Pool boiling is extensively utilized in various fields, such as electronics cooling, battery cooling, nuclear power plants, and chemical processing. In the process of temperature control through boiling, the generation and transportation of bubbles play a crucial role in calculating the heat transfer capacity of the system. Therefore, it holds immense significance to gain a profound understanding of the mechanisms underlying bubble formation, growth, and detachment. Currently, numerous mechanistic explanations and empirical correlations have been proposed to elucidate various parameters of bubbles during the boiling process. These findings were considered valuable references in selecting appropriate boiling media and designing efficient heating surfaces. To comprehensively present the progress of bubble formation and heat transfer during the boiling process, the forces exerted on the bubbles was highlighted in this article. And a meticulous review on bubble force analysis and correlation formulas pertaining to various relevant parameters (e.g. nucleation sites density, bubble growth rate, detachment diameter, detachment frequency) were conducted. This review article also were expected to provide a novel foundation for further exploration of enhanced boiling heat transfer.

Bending waveguide is one of the building blocks of on-chip photonic integrated circuits (PICs). Previous bending waveguides (BWs) usually suffer from larger footprint size in the bending region, which is are not conducive to develop large-scale and high-density PICs. To this end, we propose and numerically investigate an ultra-compact silicon-on-insulator 180-degree waveguide bend with a bending radius of 2 µm. Being different from the previous BWs, the design is based on topologically optimized digital metamaterials, which is implemented by using intelligent genetic algorithms. Simulated results show that the proposed structure has a -1.36 dB bend efficiency per 180-degree at a wavelength of 1.55 µm and occupies a footprint of only 4 × 4 µm2. Moreover, the crosstalk is <−20 dB and the loss is <2.6 dB over a >100 nm operating bandwidth (1.6-1.7 µm) for TE0 modes. With the wide operating bandwidth and ultra-compact footprint, the proposed silicon-based 180-degree waveguide bend shows a potential application as the fundamental component for optical communication interconnect in the ultra-highly integrated photonic circuit.

Osteoporosis is a common problem in orthopedic medicine, and it has become an important medical issue in orthopedics as Taiwan is gradually becoming an aging society. In the diagnosis of osteoporosis, the bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the main criterion for orthopedic diagnosis of osteoporosis, but due to the high cost of this equipment and the lower penetration rate of the equipment compared to the X-ray images, the problem of osteoporosis has not been effectively solved for many people who suffer from osteoporosis, however, at present, in clinical diagnosis, doctors are not yet able to accurately interpret X-ray images for osteoporosis manually, and must rely on the data obtained from DXA. In recent years, with the continuous development of artificial intelligence, especially in machine learning and deep learning, significant progress has been made in image recognition. Therefore, it is worthwhile to revisit whether it is possible to use a convolutional neural network model to read a hip X-ray image and then predict the patient's BMD. In this study, we proposed a hip X-ray image segmentation model and a hip X-ray image recognition classification model. First, we used the U-Net model as a framework to segment the femoral neck, greater trochanter, wards triangle, and the total hip in the hip X-ray images. We then performed image matting and data augmentation. Finally, we constructed a predictive model for osteoporosis using deep learning algorithms. In the segmentation experiments, we used intersection over union (IoU) as the evaluation metric for image segmentation, and both the U-Net and U-Net++ models achieved segmentation results greater than or equal to 0.5. In the classification experiments, using the T-score as the classification basis, the total hip using the DenseNet121 model has the highest accuracy of 74%.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. Controlling and curing diabetes in its early stages is the most significant technique to avoid its effects of diabetes. However, lack of awareness and expensive clinical tests are the primary reasons to skip clinical diagnosis and take preventive methods in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, the study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes. Because clinical features are not always known to all people from lower-income countries. To find the best fit supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. The study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for growing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

The global construction sector contributes a significant share of total greenhouse gas (GHG) emissions. In Australia, infrastructure activity alone generates 18% of the GHG emissions budget. The use of low-embodied carbon building materials is crucial to achieving sustainability in the construction sector and to fulfill national and international climate goals. Industrial hemp (Cannabis sativa L.) is considered a promising feedstock for sustainable construction materials because of its biogenic carbon content, fast-growing cycles with low agricultural input requirements, and technical functionality which is comparable to traditional materials. This study has applied the life cycle assessment (LCA) guideline of ISO 14040:2006 to estimate the carbon footprint (CF) expressed in carbon dioxide equivalent (CO2eq) emissions of hemp-based building materials in Western Australia. The functional unit is 1 m2 of hemp-based board, and the system boundary includes cradle-to-gate stages, i.e., pre-farm, on-farm, and post-farm activities. The production of 1 m2 of hemp-based board is estimated to be - 2.302 kgCO2eq. Electricity from the public grid for lignin extraction during the post-farm stage is the main contributor to total CO2eq emissions (26%), followed by urea production (14%) during the pre-farm stage. Overall, the use of electricity from the SWIS during the post-farm stage accounts for 45% of total emissions. Sensitivity analysis shows that the CF of hemp-based boards is highly sensitive to the source of energy, i.e., total replacement of the SWIS by solar power decreases the CF from - 2.30 to -6.07 kgCO2eq (164%). The results suggest that hemp-based boards exhibit lower embodied GHG emissions compared to traditional materials, such as gypsum plasterboards.

This research describes a detailed analysis in the enactment of a Lean Tool, the Last Planner System during pandemic period which helps in optimizing the resources for better coordination among all stakeholders in pandemic period. LPS as it is known, focusses on minimizing the factors such as uncertainties, barriers and variability to make construction projects more flexible for better project management. These include variations and deviations, supervision, delays in approvals, change resistance, subcontractor dedication which are all related to various stakeholders in the project. Following that, a Design Science Research technique is used to evaluate the effect of applying LPS in buildings. An action strategy is being used to attain this goal, and four case studies were being documented which were concerned in the implementation of LPS in the building of the Boy's Hostel, Research Scholar Quarters, Faculty Housing, and Girls' Hostel at Chennai, Tamilnadu, India. Data was gathered by observation of site activities, interviews, documentation analysis, and a questionnaire survey and grouped into various factors. While adopting the LPS tool in the research the factors affecting the implementation were found in the covid-19 pandemic period. Further these factors were anlayzed, measured, ranked and validated for adopting in projects.

Previous studies to recognize negative emotions (e.g. disgust, fear, sadness) for mental health care have used heavy equipment directly attaching electroencephalogram (EEG) electrodes to the head, making it difficult to use in daily life, and they have proposed binary classification methods to determine whether negative emotion or not. To tackle this problem, we propose a negative emotion recognition system to collect multimodal biosignal data such as five EEG signals in an EEG headset and heart rate, galvanic skin response, and skin temperature in a smart band for classifying multiple negative emotions. It consists of android Internet of Things (IoT) application, an oneM2M-compliant IoT server, and a machine learning server. The android IoT application upload the biosignal data to the IoT server. By using the biosignal data stored in the IoT server, the machine learning server recognizes the negative emotions of disgust, fear, and sadness using a multi-class support vector machine (SVM) model with a radial basis function kernel (RBF). The experimental results showed that the multi-class SVM model achieved 93% accuracy when considering all the multimodal biosignal data. Moreover, when considering only data in the smart band, it could achieve 98% accuracy by optimizing the hyper-parameter of the RBF kernel.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

The study focuses on solving urban challenges such as temperature reduction, urban stormwater management, noise reduction, air quality improvement, and CO2 concentrations reduction, and suggests terrestrial and freshwater habitat types (HTs) found in Europe, as innovative forms of nature-based solutions (NBS) for them. Establishment of native HTs in different urban environments to solve urban challenges would enhance biodiversity at different levels and integrate this aspect in urban planning. This contribution builds on the recognition that surfaces of vegetated terrain are the most versatile NBS in addressing the broadest range of environmental problems in urban areas and that the processes running within these green spaces offer the key to socio-ecological improvements of such areas. Employing narrative literature review, qualitative content analysis, and interdisciplinary expert discussion this paper defines why and how native HTs of unchanged nature can be implemented as NBS in the urban environment, indicate potential HTs for specific urban challenges and presents an approach to the inclusion of HTs as NBS into spatial planning documents at national, regional, and local levels. The proposed planning approach attributes added value to HT, and by linking the concepts of NBS and HT integrates them into urban planning.

Developing low-cost and stable materials for converting solar energy into electricity is vital in meeting the world's energy demand. Metal-organic frameworks (MOFs) have gained attention for solar cells due to their natural porous architectures and tunable chemical structures. This review commences with an exploration of the synthetic methods of MOFs. Moreover, we discuss the various roles of MOFs, including photoanodes and counter electrodes in dye-sensitized solar cells and interfacial layers and charge carriers in perovskite solar cells. Ultimately, burdensome tasks and possible directions for advancing MOFs-based nanomaterials are provided for solar cells.

In the work the enhanced security of unmanned aerial vehicles (UAVs) electronics in RF environment was investigated. UAVs are commonly used in many areas and the cellular networks as the parallel helping networks for systems: LTE, 5G or especially for future 6G are very perspective and needed. For effective communications in channels: “air-to-ground” as well as “drone-to-drone” the important task is undisturbed work of transportation platforms, ie. UAV. However, high level of outside electromagnetic radiation can affect the correct electronics work. The problem is due to non-conformity of RF environment regulations. The electromagnetic compatibility (EMC) standards guarantee correct electronics work when the radiation level is not higher than established EMC levels. In case of commercially available devices, like UAVs, this level is 3 V/m. On the other hand, permissive exposure level, treated as RF environment emitted by cellular base stations, can reach the level of 61 V/m. Such level of electromagnetic wave pollution, can damage unprotected electronics with fatal consequences. This can cause the unchecked drone flight or unchecked fall down. As a result, due to unchecked flight, the UAV can cause the sudden communication interruption with operator or other drones. To reduce the level of damaging RF field we propose to cover the UAV housing with microwave absorber. In this case absorber must ensure good electromagnetic protection as well as lightweight and resistant on weather conditions. The shielding property of reduced graphene oxide (RGO) as an absorber was investigated. For proposed material the shielding effectiveness (SE) was investigated in the frequency range from 100 MHz to 10 GHz. It has been proved that such material can be a good candidate as an absorber for UAV having low reflection coefficient and high absorption ability. In this work the shielding effectiveness of RGO was analyzed for two layers of 3 and 5 mm at typical frequencies as RF environment, ie. 3.6 GHz (5G system) and 5.8 GHz (LTE). Investigated absorber guarantees the value of SE of 25 dB and 30 dB for 3 mm layer at 3.6 GHz and 5.8 GHz respectively and of 29 dB and 39.5 dB for 5 mm layer at these frequencies respectively.

As the demands in reliability and high-degree of automation in industrial applications are increasing exponentially, the current Industry 4.0 trend lies in migrating towards smarter technologies that increase flexibility and provide autonomous operation. To tackle this, manufacturers shifted towards Real-Time Ethernet communication which supports autonomous operation, faster sensor/actuator configuration and high degree of determinism. Nevertheless, the uninterrupted operating principle of legacy industrial systems as well as the system complexity and heterogeneity constitutes the transition to Real-Time Ethernet very challenging. This article proposes a novel method allowing to automate the generation of executable code for Real-Time Ethernet architectures based on high-level programming models. The method is based on a programming model and modular code artifacts for the hardware architecture, employing Real-Time Ethernet communication. Through an tool-supported algorithm the high-level model is linked to the architecture's communication primitives and interfaces. The tool-support reduces significantly the development and debugging effort for Real-Time Ethernet applications and increases the application reliability, since the generated code is based on verified programming models. Additionally, the tool creates dedicated files, which allow automated industrial network configuration without any human intervention. Finally, by embedding firewall policies into the code generation process, the method guarantees cyber-resilience for the Real-Time Ethernet architecture. We demonstrate the proposed method in Programmable Logic Controllers (PLCs) of a Public Power Corporation's Hydroelectric Power Plant, which control the temperature and rotor speed of a power generator. The results demonstrate the method's ability to generate rapidly trustworthy and fault-tolerant code for the autonomous plant operation. The hurdles in time and effort for developing, configuring and debugging Real-Time Ethernet applications can be minimized through a high-level programming model that allows automated generation of executable code that is reusable for similar applications. Such time and effort reduction may pave the way towards the Industry 4.0 area.

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, which has attracted extensive attention in recent years with the development of deep learning. Currently, mainstream oriented object detectors are based on preset anchor boxes. This method increases the computational load of the network and cause a large amount of anchor box redundancy. To solve this problem, we propose anchor-free oriented object detection based on the Gaussian centerness(AOGC), a single-stage anchor-free detection method. Our method uses contextual attention FPN(CAFPN) to obtain the contextual information of the target. Then we design a label assignment method for oriented objects. Finally, we develop a Gaussian kernel-based centerness branch, which can effectively determine the significance of different anchors. AOGC achieves mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. AOGC exhibits superior performance to other methods in oriented anchor-free object detection methods.

Previous studies have indicated that facial expressions can serve as an objective evaluation method for hedonics (overall pleasure) of food and beverages. In this study, we aimed to validate the findings of our previous research, which demonstrated that facial expressions induced by tastants can predict the perceived hedonic ratings of these tastants. Facial expressions of 29 female participants (aged 18-55 years) were recorded using a digital camera while they consumed 12 different concentrations of solutions representing five basic tastes. The facial expressions were then analyzed using the widely-used facial expression analysis application, FaceReader, to identify seven emotions (surprise, happiness, scare, neutral, disgust, sadness, and anger) with scores ranging from 0 to 1. Participants also rated the hedonics of each solution on a scale from -5 (extremely unpleasant) to +5 (extremely pleasant). A multiple linear regression analysis was conducted to develop a formula to predict perceived hedonic ratings. The formula's applicability was tested by analyzing emotion scores for 11 additional taste solutions consumed by 20 other participants. The predicted hedonic ratings demonstrated good correlation and concordance with the perceived ratings, supporting the validity of our previous findings using different software and taste stimuli among diverse participants.

This preliminary study introduces the incorporation and chemical stabilization of sulfur waste into geopolymers matrix and explores the concept of material production for further environmental and engineering solutions. Geopolymers were synthesized by dissolving sulfur in the alkaline activator of sodium hydroxide and sodium silicate in different proportions. The Alkaline solution was then mixed with metakaolin to synthesize the geopolymer matrix. Adding sulfur from 0 wt.% to 5 wt.%, compared with metakaolin, was found to lead to an increase in the compressive strength of the geopolymer from 22.5 MPa to 29.9MPa. When sulfur is between 5 wt.% and 15 wt.%, a decrease in compressive strength was observed to 15.7 MPa, which can be explained by defects and voids in the geopolymer microstructure through the solubility of excess sulfur and thus the formation of defects and voids in the geopolymer microstructure. Because of the incorporation of sulfur into the geo-polymer, a compact and dense microstructure is formed as reported in the SEM analysis. XRD analysis shows that besides quartz and Analcime, a new phase, Al2·H10·O17·S3, is also formed as a result of sulfur dissolution in alkaline activator of geopolymer.

The COVID-19 pandemic has posed a significant threat to society in recent times, endangering human health, life, and economic well-being. The disease spreads quickly due to the highly infectious SARS-CoV-2 virus, which has undergone numerous mutations. Despite intense research efforts by the scientific community since its emergence in 2019, no effective therapeutics have been discovered yet. While some repurposed drugs have been used to control the global outbreak and save lives, none have proven universally effective, particularly for severely infected patients. Although the spread of the disease is generally under control, anti-SARS-CoV-2 agents are still needed to combat current and future infections. This study reviews some of the most promising repurposed drugs containing indolyl heterocycle, which is an essential scaffold of many alkaloids with diverse bio-properties in various biological fields. The study also discusses natural and synthetic indole-containing compounds with anti-SARS-CoV-2 properties, as well as computer-aided drug design (in-silico studies) for optimizing anti-SARS-CoV-2 hits/leads.

Depression is one of the psychiatric disorders characterized by anxiety, pessimism, and suicidal tendencies, which seriously affect the quality of life of patients and their families. In this paper, we used polynomial-based kernel Granger causality values as network node connectivity indicators to construct brain networks for 5 depressed patients and 11 healthy individuals’ magnetoencephalogram(MEG) under positive, neutral, and negative emotional stimuli, respectively, and found that depressed patients had closer information exchange between frontal and occipital regions compared to healthy individuals and other brain regions, and fewer causal connections in parietal and central regions. Further analysis of the topological properties of the network revealed that depressed patients had higher mean degrees under negative stimuli (p=0.008)and lower mean clustering coefficients than healthy individuals(p=0.034). Comparing the mean degree and mean clustering coefficient of the same sample under different emotional stimuli, we found that depressed patients had the greater mean degree and mean clustering coefficient under negative stimuli than neutral and positive stimuli. We also found that patients’ feature path lengths under negative and neutral stimuli significantly deviated from small-world attributes. The results suggest that analysis of nuclear Granger causality-based brain networks can effectively characterize depression pathology.

Herein, we report on a versatile system composed of two Ni(II)-based coordination compounds: a 3D metal-organic framework of {[Ni(μ-3isoani)2]·DMF}n (1) formula and a 0D monomeric [Ni(3isoani)2(H2O)4] (2) complex (where 3isoani = 3-aminoisonicotinato and DMF = dimethylformamide) which show mutual solvent-induced reversible transformations into each other. These 1 ↔ 2 transformations are triggered by the exposure of one compound to the solvent present in the other, that is, the MOF (1) in water and the monomer (2) in DMF. The process is easily followed by the naked eye because it involves a colour change from green (1)-to-brownish green (2), making the system potentially interesting to act as a sensor of those solvents. Moreover, these compounds present very different magnetic properties since 1 shows field-induced single-molecule magnet (SMM) behaviour, which corresponds to one of the few Ni(II)-based compounds showing this property, whereas 2 behaves as a regular paramagnet, all of which is explained according to a careful study of the magnetic properties by means of experimental direct-current (dc) and alternating-current (ac) measurements as well as complete active space self-consistent field (CASSCF) calculations.

The development of sustainable tourism in the territory requires the monitoring of all diverse aspects of the tourism sector and the creation of information technologies that provide the collection, analysis, processing and visualization of relevant information. The aim of this work is to describe the concept of the tourism monitoring service in Irkutsk Oblast as part of the Lake Baikal area, define methods and software of implementing this service, and present individual results of its prototyping. The creation of a service includes the development and software implementation of methods for collecting, processing and visualizing data, as well as ensuring the use of the functionality of existing cloud platforms for the main information processes. Data collection methods are based on Application Programming Interface and web scraping technologies, processing methods include statistical methods and artificial intelligence methods, visualization methods are based on Business Intelligent systems methods. In this paper described the concept of service, justified methods of data analysis and visualization, built an ontological model, defined the content of dashboards, implemented a prototype of a service, and demonstrated the results of data collection, processing and presentation obtained from the research. Currently, information has been collected and processed for the following tourist attractions: 685 accommodation facilities, 16 services, 809 catering establishments, 352 attractions, 94 tourist routes and excursions. The proposed technology (methods and software) of the territory tourism monitoring service has demonstrated its effectiveness.

Matrix Factorization is a long established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources, we present a novel parallel computation framework utilizing community information available in the rating network. Our proposed approach named as Community-based Matrix Factorization(CBMF), parallelizes matrix factorization technique by dividing the network into communities using existing community detection algorithms. We prove that this parallel approach not only increases the quality of recommendations in connection with Root Mean Square Error (RMSE), but also yields substantial performance improvement. We empirically evaluate our idea on diverse datasets and present comprehensive experimental results. These results serve as empirical evidence of the effectiveness and performance gains offered by our parallel computation framework.

The development of biosensors for target detection plays a crucial role in advancing various fields of bioscience. This work presents the development of a genosensor that exploits the colorimetric phenol-sulfuric acid sugar reaction for the detection of DNA, and RNA as specific targets, and DNA intercalator molecules. The biosensor combines simplicity and reliability to create a novel bio-assay for accurate and rapid analysis. A 96-well microplate based on polystyrene polymer was used as the platform for an unmodified capture DNA immobilization via a silanization process and with (3-Aminopropyl) triethoxysilane (APTES). After that, a hybridization step was carried out to catch the target molecule, followed by adding phenol and sulfuric acid to quantify the amount of DNA or RNA sugar backbone. This reaction generates a yellow-orange color on the wells measured at 490 nm that was proportional to the target concentration. Under the optimum conditions, a calibration curve was obtained for each target. The developed biosensor demonstrated high sensitivity, good selectivity, and linear response over a wide concentration range for DNA and RNA targets. Additionally, the biosensor was successfully employed for the detection of DNA intercalator agents that inhibit the hybridization of DNA complementary to the immobilized capture DNA. The developed biosensor offers a potential tool for sensitive and selective detection in various applications, including virus diagnosis, genetic analysis, pathogenic bacteria monitoring, and drug discovery.

We present a new study of Jupiter atmosphere’s dynamics using for the first time the extremely high resolution capabilities of VLT/ESPRESSO to retrieve wind velocities in Jupiter’s troposphere, with a dedicated ground-based Doppler velocimetry method. These results are complemented by a deeper analysis of Cassini data during its flyby of Jupiter in December 2000, performing cloud tracking at visible wavelengths, obtaining a more comprehensive dynamical interpretation. We explore the effectiveness of this new method to measure winds in Jupiter, using high resolution spectroscopy data obtained from ESPRESSO observations performed in July 2019, with a Doppler velocimetry method based on back scattered solar radiation in the visible range. Coupled with our ground based results, we retrieved a latitudinal and longitudinal profile of Jupiter’s winds along select bands of the atmosphere. Comparing the results between cloud tracking methods, based on previous reference observations, and our new Doppler velocimetry approach we found a good agreement between them, demonstrating the eectiveness of this technique. The winds obtained in this exploratory study have a two-fold relevance: they contribute for the temporal and spatial variability study of Jupiter troposphere’s dynamics, and also the results presented here validate this Doppler technique to study the dynamics of Jupiter’s atmosphere and pave the way for further exploration of a broader region of Jupiter’s disk for a more comprehensive retrieval of winds and to evaluate their spatial and temporal variability.

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies, the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms.

Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time–frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods.

We describe a machine learning (ML) approach to process the signals collected from Covid-19 optical-based detector. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were used to process both raw data and feature engineering data, and high performances for qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/ml has been achieved. Valid detection experiments contain 486 negative and 108 positive samples; and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contains 36 negative samples and 732 positive samples. Data distribution patterns of the valid and control detection dataset, based on T-distributed Stochastic Neighbor Embedding (t-SNE), was used to study the distinguishability between positive and negative samples, and explain the ML prediction performances. This work demonstrates that ML can be a generalized effective approach to process signals and dataset of biosensors dependent on resonant modes as biosensing mechanism.

The paper deals with the reverse engineering, namely the as built documentation of the actual construction. The input is data measured in field using geodetic methods. In addition to modern methods of surveying 3D objects, such as laser scanning, it is still necessary to use classic surveying using a total station. The bottle neck of the process is the creation of documentation of the construction, which is still created manually in the appropriate CAD/BIM software. The goal of the research was to find a method that would reduce the amount of manual work when drawing documentation in CAD/BIM to a minimum. The core of the solution is the use of a topology that interconnects the points measured in field. The entire procedure has 2 parts: 1. Creating a topo-logical drawing template in a suitable CAD/GIS software (digital sketch), 2. Adding geometry to this sketch and creating a drawing using topological codes. The method was verified in practice by applying it to several specific buildings in the Czech Republic. The practical application of the method demonstrated a 30% time saving, a reduction in work and error rate in the entire process.

ORB (Oriented FAST and Rotated Brief) features are the most commonly used features in visual SLAM and visual odometry that have high computational speed, and rotation scale invariance. However, due to the homogenization of ORB features, the corner properties of specific features are poor in some artificial environments, and it can lead to poor matching performance when the environmental texture is not rich. The underlying reason is that the pixel grayscale changes in the less-richly textured regions on the image are not obvious, leading to a certain degree of mis-matching in the matching process. At the same time, when the camera motion speed is low, there is much overlap between adjacent frames. This results in minimal or almost no changes in the projection of feature points, and the system is highly sensitive to errors, In this case, even small errors can cause significant fluctuations in the calculation results. In response to the above issues, an improved feature point method for stereo vision mileage calculation is proposed to solve the problem of system stability deterioration when cameras move at low speeds in artificial envi-ronments. Firstly, weight calculation based on different texture regions is used to solve the problem of low corner properties of feature points caused by environmental texture differences. Then, keyframes are used for motion model estimation to improve the system's stability under low-speed motion. The experimental results of the dataset showed that the key frame rate was optimal between 10% and 12%, and the positioning accuracy was higher than these open-source systems compared with three common open-source VO systems.

We report the use of four-layer graphene (4LG) as a highly reliable transparent conductive electrode (TCE) for polymer-dispersed liquid crystal (PDLC)-based smart window devices. The adhesion between 4LG and the substrate was successfully improved through a water-induced interface-cleaning (WIIC) process. We compared the performance of a device with a WIIC-processed 4LG electrode with that of devices with a conventional Indium tin oxide (ITO) electrode and a 4LG electrode without a WIIC. With the application of the WIIC process, the PDLC smart window with a 4LG electrode exhibited reduced turn-on voltage and haze compared to 4LG without the WIIC process and characteristics comparable to those of the ITO electrode. Based on our observations, we conclude that 4LG with WIIC is a promising TCE for flexible smart windows.

Drugs-drugs interactions(DDI) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is considered the center of a substructure, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differ- ences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. To address these issues, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

When implementing SVMs, two major problems are encountered: (a) the number of local minima increases exponentially with the number of samples and (b) the quantity of required computer storage, required for a regular quadratic programming solver, increases by an exponential mag-nitude as the problem size expands. The Kernel-Adatron family of algorithms gaining attention lately which has allowed it to handle very large classification and regression problems. Howev-er, these methods treat different types of samples (Noise, border, and core) in the same manner, which causes searches in unpromising areas and increases the number of iterations. In this work, we introduce a hybrid method to overcome these shortcomings, namely Optimal Recurrent Neu-ral Network Density Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) characterization of different samples, (b) elimination of samples with a low probability of being a support vector, (c) construction of an appropriate recurrent neural network based on an original energy function, and (d) solution of the system of differential equations, managing the dynamics of the RNN, using the Euler-Cauchy method involving an optimal time step. The RNN remembers the regions explored during the search process thanks to its recurrent architecture. We demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has a very low time complexity compared to RNN-SVM with constant time step, and KAs-SVM. Several experiments were performed on academic data sets. We used several classification performances measures to compare Opt-RNN-DBSVM to different classification methods and the results obtained show the good performance of the proposed method.

Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. As lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to the unfair status in the distribution process. As a result, the honest participants’ interest will be harmed. As an important supporting infrastructure of Internet of things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, an blockchain based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named as proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain, theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.

The prevalence of antiviral drugs (AVTs) has seen a substantial increase in response to the COVID-19 pandemic, leading to heightened concentrations of these pharmaceuticals in wastewater systems. The hydrophilic nature of AVTs has been identified as a significant factor contributing to the low degradation efficiency observed in wastewater treatment plants. This characteristic often necessitates the implementation of additional treatment steps to achieve complete degradation of AVTs. Semiconductor-based photocatalysis has garnered considerable attention due to its promising potential in achieving efficient degradation rates and subsequent mineralization of pollutants, leveraging the inexhaustible energy of sunlight. However, in recent years, there have been few comprehensive reports that have thoroughly summarized and analyzed the application of photocatalysis for the removal of AVTs. This review commences by summarizing the types and occurrence of AVTs. Furthermore, it places a significant emphasis on delivering a comprehensive summary and analysis of the characteristics pertaining to the photocatalytic elimination of AVTs. Ultimately, the review sheds light on the identified research gaps and key concerns, offering invaluable insights to steer future investigations in this field.

Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the ”rule of five (RO5).” Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is difficult. In this study, we propose a molecule generation model based on deep reinforcement learning, specialized for generating PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. The generated virtual compounds possess the desired properties for PPI inhibitors, and show similarity to commercially available PPI libraries. These generated virtual compounds are freely available as a virtual library.

Wireless sensor networks are usually applied in hostile areas where nodes are easy to be moni-tored and captured by adversary. Key distribution is an essential primitive to provide most of security mechanism. However, the characteristic of limited resources of sensors restricts the direct use of conventional key distribution schemes. In this paper, a complete security key distribution scheme based on asymmetric cryptography technology is proposed in both static and mobile scenarios. Mutual authentication is guaranteed using challenge-response mechanism. The per-formance evaluation and security analysis show that the proposed scheme with low complexity not only provides better security for wireless sensor networks, but also reduces storage overhead and key exposure risks.

To solve the problem that the common long-tailed classification method does not use the semantic features of the original label text of the image, and the difference between the classification accuracy of most classes and minority classes is large, the long-tailed image classification method based on enhanced contrast visual language trains the head class and tail class samples separately, uses text image to pre-train the information, and uses enhanced momentum contrast loss function and RandAugment enhancement to improve the learning of tail class samples. On the ImageNet-LT long-tailed dataset, the enhanced contrastive visual-language based long-tailed image classification method has improved all class accuracy, tail class accuracy, middle class accuracy, and F1 values by 3.4%, 7.6%, 3.5%, and 11.2%, respectively, compared to the BALLAD method. The difference in accuracy between the head class and tail class is reduced by 1.6% compared to the BALLAD method. The results of three comparative experiments indicate that the long-tailed image classification method based on enhanced contrastive visual-language has improved the performance of tail classes and reduced the accuracy difference between majority and minority classes.

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in multipath fading time-varying channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

This paper proposes an AI-based approach to overcome the limitations of the SHM system in measuring global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An ANN is used to establish the solution relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize accuracy by considering factors like the number of sensors, neural network framework, and convergence criteria. The accuracy of the ANN is verified through a real-scale model test, demonstrating 93.6% accuracy. This technology represents a significant advancement, enhancing the practicality of the structural health monitoring (SHM) system from “point monitoring" to “field monitoring".

Epsilon-near-zero nanoantennas can be used to tune the far-field radiation pattern due to their exceptionally large intensity-dependent refractive index. Here, we propose hybrid optical antenna based on indium tin oxide (ITO) to optically tune the deflection of radiation. In particular, a hybrid structure antenna of ITO and dielectric material, which makes the deflection angle changes 17 ∘ as incident intensities increases. Finally, the array of ITO or hybrid nanodisks can enhance the unidirectionality to be needle-like, with the angular beam width α&lt; 8∘ of main lobe. The deflection angle of radiation pattern response with needle-like lobe pave the way for further studies and applications in beam steering and optical modulation where dynamic control of the nanoantennas is highly desirable.

We propose a deep reinforcement learning based manipulator path tracking method to solve the computationally difficult and non-unique problem of manipulator path tracking methods based on inverse kinematics. By transforming the path tracking task into a sequence decision problem, our method adopts an end-to-end learning method for closed-loop control and avoids the process of finding the inverse solution. We first explored the feasibility of the deep reinforcement learning method in the path tracking of the manipulator. After verifying the feasibility, the path tracking of the multi-degree-of-freedom(multi-DOF) manipulator was realized by combining the maximum entropy deep reinforcement learning algorithm. The experimental results show that our method has a good effect on the path tracking of the manipulator, which not only avoids the process of finding the inverse kinematics solution, but also requires no dynamic model. Therefore, we believe that our method has great significance in the study of manipulator path tracking.

Graphene-based nanomaterials (GNMs), including graphene, graphene oxide, reduced graphene oxide, and graphene quantum dots, may have direct anticancer activity or be used as nanocarri-ers for antitumor drugs. GNMs usually enter tumor cells by endocytosis and can accumulate in lysosomes. This accumulation prevents drugs bound to GNMs from reaching their targets, sup-pressing their anticancer effects. A number of chemical modifications are made to GNMs to facili-tate the separation of anticancer drugs from GNMs at low lysosomal pH and to enable lysosomal escape of drugs. Lysosomal escape may be associated with oxidative stress, permeabilization of the unstable membrane of cancer cell lysosomes, release of lysosomal enzymes into the cytoplasm, and cell death. GNMs can prevent or stimulate tumor cell death by inducing protective autopha-gy or suppressing autolysosomal degradation, respectively. Furthermore, because GNMs prevent bound fluorescent agents from emitting light, their separation in lysosomes may enable tumor cell identification and therapy monitoring. In this review, we explain how the characteristics of the lysosomal microenvironment and the unique features of tumor cell lysosomes can be exploited for GNM-based cancer therapy.

Sparse direction-of-arrival (DOA) estimation of wideband signal has attracted widespread researches for its unique high-resolution performance. Numerous existing methods based on sparse Bayesian learning (SBL) don’t possess the ability to enhance sparsity even if they have already enjoyed high favor among sparse recovery approaches. In view of this, we propose a novel hierarchical Bayesian prior framework that enhance sparsity evidently and derive its corresponding iterative algorithm. On analysis, the computational complexity of the iterative are less than most existing other state-of-the-art algorithms. Not only that, proposed method possesses high angular estimation precision and sparsity performance by utilizing joint sparsity of multiple measure vector (MMV) models. Last but not least, the method obtains the ability to stabilize the estimated values between different frequencies or snapshots such that flat spatial spectrum. Extensive simulation results are presented to prove the superior performance of our methods

Hemorrhage is a detrimental event present in traumatic injury, surgery, and disorders of bleeding, that can become life-threatening if not properly managed. Moreover, uncontrolled bleeding can complicate surgical interventions, altering the outcome of surgical procedures. Therefore, to reduce the risk of complications and decrease the risk of morbidity and mortality associated with hemorrhage, it is necessary to use an effective hemostatic agent that ensures immediate control of bleeding. In recent years, there have been increasingly rapid advances in developing novel generation of biomaterials with hemostatic properties. Nowadays, a wide array of topical hemostatic agents is available, including chitosan-based biomaterials that have shown outstanding properties such as: antibacterial, antifungal, hemostatic, analgesic activity, in addition to their biocompatibility, biodegradability, and wound-healing effects. This review provides an analysis of chitosan-based hemostatic and discusses the progress made in their performance, mechanism of action, efficacy, cost, and safety in recent years.

This study aimed at exploring what role artificial intelligence techniques could play in the futural numerical analysis. In this paper, a convolutional neural network techniques based on modified loss function is proposed as a surrogate of finite element method(FEM). Several surrogate-based physics-informed neural networks(PINNs) are developed to solve representative boundary value problems based on elliptic partial differential equations (PDEs). Results from the proposed surrogate-based approach are in good agreement with ones from conventional FEM. It is found that modification of the loss function could improve the prediction accuracy of the neural network. It is indicated that to some extent the artificial intelligence technique could replace conventional numerical analysis as a great surrogate model.

The paper deals with applying Artificial Intelligence techniques to examine CHIRP-recorded data in sand and sandstone sea-bottom sites. The provided analysis of the state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom were rarely used as the features for machine learning models. The results of the examination indicate that types of sea bottom can be quantitatively characterized by applying logistic regression models to either the backscatter time series of a frequency-modulated signal or the spectrum of that backscatter. The examination accuracy reached 90% for the time series and 94% for the spectra. The application of spectral data as features for more advanced machine learning algorithms, and the advantages of its combination with other types of data have great potential for future research and the enhancement of remote marine soil classification.

With the rise of piano teaching in recent years, many people have joined the team of piano learners. However, the expensive cost of manual instruction and the unique one-on-one teaching model have made piano learning an extravagant event. Most existing approaches based on the audio modality aim to evaluate piano players' skills. Unfortunately, these methods ignored the information contained in the video, which led to a one-sided and simplistic evaluation of the piano player's skills. More recently, multimodal-based methods are proposed to assess the skill level of piano players using both video and audio information. However, existing multimodal approaches use shallow networks to extract video and audio features, which are deficient in extracting complex spatio-temporal and time-frequency features from piano performance. Furthermore, the fingering and the pitch-rhythm information of the piano performance is contained in the spatio-temporal and time-frequency features, respectively. In the paper, we propose a ResNet-based audio-visual fusion model that combines video and audio features to assess the skill level of piano players. Firstly, ResNet18-3D is used as the backbone network for our visual branches, which can extract feature information from the video data. Then, we consider ResNet18-2D as the backbone network of the aural branch and extract the feature information from the audio data. The extracted video features are fused with the audio features to generate multimodal features for the final piano skill evaluation. The experimental results on the PISA dataset show that our proposed audio-visual fusion model, with a validation accuracy of 70.80%, outperforms the state-of-the-art methods in both performance and efficiency. Then, we also explore the impact of different layers of ResNet on model performance, and the experimental results show that the audio-visual fusion model dealing with the piano skill assessment problem can make full use of both feature information when the number of video features is close to the number of audio features.

In today's world, news events are emerging incessantly. Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on translators would be costly and inefficient, while depending solely on translation systems would incur considerable performance overheads in invoking translation interfaces and storing translated texts. To address this issue, we mainly focus on the clustering problem of cross-lingual news. To be specific, we use a combination of sentence vector representations of news headlines in a mixed semantic space and the topic probability distributions of news content to represent a news article. In the training of cross-lingual models, we employ knowledge distillation techniques to fit two semantic spaces into a mixed semantic space. We abandon traditional static clustering methods like K-Means and AGNES in favor of the incremental clustering algorithm Single-Pass, which we further modify to better suit cross-lingual news clustering scenarios. Our main contributions are as follows: (1) We adopt the English standard BERT as the teacher model and XLM-Roberta as the student model, training a cross-lingual model through knowledge distillation that can represent sentence-level bilingual texts in both Chinese and English. (2) We use the LDA topic model to represent news as a combination of cross-lingual vectors for headlines and topic probability distributions for content, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation. (3) We adapt the Single-Pass clustering algorithm for the news context to make it more applicable. Our optimizations of Single-Pass include adjusting the distance algorithm between samples and clusters, adding cluster merging operations, and incorporating a news time parameter.

3D point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we design an augmented sampling and grouping (ASG) module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to mine point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first try to use a linearly-normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module is adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that our method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which was 0.5% higher than PCT. Our network also archived 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.

1) Background: Genomic medicine harbors the real potential to improve the health and healthcare jour-ney of patients, care provider experiences, and improve health system efficiency – even reducing health care costs. There is expected to be an exponential growth in medically necessary new genome- based tests and test approaches in coming years. Testing can also create scientific research and commercial opportu-nities beyond healthcare decision-making. The purpose of this research is to generate a better under-standing of Canada’s state of readiness for genomic medicine, and to provide some insights for other healthcare systems; (2) Methods: a mixed-methodsapproach of literature review and key informant in-terviews with a purposive sample of experts was used. Health system readiness was assessed using a pre-viously published set of conditions. (3) Results: Canada has created some of the established conditions but more needs to be done to improve the state of readiness for genome-based medicine. Important gaps are the need for linked information systems and data integration; evaluative processes that are timely, and transparent; navigational tools for care providers; dedicated funding to facilitate rapid onboarding and supports test development and proficiency testing; and broader engagement with a broader set of inno-vation stakeholders. These findings highlight the known role of organizational context, social influence, and other factors that are known to affect the diffusion of innovation within health systems

Internet of Things (IoT) is a massive network of tiny devices connected internally and to the in-ternet. It is uniquely identified in the network (i.e. dedicated IP) and can share the information with other devices. However, the low power and low resources that distinguish IoT devices render them unsecure and targeted by different kinds of attacks since IoT devices cannot toler-ate heavy security models. Also, due to the heavy nature of famous protocols such as HyperText Transport Protocol (HTTP), it is costly to be used with IoT devices, and alternatively, different lightweight protocols are implemented to fit IoT devices. One of the prevailing protocols used over IoT networks is the Constrained Application Protocol (CoAP). Therefore, CoAP is popular, and that makes it targeted by different types of attacks. One of the major attacks that target CoAP is distributed denial of service (DDoS) attacks. DDoS aims to overwhelm the resources of the target and make them unavailable to legitimate users. As a result, different kinds of meth-ods were used to secure CoAP against DDoS attacks such as Datagram Transport Layer Secu-rity (DTLS) and Lightweight and Secure Protocol for Wireless Sensor Networks (LSPWSN). However, the existing models suffer from two issues: DTLS is not designed for constrained devices and is considered a heavy protocol. Besides, LSPWSN is working over the network layer, not in the application layer that CoAP works on. In this paper, we build a machine learn-ing model that can detect the DDoS attacks against CoAP with an accuracy of 98%. The CIDAD dataset is extended from ~11000 to 100,000 samples using GANs because it has fewer samples of malware (less than 0.2% of the total dataset). Our model outperforms the existing models that target securing CoAP in the application layer and obtains 93% of accuracy.

Speech emotion recognition is a critical component for achieving natural human-robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral-temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to recognize emotions from the modulation-filtered cochleagram. The channel-level attention and spatial-level attention modules are used to capture emotional saliency maps of channel and spatial feature representations from the 3D convolution feature maps, respectively. Furthermore, the temporal-level attention module captures significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the IEMOCAP dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves a better unweighted accuracy of 71% in categorical emotion recognition than several existing approaches in the experiments. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

This review focus on a critical analysis of nanocatalysts for Advanced Reductive Processes (ARP) and Oxidation Processes (AOP) designed for the degradation of poly/perfluoroalkyl substances (PFAS) in water. Ozone, ultraviolet and photocatalyzed ARP and/or AOP will be the basic treatment technologies. Besides the review of the nanomaterials with greater potential as catalyst for advanced processes of PFAS in water, the perspectives for its future development considering sustainability considerations will be discussed. Moreover, a brief analysis of the current state of the art of the ARP and AOP for the treatment of PFAS in water will be presented.

In an $m$-person symmetric game, all players are identical and indistinguishable. In this paper, we find that the payoff tensor of the player $k$ in an $m$-person symmetric game is $k$-mode symmetric, and the payoff tensors of two different individuals are the transpose of each other. Furthermore, we reformulate the $m$-person symmetric game as a tensor complementary problem and demonstrate that locating a symmetric Nash equilibrium is equivalent to finding a solution to the resulting tensor complementary problem. Finally, we use the hyperplane projection algorithm to solve the resulting tensor complementary problem, and we present some numerical results to find the symmetric Nash equilibrium.

As a new distributed machine learning (ML) approach, federated learning (FL) shows the great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning assisted FL framework. Then the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a deep Q-netwok (DQN) based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but has stronger robustness to resist malicious nodes.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process but, on the other hand, the electrical system has to face the problem of unbalances. Renewable Energies Sources (RES) are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced

This study aimed at assessing the extent to which the Saudi general population has embraced digital health medical applications to meet their health-related needs so that the Saudi Ministry of Health and government can appropriately be guided on scaling up of digital health across the country. As such, this study was guided by the question of to what extent do the Saudi people use digital health mobile-based applications?. This was a cross-sectional study utilizing snowballing sampling approach. Frequencies, Chi-square, and Spearman rank correlation statistics were used to offer descriptive and inferential analysis of the variables. The majority of the participants were economically able to afford smart devices that have medical apps, had at least an app on such devices, and highly regarded the benefits of the apps. Unfortunately, their understanding of how to use such apps was limited and this posed a barrier to embracing digital health alongside difficulty downloading apps, and medical ethical concerns. Although there is a willingness, extra effort is needed from the Saudi Ministry of Health and government to promote the uptake of digital health in Saudi Arabia.

Traditional camera sensors rely on human eyes for observation. However, the human eye 1 is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2 human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3 Target recognition technology is an important technology to judge the target category in camera 4 sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5 proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6 size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7 existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8 preserve the context feature information. The feature fusion network was improved to effectively 9 combine shallow information and deep information. A new network structure was proposed to 10 effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11 YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12 were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13 accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14 accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15 more accurate than YOLOv8.

Microwave photonic (MWP) transversal signal processors offer a compelling solution for realizing versatile high-speed information processing by combining the advantages of reconfigurable electrical digital signal processing and high-bandwidth photonic processing. With the capability of generating a number of discrete wavelengths from micro-scale resonators, optical microcombs are powerful multi-wavelength sources for implementing MWP transversal signal processors with significantly reduced size, power consumption, and complexity. By using microcomb-based MWP transversal signal processors, a diverse range of signal processing functions have been demonstrated recently. In this paper, we provide a detailed analysis for the processing inaccuracy that are induced by the imperfect response of experimental components. First, we investigate the errors arising from different sources including imperfections in the microcombs, the chirp of electro-optic modulators, chromatic dispersion of the dispersive module, shaping errors of the optical spectral shapers, and noise of the photodetector. Next, we provide a global picture quantifying the impact of different error sources on the overall system performance. Finally, we introduce feedback control to compensate the errors caused by experimental imperfections and achieve significantly improved accuracy. These results provide a guide for optimizing the accuracy of microcomb-based MWP transversal signal processors.

In order to reduce the influence of ground vibration generated by tractor during driving and operation on driving stability and driver ride comfort, this paper proposes a control method of tractor active seat suspension based on model predictive control. By establishing a 3-degree of freedom tractor active seat suspension model and a ground excitation input model, a new model predictive controller for the active seat suspension system is designed. The vertical acceleration of the seat center of mass, the seat suspension stroke and the tire dynamic displacement are optimized by limiting the dynamic deflection of the seat suspension and the active force output of the actuator. The control model of MPC tractor active seat suspension system is built and its performance is simulated and verified. The results show that compared with the passive seat suspension and the PID seat suspension, the tractor active seat suspension based on model predictive control (MPC) has better damping characteristics and improves ride comfort.

Machine learning (ML) is widely used in the field of crop-growing information identification based on high-resolution remote sensing images. With Baoying County in Jiangsu Province, China, as the study area, this paper used Sentinel-2 images during the winter wheat growth period to construct its spectral, textural, and topographic features during its growth period and proposes a winter wheat-growing area extraction method based on the extreme gradient boosting (XGBoost) algorithm, which was compared with traditional ML algorithms such as the support vector machine (SVM), classification and regression tree (CART), and random forest (RF) algorithms. The results indicated that (1) a winter wheat-growing area identification model based on the XGBoost algorithm was successfully constructed based on Sentinel-2 images, considering 27 spectral, textural, and topographic features; (2) the constructed model could effectively extract winter wheat in the study area with an overall accuracy of 93.43% and only a small error compared with the actual winter wheat-growing area in Baoying County, meeting the accuracy requirement for crop identification in the study area; and (3) the deep learning algorithm XGBoost outperformed the three traditional ML algorithms, among which the RF algorithm was better than the SVM and CART algorithms, both of which had poor identification performance and a large error compared with the actual growing area. This paper provides a scientific basis for the accurate extraction of winter wheat-growing areas and further research on winter wheat growth monitoring and yield estimation.

To explore the effects of pymetrozine on the reproductive behavior of N. lugens, we established a bioassay method to accurately evaluate the toxicity of pymetrozine in N. lugens and establish the level of pymetrozine resistance of N. lugens in the field. In this study, pymetrozine’s effects on the fecundity of N. lugens were evaluated using the topical application method and rice-seedling dipping method. Moreover, the resistance of N. lugens to pymetrozine in a pymetrozine-resistant strain (Pym-R) and two field populations (YZ21 and QS21) was determined using the rice-seedling dipping method and fecundity assay methods. The results showed that treatment of N. lugens third-instar nymphs with LC15, LC50, and LC85 doses of pymetrozine resulted in significantly reduced male fertility and female fecundity. In addition, N. lugens adults treated with pymetrozine using the rice-seedling dipping and topical application method also exhibited significantly in-hibited male courtship, fertility, and female receptivity. Using the rice-stem dipping method, pymetrozine resistance was shown to be at high levels in Pym-R (194.6-fold), YZ21 (205.9-fold), and QS21 (212.8-fold), with LC50 values of 522.520 mg/L (Pym-R), 552.962 mg/L (YZ21), and 571.315 (QS21) mg/L. However, when using the rice-seedling dipping or topical application fe-cundity assay method, Pym-R (EC50: 14.370 mg/L, RR=12.4-fold; ED50: 0.560 ng/adult, RR=10.8-fold), YZ21 (EC50: 12.890 mg/L, RR=11.2-fold; ED50: 0.280 ng/adult; RR=5.4-fold), and QS21 (EC50: 13.70 mg/L, RR=11.9-fold) exhibited moderate or low levels of resistance to pymet-rozine. Our studies show that pymetrozine can significantly inhibit the fecundity of N. lugens. The fecundity assay results showed that N. lugens only developed low to moderate levels of resistance to pymetrozine, indicating that pymetrozine can still achieve effective control on the next gen-eration of N. lugens populations.

The concentration of data from multiple links to a single output is an essential task performed by High-Energy Physics (HEP) Data Acquisition Systems (DAQs). At high and varying data rates combined with the large width of the concentrator’s output interface, this task is non-trivial. This paper presents a concentrator based on the Beneš network, which provides efficient concentration without using a high-frequency clock internally. It warrants that empty data are eliminated and does not disturb the data time-ordering if the data rates significantly differ between inputs. Additionally, it is well suited to FPGA implementation. It is based on simple data-routing primitives and may be fully pipelined.

With the rise of powerful computational technologies, healthcare systems are going through a paradigm shift to the era of healthcare 5.0, also known as" smart healthcare". This new-age healthcare system not only enhances the life of patients but also aims to reduce healthcare costs significantly. People's health data collected through various means, including various Internet of Things (IoT) devices, is too sensitive to be regulated by particular organisations owing to increasing privacy and security threats. The sheer size of data makes it difficult for centralised bodies to regulate, due to which third-party involvement is introduced, which again poses threats to the privacy of the data holder. Motivated by the abovementioned gaps, we propose a decentralised approach using Ethereum blockchain technology and Oasis protocol. The patient-driven solution approach effectively solves the privacy problem by providing complete control of data to the owner. Along with this, byproducts such as fine-grained access control and on-chain data processing capabilities are also obtained.

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human-computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey provides an in-depth exploration of the vision-based human action recognition field, comprehensively offering the available techniques and their evolution, highlighting the cutting-edge ideas driving its development. We also analyze the most used keywords in research papers over the past years to identify trends and predict possible future directions. Hence, this concise survey helps researchers understand the breadth of existing approaches, evaluate current research trends, and stay up-to-date on potential developments.

Precise monitoring of different environmental parameters and contaminations during food processing and storage is a key factor for maintaining its safety and nutritional value. Thus, developing reliable, efficient, cost-effective sensor devices for these purposes is of utmost importance. In this paper, we show that Poly-(diallyl-dimethylammonium chloride)/reduced Graphene oxide (PDAC/rGO) films produced by a simple Layer-by-Layer deposition can be effectively used to monitor temperature, relative humidity and the presence of volatile organic compounds as indicators for spoilage odors. At the same time, they show potential for electrochemical detection of organophosphate pesticide dimethoate. By monitoring the resistance/impedance changes during temperature and relative humidity variations or upon the exposure of PDAC/rGO films to methanol, good linear responses were obtained in the temperature range of 10-100 °C, 15-95 % relative humidity, and 35 ppm - 55 ppm of methanol. Moreover, linearity in the electrochemical detection of dimethoate is shown for the concentrations in the order of 102 µmol dm−3. The analytical response to different external stimuli and analytes depends on the number of layers deposited, affecting sensors’ sensitivity, response and recovery time, and long-term stability. The presented results could serve as a starting point for developing advanced multimodal sensor devices and sensor arrays with high potential for analytical applications in food safety and quality monitoring.

In the process of image feature extraction, noise in the image will greatly affect the accuracy of edge detection. In this paper, the image is filtered to remove noise before edge detection by using the algorithm of wavelet transformation. Different wavelet functions are used to decompose image. Based on the experimental results, the best denoising wavelet function is selected. Canny algorithm is used to detect the edge of the denoised image, and the result of edge detection is evaluated according to the Pratt quality factor. It is proved that wavelet transform can improve the edge detection results.

Magnetic nanoparticles based on iron oxides (MNPs-Fe) with magnetite or maghemite phases have been widely employed in bio-applications. Thus, they have been used as contrast agents in magnetic resonance imaging (MRI) and oncological treatments through different therapies. Besides, due to the vast health problem of multidrug-resistant bacterial infections, several studies have proposed MNPs-Fe as photothermal agents (PTAs) within antibacterial photothermal therapy (PTT). This work presents a quick and easy green synthesis (GS) to obtain MNPs-Fe using orange peel extract from orange waste from local commerce, which presents an environmentally friendly approach compared to traditional methods such as coprecipitation. The GS can be irradiated with microwaves to reduce the synthesis time drastically. We evaluated the weight yield of the GS and the physical-chemical and magnetic features of the synthesized MNPs-Fe. Besides their cytotoxicity in animal cell line ATCC RAW 264.7, their antibacterial activity against Staphylococcus Aureus (S. Aureus) and Escherichia Coli (E. Coli) was assessed. We found that the MNPs-Fe synthesized using the GS, with 50% v/v of NH4OH and 50% v/v of orange peel extract (50GS-MNPs-Fe) had an excellent weight yield, negligible cytotoxicity for concentrations of MNPs-Fe below 250 µg·mL-1 in 24 hours, and 8 days. In the MNPs-Fe surface, we identified a coating of organic molecules (~ 25 nm) such as terpenes, aldehydes, etc. MNPs-Fe inhibited S. Aureus and 2.54 log10 (CFU) of E. Coli under red LED light irradiation (630 nm, 65.5 mW·cm-2, 30 min). Likewise, they exhibited a superparamagnetic (SPM) behavior for temperatures above 60 K, with a size of 49.3±9.6 nm and saturation magnetization (Ms) of 72.83 and 44.16 emu·g-1 at 60 and 300 K, respectively. Therefore, 50GS-MNPs-Fe are excellent candidates as broad-spectrum PTAs in antibacterial PTT, magnetic hyperthermia (MH), or MRI.

Recommender systems as an effective information filtering system can be used to obtain information through the user's explicit or implicit behavior. On the one hand finding items that may be of interest to the user. On the other hand, the recommendation facilitates the interaction between the user and the item to increase the revenue. Recommender systems have been widely used in various fields, such as e-commerce, travel recommendation, online books and movies, social networks, etc, which can satisfy the intrinsic implicit needs of users through personalized services. In recent years, the development of deep learning has further improved the performance of recommendation systems. Although these methods improve the performance of the recommendation system, when the number of users and products increases, the recommendation system may face sparsity and cold start problems, and thus cannot achieve personalized recommendations. Knowledge graphs, which are structured data, have become the choice of many algorithms due to the high quality and wide scale of the data, and therefore many recommendation algorithms combined with knowledge graphs have emerged as a popular new direction in recommendation systems. These algorithms are able to preserve the rich connections between different entities. Moreover, when constructing the features of an entity, the entities that are far away from the central entity can also be utilized. Entities are no longer only directly connected to each other. To address the shortcomings of existing recommendation algorithms, this paper designs the recommendation algorithm GPRE using graph neural networks. GPRE focuses on expressing the user's features. The graph neural network provides GPRE with a strong generalization capability for modeling, which can provide long-range semantics between users and entities, as well as selective entity selection in the auxiliary graph neural network. Explicit semantic links are established between remote and central nodes to reduce the introduction of noise. In this paper, experiments are conducted on real-world datasets and the results are compared with baselines. The experimental results show that GPRE performs well on the experimental dataset.

Nanomedicine holds great potential to devise better drug delivery systems (DDSs). However, many reported nanomedicines still fall short of commercial requirements including specific targetability, scale-up manufacturing and safety. Cell/tissue based carriers, including cell membrane vehicle and exosome, are biocompatible and targeting platforms but usually suffered from low yields and unstable reproducibility. Here in this study, we proposed the concept and preparation of reconstituted lipid nanoparticles (rLNPs) to develop highly reproducible cell/tissue based lipid nanoparticles (LNPs) for drug delivery, which holds the potential as a versatile drug delivery platform. The whole lipids of cell or tissue were firstly extracted and then prepared into rLNPs using solvent diffusion method. In this way, the preparation of ultra-small (~20 nm) rLNPs can be easily applied to both cell (mouse breast cancer cell line, 4T1) and tissue (mouse liver tissue). Our results demonstrated that mouse liver tissue derived rLNPs can be further labeled/modified with imaging, targeting or other functional moieties. Furthermore, rLNPs were highly biocompatible and capable of loading different drugs including doxorubicin hydrochloride (Dox) and curcumin (Cur). Most importantly, Dox loaded rLNPs (rLNPs/Dox) showed preferable in vitro and in vivo anticancer performance. Therefore, rLNPs might be a versatile drug delivery platform for future application in the treatment of a variety of diseases.

The prevalence of obesity in Korean adults was 56.1% in 2019. This study aimed to find obesity phenotypes from the KNHANES(2015–2019) and determine the risk factors associated with each type. In 15,179 participants, three obesity phenotypes, high BMI (short heavy obesity, n=1,685), high waist circumference (WC) (tall abdominal obesity, n=549), and BMI/WC combination (heavy abdominal obesity, n=3,538), were compared to non-OB (n=9,425). Marital status, lower income and education levels were highly associated with abdominal obesity. Insufficient exercise or skipping breakfast were associated with the WC or BMI/WC obesity type. The BMI/WC risk was 3.25 times higher in males with high TG and 4.6–4.8 times higher in females with high FAS or TG, and the risk increased to 7-8 times higher in three combinations (HYP+HDLc+FBS) compared to the control. The K-Score to test balanced diets was lower in BMI/WC than in others. Low intakes of FI-4(fruits) in men and FI-2(meat), FI-3 (vegetables), and FI-5 (milk/milk products) in women with BMI/WC and WC types, but BMI type was not associated with diets. Compared to BMI, the BMI/WC or WC obesity type was an indicator of metabolically unhealthy obesity associated with obesogenic environments including food/diets pattern and unhealthy behaviors.

Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-based Read-Across predictions. In this work, we have generated a simple, interpretable, and transferable quantitative-RASAR (q-RASAR) model which can efficiently predict the cytotoxicity of TiO2-based multi-component nanomaterials. The data set involves 29 TiO2-based nanomaterials which contain specific amounts of noble metal precursors in the form of Ag, Au, Pd, and Pt. The data set was rationally divided into training and test sets and the Read-Across-based predictions for the test set were generated using the tool Read-Across-v4.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home. The hyperparameters were optimized based on the training set data and using this optimized setting, the Read-Across-based predictions for the test set were obtained. The optimized hyperparameters and the similarity approach, which yields the best predictions, were used to calculate the similarity and error-based RASAR descriptors using the tool RASAR-Desc-Calc-v2.0 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home. These RASAR descriptors were then clubbed with the physicochemical descriptors and were subjected to features selection using the tool Best Subset Selection v2.1 available from https://dtclab.webs.com/software-tools. The final set of selected descriptors was used to develop multiple linear regression based q-RASAR models, which were validated using stringent criteria as per the OECD guidelines. Finally, a random forest model was also developed with the selected descriptors. The final machine learning model can efficiently predict the cytotoxicity of TiO2-based multi-component nanomaterials superseding previously reported models in the prediction quality.

The discovery of first ATP-binding cassette (ABC) transporter, whose overexpression in cancer cells is responsible for exporting anticancer drugs out of tumor cells, initiated enormous efforts to overcome tumor cell multidrug resistance (MDR) by inhibition of ABC-transporter. Because of its many physiological functions, diverse studies have been conducted on the mechanism, function and regulation of this important group of transmembrane transport proteins. In this review, we will focus on the structural aspects of this transporter superfamily. Since the resolution revolution of electron microscope, experimentally solved structures increased rapidly. A summary of the structures available and an overview of recent structure-based studies are provided. More specifically, the artificial intelligence (AI)-based predictions from AlphaFold 2 will be discussed.

Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as COD and TP makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappears or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.

Detailed flow distributions in vascular systems are the key to identifying hemodynamic risk factors for the development and progression of vascular diseases. Although computational fluid dynamics (CFD) has been widely used in bioengineering research on hemodynamics predictions, not only are high-fidelity CFD simulations time-consuming and computing-expensive, but also not friendly to clinical applications due to the difficulty of comprehensive numerical calculations. Machine learning (ML) algorithums to estimate the flow field in vascular systems based on the angiographic images of the blood flow using existed diagnostic tools are emerging as a new pathway to facilitate the mapping of hemodynamics. In present work, the dye injection in a water flow was simulated as an analogy of the contrast perfusion in blood flow using CFD. In the simulation, the light passes through the flow field and generates projective images, as an analogy of X-ray imaging. The simulations provide both the ground truth velocity field and the projective images of the flow with dye patterns. A rough velocity field was estimated using the optical flow method (OFM) based on projective images. ML algorithums are then trained using the ground truth CFD data and the OFM velocity estimation as the input. Finally, the interpretable (logistic regression) and deep (neural networks, convolutional neural networks, long short term memory) machine learning models are validated by using parallel in vitro experiments on the same flow setup. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% in average for the v-velocity estimation.

In recent years, solid state terahertz (THz) modulators have obtained rapid progress with the widespread use of two-dimensional (2D) materials in the field of THz; however, challenges remain in preparing flexible THz modulators. In this study, flexible ferromagnetic nematic materials were prepared by doping thermotropic nematic liquid crystals 5CB into magnetic fluids, and the influence of hydrogen bonding in water was reduced by a self-made cyclic olefin copolymer (COC) microfluidic chip. THz modulation characteristics of magnetic fluid and ferromagnetic nematic liquid crystal (FNLC) under the induction of external magnetic field were compared using a THz time domain spectroscopy system. Under the action of a 91 mT magnetic field, the magnetic fluid has a maximum modulation depth (MD) of 54%. Under the same magnetic field, the maximum MD of the ferromagnetic nematic liquid crystal materials increase to 78% because of the rearrangement of Fe3O4 nanoparticles induced by the topological defect of the liquid crystal. We demonstrate that the magneto-optical effect is significantly enhanced in the ferromagnetic nematic liquid crystal hybrid system. This strategy of doping thermotropic nematic liquid crystals to enhance the magneto-optical effect has great potential for THz filtering, modulation, and sensing applications.

When designing rule-based dialogue systems, the need for elaborate design by the designer is a challenge for dialogue systems. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data, and more specifically on opinion dialogue where the system models the user. In opinion dialogue, there has not been much study of topic transition methods for modeling users while maintaining their motivation for dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user's personality characteristics. The content of questions can give the user a sense of the system's intention to understand them. We also reported the possibility that the system's rational intention contributes to the user's motivation for dialogue with the system.

One of the significant challenges today in the brain-machine interface using invasive methods is the stability of the chronic record. In recent years, polymer-based electrodes have gained notoriety for achieving mechanical strength values close to that of brain tissue, promoting a lower immune response to the implant. In this work, we fabricated fully polymeric electrodes based on PEDOT:PSS for neural recording in Wistar rats. We characterized the electrical properties and both in-vitro and in-vivo functionality of the electrodes. Also, we employed histological processing and microscopical visualization to evaluate tecidual immune response in 7, 14, and 21 days post-implant days. Electrodes with 400-micrometer channels showed a 12dB signal-to-noise ratio. Local field potentials were characterized under two conditions: anesthetized and free-moving. There was a proliferation of microglia to the tissue-electrode interface in the first days, with a decrease after 14 days. Astrocytes also migrated to the interface, but there was no continuous recruitment of these cells in the tissue, showing inflammatory stability at 21 days. The signal was not affected by this inflammatory action, demonstrating that fully polymeric electrodes can be an alternative to prolong the valuable time of neural recordings.

There is a considerable amount of research in online social networks, most of which focuses on the structural analysis of social graphs. The interpersonal relationships of social networks, especially friend circle, can solve the cold start and sparsity problems, and through the relationship between social networks can effectively recommend users' favorite items (items), such as music , videos, brands/products, preferred tags, location, services, etc. User relationships in social networks are diverse and there are many different perspectives on different social networks. Associations among users can form multi-layered composite networks, and multi-layered social networks present new challenges and opportunities. Different relationships can influence users' preferences to different degrees, which in turn affects their behavior. Therefore, fusing multiple social networks is an effective way to improve recommendation. Although some studies have started to address multiple social network recommendations, simple linear superposition cannot reflect the coupling and nonlinear association between multiple social networks. In this paper, we propose a graph neural network recommendation model under social relationships based on this background. We first propose to compute the 2nd order collaborative signals and their intensities directly from the neighboring matrix for updating the node embedding of the graph convolution layer. Secondly by embedding historical evaluations, various social networks constituting different dimensions, the attention integration of user preferences by different social networks is achieved, and its effectiveness and scalability are demonstrated in theoretical derivation and experimental validation. The theoretical derivation and experimental validation demonstrate its effectiveness and scalability.

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional Artificial Neural Network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and Exhaust Gas Temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

Previous studies have shown, in the United States (U.S.), that non-White communities are exposed to significantly higher temperatures in urban environments than complementary White populations. Studies highlighting this disparity have usually been cross-sectional and are therefore “snapshots” in time. Using surface urban heat island (SUHI) intensity data, U.S. Census 2020 population counts, and a measure of residential segregation, this study performs a comparative analysis between census tracts identified as prevalent for White, Black, Hispanic and Asian populations and their thermal exposure from 2003 – 2018. The analysis concentrates on the top 200 most populous U.S. cities. SUHI intensity is shown to be increasing through time for all examined tracts. However, the increase is only statistically significant for White and Black prevalent zones. There is a 1.25K to ~2.00K higher degree of thermal exposure on average for non-White relative to White prevalent areas. When examined on an inter-city basis, White and Black prevalent tracts had the largest disparity, as measured by SUHI intensity, in New Orleans, LA, USA by &lt; 6.00K. Hispanic (&gt;7.00K) and Asian (&lt;6.75K) prevalent tracts were greatest in intensity in San Jose, CA, USA. To further explore temporal patterns, two models were developed using a Bayesian hierarchical spatial temporal framework. One models the effect of varying the percentages of each population group relative to SUHI intensity within all examined tracts. Increases in percentages of Black, Hispanic, and Asian populations contributed to statistically significant increases in SUHI intensity. White increases in population percentage lowered SUHI temperature intensity. Throughout all modeled tracts, there is a statistically significant 0.01K per year average increase in SUHI intensity. A second model tests the effect of residential segregation on thermal inequity across all examined cities. Residential segregation, indeed, has a statistically significant positive association with SUHI intensity from this portion of the analysis. Similarly, there is a statistically significant 0.01K increase in average SUHI intensity per year for all cities. Results from this study can be used to guide and prioritize intervention strategies and furthers urgency related to social, climatic, and environmental justice concerns.

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architecture that harnesses the potential of edge devices, reduces network latency, and lowers infrastructure cost to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. To address these concerns, we introduce a decentralized architecture tailored for large scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. The solution combines a blockchain consensus algorithm and verifiable random functions to ensure scalability, fault tolerance, transparency, and no single point of failure. We build on our previously presented theoretical simulations with many protocol improvements and an implementation tested in a use case related to monitoring a Slovenian cultural heritage building located in Bled, Slovenia.

Superconducting split ring resonator arrays allow to overcome two main limitations affecting metallic metamaterial resonating in the Terahertz (THz) range: Ohmic losses and tunability of their optical response. In this work, we design and experimentally realize direct and complementary square arrays of superconducting YBa2Cu3O7 (YBCO) split ring resonators working in the THz spectral range. The main purpose of this paper is to show how the metamaterial resonances can be tuned by temperature (T) when crossing the superconducting transition temperature Tc of YBCO. The tuning property can be quantified by describing the THz transmittance of the patterned YBCO films vs. T through a model of coupled resonators. This model allows us to estimate the THz resonances of split-ring arrays and their interaction, showing how the kinetic inductance Lk in the superconducting state is the main parameter affecting the metamaterial properties.

The paper is based on the scientific outcome of a PhD Thesis. It introduces the generic, model-based, reusable, and extensible conceptual framework to incorporate Facility Management data based within the three-dimensional model-based design and construction of an asset to enable smart applications, which are introduced. The conceptual framework is composed of empirical data from expert interviews, questionnaires, and factual analysis from 13 projects of varied sizes of public and private clients. It shows which phases need which data, who needs them, and which added value can be generated if intelligent data structuring is used at the beginning of the construction project and bridges the gap between requirement and practice. The term “smart application” is introduced.

In heterogeneous networks, the random walks based on meta-path requires prior knowledge and lacks flexibility. And the random walks based on non-meta-path only considers the number of node types, but does not consider the influence of schema and topology between node types in real networks. To solve the above problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint). Firstly, to realize the flexible walks, we design a Type strategy, which is the node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is the node selection strategy based on the adjacency relationship between nodes. The Type & Inner strategy can realize the random walks based on meta-path, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes embedding based on the random walks and Skip-Gram. Finally, in classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results shown that F1-Score and NMI of HNE-RWTIC outperform state-of-the-art approaches.

Digital twins are the foundation of autonomous off-road vehicles. Dynamic reduction methods are one of several ways to develop digital twins for off-road vehicles. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results of digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built control, electrical, and control systems.

This study identified high-risk locations (hotspots), using geographic information systems (GIS) and spatial analysis. Five years of accident data (2013-2017) for the Lokoja-Abuja-Kaduna highway in Nigeria were used. Accident concentration analysis was carried out using the mean center analysis and Kernel density estimation method. These locations were further verified using Moran’s I Statistics (Spatial Autocorrelation) to determine their clustering with statistical significance. Fishnet polygon and Network spatial weight matrix approaches of Getis-Ord Gi* statistic for hotspot analysis were used for the hotspot analysis. Hotspots exist for 2013, 2014, and 2017 with a significance level between 95% - 99%. However, no hotspots exist for 2014 and 2015 since the pattern is random. The spatial autocorrelation analysis of the overall accident locations with a z-score = 0.0575, p-value = 0.9542, and Moran's I statistic = -0.0089 showed that the distribution of accidents on the study route is random. Thus, preventive measures for hotspot locations should be based on a yearly hotspot analysis. The average daily traffic values of 31,270 and 16,303 were obtained for the Northbound and Southbound directions of the Abaji-Abuja section. The results show that hotspot locations with high confidence levels are at points where there are geometric features.

A single fracture is the basic unit of fracture medium, and the roughness of fracture wall surface is an important factor influencing hydraulic characteristics of the flow in bedrock fracture. However, effects of the shape and density of roughness elements (various bulges/pits on rough fracture wall surfaces) on water flow in a single rough fracture have not been thoroughly discovered. Thus the water flow in single fracture with different shapes and densities of roughness elements was simulated by using Fluent software in this study. The results show that in wider fractures the flow rate mainly depends on fracture aperture, while in narrow and close fracture medium the surface roughness of fracture wall is the main factor of head loss of seepage; there is a negative power exponential relation between the hydraulic gradient index and the average fracture aperture, i.e. with the increase of fracture aperture, the relative roughness of fracture and the influence weight of hydraulic gradient both decrease; and in symmetrical-uncoupled fractures there is a super-cubic relation between the discharge per unit width and average aperture. Above results would help to deepen the understanding of rough fracture seepage.

Plagiarism means taking another person’s work and not giving any credit to them for it. Plagiarism is one of the most serious problems in academia and among researchers. Even though there are multiple tools available to detect plagiarism in a document but most of them are domain-specific and designed to work in English texts, but plagiarism is not limited to a single language only. Bengali is the most widely spoken language of Bangladesh and the second most spoken language in India with 300 million native speakers and 37 million second-language speakers. Plagiarism detection requires a large corpus for comparison. Bengali Literature has a history of 1300 years. Hence most Bengali Literature books are not yet digitalized properly. As there was no such corpus present for our purpose so we have collected Bengali Literature books from the National Digital Library of India and with a comprehensive methodology extracted texts from it and constructed our corpus. Our experimental results find out average accuracy between 72.10 % - 79.89 % in text extraction using OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have built a web application for end-user and successfully tested it for Plagiarism detection in Bengali texts. In future, we aim to construct a corpus with more books for more accurate detection.

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

To detect aberrant human behaviors from large volume of passive data collected by smartphones in real time, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic is a weighted average, with more weight on the between-individual component when there are little data available for the individual and more weight on the within-individual component when the data are adequate. The algorithm takes only O(1) run time in each update and the required memory usage is fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity and specificity, is consistently better than or equal to the offline method that it builds upon depending on the sample size of the individual data.

Powder mixture with given molar ratio Ca/P = 1.67 consisting of brushite (calcium hydrophosphate dihydrate) CaHPO4·2H2O, calcium oxalate monohydrate CaC2O4·H2O in form of whewellite and weddellite and some quantity of quasi-amorphous phase was obtained as a result of the interaction of hydroxyapatite powder Ca10(PO4)6(OH)2 with an aqueous solution of oxalic acid H2C2O4 at a molar ratio of Ca10(PO4)6(OH)2/H2C2O4 = 1:4 under mechanical activation conditions. This powder mixture was used to produce microporous monophase ceramics based on hydroxyapatite Ca10(PO4)6(OH)2 with aperient density of 1.25 g/cm3 after firing at 1200 oC. Microporosity of sintered ceramics was formed due to presence of particles with plate-like morphology, restraining shrinkage during sintering. Microporous ceramics based on hydroxyapatite Ca10(PO4)6(OH)2 with roughness of the surface as a consequence of the created microporosity can be recommended as a biocompatible material for the bone defects treatment and as a substrate for bone cell cultivation.

Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in Network Intrusion Detection System (NIDS) technology, most implementations still depend on signature-based intrusion detection systems, which can’t identify unknown attacks. Deep learning can help NIDS to detect novel threats since it has a strong generalization ability. The deep neural network’s architecture has a significant impact on the model’s results. We propose a genetic algorithm based model to find the optimal number of hidden layers and the number of neurons in each layer of the deep neural network (DNN) architecture for the network intrusion detection binary classification problem. Experimental results demonstrate that the proposed DNN architecture shows better performance than classical machine learning algorithms at a lower computational cost.

In remote sensing applications, one of the key points is the acquisition, real-time pre-processing and storage of information. Due to the large amount of information present in the form of images or videos, compression of this data is necessary. Compressed sensing (CS) is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, using a minimal number of non-adaptive linear measurements. After this CS process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose in this work, a CS reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by the augmented Lagrangian which avoids the difficult problem of non-linearity and non-differentiability of the regularization terms. The proposed algorithm, called denoising compressed sensing by regularizations terms (DCSR), will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising, and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach allows to gain up to 25% in terms of denoising efficiency, and visual quality using two metrics: PSNR and SSIM.

The process of water treatment by nanoparticles is one of the most considerable subjects in the cross-field of hydrodynamics, chemistry and mathematics. This paper is dedicated to the case of the flows that appear when squeezing and stretching a spongy with a mix of water with nanoparticles and contaminants. It is assumed that fluid is homogeneous at the starting moment, the parameters of the nanoparticles and contaminants are known, and there is a constant non-homogeneous magnetic field applied to the system. The flow starts moving when the walls of the channel shift to each other. Exact and numerical solutions of the system of ordinary differential equations are used to receive the results. The article gives an answer to the question about stability of the flow and proposes the technique to evaluate the essential characteristics of the system to achieve the treatment process efficiency. The main result is that the considered system shows excellent treatment properties during some part of squeezing stage. This effect does not appear without magnetic field.

Radar observation data with high temporal and spatial resolution are used in the data assimilation experiment to improve precipitation forecast of a numerical model. The numerical model considered in this study is Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factor using higher resolution WRF and compared with radar observations in South Korea. To compare the precipitation forecast characteristics of three-dimensional variational (3D-Var) assimilation of radar data, four experiments are performed based on different precipitation types. Comparisons of the 24-h accumulated rainfall with Automatic Weather Station (AWS) data, Contoured Frequency by Altitude Diagram (CFAD), Time Height Cross Sections (THCS), and vertical hydrometeor profiles are used to evaluate and compare the accuracy. The model simulations are performed with and with-out 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The radar data assimilation experiment improved the location of precipitation area and rainfall intensity compared to the control run. Especially, for the two convective cases, simulating mesoscale convective system was greatly improved.

Klebsiella pneumonia (K. pneumoniae) is a Gram-negative bacterium that causes nosocomial infections in the lung, bloodstream, and urinary tract. Therefore, detecting K. pneumoniae in early time is important in preventing severe infections. However, clinical detection of K. pneumoniae requires a long time of agar plate culture. Nucleic acid detection like qPCR is precise but requires expensive equipment. Recent research reveals that collateral cleavage activity of CRISPR-LbCas12a has been applied in nucleic acid detection. In this study, PCR combined with CRISPR-LbCas12a targeting the K. pneumoniae system was established. This system showed excellent detection specificity and sensitivity in both bench work and clinical samples. Due to its advantages, its application can meet different detection requirements in health centers where qPCR is not accessible.

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

The emergence of the COVID-19 pandemic has propelled the use of alcohol-based hand sanitizers to the fore as a SARS-CoV-2 control measure. To be effective these products must comply with relevant quality parameters such as alcohol concentration, methanol limits and purity. The current study was designed to determine the quality of alcohol-based hand sanitizer products in the Nairobi metropolitan area. For this purpose, 74 commercially marketed samples were collected and subjected to analysis by gas chromatography. Only three samples (4.1%) complied with the regulatory specifications for alcohol content, methanol limits and pH. Five samples (6.8%) complied with the specification for alcohol content but did not meet methanol or pH limits. A total of 44 (59.5%) samples had methanol levels that exceeded threshold limits. Eleven samples (14.9%) were found with methanol substitution (i.e., methanol, instead of ethanol or isopropanol, was the main alcohol component). The results show that users of alcohol-based hand sanitizers are being exposed to substandard and falsified products which in addition to being non-efficacious pose harm due to unacceptable levels of toxic impurities. Regular, routine post-market surveillance is needed to prevent such products from reaching the market.

Recently, spam emails have become a significant problem with the expanding usage of the Internet. It is to some extend obvious to filter emails. A spam filter is a system that detects undesired and malicious emails and blocks them from getting into the users' inboxes. Spam filters check emails for something "suspicious" in terms of text, email address, header, attachments, and language. However, we have used different features such as word2vec, word n-grams, character n-grams, and a combination of variable length n-grams for comparative analysis in our proposed approach. Different machine learning models such as support vector machine (SVM), decision tree (DT), logistic regression (LR), and multinomial naïve bayes (MNB) are applied to train the extracted features. We use different evaluation metrics such as precision, recall, f1-score, and accuracy to evaluate the experimental results. Among them, SVM provides 97.6 \% of accuracy, 98.8\% of precision, and 94.9\% of f1-score using a combination of n-gram features.

Simple measures often couldn’t count a deep complexity. In the case of semantic complexity, conventional readability formulas share a common style, a common sort of achievements and a common borders of limitation: These formulas lack a semantics-aware approach and as a result, a precise measurement of semantic complexity couldn’t be done. In this paper, we introduce DASTEX, a novel semantics-aware complexity measure for semantic complexity of text. By DASTEX, a new layer of complexity analysis are opened for NLP, cognitive and computational tasks. This measure benefits from an intuitionistic underlying formal model which consider semantic as a lattice of intuitions. This yields to a well-defined definition for semantic of a text and its complexity. DASTEX is a practical analysis method upon this formal model. So a complete suite of idea, model and method are prepared to result in a simple but yet deep measure for semantic complexity of text. The evaluation of the proposed approach is done by 4 Experiments. The results show DASTEX is capable of measuring the semantic complexity of text in 6 application-tasks.