Background/Objectives: Transforaminal endoscopic lumbar discectomy (TELD) under local anesthesia is a promising minimally invasive surgical option for intractable lumbar disc herniation (LDH). However, our understanding of access pain prediction during foraminal pathological procedures is limited. To our knowledge, no predictive rules for access pain during TELD have been established for foraminal or extraforaminal LDH. This study, with its potential to predict access pain during TELD and to discuss strategies for pain prevention and management, could significantly impact the field of endoscopic spine surgery. Methods: This observational study included 73 consecutive patients who underwent TELD for foraminal or extraforaminal LDH between January 2017 and December 2022. Preoperative clinical and radiographic factors affecting significant access pain and the impact of access pain on clinical outcomes were evaluated. Results: The rate of significant access pain was 13.70% (10 of 73 patients). Extraforaminal LDH tended to cause more severe pain than foraminal LDH during TELD under local anesthesia (p < 0.05). Although the degree of access pain was not related to global outcomes, increased pain was strongly associated with a prolonged operative time and length of hospital stay (p < 0.05). Conclusions: TELD can be an effective surgical option for foraminal or extraforaminal LDH under local anesthesia. More access pain may develop during TELD for extraforaminal LDH. The extraforaminal component of LDH can narrow the safety working zone. Significant access pain may prolong the duration of surgery and hospitalization. Thus, a specialized technique is required for the clinical success of TELD.

Heart failure is a major global health concern, especially in Ethiopia. Numerous studies have analyzed heart failure data to inform decision-making, but these often struggle with limitations to accurately capture death dynamics and account for within-cluster dependence and heterogeneity. Addressing these limitations, this study is then to incorporate the dependence and to analyze heart failure data to estimate survival time and identify risk factors affecting patient survival. The data, obtained from 497 patients at Jimma University Medical Center in Ethiopia, were collected between July 2015 and January 2019. Residence was considered as the clustering factor in the analysis. We employed the Bayesian accelerated failure time (AFT), and Bayesian AFT shared gamma frailty models, comparing their performance using the Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). The Bayesian log-normal AFT shared gamma frailty model had the lowest DIC andWAIC, with well capturing cluster dependency that attributed to unobserved heterogeneity between patient residences. Unlike other methods that use Markov-Chain Monte-Carlo (MCMC), we applied the Integrated Nested Laplace Approximation (INLA) to reduce computational load. The study found that 39.44% of patients died, while 60.56% were censored, with a median survival time of 34 months. Another interesting finding of this study is by adding frailty into the Bayesian AFT models has boosted the performance in fitting the heart failure dataset. Significant factors reducing survival time included age, chronic kidney disease, heart failure history, diabetes, heart failure etiology, hypertension, anemia, smoking, and heart failure stage. The Bayesian model also showed lower standard errors for parameters compared to other methods, highlighting the importance of frailty models in accurately assessing survival time in heart failure patients.

With the significant annual increase in the market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising.To achieve efficient and low energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processing hourly time-series data of temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time-series features. After augmentation, the root mean square error (RMSE) of temperature prediction decreased by 27.3%, and the RMSE of humidity prediction decreased by 32.8%, significantly improving the accuracy of multi-step predictions and providing substantial industrial application value.

Capulus danieli, a distinct member of Capulidae, exhibits a unique ecological behaviour by attaching and drilling onto the pecten, distinguishing itself from other gastropods. Herein, we present the first characterisation of the mitochondrial genome of Capulus danieli. The results showed that the genome spans 15600 base pairs, with an A + T content of 71.12% and a G + C content of 28.88%. Within this genetic makeup, we identified 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Phylogenetic analysis revealed a close evolutionary relationship between Capulus danieli and members of the Ficoidea superfamily. The divergence time estimation suggested that Capulus danieli diverged approximately 52.29 million years ago. These findings significantly contribute to our understanding of the evolutionary history and genetic architecture of Capulus danieli, shedding light on its unique ecological adaptations to the marine environment.

Open-pit uranium mining in the south Texas Gulf Coast region was active during the second half of the 20th century. Although some mines have extensive records documenting the locations and extents with descriptions of reclamation procedures and timelines, abandoned and reclaimed mines with minimal documentation concurrently exist where little is known about locations, mine waste volumes, and reclamation status. Advanced mapping of these mining operations is helpful for producing basic mine land inventories and assessments of mine waste; identifying potential undeveloped resources and environmental health issues; and for developing proper management, restoration, and reclamation strategies. We studied known reclaimed and unreclaimed mine sites to develop a spatiotemporal mapping approach utilizing multiple techniques in remote sensing as a proof of concept. A mapping classification system was developed to identify uranium mine waste-rock piles, mine pits (with and without waste water), the total area of land disturbance due to mining, and to differentiate abandoned and reclaimed mine features using lidar (Light Detection and Ranging) analysis and multispectral temporal analysis. Visual interpretation of Landsat imagery and time-series analysis revealed the temporal life-cycle of mining, historical areas of disturbance, and reclamation; however, mine features below vegetation canopies are not always visible in satellite imagery. Lidar laser scanning penetrates the vegetation canopy to measure the topographic elevation and exposes anthropogenic modifications to the landscape. Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features, and expansive areas of mining disturbance. The spatiotemporal mapping approach proved to be useful for identifying and characterizing known mine features, areas of disturbance, and reclamation status. Historical pre-mining topographic data were available at only 4 of the mines for comparison where the cut-and-fill tool estimated 21% less waste-rock volume, indicating that information about pre-mining terrain topography and/or subsurface mine waste geometry is needed to improve accuracies for quantifying buried mine waste volume at reclaimed mine sites.

Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep learning techniques, particularly advanced neural network architectures, in predicting photometric metallicity from time-series data. Moreover, our deep learning models demonstrated notable predictive performance over experimentation and analysis. Through GRU-based models we achieved a low mean absolute error of 0.056 and a high $R^2$ regression performance of 0.9401 measured by cross-validation, showcasing the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena.

Academic procrastination, more than merely postponing tasks, represents a significant failure in the self-regulation process of learning. Research on study skills highlights academic time management as one of the crucial elements of learning strategies and sustainable education. These abilities will help achieve the Sustainable Development Goals fourth goal. Therefore, this study aimed to understand how students plan time management in school activities and its influence on study procrastination, analyzing differences between genders and the hours students spend studying. The Time Management Planning Inventory, the Study Procrastination Questionnaire, and a personal and school data sheet were used. A sample of 506 students from elementary schools in northern Portugal was utilized. The results revealed that gender and study hours significantly influence how students plan time management for school tasks in the short and long term. This variable also showed a significant impact on the procrastination of study activities. The practical implications of this study are substantial, as they provide educators and researchers insights into the factors influencing academic procrastination and the role of time management planning and study hours. These insights can be applied to develop effective strategies to reduce academic procrastination and promote sustainable education.

This study introduces a novel method that combines Generative Adversarial Networks (GANs) with conformal prediction to revolutionize the domain of stock ticker forecasting. Our approach enhances prediction accuracy and offers dependable prediction intervals to gauge uncertainty, surpassing current GAN-based techniques in unpredictable market scenarios. We made a validation using AAPL stock data, showing the ability of our model to generate accurate forecasts and reliable assessments of prediction uncertainty. The prediction intervals generated provide investors and risk managers with a powerful tool to make informed decisions. Our approach represents a notable advancement in financial prediction as it improves the clarity and reliability of forecasts. By incorporating conformal prediction into the GAN framework, the reliability of forecasts is improved, providing a thorough evaluation of uncertainty that is crucial for successful risk management. This framework has the potential to transform the field of predictive analytics in finance. It shows a solution that balances accuracy and uncertainty quantification. The results clearly show the effectiveness of this method, paving the way for its potential application in a wide range of financial prediction scenarios.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

A field study was conducted at the Eastern Mediterranean Agricultural Research Institute, Adana, Turkey, during the 2022/23 cropping seasons to examine the impact of Mepiquat chloride dosage applied at different intervals to optimize growth, yield characteristics enhancement and improve fiber quality attributes of cotton. The study employed a split-split plot design with three replications. The results revealed that differences among treatments were not statistically significant for most parameters investigated. The observed discrepancies indicated that all treatments tested in 2012 delayed cotton development, whereas in 2023, they led to premium fibers. In 2023, the fiber percentage was highest in the control treatment, and in 2022, it was highest at 400 mL·ha-1. Across all years, the Selin variety exhibited the highest fiber percentage when Mepiquat chloride was applied at the bolling stage. The shoot-root ratio was highest at 400 mL·ha-1 during harvest in 2022 and at 0 mL·ha-1 during the reproductive stage in 2023. Overall, the results demonstrated that the application of Mepiquat chloride altered the physiological characteristics of the cotton plants, leading to bottom-up effects on cotton performance. This implies that the effectiveness of Mepiquat chloride in cotton production to alleviate drought stress is influenced by weather-related conditions.

In economics, especially econometrics, fixed-effects are widely recognized as powerful tools for heterogeneity analysis of longitudinal data sets. However, the existing research pays more attention to such as the variable selection, parameter estimation and dimension reduction on linear relationships with too many assumptions while nonlinear relationships are ubiquitous. In this paper, an extension fixed-effects model is proposed on the curve_fit nonlinear least squares. First, with the help of expert knowledge, the series regressions between independent variables including time and dependent variable is estimated on each series data, and the parameters are estimated by curve_fit. Similarly, the coeffection between independent variables including time and dependent variable is also initially estimated on the whole panel data. Second, the distance between regressions is defined, and then the coeffect function between the series regressions is obtained on the initial co-effect with curve_fit. Third, the time-effects and individual fixed-effects for each series data in the panel data are estimated on the differences of series regressions and co-effect function. Additionally, the effectiveness of the proposed method is varified on the synthetic data.

Event data loggers are frequently used to record the date and time of tip events in tipping-bucket rain gauges. The HOBO® pendant event data logger is one such commercially-available device commonly used for this purpose. It can record the contact closure of a TBGR reed switch at a maximum timing resolution of 1 second, tied to the timing of the logger clock, which is set each time the logger is launched. These event loggers are ideal for the routine recording of rainfall. This paper addresses the issue of whether they can also be relied upon when estimating short-term intensities, for which they were not designed. New experiments show that for a series of switch closures at fixed intervals other than exact multiples of 1 s, the HOBO® logger fails to record evenly-spaced tip events. Thus, for example, with pulses at fixed 2.75 s intervals, the logger records some events as occurring at 2 s intervals, and others at 3 s intervals. The duration between logged events is correct only when the mean is calculated for a long series of logged events. In natural rainfall, tip events can occur at any time, and inter-tip times, from which intensity can be estimated, will generally not be an integral number of seconds. Consequently, particularly in intense rain, the logger behaviour just described can lead to erroneous estimates of the rainfall rate estimated from the duration of individual inter-tip times. Possible solutions are discussed.

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD are organized into a spatial (raster) grid, and are available in consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs & Grasses there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will be not-EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data, and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.

Online food delivery services are rapidly growing in popularity, making customer satisfaction critical for company success in a competitive market. Accurate delivery time predictions are key to ensuring high customer satisfaction. While various methods for travel time estimation exist, effective data analysis and processing are often overlooked. This paper addresses this gap by leveraging spatial data analysis and preprocessing techniques to enhance the data quality used in Bayesian models for predicting food delivery times. We utilized the OSRM API to generate routes that accurately reflect real-world conditions. Next, we visualized these routes using various techniques to identify and examine suspicious results. Our analysis of route distribution identified two groups of outliers, leading us to establish an appropriate boundary for maximum route distance to be used in future Bayesian modelling. The spatial analysis revealed that these outliers were primarily deliveries to the outskirts or beyond the city limits. By refining the data quality through these methods, we aim to improve the accuracy of delivery time predictions, ultimately enhancing customer satisfaction.

The e-commerce sector is in a constant state of growth and evolution, particularly within its subdomain of online food delivery. As such, ensuring customer satisfaction is critical for companies working in this field. One way to achieve this is by providing accurate delivery time estimation. While companies can track couriers via GPS, they often lack real-time data on traffic and road conditions, complicating delivery time predictions. To address this, a range of statistical and machine learning techniques are being employed, including neural networks and specialized expert systems, with different degree of success. One issue with neural networks and machine learning models is their heavy dependence on vast, high quality data. To mitigate this issue we propose two Bayesian generalized linear models to predict time of the delivery. Utilizing a linear combination of predictor variables, we generate practical range of outputs with Hamiltonian Monte Carlo sampling method. These models offer a balance of generality and adaptability, allowing for tuning with expert knowledge. They were compared with PSIS-LOO and WAIC criteria. Results show that both models accurately estimated delivery times from the dataset, while maintaining numerical stability. Model with more predictor variables proved to be more accurate.

Immortal-time bias (ITB) is known to be common in cohort studies and distorts the association estimates between treated and untreated groups. We used data from the last of two large studies in an Italian province on COVID-19 vaccines safety and effectiveness incurred this bias, and aligned the entire population on a single index date, to correct the ITB. We considered the "all-cause deaths" outcome to compare the survival curves between the unvaccinated group and the various vaccination statuses. The all-cause deaths Hazard Ratios in univariate analysis for unvaccinated (reference) versus vaccinated with 1, 2, 3/4 doses were 0.88 (CI95: 0.78 –1.00; p-value 0.044), 1.23 (1.16–1.32; p-value ≤0.001) and 1.21 (1.14–1.29; p-value ≤0.001), respectively. The multivariate values were 2.40 (2.00–2.88; p-value <0.0001), 1.98 (1.75–2.24; p-value <0.0001), 0.99 (0.90–1.09; ns). The possible explanations of the trend of the Hazard Ratios as vaccinations increase could be a harvesting effect; a calendar-time bias, accounting for seasonality and pandemic waves; a case-counting windows bias; a healthy-vaccinee bias; or some their combination. With two and even with 3/4 doses the calculated Restricted Mean Survival Time and Restricted Mean Time Lost have shown a small but significant downside for the vaccinated populations.

A flat-plate unglazed solar water heater (SWH) with polymer thermal absorber was developed and tested. Polymer thermal absorber as a viable alternative to metal thermal absorber that is found in most SWH systems. The performance of this polymer SWH system is measured based on inlet and outlet water temperature, water flowrate, ambient air temperature and solar irradiance. The polymer thermal absorbers are hollow Polyvinyl Chloride (PVC) pipes with 20mm external diameter and 3 mm thick, painted in black to enhance radiation absorption. The pipes are arranged in a rectangular spiral inward-outward opposing-flow (RSioof). The collector pipes are placed in a 1 m2 square enclosure with bottom insulation and reflective surface for better radiation capture. Water circulation is a closed loop with a 16-liter uninsulated storage tank, driven by a pump and controlled by valves to maintain a mass flow rate of 0.00031 to 0.00034 kg/s. The test was conducted under partially clouded sky from 9 am to 5 pm, with solar irradiance between 105 and 1003 W/m2 and 27-36oC ambient air temperature. The SHW produced outlet hot water at 65oC by mid-day and maintained the storage temperature at 63oC until the end of test duration. Photo-thermal energy conversion was recorded with 23% maximum value. Results indicate that a flat plate solar water heater with polymer thermal absorber in RSioof design can be an effective alternative to the metal thermal absorber solar water heater and can be further optimized with glazing and tube sizing.

Unmanned aerial vehicles (UAVs, drones) are not just a technological achievement based on modern ideas of artificial intelligence; they also provide a sustainable solution for green technologies in logistics, transport and material handling. In particular, using battery-powered UAVs to transport products can significantly decrease energy and fuel expenses, reduce environmental pollution, and improve the efficiency of clean technologies through improved energy-saving efficiency. We consider the problem of maximizing the average environmental benefit of a fleet of drones given a periodic schedule of tasks performed by the fleet of vehicles. To solve the problem efficiently, we formulate it as an optimization problem on an infinite periodic graph and reduce it to a special type of parametric assignment problem. We exactly solve the problem under consideration in O(n³) time, where n is the number of tasks performed by vehicles.

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

Background. Escherichia coli commonly causes catheter-related bloodstream infection (C-RBSI) in specific populations. The differential time to positivity (DTTP) technique is the recommended conservative procedure for diagnosing C-RBSIs. Methods. We conducted a retrospective study of episodes in which E. coli was isolated from catheter lumens obtained using the DTTP technique. We analysed microbiological and clinical data based on the DTTP technique as either catheter colonization, C-RBSI, or non-C-RBSI. Results. We included 89 catheter blood cultures classified as follows: catheter colonization, 33.7%; C-RSBI, 9.0%; and non-C-RBSI, 57,3%. Catheter withdrawal was 15.7% without positive catheter-tip cultures. We found no statistically significant differences in catheter type, antibiotic treatment, or clinical outcome among groups, except for the frequency of catheter lock therapy or in the frequency of successful treatment. Mortality was associated with C-RBSI in only 1 patient. Conclusion. E. coli bacteremia diagnosed by the DTTP technique was non-catheter related in most patients. As the majority of the catheters were retained, E. coli bacteremia could not be microbiologically confirmed or ruled out as catheter related by catheter-tip culture. Future studies are needed to assess the profitability of the DTTP technique for diagnosing E. coli C-RBSIs..

A difficult open problem that has never been fully resolved in the literature to date is the behaviour of the (TDCDP) third approximation. As a giant step towards modern control theory, this cutting-edge paper will advance control theory and other relevant inter-disciplinary subjects. Because control theory is important in industry and engineering, this study will be especially beneficial to anyone working in these disciplines who wish to understand more about recent advances in control theory settings. In contrast, the wealth of applications of Fokker Planck Kolmogorov (FPK) equations numerous disciplines As a result, the study instantly gains more taste and credibility. At the end of the paper, there are several challenging open problems, some concluding remarks, and recommendations for further investigation.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

In the light of almost a century of struggle to make (common) sense of Quantum Mechanics and to reconcile it with General Relativity, it is proposed to (for some time) forget about quantizing gravity or striving for one Theory of Everything or “Weltformel”, which would describe the whole of reality without any joints or suture marks. Instead of one single monolithic formalism, a three-legged compound approach is argued for. Quantum Mechanics, Relativity and Thermodynamics are proposed as the main pillars of reality, each with its well-defined realm, specific features, and clearly marked interfaces between the three of them. Not only classical reality, which is rather directly accessible to us, is then comprehensively modelled by their encompassing combination. Quantum phenomena are understood as undoubtedly lying at the bottom of classical physics and at the same time, they become “fully real” only when embedded in classical frames between preparation and measurement in time. It is then where thermodynamics steps in and provides the mediating glue. The aim of this short contribution is not to deliver novel quantitative results but rather to propose a comprehensive research program and to coarsely lay out a very roughly coherent self-referential sketch starting from the beginning of the one universe, which we inhabit.

This retrospective study examines 248 test-bolus examinations preceding contrast-enhanced magnetic resonance angiography (CE-MRA) to extract clinically relevant data for critical limb ischemia (CLI) management. Methods involved a retrospective review of test-bolus exams, analysing 60 graphs for time-to-peak (TTP), full-width half-maximum (FWHM) time, and time to continual rise in signal intensity. These values were correlated with heart function parameters (ejection fraction, ASA-Classification, Lee-Index, and MET-Score). Results indicate a mean TTP of 31.2 ± 7.3s, showing a correlation between ejection fraction and ASA classification. Patients with atrial fibrillation exhibited prolonged TTP compared to those without. Despite population heterogeneity, findings facilitate risk stratification for limb-saving interventions in CLI. TTP emerges as a potential clinical cardiovascular parameter and a risk factor for vascular interventions. Given the variation in injection protocols across centres, this study underscores the importance of precise bolus arrival time documentation for future multicentre studies.

Sharding shows great potential for extending the blockchain. The current challenge facing sharded blockchain technology lies in addressing the extended transaction confirmation times caused by isolated states between shards and unbalanced transaction allocation strategies. These factors contribute to an increase in cross-shard transactions and disproportionate shard workload, ultimately resulting in indefinite confirmation delays for cross-shard transactions. A critical priority for sharded blockchain systems is to conduct a comprehensive qualitative analysis to better understand and mitigate the prolonged transaction confirmation times. We introduce a Product-Form Queue Network (PFQN) model to address the transaction confirmation time problem in sharded blockchains and incorporate a new confirmation queue to more accurately simulate the actual transaction confirmation process in the blockchain. In addition, we provide a detailed quantitative analysis of the relationship between network load, consensus efficiency, and security in sharded blockchains, laying a theoretical foundation for achieving robustness and efficiency in sharded blockchains. This research not only contributes to addressing the scalability issues in sharded blockchains but also offers a new perspective for future research directions.

This study aimed to evaluate the effects of Blazepod technology when incorporated with various training regimens on the essential elements of physical fitness in volleyball, such as agility, speed, visual response time, and vertical leap. Thirty-six participants (age :17.33±0.48 y, height: 183.78±6.34 cm, weight: 77.14±5.16 kg) were in-volved in this study. In randomized design, three equal groups with two experimental groups and one control group were involved in the study over a period of six weeks: (GE1) used Blazepod technology to workout at a low level; (GE2) used Blazepod technology to workout at a higher intensity; (CG) did not employ Blazepod technology, but it adhered to the same fundamental training curriculum. We conducted agility, speed, reaction time, and vertical jump tests before, midway and after the study to determine optimal training volume, intensity, and frequency. A significant main effect of time (P0.01), and time×group interaction (P>0.001) were found for all tests. Post hoc test showed that the two experimental groups gadget significantly improved in all performance variables (P

High seismic activity in the Himalayas and the increasing urbanization of its surrounding areas in the northern Indian region pose significant threats to both lives and properties. To mitigate the risk of seismic events, an earthquake early warning system can be used in the region. This system would be particularly beneficial to cities and towns in the mountainous and foothill regions that are located near earthquake sources. The government and the science and engineering community have re-sponded to this call by establishing the Uttarakhand State Earthquake Early Warning System (UEEWS). The system became fully operational on August 4, 2021, after being launched by the government of Uttarakhand. The UEEWS includes 170 accelerometers in the seismogenic part of the Uttarakhand region of the Himalayas. Ground motion data is transmitted to a central server through a dedicated private telecommunication network 24 hours a day, seven days a week.The system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile application installed on users' devices and sirens installed in government-owned public buildings. The UEEWS has successfully issued alerts for light earthquakes that occurred in the instrumented region and warnings for moderate earthquakes that occurred in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of the instrumentation, adaptation of attributes and their relation to earthquake parameters, the operational flow of the system, and information about the dissemination of warnings

Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tubes signals would serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method are presented.

The flow properties of pellets or granules are crucial for further processing the drug dosage forms. Optimal compression or filling the multiparticulate dosage forms into capsules is influenced by forces between discrete particles, which could be partially characterized by flow properties. Several techniques were developed for the examination of flowability, including static and dynamic methods applying empirical studies and the up-to-date chaos theory; however newest methods seem only to be powerful with the supplement of empirical principles. Our experiments try to refine both the technique of analysis and the methods by finding new, alternative ways. Our approach to the flowability measurements was to set up a dynamic time-dependent model which combines empirical observations and the chaos theory on a geometrical basis, thus find new aspects regarding flow properties of pellets and granules that could be relevant for drug developers.

In this paper, we consider an epidemiological Susceptible-Infected-Susceptible (SIS) model in which the individuals cured from the infection return back into the group of people endangered by the infection. Delay differential equations were introduced to create more realistic models since many processes depend on past history. Many different mathematical methods, including the Susceptible-Infected-Recovered (SIR) model as well as derivatives thereof, can be used to prognose the spread of many various infectious diseases, including Covid-19.We use a nonlinear differential system with time delay as a mathematical formulation of this model. It is examined by the existence of a positive solution which is bounded by two functions $k_1, k_2\in C([t_0,\infty),(0,\infty))$ with constants $\lambda_1 > 0,\,\lambda_2 > 0$ where $0<\lambda_1\leq \lambda_2$. The conditions are put in place for existence of a positive solution that approaches zero in infinity, if $t\to\infty$. Infectious diseases accompany a person throughout their entire life, and in the history of mankind often appeared in frequent epidemics caused high mortality. The occurrence of infectious diseases in the population represents a serious health, social and economic problem. There is a lot of ambiguity concerning the time span and final spread of the epidemic thus making it very demanding for the governing bodies, healthcare institutions, and economic sector to precisely judge the future development.Several examples are shown at the end of this paper.

In this paper, a class of coupled competitive neural networks, which subject to disturbance and discontinuous activation functions, is concerned. To realize the fixed-time quasi-bipartite synchronization, an aperiodic intermittent controller is initially designed. Subsequently, by combining the fixed-time stability theory and nonsmooth analysis, several criteria are established to ensure the bipartite synchronization in fixed time. Moreover, synchronization error bounds and settling time estimates are provided. Finally, numerical simulations are presented to verify the main results.

Smoker’s paradox usually refers to the common observation of a favourable outcome of smoker patients in acute myocardial infarction. The interpretation of this phenomenon is still controversial and not fully explained. We aimed at assessing whether smoking could be associated with mortality in a large cohort of prospectively enrolled patients with ST segment elevation myocardial infarction (STEMI) referred to primary percutaneous coronary intervention (pPCI). From April 2006 to December 2018 a population of 2456 STEMI patients treated with primary PCI were prospectively enrolled in the MATRIX registry. Ischemic time, clinical, demographics, angiographic data and 1-year follow-up were collected. Among 2546 patients admitted with STEMI 1007 (41%) were current smokers. Smokers were 10 years younger and had lower crude in-hospital and 1-year mortality (1.5% vs 6%, p&lt;0.0001 and 5% vs 11%, p&lt;0.0001), shorter ischemic time (203 [147-299] vs 220 [154-334] minutes, p=0.002) and shorter decision time (60 [30-135] vs 70 [36-170] minutes, p=0.0063). After propensity-matching, age, cardiogenic shock and low TIMI score were associated with in-hospital mortality, while smoking habit was still associated with reduced mortality. Smoking was also associated with reduced death rate at 1-year follow-up (HR 0.54, 95% CI [0.37 - 0.78]; p&lt;0.001). Our data show a protective link between smoking status and mortality in patients with STEMI, the so called “smoker’s paradox”. This could be explained not only by the shorter ischemic time and younger age of the smoking population but also by a specific pathogenetic effect of smoking underlying the onset of STEMI and response to therapies.

The retrospective noninterventional study investigated the kinetic energy of video images of 18 fertilized eggs (7 were normal and 11 were abnormal) recorded by a time-lapse device leading up to the beginning of the first cleavage. The norm values of cytoplasmic particles were measured by the optical flow method. Three phases profile for normal cases were found regarding the kinetic energy that were 2.199×10⁻²⁴±2.076×10⁻²⁴, 2.369×10⁻²⁴±1.255×10⁻²⁴, and 1.078×10⁻²⁴±4.720×10⁻²⁵ (J) for the phases 1, 2, and 3, respectively. In phase 2, the energies were 2.369 × 10⁻²⁴ ± 1.255 × 10⁻²⁴ and 4.694 × 10⁻²⁴ ± 2.996 × 10⁻²⁴ (J) (mean ± SD, P = 0.0372), and the time required was 8.114 ± 2.937 and 6.018 ± 5.685 (H) (P = 0.0413) for the normal and abnormal cases, respectively. The kinetic energy change was considered a condition for applying the free energy principle that states that, for any self-organized system to be in equilibrium in its environment, it must minimize its informational free energy. The kinetic energy while interpreting it in terms of the free energy principle suggesting clinical usefulness would further our understanding of the phenomenon of fertilized egg development with respect to the birth of human life.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations

This study investigates the suitability of the timestamp approach algorithm for predicting blood pressure (BP) using wearable and comfortable devices, showcasing its accuracy even with low-frequency sampled signals. Developed within the SINTEC European project, the algorithm utilizes Electrocardiogram (ECG) and Photoplethysmogram (PPG) sensors, demonstrating promising results in both laboratory and hospital settings. The research emphasizes the algorithm’s potential to transform continuous and non-invasive BP monitoring through wearable technology, offering frequent measurements with minimal discomfort. While the study shows promise, further exploration is needed, focusing on optimizing machine learning coefficients for long-term monitoring applications and addressing potential changes over time. Technological improvements, including the implementation of edge computing algorithms, hold the key to enhancing BP accuracy, denoising techniques, and integrating alarm systems for future wearable solutions. This study emphasizes the adaptability of the algorithm for diverse healthcare environments, paving the way for broader applications in convenient and reliable BP monitoring.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

In this study, the effect of increasing exposure time on microstructures, defectiveness, mechanical properties and corrosion behavior of selective laser melted sample Al-Si10-Mg powder have been investigated. The sample were processed at the same power (375 W) and scan speed (2000 mm/s) but with increasing exposure time. Exposure time equal to 40, 50 and 60 µs was applied. The features of the analyzed samples show that with increasing exposure time a better efficiency of the heat input has been obtained with larger size of melting pool and Si particles and lower defectiveness. Moreover, being the coarser microstructure associated a to lower level of voids, the average hardness is similar for the analyzed samples. The potentiodynamic curves of the samples show that the voids occurrence is more significant with respect to the scale of micro-structure on corrosion behavior being the sample processed at the highest exposure time the more resistant to corrosion. The experimental techniques used in the present study were Optical microscope, Sem microscope, Hardness and X-Ray Computed Tomography.

Milling machines have made denture fabrication possible with high accuracy in a short time. However, the relationship between the milling conditions, accuracy, and milling duration has not been clarified. This study aimed to clarify the effects of milling conditions on surface roughness and milling duration. The specimen was designed using a CAD software, and milled using PMMA disks. In milling, the parameters of finishing the specimen surface were adjusted. Three different spindle speeds and four different feed rates were set. Twelve combinations of each parameter were used for milling, and the surface roughness and milling duration were measured. Results showed that the surface roughness significantly increased with the feed rate on the slopes of the specimen. The surface roughness differed with the spindle speed on the left and right slopes. The spindle speed and feed rate did not affect the surface roughness on the flat surface. The milling duration was not affected by spindle speed but decreased as the feed rate increased. In conclusion, by increasing both the spindle speed and feed rate, the milling duration could be shortened while maintaining a constant surface quality. The optimum milling conditions were a spindle speed of 40,000 rpm and feed rate of 3,500 mm/min.

The Covid-19 crisis has been one of the most relevant economic shocks in the history of the modern economy, mainly due to the restrictions on travel and social interaction taken by the authorities (quarantines, closure of schools, artistic activities, bars, and restaurants, capacity in stores), to reduce the transmission of the virus (Sars-Cov2), having consequences on the national productions of all countries (about 7% for the loss of GDP worldwide). In our study, we analyze, through clustering methods, the effects of the level of contagion, economic projections, and economic mitigation measures taken by governments for managing the pandemic. According to the analyses, it can be concluded that a low infection rate implies a lower loss of product and economic savings when applying economic mitigation measures, taking relevance in the management of sanitary measures to reduce the transmission of the virus (and the temporality of these).

This paper offers an in-depth exploration of the complex relationship between seasonality and Autism Spectrum Disorder (ASD). It reviews existing research, providing a comprehensive summary of findings and highlighting the multifaceted dimensions of seasonality as an environmental factor that influences the etiology of ASD. The discussion encompasses various factors, including birth months, maternal health, dietary choices, and vitamin D deficiency, and delves into the intricate interplay of seasonality with environmental influences such as viral infections and solar radiation. The present study raises essential questions regarding the timing of environmental influences and the factors contributing to the rising prevalence of ASD. Ultimately, it underscores the need for future epidemiological research to incorporate more extensive investigations of environmental risk factors and employ advanced statistical analyses. This comprehensive overview contributes to a deeper understanding of how seasonality factors may be linked to the occurrence of ASD and its increasing prevalence, recognizing the multifaceted and diverse nature of these interactions.

The importance of utilizing an optimal and efficient braking system is emphasized in this study. Braking system optimization and efficiency were practically determined through successive tests on the brake dynamometer, according to road safety regulations. Subsequently, the results were correlated with practical tests involving consecutive braking at different speeds, tracking both the time and distance of braking. The coefficient of friction (COF) was established for each braking instance by utilizing the braking force measured on the brake dynamometer. This paper aims to highlight the genuine impact of the braking system by measuring and establishing its braking efficiency depending on the car’s speed, time, and distance of braking.

Abdominal entry is the critical initial step of laparoscopic surgery as it is associated with unexpected complications. Acceptable working space is a prerequisite for safe minimal access surgery. Direct trocar insertion (DTI) technique has been often reported to be time-consuming and capable of causing injuries. We aim to facilitate conventional DTI employing an easy-to-use self-designed abdominal wall-lifting device, OneShot-M. We tested its safety in a porcine model. 18 pigs were randomized into three groups (n=6): open laparoscopy (Group A), DTI (Group B), DTI using OneShot-M (Group C), suitable for a 5-mm trocar. Outcomes were access time, entry-related minor and major complications. Median access time was shorter in C (43 seconds; range: 35-49 seconds), followed by B (82.5; 72-90) and A (190.5; 182-240). Statistically significant differences in procedural time between techniques were detected (p-value = 0.0005). Pairwise comparisons exhibited a statistically significant difference between A and C in terms of access time. Only one difficulty in entry was seen in C; pigs experienced several occurrences in A and B. This pilot study suggests that OneShot-M appears to be a technically feasible, fast and safe method for pneumoperitoneum induction in a porcine model, as it provides sufficient exposure of the operative field without access-related complications. Further clinical studies are needed.

Iris laevigata Fisch. is a perennial aquatic plant with strong cold resistance and showy flowers. However, the flowering period of a single flower is only two days, greatly limiting its potential use in landscaping and the cutting-flower industry. In addition, I. laevigata is often challenged with various abiotic stresses including high salinity and drought in its native habitat. Thus, breeding novel cultivars with prolonged flowering time and higher resistance to abiotic stress is of high importance. In this study, we first performed genome-wide identification of WRKYs, key transcription factors in modulating flowering time and abiotic stress responses. We overexpressed IlWRKY22 in Arabidopsis thaliana and found the flowering time was delayed in the transgenic plants. Molecular characterization further revealed that IlWRKY22 promotes the expression of CO and GA20OX genes in the photoperiod and gibberellin pathways, respectively, and inhibits the expression of SPL3 in the aging pathway. We also created transgenic Nicotiana tabacum overexpressing IlWRKY22, which showed significantly improved resistance to both salt and drought compared to control plants. Specifically, the photosynthetic rate, maximum potential quantum efficiency of Photosystem II (Fv/Fm), and chlorophyll content were higher in the transgenic plants, which was accompanied with higher antioxidant enzyme activity and lower levels of reactive oxygen species. Thus, our study revealed a unique dual function of IlWRKY22, an excellent candidate gene for breeding novel I. laevigata cultivars of desirable traits.

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study aimed to investigate whether it is possible to calculate the time constant of the semiconductor survey meter using the X-ray equipment installed in each medical facility and whether it is possible to calibrate the semiconductor survey meter. Attach an additional filter to the medical X-ray system to satisfy the standards of N-60 to N-120, add more copper plates from there, and calculate the first and second half-value layers to compare with N-60 to N-120 values. Next, we will measure the leakage dose using a medical X-ray system and calculate the time constant of the survey meter. The survey meter is then calibrated and compared with the calibration factors in industrial X-ray system. Per experimental results, it is possible to reproduce the N-60 to N-120 radiation quality using a medical X-ray system and to calculate the time constant from the measured results assuming actual leakage dosimetry using that radiation quality. We also found that the calibration factor was equivalent to that of an industrial X-ray device. It was revealed that semiconductor survey meters can be calibrated using a medical X-ray system.

This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country's administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.

This work investigates the sliding mode control (SMC) problem for a class of uncertain switched systems subject to asynchronous switching and an assigned finite time constraint. Two important issues are how to ensure the reachability of state trajectories within the assigned time and the input-to-output finite-time stability (IO-FTS) of the closed-loop switched systems during the whole phase under asynchronous switching. To achieve these objectives, an asynchronous sliding mode controller with adjustable parameters is constructed to drive the state trajectories onto the sliding surface during the assigned finite-time interval. By means of the partitioning strategy, sufficient conditions for the IO-FTS of the closed-loop switched are derived during the whole phase [0,T] using the multiple Lyapunov function (MLF) approach. Additionally, the asynchronous characteristics are detailedly investigated while analyzing the reachability of a specified sliding surface. Finally, an illustrative example is given to illustrate the effectiveness of the proposed method.

This trial was carried out to find out the effects of the parent flocks and the hatching time of broiler chickens on the production traits and bacteriota development of animals. Two times 730 hatching eggs were collected from two different parent flocks with age of 22-23 and 65-66 weeks. In the hatchery both groups were divided into two subgroups, when the day-old chickens were separated according to their hatching time. A feeding trial was carried out afterwards, using the 4 treatments in 6 replicate floor pens, feeding commercial starter, grower, and finisher diets, that contained all the nutrients according to the breeder’s recommendations. The day-old chickens of the parent older flock and those hatched later were heavier and this advantage of the birds remained until end of the production period. The different age and origin of parent flocks failed to modify the microbiological parameters of the chicken’s ceca. However, the hatching time significantly influenced the different bacteriota diversity indices. The late hatched chickens showed higher Bacteroidetes and Actinobacteria and lower Firmicutes abundances at day 11. These treatments resulted differences in the main families, Ruminococcaceae, Lactobacillaceae, Bacteroidaceae. These differences could not be found at day 39.

Glioblastoma multiforme (GBM) and malignant gliomas are the most common primary malignant brain tumors. Temozolomide (TMZ) chemotherapy plus radiation therapy (RT), admi-nistered after debulking surgery, increased median survival time (MST) from 12.1 months with RT alone merely to 14.6 months. In this study, the action of deuterium-depleted water (DDW) on the survival of GBM patients who also received conventional therapies was investigated. Without changing the conventional treatment, the daily fluid intake of the patients was wholly replaced with DDW in 1.5-2 liters per day volume to reduce D concentration in their bodies. The primary endpoint was MST. The 55 patients involved, receiving conventional treatment and consuming DDW, showed longer MST (30 months) compared to the historical control (12.1-14.6 months). There was a massive difference between the two genders in the calculated MST values; it was 25 months in the male subgroup (n=33) and 42 months in the female subgroup (n=22). MST was 27 months without TMZ treatment (38 patients) and 42 months in the TMZ-treated group (17 patients). For the selected 31 patients, who consumed DDW in the correct way in addition to conventional treatments, MST was 30 months. Within this group, the 20 subjects who had relapsed before DDW treatment had 30 months of MST, but in those 10 subjects who were in remission when DDW treatment started, MST was 47 months. In the subgroup of patients who started DDW treatment parallel with radiotherapy, MST was 47 months again, and it was 25 months when DDW treatment started 8 weeks or later after the completion of radiotherapy. Altogether, survival times were substantially prolonged, compared to prospective clinical data of patients with primary GBM. Consequently, if conventional therapies are supplemented with D depletion, better survival is achieved in advanced stage of GBM than with the known targeted or combination therapies. Application of DDW is recommended in all stages of the disease, before surgery, parallel with radiotherapy, and repeated DDW courses are advised when remission has been achieved.

In this study, we meticulously compared the practical performance of four bootstrap methods for assessing the significance of causal analysis in time series data, recognizing that their evaluation has not been sufficiently conducted in the field. The methods investigated were uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-sieve bootstrap (ARSB), a resampling method based on autoregressive (AR) models that approximates the underlying data-generating process. Our study found that the AR-sieve bootstrap (ARSB) method outperformed the others in detecting both self-excitation and causality among variables. In contrast, the uncorrelated phase-randomized bootstrap (uPRB) and Stationary Bootstrap (SB) methods demonstrated limitations in specific scenarios. This detailed comparison highlights the need for selecting suitable bootstrap methods to ensure accurate results, ultimately guiding researchers in their choice of method for real data analysis.

The dynamic response of a Bell-and-Bloom magnetometer to a parallel (to the bias field) time-dependent field is modelled beyond the commonly assumed quasi-static regime. The results unveil features that are related to the parametric nature of the considered system. It is shown that a for low-amplitude time-dependent field different operating conditions are possible and that, beside the commonly reported low-pass-filter behaviour, a band-pass response emerges. Moreover, we numerically show that for larger field amplitude the system, due to its parametric nature, has a kind of ``non-linear'' response.

A solution is presented that describes a region of space, box or warp bubble, where time gets slowed down by an arbitrary factor, while reducing the components of the energy-impulse tensor by any chosen amount.

In Thailand, parking time violation is a major problem, especially for mini-marts. At present the task of detecting parking time violation is mainly conducted manually using Closed-Circuit Television (CCTV). This method requires additional human labour to track incoming and outgoing vehicles. Therefore, low cost time violation tracking is needed. To the best of our knowledge, there has not been any research for parking violation detection and tracking conducted for parking time limits. This paper introduces a novel parking time violation detection algorithm using the Yolov8 and DeepSORT tracking algorithms to track vehicles in consecutive frames. The presented parking violation tracking algorithm can provide a guideline for research in parking time violation detection.

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine learning based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions namely; Iran, Mozambique, Sri-Lanka, and Sudan where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction , and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 to 0.98 across the four test regions at hand.

Incremental stability analysis for time-delay systems has attracted more and more attentions for its contemporary applications in transportation processes, population dynamics, economics, satellite positions, etc. This paper researches criteria on v(t)-incremental stability, which is defined to demonstrate the convergence rate for incremental stability, for time-delay systems. Firstly, the sufficient conditions for v(t)-incremental stability for time-delay systems with continuous right-hands are studied, and several corollaries for specific cases are provided. As for time-delay systems with discontinuous right-hands, after expounding the relevant conditions for the existence and uniqueness of the Filippov solution, by using approximation methods, sufficient conditions for v(t)-incremental stability are obtained. The conclusions are applied to linear switched time-delay systems and Hopfield neural network system with composite right-hand afterwards.

Wood has been used in the construction, furniture, and automotive industries since ancient times. In areas where wood material is used, it is combined with various fasteners. The durability of the products produced using wooden materials depends on the performance of the fasteners. Since wood is a hydroscopic and biodegradable material, various changes occur in its structure when exposed to external weather conditions. Wood materials used especially in the field of construction and urban furniture are exposed to effects such as extreme temperatures, freezing, moisture, or drying depending on the seasons. In this study, the effect of freeze-thaw cycling (FTC) process on screw direct withdrawal resistance (SDWR) of plywood produced from beech, ozigo, and okoume species was investigated. In this context, the effects of screwing time (before or after), screw orientation (face or edge), number of cycles (0 to 7) in the FTC process and plywood type parameters on SDWR were investigated. As a result of the tests, when the mean SDWR values were examined according to the plywood type, the highest values were obtained in beech, ozigo, and okoume plywood, respectively. Considering the screwing time parameter, it was determined that there was no statistically significant difference between the mean SDWR values in other plywood types except beech plywood (p&lt;0.05). When the screw orientation parameter is examined, screwing in the face direction gave better results than screwing in the edge direction in all plywood types. There was a decrease in the mean SDWR values inversely proportional to the increase in the number of cycles in FTC-treated plywood.

Adaptive Cruise Control (ACC) is useful in the most dangerous maneuvers such as braking and acceleration. This study assesses how ACC modifies traffic flows by analysing the differences be-tween manual and autonomous driving in connected and autonomous vehicles (CAVs). Using a platoon of 80 vehicles, tested in pairs on the road, it was possible to define the speed trends during braking and acceleration and the reaction times to the driving maneuvers (PRT, TH, TCC) with a kinematic data detector. The interactions between the CAV and the driver, have been studied in-novatively, i.e through gaze analysis. Situations of potential danger, characterized by the braking of the vehicle that precedes the car with the driver equipped with the eye tracker tool, have been recreated, considering the influence of the driver’s ACC experience. Results statistically confirmed that with the ACC switched on the reaction times are greater than manual driving (2.4/3.8 sec); this can lead to a reduction in road safety, further motivated by the rapid decrease in speed. The interpolation between automated and human data, finally, has allowed the detection of some criticalities of the system that are fundamental in order to reach the second level of automation.

This paper gives solutions to puzzles of double Wheeler’s delayed choice experiments, a photon’s passing two paths simultaneously and wave collapse velocity measure, and shows new quantum control, new quantum oscillation and new controlling photon oscillator (that can be arranged in quantum computer and quantum network as their classical correspondences) in experiments. Furthermore, this paper obtains the measured attractive state and the new useful key technology of altering state’s evolving paths for quantum systems. We discover the reason and the technological way for the present changes the past, further show their feasible experiments of new quantum control, new quantum oscillation and new quantum oscillator. Finally, this paper achieves the collapse response time t of wave, further concretely deduces the measured collapse velocity of quantum system. In the past, the collapse velocity cannot be objectively deduced and experimentally measured.

: In this article, the authors propose an algorithmic approach to building a model of the dynamics of economic and, in particular, innovation processes. The approach under consideration is based on a complex algorithm that includes (1) decomposition of the time series into components using singular spectrum analysis; (2) recognition of the optimal component model based on fuzzy rules, and (3) creation of statistical models of individual components with their combination. It is shown that this approach corresponds to the high uncertainty characteristic of the tasks of the dynamics of innovation processes. The proposed algorithm makes it possible to create effective models that can be used both for analysis and for predicting the future states of the processes under study. The advantage of this algorithm is the possibility to expand the base of rules and components used for modeling. This is an important condition for improving the algorithm and its applicability for solving a wide range of problems.

This paper presented a method for calculating daily load curves by considering charging and discharging locations of electric vehicles to help to understand the impact of loads generated by charging and discharging of electric vehicles on the power grid. Based on the estimated PEVs’ share, the PEVs discharge power was calculated to reflect both the characteristics of the arriving vehicle in the morning and the SMP plan after establishing a assumption that the electric vehicle arrived at work in the morning and the electric vehicle arrived at home in the afternoon for each of the charging/discharging locations, that is, work and home, of electric vehicles in the city. After calculating the daily load curve for each charging/discharging power type for the PEVs charging strategy, which takes into account both the characteristics of the vehicle arriving at home in the afternoon and the TOU fare system and the characteristics of the vehicle arriving at work in the morning and the SMP fare system, it was analyzed by comparing the impact assessment on the grid by adding the existing load. The results of this paper provide an accurate understanding of the impact of PEVs charging and discharging loads on the power grid. The results should help to establish PEVs charging and discharging load management plans to prevent overloading the power grids with appropriate SMP and TOU tariffs while curbing the reinforcement and expansion of power grids as much as possible.

These days, numerous organizations and Analysis Centers (AC) offer various Ambiguity Resolution (AR) products using various methodologies. To use it for time-frequency transfer, there is no associated study. This paper chooses 16 MGEX stations with external high-precision atomic clocks to constitute 15 international time comparison links and uses AR products data from CNES, SGG, CODE, and PRIDE laboratory, using three ambiguity-fixed strategies, to thoroughly evaluate the effects of various strategies and AR products for high-precision time-frequency transfer. We reach the following results by using the IGS final clock product as a reference and comparing it to ambiguity-float. With various ambiguity-fixed procedures, the time stability STD of time transfer is increased for a single GPS, and the improvement ranges from 10% to 40%. The frequency stability has barely improved; up to 40%, the most notable improvement comes from FCB with GRM products. The time stability STD of combinations has improved after the addition of the Galileo system compared to the single GPS, and the improvement ranges from 2% to 9%. Most strategies have improved, while a few techniques have weakened with the GEC (GPS+Galileo+BDS) combination. We feel that the stability has not significantly increased with the system's increase within short-term stability after comparing multiple groups of linkages.

Fiber distributed optical fiber acoustic sensor (DAS) is generally used in distributed long-distance acoustic/vibration measurement. We found that DAS with excellent performance of low self-noise and anti-fading in combination with the plastic structure in daily life as an acoustic transducer can achieve high-sensitivity acoustic measurement at a single point rather than designing a state-of-the-art acoustic transducer or using special enhanced scattering fiber. The simple acoustic transducer we proposed for DAS can reach the sensitivity level of -106.5dB re. 1rad/μPa at a sensing range of 5.1 km, which can meet many demands on the industrial site.

In Los Angeles, at one point, the Covid-19 testing positivity rate was 6.25%, or one in sixteen. This translates to, on average, one in sixteen specimens testing positive and the vast majority testing negative. Usually, we run sixteen tests on sixteen specimens to identify the positive one(s). This process can be time consuming and expensive. Since a group of negative specimens pooled together for testing will produce a negative result, one single test could potentially eliminate many specimens. Only when the pooled specimen tests positive do we need further testing to identify the positive one(s). Based on this concept, we designed a strategy that will identify the positive specimen(s) efficiently. Assuming one in sixteen specimens is positive, we find that only four tests are needed. Furthermore, we can run them simultaneously, saving both resources and time. Although, in the real world, we cannot make the assumption of only one positive specimen, the same strategy works with slight modification and proves to be much more efficient than the conventional testing. Our strategy returns an answer 48% of the time in four tests and one time cycle. Overall, the average number of tests is seven or eight depending on the follow-up testing, and the average time cycle is about one and a half.

This joint experimental-theoretical work focuses on molecular and photophysical properties of the spiropyran-containing amphiphilic molecule in organic and aqueous solutions. Being dissolved in tested organic solvents, the system demonstrates positive photochromism, i.e. upon UV stimulus the colorless spiropyran form is transformed into colorful merocyanine isomer. However, aqueous solution of the amphiphile possesses a negative photochromism: the orange-red merocyanine form turns to be thermodynamically more stable in water, and both UV and vis stimuli lead to the partial or complete photobleaching of the solution. The explanation of this phenomenon is given on the basis of density functional theory calculations and classical modeling including thermodynamic integration. The simulations reveal that stabilization of merocyanine in water proceeds with the energy of ca. 70 kJ mol−1, and that the Helmholtz free energy of hydration of merocyanine form is 100 kJ mol−1 lower as compared to the behavior of SP isomer in water. The explanation of such a difference lies in the molecular properties of the merocyanine: after ring-opening reaction this molecule transforms into a zwitterionic form, as evidenced by the electrostatic potential plotted around the opened form. The presence of three charged groups on the periphery of a flat conjugated backbone stimulates the self-assembly of merocyanine molecules in water, ending up with the formation of elongated associates with stack-like building blocks, as shown in molecular dynamics simulations of the aqueous solution with the concentration above critical micelle concentration. Our quantitative evaluation of the hydrophilicity switching in spiropyran/merocyanine containing surfactants may prompt the search for new systems, including colloidal and polymeric ones, aiming at remote tuning of their morphology, which could give new promising shapes and patterns for the needs of modern nanotechnology.

We survey the main results emerging in the rapidly developing field that considers the geometry of thermodynamics and statistical mechanics. Our ideas of stability depend on variational principles – Maximum Entropy together with Least Action (and the isomorphic Least Exertion) – and this stability (invariance in time) indicates the centrality of the geometries of the systems. We explore a variety of examples of the application of thermodynamics in both macro- and micro-systems, and propose that irreversibility is an intrinsic and fundamental property of the physical realm (instead of being merely emergent). Using the concept of complex time, we examine the underlying mathematical unity of quantum mechanics, statistical mechanics, and geometrical thermodynamics. We show how the Planck and Boltzmann constants provide the fundamental quantum units for the phenomenological characteristics of the two Noether-conserved quantities controlling overall thermodynamic behaviour: respectively, energy and entropy production. We discuss the far-reaching isomorphisms (between all of complex time, temperature and the system symmetry) that always exist for Maximum Entropy systems, even as they exhibit either irreversible (dissipative, yet still dynamically stable) or reversible (including stable and static geometries) behaviours.

This paper presents a cooperative parallel tabu search meta-heuristic for the vehicle routing problem with time windows. It is based on the scheme in which several search threads cooperate by asynchronously exchanging information on the best solutions identified. The exchanges are performed through a mechanism called adaptive memory which holds and manages a pool of solutions. This enforces the asynchronous strategy of information exchanges and ensures the independence of the individual search threads. Each of these independent threads implements a tabu search meta-heuristic. Comprehensive computational experiments and comparisons to best known solutions show that the proposed cooperative parallel tabu search algorithm is able to achieve 48 new best solutions on VRPTW benchmark instances.

We consider partition functions, in the form of state sums, and associated probabilistic measures for aperiodic substrates described by model sets and their associated tiling spaces. We propose model set tiling spaces as microscopic models for small scales in the context of quantum gravity. Model sets possess special self-similarity properties that allow us to consider implications on large and observable scales from the underlying (non-ergodic) dynamics. In particular we consider the implication of the underlying aperiodic substrate for the well known problem of time in quantum gravity, and propose a correspondence between small and large scales, the so-called ergodic correspondence, that addresses the emergence of matter properties and spacetime structure. In the process we find a possible bound in the mass spectrum of fundamental particles.

Leisure-time physical activity (LTPA) is associated with access and use of public open spaces. The “President João Goulart Elevated Avenue” and current denominated “Minhocão” is a facility for leisure activities that are open for people during the night/weekends. The aim of this study was to examine if the prevalence of LTPA among individuals living in the surroundings of Minhocão is different according to proximity to, and use of, the facility. We conducted a cross-sectional study with cluster sampling with people aged ≥18 years who lived in households until 500m and between 501m to 1500m of Minhocão. The survey was conducted between December/2017 until March/2019 with an electronic questionnaire self-responded. We conducted bivariate analysis and Poisson regression to examine possible differences in LTPA according to the proximity of residences and use of Minhocão. The analysis used post-stratification weights. A total of 12,030 telephone numbers of people were drawn (≤500m = 6,942; and &gt;500m to ≤1500m = 5,088). The final sample analyzed were of 235 residents who returned the questionnaires. There was a higher prevalence of individuals engaging in at least 150 minutes per week of LTPA among users than non-users (Prevalence Ratio=2.23, IC95%1.72-2.90). People who used the park had higher prevalence of all types of LTPA than non-users. The results can serve to inform government decision-making on the future of Minhocão.

(1) Background: Deep hypothermic circulatory arrest (DHCA) with selective antegrade cerebral perfusion (ACP) is an established cerebral protection technique for the conduction of complex surgical procedures involving the aortic arch. It is controversial if the duration of DHCA is associated with adverse outcome in patients with acute type A aortic dissection (AAAD). Our goal was to investigate whether DHCA time was associated with surgical outcome in patients undergoing a surgical treatment of AAAD. (2) Methods: 410 Patients were divided into two groups concerning the DHCA time less than 60 minutes and equal or longer than 60 minutes. (3) Results: Patients with longer DHCA time were significantly younger (p=0.001). Intraoperatively, complex procedures with aortic arch surgery were more common in patients with longer DHCA time (p&lt;0.001). Accordingly, cardiopulmonary bypass (p&lt;0.001), cross-clamping (p&lt;0.001) and DHCA times (p&lt;0.001) were significantly longer in this group. Postoperatively, only the duration of mechanical ventilation (p&lt;0.001) and the rate of tracheotomy were significantly higher in these patients. 30-day mortality was satisfactory for both groups (p=0.746). (4) Conclusions: Our results showed that improvements in perioperative management including ACP allow a successful performance of surgical treatment of AAAD under DHCA with duration of even longer than 60 minutes.

We report the results of the characterization of local Monte Carlo (MC) dynamics of an equilibrium bond fluctuation model polymer matrix (BFM), in time interval typical for MC simulations of non-linear optical phenomena in host-guest systems. The study contributes to the physical picture of the dynamical aspects of quasi-binary mosaic states characterized previously in the static regime. The polymer dynamics was studied at three temperatures (below, above and close to the glass transition), using time-dependent generalization of the static parameters which characterize local free volume and local mobility of the matrix. Those parameters play the central role in the kinetic MC model of host-guest systems. The analysis was done in terms of the probability distributions of instantaneous and time-averaged local parameters. The main result is the characterization of time scales characteristic of various local structural processes. Slowing down effects close to the glass transition are clearly marked. The approach yields an elegant geometric criterion for the glass transition temperature. A simplified quantitative physical picture of the dynamics of guest molecules dispersed in BFM matrix at low temperatures offers a starting point for stochastic modeling of host-guest systems.

Copper is a heavy metal used in many industries and known for its negative impacts on the environment and human’s health. A novel structured metal organic framework (MOF) was used for copper adsorption for this work. SEM, EDAX, XRD and FTIR were done to confirm the structure of MOF prepared. MOF of 0.05 gm was used to examine its ability in Cu+ 2 removals with different initial concentrations of Cu+2 and pH values (5, 7 and 9). The prepared MOF was able to achieve Cu removal with 94.6%, 93%, 91.5%, and 92.5 % for the initial concentrations of 5, 10, 15, and 20 ppm respectively. It also performed very well for pH 5 and 7 with average removal ranging from 93.9%-95% for pH 5 and 7 for the initial concentrations of 5, 10, and 15 respectively which indicate that the prepared MOF is of high ability in Cu+ 2 removal.

Holographic projection is a simple projection because it enlarges or reduces reconstructed images without using a zoom lens. However, one major problem associated with this projection is the deterioration of image quality as the reconstructed image enlarges. In this paper, we propose a time-division holographic projection, in which the original image is divided into blocks and the holograms of each block are calculated. Using a digital micromirror device (DMD), the holograms were projected at high speed to obtain the entire reconstructed image. However, the holograms on the DMD need to be binarized, thereby causing uneven brightness between the divided blocks. We correct this by controlling the displaying time of each hologram. Additionally, combining both the proposed and noise reduction methods, the image quality of the reconstructed image was improved. Results from the simulation and optical reconstructions show we obtained a full-color reconstruction image with reduced noise and uneven brightness.

The study of the area model of Pinus tabulaeformis forest provides an important reference for improving the management of Pinus tabulaeformis and revealing the growth law of Pinus tabulaeformis. According to the classification method proposed by Munro, stand growth and harvest prediction models are divided into three categories: full stand model, single wood model and diameter distribution model. Based on the fixed sample data of Shangluo Pinus tabulaeformis, the spatial instead of time method is used to process the data, and the weight coefficient of each model in the combined prediction model is calculated by using the optimal weighting method. The single wood model, the whole forest model and the diameter distribution model are combined by the combined prediction method to integrate the fault area prediction of Pinus tabulaeformis forest. The results show that the combined prediction method is more accurate than the single model (single wood model, whole forest model and diameter distribution model). At the same time, the method can improve the compatibility of the forest break area prediction model, ensure the consistency of the forest break area prediction, and provide a new direction for the research of forest resource monitoring and investigation.

The onset of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked unprecedented change. Due to the public health guidelines imposed during the COVID-19 pandemic, there is no longer sufficient street traffic for remaining buskers to generate sufficient revenue, leading a majority of street musicians to pursue remote music production. However, real-time music production is notoriously difficult due to the excessively high latencies that current video call platforms such as Zoom and Google Meet harbor. In this paper, we propose an architecture for a platform with end-to-end, near-lossless audio transmission tailored specifically to online joint music production, called Latent Space. We discuss the usage of a recurrent autoencoder with sequence-aware encoding (RAES) and a 1D convolutional layer for audio compression, which we dub ClefNet, as well as propose a new evaluation metric for naive autoencoders (AEs), MSE-DTW loss, which combines the traditional mean square error (MSE) loss function with dynamic time warping (DTW) to prevent an increase in loss when the target sequence predicted by the AE is strictly a temporal variation of the source sequence. Moreover, we detail the logistics of a live system implementation which uses the Web Audio API to extract raw audio samples in real-time to feed into our client-side model before relaying the traffic using peer-to-peer WebRTC technology. The Latent Space platform can be accessed at https://latent-space.tech, and the code and data can be found under the MIT License at https://github.com/rvignav/ClefNet.

Satellite Image Time Series (SITS) is a data set which includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two scenes or three scenes in different time phases). However, these methods are not suitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, this study proposed a Nonlinear Radiometric Normalization Model (named NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algroithm (GA) . In this method, GA was used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoided the selection of a single reference image. ANN was used to obtain the optimal solution of error function, which minimized the radiometric distortion between different images in SITS. SITS composed of 21 Landsat-8 images in Tianjin City from October 2017 to January 2019 were selected to test the method. We compared NMAG with other two contrasting method (refered as CM1 and CM2), and found that the average of root mean square error $(\mu_{RMSE})$ of NMAG $(497.22)$ was significantly smaller than those of CM1 $(641.39)$ and CM2 $(543.47)$, and the accuracy of normalized SITS obtained by using NMAG has increased by 22.4\% and 8.5\% toward CM1 and CM2, respectively. These experimental results confirmed the effectiveness of NMAG in the reduction of radiometric distortion caused by continuously changing noise between images in SITS.

(1) Introduction: The unprecedented coronavirus disease 2019 (COVID-19) epidemic has caused millions of infections worldwide and represents a significant challenge facing modern health care systems. This study was conducted to investigate the impact of lockdown measures on regional neonate services, which might be used to predict the long-term effects of medical behavior. (2) Methods: Using hospital information system (HIS) Statistics data from 4 January 2019 to 27 August 2020, an interrupted time-series analysis was employed to compare changes and trends in hospital admissions and disease spectrum before and after the lockdown interventions. Furthermore, this study was designed to evaluate whether the pandemic influenced newborns' healthcare behavior. (3) Results: Overall, 13,540 infants were admitted to the NICU during the pre-COVID period (n = 12082) and COVID-impacted period (n =4558). The patients' age at admission were younger than that of the pre-COVID-19 period (median age 5d vs. 6d after birth, p＜0.001). The overall number of neonate visits consistently decreased from the first days of the lockdown measures (24 January 2020). The disease spectrum for respiratory system, infectious diseases, and gastrointestinal disease indicated no declined immediately after intervention(p =0.079, p =0.113, p =0.060, respectively). There was an immediate decline in the volume of Jaundice-related conditions (p<0.001) after lockdown measures. The percentage of patients who suffer from respiratory system and infectious diseases has decreased (p =0.005 and p =0.002). However, a relatively high percentage of patients admitted to the neonatal intensive care unit (NICU) presented with Jaundice-related conditions (p <0.001). (4) Conclusions: In summary, the COVID-19 pandemic profoundly impacted the regional neonate services. However, it is still unclear what might be the effect of long-term effects from pandemic.

Abstract: Increasing physical activity (PA) and personal and social values are two of the greatest demands in the current educational system. This study examined the effect of a hybrid pro-gramme based on the Personal and Social Responsibility Model and Gamification. A total of 58 students (13.89 years old, SD = 1.14) in two groups (Experimental and Control) participated dur-ing a complete academic year. Motivation, physical activity and sedentary behaviour were as-sessed though questionnaires. Physical fitness was evaluated using previously validated field tests. The results showed significant differences over time between EG and CG in afterschool physical activity (APA) at the weekend (p = 0.003), sedentary time (p = 0.04) and speed-agility (p = 0.04). There were no significant differences in motivation. Regarding the intervention, speed-agility (p = 0.000), strength (p = 0.000), agility (p = 0.000), cardiorespiratory fitness (p = 0.001), APA-weekend (p = 0.000), APA-week (p= 0.000) and sedentary time (p = 0.000) increased signifi-cantly in the EG. Speed-agility (p = 0.000), APA-weekend (p = 0.03) and sedentary time (p = 0.008) increased in the CG. The use of this hybrid program can be useful to produced improvements in physical fitness, physical activity and sedentary hevaviours. The use of a hybrid program based on teaching personal and social responsibility and gamification strategies, produced improvements in physical fitness, physical activity and sedentary behavior, but not in motivation variables. Further research is demanded in order to contrast this relevant findings due to the potential drawbacks and diversity found in the scientific literature among model-based practice.

Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driven equipment (e.g., a pump) to maintain the fluid circulation. However, relying on buoyancy as the sole driving force may lead to several potential difficulties, one of which is generation of (possibly) time-varying nonlinearities in the dynamical system, where a difference in the time scales of heat transfer and fluid flow causes the flow to change from a steady-state regime to either an oscillatory regime or a flow-reversal regime, both of which are undesirable. In this paper, tools of symbolic time series analysis (e.g., probabilistic finite state automata (PFSA)) are proposed to identify selected regimes of operation in NCL systems, where the underlying principle is built upon the concept of pattern classification from measurements of fluid-flow dynamics. The proposed method is shown to be capable of identifying the current regime of operation from the initial time response under a given set of operational parameters. The efficacy of regime classification is demonstrated by testing on two data sets, generated from numerical simulation of a MATLAB SimuLink model that has previously been validated with experimental data. The results of the proposed PFSA-based classification are compared with those of a hidden Markov model (HMM) that serves as the baseline.

Time is a component quantity of various measurements and is also used on its own to sequence events, to compare the duration of events or the intervals between them, and to quantify rates of change of quantities in material reality or in the conscious experience [1]. The measuring equipment well might happen to be a clock. By making a presupposition that clock is Time, it is perceived experimentally that “Time exhibits wave-like properties”

The scalar field of space-time film is considered as unified fundamental field. The field model under consideration is the space-time generalization of the model for a two-dimensional thin film. The force and metrical interactions between solitons are considered. These interactions correspond to the electromagnetic and gravitational interactions respectively. The metrical interaction and its correspondence to the gravitational one are considered in detail. The practical applications of this approach are discussed.

In recent years, the photophysical properties of crystalline metal-organic frameworks (MOFs) have become increasingly relevant for their potential application in light-emitting devices, photovoltaics, nonlinear optics and sensing. The availability of high-quality experimental data for such systems makes them ideally suited for a validation of quantum mechanical simulations, aiming at an in-depth atomistic understanding of photophysical phenomena. Here we present a computational DFT study of the absorption and emission characteristics of a Zn-based surface-anchored metal-organic framework (Zn-SURMOF-2) containing anthracenedibenzoic acid (ADB) as linker. Combining band-structure and cluster-based simulations on ADB chromophores in various conformations and aggregation states, we are able to provide a detailed explanation of the experimentally observed photophysical properties of Zn-ADB SURMOF-2: The unexpected (weak) red-shift of the absorption maxima upon incorporating ADB chromophores into SURMOF-2 can be explained by a combination of excitonic coupling effects with conformational changes of the chromophores already in their ground state. As far as the unusually large red-shift of the emission of Zn-ADB SURMOF-2 is concerned, based on our simulations, we attribute it to a modification of the exciton coupling compared to conventional H-aggregates, which results from a relative slip of the centers of neighboring chromophores upon incorporation in Zn-ADB SURMOF-2.

This study investigated how fashion is expressed on social media platforms from a media ecology perspective of sustainable digital space-time, as society evolves into a digital ecosystem. Media ecology concerns how the media environment transforms human experience and impacts society and culture. A theoretical review of media ecology was conducted, and the Instagram account of global fashion influencer Susanna Lau (@susiebubble) was analyzed as a case study. In total, 300 fashion-related images were collected out of 5,817 uploaded to Lau’s Instagram between May 2012 and June 2019. These were analyzed—alongside their titles, content, hashtags, and commentaries—for visual phenomena conveying everyday divisions between spatiality and temporality, public and private, real and virtual, and geography and culture, which demonstrate ambiguous boundaries. The analysis revealed that the images reflect nuances of digital time and space as they emerge in social media, and represent a nonboundary of style across the binaries of work and leisure, public and private, real and virtual, and geography and culture—signifying a sustainable digital lifestyle. These findings illustrate how our changing daily lives are visualized through fashion on a sustainable digital platform, and suggest ongoing research into the practical impact of technological advances on fashion.

An application of deep convolutional neural network and recurrence plot for financial market movement prediction is presented. Though it is challenging and subjective to interpret its information, the pattern formed by a recurrence plot provide a useful insight into the dy- namical system. We used a recurrence plot of seven financial time series to train a deep neural network for financial market movement predic- tion. Our approach is tested on our dataset and achieved an average of 53.25% classification accuracy. The result suggests that a well trained deep convolutional neural network can learn a recurrence plot and pre- dict a financial market direction.

Knowledge of a lasers beam’s profile throughout a laser system and experiment can help immensely in diagnosing laser problems and assisting in beam alignment and focusing at a sample. Obtaining such profiles is a trivial task in the ultraviolet-visible wavelength range but more challenging with near-infrared to infrared beams. Scientific grade bolometer arrays, suitable for such a task, do exist but are extremely costly, relatively large and have a large pixel size, of the order of 80 μm, which is adequate for profiling larger beams but poses an issue when trying to profile sub 100 μm beams for example at a focal point. This communication identifies a micro-bolometer array for near- to mid-infrared laser beam profiling, which is extremely low cost. In addition, the device is very compact, enabling use in confined spaces, and has a small, 12 μm, pixel size permitting the profiling of focused laser beams. The best scientific grade device identified has a pixel size of 17 μm. This device is a powerful tool for infrared laser spectroscopists, reducing the time required to measure the spot size of beams and to achieve spatial overlap of multiple infrared beams as used in two-dimensional infrared spectroscopy, saving many hours of setup time. The use of the bolometer array as a spectrographic detector and probe of long-term beam drifts is also demonstrated.

Sorghum in Australia is grown in water-limited environments of varying extent, generating substantial genotype × environment interaction (GEI). Much of the yield variation and GEI results from variations in flowering time and tillering through their effects on canopy development. The confounding effects of flowering and tillering complicate the interpretation of breeding trials. In this study, we evaluated the impacts of both flowering time (DTF) and tillering capacity (FTN) on yield of 1741 unique test hybrids derived from three common female testers in 21 yield testing trials (48 tester/trial combinations) across the major sorghum production regions in Australia in three seasons. Contributions of DTF and FTN to genetic variation in grain yield were significant in 14 and 12 tester/trial combinations, respectively. The proportion of genetic variance in grain yield explained by DTF and FTN ranged from 0.2% to 61.0% and from 1.4% to 56.9%, respectively, depending on trials and genetic background of female testers. The relationship of DTF or FTN with grain yield of hybrids was frequently positive, but varied across the genetic background of testers. Accounting for the effects of DTF and FTN using linear models did not substantially increase the between trial genetic correlations for grain yield. The results suggested that other factors affecting canopy development dynamics and grain yield might contribute GEI and/or the linear approach to account for DTF and FTN on grain yield did not capture the complex non-linear interactions.

Temperature field is an essential attribute of metal additive manufacturing in view of its bearings on the prediction, control, and optimization of residual stress, part distortion, fatigue, balling effect, etc. This work provides an analytical physics-based approach to investigate the effect of scan strategy parameters including time delay between two irradiations and hatching space on thermal material properties and melt pool geometry. This approach is performed through the analysis of the distribution of material properties and temperature profile in three-dimensional space. The moving point heat source approach is used to predict the temperature field. To predict the temperature field during the additive manufacturing process some important phenomena are considered. 1) Due to the high magnitude of temperature in the presence of the laser, the temperature gradient is usually high which has a crucial influence on thermal material properties. Consequently, the thermal material properties of stainless steel grade 316L are considered to be temperature-dependent. 2) Due to the repeated heating and cooling, part usually undergoes several melting and solidification cycles. This physical phenomenon is considered by modifying the heat capacity using the latent heat of melting. 3) The multi-layer aspect of metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the successive layers has an impact on heat transfer mechanisms. 4) Effect of heat affected zone on thermal material properties is considered by the superposition of material properties in regions where the temperature fields of two consecutive irradiations have an overlap since the consecutive irradiations change the behavior of the material properties. The goals are to 1) investigate the effects of temperature-sensitive material properties and constant material properties on the temperature field. 2) Study the behavior of thermal material properties under different scan strategies. 3) Study the importance of considering the effect of heat affected zone on thermal material through the prediction of melt pool geometry. 4) Investigate the effect of hatching space on melt pool geometry. This work is purely employed physics-based analytical models to predict the behavior of material properties and temperature field under different process conditions, and no finite element modeling is used.

This paper discusses the metonymic uses of the greek deictic adverbs εδώ [here] and εκεί [there] in the language of politics. The paper draws examples from political speeches which taken place in the Hellenic Parliament during 2011 and discussed the financial situation of Greece during that time. The paper discusses the multiple senses of these deictic adverbs and suggests that the temporal and spatial denotations of εδώ and εκεί are subject to image schemas. It is argued that the image schemas in which εδώ and εκεί are rooted have a metonymic basis. The paper also suggests that the spatio-temporal senses of εδώ and εκεί go beyond their deictic function due to their metonymic basis.

This is a short note on a new way to describe Haug's newly introduced maximum velocity for matter in relation to the Schwarzschild radius. This leads to a probabilistic Schwarzschild radius for elementary particles with mass smaller than the Planck mass. In addition, our maximum velocity, when linked to the Schwarzschild radius, seems to predict that particles just at that radius cannot move. This implies that radiation from the Schwarzschild radius not can undergo velocity time dilation. Our maximum velocity of matter, therefore, seems to predict no time dilation, even in high Z quasars, as has surprisingly been observed recently.

The crucial stages in the geochemical evolution of the Earth’s crust, ocean, and atmosphere could be explained by the assumed low-energy nuclear reactions (LENR) that are triggered by seismic activity. LENR result in the fission of medium-weight elements accompanied by neutron emissions, involving Fe and Ni as starting elements, and C, N, O as resultants. Geochemical data and experimental evidences support the LENR hypothesis. A spectral analysis of the period 1955-2013 shows common cycles between interannual changes in atmospheric CO2 growth rate and global seismic-moment release, whereas the trending behavior of the atmospheric CO2 was in response to the anthropogenic emissions. Assuming a correlation between such seismic and atmospheric fluctuations, the latter could be explained by cycles of worldwide seismicity, which would trigger massively LENR in the Earth’s Crust. In this framework, LENR from active faults could be considered as a relevant cause of carbon formation and degassing of freshly-formed CO2 during seismic activity. However, further studies are necessary to validate the present hypothesis which, at the present time, mainly aims to stimulate debate on the models which regulates atmospheric CO2.

Molecular overtone transitions provide optical frequency transitions sensitive to variation in the proton-to-electron mass ratio (μ = m_p/m_e). However, robust molecular state preparation presents a challenge critical for achieving high precision. Here, we characterize infrared and optical-frequency broadband laser cooling schemes for TeH⁺, a species with multiple electronic transitions amenable to sustained laser control. Using rate equations to simulate laser cooling population dynamics, we estimate the fractional sensitivity to μ attainable using TeH⁺. We find that laser cooling of TeH⁺ can lead to significant improvements on current μ variation limits.

This study aims at an acoustic design of the classical concert hall and evaluation of the acoustic performance. In terms of three acoustic parameters (i.e., reverberation time (RT), clarity (C80), and lateral fraction (LF)), this study performed acoustic simulation modeling and site measurement with the K Art Hall located in South Korea as a case study. First, in order to meet the acoustic performance of the K Art Hall (target RT: 1.4~1.7 seconds, target C80: -2dB or more +2dB or less, and target LF: 10~35%), the finish materials and shape of the room as an interior acoustic design were determined. Second, the average values of the RT, C80, and LF using the acoustic simulation modeling were estimated at 1.4 second, 1.2~1.6 dB, and 29%, respectively. Third, the average values of the RT, C80, and LF through site measuring were measured at 1.5~1.64 second, 0.07~1.31dB, and 22.22~31.37%, respectively. Thus, the results of both the acoustic simulation modeling and site measuring were analyzed so as to satisfy the target acoustic performance. The results of this study will help the decision-makers (i.e., owner, construction managers, etc.) to plan the classical concert hall in terms of the RT, C80, and LF.

The importance of vertical jump in sport fields and rehabilitation is widely recognized. Furthermore, Force-Time variables of vertical jump are factors affecting jumping height. Exclusive review of each of this variables, in eccentric and concentric phases, can lead to a specific focus on them during jumping exercises. So, the aims of his study were to a) reviewing the relationship between force-time curve variables of eccentric and concentric phases with jump height and b) description of this variables in Iran national youth volleyball players society. This is an observational study. 12 elite volleyball player (Male, Iran national youth volleyball players, 17±0.7 years) have participated in this study. Correlation between Force-Time variables - included peak force (PF), relative peak force (RPP), peak power (PP), average power (AP), relative peak power (RPP), and Modified Reactive Strength Index (MRSI) - in eccentric and concentric phases and ultimate jump height has been studied. Results showed that the average power (r=0.7) and relative peak force (r=0.75) of concentric phase and MRSI (r=0.83) have significant correlation with ultimate jump height (JH). Relative peak power and average power of concentric phase can massively effect Jump Height in sports like volleyball, which vertical jump is an integral part of them. Focus on both of these factors, which has been studied in this research, in training programs, can improve athlete jump performance significantly.

This paper studies the duration patterns of unemployment spells for immigrants and the determinants of unemployment’s completion into one of a number of studied labour market states in Finland. We estimate a duration model for unemployment with competing risks of its terminating into employment, labour market training or economic inactivity. Taking into account the wide period of observation and opportunities to analyse processes of labour market integration during various periods of economic development in Finland, in combination with the individualistic character of the labour careers of immigrants, this research is beneficial owing to the many various findings concerning labour market integration of immigrants. The approach undertaken in this research has a dualistic “descriptive-dynamic” character under which integration is understood as a never-ending process, which is conditioned by a time period of long-term existence and a context of solitary action. We find that transitions out of unemployment spells have a cyclical character; after every new “cycle” in unemployment, the probability of terminating unemployment decreases further. We also find that ascriptive factors make sense in the process of job-placement of immigrants from unemployment. Therefore, the gender, education and age of immigrants, as well as the effect of the period in which first unemployment occurred, potentially predict transitions out of unemployment and further labour market integration of immigrants.

Mesoporous MgAl2O4+ LnFe0.7Ni0.3O3 (Ln=La, Pr) nanocomposites were prepared by inexpensive one-pot procedure with Pluronic P123 copolymer, Ni+Ru active component was supported by wet impregnation. The real structure of samples was studied by XRD and TEM with EDX, surface properties by FTIRS of adsorbed CO, reactivity by H2 –TPR, catalytic activity was tested in ethanol steam reforming. Disordering of the real structure of nanocomposite supports due to incorporation of transition metal cations into MgAl2O4 results in developed metal-support interface and domination of single surface metal centers. This provides a high catalytic activity in reaction of ethanol steam reforming in the intermediate temperature range ~550 oC close to that of the best known catalysts, and stability to coking. A higher activity for Pr-containing catalyst is provided by a high reactivity of surface oxygen species bound with Pr cations.

Avian mutations in vaccine strains obtained from embryonated eggs could impair vaccine effec-tiveness. We performed a systematic review and meta-analysis of the adjusted relative vaccine effectiveness (arVE) of seed cell-cultured influenza vaccines (ccIV) compared to egg-based influ-enza vaccines (eIV) in preventing laboratory-confirmed influenza related outcomes (IRO) or IRO by clinical codes, in subjects 18 and over. We completed the literature search in January 2021; ap-plied exclusion criteria, evaluated risk of bias of the evidence, and performed heterogeneity, pub-lication bias, qualitative, quantitative and sensitivity analyses. All estimates were computed us-ing a random approach. International Prospective Register of Systematic Reviews, CRD42021228290. We identified 12 publications that reported 26 adjusted arVE results. Five publications reported 13 laboratory confirmed arVE and seven reported 13 code-ascertained arVE. Nine publications with 22 results were at low risk of bias. Heterogeneity was explained by season and risk of bias. We found a significant 11% (8 to 14%) adjusted arVE favoring ccIV in preventing any IRO in the 2017-2018 influenza season. The arVE was 3% (-01 to 7%) in the 2018-2019 influenza season. We found moderate evidence of a significant advantage of the ccIV in preventing IRO, compared to eIV, in a well-matched A(H3N2) predominant season.

Cardiovascular diseases are one of the leading causes of death in the world. The capability to direct the differentiation of human pluripotent stem cells (hPSCs) into functional cardiomyocytes (CMs) provides excellent opportunities for disease modeling and novel cell-based cardiac therapies. However, current hPSC-derived cardiomyocytes (hPSC-CMs) remain largely immature, limiting experimental and clinical uses. New insights into the maturation process of adult-like hPSC-CMs are critical. In this paper, we developed a data-driven pipeline to quantify cell maturity, based on gene expression input spanning different stages of cardiac development. First, we determined that culture time can serve as a proxy of the maturity level of hPSC-CMs. Second, we selected key heart-related genes whose expression varies with culture time, based on machine learning algorithms that are applied to accurately predict culture time. Our results demonstrate that the average discrepancy between the predicted culture time and the observed culture time can be reduced to 4.461 days. Furthermore, Gene CASQ2 (Calsequestrin 2), whose protein plays a role in the storage and transportation of calcium ions, has been identified as the most important cardiac gene that correlates with the culture duration.

The transport sector is heavily relying on the use of fossil fuels that are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity, through e-fuels production, are emerging to power the transport sector. To ensure environmental benefits over this transition, an accurate estimation of the impact of the use of electricity is needed. This work presents a temporal analysis of carbon intensity of e-fuels using grid electricity in countries members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for to produce low-carbon e-fuels according to the European Union legislative framework. This was achieved, building on top of the existing EcoDynElec tool, by developing EcoDynElec_xr a python tool enabling, with an hourly time resolution, the calculation, visualisation and the analysis of the historical time-series of electricity mix from the ENTSO-E. The results highlight that, in 2023, a minority of European countries were reaching low carbon intensity of electricity that enables the use of grid electricity for green electrolytic hydrogen production. The methodological hypothesis to consider the consumed electricity mix instead of the production mix, and the considered time step, are of paramount importance and drastically impact the potential load factor of green hydrogen production. The developed tool and calculated data are released under an open-source license to ensure transparency, results reproducibility, reuse on newer data, or for other purposes.

Background/Objectives: Obstructive Sleep Apnoea Syndrome (OSA) in children disrupts standard breathing patterns and sleep architecture, potentially leading to severe consequences. Early identification and intervention for OSA are crucial to prevent these issues. This study explored the relationships between waiting times for polysomnography (PSG), clinical history, age at the time of PSG, and PSG outcomes in children; Methods: Two hundred and fourteen children were prospectively enrolled. Data were extracted from medical records on the age at referral for PSG and at the time of its execution, the waiting time for PSG, diagnosis (primary snoring, mild, moderate, and severe OSA), history of snoring and apnoea, medications taken, surgical interventions, passive smoke exposure, and allergies. The records also included the Pediatric Sleep Questionnaire (PSQ); Results: The mean age at referral for PSG was 5.2 (2.8) years, the waiting time for PSG was 5.2 (5.0) months, and the age at the time of PSG was 5.6 (2.8) years. Children with a shorter history of OSA (≤12 months) underwent PSG at an earlier age (4.9 (2.7) years) compared to those with a more extended history (6.0 (2.7) years; p=0.006). A younger age at referral for PSG was associated with a lower PSQ score (r=0.167; p=0.015), a shorter history of apnoea (r=0.216; p=0.003) and snoring (r=0.187; p=0.023), but also with greater OSA severity, expressed as oxygen desaturation index (r=-0.152; p=0.026). In the correlation analysis, the waiting time for PSG, adjusted for age at referral, was longer in children with a prolonged history of apnoea (r=0.195; p=0.023) and snoring (r=0.259; p=0.002) but with a lower PSQ score (r=-0.172; p=0.045); Conclusions: Some children receive delayed treatment, increasing the risk of long-term complications. In evaluating children with suspected OSA, the duration of symptoms and the PSQ score at referral emerge as crucial factors that should be considered to optimise prioritisation for PSG access.

The finite-time turnpike property describes the situation in an optimal control problem where an optimal trajectory reaches the desired state before the end of the time interval and remains there. We consider a machine learning problem with a neural ordinary differential equation that can be seen as a homogenization of a deep ResNet. We show that with appropriate scaling of the quadratic control cost and the non-smooth tracking term the optimal control problem has the finite-time turnpike property, that is the desired state is reached in the interior of the time interval and the optimal state remains there until the terminal time $T$. This property is useful to achieve a compromise between the depth of the network and the size of the optimal system parameters which we hope will be useful to determine optimal depths for neural network architectures in the future.

Cementitious materials exhibit excellent binding properties and micropore structures that support the stabilization of TiO2 nanoparticles within the matrix. Therefore, these materials are ideal for the development of functional construction products with TiO2 nanoparticles. This study investigated the degree of hydration, microstructure, and mechanical properties of cement-containing TiO2 nanoparticles. The experimental results demonstrated that TiO2 nanoparticles accelerated the hydration rate of cement, reducing the setting times. Moreover, TiO2 nanoparticles changed matrix microstructure, decreasing the porosity and improving early-age strength. Although TiO2 nanoparticles function as nucleation sites in the cement hydration reaction, the tendency of the particles to clump implies that proper dispersion should be ensured when mixing in large quantities. When developing new building materials incorporating TiO2 nanoparticles, considering the effect of TiO2 nanoparticles on the hydration reaction of cementitious materials is crucial.

Optimization techniques are pivotal across various scientific domains, typically involving solving problems among feasible alternatives based on specific goals, alternatives, or constraints. In mathematical optimization, traditional methods like Simulated Annealing (SA) do not guarantee finding global minima/optima, especially in cases that contain high dimensions in objective functions. This paper introduces a novel approach where a free-energy driven self-adaptive SA algorithm is designed to handle such cases by incorporating free-energy costs within the Metropolis-Hastings framework. This algorithm not only dynamically adjusts to the changing dimensions of the objective function, but it also enhances a faster optimization process. Furthermore, as examples we demonstrate its capability on a convex unimodal function and the non-convex Rastrigin function, revealing faster convergence to search global minima compared to standard SA algorithm. At last, parameter estimation of noisy exponential data has been executed by our Simulated Annealing driven by the Free-Energy (SAFE) algorithm. Our results suggest that this novel approach may significantly improve optimization speed and accuracy, providing a robust tool for complex multidimensional optimization problems.