Miracle-dz/UniFAR-FTUs huggingface dataset:
BEHAVE : Fire Behavior Prediction and Fuel Modeling System -- FUEL Subsystem

PREFACE Over the past decade the science of fire modeling has made great advances. The 13 original fire behavior fuel models have been used successfully to represent a wide array of fuel types in the United States. Nevertheless , fi re managers, who are using fire predictions in an increasing number of appl ications, have found that existing fuel models do not adequately match some fuel situations. They therefore have developed a need for techniques that will enable them to modify existing fuel rlodels or to devise entirely new ones. The purpose of this publication is to provide them with this capabil ity. The FUELS subsysterl of BEHAVE contains programs that will enable fire managers to assemble fuel models and test their performance before releasing them for operational use. Fuel modeling is not yet a rigorous process; consequently science and good judgment are both needed. Nevertheless , pilot tests have shown that the methods are ready for application in the field by well-trained personnel. The programs contain new and simplified procedures for examining fuels in the field and developing fuel models. It is not always necessary to construct new models, however; modifications to existing models may be sufficient in some cases, while in others more rigorous field inventory procedures may be desirable. There are four ways to obtain a fuel model for operational use in BEHAVE: 1. Choose one of the 13 standard rlOdels. 2. Modify one of the 13 standard models. 3. Use measured data taken by inventory techniques. 4. Use the new fuel rlOdeling procedures described in this manual. The fastest solution is choosing one of the standard 13 models (Anderson 1982). I f that does not satisfy the user, the most representative model of the 13 can be modified. For example, one can change loading and depth, add green fuel, make it a dynamic rlodel, and so on. If modification is not satisfactory, the next fastest expedient would be to use our new procedures. Although any rlethod of measuring and model ing fuels yields only approximate answers, our new procedures are simple, inexpensive, and rapid to use. But if the user prefers to inventory, or to use previously inventoried data , the programs will accommodate the fuel loads by size class and will assist the user in providing information needed to assemble a complete fuel model. Several features built into the modeling program contribute to reasonable fuel …
Enhancing the Channel Quality in Cognitive network by Traffic Reduction Method

Cognitive radio (CR) has some specific unique characteristics like flexibility, interoperability and adaptability. Optimal technological candidate is particularly contributed which reduces the challenges towards spectrum scarceness sin multimedia communications. Moreover, this communication over CR networks produces few severe issues like delay-sensitive, bandwidth-hungry, and loss-tolerant service and particularly ensuring quality of service (QoS). Improved Dynamic Optimization Algorithm (IDOA) is proposed for spectrum sensing in cognitive radio networks (CRN) where for the Secondary User (SU), data traffic is considered. From the comparison of several works in the literature, it is assumed that SU have no packets to transmit always; but actually data sent per second is considered instead of channel capacity as the possible throughput with which the problem of sensing-throughput tradeoff is reformulated. The parameters such as probability of availability, Signal to Interference Noise Ratio (SINR), Processing time and transmission time are considered to analyze the efficiency of the IDOA approach proposed which is compared with two state of methods namely Medium Access Control (MAC) and Dijikstra Algorithm (DA).
VNN1 as a potential biomarker for sepsis diagnosis and its implications in immune infiltration and tumor prognosis

Background This study explored novel biomarkers for diagnosing sepsis, a severe disease prevalent in clinical settings, particularly threatening to elderly patients. Methods Using microarray gene expression datasets and fatty acid metabolism signatures, we identified differentially expressed genes between sepsis and healthy control groups. Correlations between candidate genes, immune cells, and immune function were assessed. Logistic regression analysis and single-gene GSEA analysis were performed to identify potential biomarkers. The biomarkers’ association with different types of tumors was investigated. Results Twelve genes related to fatty acid metabolism were excluded. CA4, OLAN, and VNN1 were found relevant to immune cells and function. Among these, only VNN1 showed statistical significance (p < 0.05), with a strong area under the ROC curve (0.995). High VNN1 expression indicated activation of certain metabolic pathways, while low expression suggested potential autoimmune responses. VNN1 was up-regulated in eight tumors and down-regulated in eight others. High VNN1 expression was linked to poor prognosis in six types of tumors, and low expression was linked to poor prognosis in four types of tumors. VNN1 expression showed correlations with stromal scores, immune scores, and cancer purity in different types of tumors. Conclusion VNN1 holds promise as a potential biomarker for sepsis diagnosis and is significant in identifying immune infiltration in tumor tissue and predicting tumor prognosis.
Measures of Fish Behavior as Indicators of Sublethal Toxicosis During Standard Toxicity Tests

Behavioral functions essential for growth and survival can be dramatically altered by sublethal exposure to toxicants. Measures of these behavioral responses are effective in detecting adverse effects of sublethal contaminant exposure. Behavioral responses of fishes can be qualitatively and quantitatively evaluated during routine toxicity tests. At selected intervals of exposure, qualitative evaluations are accomplished through direct observations, whereas video recordings are used for quantitative evaluations. Standardized procedures for behavioral evaluation are readily applicable to different fish species and provide rapid, sensitive, and ecologically relevant assessments of sublethal exposure. The methods are readily applied to standardized test protocols.
Potential Galactagogue Effect of Tri-Than-Thip Remedy on Milk Synthesis in Lactating Rats: Impact on Milk Production-Related Parameters

Herbal galactagogues have been widely used as a treatment for postpartum hypogalactia due to the potential side effects associated with pharmacological therapy. Tri-Than-Thip (Tri-TT) is a Thai herbal medicine remedy that contains three main components: Cassia fistula, Pithecellobium dulce, and Ficus benjamina. These components are believed to have properties that contribute to milk production. However, despite the traditional use of Tri-TT, there is a lack of academic evidence supporting its efficacy in enhancing milk production. Therefore, the purpose of this study was to investigate the effect of Tri-TT on milk production and determine if it has a galactagogue effect. The weight suckle weight model was used to determine total milk production in lactating rats, while histological analysis was performed to assess the alveolar diameter of the mammary gland. The findings of this study revealed a significant increase in total milk production among lactating rats treated with 500 mg/kg of Tri-TT, compared to the control group. Furthermore, both the Tri-TT and Domperidone-treated groups exhibited a larger alveolar diameter of the mammary gland in comparison to the control group. In summary, these findings provide supportive evidence for the galactagogue activity of Tri-TT. The observed enhancement in milk production may be associated with Tri-TT could potentially be attributed to its ability to widen the alveolar diameter of the mammary gland, thereby facilitating increased milk volume.
Pearls and pitfalls in contemporary management of marginal velopharyngeal inadequacy among children with cleft palate

Marginal velopharyngeal inadequacy (MVPI) is a particular status of velopharyngeal closure after cleft palate repair. The physiological and phonological characteristics of patients with MVPI are significantly different from those with typical velopharyngeal insufficiency. The pathological mechanisms and diagnostic criteria of MVPI are still controversial, and there is limited evidence to guide the selection of surgical and non- surgical management options and a lack of recognized standards for treatment protocols. Based on a systematic study of the relevant literatures, this review identifies specific problems that are currently under-recognized in the diagnosis and treatment of MVPI and provides guidelines for further exploration of standardized and reasonable intervention protocols for MVPI.
A Blockchain-based Method for Monitoring User-plane Congestion in Mobile Core Network

Nowadays, 5G NPN network is flourishing and the scale of PLMN is gradually expanding, that leading to the emergence of numerous demand and scenarios for cross-network communication among multiple operators. In this paper, we focus on the issues of delay feedback, and congestion monitoring for cross-network communication. Specifically, we investigate the forwarding unit UPF in 5G mobile communication core network and propose a monitoring method based on blockchain. We employ blockchain to save ledger data, ensuring the traceability of packet forwarding latency. By utilizing MPT, we can reduce the storage burden of the blockchain, we also analyze the storage cost of introducing this trust mechanism locally on UPF. A 5G core network prototype system is implemented to validate the feasibility of this method. It can detect congested nodes and paths in the system with the precise timing and synchronization.
GAN-based Image-to-Image Translation of Fundus Photography: Topcon to Eidon

This paper proposes a technique to translate Topcon to Eidon fundus photography using a multimodal image-to-image translation technique based on the BicycleGAN model. The proposed approach aims to address the limitations of Topcon-type fundus photography and leverage the advantages of Eidon-type fundus photography, which captures high-resolution images of the retina using a confocal scanning technique. To this end, we constructed a dataset comprising 475 pairs of Topcon and Eidon fundus images from the same subjects, and we used it to train the proposed model. We evaluated the generated Eidon-type fundus images qualitatively and quantitatively using various image quality metrics, including SSIM, MSE, PSNR and FID. Our results demonstrate that the proposed technique can effectively translate Topcon-type fundus photography to high-quality Eidon-type fundus photography. This technique could potentially enhance the diagnosis and treatment of retinal diseases by providing high-resolution images that capture fine details of the retina.
Unconventional Cardiovascular Risk Factors and Systematic Coronary Risk Estimation (SCORE) in the Lebanese Rural Population: The Forgotten Factors

Purpose To evaluate the correlation between unconventional risk factors and the Systematic Coronary Risk Estimation (SCORE), and estimate the prevalence of conventional and unconventional cardiovascular (CV) risk factors in the rural Lebanese population in order to assess their CV risk. Methods This is a retrospective descriptive study conducted between November 2017 and June 2019 among the Lebanese rural population. The risk factors were analyzed from the files of the patients who presented for the CV disease screening days organized by a non governmental organization. The CV risk estimation tool is the SCORE. The classification of socio-economic level ranges from zero (low level) to 3 (high level). Results A total of 433 patients were included. The prevalence of hypertension, diabetes, dyslipidemia, smoking, and metabolic syndrome was 45.1%, 31.2%, 39.2%, 50% and 42.9% respectively. Only 13.6% of hypertensive patients and 6.7% of diabetics were controlled. A total of 0 or 1 point for the classification of socio-economic status was found in 62.6% of cases. A family history of CV diseases was present in 87.3% of participants. The SCORE was correlated with diabetes and metabolic syndrome (p = 0.000), without being correlated to socio-economic status (HR = −0.104; p = 0.059) or to family history (p = 0.834). Conclusion The socio-economic status and the family history of CV disease must be evaluated in addition to the classical risk calculation of the SCORE to better pinpoint the actual risk of the targeted population. The risk factors are prevalent but poorly controlled, hence the need for a national effort to ensure better care for the rural Lebanese population.
Forecast Model for Scheduling an HPC Application on CPU and GPU Architecture

Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases.
Emotion Recognition Using Speech Processing

Since many years ago, emotion identification from voice processing has been a significant factor in applications involving human-machine interfaces. In the psychological well-being of humans, emotions are pertinent. It serves as an agent for communicating one's viewpoint or mental condition to others. Speech Emotion Recognition (SER) is a manner of detecting the speaker's emotional state from the speech signal. Any computer system with limited processing resources may be programmed to sense or generate the few universal feelings, like Neutral, Anger, Happiness, and Sadness as needed. The following characteristics are retrieved in this work: Mel-frequency cepstral coefficients (MFCC), Chromogram, Mel scaled spectrogram, Spectral contrast, and Tonal Centroid. The emotion in this study is regarded using a deep neural network. The categorization of the speech in the output layer is done using Softmax. We used the combined 1440 audio recordings from 24 individuals in the RAVDASS speech dataset for training. Speech DNN has a 96% accuracy rate for emotion identification, which is greater than other algorithms like KNN, LDA, and SMO. Automatic emotion detection from human speech is becoming increasingly prevalent these days as it boosts interactions between computers and humans.
A YOLOv7 Forest Fire Detection System with Edge Computing

Forest fires may cause serious human and economic losses, and it is also a key technology linked to realizing forest intelligent fire detection and prevention. Therefore, the study of forest fire detection and early warning system has great social application value. However, the traditional methods of detecting fires have weak real-time and detection capabilities. The new generation of deep learning technology, especially convolutional neural networks, provides new means and methods for fire detection. The convolutional neural network has the characteristics of good fault tolerance, self-adaptability, self-learning ability, and weight sharing, which makes the convolutional neural network can identify fires in a working principle close to that of the human eye. The fire detection method based on deep learning has high accuracy and robustness. However, due to its large number of parameters and a large number of calculations, it is difficult to be practically applied. With the development of edge computing, it is now possible to use the edge instead of the cloud for computing, which will greatly reduce latency. This paper collects a large number of forest fire images as a data set, trains a model that can recognize forest fires based on the YOLOv7 network, and further deploys the trained model to the edge server RK3588. Finally, a front-end visual interface capable of displaying fire recognition results was built using Pyqt5. The model in this method has good robustness and generalization ability, and the application of edge computing in fire forest detection further improves the speed of fire detection, which provides a new way for forest fire detection.
A study on power and delay reduction techniques of latched comparator

Comparators play the role of brain in any conversion system as the precision of any Analog to digital converter solely depends upon the performance metrics of comparator Thus it is important to optimize the trade-off between two main parameters, one is speed and other is power consumption. Together with power consumption, a comparator's design must consider several performance metrics, including input-referred offset voltage, delay, voltage gain, supply voltage, kickback noise and common-mode voltage level. A combination of low power with less delay in dynamic comparators is demanded for efficient analog to digital converters. This work focuses on contrasting and analyzing various research works that have been reported on power and delay optimization. Also, modified shared charge reset technique where we have sustained preamplifier gain until comparison phase. Also, power is optimized fixed value for a wider range of common mode voltage.
Fast reconstruction of the orbital velocity field of sea surface by sinusoidal decomposition neural network

A neural network called sinusoidal decomposition neural network (SDNN) is proposed to reconstruct the digital elevation model (DEM) and orbital velocity field (OVF) of sea surface. According to the linear wave theory, DEM can be regarded as the superposition of a series of sine waves, from which OVF can be obtained. The SDNN adopts a fully connected network (FCN) to fit the DEM, which is similar to the inverse discrete Fourier transform (IDFT) model and regression model. The two-dimensional and three-dimensional SDNN are introduced in detail and their validities are demonstrated. A major advantage of the SDNN is that it requires only one scene of the wave height to reconstruct the OVF. By the applications to wind-driven sea surface and ship wake, respectively, the correctness and efficiency of the reconstruction are verified.
Select-and-Combine (SAC): A Novel Multi-stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets

Many practical systems for image-based surface reconstruction employ a stereo/multi-stereo paradigm, due to its ability to scale for large scenes and its ease of implementation for out-of-core operations. In this process, multiple and abundant depth maps from stereo matching must be combined and fused into a single, consistent, and clean point cloud. However, the noises and outliers caused by stereo matching and the heterogenous geometric errors of the poses present a challenge for existing fusion algorithms, since they mostly assume Gaussian errors and predict fused results based on data from local spatial neighborhoods, which may inherit uncertainties from multiple depths resulting in lowered accuracy. In this article, we propose a novel depth fusion paradigm, that instead of numerically fusing points from multiple depth maps, selects the best depth map per point, and combines them into a single and clean point cloud. This paradigm, called select-and-combine (SAC), is achieved through modeling the point level fusion using local Markov Netlets, a micro-network over point across neighboring views for depth/view selection, followed by a Netlets collapse process for point combination. The Markov Netlets are optimized such that they can inherently leverage spatial consistencies among depth maps of neighboring views, thus they can address errors beyond Gaussian ones. Our experiment results show that our approach outperforms existing depth fusion approaches by increasing the F1 score that considers both accuracy and completeness by 2.07% compared to the best existing method. Finally, our approach generates clearer point clouds that are 18% less redundant while with a higher accuracy before fusion.
Improved Dynamic Comparator With Adaptive Delay Line for the Latch Conduction and Regenerative Feedback Assisted FIA

This article presents a low-noise energy-efficient comparator design. Firstly, by using an adaptive delay line based on the differential voltage at the output of the pre-amplifier, the delayed clock turns on the latch stage after the pre-amplifier stage where the delay is automatically changed depending on the input voltage level. Secondly, a regenerative feedback assisted Dynamic Floating Inverter Amplifier (FIA) is used to increase integration speed and gain. For Successive-approximation-register (SAR) ADCs where each bit is determined sequentially and the input voltage level differs in each cycle, the comparator resolves small analog inputs up to twice per conversion theoretically. The time taken by the proposed comparator to resolve analog input into digital output (called CLK-OUT delay) only increases in the small signal voltage range (near its input-referred noise level) which has less impact on SAR ADCs sample rate. The proposed comparator in 28 nm process technology achieves 66.37-uV input-referred noise while consuming approximately 780 fJ per comparison under a 0.9-V supply. This represents greater than 20-time energy efficiency boost compared to the previous comparator with a dynamic floating inverter pre-amplifier.
Yolo-MSAPF: Multiscale Alignment Fusion With Parallel Feature Filtering Model for High Accuracy Weld Defect Detection

This work aims to improve the low accuracy caused by interference information during real-time weld surface detection. First, a weld surface dataset with 7580 pictures containing eight types of defects was established. Second, an improved detection model named Yolo-MSAPF was designed based on Yolo-v5 model and verified by the self-established database. Finally, a real-time detection system was built to analyze the performance of the detection model in an industrial environment. The design principle of the Yolo-MSAPF is to eliminate interference information but enhance necessary features in each scale as much as possible by multiscale alignment fusion (MSAF) with parallel features filtering (PFF) modules (i.e., MSAPF strategy). For the MSAF, not only the accuracy but the richness of fused features is guaranteed by aligning features at one level to fuse all other scales. After that, the fused features in each scale were individually filtered out in parallel spatial and channel in the PFF module. The results show that rate for the images with missing detection for eight types of defects sharply drops from 21.47% to 1.68% when the MSAPF strategy is used. Moreover, it is worth noting that the mAP@0.5 of the Yolo-MSAPF model reaches 95.3%, which is similar to the Yolo-v7 model, while the number of parameters is reduced by 30.1%, compared with the baseline model. Additionally, the great ability to screen out unqualified weld is also verified in the industrial environment. Soon, code and dataset will be available at https://github.com/Luckycat518/Yolo-MSAPF.