Specific Intervals Research Articles

The role of color doppler in high risk pregnancies: A prospective comparative study

Pregnancy can be considered as the most beautiful period of a woman’s life. The recent medical advances are helpful in taking very good care of the female throughout her pregnancy. The pregnancy is constantly observed by clinical examination, laboratory investigations, and radiographic examinations at specific intervals throughout its course.To study the role of doppler ultrasound in high-risk pregnancies.This is a prospective comparative study was conducted in department of Obstetrics and Gynaecology at Government medical college, Nagpur. The study period was 12 months. 232 patients were included in this study.In our study, 2 patients had Chronic hypertension, 2 patients had GDM, 1 patient had Gestational hypertension, 3 patients had Anaemia, 6 patients had Heart Disease, 5 patients had HIV positive,15 patients had Hypothyroidism, 2 patients had Increased BP, 1 patient had Overt DM,6 patients had Pre-eclampsia,1 patient had previous abortion, 4 patients had Rh negative, 5 patients had Sickle Cell Disease(SS) and 15 patients had Sickle Cell trait(AS). Distribution of normal doppler changes in cases according to high risk factor was statistically significant (p<0.0001).In our study, NICU Admission with doppler changes was statistically significant and Association of Baby weight in cases according to doppler changes in uterine artery and Baby weight in cases according to doppler changes in umbilical artery was not statistically significant.

The Maximal and Minimal Distributions of Wealth Processes in Black–Scholes Markets

The Black–Scholes formula is an important formula for pricing a contingent claim in complete financial markets. This formula can be obtained under the assumption that the investor’s strategy is carried out according to a self-financing criterion; hence, there arise a set of self-financing portfolios corresponding to different contingent claims. The natural questions are: If an investor invests according to self-financing portfolios in the financial market, what are the maximal and minimal distributions of the investor’s wealth on some specific interval at the terminal time? Furthermore, if such distributions exist, how can the corresponding optimal portfolios be constructed? The present study applies the theory of backward stochastic differential equations in order to obtain an affirmative answer to the above questions. That is, the explicit formulations for the maximal and minimal distributions of wealth when adopting self-financing strategies would be derived, and the corresponding optimal (self-financing) portfolios would be constructed. Furthermore, this would verify the benefits of diversified portfolios in financial markets: that is, do not put all your eggs in the same basket.

Effect of Different Spacing Level and Growth Stages of Chekkurmanis (Sauropus androgynus L.) on Plant Growth

Background: Chekkurmanis, is a shrubby glabrous perennial green leafy vegetable, extensively grown in warm humid tropics. It is called as “Multivitamin / Multigreen” plant. Micronutrient deficiency has spread all over the world and it is termed as “Hidden Hunger”. In this regard, chekkurmanis is the chief source of many minerals and nutrients is help to eradicate these deficiencies. The yield enhancement and improvement on plant growth characters could be manipulated by adopting optimized cultural practices, especially plant spacing and stage of harvest. With the background, present study was taken up to examine the variations in morphologically; under different spacing’s in relation to different growth stages were assessed and discussed. Methods: The research study was carried during 2019-2020. The healthy rooted cuttings collected from Orchard, Agricultural College and Research Institute, Madurai, Tamil Nadu. The research trial consisted of two factors different spacing’s (Factor I) and specific days interval (Factor II). Result: Based on the results, it can be suggested that growth characters i.e. plant height, number of branches, number of leaves per plant and petiole length parameters were significantly higher under closer spacing at 180 days after planting (S1D3). While, Maximum leaf length, leaf breadth and plant spread were recorded under wider spacing at 180 days after planting (S5D3).

ROBUST LEARNING AND ACCURATE PREDICTION OF WIRELESS TRAFFIC USING IMPROVED NEURAL NETWORK

The proposed system introduces a novel Deep Metric Algorithm as a cornerstone for enhancing the feasibility and optimality of network traffic prediction within the dynamic landscape of cloud technologies. The Deep Metric Algorithm is designed to operate as a physically interpretable probabilistic model that captures network-wide traffic patterns. Unlike traditional methods that rely solely on current traffic states observed at specific intervals, this algorithm leverages an initially expensive set of measurements to construct a network-specific traffic model.The algorithm's innovation lies in its ability to utilize this network-specific model alongside readily available, less expensive measurements, thereby significantly improving the accuracy of traffic predictions. By employing the Deep Metric Algorithm, the system can predict traffic fluctuations not only over observed links but also extend its predictions to unobserved links. This is a crucial advancement as it enables the system to offer more comprehensive insights into network behavior, even where direct measurements might be limited.One notable strength of the proposed Deep Metric Algorithm is its demonstrated applicability across various traffic periods within the same network. This adaptability ensures that the learned model remains effective over time, showcasing its potential utility in optimizing network performance as it evolves. The algorithm's predictive capabilities, coupled with its ability to adapt to changing network conditions, position it as a valuable tool for load-aware resource management and predictive control, thereby contributing to the efficient management of multi-provider networks in the evolving cloud landscape. KEYWORDS: Quality of Service (QoS), network traffic, accurate predictions, network-specific traffic model, Deep Metric Algorithm.

Linking social media addiction and student retention through the lenses of student engagement

PurposeThis research is designed to explore the relationship between social media addiction, student engagement and student retention. Social media addiction is dealt with as an independent variable student engagement acts as a mediating variable and student retention as a dependent variable.Design/methodology/approachThis is a cross-sectional and quantitative research. Primary data are collected from 600 respondents (university students) with the help of a structured questionnaire. Multistage sampling techniques, i.e. simple random sampling and judgment sampling, are used for the selection of respondents.FindingsResults indicate that for direct relationships, social media addiction has a significant positive impact on student engagement and student retention, respectively, while student engagement is partially mediating the relationship between social media addiction with student retention.Research limitations/implicationsIn the future, these kinds of research may also be conducted on students of different universities in Pakistan, which are located in other cities of Pakistan besides Lahore. This research provides a practical framework for the higher authorities of the universities of Pakistan and explains how the use of media positively fosters the levels of student retention directly and indirectly through the path of student engagement. It is commonly believed that media addiction is bad but the result of this research indicates that anything is not dangerous but depends upon its use, media addiction itself is not bad but if someone uses this for a good purpose in limitation then it has better outcomes. The result indicates that the media addiction of students has a positive impact on student retention. This means that if someone uses media for a positive purpose then he/she will use it as a supporting tool for success. Longitudinal research on these variables will also help to check the status after a specific interval of time.Practical implicationsThe current study will help the practitioners or policymakers (Managers) of higher education institutions by providing practical insights into the positive use of media by students for increasing their knowledge and grades. This research can also help practitioners or policymakers to focus their students on the positive use of social media for fostering the levels of student retention.Originality/valueTo the best of the researcher’s knowledge, no previous study has been done to incorporate social media addiction and student engagement in a single model in the Pakistani cultural context. Similarly, the relationship of variables social media addiction with student engagement is rarely checked empirically because the research of Wang et al. (2011) proposed that social media addiction has a relationship with student engagement so that is why this is the rationale of the research is to check this empirically. Moreover, this study is an initial effort to check the mediating effect of student engagement in the relationship between social media addiction and student retention. This research is also proposing the framework of social media addiction, student engagement and student retention based on the social exchange theory (SET).

A trajectory data-driven approach for traffic risk prediction: incorporating variable interactions and pre-screening

ABSTRACT Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the impact of the traffic flow parameters and their interaction terms on risk prediction performance, employing a variable pre-screening approach (i.e. Smoothly Clipped Absolute Deviation (SCAD)). A research framework is proposed for more efficient risk prediction, and a detailed case study is further conducted using the proposed approach. In the case study, real vehicle trajectory data from HighD are processed and used, which can be aggregated to extract both traffic flow parameters and corresponding risk data during a specific time interval. As for the risk detection, Time-to-Collision (TTC) index is utilized to identify risky conditions. For different lanes (i.e. inner, middle and outer lanes), the impact of variables, including interaction terms, on risk is explored using the SCAD-logistic models. Furthermore, machine learning methods are employed to compare the risk prediction performance before and after considering interaction terms, as well as before and after variable pre-screening. Finally, the superiority of the machine learning models after SCAD-based variable pre-screening is demonstrated. Results indicate that the interaction terms between traffic flow parameters have significant impacts on the traffic risk. Besides, considering interaction terms and variable pre-screening can improve risk prediction accuracy. Furthermore, the proposed models outperform Random Forest (RF) in terms of predicting traffic risk, achieving a maximum 21.24% accuracy improvement and reducing computational time by up to 31.51%. Findings of this study are expected to contribute to the high-precision prediction of real-time risk in the future.

Spatial Analysis of Leachate Penetration at Lemna Dumpsite, Calabar: Implications for Sustainable Waste Management in Cross River State

This study rigorously investigated the spatial analysis of leachate penetration at Lemna dumpsite, located in Calabar, Cross River State, Nigeria. Purposeful soil sampling, performed at specific intervals (5 meters, 25 meters, and 50 meters) along the Electrical Resistivity profile line within the dumpsite, was augmented by water sample collection from five boreholes near Lemna dumpsite. Utilizing Electrical Resistivity Tomography (ERT) and Vertical Electric Sounding (VES) survey techniques, resistivity data were systematically gathered to comprehensively analyze the Leachate Penetration in the Lemna dumpsite. Laboratory analysis of soil and borehole water quality focused on Benzene, Toluene, Ethylbenzene, and Xylene (BTEX), with paired sample t-tests applied for statistical scrutiny. Analyzing the ERT and VES data employed sophisticated techniques embedded in Resistivity Two Dimension Invasion software and Advanced Geosciences Incorporation Earth Imager software. Substantial disparities (p<0.05) emerged in the paired sample t-tests for BTEX in soil compared to National Environmental Standard Regulation and Enforcement Agency (NESREA) limits. Similarly, BTEX in borehole water displayed significant differences (p<0.05) when compared to World Health Organization (WHO) standards, raising alarming concerns about the safety and portability of groundwater in the area. The examination of dumpsite leachate penetration revealed a resistivity anomaly of 8.01Ωm and an inverse depth of 12.4 m, underscoring profound environmental implications and necessitating immediate remediation efforts. Additionally, Vulnerability and Aquifer Protective Capacity Index (VES) results, with a rating of <0.1, indicated severely compromised aquifer protective capacity, emphasizing the vulnerability of groundwater resources to further contamination. Our study advocates for strategic management, remediation, and monitoring measures to prevent contamination and safeguard water quality in the region.

First trimester specific reference interval of TSH, FT3, FT4 in population of Oran (Algeria)

First trimester specific reference interval of TSH, FT3, FT4 in population of Oran (Algeria)

Evaluation of oral health status in pregnant women and its correlation with calcium and phosphate levels.

This study aimed to analyze the oral health conditions of pregnant women. The analysis involves evaluating two key indices: the decayed, missing, and filled teeth (DMFT) index and the basic erosive wear examination (BEWE) index. Furthermore, this study investigated potential correlations between calcium (Ca) and phosphate (P) levels within specific time intervals and the aforementioned oral health indices.

Quantitative detection of mpox antigen using time-resolved fluorescence immunochromatography.

Recently, concern has been raised about the spread of human mpox virus, and the demand for rapid detection reagents for mpox virus has increased. This study aims to establish a time-resolved fluorescence immunochromatography (TRFICO) method for qualitative/quantitative detection of mpox virus. A double-antibody sandwich TRFICO method was optimized and established using mpox recombinant fusion antigen and its paired monoclonal antibody. The test performance of the method was evaluated using mpox fusion antigen and control serum, including sensitivity, linearity range, specificity, precision, and reference interval. We successfully established a TRFICO method for qualitative/quantitative detection of mpox antigen, its linearity range 0-100ng/mL, analytical sensitivity 0.017ng/mL, and reference intervals greater than 0.045ng/mL. No cross-reaction was detected with various poxvirus and clinical negative controls, with good specificity. All average recoveries of the intra- and inter-batch ranged from 81.33% to 97.83%, and all CVs were below 10%. Additionally, the TRFICO strips can be stably stored at 37°C for 7days without significant changes in the fluorescence intensity. This TRFICO method, with high sensitivity, linearity range, specificity, precision, and stability with 16-min detection time, provides a new option for qualitative/quantitative and convenient testing of mpox virus.

Analyzing Players' Tactical Positions and Movement Trajectories in Soccer Matches Using Machine Vision Algorithms

Tactical positions and movement trajectories in soccer matches using machine vision algorithms involves leveraging computer vision techniques to track players and the ball throughout the game. By processing video feeds from multiple cameras, machine vision algorithms can identify players, recognize their positions on the field, and map out their movement trajectories over time. This analysis provides valuable insights into team formations, player positioning, and tactical strategies employed during matches. Coaches and analysts can use this information to assess team performance, identify patterns in gameplay, and make data-driven decisions to optimize tactics and training regimes. Machine vision algorithms enhance the understanding of soccer dynamics, facilitating more strategic and effective approaches to coaching and gameplay.. This paper presents a comprehensive analysis of player movement, tactical positions, and detection in soccer matches using machine learning algorithms. Leveraging a simulation environment built upon a Recurrent Neural Network (RNN) model trained on historical match data, we investigate the efficacy of the model in accurately predicting future player positions based on contextual features such as player trajectories and ball movement. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, providing valuable insights for coaches and analysts in understanding team strategies and adapting to match dynamics. Additionally, we evaluate player detection systems to assess their capabilities and limitations, laying the groundwork for future improvements in player tracking technologies. Our findings underscore the potential of machine learning in soccer analytics, offering actionable insights for enhancing performance analysis, strategic decision-making, and overall understanding of the game. Our results show a high prediction accuracy, with an average Mean Squared Error (MSE) of 0.05. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, achieving an overall prediction accuracy of 85%. Additionally, we evaluate player detection systems to assess their capabilities and limitations, achieving a detection accuracy of 90%.

3D DEWS digital parametric modeling and manufacturing for obtaining the post-cracking parameters of SFRC

A pullout analytical model is introduced to simulate the Double Edge Wedge Splitting (DEWS) test to obtain the tensile post-peak response of steel fiber reinforced concretes. One of the main barriers to the widespread utilization of FRC as a structural system is the anisotropy of the material. This augments the uncertainty of the material behavior (fibers random distribution and orientation in the bulk concrete), increases the dispersion of the test results, and requires the performance of characterization tests to check the residual tensile stresses at specific crack opening intervals. The characterization tests proposed in the literature (e.g., Barcelona, Montevideo, Wedge Splitting Test, EN 14651) are time-consuming and relatively complex, demanding careful preparation and hydraulic presses with data acquisition systems. This paper aims to contribute to the discussion of alternatives for FRC characterization procedures by the introduction of a pullout analytical model to predict FRC constitutive law based on DEWS tests. The pullout analytical model is calibrated with pullout tests considering straight and hooked-end steel fibers with different strengths, embedment lengths, inclinations, and diameters. A 3D digital model reproducing the experimental DEWS samples is developed, considering the fibers’ randomness distribution, orientation, and wall effect. The analyses are compared with four different experimental tests. The results define a linear post-peak tensile constitutive law, considering the mean residual stress values at different COD values. The model was able to reproduce the specimens’ production procedures and the fibers’ distribution and orientation correctly. The results demonstrated a good agreement with the experimental data.

A Multi-Objective Optimization Method for Single Intersection Signals Considering Low Emissions

The exponential growth of urban centers has exacerbated the prevalence of traffic-related issues. This surge has amplified the conflict between the escalating need for travel among individuals and the constricted availability of road infrastructure. Consequently, the escalation of traffic accidents and the exacerbation of environmental pollution have emerged as increasingly pressing concerns. Urban road intersections, serving as pivotal junctures for vehicle convergence and dispersal, have remained a focal point for scholarly inquiry regarding enhanced operational efficacy and safety. Concurrently, vehicles navigating intersections are subject to external influences, such as pedestrian crossings and signal controls, causing frequent fluctuations in their operational dynamics. These fluctuations contribute to heightened exhaust emissions, exacerbating air pollution and posing health risks to pedestrians frequenting these intersections. A reasonable signal timing scheme can enable more vehicles to pass through the intersection safely and smoothly and reduce the pollutants generated by transportation. Therefore, optimizing signal timing schemes at intersections to alleviate traffic problems is a topic that needs to be studied urgently. In this paper, the emission model based on specific power is analyzed. Through an analysis of the correlation between specific power distribution intervals and the emission rates of individual pollutants, it has been observed that vehicle emission rates are at their lowest during idle speed, progressively increasing with rising vehicle speeds. Investigation into specific power distribution based on variables, such as vehicle type, frequency of stops, and varying delays, has led to the deduction that the peak specific power of vehicles at intersections consistently occurs within the (0, 1) interval. Furthermore, it has been established that high-saturation intersections exhibit higher peak specific power compared to low-saturation intersections.

Comparison of Candida colonization in intensive care unit patients with and without COVID-19: First prospective cohort study from Turkey.

Candida species are the primary cause of fungal infections in intensive care units (ICUs). Despite the increasing prevalence of Candida-related infections, monitoring the progression of these infections from colonization in COVID-19 ICU patients lacks sufficient information. This study aims prospectively to compare 62 COVID-19 and 60 non-COVID-19 ICU patients from admission to discharge in terms of colonization development, rates, isolated Candida species, risk factors, and Candida infections during hospitalization. A total of 1464 samples collected at specific time intervals from various body sites [mouth, skin (axilla), rectal, and urine]. All samples were inoculated onto CHROMagar Candida and CHROMagar Candida Plus media, with isolates identified using MALDI-TOF MS. COVID-19 patients exhibited significantly higher colonization rates in oral, rectal, and urine samples compared to non-COVID-19 patients, (p<0.05). Among the Candida species, non-albicans Candida was more frequently detected in COVID-19 patients, particularly in oral (75.8%-25%; p<0.001) and rectal regions (74.19%-46.66%; p< 0.05). Colonization with mixed Candida species was also more prevalent in the oropharyngeal region (p<0.05). Mechanical ventilation and corticosteroid use emerged as elevated risk factors among COVID-19 patients (p<0.05). Despite the colonization prevalence, both COVID-19-positive and negative patients exhibited low incidences of Candida infections, with rates of 9.67% (n=6/62) and 6.67% (n=3/60), respectively. Consequently, although Candida colonization rates were higher in COVID-19 ICU patients, there was no significant difference in Candida infection development compared to the non-COVID-19 group. However, the elevated rate of non-albicans Candida isolates highlights potential future infections, particularly given their intrinsic resistance in prophylactic or empirical treatments if needed. Additionally, the high rate of mixed colonization emphasizes the importance of using chromogenic media for routine evaluation.

On the Fidelity of RANS-Based Turbulence Models in Modeling the Laminar Separation Bubble and Ice-Induced Separation Bubble at Low Reynolds Numbers on Unmanned Aerial Vehicle Airfoil

The operational regime of Unmanned Aerial Vehicles (UAVs) is distinguished by the dominance of laminar flow and the flow field is characterized by the appearance of Laminar Separation Bubbles (LSBs). Ice accretion on the leading side of the airfoil leads to the formation of an Ice-induced Separation Bubble (ISB). These separation bubbles have a considerable influence on the pressure, heat flux, and shear stress distribution on the surface of airfoils and can affect the prediction of aerodynamic coefficients. Therefore, it is necessary to capture these separation bubbles in the numerical simulations. Previous studies have shown that these bubbles can be modeled successfully using the Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) but are computationally costly. Also, for numerical modeling of ice accretion, the flow field needs to be recomputed at specific intervals, thus making LES and DNS unsuitable for ice accretion simulations. Thus, it is necessary to come up with a Reynolds-Averaged Navier–Stokes (RANS) equation-based model that can predict the LSBs and ISBs as accurately as possible. Numerical studies were performed to assess the fidelity of various RANS turbulence models in predicting LSBs and ISBs. The findings are compared with the experimental and LES data available in the literature. The structure of these bubbles is only studied from a pressure coefficient perspective, so an attempt is made in these studies to explain it using the skin friction coefficient distribution. The results indicate the importance of the use of transition-based models when dealing with low-Reynolds-number applications that involve LSB. ISB can be predicted by conventional RANS models but are subjected to high levels of uncertainty. Possible recommendations were made with respect to turbulence models when dealing with flows involving LSBs and ISBs, especially for ice accretion simulations.

Randomized controlled open-label trial to evaluate prioritization software for the secondary triage of patients in the pediatric emergency department

BackgroundThe continual increase in patient attendance at the emergency department (ED) is a worldwide health issue. The aim of this study was to determine whether the use of a secondary prioritization software reduces the patients’ median length of stay (LOS) in the pediatric ED.MethodsA randomized, controlled, open-label trial was conducted over a 30-day period between March 15th and April 23rd 2021 at Lille University Hospital. Work days were randomized to use the patient prioritization software or the pediatric ED’s standard dashboard. All time intervals between admission and discharge were recorded prospectively by a physician not involved in patient care during the study period. The study’s primary endpoint was the LOS in the pediatric ED, which was expected to be 15 min shorter in the intervention group than in the control group. The secondary endpoints were specific time intervals during the stay in the pediatric ED and levels of staff satisfaction.Results1599 patients were included: 798 in the intervention group and 801 in the control group. The median [interquartile range] LOS was 172 min [113–255] in the intervention group and 167 min [108–254) in the control group (p = 0.46). In the intervention group, the time interval between admission to the first medical evaluation for high-priority patients and the time interval between the senior physician’s final evaluation and patient discharge were shorter (p < 0.01). The median satisfaction score was 68 [55–80] (average).ConclusionThe patients’ total LOS was not significantly shorter on days of intervention. However, use of the electronic patient prioritization tool was associated with significant decreases in some important time intervals during care in the pediatric ED.ClinicalTrials.gov: NCT05994196Trial registration number: NCT05994196. Date of registration: August 16th, 2023

Reference intervals of circulating secretoneurin concentrations determined in a large cohort of community dwellers: the HUNT study.

Secretoneurin (SN) is a novel cardiac biomarker that associates with the risk of mortality and dysfunctional cardiomyocyte Ca2+ handling in heart failure patients. Reference intervals for SN are unknown. SN was measured with a CE-marked ELISA in healthy community dwellers from the fourth wave of the Trøndelag Health Study (HUNT4) conducted in 2017-2019. The common, sex and age specific 90th, 95th, 97.5th and 99th percentiles were calculated using the non-parametric method and outlier exclusion according to the Reed test. The applicability of sex and age specific reference intervals were investigated using Harris and Boyd test. We also estimated the percentiles in a subset with normal findings on echocardiographic screening. The total cohort included 887 persons (56.4 % women). After echocardiographic screening 122 persons were excluded, leaving a total of 765 persons (57.8 % women). The 97.5th percentile (95 % CI in brackets) of SN was 59.7 (57.5-62.1) pmol/L in the total population and 58.6 (57.1-62.1) pmol/L after echocardiography screening. In general, slightly higher percentiles were found in women and elderly participants, but less than 4 % in these subgroups had concentrations deviating from the common 97.5th percentile. Low BMI or eGFR was also associated with higher concentrations of SN. Upper reference limits for SN were similar amongst healthy adult community dwellers regardless of prescreening including cardiac echocardiography or not. Women and elderly showed higher concentrations of SN, but the differences were not sufficiently large to justify age and sex stratified upper reference limits.

Depression, anxiety, and insomnia symptoms among Chinese college students: A network analysis across pandemic stages

BackgroundAs the stages of the COVID-19 pandemic evolved, the symptoms of depression, anxiety, and insomnia have increasingly manifested among Chinese college students. The aim of this study is to investigate the relationships between these symptoms through network analysis among Chinese college students during COVID-19. MethodA three-wave cross-sectional survey was conducted at 22 colleges in Guangdong Province, involving 381,152 students during three specific time intervals: T1 (baseline, February 3 to 10, 2020), T2 (19 months after baseline, June 10 to 18, 2021), and T3 (37 months after baseline, March 15 to April 22, 2023). Depression (PHQ-9), anxiety (GAD-7), and insomnia (YSIS) were used separately. We analyzed two key network indices: “Expected influence” and “Bridge expected influence”. Network stability was assessed through a case-dropping bootstrap program. ResultThe effective sample sizes for the three periods were as follows: T1 - 164,101 (103,645 females, 63.2 %), T2 - 86,767 (52,146 females, 60.1 %), and T3 - 130,284 (76,720 females, 58.9 %). Across these three periods, the key central symptoms were “Fatigue” (PHQ4), “Restlessness” (GAD5), “Uncontrollable worrying” (GAD2), “Worry too much” (GAD3) and “Sleep insufficiency” (YSIS6). Notably, “Fatigue” (PHQ4), “Restlessness” (GAD5) and “Irritability” (GAD6) consistently served as bridge symptoms. In the T1 and T2 period, “Motor” (PHQ8) acted as a bridge symptom but weakened in T3. ConclusionThroughout the three periods, the mental health issues among Chinese college students displayed characteristics of somatization within the depression-anxiety-insomnia comorbidity network. Over time, anxiety symptoms appeared to become more prominent. Consequently, this study highlights the importance of accurately identifying and promptly intervening in these core symptoms of mental health among college students, as these symptoms may evolve across different stages of a pandemic.

An Algorithm to Estimate the Power Spectral Density From Allan Deviation.

Complex architectures for wireless communications, digital electronics, and space-based navigation interlink several oscillator-based devices such as clocks, transponders, and synthesizers. Estimators characterizing their stability are critical for addressing the impact of random fluctuations (noise) on the overall system performance. Manufacturers typically specify this as an Allan/Hadamard Variance (AVAR/HVAR) profile in the time domain. However, stochastic processes constituting the noise are more thoroughly described in the frequency domain by the power spectral density function (PSD). Both are second-moment measures of the time series, but it is only possible to translate unambiguously from the PSD to the AVAR/HVAR, not vice versa, except in the case of a single noise type, a rather unrealistic case. This note presents an analytical method to generate an approximated PSD expressed as a set of power-laws defined in specific intervals in the frequency domain, starting from an AVAR/HVAR expressed as a set of power-laws in the time domain. The proposed algorithm is straightforward to implement, applicable to all noise types (and combinations thereof), and can be self-validated by reconstructing the corresponding AVAR/HVAR by direct computation. Coupling with well-established algorithms relying on the PSD for power-law noise generation, the ensuing method encompasses the capability for generating multicolored noise in end-to-end simulations, as demonstrated hereby for NASA's deep space atomic clock. We also report on the limitations of the algorithm and analytical expressions of the continuous version of the algorithm.

Demonstrating a new evaluation method on ReLU based Neural Networks for classification problems

Deep neural networks, which have proven to be effective methods to solve complex problems, can even be applied in decision systems controlling critical processes. However, in such applications the outcomes of the neural network must be checked if it we have a clear understanding regarding the operation of network at given input intervals. The most straightforward, though often computationally expensive approach for this checking process evaluates the network at discrete input points and estimates the expected outputs at the given interval. The present research aims to develop a novel approach that can identify in the case of specific input intervals whether the operation process and the output of a neural network can be considered as known or unknown. During the presented case study, we investigated the ReLU (Rectified Linear Unit) and Sigmoid activation functions, using the double moon and the Banknote Authentication classification problems for demonstration.Our method can be applied to identify certain input intervals where the given neural network cannot support critical decisions. The evaluation is performed based on a nonlinear system of equations and inequalities built on arbitrary continuous activation functions. To define the critical intervals of the input variables (i.e. where the decision-making system should not be relied on), those input variable combinations are identified which result in a non-expected output value. This inverse logic is intended to identify intervals of the input variables where the response of the system and the correct decision are not identical. The presented demonstration examples supported our assumption that the number of the neurons and the dimension of the decision space have a significant impact on the complexity of the evaluation process.