This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on the technologies for gait characteristic assessment, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigen-analysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

This article explores the required amount of time series points from a high-speed traffic network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series, followed by addressing the minimum amount of points required to obtain accurate estimates of the Hurst exponent in real-time. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behavior, standard deviation, mean square error, and convergence using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed network based on the IEEE 802.3ab standard.

FeIFeI Fe2(S2C3H6)(CO)6(µ-CO) (1a-CO) and its FeIFeII cationic species (2a+-CO) are the simplest model of the CO-inhibited [FeFe] hydrogenase active site, which is known to undergo CO photolysis within a temperature- dependent process whose products and mechanism are still a matter of debate. Using Density Functional Theory (DFT) and Time-Dependent Density Functional Theory (TDDFT) computations, the ground state and low-lying excited state potential energy surfaces (PESs) of 1a-CO and 2a+-CO have been explored aimed at elucidating the dynamics of the CO photolysis yielding Fe2(S2C3H6)(CO)6 (1a) and Fe2(S2C3H6)(CO)6+ (2a+), two simple models of the catalytic site of the enzyme. Two main results came out from these investigations. First, a-CO and 2a+-CO are both bound with respect to any CO dissociation with lowest free energy barriers around 10 kcal mol-1, suggesting that at least 2a+-CO might be synthetized. Second, focusing on the cationic form, we found at least two clear excited state channels along the PESs of 2a+-CO that are unbound with respect to equatorial CO dissociation.

Structural and morphological properties of the hydronium-potassium jarosite microstructures were investigated in this work, and their electrical properties were evaluated. All microstructures were synthesized at a reasonable temperature of 343 K with a reduced reaction time of 3 hours. Increase in the pH from 0.8 to 2.1 decreased the particle sized from 3 µm to 200 nm and increasing the aging time from 0, 3 to 7 days resulted in semispherical, spherical and euhedreal jarosite structures, respectively. A Rietveld analysis also was done, finding that increasing pH, the amount of hydronium substitution by potassium in the cationic site also increases, having a 77.72 % of hydronium jarosite (JH) plus 22.29 % potassium jarosite (JK) at pH 0.8; 82.44 % (JH) and 17.56 % (JK) at pH 1.1, and 89.98 % (JH) plus 10.02 % (JK) at pH 2.1. The results obtained in this work show that the obtained hydronium potassium jarosite microstructures with reduced particle size and euhedreal morphology can be used as anode materials for improving the life time of lithium ion batteries, due that during the analysis of the voltage obtained using electrodes made with this particles and graphite, this ranged from 0.89 to 1.36 V.

We aimed to investigate the ability of children aged 5–14 years old (preschoolers, primary schoolers, and preteens) to assess and anticipate time intervals. 287 Russian children aged 5–14 years old and 26 adults of control group participated in our study. The neuropsychological assessment, Wechsler Intelligence Scale for Children and a battery of time-related tests were applied. All groups of children overestimated the event’s duration, although the accuracy of the second estimations increased among the participants aged 6–8 years after a prompt was offered. A zone of proximal development for time anticipation task was detected for children aged 9-11 years, when the prompt could significantly improve the accuracy of time perception. The participants overestimated the duration of both upcoming and past events, with the degree of overestimation being found to be negatively correlated with age. Further, a higher degree of accuracy in terms of time estimation was found to be correlated with higher scores on the attention and memory tests, and accuracy of time anticipation was associated with scores of praxis test.

The aim was to examine the sociodemographic predictors associated with changes in movement behaviours (physical activity, screen time and sleep) among toddlers and pre-schoolers during the early stages of the coronavirus disease 2019 pandemic in Chile. Caregivers of 1- to 5-year-old children completed an online survey between March 30th and April 27th, 2020. Information about the child's movement behaviours before (retrospectively) and during the pandemic, as well as family characteristics were reported. In total, 3,157 participants provided complete data (mean children age: 3.1±1.38 years). During early stages of the pandemic, time spent in physical activity decreased, recreational screen time and sleep duration increased, and sleep quality declined. Toddlers and pre-schoolers with space to play at home and living in rural areas experienced an attenuated impact of the pandemic restrictions on their physical activity levels, screen time, and sleep quality. Older children, those whose caregivers had a higher educational level, and children living in apartments had greater changes, mainly a decrease in total physical activity and increase in screen time. This study has shown the significant impact of the pandemic restrictions on movement behaviours in toddlers and pre-schoolers in Chile.

Previously, various techniques have been proposed for reducing the lift-off effect on the thickness measurement of the non-magnetic films, including the peak-frequency feature and phase feature in the Dodd-Deed analytical formulation. To realise a real-time feedback response on the thickness monitoring, the phase term in the Dodd-Deeds formulation must be taken off the integration. Previous methods were based on the slow change rate of the phase term when compared to the rest of the term – the magnitude term. However, the change rate of the phase term is still considerable for a range of working frequencies. In this paper, a high-frequency feature has been found. That is, the ratio between the imaginary and real part of the phase term is proportional to the integral variable under high frequencies. Based on this proportion relationship, the phase term has been taken out; and a thickness algorithm has been proposed. By combing the measured impedance from the custom-built sensor (three coils), the thickness of the metallic film can be reconstructed. Experiments have been carried out for the verification of the proposed scenario. Results show that the thickness of the metal film can be reconstructed with a small error of less than 2 %, and immune to a reasonable range of lift-offs.

Room-temperature phosphorescent (RTP) materials have been attracted tremendous interest owing to their unique material characteristics and potential applications for state-of-the-art optoelectronic devices. Recently, we have reported a synthesis and fundamental photophysical properties of new RTP materials based on benzil, i.e., fluorinated monobenzil derivative and fluorinated and non-fluorinated bisbenzil derivative analogues [Yamada, S. et al, Beilstein J. Org. Chem. 2020, 16, 1154–1162.]. To further understand their RTP properties, here we investigated the excited-state dynamics and photostability of the derivatives by means of time-resolved and steady-state photoluminescence spectroscopies. For these derivatives, clear RTP emissions with lifetimes on the microsecond timescale were identified. Among them, the monobenzil derivative was found to be the most efficient RTP material, showing both the longest lifetime and highest amplitude RTP emission. Time-resolved photoluminescence spectra measured at 77 K and density functional theory calculations revealed the existence of a second excited triplet state in the vicinity of the first excited singlet state for the monobenzil derivative, indicative of the presence of a fast intersystem crossing pathway. A discussion of the correlation between the excited state dynamics, emission properties, and conformational flexibility of the three derivatives is presented.

In Cameroon, alcoholic beverages remain the main consumed drink. In the Northern regions, indigenous sorghum beers are popular and widely consumed in an actively fermenting state by the people. In this study, some physicochemical parameters of the alcoholic sorghum beverages and correlations between them were evaluated during fermentation for 10 days. The indigenous white and red beers were produced at the laboratory scale assisted by experimented producers and some parameters (pH, total acidity, alcohol, sugars, density, total solids, temperature, and conductivity) were measured on the wort and fermented beverages. The pH decreases from 3.2 to 2.4 and 3.11 to 2.41; total acidity increases from 1.07 to 5.1 g/l and 0.5 to 4.6 g/l; alcohol enhances from 0 to 9.5% and 0 to 6.8% (v/v); total solids drop from 13.6 to 5°P and 12.2 to 3.3°P were observed respectively in the white and red sorghum beers. The multivariate analysis showed a good correlation between consumption of sugar, the decrease of total solids and density with the decrease in pH. It was also shown that, a perfect link exist between the production of alcohol and organic acids. The hierarchical analysis showed that indigenous red beer samples fermented for 1 and 2 days and those fermented for 4 to 10 days are related and could be separate in two distinct groups, whereas white turbid beer samples are separated in three different groups, those fermented for 1 to 4 days, those 5 to 6 days and those 7 to 10 days. The results obtained could serve as a guide to better understand the fermentation process of the indigenous alcoholic sorghum-based beverages.

Time-restricted feeding (TRF) showed a potent effect in preventing obesity and improving metabolic outcomes in several animal model of obesity; however, there is, as yet, scarce evidence about its effectiveness against obesogenic challenge that more accurately mimic the human Western diets, such as cafeteria diet. Moreover, the mechanism for its efficacy is poorly understood. White adipose browning has been linked to body weight loss. Herein, we tested whether TRF has the potential to induce browning of inguinal white adipose tissue (iWAT) and to attenuate obesity and associated dyslipidemia in cafeteria diet-induced obesity model. Male Wistar rats, fed normal laboratory chow (NC) or cafeteria diet (CAF) for 16 weeks, were subdivided into two groups that were subjected to either ad libitum (ad lib, A) or TRF (R) for 8 hours per day. Rats under TRF regimen had a lower body weight gain and adiposity compared with their diet-matched ad lib rats, despite equivalent levels of food intake and locomotor activity. In addition, TRF improved the deranged lipid profile [total cholesterol (TC); triglycerides (TG); high density lipoprotein (HDL-c); low density lipoprotein (LDL-c)] and atherogenic indices [atherogenic index of plasma (AIP); atherogenic coefficient (AC); coronary risk index (CRI)] in rats fed CAF diet. Remarkably, TRF resulted in decreased size of adipocytes and induced emergence of multilocular brown-like adipocytes in iWAT of NC- and CAF-fed rats. Protein expression of browning markers, such as uncoupling protein-1 (UCP1) and peroxisome proliferator activated receptor gamma coactivator 1-alpha (PGC1α) in iWAT were also up-regulated in time restricted NC- or CAF-fed rats. These findings suggest that TRF regimen is an effective strategy to improve obesity and associated dyslipidemia induced by CAF-diet, probably via a mechanism involving WAT browning process.

A lot of previous research have proposed various frameworks and algorithms to optimize routes to reduce the total transportation cost, which accounts for over 70% of overall logistics cost. However, it is very hard to find the cases applied the mathematical models or algorithms to the practical business environment cases, especially daily operating logistics services like convenient stores. Most of previous research have considered the developing an optimal algorithm which can solve the mathematical problem within the practical time while satisfying all constraints such as the capacity of delivery and pick-up, and time windows. For the daily pick-up and delivery service like supporting several convenient stores, it is required to consider the unit transporting container as well as the demand, capacity of trucks, traveling distance and traffic congestion. Especially, the reusable transporting container, trays, should be regarded as the important asset of logistics center. However, if the mathematical model focuses on only satisfying constraints related delivery and not considering the cost of trays, it is often to leave the empty trays on the pick-up points when there is not enough space in the track. In this research, it has been proposed to build the mathematical model for optimizing pick-up and delivery plans by extending the general vehicle routing problem of simultaneous delivery and pickup with time windows while considering left-over cost. With the numerical experiments, it has been proved that the proposed model may reduce the total delivery cost. It may be possible to apply the proposed approach to the various logistics business which uses the reusable transporting container like shipping containers, refrigerating containers, trays, and pallets.

We propose a Gauss model (GM), a map from time to the bell-shaped Gauss function to model the deaths per day and country, as a quick and simple model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e. initial exponential spread to eventual saturation, we apply the GM to existing data, as of April 2, 2020, from 25 countries during first corona pandemic wave and study the model's predictions. We find that logarithmic daily fatalities caused by Covid-19 are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical chi2-fit with 95\% confidence, we are able to obtain the characteristic parameters of the GM, i.e. a width, peak height and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced.

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper we automate a method (thresholding of the short-wave infrared band) for classifying inundation, using Landsat imagery and Google Earth Engine. We demonstrate the method in the Okavango Delta, northern Botswana, a complex case study due to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery. Inundation classifications in the Okavango Delta have predominately been implemented on broad spatial resolution images. We present the longest time series to date (1990-2019) of inundation maps at high spatial resolution (30m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with accuracy ranging from 91.5 - 98.1%. Use of Landsat imagery resulted in consistently lower estimates of inundation extent than previous studies, likely due to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use.

In 1922, Albert Einstein visited Paris and interacted extensively with an illustrious section of the French academia. In overfilled sessions at the Coll\`{e}ge de France and the Sorbonne, Einstein explained his theories of relativity, and prominent physicists, mathematicians and philosophers listened, debated, questioned and explored facets of relativity. The 1922 visit had its echoes in the life and works of many who participated, particularly decisive for Einstein and the philosopher Henri Bergson. This essay examines that eventful visit, focusing on the physical and logical aspects of Bergson's critique, with physics commentaries, linking prominent French physicists and mathematicians Langevin, Painlev\'{e}, Hadamard, Becquerel, Sagnac, and Kastler. I give particular attention to the logical and empirical accuracy of the physics issues involved, delineating Bergson's exact reasoning for his philosophical enthusiasm in Einstein's theory and for the ensuing critique. Bergson's philosophical stand on duration and simultaneity is reassessed in the context of later developments in cosmological physics as well as the wealth of empirical data involving comparison of atomic clocks. Finally we are led naturally to a surprising completion of the philosopher's program on universal time, duration and simultaneity, in harmony with the time of the physicist. In the appendices after the main text I also give the physics background and easily verifiable proofs for the assertions made in the text, pertaining to relativity, simultaneity and time dilation, clearly distinguishing beliefs and facts.

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

During the casting in a warmer tropical temperature, a setting time delay is required to maintain the workability of the concrete, commonly achieved by the addition of admixtures i.e. silica fume (SF), fly ash (FA), and Plastrocrete®. However, high sugar content Coca-Cola in niche conditions is proposed as an ingredient for delaying concrete setting time in combination with conventional admixtures. This research aims to compare the setting time of admixtures from Coca-Cola and Plastocrete® RT6 plus in concrete mixing with control data of concrete mixed with SF or FA. The second aim is to measure the compression strengths between combinations of Coca-Cola and Plastocrete® RT6 plus. Concretes were produced with admixtures of SF, FA, Plastocrete® RT6plus, or Coca-Cola. The concrete used to control was f'c20 and f'c 25, while other concrete mixes were produced with the addition of Coca-Cola at 0.15% from the weight of cement at variation of moisture treatments. The first method to produce concrete (f'c20+Plas0.23%+Cola0.15% and f'c25+Plas0.23%+Cola0.15%) did not employ water reduction. The second concrete productions (f'c25+Plas0.46%+Cola0.15% and f'c25+Plas0.46%+Cola0.15%) reduced the addition of water at 8.8% (v/w). The first concrete production method had a setting time 44% longer than control. The reduced water concrete in the second productions had a setting time 34% longer than control. Meanwhile, the Plastocrete® RT6 Plus admixture with the reduced water delayed the concrete setting time by 26% longer than control. The delayed setting time of Plastocrete® RT6 Plus admixture with reduction of water was shorter than in the treatment with Coca-Cola. The combination of the addition of Coca-Cola with Plastocrete® RT6 plus by reducing the amount of adding Coca-Cola to 0.10% with Plastocrete® RT6 plus can delay concrete setting time by 51% longer than normal concrete and increase concrete compressive strength by 13% higher than normal concrete. Mixing Coca-Cola with Plastocrete® RT6 plus not only provided an optimal delay effect on setting time but also significantly increase the compressive strength that was desired during the casting in warm tropical weather applied in building construction of Mulawarman University, Samarinda, Indonesia.

The general theory of relativity (GR) is known to be invariant under smooth coordinate transformations (diffeomorphism). This group has a subgroup known as the Lorentz group of symmetry which is manifested in the weak field approximation to GR. The dominant operator in the weak field equation of GR is thus the d'Alembert (wave) operator which has a retarded potential solution. Galaxies are huge physical systems having dimensions of many tens of thousands of light years. Thus any change at the galactic center will be noticed at the rim only tens of thousands of years later. Those retardation effects are neglected in present day galactic modelling used to calculate rotational velocities of matter in the rims of the galaxy and surrounding gas. The significant differences between the predictions of Newtonian instantaneous action at a distance and observed velocities are usually explained by either assuming dark matter or by modifying the laws of gravity (MOND). In this paper we will show that taking general relativity seriously without neglecting retardation effects one can explain the radial velocities of galactic matter in the M33 galaxy without postulating dark matter.

Millimeter wave wide-band imaging is widely studied for a variety of applications. However real-time millimeter wave wide-band imaging at frequencies above 30GHz for moving targets in a large field of view has not been realized commercially. A 2D sparse array with transmitter multiplexing is a promising solution to this problem. In this article, a method combining compressed sensing and orthogonal coded multiplexing was proposed, and the imaging performance was analyzed for different reconstruction algorithms and observation matrices by imaging simulation for a continuous object. Also the influence on the dynamic range of the original signal introduced by orthogonal coded multiplexing was studied. This work demonstrated that the proposed method was effective in reconstructing the image with a real-time capability. It is shown that different algorithms and matrices resulted in distinct performances, while the evaluation parameter selection also played a role. This work provided useful instructions for both the hardware and software design of a real-time 3D millimeter wave imaging system in the future.

While the container crane is an important part of daily port operations, it has received little attention compared with other infrastructures, such as buildings and bridges. Crane collapse due to earthquake affects the operation of the port, and indirectly impacts the economy. This study proposes fragility analyses for various damage levels of the container crane that allow the port owner and partners to better understand the seismic vulnerability presented by container cranes. A large quantity of nonlinear time history analyses was applied for a three-dimensional (3D) finite element model to quantify the vulnerability of the container crane in considering the uplift and derailment behavior. The uncertainty of demand and capacity of the crane structures were also considered through random variables, i.e. elastic modulus of members, ground motion profile, and intensity. The results analyzed in the case of a Korean container crane showed that the probability of exceeding the first uplift with or without derailment is shown before the crane reaches the structure’s limit states. This means that under low seismic excitation, the crane might be derailed without any structural damage. But when the crane reaches the minor damage state, it is always coupled with a certain probability of uplift with or without derailment. This study also proposes the fragility curves developed for different structural periods to enable port stakeholders to assess the risk of their container crane.

Real-time evolution of the electron densities under excitations in La₂CuO₄ was calculated by the time-dependent density functional theory (TDDFT). The author found, for the first time, under excitations, the electron cloud of Cu²⁺ changes obviously and the characteristic frequencies are 83 meV and 36 meV, respectively, for two different modes. The results are unexpected and close to that of lattice vibrations. The results show that the electron cloud of Cu²⁺ (just like the lattice) can be the electron-pairing medium in high temperature copper oxide superconductors.

We give an exact mathematical construction of a spacelike order $<$, which is dual to the standard chronological order $\ll$ in the $n$-dimensional Minkowski space $M^n$ and we discuss its order-theoretic, geometrical as well as its topological implications, conjecturing a possible extension to curved spacetimes.

Printed electronic devices are attracting significant interest due to their versatility and low cost; however, quality control during manufacturing is a significant challenge, preventing the widespread adoption of this promising technology. We show that terahertz (THz) radiation can be used for the in situ inspection of printed electronic devices, as confirmed through a comparison with conventional electrical conductivity methods. Our in situ method consists of printing a simple test pattern exhibiting a distinct signature in the THz range that enables the precise characterization of {the static} electrical conductivities of the printed ink. We demonstrate that contactless dual-wavelength THz spectroscopy analysis, which requires only a single THz measurement, is more precise and repeatable than the conventional four-point probe conductivity measurement method. Our results open the door to a simple strategy for performing contactless quality control in real time of printed electronic devices at any stage of its production line.

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

The variability of meteorological parameters such as temperature and precipitation, and climatic conditions such as intense droughts, are known to impact vegetation health over southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the El Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole/Dipole Mode Index (DMI) is important as these factors drive the variability of temperature and precipitation. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform in order to analyze the response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP), during the study period. The MODIS-enhanced vegetation index and burned area index were also analyzed for this period. The area-averaged Modern Retrospective Analysis for Research Application (MERRA) model maximum temperature and precipitation were also extracted using the JavaScript code editor in the GEE platform. This procedure demonstrated a strong reversal of both the NDVI and Enhanced Vegetation Index (EVI), leading to signs of a sudden increase of burned areas (strong BAI) during the strongest El Niño period. Both the Theilsen method and the Mann–Kendall test showed no significant greening or browning trends over the whole time series, although the annual Mann–Kendall test, in 2003 and 2014–2015, indicated significant browning trends due to the most recent strongest El Niño. Moreover, a multi-linear regression model seems to indicate a significant influence of both ENSO activity and precipitation. Our results indicate that the recent 2014–2016 drought altered the vegetation condition in the HiP. We conclude that it is vital to exploit freely available GEE resources to develop drought monitoring vegetation systems, and to integrate climate information for analyzing its influence on protected areas, especially in data-poor counties.

This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place but also has causal influence on the number of such individuals. By creating an index based on human mobility data, it becomes possible to predict the influence of urban development on future residential movements. In this paper, we propose a method called the travel cost method for multiple places (TCM4MP) by extending the conventional travel cost method (TCM). We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of a neighborhood. However, conventional TCM does not assume that the opportunity cost of travel time varies according to the departure place. In this paper, TCM4MP is proposed to estimate the opportunity cost of travel time with respect to the departure place. We consider such estimation to be possible due to the use of massive mobility data. We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of the neighborhood. Therefore, we consider that the opportunity cost of travel time has a causal influence on future residential mobility. In this paper, the validity of the proposed method is tested using the smart card data of public transportation in Western Japan. Our proposed method is beneficial for urban planners in estimating the effects of urban development and detecting the shrinkage and growth of a population.

The active layer thickness (ALT) is affected by local soil material and surface vegetation coverage in Tibetan plateau permafrost region. The human activities and engineering construction along the Qinghai-Tibet Highway (QTH) destroyed the surface vegetation coverage, which have seriously influenced the ALT and heat exchange balance. Meanwhile, the changes of the detailed internal structure in the active layer will affect the stability of engineering infrastructure. Ground-penetrating radar (GPR) was used to detect the ALT in the permafrost region. However, researches on the detailed structure of the active layer and spatial variation information of ALT are rare, the main reason given for this is that the lack of advanced imaging techniques to enhance the GPR images. In this paper, the researchers focus on solving this problem by applying the reverse time migration (RTM) method to GPR field data acquired in the Beiluhe region. Analyses of the results led to conclusions as follows: a) The RTM algorithm is proved to be accurate for imaging the characteristics of the active layer through the dielectric constant model obtained by common midpoint and GPR data velocity analysis. b) The result of RTM profiles show the loose structure and graben-like fine internal structure. Combining with drill data and soil materials information, the researchers deduce the graben-like structure might be coarse gravel layer, which may be related to subgrade settlements in some local area. c) The 2D contour maps not only show the spatial variation feature of ALT at two sides of the highway, but also can compensate the defect of the research for single 2D survey line. The ALT is about 0.6~0.8 m in most areas, and about 0.8~0.9 m near the highway at west side of the QTH on the 2D contour maps. From the RTM interpretation profile, the closer to the plash, the deeper thawed active layer. The detailed internal structure of the active layer and 2D contour maps of ALT have great significance for the study of fine internal structure and the characteristic of ALT spatial variation in the Tibetan plateau under the background of climatic change.

The development of efficient luminescent systems, such as microcavities, solid state lasers, integrated optical amplifiers, optical sensors is the main topic in glass photonics. The building blocks of these systems are glass-ceramics activated by rare earth ions because they exhibit specific morphologic, structural and spectroscopic properties. Among various materials that could be used as nanocrystals to be imbedded in silica matrix, tin dioxide presents some interesting peculiarities, e.g. the presence of tin dioxide nanocrystals allows increase in both solubility and emission of rare earth ions. Here, we focus our attention on Er³⁺ - doped silica – tin dioxide photonic glass-ceramics fabricated by sol-gel route. Although the SiO₂-SnO₂:Er³⁺ could be fabricated in different geometrical systems: thin films, monoliths and planar waveguides we herein limit ourselves to the monoliths. The effective role of tin dioxide as luminescence sensitizer for Er³⁺ ions is confirmed by spectroscopic measurements and detailed fabrication protocols are discussed.

Lean is one of the systematic approach to achieve higher value for organizations through eliminate non-value-added activities. It is an integrated set of tools, techniques, and principles designed to optimize cost, quality and delivery while improving safety. In Vietnam, industry waste management and treatment has become serious issue. The aim of this research is to present the effective of Lean application for industrial wastes collecting and delivery improvement. Through a case study, this paper showed the way of Lean tools and principles applied for wastes management and treatment such as Value Stream Mapping, Pull system, Visual Control, and Andon.... to get benefit on both economic and environment. In addition, the results introduced a good experience for Vietnamese enterprises in cost saving and sustainable development in waste management.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

This paper is aimed at studying the temporal evolution of the surface displacements occurred over the past few years in the ocean-reclaimed platforms of the Shanghai megacity (China), which are mainly ascribable to consolidation processes of large dredger fills and alluvial deposits. With respect to previous analyses carried out over the same area, this work provides a joint multi-platform differential interferometry synthetic aperture radar (DInSAR) analysis, based on the application of the advanced Small BAseline Subset (SBAS) algorithm. This led us to retrieve long-term deformation time-series that are helpful for a better understanding of the on-going deformation phenomena. To this aim, we have exploited two sequences of SAR data collected by the ASAR/ENVISAT and by the COSMO-SkyMed (CSK) sensors, respectively, spanning the whole time period from 2007 to 2016. Unfortunately, the large time gap (of about three years) existing between the available ASAR/ENVISAT and CSK datasets gave rise to additional difficulties for their combination. Nevertheless, this problem has been faced by benefiting from the knowledge of a time-dependent model describing the temporal evolution of the expected deformations affecting the Shanghai ocean-reclaimed platforms.

This paper presents a real-time eye blink detection system using computer vision techniques and machine learning. The system uti- lizes the MediaPipe face mesh model for facial landmark detection and calculates the Eye Aspect Ratio (EAR) to determine the eye state. Results demonstrate high accuracy and responsiveness, indicating po- tential applications in driver drowsiness detection, human-computer interaction, and medical diagnostics.

This study investigated the optimal treatment conditions to improve the quality of white pan bread by applying high-voltage pulsed electric fields (PEF) technology with varying electric field treatment times to bread dough. The treatment was conducted at an electric field strength of 2 kV/cm, frequency of 10 Hz, and a pulse width of 100 µs, with varying treatment time of 10, 20, and 30 min. When PEF was applied for 10 min, the treatment dough’s fermentation expansion rate was significantly higher. Bread made with the 10 min treated dough also exhibited higher weight, volume, specific volume, and moisture content, along with lower oven spring and baking loss rate. During storage, the 10 min treated samples maintained higher moisture content, while the 20 and 30 min treated samples showed no significant difference. In terms of texture profile analysis, the treated samples had lower hardness and chewiness compared to the control, while springiness, cohesiveness, and resilience tended to be slightly higher. In terms of texture, the treated samples had lower hardness and chewiness, but higher springiness, cohesiveness, and resilience compared to the control group.

Background/Objectives: Informed consent for anesthesia poses both legal challenges and problems of understandable communication. Fulfilling all requirements through anesthesiologists directly interacting with patients is a time- and staff-consuming strategy. Given today's smart technologies, notably including virtual reality (VR), we explored in a prospective randomized study whether 'VR-assisted informed consent' could improve this situation. Methods: Fifty patients scheduled for orthopedic surgery were randomized. In the control group, informed consent was obtained via patient-specialist dialogs only. The patients in the study group, wearing a head-mount display, watched an 8-minute immersive 3D movie with standard explanations of general anesthesia, followed by a patient-specialist dialog to address open question. The time spent on the dialogs in both groups was evaluated as primary outcome variable. As secondary variables, we analyzed both a three-item Likert scale on patient satisfaction with the VR experience and cost differences between both groups. Results: Patient-specialist dialogs were carried on for median (IQR) durations of 93 (20–182) seconds in the study group versus 665 (261–829) seconds in the control group (P&lt;0.001). All patients exposed to VR rated this experience as favorable (87.5%) or neutral (12.5%). Based on anesthesiologists' incomes in the US and UK, our approach would reduce the staff expenditure for each patient-specialist dialog by median amounts of ≈40 or ≈11 US dollars, respectively (2× P&lt;0.001). Conclusions: 'VR-assisted informed consent' for anesthesia is well accepted by patients and reduces the time requirements for patient-specialist dialogs, thus pointing out a potential avenue towards increasing the work-time efficiency of anesthesiologists.

In an era of relentless technological change, accelerating the pace of innovation has become a critical imperative for organizations seeking to maintain a competitive edge and drive meaningful societal progress. Yet the complex, multifaceted innovation process - from ideation to commercialization - is often plagued by bottlenecks and challenges that can significantly delay the time-to-market for groundbreaking new products, services, and business models. This pioneering article represents an outstanding contribution to the burgeoning field of innovationology, offering a comprehensive, evidence-based playbook for shortening the innovation lifecycle. Drawing on an extensive review of over 180 cutting-edge academic sources, the paper maps the key stages of the innovation process - idea generation, concept development, prototyping, testing, and launch - and illuminates cutting-edge strategies that innovators can deploy to overcome common barriers and drive transformative innovations to market more quickly. From advanced idea generation techniques like design thinking and AI-powered creativity to innovative approaches to rapid prototyping, virtual experimentation, and data-driven commercialization, this seminal work equips scholars, practitioners, and policymakers with a transformative, globally-relevant toolkit for accelerating the innovation lifecycle. By synthesizing insights from a diverse array of industries and disciplines, the article establishes a new benchmark for innovationology research and empowers organizations to cultivate the conditions needed to translate visionary ideas into game-changing realities with unprecedented speed and efficiency. As the world grapples with ever-more complex, interconnected challenges, the ability to rapidly develop and scale breakthrough innovations has never been more crucial. This paradigm-shifting contribution to the field of innovationology represents a vital resource for driving the bold, disruptive changes needed to shape a more sustainable, equitable future.

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

Wearable devices have a significant impact on society and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using IoT can be a crucial tool for emergency management, reducing risk in rescue operations and ensuring the safety of workers.The massive amounts of data, high network traffic, and computational demands of a typical IoT monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities.This article presents a cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable IoT device. It enables the management of ECG, breathing patterns, and other vital signs during work sessions, and sends real-time alarm events to a personnel management center. The platform was extensively used and tested in a large-scale monitoring application for tracking the activities of emergency room and ambulance workers, and the results of this testing are presented.

Spacecraft recovery technology is crucial in the field of aerospace, in which the parachute plays a key role in slowing down the descent speed of the spacecraft and realizing a smooth landing. In order to construct a dynamically adjustable parachute deployment strategy, it is necessary to measure the parachute dynamic load accurately in real time. However, the existing sensor measurement scheme is difficult to meet the measurement requirements due to its large structure and complex wiring. In order to meet the current demand for real-time measurement of parachute cords dynamic load, a miniature measuring instrument is designed.According to the function and technical requirements of the miniature measuring instrument, the hardware modules of the acquisition system are selected and designed, and the integration debugging and performance optimization of the microcontroller module, A/D sampling module, signal acquisition circuit and power supply module are carried out. The software of the parachute cords tension acquisition system based on the miniature measuring instrument is developed. The elastomer is modeled by using SolidWorks and statically analyzed by using AnsysWorkbench finite element analysis software. Then the final structure of the elastomer and the pasting position of the strain gage are determined through the results analysis as well as experimental verification. The hardware module of the signal acquisition system for the miniature measuring instrument is then encapsulated. The force value of the miniature measuring instrument is calibrated and tested many times by using the microcomputer-controlled electronic universal testing machine. The experimental results show that the designed miniature measuring instrument has accurate data, strong stability and good real-time performance, which meets the demand for real-time accurate measurement of miniature measuring instrument, and can provide reliable data for parachute cords parameter validation and stepless unfolding design.

Our decisions today are part of an economic thinking which aims to fix problems and increase the efficiency of capital markets. Our economy is still largely approached as a system which is unre-lated to our environment or social returns like community well-being, social cohesion, equitable distribution of resources, etc. To fully incorporate sustainability into cost-benefit analysis, busi-nesses need to change their financial approach, showcasing how long-term thinking impacts prof-its. Money's value is not static and evolves with time and technology. We propose to introduce a variation of cost/benefit and present value calculations. Precise calculations reveal the benefits of long-term strategies over short-term profit focus. As financial systems evolve and technology advances, the accurate calculation of money's time value will continue to play a crucial role in shaping economic landscapes and guiding financial choices. In this article, we demonstrate that updated calculations could be a key enabler to visualize the benefits of long-term thinking or “patient finance” as compared to a short-term focus on profit. We introduce the concept and calculation of a Present Future Value (PFV).

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected nine months of real-time Wuhan traffic monitoring data from Amap. We proposed Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long and short-term memory networks (LSTM) and convolutional neural networks (CNN) to effectively achieve long-term anomaly detection at the city level. First, we convert the traffic flow data into image representation, which enables the capture of the spatial correlation between traffic flow and roads and the correlation between traffic flow and roads, as well as the surrounding environment. Second, The model utilizes convolution kernels of different sizes to extract spatial features at road-level, regional-level, and city-level scales, while incorporating temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic control strategies.

In order to reduce the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is cases of exceeding the maximum permissible concentrations of pollutants in wastewater. The goal of the method for detecting complex anomalies in multidimensional time series obtained from smart energy station’s sensor channels is to increase the accuracy of detecting pollution levels in industrial wastewater. To achieve this goal, the following tasks were solved: methods for detecting time series anomalies were analyzed, the method for detecting complex anomalies was developed, software implementation of the algorithm was carried out and experiments were conducted. The developed method is proposed to be used in an smart energy monitoring system.

Intermittent religious fasting increases a risk of hypo- and hyperglycemia in individuals with diabetes, but its impact in those without diabetes is poorly investigated. The study aim was to examine the effects of religious Bahá'í fasting (BF) on glycemic control and variability and compare these effects with time-restricted eating (TRE). In a three-arm randomized controlled trial, 16 subjects without diabetes were assigned to a BF, 16:8 TRE or control group. Continuous glucose monitoring and food intake documentation was conducted before and during the 19 days of intervention, and 24-hour mean glucose, glycemic variability indices, and glucotypes were assessed. BF and TRE groups, but not the control group, markedly reduced the daily eating window while maintaining macronutrient composition. Only the BF group decreased caloric intake (-677.8 ± 357.6 kcal, p=0.013), body weight (-1.92 ± 0.95 kg, p=0.011) and BMI (-0.65 ± 0.28 kg, p=0.006). Higher maximum glucose values were observed during BF in the within-group (+1.41 ± 1.04, p=0.039) and between-group comparison (BF vs. Control: p= 0.010 and TRE vs. BF: p= 0.022). However, there were no alterations of 24-hour mean glucose, intra- and inter-day glycemic variability indices, or glucotypes in any group. Proportions of time above and below range (70-180mg/dl) and HbA1c values remained unchanged. BF and TRE do not exhibit negative effects on glycemic control and variability in subjects without diabetes.

The extent of correlation or co-movement among the returns of developed and/or emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, we analyzed the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners. The Dynamic Conditional Correlation - Exponential Generalized Autoregressive Conditional Heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibit time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 participants), teams collaborated over Zoom to set up a virtual Mars station using custom simulation software. Participants were divided into 14 experimental teams which were shown the virtual mirror, and 14 control teams without it. Team performance was measured by the improvement in the Mars simulation output quality. Our analysis using correlation, multilevel regression, and machine learning revealed that fewer interruptions but an increasing number over time correlated with higher performance. Higher vocal arousal and happiness also enhanced performance. We confirmed that female presence in teams boosts performance. SHAP values indicated that high variability in happiness, head movement, and positive facial valence—an “emotional rollercoaster”—positively predicted team performance. The experimental group outperformed the control group, suggesting that virtual mirroring improves virtual teamwork and that interrupting each other more, while speaking less, leads to better results.

The open data market is growing, driven by laws requiring governments to make data publicly available. However, the true value of this market lies in the products and services derived from open data. In Slovenia, the open data market has been growing since 2015, especially in the field of mobility. Platforms such as Avtolog.si facilitate the retrieval of vehicle data via vehicle identification numbers, while tocen.si, ojpp.si, and brezavta.si aggregate mobility data, focusing on sustainable and micromobile public transportation services. These platforms use open government data and private sector open data, and are freely accessible. This study examines the use of open data in the Slovenian mobility applications and similar solutions developed in other countries. By mapping these applications, we gain valuable insights into potential advances in the field, and provide a useful resource for future developers worldwide. By focusing on the socio-economic impact of these innovations, the study identifies methods for evaluating such applications. This approach highlights how transforming open data into user-friendly products and services can deepen our understanding of their broader societal and economic benefits. While open data is valuable in its own right, its full transformative potential is realized through innovative products that support sustainable lifestyles and promote environmentally conscious behavior.

The aim of the present work was to map Estonian primary school students' ratings of symptoms of digital addiction and to identify both class and gender differences. The sample consisted of 5493 primary school pupils from 46 schools in Estonia participating in the 2022 Student Survey conducted by the Tallinn University Centre for Educational Innovation (CIE). The results showed that the digital addiction symptoms scores were highest in 8th and 9th grades. Girls and those who did not disclose their gender, scored higher than boys on digital addiction symptom statements. Symptoms of digital addiction were moderately positively related to screen time during school days and weekends and to school burnout and weakly negatively related to feeling refreshed in the morning.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

Academics in healthcare grapple with the challenge of efficiently managing time while addressing diverse roles and responsibilities, leading to heightened levels of anxiety and stress within the academic community. The primary objective of this study is to offer a sustainable and systemic solution to the prevalent time-management challenges in healthcare academia, through the development of a predictive model. Employing systemic analysis software and adopting a systemic management approach, we crafted a model to address academic time-management concerns. The dynamics of time allocation for academicians are governed by three fundamental pillars: teaching, research, and administrative tasks. Significantly prioritizing administrative tasks depletes valuable academic time, making time management within academia excessively costly. This occurs as highly skilled individuals find themselves diverted towards administrative duties at the expense of their academic roles diminishing resilience levels. The proposed Timebooster Academic Systemic Model (TASM) advocates for the cultivation of soft skills in academia, en-compassing prioritization, seeking assistance, and addressing tendencies of perfectionism and procrastination. Additionally, TASM recommends the implementation of full automation, delegation of administrative responsibilities, and the mitigation of both overt and covert time-consuming elements. Furthermore, TASM reports on the pivotal role of the academic resilient manager-leaders, who actively contribute to their own satisfaction and academic growth by enhancing collaboration and teamwork. Time-management challenges emerge as a critical determinant of academic well-being and resilience. Systemic models, exemplified by TASM, offer a valuable framework for investigating prioritization concerns, identifying administrative impediments in academic processes, and managing low-value time-consuming elements. The TASM model, along with the systemic philosophy underpinning its design, presents an avenue for advancing the exploration of time-management phenomena within the context of healthcare academia.

In the field of railroad safety, effective detection of surface cracks is critical, necessitating reliable, high-speed non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of ’lift-off noise’ due to varying distances between the probe and test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver coils, aiming to enhance detection sensitivity and minimize lift-off impact. The study optimizes ECT probes employing different driver coils, emphasizing three main objectives: a) quantitatively evaluating each probe using signal- to-noise ratio (SNR) and outlining a real-time data processing algorithm based on SNR methodology; b) exploring the frequency range proximal to the electrical resonance of the receiver coil; c) examining sensitivity variations across varying lift-off distances. The experimental outcomes indicate that the newly designed probe with figure 8-shape driver coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive SNR exceeding 100 for defects with minimal dimensions of 1 mm in width and depth. Simulation results closely align with experimental findings, validating the investigation of optimal operational frequency and lift-off distance for selected probe performance, determined to be 0.3 MHz and 0.5 mm, respectively. The realization of this project would lead to notable advancements in enhancing railroad safety by improving crack detection efficiency.

The social- and self-directed behaviours of two golden lion tamarins (Leontopithecus rosalia) was investigated while having residence in the tropical house of Aalborg Zoo, Denmark. The behaviour of the female had been distinct from the male since 2018. Despite several diagnostic examinations and medicinal treatments, the cause of the aberrant and apparently stereotypical behavioural patterns remained undetermined, which initiated this study. The pair were recorded throughout a predetermined observation period and these recordings were analysed to illuminate the behaviours of the individuals. The results showed that there were differences in the type, frequency, and durations of both social- and self-directed behaviours between the sick female and the healthy male. The female expressed both social- and self-directed behaviours more frequently and for longer durations, while the male were more socially reserved and did not engage in social behaviours as frequently. Furthermore, subsequent necropsy of the female revealed uro-genital disease.

This paper presents a groundbreaking approach to real-time image processing in electronic component assembly, enhancing quality control in manufacturing. By capturing images from pick and place machines between component pickup and mounting, defects are identified and addressed in-line, significantly reducing the likelihood of defective products. Leveraging fast network protocols like gRPC and orchestration with Kubernetes, along with C++ programming and TensorFlow, this method achieves an average turnaround time of less than 5 milliseconds. Tested on 20 live production machines, it ensures compliance with IPC-A-610, and IPC-STD-J-001 standards while optimizing production efficiency and reliability.

The inter-tip times (ITTs) of tipping-bucket rain gauges potentially provide the highest-resolution intensity data that can be acquired from this type of gauge. At an intensity of 100 mm h-1, a typical gauge with a sensitivity of 0.2 mm of rainfall would be expected to tip every 7.2 seconds. However, TBRGs are often equipped with syphons, to reduce the dynamic calibration error that results from continued (and unmeasured) inflow to a bucket as it tips. This increases the accuracy of rainfall depth recording, but the time to fill and empty the syphon can reduce the ability of a TBRG to respond to (and for the ITTs to reflect) short-term intensity fluctuations. This ability is already limited by the discretisation arising from the filling and emptying of the buckets themselves. Laboratory tests with controlled water inflow rates were performed using two high-quality TBRGs, one a 'straight-through' design and the other syphon-equipped. These confirmed that at all intensities at which the syphon operates, a regular sequence of fixed-duration ITTs (such as the 7.2 s mentioned above) does not occur. Rather, the ITTs are perturbed by the syphon cycling. The gauges were also co-located in the field, and linked to carefully synchronised event data loggers. Data collected during several rainfall events revealed differences in the ITTs, and again confirm that the ITT sequence of a syphon-equipped TBRG exhibits artefacts related to syphon operation, that are not present in the 'straight-through' data. These artefacts can result in ITT differences of many minutes, depending on the rainfall intensity, and are problematic for the use of ITTs to estimate intensity. Peaks and troughs in the intensity profile also differed between the two gauges. It is recommended that in the application of TBRGs for studies where short-term intensity data are required, 'straight-through' gauges should be used, and syphon-equipped gauges avoided.

The Simulation analogy presented in this work enhances the accessibility of abstract quantum theories, specifically the stochastic hydrodyanimc model (SQHM), by relating them to our daily experiences. The SQHM incorporates the influence of fluctuating gravitational background, resembling dark energy, into quantum equations. This model successfully addresses key aspects of Objective-Collapse Theories, including resolving the 'tails' problem through the definition of quantum potential length in addition to the De Broglie length, beyond which coherent Schrödinger quantum behavior and wavefunction tails cannot be maintained. The SQHM emphasizes that an external environment is unnecessary, asserting that the quantum stochastic behavior leading to wave-function collapse can be an inherent property of a system in a spacetime with fluctuating metrics. Embedded in relativistic quantum mechanics, the theory establishes a coherent link between the uncertainty principle and the constancy of light speed, aligning seamlessly with finite information transmission speed. Within a fluctuating quantum system, the SQHM derives the indeterminacy relation between energy and time, offering insights into measurement processes impossible within a finite time interval in a truly quantum global system. Experimental validation is found in confirming the Lindemann constant for solid lattice melting points and the transition from fluid to superfluid states. The SQHM's self-consistency lies in its ability to describe wavefunction collapse and the measure process. Additionally, the theory resolves the Pre-existing Reality problem by showing that large-scale systems naturally decay into decoherent states stable in time. Continuing, the paper demonstrates that the physical dynamics of SQHM can be analogized to a computer simulation employing optimization procedures for realization. This perspective elucidates the concept of time in contemporary reality and enriches our comprehension of free will. The overall framework introduces an irreversible process impacting the manifestation of macroscopic reality at the present time, asserting that the multiverse exists solely in future states, with the past comprising the formed universe after the current moment. Decoherence at the present time functions as a reduction of the multiverse to a single universe. The macroscopic reality, characterized by fractal consistency where microscopic domains with quantum properties coexist, provides insights into how our consciousness apprehends dynamic reality, the spontaneous emergence of gravity in discrete spacetime evolution, and the attainment of the classical General Relativity limit in Quantum Loop Gravity and Causal Dynamical Triangulation. The Simulation Analogy highlights a strategy focused on minimizing information processing, facilitating the universal simulation in solving its predetermined problem. From within, reality becomes the manifestation of specific physical laws emerging from the inherent structure of the simulation devised to address its particular issue. In this context, the reality simulation appears to employ an entropic optimization strategy, minimizing information loss while efficiently compressing data in line with the simulation's intended purpose.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

Early-onset colorectal cancer (EOCRC) is defined as a diagnosis of colorectal cancer (CRC) in individuals younger than 50 years of age. While overall CRC rates in the United States (US) decreased between 2001 and 2018, EOCRC rates have increased. This research project aims to evaluate the feasibility and acceptability of Time-Restricted Eating (TRE), Mindfulness, or TRE combined with Mindfulness among young to middle-aged adults at risk of EOCRC. Forty-eight participants will be randomly assigned to one of four groups: TRE, Mindfulness, TRE &amp; Mindfulness, or Control. Data on feasibility, adherence, and acceptability will be collected. Measures assessed at baseline and post-intervention will include body weight, body composition, dietary intake, physical activity, sleep behavior, circulating biomarkers, hair cortisol, and gut microbiome. Effects of the intervention on the following will be examined: 1) acceptability and feasibility; 2) body weight, body composition, and adherence to TRE; 3) circulating metabolic, inflammation, and oxidative stress biomarkers; 4) intestinal inflammation; and 5) the gut microbiome. TRE combined with Mindfulness holds promise for stress reduction and weight management among individuals at risk of EOCRC. The results of this pilot study will inform the design and development of larger trials aimed at preventing risk factors associated with EOCRC.

In emergency situations, every second counts for an ambulance navigating through traffic. Efficient use of traffic light systems can play a crucial role in minimizing response time. This paper introduces a novel automated Real-Time Ambulance in an Emergency Detector (RTAIAED). The proposed system uses special Lookout Stations (LS) suitably positioned at a certain distance from each involved traffic light (TL), to obtain timely and safe transitions to green lights as the Ambulance in an Emergency (AIAE) approaches. The RTAIAED is particularly pertinent on one-way roads, addressing the challenge of regulating the sequence of traffic-lights-signals so as to minimize the time needed to safely grant the green signal to the AIAE. The proposed solution leverages a part-based model made of elementary detectors for video analysis, specifically realized with a customized YOLOv8 model, and for audio analysis, thanks to an additional neural network based on Mel Frequency Cepstral Coefficients (MFCCs). This way, the RTAIAED ensures the accurate and robust identification of an AIAE heading towards a traffic light in time to ensure a green light thanks to the strategic positioning of the LS detectors. Extensive experiments demonstrate the robustness of the approach and its reliable application in real-world scenarios thanks to its predictions in real-time, showcasing the ability to detect AIAEs even in challenging conditions such as noisy environments, nighttime, or adverse weather conditions, provided a suitable-quality camera is appropriately positioned. The proposed system can also find application in traffic flow management.

Time-modulated sparse circular array (TMSCA) with elements uniformly excited is designed and synthesized in this paper. By utilizing rotationally symmetric structure, the array geometry and the time modulation of the TMSCA is presented, and the radiation patterns in frequency domain are formulated. In order to achieve the desired radiation pattern at the operating frequency and suppress the sideband radiation, the synthesis problem is established and solved by the proposed harmony search-convex programming (HS-CP) algorithm in two dependent optimizations. Two designs are presented, and the numerical results indicate that the TMSCA with rotationally symmetric structure has the advantages of designing time-modulated sparse arrays with superior performance.

Breast and prostate cancer lead incidence rankings in Chile, with obesity and overweight as important risk factors involved in prognosis. Nutritional interventions have shown to be beneficial therapies in patients with cancer, improving anthropometric and biochemical measurements. This study investigates the feasibility and effectiveness of Calorie Restriction and Time-Restricted Feeding in overweight or obese patients undergoing curative radiotherapy for breast and prostate cancer. The research was conducted as a prospective, interventional, non-randomized clinical trial, involving 27 participants, of whom 23 completed the study. Findings indicate that both interventions are feasible and effective. Notably, Time-Restricted Feeding demonstrated superior adherence and more significant improvements in weight loss and metabolic parameters. A mean weight reduction of 3.6 kg and waist circumference decrease of 4.9 cm was observed across participants. Serum levels of glucose, insulin, LDL, HDL and TG showed an average reduction, indicating positive metabolic changes. Results suggest that Time-Restricted Feeding could be a superior beneficial dietary intervention for cancer patients undergoing radiotherapy, due to its ease of adherence and positive impact on metabolic health. This research contributes to the growing evidence on the role of nutritional interventions in cancer treatment and underscores the need for more extensive studies to validate these findings.

In this paper we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average (ARMA) processes. Using individual demand streams will always lead to a superior forecast compared to any aggregates, however we show that if several aggregated clusters are formed in a structured manner then these subaggregated clusters will lead to a forecast with minimal increase in mean-squared forecast error. We show this result based on theoretical MSFE obtained directly from the models generating the clusters as well as estimated MSFE obtained directly from simulated demand observations. We suggest a pivot-algorithm, that we call Pivot Clustering, to create these clusters. We also provide theoretical results to investigate sub-aggregation, including for special cases such as, aggregating MA(1) streams and ARMA streams with similar or same parameters.

The healthcare domain is increasingly adopting IoT and Electronic Health Record (EHR) systems, generating vast volumes of healthcare data. This shift is driven by the growing need of delivering the right information to the right individuals, at the right time. The latter underscores the importance of adopting a comprehensive strategy for efficiently collecting, utilizing, and analyzing health-related data to not only enhance overall healthcare management but also for the provision of timely and personalized prevention strategies. The latter is of highest importance especially in scenarios where lack of effective treatments or poor survival rates (such in pancreatic cancer) renders typical healthcare strategies ineffective. In this article, we introduce an innovative and integrated platform that is specifically designed and developed for accessing, processing, and analyzing data in challenging healthcare scenarios, such as dealing with pancreatic cancer. This platform, called iHelp, combines multidisciplinary technologies and provides healthcare professionals reliable risk modelling, analysis, and prediction techniques so that individuals (at risk of developing pancreatic cancer) can be provided with timely, reliable, and personalized prevention and intervention measures. A key innovation in the iHelp platform is the standardized data management approach called Holistic Health Records (HHRs) that facilitate the capturing of all health determinants in a standardized and well-structured way for processing towards the provision of health risk detection and personalized healthcare decision support. In the development of iHelp platform, the HHRs are evaluated through different real-world healthcare datasets, including datasets coming from hospital systems, data from wearables, questionnaires, and mobile applications.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

Optimum postharvest storage conditions increase the shelf life and postharvest quality of horticultural crops. The effects of forced-air precooling (FAP) and modified atmosphere packaging (MAP) on shelf life, physicochemical quality, and health-promoting properties of bell pepper (Capsicum annuum L. cv. Nagano) harvested at 90% and 50% coloring stages in May and July respectively, stored at 11℃, 95% relative humidity were assessed. Fruits were treated with FAP + 30 μm polyethylene liner (FOLO), FAP (FOLX), 30 μm polyethylene liner (FXLO), and control (FXLX). The quality attributes, viz. weight loss, firmness, color, SSC, soluble sugars, TPC, TFC, DPPH, and ABTS were evaluated. FOLO maintained sensory quality (weight loss, firmness, and color), physicochemical (soluble solids content and soluble sugars), and health-promoting properties compared to other treatments during storage. The investigated parameters differed significantly (p &lt; 0.05) among treatments except soluble sugars. The 50% coloring fruits had a huge variation between treatments than 90% coloring. The results revealed more TPC and antioxidant capacity in 50% than in 90% coloring fruits. The study highlights the need to consider the ideal fruit coloring stage at harvest under the effect of FAP and MAP treatments in preserving bell pepper's postharvest quality and shelf life.

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.

Taiwan is highly susceptible to global warming, experiencing a 1.4°C increase in air temperatures from 1911-2005, which is twice the average for the Northern Hemisphere. This has led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To improve forecast reliability, statistical post-processing is needed to address systematic errors. In September 2020, NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and Quantile mapping (QQ) approaches to predict daily maximum temperatures and extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias, RMSE, and highest correlation coefficient. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method was more effective in predicting summer daily Tmax and its extremes on an extended-range time scale deterministic and ensemble probabilistic forecasts over Taiwan.

The probability distribution of the interevent time between two successive earthquakes has been subject of numerous studies due to its key role in seismic hazard assessment. In recent decades, many distributions have been considered and there has been a long debate about the possible universality of the shape of this distribution when the interevent times are suitably rescaled. In this work we aim to find out if there is a link between the different phases of a seismic cycle and the variations in the distribution that best fits the interevent times. To do this, we consider the seismic activity related to the Mw 6.1 L’Aquila earthquake that occurred on April 6, 2009 in central Italy by analyzing the sequence of events recorded from April 2005 to July 2009, and then the seismic activity linked to the sequence of the Amatrice-Norcia earthquakes of Mw 6 and 6.5 respectively and recorded in the period from January 2009 to June 2018. We take into account some of the most studied distributions in the literature: q-exponential, q-generalized gamma, gamma and exponential distributions and, according to the Bayesian paradigm, we compare the value of their posterior marginal likelihood in shifting time windows with a fixed number of data. The results suggest that the distribution providing the best performance changes over time and its variations may be associated with different phases of the seismic crisis.

Pacing strategy refers to the distribution of effort and speed throughout the race to achieve optimal performance. The study aims to understand whether the choice of pacing strategy in swimming depends on the length of competitions and how sex, age, and performance level influence this strategy. Participants were the finalists of the 800-meter and 1500-meter freestyle events at the elite and junior world championships in 2022-2023. Race outcomes and pacing parameters were compared between the two distances and across different groups of swimmers. Swimmers in both distances did not break world records. Pacing strategy generally followed a U-shape with significant differences in the frequency and duration of speed changes between the two distances. The 800-meter exhibited more frequent changes in acceleration while the 1500-meter events generally followed a more consistent time series pattern. There were differences in pacing strategies between males and females and between junior and elite swimmers. Swimmers closer to world records showed more consistent pacing patterns compared to those farther from records. The study suggests that pacing strategies are influenced by race distance, sex, age, and performance level. The research highlights the complex interplay between physiological and psychological factors that shape a swimmer's decision-making during a race.

There are two purposes for this article. The first purpose is to perform a reiew of previous research on global supply chain management (GSCM) principles that can adapt and survive adversity, such as the COVID-19 pandemic. The second purpose is to put forth a conceptual framework for the GSCM that is sustainable in the event of future turbulence similar to that experienced during the COVID-19 pandemic. With regard to the issues raised, the article posits the following questions. Is there, perhaps, a sustainable post-COVID-19 pandemic GSCM? Can the COVID-19 pandemic-related international logistics system (GSC) turbulences be predicted with certainty using these sustainable post-COVID-19 pandemic GSCM concepts? Putting it all together, the paper concludes that, while debatable, sustainable GSCM is feasible and can be accomplished utilizing JIT, supply chain mitigation, and supply chain visibility, as demonstrated by the manufacturing firms referenced in the article.

A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to wake effect. The main engine of the algorithm is a gradient-descent method, in which an analogy is established between power deficit and repulsive electrostatic potential energy. To achieve a maximum dispersion in the initial solution, throughout the optimization process, new turbines are included in the centre of the concession area, and quickly expand outward looking for areas with less potential, in turn pushing the previous ones. When the optimization process ends, to avoid local maximums, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a potential map of the entire area is created and a greedy algorithm includes new turbines to complete the layout with the final number of turbines. To speed up the search process and the creation of the potential map, two simplifications have been validated and added: the spatial distribution of velocities has been converted into its equivalent with point symmetry; and, for turbines affected by multiple wakes, the power deficit has been calculated by using a linear aggregation of powers, instead of the usual linear (or quadratic) aggregation of speeds. Owing to this type of aggregation, the process is accelerated in a factor of 90.

The influence of cuff inflations on nighttime measurements during 24h ambulatory blood pressure (BP) measurements is unknown. We investigated the potential effect of cuff-inflations on asleep parameters using measurements taken simultaneously with a cuffless device using pulse-transit-time (PTT). On the first day of measurement, standard cuff-based 24h BP and cuf-fless measurements were simultaneously performed on right and left arms (CUFF/PTT-D). 1-2 days after the first measurement, the cuffless device was worn alone (PTT-D). Only data from the cuffless device was analyzed. The following mean asleep parameters were analyzed: systolic and diastolic BP, arousals, sleep efficiency, total arousals, arousal per hour and desaturations. 21 in-dividuals were prospectively enrolled. Mean (SD) age was 47(±15) years and 57% were female. Mean systolic asleep BP for the during CUFF/PTT-D and during PTT-D were 131(±21) and 131(±26) mmHg respectively. Mean diastolic asleep BP values during CUFF/PTT-D and during PTT-D were 80(±14) and 84(±14) mmHg respectively (p=0.860, p=0.100 respectively). Systolic and diastolic asleep mean difference was 0.1 (±18.0) and -3.6 (±9.8) mmHg, respectively. There were significantly more total arousals during PTT-D (p=0.042). There was no significant difference seen in sleep efficiency (p=0.339) or desaturations (p=0.896) between the two measurements. We could not show any significant impact from cuff inflations during asleep PTT-D measurements.

PM-Gati-Shakti Initiative, integration of ministries, including railways, ports, waterways, logistic infrastructure, mass transport, airports, and roads. Aimed at enhancing connectivity and bolstering the competitiveness of Indian businesses, the initiative focuses on six pivotal pillars known as "Connectivity for Productivity": comprehensiveness, prioritization, optimization, synchronization, analytical, and dynamic. In this study, we explore the application of these pillars to address the problem of "Maximum Demand Forecasting in Delhi." Electricity forecasting plays a very significant role in the power grid as it is required to maintain a balance between supply and load demand at all times, to provide a quality electricity supply, for Financial planning, generation reserve, and many more. Forecasting helps not only in Production Planning but also in Scheduling like Import / Export which is very often in India and mostly required by the rural areas and North Eastern Regions of India. As Electrical Forecasting includes many factors which cannot be detected by the models out there, We use Classical Forecasting Techniques to extract the seasonal patterns from the daily data of Maximum Demand for the Union Territory Delhi. This research contributes to the power supply industry by helping to reduce the occurrence of disasters such as blackouts, power cuts, and increased tariffs imposed by regulatory commissions. The forecasting techniques can also help in reducing OD and UD of Power for different regions. We use the Data provided by a department from the Ministry of Power and use different forecast models including Seasonal forecasts for daily data.

Accurate prediction of crop production is essential in effectively managing agricultural countries' food security and economic resilience. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop production forecasting. We predict the quarterly production of 325 crops (including fruits, vegetables, cereals, non-food, and industrial crops) across 83 provinces in the Philippines. Using a comprehensive dataset of 10,949 time series over 13 years, we demonstrate that a global forecasting approach using a state-of-the-art deep learning architecture, the transformer, significantly outperforms traditional local forecasting approaches built on statistical and baseline methods. By leveraging cross-series information, our proposed way is scalable and works well even with time series that are short, sparse, intermittent, or exhibit structural breaks/regime shifts. The results of this study further advance the field of applied forecasting in agricultural production and provide a practical and effective decision-support tool for policymakers that oversee the farm sector on a national scale.

Abstract: The diffusion of competition under the coexistence of multi-generation products has become one of the important challenges faced by enterprises in their daily and sustainable operations. At the same time, the competition between different brands has intensified the difficulty and complexity of decision-making in the process of multi-generation product operation, Therefore, based on the Norton-Bass model diffusion process, this paper introduces two marketing variables, dynamic price and quality level, builds a multi-generation product diffusion model under the dual-brand competition, and analyzes the impact of the company's revenue on the launch time to market, pricing, quality or technical level. By using system dynamics (SD) method, From the perspective of strong brand and weak brand enterprises, the competition diffusion model is built and simulated. The simulation shows that: i) When enterprises have the same brand competitiveness, reducing the pricing level cannot obtain more revenue, but diminish the overall revenue of the industry; Raising the pricing level can get more revenue, but also improve the revenue of competitors. ii) When the competitive strength of enterprises is different, the strong brand tends to maintain a stable pricing on the basis of improving the quality level, or slightly raise the price. Weak brands tend to raise the pricing of new products significantly on the basis of improving the quality level. iii) The launch time to market decision of new product is influenced by the degree of product quality upgrade. Therefore, the frequency of releasing new products should trade-off against the degree of technological upgrading of product quality. This paper provides the theoretical basis and new insights for the new product launch and operation decision of enterprises.

Background: Permanent Daylight Savings Time (DST) may improve road safety by providing more daylight in the evening but could merely shift risk to morning commutes or increase risk due to fatigue and circadian misalignment. Methods: To identify how potential daylight exposure and fatigue risk could differ between permanent DST versus permanent Standard Time (ST) or current time arrangements (CTA), generic work and school schedules in five United States cities were modeled in SAFTE-FAST biomathematical modeling software. Commute data were categorized by morning (0700-0900) and evening (1600-1800) rush hours. Results: Percent darkness was greater under DST compared to ST for the total waking day (t=2.59, p=0.03) and sleep periods (t=2.46, p=0.045). Waketimes occurred before sunrise 63%±41% percent of the time under DST compared to CTA (42%±37%) or ST (33%±38%; F(2, 74)=76.37; p<0.001). Percent darkness was greater during morning (16%±31%) and lower during evening rush hour (0%±0%) in DST compare to either CTA (morning:7%±23%; evening:7%±14%) or ST (morning:7%±23%; evening:7%±15%). Discussion: Morning rush hour overlapped with students’ commutes and shift worker reverse commutes, which may increase traffic congestion and risk compared to evening rush hour. Switching to permanent DST may be more disruptive than either switching to ST or keeping CTA without noticeable benefit to fatigue or potential daylight exposure.

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE by 30%, RMSE by 25%, and MAPE by 20% compared to the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE by 10%, RMSE by 20%, and MAPE by 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%. These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

When drilling oil and gas wells, hole cleaning efficiency is crucial, particularly in the curved or severely deviated sections. Although many hole-cleaning procedures and models have been developed, most of them have substantial limitations or are difficult to apply in real time. This study aimed to develop a model for the hole cleaning index (HCI) that could be integrated into the drilling operations to provide an automated and real-time evaluation of deviated drilling hole cleaning. The new model herein was developed based on the mechanical drilling parameters, enhanced estimated drilling fluid properties, and cuttings characteristics. This HCI model was validated and tested in the field, as it was applied when drilling 12.25”-intermediate directional sections in two wells with a total length of approximately 2000 ft each. The integration of the HCI helped to attain a much better well drilling performance (50% enhancement) and mitigation of potential problems like pipe sticking and the slower rate of penetration. Since the developed index incorporates the changes in wellbore geometry and other spontaneous field data, the new model could be utilized for real-time optimization and intermediate interventions by drilling teams, unlike commercial software tools which are only useful during the planning phase. For this reason, the HCI can be linked to the driller's control panel to provide timely evaluation and corrective measures related to hole cleaning.

Global models have been developed to tackle the challenge of forecasting sets of series that are related or share similarities, but not for heterogeneous datasets. Various methods of partitioning by relatedness have been introduced to enhance the similarities of the set, resulting in improved forecasting accuracy but often at the cost of a reduced sample size, which could be harmful. To shed light on how the relatedness between series impacts the effectiveness of global models in real-world demand forecasting problems we perform an extensive empirical study using the M5 competition dataset. We examined cross-learning scenarios driven by the product hierarchy commonly employed in retail planning, which allow global models to capture interdependencies across products and regions more effectively. Our findings show that global models outperform state-of-the-art local benchmarks by a considerable margin, indicating that they are not inherently more limited than local models and can handle unrelated time series data effectively. The accuracy of data partitioning approaches increases, as the size of the data pools and the models' complexity decrease. However, there is a trade-off between data availability and data relatedness. Smaller data pools lead to increased similarity among time series, making it easier to capture cross-product and cross-region dependencies, but this comes at the cost of a reduced sample, which may not be beneficial. Finally, it's worth noting that the successful implementation of global models for heterogeneous datasets can significantly impact forecasting practice.

During disaster response, clouds or darkness can prevent the use of optical images for detecting consequences of natural disasters, including landslides. In these situations, radar images can be used to detect changes more rapidly. However, Synthetic Aperture Radar (SAR) backscatter intensity images are underutilized for landslide detection. Unfortunately, there remains a lack of understanding about how to interpret landslide signatures in SAR imagery. In this study, we investigate how the morphometric features and material properties of landslides, and preexisting land cover, control their expression in SAR backscatter intensity change images. Trends in the spatial and temporal signatures of over 1000 landslides in 30 diverse case studies are investigated, using multi-temporal composites and dense time-series of Sentinel-1 C-band SAR backscatter intensity data. The results show that the orientation of landslide surfaces relative to the sensor, pre-existing land cover, and the roughness of the landslide surface, determine whether landslides will produce an increase or decrease in backscatter intensity values. In certain cases, we can identify morphometric features of landslides (e.g. scarps, transit zone, deposits, ponding) and material properties. Generally, we see that landslides appear most clearly with a strong increase in intensity when they occur in herbaceous vegetation or non-vegetated ground surfaces, due to an increase in surface roughness. While in forested or densely vegetated areas, landslides produce a more complex signature with both decreases due to radar shadow and vegetation removal, and an adjacent edge of increased intensity due to double bounce and direct return from vertical tree trunks and convex edges. In most cases, rough deposits produce an increase in intensity, while smooth deposits (e.g. from mudslides) exhibit specular reflection, and thus show decreased values. Landslides are less visible in cases with pre-event very rough ground, or mixed vegetation conditions. The conceptual model developed can aid interpretation of landslides in SAR imagery, and provide domain knowledge needed to train models for automatic landslide detection.

Application of pulsed optical sources on the base of stable mode-locked lasers, which are known for their very low time jitter, provides the opportunity for creation a high precision photonic analogue to digital converters of signals in the microwave range. However, the repetition rate of modern commercially available mode-locked lasers is limited to a few gigahertz. The increase of repetition rate is possible using the schemes that implement a passive chirp of ultra-short pulses prior to electro-optic amplitude modulator, which is driven by the signal under test, and demultiplexing of modulated signal after a modulator. In the given article we analyzed a continuous time-stretch chirp using single mode fiber as dispersive element. The limitations of input signal bandwidth and source pulses energy are considered.

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

Sickle cell disorder (SCD) is a multisystem disease with heterogeneous phenotypes. Although all patients have the mutated haemoglobin (Hb) in the SS phenotype, the severity and frequency of complications are variable. When exposed to low oxygen tension, the Hb molecule becomes dense and, forms tactoids which, lead to the peculiar sickled shapes of the affected red blood cells, giving the disorder its name. This sickle cell morphology is responsible for the profound and widespread pathologies associated with this disorder, such as vaso-occlusive crisis, (VOC). How much of the clinical manifestation is due to sickled erythrocytes and what is due to the relative contributions of other elements in the blood, especially in the microcapillary circulation, is usually not visualized and quantified for each patient during clinical management. Here, we used a microfluidic microcirculation mimetic (MMM) which has 187 capillary-like constrictions to impose deformations on erythrocytes of SCD patients, visualizing and characterizing the morpho-rheological properties of the cells in normoxic, hypoxic (using of sodium meta-bisulfite) and in treatment conditions (using hydroxyurea). The MMM enabled a patient-specific quantification of shape descriptors (circularity and roundness) and transit time through the capillary constrictions, which are readouts for morphorheological properties implicated in VOC. Our results demonstrate the feasibility of microfluidics-based monitoring of individual patients for personalized care in the context of SCD complications such as VOC, even in re-source-challenged settings.

Planck's constant plays an important role in quantum mechanics but has little physical meaning apart from its contribution to the formulas. Evaluating each of the constant's unit dimensions gives a more granular account of the physical phenomena described by the equations. These formulas show how classical and quantum mechanical equations are related, and they give consistent descriptions of phenomena like momentum and energy in classical and quantum systems.

We developed a novel approach in the field of spatiotemporal modelling, based on the spatialisation of time: the Timescape algorithm. It is especially aimed at sparsely distributed datasets in ecological research, whose spatial and temporal variability is strongly entangled. The algorithm is based on the definition of a spatiotemporal distance that incorporates a causality constraint and that is capable of accommodating the seasonal behaviour of the modelled variable as well. The actual modelling is conducted exploiting any established spatial interpolation technique, substituting the ordinary spatial distance with our Timescape distance, thus sorting, from the same input set of observations, those causally related to each estimated value at a given site and time. The notion of causality is expressed topologically and it has to be tuned for each particular case. The Timescape algorithm originates from the field of stable isotopes spatial modelling (isoscapes), but in principle it can be used to model any real scalar random field distribution.

Dielectric spectroscopy (DS) is a promising cell screening method that can be used for diagnostic and drug discovery purposes. The primary challenge of using DS in physiological buffers is the electrode polarization (EP) that overwhelms the impedance signal within a large frequency range. These effects further amplify with miniaturization of the measurement electrodes. In this study, we present a microfluidic system and the associated equivalent circuit models for real-time measurements of cell membrane capacitance and cytoplasm resistance in physiological buffers with 10s increments. The current device captures several hundreds of biological cells in individual microwells through gravitational settling and measures the system’s impedance using microelectrodes covered with dendritic gold nanostructures. Using PC-3 cells (a highly metastatic prostate cancer cell line) suspended in cell growth media (CGM), we demonstrate stable measurements of cell membrane capacitance and cytoplasm resistance in the device for over 15 minutes. We also describe a consistent application of the equivalent circuit model, starting from the reference measurements used to determine the system parameters. The circuit model is tested using devices with varying dimensions, and the obtained cell parameters between different devices are nearly identical. Further analyses of the impedance data have shown that accurate cell membrane capacitance and cytoplasm resistance can be extracted using a limited number of measurements in the 5 MHz to 10 MHz range. This will potentially reduce the timescale required for real-time DS measurements below 1s. Overall the new microfluidic device can be used for dielectric characterization of biological cells in physiological buffers for various cell screening applications.

In this paper, a new design of a helium-assisted hybrid drone is proposed for flight time enhancement. As is widely known, most of the drones with a VTOL (vertical take-off and landing) feature have a short operation time, limiting their capability to carry out sustainable operations for the given missions. Thus, with the clear goal of enhancing the flight time, this study aims to develop a hybrid drone system, where a helium balloon is used to provide a lifting force for this purpose. The proposed design for the hybrid drone has several benefits including easiness to manufacture and relatively small size when compared to other types of hybrid drones. Various analyses are conducted for the design of the hybrid drone system including the balloon shape and size, buoyant force, flight time, and connector design. Since stability and performance are one of the most important issues for the new design, the pole location analysis is conducted based on the control theory. This rigorous analysis provides that the proposed hybrid drone design is stable as well as robust against swinging motions. To validate the effectiveness of the proposed design and flight time enhancement, simulations were conducted and experimental results are also provided using the manufactured hybrid drone system. Through the real experiments, it is proved that the hybrid drone can increase the flight time more than 2.5 times while guaranteeing stable motions.

Informational (Shannon) measures of symmetry are introduced and analyzed for the patterns built of 1D and 2D shapes. The informational measure of symmetry Hsym (G) characterizes the an averaged uncertainty in the presence of symmetry elements from the group G in a given pattern; whereas the Shannon-like measure of symmetry Ωsym (G) quantifies averaged uncertainty of appearance of shapes possessing in total n elements of symmetry belonging to group G in a given pattern. Hsym(G1)=Ωsym(G1)=0 for the patterns built of irregular, non-symmetric shapes. Both of informational measures of symmetry are intensive parameters of the pattern and do not depend on the number of shapes, their size and area of the pattern. They are also insensitive to the long-range order inherent for the pattern. Informational measures of symmetry of fractal patterns are addressed. The mixed patterns including curves and shapes are considered. Time evolution of the Shannon measures of symmetry is treated. The close-packed and dispersed 2D patterns are analyzed.

The Einstein’s Boxes paradox was developed by Einstein, de Broglie, Heisenberg, and others to demonstrate the incompleteness of the Copenhagen Formulation of quantum mechanics. I explain the paradox using the Copenhagen Formulation. I then show how a Time-Symmetric Formulation of quantum mechanics resolves the paradox in the way envisioned by Einstein and de Broglie. Finally, I describe an experiment that can distinguish between these two formulations.

This paper presents a study on the influence of the frequency variation of a nonlinear1 control allocation technique execution, developed by the author [1], named by Fast Control2 Allocation (FCA) for the Quadrotor Tilt-Rotor (QTR) aircraft. Then, through Software In The3 Loop (SITL) simulation, the proposed work considers the use of Gazebo, QGroundControl, and4 Matlab applications, where different frequencies of the FCA can be implemented separated in5 Matlab, always analyzing the QTR stability conditions from the virtual environment performed in6 Gazebo. TheresultsshowedthattheFCAneedsatleast200HzoffrequencyfortheQTRsafeflight7 conditions, i. e., 2 times smaller than the main control loop frequency, 400 Hz. Lower frequencies8 than this one would case instability or crashes during the QTR Operation.

About a century ago, in the spirit of ancient atomism, the quantum of light was renamed the photon to suggest its primacy as the fundamental element of everything. Since the photon carries energy in its period of time, a flux of photons inexorably embodies a flow of time. Time comprises periods as a trek comprises legs. The flows of quanta naturally select optimal paths, i.e., geodesics, to level out energy differences in the least time. While the flow equation can be written, it cannot be solved because the flows affect their driving forces, affecting the flows, and so on. As the forces, i.e., causes, and changes in motions, i.e., consequences, cannot be separated, the future remains unpredictable, however not all arbitrary but bounded by free energy. Eventually, when the system has attained a stationary state, where forces tally, there are no causes and no consequences. Then time does not advance as the quanta only orbit on and on.

The anisotropy in the CMBR detected by the COBE satellite has sprouted the research and theories of the void that got created, after being washed away by a parallel universe after its collision. This details of the roadmap to the parallel universe, that are previously been entangled, but now, separated, with the concept of 2-time dimensions as a time curve of containing the closed curves embedded on the surface area, thereby time running backward in orientation in the parallel universe, along with the perspective of eternal inflation and divided subspaces giving birth to twin universes are provided here. This time curve being responsible for creating an exponentiality of time frames, growing in layers, giving us the perception of the confabulations being an indication of the previously connected worlds.

In this study, the effect of hot stamping forming process on the properties change of compound layer in Al–Si coated high-strength steel sheet with different thickness was investigated. The coating layer in hot stamped high-strength steel sheet was composed of a diffusion layer and a compound layer. As the hot stamping temperature and holding time for forming increased, the thickness of the compound layer in the Al–Si coated high-strength steel sheet increased. The thickness of coating layer with the hot stamping for 500 sec at 890, 910 and 950 °C was obtained 8.9, 11.8, and 20.6 micrometer, respectively. The thickness of coating layer with hot stamping for 180, 500 and 550 sec at 910 ^oC was obtained 5.7, 11.8, and 17.8 micrometer, respectively. The compound layer in hot stamped high-strength steel sheet with Al–Si coating layer consists of Al_5.4Fe₂ and Fe₂Si phases. In the compound layer, Al and Si were irregularly distributed.

An earthquake of Mw6.4 hit the coastal zone of Albania on 26 November 2019, at 02:54:11 UTC. It was intensively felt at about 34km far away, in Tirana City, where damages and lives lost occurred. To emphasize a geomagnetic signature before the onset of this earthquake, the data collected on the interval 15 October–30 November 2019, at the Panagjurishte (PAG)-Bulgaria and Surlari (SUA)-Romania observatories are analyzed by using both the polarization parameter (BPOL)-time invariant in non-seismic conditions, becoming unstable before this seismic event, and the strain effect for geomagnetic signal identification. Consequently, BPOL time series and its standard deviations are performed for the both sites using ULF-FFT band-pass filtering. A statistical analysis, based on a standardized random variable equation, was applied to emphasize on the BPOL*(PAG) and ABS BPOL*(PAG) time series the anomalous signal’s singularity and, to differentiate the transient local anomalies due to Mw6.4earthquake, from the internal and external parts of the geomagnetic field, taken PAG observatory as reference. Finally, the ABS BPOL*(PAG-SUA) time series are obtained on the interval 1-30 November, 2019, where a geomagnetic signature greater than 2.0, was detected on 23 November and the lead time was 3 days before the onset of Mw6.4earthquake.

The high-power Asymmetric half-bridge Converter (AHBC) LED constant current driver controlled by digital current mode is a fourth-order system. Static operating point, parasitic resistance, load characteristics, sampling effect, modulation mode and loop delay will have great influence on its dynamic performance. In this paper, the small-signal pulse transfer function of the driver is established by the discrete-time modeling method for the two operating points corresponding to the three modulation modes of the trailing edge, leading edge and double edge. And, the effects of parasitic parameters, delay effect, sampling effect and load effect are fully considered in modeling. For a large number of complex exponential matrix operations, the first order Taylor formula is used for approximate calculation after the coefficient matrix is obtained by substituting the data. Then, Matlab software is used to compare and analyze the discrete-time model and the discrete-average model. The results show that the proposed discrete-time model can more accurately characterize the resonant peak and high-frequency dynamic characteristics, and is very suitable for the design of high frequency digital controller.

Viral entry is the first stage in the virus replication cycle and, for enveloped viruses, is mediated by virally encoded glycoproteins. Viral glycoproteins have different receptor affinities and triggering mechanisms. We employed vesicular stomatitis virus (VSV), a BSL-2 enveloped virus that can incorporate non-native glycoproteins, to examine the entry efficiencies of diverse viral glycoproteins. To compare glycoprotein-mediated entry efficiencies of: VSV G, SARS-CoV-2 S, EBOV GP, LASV GP, and CHIKV E we produced recombinant VSV (rVSV) viruses that produce the five glycoproteins. The rVSV virions encoded a nano luciferase-PEST (NLucP) reporter gene, which we used in combination with the live-cell substrate Endurazine™ to monitor viral entry kinetics in real time. Our data indicate that rVSV particles with glycoproteins that require more post-internalization priming typically demonstrate delayed entry in comparison to VSV G. In addition to determining the time required for each virus to complete entry, we also used our system to evaluate viral cell surface receptor preferences, monitor fusion, and elucidate endocytosis mechanisms. This system can be rapidly employed to examine diverse viral glycoproteins and their entry requirements.

In the steel industries, workers are exposed to heat and ambient thermal stresses on a daily basis, leading to discomfort and limited performance. In this study, the main purpose is to investigate the effect of climate heat stress on the rate of accidents in the workplace for workers for 5 consecutive years. The data of this study were received without any sampling through the HSE Center for Steel Industry and meteorological data from 2015 to 2019 from Isfahan Meteorological station. The daily number of casualties among workers in the steel industry during 2015-2019 by adjusting seasonal patterns, months, effects of the day of the week and other meteorological factors on the average daily temperature using the studied model has a decreasing effect. Eviews software (version 8) was used to model and investigate the relationship between events and meteorological variables. The mean temperature was at least 40.2-2 and at most 70.34 ° C, respectively. In the time-series study for the main model, the number of accidents shows a direct relationship with the average temperature and wind speed. Climatic indices of humidity and rainfall have the least impact on accidents compared to temperature and wind speed. A strong correlation was shown between the increase in average ambient temperature and the rate of accidents over the past 5 years. Given the fundamental differences in studies of environmental exposure and wind speed over heat stress, further analysis in workers should be considered.

Specifically this work is based on the concept of ‘dark energy’ as a phenomenal effect of expansion through which a theoretical value of Hubble’s constant has been introduced. It is a constant that is based on a postulate that the square root of ratio of the Siva’s constant ‘K’ and Hubble’s constant ‘H’ is exactly equal to the diameter of the neutral hydrogen atom. That is the, exact theoretical value of Hubble’s constant. This is always constant .However, space-time conversions and the changes in the velocity of light will affect the distance and velocity in Hubble’s equation. Deviation factors in velocity, distance and the Hubble’s constant have been calculated separately which are affected by change of velocity of light with expansion of space time. The observations are related to red shift. So the change factor in terms of Hubble’s constant has been calculated with respect to red shift. Thus a factor which affects the theoretical Hubble’s constant has been calculated to find the exact value of Hubble’s constant that satisfies the experimental results. The present theoretical value of Hubble’s constant has been calculated. The experimental results by plank, ACT (Atacama Cosmology Telescope), WMAP (Wilkinson Microwave Anisotropy Probe) etc. experiments are at par with the theoretical result. Thus the ontology of space time introduced in this theoretical work supposed to be correct and leads to a new theory of origin of universe for which a brief description has been provided.

A 47-year-old physician suddenly noticed a persistent difficulty maintaining attention. He was awake, alert, and oriented. After two hours he developed fever, ageusia, and anosmia. He denied any previous history of psychiatric illness and was hydrated at the time of the subjective attention impairment. On admission, the patient remained oriented. He reported the persistence of attention problems, anosmia, and mild fatigue. The oxygen saturation 99% while he was breathing ambient air. Laboratory tests were unremarkable. A high-resolution computed tomography of the chest was normal. Nasopharyngeal and throat swabs specimens on reverse transcription-polymerase chain reaction analysis tested positive for SARS-CoV2. On illness day 3, the examination was unchanged, but he continued to complain of difficulties to stay focused. Then, he performed an objective attention test. The test demonstrated a moderate attentional impairment. On day 6, the patient reported a subjective worse in his concentration and performed a second test. Although his physical examination remained normal, the attention performance was worse as compared to day 3. Eight hours after worsening of attention impairment, the patient’s oxygen saturation dropped to 94%. From illness days 9 to 14, the patient evolved with clinical improvement. On day 10, a third objective attention test indicated a mild deficit. On day 16, he did not report any other symptom and the attention test was completely normal. Then, the patient returned to work. Neurological symptoms had been previously described in COVID- 19 patients. However, no previous research had investigated early cognitive deficits preceding the traditional symptoms.