Miracle-dz/UniFAR-FTUs huggingface dataset:
A Smoke Detection Model Based on Improved YOLOv5

Fast and accurate smoke detection is very important for reducing fire damage. Due to the complexity and changeable nature of smoke scenes, existing smoke detection technology has the problems of a low detection rate and a high false negative rate, and the robustness and generalization ability of the algorithms are not high. Therefore, this paper proposes a smoke detection model based on the improved YOLOv5. First, a large number of real smoke and synthetic smoke images were collected to form a dataset. Different loss functions (GIoU, DIoU, CIoU) were used on three different models of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l), and YOLOv5m was used as the baseline model. Then, because of the problem of small numbers of smoke training samples, the mosaic enhancement method was used to randomly crop, scale and arrange nine images to form new images. To solve the problem of inaccurate anchor box prior information in YOLOv5, a dynamic anchor box mechanism is proposed. An anchor box was generated for the training dataset through the k-means++ clustering algorithm. The dynamic anchor box module was added to the model, and the size and position of the anchor box were dynamically updated in the network training process. Aiming at the problem of unbalanced feature maps in different scales of YOLOv5, an attention mechanism is proposed to improve the network detection performance by adding channel attention and spatial attention to the original network structure. Compared with the traditional deep learning algorithm, the detection performance of the improved algorithm in this paper was is 4.4% higher than the mAP of the baseline model, and the detection speed reached 85 FPS, which is obviously better and can meet engineering application requirements.
Quantifying the Flexibility of Real-Time Systems

In this paper we define the flexibility of a system as its capability to schedule a new task. We present an approach to quantify the flexibility of a system. More importantly, we show that it is possible under certain conditions to identify the task that will directly induce the limitations on a possible software update. If performed at design time, such a result can be used to adjust the system design by giving more slack to the limiting task. We illustrate how these results apply to a simple system.
Sleep Architecture in Progressive Supranuclear Palsy: A Video-Polysomnography Study

Background: Sleep disturbances have been reported to occur in progressive supranuclear palsy (PSP). The anatomical regions affected in PSP and those regulating sleep and wake cycle like dorsal raphe nucleus, locus coeruleus (LC), and pedunculopontine nucleus (PPN) overlap. There is a paucity of polysomnographic studies in PSP and they have shown altered sleep architecture. Objective: To study the sleep architecture in patients with PSP using video-polysomnography (vPSG) and correlate it with the disease severity and duration. Methods: This was a prospective, cross-sectional, case-control, single-center study. A total of 22 patients with PSP and 15 age and gender-matched controls were recruited. The cases and controls underwent clinical assessment, face-to-face interviews with sleep questionnaires, anxiety and depression scales, and one overnight vPSG. The sleep architecture was analyzed in detail. Results: The sleep architecture was altered as compared to the controls. The total sleep time, stage N2 duration, stage N3 duration, rapid-eye-movement (REM) sleep duration, sleep efficiency %, and N2%, N3%, and REM% were significantly lesser in PSP patients. The wake duration, wake after sleep onset (WASO) duration, wake%, WASO%, stage N1 duration was significantly greater in PSP patients. The stage N2 and N3 latencies were significantly prolonged in patients. REM sleep without atonia was noted in four patients and no patients had vPSG proven REM sleep behavior disorder. Conclusions: Sleep architecture is altered in PSP even during the early stages of the disease. There is reduced total sleep including both non-REM and REM sleep, sleep efficiency, prolonged sleep latencies, and increased wake duration. This correlates with the neurodegenerative processes affecting the anatomical region regulating the sleep/wake cycle like dorsal raphe nucleus, locus coeruleus (LC), pedunculopontine nucleus (PPN).
Fishing for synucleinopathy models

Synucleinopathies such as Parkinson’s disease (PD) are incurable neurodegenerative conditions characterised by the abnormal aggregation of α -synuclein protein in neuronal cells. In PD, fibrillary synuclein aggregation forms Lewy bodies and Lewy neurites in the substantia nigra and cortex on the brain. Dementia with Lewy bodies and multiple system atrophy are also associated with α -synuclein protein abnormalities. α -synuclein is one of three synuclein proteins, and while its precise function is still unknown, one hypothesis posits that α -synuclein propagates from the enteric nervous system through the vagus nerve and into the brain, resulting in synucleinopathy. Studies on synucleinopathies should thus encompass not only the central nervous system but must necessarily include the gut and microbiome. The zebrafish ( Danio rerio ) is a well-established model for human neuronal pathologies and have been used in studies ranging from genetic models of hereditary disorders to neurotoxin-induced neurodegeneration as well as gut-brain-axis studies. There is significant genetic homology between zebrafish and mammalian vertebrates which is what makes the zebrafish so amenable to modelling human conditions but in the case of synucleinopathies, the zebrafish notably does not possess an α -synuclein homolog. Synuclein orthologs are present in the zebrafish however, and transgenic zebrafish that carry human α -synuclein have been generated. In addition, the zebrafish is a highly advantageous model and ideal replacement for reducing the use of mammalian models. This review discusses the application of the zebrafish as a model for synucleinopathies in efforts to further understand synuclein function and explore therapeutic strategies.
Study on the Mechanism of Action of Paclitaxel-Loaded Polylactic-co-glycolic Acid Nanoparticles in Non-Small-Cell Lung Carcinoma Cells

Objective To study effective carriers that can enhance the antitumor effect of paclitaxel (PTX). Methods PTX-loaded polylactic-co-glycolic acid (PLGA) nanoparticles (NPs) (PTX-PLGA NPs), constructed using the emulsification solvent evaporation method, were characterized by scanning electron microscopy and dynamic light scattering. Non-small-cell lung carcinoma (NSCLC) cells were divided into the dimethyl sulfoxide (DMSO) group, PLGA NPs group, PTX group, and PTX-PLGA NPs group. Cell viability was detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), cell apoptosis was determined by flow cytometry, and cell migration and invasion were assessed using Transwell assay. Results PTX-PLGA NPs were smooth in the surface and spherical in shape, with a particle size of 268 ± 1.3 nm. Both PTX and PTX-PLGA NPs could effectively inhibit the activity of A549 and H1650 cells. At 12 and 24 h, PTX-PLGA NPs presented weaker inhibition on the activity of NSCLC cells than PTX, but at 48 and 72 h, PTX-PLGA NPs presented stronger inhibition. Compared with PTX, PTX-PLGA NPs were more effective in enhancing apoptosis and inhibiting migration and invasion of NSCLC cells. Conclusion With good sustained release and the ability to promote cellular uptake, PTX-PLGA NPs can strongly inhibit the malignant activities of NSCLC cells, which can be used as a promising drug carrier.
Comparison of AC Choppers Fed Single Phase Induction Motor Close Loop Characteristics.

This paper presents comparison of closed loop results of single, two and four switch AC Chopper fed single phase induction motor. The Dynamic response of open loop system is poor. Closed loop system is used to improve the dynamic response. This paper deals with modeling and simulation of one switch, two switch and four switch AC Chopper fed single phase induction motor drives. The pulse width modulated AC chopper fed single phase induction motor is implemented using an Atmel 89C51 microcontroller. The intention is to save energy in operations using induction motors. At no load, 70% of the energy can be saved that decreases with an increase in the load. From the simulation results, it is seen that the pulse width modulated (PWM) AC chopper system has lesser harmonics than the phase controlled AC chopper system, and hence it is used in the present work. Closed –loop control scheme to implement the energy–saving in the single phase induction motor drive system is designed and presented. The possibility of energy saving is explored in loads like cutting, punching and drilling operations, where most of the time induction motors runs at no load.
Metabolic Profile in a Cohort of Young Sicilian Patients with Klinefelter's Syndrome: The Role of Irisin

Klinefelter's syndrome (KS) is the main cause of hypogonadism and infertility in men and is often related to obesity, metabolic syndrome, and diabetes. The purpose of our real-life observational study was to investigate the metabolic and anthropometric parameters in a population of patients with Klinefelter syndrome compared to a group of healthy age-matched subjects. Methods. In our study, 25 consecutive Caucasian adult outpatients (age range 21–52 years, mean age 32.9 ± 12.2) with KS in testosterone replacement therapy and 30 healthy men (age range 25–45 years, mean age 32.4 ± 7.62) were studied. In both groups of subjects, anthropometric indices, lipid profile, glucose metabolic parameters, HbA1c, the homeostasis model assessment estimate of HOMA-insulin resistance (IR), and the insulin sensitivity index (ISI) were evaluated. In addition, we assessed the complete hormonal gonadic status and irisin values in both groups of patients. Results. No significant differences were found in BMI and total blood testosterone levels between KS and control subjects. Patients with KS had significantly higher values of WC (p=0.028), HbA1c (p=0.018), HOMA-IR (p < 0.001), FSH (p < 0.001), LH (p < 0.001), estradiol (p=0.001), and irisin (p=0.029) and significantly lower HDL-cholesterol (p=0.002), AMH (p < 0.001), inhibin B (p < 0.001), and ISI-Matsuda (p < 0.001) compared to healthy controls. Univariate analysis revealed an inverse correlation between irisin and ISI-Matsuda (r = −0.128; p=0.010). These data were then confirmed in multivariate analysis. Conclusions. KS is characterized by early development of metabolic syndrome and in particular by alterations of the glucose metabolism, independently of testosterone levels serum and BMI. Irisin blood levels of Klinefelter's patients are higher than in healthy subjects and positively correlate with the degree of insulin resistance.
Safety Prediction Method for Freeway Facilities with High-Occupancy Lanes

The objective of this paper is to describe the development of a safety prediction method for freeways with high-occupancy vehicle (HOV) and high-occupancy toll (HOT) lanes, collectively referred to as HO lanes. This method has been developed and documented in a manner that is consistent with the safety evaluation methods in Part C of the Highway Safety Manual (HSM). Such a predictive methodology would assist state departments of transportation in explicitly considering safety performance impacts when planning, designing, and operating freeway facilities with HO lanes. Data were collected in California and Washington to support development of the predictive methodology. This method focuses on the evaluation of one freeway travel direction with each application. The paper summarizes key differences and similarities between this method and the current predictive method for freeways in Chapter 18 of the HSM Supplement. The method includes models for predicting total crash frequency and multiple-vehicle crash frequency. The method applies to freeway facilities with continuous HO lane access, buffer-separated HO lanes with intermittent access, and barrier/pylon-separate HO lanes with intermittent access between the HO lane(s) and the general-purpose lanes. The method does not differentiate between HOV and HOT designation.
Analysis of composite plates by using mechanics of structure genome and comparison with ANSYS

Zhao, Banghua MSAA, Purdue University, December 2016. Analysis of Composite Plates by Using Mechanics of Structure Genome and Comparison with ANSYS. Major Professor: Wenbin Yu. Motivated by a recently discovered concept, Structure Genome (SG) which is defined as the smallest mathematical building block of a structure, a new approach named Mechanics of Structure Genome (MSG) to model and analyze composite plates is introduced. MSG is implemented in a general-purpose code named SwiftComp, which provides the constitutive models needed in structural analysis by homogenization and pointwise local fields by dehomogenization. To improve the user friendliness of SwiftComp, a simple graphic user interface (GUI) based on ANSYS Mechanical APDL platform, called ANSYS-SwiftComp GUI is developed, which provides a convenient way to create some common SG models or arbitrary customized SG models in ANSYS and invoke SwiftComp to perform homogenization and dehomogenization. The global structural analysis can also be handled in ANSYS after homogenization, which could predict the global behavior and provide needed inputs for dehomogenization. To demonstrate the accuracy and efficiency of the MSG approach, several numerical cases are studied and compared using both MSG and ANSYS. In the ANSYS approach, 3D solid element models (ANSYS 3D approach) are used as reference models and the 2D shell element models created by ANSYS Composite PrepPost (ACP approach) are compared with the MSG approach. The results of the MSG approach agree well with the ANSYS 3D approach while being as efficient as the ACP approach. Therefore, the MSG approach provides an efficient and accurate new way to model composite plates.
Temporary Increased LDL-C in Offspring with Extreme Elevation of Maternal Preconception Estradiol: A Retrospective Cohort Study

Objective To investigate the effect of maternal estradiol (E2) elevation on long-term metabolic manifestations in the offspring. Study Design and Setting This was a retrospective cohort study. Overall, 3690 children conceived by in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) between July 2014 and December 2017 were recruited and divided into four groups categorized by maternal E2 quartiles (Q1, <2420; Q2, 2420–3839; Q3, 3839–5599; and Q4, ≥5599 pg/mL). The metabolic profiles were measured during childhood. Linear mixed models were used to evaluate the association between maternal E2 elevation and metabolic phenotypes of the offspring. Results Lipoprotein cholesterol (LDL-C) was significantly higher in the highest quartile group than in the lowest quartile group during infancy (adjusted mean difference [95% confidence interval, CI]): 0.11 [0.02, 0.20], P = 0.005), but the difference disappeared in the later childhood phase. In children born after fresh embryo transfer, LDL-C showed an increasing trend with the increase in maternal E2 level (adjusted mean difference [95% CI]: Q2 vs Q1, −0.01 [−0.11, 0.08], Q3 vs Q1, 0.06 [−0.04, 0.15], Q4 vs Q1, 0.10 [0, 0.20]). Other metabolic variables were comparable across increasing quartiles of maternal E2 levels. Conclusion This study demonstrates a temporary increase in LDL-C levels in infants with higher levels of maternal preconception E2 levels. However, the long-term safety of hyperestrogens after ovarian stimulation in the next generation is favorable. The mechanism underlying the transiently increased metabolic dysfunction risk in infants conceived by IVF/ICSI requires investigation in future studies.
Distributed Radio Interference Localization of Wireless Sensor Networks

A novel distributed localization method based on radio interference(DRIPS) is proposed to reduce the complexity of existing localization methods for wireless sensor networks.The DRIPS utilizes two anchors to create an interference signal.Based on the Doppler effect produced by the movement of one transmitter,each sensor in a network can obtain the angle information with the mobile anchor through the Doppler shift measurement of locally received signal strength indication signal.Finally,each sensor utilizes multi-sets of the angle information and the location of the mobile anchor to estimate its optimal geographical location independently.The DRIPS only relies on radio transceivers without the use of extra measurement equipments,and the sensor node conducts localization in a completely distributed manner without information exchange or centralized processing.Simulation results show that the proposed method is effective and that the average localization error in a network will be less than 1 m when the measurement accuracy of Doppler shift is 1 Hz.
Developments in Algorithms for Sequence Alignment: A Review

The continuous development of sequencing technologies has enabled researchers to obtain large amounts of biological sequence data, and this has resulted in increasing demands for software that can perform sequence alignment fast and accurately. A number of algorithms and tools for sequence alignment have been designed to meet the various needs of biologists. Here, the ideas that prevail in the research of sequence alignment and some quality estimation methods for multiple sequence alignment tools are summarized.
Amino Surface Modification and Fluorescent Labelling of Porous Hollow Organosilica Particles: Optimization and Characterization

Surface modification of silica nanoparticles with organic functional groups while maintaining colloidal stability remains a synthetic challenge. This work aimed to prepare highly dispersed porous hollow organosilica particles (pHOPs) with amino surface modification. The amino-surface modification of pHOPs was carried out with 3-aminopropyl(diethoxy)methylsilane (APDEMS) under various reaction parameters, and the optimal pHOP-NH2 sample was selected and labelled with fluorescein isothiocyanate (FITC) to achieve fluorescent pHOPs (F-HOPs). The prepared pHOPs were thoroughly characterized by transmission electron microscopy, dynamic light scattering, FT-IR, UV-Vis and fluorescence spectroscopies, and microfluidic resistive pulse sensing. The optimal amino surface modification of pHOPs with APDEMS was at pH 10.2, at 60 °C temperature with 10 min reaction time. The positive Zeta potential of pHOP-NH2 in an acidic environment and the appearance of vibrations characteristic to the surface amino groups on the FT-IR spectra prove the successful surface modification. A red-shift in the absorbance spectrum and the appearance of bands characteristic to secondary amines in the FTIR spectrum of F-HOP confirmed the covalent attachment of FITC to pHOP-NH2. This study provides a step-by-step synthetic optimization and characterization of fluorescently labelled organosilica particles to enhance their optical properties and extend their applications.
Bounded Energy Collisions in Human–Robot Cooperative Transportation

In this article, we introduce the notion of bounded energy collision for physical human–robot collaboration to allow safe interaction with the environment, by keeping the collision impact within predetermined bounds. The proposed method is based on energy dissipation, implemented by a variable damping strategy along the direction of imminent collisions with the environment, and fully complies with any physical human–robot interaction strategy that combines an indirect force control (e.g., impedance control) with an estimation of the human motion intention. Finally, extensive experimental studies clarify and verify the proposed scheme.
Inference of User Desires to Spread Disinformation Based on Social Situation Analytics and Group Effect

The dissemination of digital disinformation in online social networks (OSNs) has been the subject of extensive research, although many challenges remain, including the analysis and control of disinformation dissemination across different platforms (i.e., cross-platform). In this article, we investigate and analyze the spreading patterns and regularities of disinformation both within a single platform and across platforms. To explore the complex relationship between user propagation desire and behaviour within the same group, a user propagation desire inference model based on propagation characteristics (behaviour characteristics and time characteristics) and a bidirectional backpropagation (B-BP) deep neural network are constructed. Then, to avoid overfitting due to the interaction of users’ propagation behaviour and the correlation among propagation characteristics, a novel adaptive weighted particle swarm optimization evolutionary algorithm is utilized to further optimize the B-BP deep neural network. We design and conduct a series of evaluation experiments on the current global hot topics including but not limited to novel coronavirus-19 pandemic (COVID-19), food safety, medical and health, and environmental protection. By using a real-world social platform and its social situation metadata analysis, the experimental results show that the proposed method not only accurately predicts the level of user propagation desire under multiple behaviour interactions but also facilitates social platform managers in handling disinformation disseminators. Our findings reveal that the intensity of social users’ desires to spread disinformation is related to the topics and groups that users are interested in, while the propagation motivation of social users is not strong under topics that users are not interested in. Our studies also demonstrate that social users with propagation desires tend to utilize their familiar social platforms and local circles for communication, and the behaviour and desire to spread disinformation to the cross-platform are not strong. We posit that these findings can help inform online and, fine-grained governance and mitigation strategies other than “one size fits all” approaches (e.g., “account prohibition and deletion”), and hopefully minimize disinformation dissemination.
Psychological Analysis for Depression Detection from Social Networking Sites

Rapid technological advancements are altering people's communication styles. With the growth of the Internet, social networks (Twitter, Facebook, Telegram, and Instagram) have become popular forums for people to share their thoughts, psychological behavior, and emotions. Psychological analysis analyzes text and extracts facts, features, and important information from the opinions of users. Researchers working on psychological analysis rely on social networks for the detection of depression-related behavior and activity. Social networks provide innumerable data on mindsets of a person's onset of depression, such as low sociology and activities such as undergoing medical treatment, a primary emphasis on oneself, and a high rate of activity during the day and night. In this paper, we used five machine learning classifiers—decision trees, K-nearest neighbor, support vector machines, logistic regression, and LSTM—for depression detection in tweets. The dataset is collected in two forms—balanced and imbalanced—where the oversampling of techniques is studied technically. The results show that the LSTM classification model outperforms the other baseline models in the depression detection healthcare approach for both balanced and imbalanced data.