Miracle-dz/UniFAR-FTUs huggingface dataset:
Interference mitigate based on an improved zero forcing algorithm for multiuser MIMO-OFDM Systems

In multiuser MIMO-OFDM systems,different users sharing the same subcarriers to transmit introduced cochannel interference(CCI). This made the BER of systems decrease heavily.As to the downlink,a new grouping zero forcing algorithm which coordinated transmitter-receiver processing was proposed to mitigate CCI.Jointing Tx-Rx processing decreased the number of antennas and increased the channel equivalent gain.Through grouping users and solving null space intersection of user groups' channel matrices,the computation complexity decreased.Simulation results show that,compared with classic zero forcing algorithm,the improved zero forcing algorithm,in different data rate and system BER limit,can decrease the required antenna number and computation complexity,and reduce the required overall power too.
A Survey on Patient Information Management System Using IOT and Cloud Computing Techniques

Clinics all around the world are using more recent, cutting-edge technology. Telecommunications and information improvements are to blame for the emergence of the Internet of Objects (IoT). In past years, new opportunities for data collection and wiser judgments have emerged with the incorporation of the Internet of Everything with hospital medical equipment, as well as with individuals and their surroundings. In the future of IoT for healthcare, various and distributed devices will collect, analyse, and transmit genuine medical data to open clouds, enabling the accrual, piling, and analysis of crucial data streams in numerous new procedures as well as the increased expression of context-dependent intimations. See some may advance the current development of traditional healthcare model toward engaged medicine by using sensors (ambient, wearable, and implants) dispersed across local surroundings with the goal of tracking patient's health and launching remotely treatment. The management of the massive amount of cloud storage is done by fusing edge computing with the Internet of Things. Recent studies have shown that the cloud and IoT integration significantly affect remote healthcare. This survey study focuses on the many applications of edge computing IoT devices to improve people's health while ensuring the accuracy of medications and medication delivery.
Synthesis and generation for 3D architecture volume with generative modeling

Generative design in architecture has long been studied, yet most algorithms are parameter-based and require explicit rules, and the design solutions are heavily experience-based. In the absence of a real understanding of the generation process of designing architecture and consensus evaluation matrices, empirical knowledge may be difficult to apply to similar projects or deliver to the next generation. We propose a workflow in the early design phase to synthesize and generate building morphology with artificial neural networks. Using 3D building models from the financial district of New York City as a case study, this research shows that neural networks can capture the implicit features and styles of the input dataset and create a population of design solutions that are coherent with the styles. We constructed our database using two different data representation formats, voxel matrix and signed distance function, to investigate the effect of shape representations on the performance of the generation of building shapes. A generative adversarial neural network and an auto decoder were used to generate the volume. Our study establishes the use of implicit learning to inform the design solution. Results show that both networks can grasp the implicit building forms and generate them with a similar style to the input data, between which the auto decoder with signed distance function representation provides the highest resolution results.
Optimal fusion full-order estimators for discrete-time stochastic singular systems

The optimal fusion problem for the state estimation of discrete-time stochastic singular systems with multiple sensors and correlated measurement noise is considered. The key idea is to convert the original singular system into an equivalent group of nonsingular systems. Based on the state estimation for each local nonsingular system,the optimal full-order filters and smoothers are obtained for the original system by using the optimal weighted fusion algorithms in the linear minimum variance sense. A simulation example shows that the fusion estimator is better than each local one.
Iot – Enabled Technologies for Sustainable Smart Agriculture and their Comprehensive Survey

Both the quality and amount of food demanded have increased, necessitating agricultural technology and intensification. Agricultural innovation is booming thanks to the Internet of Things, or IoT, is a cutting-edge technology. Research institutions and scientific organizations are creating IoT-based goods and solutions to solve a range of agricultural concerns. This study offers a thorough literature assessment by examining IoT technology and their current applications in a range of farming-related businesses. The comprehensive reviews of the literature conducted for this study were entirely based a review of the literature publications published in government publications during the previous ten years. Carefully selected articles from a wide variety have been arranged into courses. The major objective gathers all pertinent studies on IoT agricultural applications, sensors/devices, communication protocols, and community types. Additionally, it addresses the main problems and challenges that are the focus of modern agricultural research.
A Hydrophilic Sulfated Resveratrol Derivative for Topical Application: Sensitization and Anti-Allergic Potential

Resveratrol (RSV), a naturally occurring metabolite, is widely used in skincare products, but its hydrophobicity impairs its own incorporation into cosmetic formulations. RSV-GS is a synthetic hydrophilic sulfated glycosylated derivative inspired by marine natural products that present a lower cytotoxicity than RSV while exhibiting similar levels of bioactivity. Herein, we predict the skin sensitization potential of this new compound using an in vitro approach based on the OECD 442E guideline. Furthermore, the anti-allergic potential of RSV-GS was also disclosed. The monocyte THP-1 cell line was stimulated with RSV and RSV-GS in the presence or absence of the extreme skin allergen 1-fluoro-2,4-dinitrobenzene (DNFB). The results demonstrated that RSV-GS alone (500 µM) evoked a relative fluorescence index (RFI) lower than the thresholds established by the OECD guideline for CD54 (200%) and CD86 (150%), indicating the absence of a skin sensitization potential. Interestingly, in the presence of the skin allergen DNFB, RSV-GS exhibited the ability to rescue the DNFB-induced maturation of THP-1 cells, with RFI values lower than those for RSV, suggesting the potential of RSV-GS to mitigate skin sensitization evoked by allergens and, consequently, allergic contact dermatitis. These results open new avenues for the use of RSV-GS as a safe and anti-allergic active cosmetic ingredient.
A Technique for Early Identification of Network Separation to Enhance Frequency Resilience

Wind and solar photovoltaic (PV) energy have transformed power systems in the last decade. Integration of renewable energy into grids has resulted in cleaner and greener energy generation and distribution than the conventional fossil-fuel-based grids. However, the variability and asynchrony associated with the renewable-based generation pose security threats such as blackouts following major generation contingency events. Early identification of such serious network separation events is crucial for grid operators to take prompt action and avoid blackouts. To this end, this paper proposes a Long-Short Term Memory (LSTM) based artificial neural network model to forecast network bottlenecks in an early stage. The proposed model is further enhanced by the inclusion of stacked LSTM with more hidden layers than the conventional LSTM. The model is then implemented on a modified Southeast Australian (SA) Power System model with substantial renewable integration. All the dynamic simulations are conducted in the PSSE platform. This platform is also used for time series data mining. The model is then trained and validated on the constructed dataset concerning the real power system. In addition, several performance metrics are evaluated to check the performance of forecasting. The findings show that the model successfully detects and anticipates the tie line overflow during a major synchronous generator loss occurrence every time. Furthermore, all of the performance evaluation metrics validate the proposed model’s goodness of fit. Therefore, the proposed technique utilizing a generalized approach has the potential to be deployed as an effective tool for forecasting network separation events in low-inertia power grids.
Design of a Low-power Computational Unit using a Pipelined Vedic Multiplier

The quest to minimize power consumption in portable battery-operated devices has led to the pursuit of innovative techniques for power reduction in digital circuits. Modern Central Processing Units (CPU) and graphics processing units incorporate powerful and complex computational units within their processors. The performance of the computational unit is a direct indication of the performance of the CPU. There has been considerable research in developing computational units to accomplish fast and power-efficient performance. One of the significant challenges in ASIC design is power dissipation. Low power design strategies that provide substantial power savings with reduced overhead in terms of delay are desirable. This research concentrates on the design of a computational unit with a pipelined Vedic multiplier, an adder/subtractor, and a logic module with operand isolation optimized for low power. The synthesis of the proposed computational unit was performed using 45 and 90 nm technology libraries in an Electronic Design Automation (EDA) Tool. The findings indicate that the proposed architecture exhibited reduced power and delay compared to existing designs.
Battery Capacity Trajectory Prediction with Multi-output Gaussian Process

Battery health monitoring is critical for the safe management and sustainable maintenance of electrical equipment. The uncertainty of battery usage scenarios and the huge cost of aging experiments make it a challenge to construct accurate and general-purpose battery lifetime prediction models. In this paper, based on the multi-output Gaussian process (MOGP) with transfer learning, the battery aging data under different working conditions can be applied to accurately predict the capacity trajectory. The performance of the two dominant MOGP models, symmetric and asymmetric, in battery capacity prediction, is thoroughly analyzed, and compared with other machine learning algorithms. Two different types of batteries with different working conditions are used to verify the performance of the models. Considering the performance of the model for different aging degrees, the battery degradation is divided into two stages: early stage and late stage. The MOGPs are proved to be the best performer. The asymmetric MOGP is suitable for the rapid prediction of batteries in the late aging stage, while the symmetrical MOGP can accurately predict the change of capacity trajectory and has high robustness to batteries at different aging stages. The average mean absolute errors (MAEs) of the symmetrical MOGP with three outputs for the early prediction of different batteries are only 0.027Ah and 0.017Ah, respectively.
Forgery Resistant Scrambled Image Watermarking

Collusion is a mechanism where some secret information shared between many peers is forged to use for illegal purpose. The watermark embedded in an image is the secret information which can be used to claim the originality by its owner. The images delivered to different peers have different watermarks embedded into them. All those malicious attackers can compare the watermarked images to determine the common places where the watermark has been embedded; hence the watermark can be attacked through collusion. So a new technique for watermarking which is collusion resistant has been proposed here. This scheme uses averaged coefficients based discrete cosine transform to embed the watermark at different areas in different images. The main advantage of the scheme is that the image is scrambled before embedding of the watermark and descrambled after embedding. This leads to spreading of the watermarking information throughout the watermarked image and it is very difficult to detect it. The correlation results show that the watermark is very robust.
Progress and effect of spectral data pretreatment in NIR analytical technique

Pretreatment of near-infrared spectra to remove unwanted is considered to be a crucial step for the pat- tern recognition,calibration transfer and construction of a quantitative calibration model.It is intended to provide an overview of the different methods for spectral data pretreatment.In the paper,the typical and commonly used pre- treatment methods are described and compared the advantage with the disadvantage of smoothing,multiplicaive seater correction(MSC) and standard normal variate correction(SNV),etc.Some newly developed methods such as wavelet transform (WT),orthogonal signal correction (OSC)methods are introduced in detail.The algorithms and ap- plications of those methods in NIR analysis are given and discussed.
Intradermal Immunization of Soluble Influenza HA Derived from a Lethal Virus Induces High Magnitude and Breadth of Antibody Responses and Provides Complete Protection In Vivo

Immunogens mimicking the native-like structure of surface-exposed viral antigens are considered promising vaccine candidates. Influenza viruses are important zoonotic respiratory viruses with high pandemic potential. Recombinant soluble hemagglutinin (HA) glycoprotein-based protein subunit vaccines against Influenza have been shown to induce protective efficacy when administered intramuscularly. Here, we have expressed a recombinant soluble trimeric HA protein in Expi 293F cells and purified the protein derived from the Inf A/Guangdong-Maonan/ SWL1536/2019 virus which was found to be highly virulent in the mouse. The trimeric HA protein was found to be in the oligomeric state, highly stable, and the efficacy study in the BALB/c mouse challenge model through intradermal immunization with the prime-boost regimen conferred complete protection against a high lethal dose of homologous and mouse-adapted InfA/PR8 virus challenge. Furthermore, the immunogen induced high hemagglutinin inhibition (HI) titers and showed cross-protection against other Inf A and Inf B subtypes. The results are promising and warrant trimeric HA as a suitable vaccine candidate.
Differentiated or integrated? The influence of European Administrative Networks on differentiated implementation of EU environmental policy

Abstract Differentiation has become a core feature not only in the European integration process but also in the implementation of European policies. Among the means to address differentiated implementation are European Administrative Networks (EANs). Through the exchange of information, best practices and advice, EANs are expected to contribute to more uniform implementation. However, the ways and extent to which they do so remain largely unexplored. This article addresses this gap through the empirical analysis of the European Network of Heads of Environmental Protection Agencies (EPA Network), drawing on a unique survey dataset and social network analysis. On the one hand, the findings uncover the EPA Network as a voluntary and informal network with a fragmented and clustered structure around key members. At the same time, several participants have recognised the network as a valuable asset to improving national implementation and developing innovative policy initiatives. Therefore, while there is significant diversity within and across the network, the network’s perceived contributions and added value point to the considerable importance of these instruments in EU policy implementation.
Thomas–Fermi theory with an external magnetic field

Of concern is a rigorous Thomas–Fermi theory of ground state electron densities for quantum mechanical systems in an external magnetic field. The energy functional takes the form E(ρ1,ρ2)=∑2i=1ci ∫R3ρi (x)5/3 dx + (1)/(2) ∫R3∫R3[ρ(x)ρ(y)/‖x−y‖]dx dy +∫R3V(x)ρ(x)dx +∫R3(B(x)(ρ1(x)−ρ2(x))dx; here ci is a positive constant, ρ1 [resp. ρ2] is the density of spin‐up [resp. spin‐down] electrons, ρ=ρ1+ρ2 is the total electron density, V is a given potential (typically a Coulomb potential describing electron–nuclear attraction), and B describes the effect of the external magnetic field. Let Ni=∫R3ρi(x)dx be the number of spin‐up and spin‐down electrons for i=1,2, and let N=N1+N2 be the total number of electrons. Specifying N and minimizing E(ρ1,ρ2) generally leads to a spin polarized system. For example, if B≤0 and B■0, then ρ1≥ρ2 and N1>N2. This and a number of related results are proved.
Improving the intelligibility of dysarthric speech using a time domain pitch synchronous-based approach

Dysarthria is a motor speech impairment that reduces the intelligibility of speech. Observations indicate that for different types of dysarthria, the fundamental frequency, intensity, and speech rate of speech are distinct from those of unimpaired speakers. Therefore, the proposed enhancement technique modifies these parameters so that they fall in the range for unimpaired speakers. The fundamental frequency and speech rate of dysarthric speech are modified using the time domain pitch synchronous overlap and add (TD-PSOLA) algorithm. Then its intensity is modified using the fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT)-based approach. This technique is applied to impaired speech samples of ten dysarthric speakers. After enhancement, the intelligibility of impaired and enhanced dysarthric speech is evaluated. The change in the intelligibility of impaired and enhanced dysarthric speech is evaluated using the rating scale and word count methods. The improvement in intelligibility is significant for speakers whose original intelligibility was poor. In contrast, the improvement in intelligibility was minimal for speakers whose intelligibility was already high. According to the rating scale method, for diverse speakers, the change in intelligibility ranges from 9% to 53%. Whereas, according to the word count method, this change in intelligibility ranges from 0% to 53%.
Application of Artificial Neural Network to predict phosphoric acid slurry viscosity

At the level of the attack unit of the phosphoric acid production process, the control and monitoring of the dynamic viscosity of phosphoric acid slurry is crucial to understand its rheological behavior. This information contributes to the resolution of problems that may be encountered in the flow. The objective of this work is to obtain a reliable artificial neural network model to predict this rheological property. First, experimental data of phosphoric acid slurry are analyzed. The dataset is composed of 468 samples with three explanatory variables namely: temperature, shear rate and solid content and a target variable which is dynamic viscosity of the phosphoric acid slurry. Results have shown that solid content has the greatest effect on the dynamic viscosity of the slurry, followed by shear rate and then comes temperature. Then, a neural network model with the topology (3-5-1) have been developed and have shown accurate predictions of the slurry viscosity.
Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal Correlation Information

The Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product is critical for global terrestrial carbon monitoring and ecosystem modeling. However, MODIS LAI is calculated on a pixel-by-pixel and day-by-day basis without using spatial or temporal correlation information, which leads to its high sensitivity of LAI to uncertainties in observed reflectance, resulting in an increased noise level in time series. While exploiting prior knowledge is a common practice to fill gaps in observations, little research has been conducted on reducing noisy fluctuations and improving the overall quality of the MODIS LAI product. To address this issue, we proposed a spatiotemporal information composition algorithm (STICA), which directly introduces prior spatiotemporal correlation and multiple quality assessment (MQA) information into the existing MODIS LAI product. STICA reduces the noise level and improves the quality of the product while maintaining the original physically based (radiative transfer model, RTM) LAI production process. In our analysis, the $R^{2}$ increased from 0.79 to 0.81 and the root-mean-square error (RMSE) decreased from 0.81 to 0.68 compared to the ground-based LAI reference. The improvement was more pronounced with the degradation of the data quality. STICA reduced noisy fluctuations in the LAI time series to varying degrees among eight biome types. In the Amazon forest, STICA significantly improved the time-series stability of LAI. Moreover, STICA can effectively eliminate abnormal declines in time series and correct for extreme outliers in LAI. We expect that the MODIS LAI reanalyzed product generated by this method will better support the application of high-quality LAI datasets.
6084 Cases of Adult Tetanus from China: A Literature Analysis

Objective To describe the clinical characteristics, treatments, and outcomes of tetanus and determine the most appropriate focus for tetanus prevention and treatment to reduce morbidity and mortality in China. Methods Four databases, including the Chinese Bio-Medical Literature Database, Chinese National Knowledge Infrastructure, Chinese Scientific Journal Database, and Wan-fang Data, were searched from 1 January, 2000 to 30 October, 2022. Results In total, 151 articles including 6084 tetanus patients met the inclusion criteria. Additionally, 5925 patients had their gender recorded in detail, among which 66.67% (3950/5925) were male, and 33.33% (1975/ 5925) were female. The average age in the detailed records was reported in 4773 cases, with an overall average age of 46.69. The number of patients’ places of residence was 580. Those from rural areas comprised the highest percentage with 88.62% (514 / 580). The causes of injury were recorded in 1592 cases in total; injuries caused by metals, wood, and wooden spikes accounted for the highest percentage with 54.52% (868/1592). Patient outcomes were recorded in 4305 cases, with a mortality of 9.34% (402/4305). The leading causes of death included treatment terminated by family members, asphyxia due to persistent spasms, respiratory failure, and autonomic dysfunction, family automatic abandonment and asphyxia accounted for the highest percentage, both 24.00% (54/225). Conclusion The overall success rate of tetanus treatment in China has dramatically improved, but the prevention and control of non-neonatal tetanus is still challenging. Focus should be placed on the prevention of adult tetanus and standardizing the use of sedative and spasmolytic drugs. Additionally, medical professionals should popularize tetanus prevention and treatment knowledge among the people and strengthen training in grass-roots hospitals.