change Detection Dataset and 0.89 on the Fish4Knowledge dataset.

Opportunities and challenges in the development of artificial intelligence research in spinal surgery

Artificial intelligence has emerged as a game-changer in the field of spine surgery, offering transformative diagnostic and therapeutic approaches for spinal conditions. The application of AI in spine research encompasses a diverse range of diseases, with usage scenarios becoming increasingly widespread and technological integration going deeper. AI technology shows immense promise and value in the diagnosis of spinal diseases, the formulation of treatment strategies, surgical navigation, prognostic evaluation, and postoperative rehabilitation. Through deep learning and machine learning, AI can aid doctors in enhancing diagnostic accuracy, creating personalized treatment plans, and executing precise maneuvers during surgery, thus improving operational safety. Moreover, AI can provide intelligent support for patients' postoperative recovery, facilitating the restoration of their functions. However, current research is still in its nascent stage and confronts several challenges, such as the lack of standardized databases, the simplicity of algorithmic learning models, the inadequate fusion of multi-modal clinical information, and the limited clinical applicability. By developing open-source, standardized spine databases, incorporating innovative intelligent core algorithms, and establishing normalized screening, diagnostic, and predictive models for spinal conditions, we trust that we can drive the innovation and refinement of diagnostic and treatment technologies in spine surgery. This will realize an effective conjunction between technological provision and clinical demands, continuously elevating the intelligence level of spine surgery and offering safer, more effective medical services to a vast array of patients.

Adaptive superpixel segmentation and pigment identification of colored relics based on visible spectral images

This work explores the extraction of the spatial distribution and chemical composition information of pigments in colored relics through visible spectral images. An adaptive superpixel segmentation method is proposed to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on the image homogeneity. These seed points are used as the initial cluster centers in an extended simple linear iterative clustering (SLIC) algorithm for visible spectral images to create superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area in the colored relics, and a pigment identification method based on the visible spectral reflectance is proposed to identify the pigments in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. Geometric features, which are characterized by the linear combination of the normalized visible spectral reflectance, its slope and its curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using the Euclidean distance to determine the pigments in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics.

Psychoacoustic assessment of automotive fan-systems

In the automotive industry, electrification trends worldwide are inducing an increase of noise sources emergences. This reinforces the acoustic assessment metrics and thresholds set by stakeholders to ensure acoustic comfort of car passengers and passersby. The main acoustic indicator, the overall Sound Pressure Level, is not relevant enough to depict pedestrian annoyance during exposure to fan-system noises. Currently, psychoacoustic assessment of these products is not systematic as the psychoacoustic metrics taken separately are not sufficient for sound quality ranking. In order to address this weakness, studies were performed by Valeo based on fan-system noise jury evaluations. It aims at providing a preference model consisting of an equation which combines several psychoacoustic metrics to assess fan-system noise annoyance; keeping in mind that Loudness -or overall SPL- high levels could mask underlying annoyances biasing its assessment. In a first step, the correlation between Overall SPL, Loudness and aeraulic power is highlighted based on standard benchmark test database. In a second step, since tonal noises affect the perceived annoyance, additional tonal metrics are analyzed versus Loudness, or acoustic classes. Tonality especially is then compared to other tonal criteria, before potential assessment of a preference model equation.

Photonic Neuromorphic Processing with On‐Chip Electrically‐Driven Microring Spiking Neuron

AbstractGuided by brain‐like temporal processing and event‐driven manner, neuromorphic computing has emerged as a competitive paradigm to realize artificial intelligence with high energy efficiency. Silicon photonics offers an ideal hardware platform with mutual foundry fabrication process and well‐developed device libraries, however, its huge potential to build integrated neuromorphic systems is significantly hindered due to the lack of scalable on‐chip photonic spiking neurons. Here, the first integrated electrically‐driven spiking neuron based on a silicon microring under the carrier injection working mode is reported, which is capable of emulating fundamental neural dynamics including excitability threshold, temporal integration, refractory period, controllable spike inhibition, and precise time encoding at a speed of 250 MHz. By programming time‐multiplexed spike representations, photonic spiking convolution is experimentally realized for image edge feature detection. Besides, a spiking convolutional neural network is constructed by combining photonic convolutional layers with a software‐implemented fully‐connected layer, which yields a classification accuracy of 94.1% on the benchmark Modified National Institute of Standards and Technology database. Moreover, it is theoretically verified that it's promising to further improve the operation speed to a gigahertz level by developing an electro‐optical co‐simulation model. The proposed microring neuron constitutes the final building block of scalable spike activation, thus representing a great breakthrough to boost the development of on‐chip neuromorphic information processing.

Unusual causes of Small bowel obstruction: a review of the literature and revisited cross-sectional imaging checklist.

Intestinal obstruction is a common surgical emergency with high morbidity and mortality. Patients presenting with features of small bowel obstruction need urgent evaluation to avoid complications such as bowel gangrene, perforation, or peritonitis. Imaging is necessary in most cases of suspected bowel obstruction, to take an appropriate decision, for apt patient management. Among the common causes of small bowel obstruction, adhesions, external herniae, malignancies, and Crohn's disease top the chart. Imaging helps in determining the presence of obstruction, the severity of obstruction, transition point, cause of obstruction, and associated complications such as strangulation, bowel gangrene, and peritonitis. This review is based on the cases with unusual causes of bowel obstruction encountered during our routine practice and also on the extensive literature search through the standard textbooks and electronic databases. Through this review we want our readers to have sound knowledge of the imaging characteristics of the uncommon yet important causes of bowel obstruction. We have also revisited and structured a checklist to simplify the approach while reporting a suspected case of small bowel obstruction. Imaging plays a key role in the diagnosis of small bowel obstruction and in determining the cause and associated complications. Apart from the common causes of small bowel obstruction, we should also be aware of the uncommon causes of small bowel obstruction and their imaging characteristics to make an accurate diagnosis and for apt patient management.

Impacting trauma care in resource-limited settings: Lessons learned from Tanzania's web-based trauma registry initiatives.

Trauma significantly impacts Tanzanian healthcare. Lacking standardized hospital-based minimal trauma data sets places further challenges for policymakers. In other resource-limited countries, implementing trauma systems and registries has reduced injury mortalities. In 2013, we introduced an electronic trauma registry, iTRAUMATM at the Tanzanian Muhimbili Orthopedic Institute (MOI) but noted several drawbacks. In 2023, we introduced a robust web-based trauma registry platform. This study assesses the feasibility and utility of implementing the platform at MOI and summarizes challenges, lessons, and results compared to existing systems. This prospective observational study involved clinicians collecting data directly on the platform at the point-of-care, following specific training. Semi-structured interviews with local stakeholders identified challenges and areas for improvement. Data were reported from July to December 2023. Data from 2930 patients showed 59% of injuries were from road traffic collisions (RTCs), with 43% of patients arriving at MOI by non-ambulances. Our findings show that non-ambulance arrivals were associated with higher injury severity (p<0.026), mortalities (p<0.017), and delayed hospital arrival (p<0.004), underscoring the critical role of prompt transport in trauma management. The new platform identified trauma care gaps, with a mean arrival-to-care time of 29.89min, prompting trauma training at MOI to enhance clinician capacities. It also demonstrated superiority over existing systems by improving data completeness, timeliness, and usability. Challenges included gaining support for the platform's functionality, technology integration, and navigating administrative changes. With continued communication, stakeholder acceptance and support were achieved. The web-based platform has become MOI's standard trauma database, demonstrating its feasibility and utility. It overcame the existing challenges of data completeness, timeliness, and usability for policymaking. Positive feedback has prompted plans to expand the platform to other hospitals, benefiting clinical benchmarking and trauma preventive efforts. Ensuring sustainability requires involvement from the Ministry of Health, ongoing training, functionality enhancements, and strengthened global partnerships.

An automatic workflow for the quantitative evaluation of bit wear based on computer vision

As global oil exploration ventures into deeper and more complex territories, drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety. Despite the publication of official bit wear evaluation standards by the International Association of Drill Contractors (IADC), the current lack of quantitative and scientific evaluation techniques means that bit wear assessments rely heavily on engineers' experience. Consequently, forming a standardized database of drilling bit information to underpin the mechanisms of bit wear and facilitate optimal design remains challenging. Therefore, an efficient and quantitative evaluation of bit wear is crucial for optimizing bit performance and improving penetration efficiency.This paper introduces an automatic standard workflow for the quantitative evaluation of bit wear and the design of a comprehensive bit information database. Initially, a method for acquiring images of worn bits at the drilling site was developed. Subsequently, the wear classification and grading models based on computer vision were established to determine bit status. The wear classification model focuses on the positioning and classification of bit cutters, while the wear grading model quantifies the extent of bit wear. After that, the automatic evaluation method of the bit wear is realized. Additionally, bit wear evaluation software was designed, integrating all necessary functions to assess bit wear in accordance with IADC standards. Finally, a drilling bit database was created by integrating bit wear data, logging data, mud-logging data, and basic drilling bit data.This workflow represents a novel approach to collecting and analyzing drilling bit information at drilling sites. It holds potential to facilitate the creation of a large-scale information database for the entire lifecycle of drilling bits, marking the inception of intelligent analysis, design, and manufacture of drilling bits, thereby enhancing performance in challenging drilling conditions.

Handwritten Character Recognition using Enhanced Artificial Neural Network

This study addresses the concerns regarding the performance of Handwritten Character Recognition (HCR) systems, focusing on the classification stage. It is widely acknowledged that the development of the classification model significantly impacts the overall performance of HCR. The problems identified specifically pertain to the classification model, particularly in the context of the Artificial Neural Network (ANN) learning problem, leading to low accuracy in recognizing handwritten characters. The objective of this study is to improve and refine the ANN classification model to achieve better HCR. To achieve this goal, this study proposed a hybrid Flower Pollination Algorithm with Artificial Neural Network (FPA-ANN) classification model for HCR. The FPA is one of the metaheuristic approaches is utilized as an optimization technique to enhance the performance of ANN, particularly by optimizing the network training process of ANN. The experimentation phase involves using the National Institute of Standards and Technology (NIST) handwritten character database. Finally, the proposed FPA-ANN classification model is analyzed based on generated confusion matrix and evaluated performance of the classification model in terms of precision, sensitivity, specificity, F-score and accuracy.

Analyzing deep textual facial patterns for human pain sentiment recognition system in smart healthcare framework

BACKGROUND: Patient sentiment analysis aids in identifying issue areas, timely remediation, and improved patient care by the healthcare professional. The relationship between pain management and patient sentiment analysis is crucial to providing patients with high-quality medical care. Therefore, a self-reported pain level assessment is required for the smart healthcare framework to determine the best course of treatment. OBJECTIVE: An efficient method for a pain sentiment recognition system has been proposed based on the analysis of human facial emotion patterns of patients in the smart healthcare framework. METHODS: The proposed system has been implemented in four phases: (i) in the first phase, the facial regions of the observation patient have been detected using the computer vision-based face detection technique; (ii) in the second phase, the extracted facial regions are analyzed using deep learning based feature representation techniques to extract discriminant and crucial facial features to analyze the level of pain emotion of patient; (iii) the level of pain emotions belongs from macro to micro facial expressions, so, some advanced feature tunning and representation techniques are built along with deep learning based features such as to distinguish low to high pain emotions among the patients in the third phase of the implementation, (iv) finally, the performance of the proposed system is enhanced using the score fusion techniques applied on the obtained deep pain recognition models for the smart healthcare framework. RESULTS: The performance of the proposed system has been tested using two standard facial pain benchmark databases, the UNBC-McMaster shoulder pain expression archive dataset and the BioVid Heat Pain Dataset, and the results are compared with some existing state-of-the-art methods employed in this research area. CONCLUSIONS: From extensive experiments and comparative studies, it has been concluded that the proposed pain sentiment recognition system performs remarkably well compared to the other pain recognition systems for the smart healthcare framework.

Cost‐Effective and Fully Hardware‐Oriented Reservoir Computing Based on IGZO/HZO Ferroelectric Thin‐Film Transistor with Electrically and Optically Distinguishable States

AbstractHardware‐based reservoir computing (RC) systems provide benefits like energy efficiency and effective predictability. The implementation of different switching characteristics for the reservoir and readout layers requires the use of different types of devices or additional processing. However, an RC system with distinguishable switching characteristics obtained by changing stimulation on a single device is not identified yet, but it is appealing in terms of process simplicity and efficient processing costs. This study develops an RC system that uses ferroelectric thin‐film transistor (FeTFT) devices with an indium gallium zinc oxide channel and Hf0.5Zr0.5O2 ferroelectric layer for both networks. The nonvolatile FeTFT utilizes the remnant polarization properties of the ferroelectric layer through electrical stimulation, showing stable retention characteristics (104 s) and long‐term potentiation/depression. By using optical stimulation, the volatile FeTFT demonstrates short‐term characteristics, such as paired‐pulse facilitation, and a 4‐bit RC system. This proves that it is possible to meet the functional requirements of both the reservoir and readout networks by simply varying the type of stimulation applied to a single FeTFT. Finally, the fully FeTFT‐based RC system can recognize digit patterns from the Modified National Institute of Standards and Technology database with a high accuracy of 90.5%.