Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Latency Short-Time Fourier Transform of Microwave Photonics Processing

Short-time Fourier transform (STFT) is a vital tool to observe spectral dynamics of non-stationary signals. Conventional STFT based on digital signal processing (DSP) usually suffers from a limited acquisition frame rate within 106 Fourier transforms per second (FTs/s). Thus, it is unable to fully intercept transient information in time and frequency shorter than a few microseconds. Further extending the acquisition frame rate can be achieved by employing the dispersion-based all-optical spectral analysis approach, while the optical dispersion usually introduces a large latency. Here, we experimentally report a low-latency STFT of microwave photonic processing, where a passive fiber loop is used to provide high value equivalent dispersion. Consequently, the latency of the STFT processing can be greatly reduced. The spectrograms of microwave signals, including instantaneous information in both time and frequency, are acquired in a real-time manner, with an acquisition frame rate of about $5 \times {10}^8$ FTs/s and a processing latency of less than 0.2 ${{\bf \mu s}}$. High temporal resolution within tens of nanosecond range can be achieved, allowing us to capture 30-ns duration frequency transients in real time with a instantaneous bandwidth of about 530 MHz. Our proposed STFT processing scheme is promising for real-time and high-speed spectral dynamic analysis for non-stational signals.
Strangers helping strangers in a strange land: Vietnamese immigrant (expectant) mothers in the US use social media to navigate health issues in acculturation

Objectives Trying to adapt to a new culture, Vietnamese (expectant) mothers in the USA gathered in few Facebook groups with thousands of members discussing pregnancy, health, and child caring issues. However, there is little research exploring how social support was given/taken among these (expectant) mothers. This empirical research aims at shedding light on how such mothers use social media groups for social support seeking/providing regarding health utilization during their acculturation process. Methods Drawing from Andersen's Behavioral Model of Health Utilization, acculturation, and online social support conceptual frameworks, this study analyzes 18 in-depth interviews with immigrant Vietnamese (expectant) mothers in the United States on the use of social media in navigating health acculturation during their pregnancy and motherhood. Results Results show that these mothers give and take all forms of social support including informational, emotional, relational, and instrumental ones. Facebook groups do not provide the best environment for improving “bonding” social capital for its members. However, these groups provide a platform where “strangers help strangers” overcome various barriers to sufficiently understand and independently access and use the official healthcare system. The groups, hence, aid these women's pregnancy and their child(ren)'s health. The informational and emotional support provided by Facebook groups among (soon-to-be) mothers helped them tremendously in overcoming acculturative stress. Moreover, with better language skills, knowledge, and experience in using health and social security systems, help-seekers tend to be transformed into help providers to deliver support for those “newcomers. ” Conclusions This research provides insights into personal experience on the uses of social media in navigating health behavior in the process of acculturation among Vietnamese immigrant (expectant) mothers in the United States. The research seeks to contribute to the conceptual frameworks and practical experience of behavioral model of health utitlization among immigrant Vietnamese ethnic immigrant pregnant women and mothers of babies and toddlers in navigating health during acculturation process in the United States. The limitations and future research suggestions are also discussed.
Output Capacitance Loss of GaN HEMTs in Steady-State Switching

The output capacitance (<inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula>) loss, a loss produced when the device's output capacitor is charged and discharged, has become a concern for GaN high electron mobility transistors (HEMTs) in high-frequency applications. This work presents a new, easy-to-implement method for the <inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula> loss characterization based on the unclamped inductive switching setup. As compared with prior approaches, this method involves the device's <sc>on</sc>-state conduction and could measure the <inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula> loss in a single pulse and the steady-state switching. The <inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula> loss of three types of the mainstream commercial GaN HEMTs is characterized, which exhibit some common dependencies including a nonmonotonic relation with the <italic>dv/dt</italic> (or resonance frequency), a linear relation with the <sc>on</sc>-state current, a power-law relation with the peak blocking voltage, and a little temperature dependence. In addition, their <inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula> losses all show minimal distinctions in a single pulse and the steady-state switching, despite the increased <sc>on</sc>-resistance in the steady-state switching. This suggests that the traps accounting for the <inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula> loss possess different detrapping time constants as compared with the traps governing the dynamic <sc>on</sc>-resistance. Finally, a unified model is established to describe the <inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula> loss of all three types of GaN HEMTs. These results provide important references for the high-frequency application of GaN HEMTs and new insights into the physical origin of their <inline-formula><tex-math notation="LaTeX">${C}_{\text{OSS}}$</tex-math></inline-formula> loss.
Paleoclimate and Paleoenvironment Reconstructions from Middle Eocene Successions at Beni-Suef, Egypt: Foraminiferal Assemblages and Geochemical Approaches

The Eocene deposits of Egypt provide a wide variety of shallow marine facies and fossil assemblages, allowing paleoenvironmental reconstructions in this warmhouse climate interval. Forty-three rock samples have been collected from two middle Eocene sections, exposed at southeast Beni-Suef area in northern Egypt. The studied outcrops are lithologicsally subdivided into two rock units named from base to top as follows: (1) the Qarara Formation (Lutetian) and (2) the El Fashn Formation (Bartonian). Most investigated rock samples showed richness in benthic foraminiferal assemblages and only rare occurrences of index planktonic foraminifera. They yielded 160 foraminifera species which belong to 4 suborders, 19 superfamilies, 34 families, and 59 genera. The stratigraphic distribution of the identified species allowed us to define four local benthic bio-ecozones: (1) Bolivina carinata Lowest Occurrence Zone (Lutetian), (2) Cibicides mabahethi/Cancris auriculus primitivus Concurrent-Range Zone, (3) Nonion scaphum Lowest Occurrence Zone, and (4) Brizalina cooki/Nonionella insecta Concurrent-Range Zone (Bartonian). These biozones are described and discussed in detail and correlated to stratigraphic equivalents in Egypt. The rareness of index planktonic foraminifera through the studied sections does not allow a precise biostratigraphic and chronostratigraphic correlation. The shale samples show low TOC values, which may be related to low productivity, high sediment influx, and/or prevailing oxic conditions. Bulk rock geochemistry, consistent with the benthic foraminifera ecological preferences, indicated that the studied sections were deposited in moderate to high oxygen levels and warm climatic conditions in a typical shelf marine setting. The identified species showed strong similarities with nearby southern Tethys areas, reflecting migration via the trans-Sahara seaway, and minor similarities with those identified from the northwestern Tethys and the North Atlantic province attributed to the change in the environmental and climatic conditions, such as cooler, latitudinal zoned climatic conditions along the northwestern Tethys, which was unsuitable for their biological demands. The warming trend identified from Lutetian to Bartonian intervals corresponds to the onset of the Middle Eocene Climatic Optimum (MECO).
Tuning of refractive index in Al-doped ZnO films by rf-sputtering using oblique angle deposition

Al-doped ZnO thin films were grown by rf-sputtering using the oblique angle deposition technique. The films are formed by tilted dense nanocolumns according to the substrate inclination during the growth. The film refractive index changed 16% due to the film nanocolumn tilt from 0° to 13.5°, which corresponded to substrate inclination from 0° to 75°, respectively. Band gap energy was found to diminish, which was associated to local redistribution of Al concentration x in the AlxZn1−xO solid solution around the x ≅ 0.02 average.
Eigenvalue results for pseudomonotone perturbations of maximal monotone operators

Let X be an infinite-dimensional real reflexive Banach space such that X and its dual X* are locally uniformly convex. Suppose that T: X⊃D(T) → 2X* is a maximal monotone multi-valued operator and C: X⊃D(C) → X* is a generalized pseudomonotone quasibounded operator with L ⊂ D(C), where L is a dense subspace of X. Applying a recent degree theory of Kartsatos and Skrypnik, we establish the existence of an eigensolution to the nonlinear inclusion 0 ∈ Tx + λCx, with a regularization method by means of the duality operator. Moreover, possible branches of eigensolutions to the above inclusion are discussed. Furthermore, we give a surjectivity result about the operator λT + C when λ is not an eigenvalue for the pair (T, C), T being single-valued and densely defined.
The Efficacy of Pretreatment 18F-FDG PET-CT-Based Deep Learning Network Structure to Predict Survival in Nasopharyngeal Carcinoma

Background: Previous studies have shown that the 5-year survival rates of patients with nasopharyngeal carcinoma (NPC) were still not ideal despite great improvement in NPC treatments. To achieve individualized treatment of NPC, we have been looking for novel models to predict the prognosis of patients with NPC. The objective of this study was to use a novel deep learning network structural model to predict the prognosis of patients with NPC and to compare it with the traditional PET-CT model combining metabolic parameters and clinical factors. Methods: A total of 173 patients were admitted to 2 institutions between July 2014 and April 2020 for the retrospective study; each received a PET-CT scan before treatment. The least absolute shrinkage and selection operator (LASSO) was employed to select some features, including SUVpeak-P, T3, age, stage II, MTV-P, N1, stage III and pathological type, which were associated with overall survival (OS) of patients. We constructed 2 survival prediction models: an improved optimized adaptive multimodal task (a 3D Coordinate Attention Convolutional Autoencoder and an uncertainty-based jointly Optimizing Cox Model, CACA-UOCM for short) and a clinical model. The predictive power of these models was assessed using the Harrell Consistency Index (C index). Overall survival of patients with NPC was compared by Kaplan–Meier and Log-rank tests. Results: The results showed that CACA-UOCM model could estimate OS (C index, 0.779 for training, 0.774 for validation, and 0.819 for testing) and divide patients into low and high mortality risk groups, which were significantly associated with OS (P < .001). However, the C-index of the model based only on clinical variables was only 0.42. Conclusions: The deep learning network model based on 18F-FDG PET/CT can serve as a reliable and powerful predictive tool for NPC and provide therapeutic strategies for individual treatment.
On totally umbilical submanifolds of Finsler spaces

The notion of a totally umbilical submanifold of a Finsler manifold is introduced. Some Gauss equations are given and some results on totally umbilical submanifolds of Riemannian manifolds are generalized. Totally umbilical submanifolds of Randers spaces are studied; a rigidity theorem and an example are given.
Single silicon synaptic device for stochastic binary spike-timing-dependent plasticity

In this paper, we present a single silicon synaptic device that enables stochastic binary spike-timing-dependent plasticity. The single silicon synaptic device, which is similar to a conventional metal-oxide-semiconductor field-effect transistor structure, is implemented using standard complementary metal-oxide-semiconductor technology. The stochastic nature of the feedback mechanism induced by weak impact ionization is experimentally demonstrated. Because of probabilistic dynamics in the feedback mechanism, the single silicon synaptic device features stochastic binary spike-timing-dependent plasticity.
Abnormal network packets identification using header information collected from Honeywall architecture

ABSTRACT Most devices are now connected through the Internet, so cybersecurity issues have raised concerns. This study proposes network services in a virtual environment to collect, analyze and identify network attacks with various techniques. Our contributions include multi-fold. First, we deployed Honeynet architecture to collect network packets, including actual cyber-attacks performed by real hackers and crackers. In the second contribution, we have leveraged some techniques to normalize data and extract header information with 29 features from 200,000 samples of many types of network attacks for abnormal packet identification with machine learning algorithms. Furthermore, we introduce an Adaptive Cybersecurity (AC) system to detect attacks and provide warnings. The system can automatically collect more data for further analysis to improve performance. Our proposed method performs better than Snort in detecting dangerous malicious attacks. Finally, we have experimented with different cyber-attack approaches to exploit the ten website security risks recommended by the Open Web Application Security Project (OWASP). From the research results, the system is expected to be able to detect cybercriminal attacks and provide early warnings to prevent a potential cyber-attack.
Hybrid fuzzy neural nets are universal approximators

It is known that regular fuzzy neural nets, based on standard fuzzy arithmetic and the extension principle, can not be universal approximators. This negative result is surprising since (regular) neural nets are universal approximators. We argue that hybrid fuzzy neural nets, not necessarily based only on standard fuzzy arithmetic, can be universal approximators. <<ETX>>
Chitosan-Based Membranes as Gentamicin Carriers for Biomedical Applications—Influence of Chitosan Molecular Weight

Over the past decade, much attention has been paid to chitosan as a potential drug carrier because of its non-toxicity, biocompatibility, biodegradability and antibacterial properties. The effect of various chitosan characteristics on its ability to carry different antibiotics is discussed in the literature. In this work, we evaluated the influence of the different molecular weights of this polymer on its potential as an antibacterial membrane after adding gentamicin (1% w/w). Three types of chitosan membranes without and with antibiotic were prepared using a solvent casting process. Their microstructures were analyzed with a 4K digital microscope, and their chemical bonds were studied using FTIR spectroscopy. Furthermore, cytocompatibility on human osteoblasts and fibroblasts as well as antibacterial activity against Staphylococcus aureus (S. aureus.) and Escherichia coli (E. coli) were assessed. We observed that the membrane prepared from medium-molecular-weight chitosan exhibited the highest contact angle (≈85°) and roughness (10.96 ± 0.21 µm) values, and its antibacterial activity was unfavorable. The maximum tensile strength and Young’s modulus of membranes improved and elongation decreased with an increase in the molecular weight of chitosan. Membranes prepared with high-molecular-weight chitosan possessed the best antibacterial activity, but mainly against S. aureus. For E. coli, is not advisable to add gentamicin to the chitosan membrane, or it is suggested to deplete its content. None of the fabricated membranes exhibited a full cytotoxic effect on osteoblastic and fibroblast cells. Based on our results, the most favorable membrane as a gentamicin carrier was obtained from high-molecular-weight chitosan.
Exploring Sex Differences of Beta-Blockers in the Treatment of Hypertension: A Systematic Review and Meta-Analysis

Aims: In the prevention of cardiovascular morbidity and mortality, early recognition and adequate treatment of hypertension are of leading importance. However, the efficacy of antihypertensives may be depending on sex disparities. Our objective was to evaluate and quantify the sex-diverse effects of beta-blockers (BB) on hypertension and cardiac function. We focussed on comparing hypertensive female versus male individuals. Methods and results: A systematic search was performed for studies on BBs from inception to May 2020. A total of 66 studies were included that contained baseline and follow up measurements on blood pressure (BP), heart rate (HR), and cardiac function. Data also had to be stratified for sex. Mean differences were calculated using a random-effects model. In females as compared to males, BB treatment decreased systolic BP 11.1 mmHg (95% CI, −14.5; −7.8) vs. 11.1 mmHg (95% CI, −14.0; −8.2), diastolic BP 8.0 mmHg (95% CI, −10.6; −5.3) vs. 8.0 mmHg (95% CI, −10.1; −6.0), and HR 10.8 beats per minute (bpm) (95% CI, −17.4; −4.2) vs. 9.8 bpm (95% CI, −11.1; −8.4)), respectively, in both sexes’ absolute and relative changes comparably. Left ventricular ejection fraction increased only in males (3.7% (95% CI, 0.6; 6.9)). Changes in left ventricular mass and cardiac output (CO) were only reported in males and changed −20.6 g (95% CI, −56.3; 15.1) and −0.1 L (95% CI, −0.5; 0.2), respectively. Conclusions: BBs comparably lowered BP and HR in both sexes. The lack of change in CO in males suggests that the reduction in BP is primarily due to a decrease in vascular resistance. Furthermore, females were underrepresented compared to males. We recommend that future research should include more females and sex-stratified data when researching the treatment effects of antihypertensives.
Study of Heat Sources and Sinks and the Generation of Available Potential Energy in the Indian Region During the Southwest Monsoon Season

Abstract The total vertical velocity and the heating field were evaluated for a portion of the Indian region for a typical monsoon day in July 1966 using a two-level geostrophic model. The generation of zonal and eddy available potential energy was computed with these values of heating and cooling. Both forms of available potential energy seem to be generated by diabatic processes. The generation of eddy available potential energy may be due to the released latent heat of condensation.
Construction and optimization of regional patrol processes for logistics finance companies

Logistics finance, as a synergistic innovation in the fields of logistics and finance, is a new way to solve the problem of difficult financing for SMEs. In this paper, a patrol process for logistics enterprises is established based on key business controls and condemnatory costs, the characteristics of patrol work are modeled, and queuing theory is used to investigate the optimal number of warehouses to be managed by a single patrol staff. On this basis, a new logistics finance business model is introduced, a many-to-many queueing theory model is used to calculate the allocation of patrol staff for a fixed number of shops in a region, and the patrol process is optimized for the new business model. The practicality of an actual logistics finance case is analyzed and verified.
Adaptive neural network observer based synchronization control of uncertain chaotic system

Abstract This paper addresses a nonlinear observer based control scheme to synchronize chaotic systems subject to uncertainties and external disturbances. It is assumed that the dynamic of slave system is not completely known. In order to compensate for the system perturbation resulting from parameter variations and mismodeling phenomena, an adaptive neural network observer is employed to handle this problem. A nonlinear observer for a class of nonlinear systems is proposed based on a generalized dynamic recurrent neural network. The weights of the proposed neural network in the observer are tuned on-line with no off-line learning phase required. Also, no exact information of the nonlinear term of the system is required and this important characteristic compensates considerable part of uncertainty. To realize control purpose, two controllers are considered. At first, PID controller is combined with proposed observer and then 2 order sliding mode controller called twisting algorithm is applied to synchronize systems. This method is implemented on the Duffing chaotic systems and simulation results confirm the effectiveness of the proposed method.
A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.
Development of a four-dimensional variational analysis system using the adjoint method at GLA. I : dynamics

Abstract Recent developments in the field of data assimilation have Pointed to variational analysis (essentially least- squares fitting, of a model solution to observed data) using the adjoint method as a new direction that holds the potential of major improvements over the current optimal interpolation (0I) method. The shortcomings of the existing OI analysis method, such as the questionable basic assumptions underlying some of the statistical formulations and the linearity between the analysis and the observation, do not exist in variational analysis. Moreover, variational analysis, by fitting a model solution to data, has the potential of avoiding the long-standing spinup problem in numerical weather prediction. Finally, when the resulting analysis is used as the initial condition for forecasting, since initialization will be performed internally to the analysis procedure, no separate initialization procedure is required before forecast starts. This paper describes the initial effort in the development.