Real-time strategy (RTS) game has proposed many challenges for AI research for its large state spaces, enormous branch factors, limited decision time and dynamic adversarial environment. To tackle above problems, the method called Adversarial Hierarchical Task Network planning (AHTN) has been proposed and achieves favorable performance. However, the HTN description it used cannot express complex relationships among tasks and impacts of environment on tasks. Moreover, the AHTN cannot handle task failures during plan execution. In this paper, we propose a modified AHTN planning algorithm named AHTNR. The algorithm introduces three elements essential task, phase and exit condition to extend the HTN description. To deal with possible task failures, the AHTNR first uses the extended HTN description to identify failed tasks. And then a novel task repair strategy is proposed based on historical information to maintain the validity of previous plan. Finally, empirical results are presented for the μRTS game, comparing AHTNR to the state-of-the-art search algorithms for RTS games.

This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

In the present study we tested the effect of small molecular weight redox molecules on collagen-induced platelet aggregation. We used N-acetylcysteine amide (AD4), the amide form of N-acetylcysteine (NAC), a thiol antioxidant with improved lipophilicity and bio-availability compared to NAC, and the thioredoxin mimetics (TXM) peptides, TXM-CB3, TXM-CB13, and TXM-CB30. All compounds significantly inhibited platelet aggregation induced by collagen, with TXM-peptides and AD4 being more effective than NAC. The levels of TxB2 and 12-HETE, the main metabolites derived from the cyclooxygenase and lipoxygenase pathways following platelet activation, were significantly reduced in the presence of AD4, TXM-peptides, or NAC, when tested at the highest concentration (0.6 mM). The effect of AD4, TXM-peptides, and NAC was tested also on the clotting time (CT) of whole blood. TXM-CB3 and TXM-CB30 showed the highest increase of CT. Furthermore, two representative compounds, TXM-CB3, and NAC, showed an increase in the an-ti-oxidant free sulfhydryl groups of plasma detected by Ellman’s method, suggesting a contribution of plasma factors to the antiaggregating effects. Our results suggest that these small molecular weight redox peptides might become useful for the prevention and/or treatment of oxidative stress conditions associated with platelet activation.

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

The fractional reduced differential transform method is a finite iterative method based on infinite fractional expansions. The result obtained is the approximation of the real value. There are few reports on the approximate error and the applicable condition. In this paper, according to the fractional expansions, we study the factors related to the approximate errors. Our research shows that the approximate errors relate not only to fractional order but also to time $t$ and increase rapidly with time $t$. This method can only be applied within a certain time range and the time range is relevant to fractional order and fractional expansions. Then, many obtained achievements may be incorrect if the applicable conditions are not satisfied. Some examples presented in this paper verify our analysis.

Improving the acquisition and retention of a new motor skill is of great importance. The present study (i) investigated the effects of difficulty manipulation strategies (gradual difficulty), combined with different modalities of feedback (FB) frequency on performance accuracy and consistency when learning a novel fine motor coordination task, and (ii) examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical education students volunteered to participate in this study. Participants were divided into three progressive difficulty groups (PDG), 100% visual FB (FB1), 50% FB (FB2), and 33% FB (FB3). Progressive difficulty was increased by the manipulation of the distance to the target; 2 m, 2.37 m, and 3.56 m. Three FB modalities were investigated (i.e.: 100% visual FB (100% FB), 50% reduced feedback condition (50% RFB), and 33% reduced feedback conditions (33% RFB)). Performance assessments were conducted following familiarization, acquisition, and retention learning phases. Two stress-conditions of dart throws were investigated (i.e.: free condition (FC) and time pressure condition (TPC)). After the learning intervention, data showed that, under the free condition, the 100% FB group had a significant improvement in accuracy during all learning phases. Under time pressure condition, for the 50% RFB and the 33% RFB group, the measured variable (accuracy and consistency) showed a significant linear improvement in performance. The association between the percentage of RFB frequencies and the task difficulty (50% group) may be a more appropriate and manageable cognitive load compared to the 33% RFB and the 100% FB group. The present findings could have practical implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be essentially based on the characteristics of the learners.

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

This study demonstrates a global, non-parametric, non-iterative optimization of time-mean value of a kind of index vibrated by time-varying forcing. It is based on the fact that the (steady) forced vibration of non-autonomous ordinary differential equation systems is well approximated by an analytical solution when the amplitude of forcing is sufficiently small and its base state without forcing is linearly stable and steady. It is applied to optimize a time-averaged heat-transfer rate on a two-dimensional thermal convection field in a square cavity with horizontal temperature difference, and the globally optimal way of vibrational forcing, i.e. the globally optimal, spatial distribution of vibrational heat and vorticity sources, is first obtained. The maximized vibrational thermal convection corresponds well to the state of internal gravity wave resonance. In contrast, the minimized thermal convection is weak, keeping the boundary layers on both sidewalls thick.

Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper we experimentally validate a physical RC system that substitutes the effect of randomness for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with a minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the `next-generation’ improvement of the traditional RC algorithm.

A major task in Functional Time Series Analysis is measuring the dependence within and between processes, for which lagged covariance and cross-covariance operators have proven to be a practical tool in well-established spaces. This article deduces estimators and asymptotic upper bounds of the estimation errors for lagged covariance and cross-covariance operators of processes in Cartesian products of abstract Hilbert spaces for fixed and increasing lag and Cartesian powers. We allow the processes to be non-centered, and to have values in different spaces when investigating the dependence between processes. Also, we discuss features of estimators for the principle components of our covariance operators.

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

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

This article is a tribute to Lewis Wolpert on the occasion of the recent 50th anniversary of the publication of his article ‘Positional Information and the Spatial Pattern of Differentiation’. This tribute relates to another of his ideas: his early ‘Progress Zone’ timing model for limb development. Recent evidence is reviewed that a mechanism sharing features with this model patterns the main body axis in early vertebrate development. This tribute celebrates the golden era of Developmental Biology.

(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<0.001). Accordingly, cardiopulmonary bypass (p<0.001), cross-clamping (p<0.001) and DHCA times (p<0.001) were significantly longer in this group. Postoperatively, only the duration of mechanical ventilation (p<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.

This article explores the cross-cultural significance of three iconic fairy tales: Cinderella, Snow White, and Sleeping Beauty. Using a comparative analysis approach, the study investigates recurring themes, motifs, and symbolism found in these tales across different cultures and time periods. The research draws on primary and secondary sources such as literary works and scholarly articles to uncover the underlying messages that resonate with audiences worldwide. The historical and cultural contexts of the tales are also examined to provide a deeper understanding of their significance in shaping cultural identity and values. The findings of the research demonstrate the enduring legacy of these tales and their continued relevance in contemporary society. The study highlights how the tales have evolved and adapted over time to reflect changing cultural values and beliefs. The interpretation of the findings offers insights into the role of storytelling in shaping cultural identity and values, and the ways in which these stories continue to inspire and captivate audiences worldwide. Overall, this research provides a comprehensive and nuanced understanding of the significance of these three fairy tales across cultures and time. By illuminating the similarities and differences in these tales, the study offers a fresh perspective on the power of storytelling and its ability to transcend cultural boundaries. The research contributes to the fields of folklore, cultural studies, and cross-cultural communication, inspiring further exploration into the captivating world of fairy tales.

This paper examines interlinkages and hedging opportunities between nine major cryptocurrencies for the period between 30 September 2015 and 4 June 2020, which notably includes the coronavirus disease 2019 (COVID-19) outbreak lasting from early 2020 through the end of the sample period. The results of dynamic conditional correlation (DCC) analysis using a minimum connectedness approach show a high degree of correlation between cryptocurrencies throughout the sample period. However, the correlations reach their minimum values during the COVID-19 pandemic, which indicates that cryptocurrencies acted as a hedge or safe haven during the stressful period of the COVID-19 pandemic. The weight of cryptocurrencies was significantly reduced and their hedging effectiveness varied greatly during the pandemic, which indicates that investors’ preferences changed during the COVID-19 period.

Since December 2019, the coronavirus disease (COVID-19) has rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, the authors use statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national level in Mexico. Two types of models are proposed: first, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model is used to estimate the middle and the end of the outbreak. Model selection will be performed using Vuong’s test. The proposed models show overall fit similar to predictive models (e.g. time series, and machine learning); however, the interpretation of parameters is less complex for decision-makers and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue.

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

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

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

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

Electric vehicles can effectively make use of the time-of-use electricity price to reduce the charging cost, meanwhile using the grid power to preheat the battery before departure is particularly important for improving the vehicle mileage and reducing the use cost.In this paper, a dynamic programming algorithm is used to optimize the battery AC charging-preheating strategy to minimize the total cost of battery charging and preheating, with the charging current and battery preheating power consumption as the control variables. The cost difference between the optimized control strategy and the conventional preheating strategy was analyzed under different ambient temperatures (-20℃~ 0℃) and different target travel time (7:00 ~ 12:00). The simulation results show that the optimized control strategy makes the battery SOC and temperature reach the set value at the user's target departure time, and the total cost of the grid is the lowest. Compared with the conventional preheating strategy, the optimized control strategy can utilize the power grid energy in the valley price area, reduce the opening time of water heating PTC in the flat and the peak price zones, and the cost utilization rate can reach 18.41% ~ 73.96%, and the cost-saving effect is significant.

We hypothesized possibility that inline registered reticulorumen pH can be as biomarker of cows reproduction and health status. Aim of this study was to evaluate the relationship of reticulorumen pH with biomarkers from automatic milking system (AMS) and some blood parameters and determinate reticulorumen pH as biomarker of cows reproduction and health status. According to cows reproductive status the cows were classified as belonging to the following four groups: 15-30 d. postpartum; 1-34 d. after insemination; 35 d. after insemination (non-pregnant); 35 d. after insemination (pregnant). According reticulorumen pH assay experimental animals were divided into four classes: 1) pH<6.22 (5.3% of cows), 2) pH - 6.22-6.42 (42.1% of cows), 3) pH - 6.42-6.62 (21.1% of cows), 4) pH >6.62 (10.5% of cows). Rumination time, body weight, milk yield, milk fat – protein ratio, milk lactose, milk somatic cell count (SCC), milk electrical conductivity of all quarters of udder were registered with the help of Lely Astronaut® A3 milking robots. The pH, temperature of the contents of cow reticulorumens and cow activity were measured using specific smaX-tec boluses. Blood gas parameters were analyzed using a blood gas analyzer (EPOC, Canada). We found that pregnant cows has higher reticulorumen pH during insemination time, comparing with non-pregnant. Cows with lower reticulorumen pH has lowest milk fat – protein ratio, and lactose concentration, and highest SCC. Cows with lowest reticulorumen pH has lowest blood pH. With increase reticulorumen pH, increases blood potasium and hematocrit, decreases CO2, saturation and sodium.

Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16\% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.

The aim of this study was to analyze the relationship between healthy vascular aging (HVA) and the Mediterranean diet alongside other lifestyles in a Spanish population aged 35 to 75 years without previous cardiovascular diseases. Methods: Cross-sectional descriptive study with 501 individuals aged 35 to 75 years recruited from five health centers by random sampling stratified by age and sex (55.90±14.24 years, 49.70% men). HVA was determined in two steps. Step 1: Subjects with vascular damage to the carotid arteries or peripheral arterial disease were classified as non-HVA. Step 2: The study population was classified by age and sex using the percentiles of the vascular aging index (VAI), with VAI ≤p25 considered HVA and >p25 considered non-HVA. The VAI was estimated using the following formula (VAI = (log (1.09) × 10 cIMT + log (1.14) cfPWV) × 39.1 + 4.76. Carotid-femoral pulse wave velocity (cfPWV) was measured with the SphygmoCor® device and carotid intima-media thickness using Sonosite Micromax® ultrasound. Mediterranean diet (MD) adherence, alcohol and tobacco use were recorded through validated questionnaires. Physical activity was assessed with the ActiGraph-GT3X® accelerometer. Results: The mean VAI value was 61.23±12.86; (Men 63.47±13.75 and women 59.04±11.54; p

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

The Dirac and Klein-Gordon equations have proven to be some of the most accurate theories for describing particle behaviour and have laid the foundations of Relatavistic Quantum Mechanics. We extend the formulations to consider a 5 Dimensional spacetime with two time dimensions (as first conceptualised by Sajjad Zahir) and extend the notions to Quantum Field Theory to arrive at the behaviour of particles through the Dirac and Klein-Gordon counterparts of the 5 Dimensional spacetime. The implications of this to anti-matter are discussed.

So far, scientific studies on high frequency welding (HF) have observed the three main parameters: anode current, welding time and pressure of upper electrode. Some authors have also observed and studied the welding frequency, the power, and the values of the dielectric constants of the materials or the angle of dielectric losses. Due to the complex effects that the parameters have on the quality of the welded joint, it is necessary to extend the research and study the mutual relationships between a large numbers of process parameters on the welded joint in order to achieve satisfactory strength of the welded joint. In this research, the number of considered parameters is increased to 17, each of which is relevant to the processes of HF welding, and their mathematical equations are presented. The research is carried out on a HF welding machine improved with a linear pneumatic actuator for lowering the upper electrode and regulating the compressive force, and a measuring machine with the appropriate measuring method for determining the breaking forces with a tension gage and performing the „T“ test is made. Numerous researches were carried out on the functional changes of the process parameters of the percentage share of the values of the coupling capacitor, the anode current and the welding time on the values of the breaking forces of the welded joints. Based on the conducted research, it was concluded that the optimization of the process parameters of HF machine must be performed before the welding process, which is determined by a certain amount of energy that must be introduced into the material. These parameters are the capacity of the output capacitor, i.e., the value of the electric current flowing through the material and the time for the development of the operating power to produce welded joints with a certain strength and quality of welded joints for a specific material used in the process.

We explore the possibility to form a physical theory in $C^4$. We argue that the expansion of our usual 4-d real space-time to a 4-d complex space-time, can serve us to describe geometrically electromagnetism and nuclear fields and unify it with gravity, in a different way that Kaluza-Klein theories do. Specifically, the electromagnetic field $A_\mu$, is included in the free geodesic equation of $C^4$. By embedding our usual 4-d real space-time in the symplectic 8-d real space-time (symplectic $R^8$ is algebraically isomorphic to $C^4$), we derive the usual geodesic equation of a charged particle in gravitational field, plus new information which is interpreted. Afterwards, we formulate and explore the extended special relativity and extended general relativity an $C^4$ or$R^8$. After embedding our usual 4-d space-time in $R^8$, two new phenomena rise naturally, that are interpreted as "dark matter" and "dark energy". A new cosmological model is presented, while the geometrical terms associated with "dark matter" and "dark energy" are investigated. Similarities, patterns and differences between "dark matter", "dark energy", ordinary matter and radiation are presented, where "dark energy" is a dynamic entity and "dark matter" reveal itself as a "mediator" betwen ordinary matter and "dark energy". Moreover, "dark matter" is deeply connected with "dark energy". Furthermore, the extended Hamilton-Jacobi equation of the extended space-time, is transformed naturally as an extended Klein-Gordon equation, in order to get in contact with quantum theories. By solving the Klein-Gordon equation analytically, we derive an eigenvalue for Higg's boson mass value at 125,173945 $Gev/c^{2}$. The extended Klein-Gordon equation, also connects Higg's boson (or vacuum) with Cosmology, due to the existence of our second "time" T (cosmological time), which serve us to connect quantum theories with Cosmology. Afterwards, in the general case, we explore the symmetries of the curved Hamilton-Jacobi equation locally, in order to investigate the consequences of a $C^4$ space-time in Standard Model. An extension to Standard Model is revealed, especially in the sector of strong nuclear field. The Stiefel manifold $SU(4)/SU(2)$ seems capable not only to describe the strong nuclear field but give us,as well, enough room to explore in the future, the possibility to explain quark confinment. Our extension, flavors firstly the unification of nuclear fields and afterwards the unification of nuclear fields with electromagnetic field. The desired grand unification, is achieved locally, through the symmetry group $GL(4,C)\simeq SO(4,4)\cap U(4)$ and we present a potential mechanism to reduce the existing particle numbers to just six. Afterwards,23 present the extended Dirac equation in $C^4$ space-time (Majorana-Weyl representation) plus a preliminary attempt to introduce a pure geometric structure for fermions. Finally, we consider a new geometric structure through n-linear forms in order to give geometric explanation for quantisation

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

This study was aimed to highlight the trends of antibiotic resistance (AR) prevalence in two annualities, 2021 and 2022, for a major bacterial pathogen, Staphylococcus aureus, that causes mastitis infections necessitating antibiotic therapy in milk producing animals. Fiftyfour isolates, 27 for each year, were obtained from routine tests for the identification of clinical mastitis agents carried out in the author institution for four provinces of Abruzzo and Molise regions, Italy. These were analyzed for phenotypic resistance to eight antibiotics used in human therapy suggested for testing by the European guidelines and for the presence of 14 transferable AR genes by qPCR tests developed in this study. The prevalence of AR genes was lower than reported in studies carried out in other countries, except for blaZ detected in 59.2% 2021 isolates and in 48.1 % 2022 isolates. However, some iso-lates with blaZ were not resistant to cefoxitin. One methicillin resistant (MRSA) strain, carrying the mecA gene, was isolated in 2022 and two multidrug resistant (MDR) isolates were identified in 2021. Other AR genes occurring were aph3’-III (two 2021 isolates), ant6-Ia (one 2021 isolate), ermB (one 2022 isolate), ermC/T (two 2021 isolates) and mph (one 2022 isolate). An interview to the veterinarians who conferred the samples indicated a good management of antibiotic therapies in farms that could explain why AR prevalence was stable in the time inter-val considered, thus underlining the effectiveness of controlled antibiotic usage in containing the AR threat.

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

In the coded pulse scanning light detection and ranging (LIDAR) system, the number of laser pulses used at a given measurement point changes depending on the modulation and the method of spreading used in optical code-division multiple access (OCDMA). The number of laser pulses determines the pulse width, output power, and duration of the pulse transmission of a measurement point. These parameters determine the maximum measurement distance of the laser and the number of measurement points that can be employed per second. In this paper, we evaluate the performance and characteristics of combinations of modulation and spreading technology that can be used for OCDMA, and study optimal combinations according to varying operating environments.

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each data with the data of its previous day in historical training time series to generate a new fluctuation trend time series(FTTS). Then, fuzzify the FTTS into fuzzy-fluctuation time series(FFTS) according to the up, equal or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of future is nonlinear, Particle Swarm Optimization (PSO) algorithm is employed to estimate the required parameters. Finally, use the acquired parameters to forecast the future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or such kinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

Background: Globally, hot cooked refined rice is consumed in large quantities and is a major contributor to dietary glycaemic load. This study aimed to compare the glycaemic potency of hot and cold stored parboiled rice to widely available medium grain white rice. Method: Twenty-eight healthy volunteers participated in a three treatment experiment where postprandial blood glucose was measured over 120 minutes after consumption of 140g of rice. Three rice samples were freshly cooked medium-grain white rice, freshly cooked parboiled rice, and parboiled rice stored overnight at 4 ºC. All rice was served warm at 65 ºC. Chewing time was recorded. Results: The 24-hours cold-stored and reheated parboiled rice resulted in a 42% and 12% lower blood glucose concentration trajectory than freshly cooked medium-grain white rice and freshly cooked parboiled rice. Chewing time for 10g cold stored parboiled rice was 6 seconds (25%) longer and was considered more palatable, visually appealing and better tasting than freshly cooked medium grain (all P<0.05). Conclusions:. For regular consumers of rice reheating cooked rice after cold storage would lower the dietary glycaemic load and long term may reduce risk for type 2 and gestational diabetes. More trials are needed to identify the significance.

Objectives: Human herpes viruses can cause life-threatening diseases in immunocompromised children, especially leukemic patients. Therefore, the aim of this study is to detect the human herpes viruses (HHV1-7) and to investigate its clinical significance in Middle Eastern Pediatric Leukemia Patients by using 2 Independent PCR assays. Methods: Detection of human herpes virus DNA has been done in blood samples of 200 pediatric leukemia patients in addition to 90 blood donors as a control group using multiplex PCR assays. When a ‘‘positive’’ result was observed, real-time PCR was performed to measure the viral load. Results: The most frequent herpes virus infection in Middle Eastern Pediatric Leukemia cases was CMV, followed by EBV, then HHV6, VZV, HHV7, HSV1, and HSV2, where they were 92/200 (46%), 76/200 (38%), 72/200 (36%), 48/200 (24%), 12/200 (6%), 8/200 (4%), and 2/200 (1%) respectively. Also, there was a statistically significance difference between leukemic patients and their controls regarding CMV, EBV, HHV6, and VZV (P <0.05). Correlation between percentage of co-infection, and clinical parameters for the 7 herpes viruses has been studied, and there is an increase in absolute neutrophilic count (ANC), total leukocyte count (TLC) and duration of fever and neutropenia in age group 6-11 years for HHV6/CMV, then in age group 12-18 years especially for EBV/CMV and CMV/HHV6. Also, our results show that multiplex PCR assay is close to single PCR assay in relation to specificity and sensitivity which in turn prove its validity for early diagnosis of herpes viral infection. Conclusions: Adopting multiplex PCR technique is helpful in screening of virus infections. It will save time, effort, cost effective and will assist in rapid diagnosis. However, the clinical relevance of the virus infection needs to be evaluated by quantitative real-time PCR which in turn will help patient's management by using appropriate antiviral treatment.

We estimate general public’s willingness to pay (WTP) for agricultural soil quality protection and improvement in Spain (n=1000) and the UK (n=984) using data from a cross-sectional survey via Qualtrics panels in March-April 2021. We use a double bound dichotomous choice contingent valuation approach to elicit the individuals’ WTP. We investigate the effect of uncertainty on the success of policies aiming at achieving soil protection. In addition, to understand the heterogeneity in individuals’ WTP for agricultural soil quality protection and improvement we model individuals’ WTP through individual’s awareness and attitudes toward agricultural soil quality protection and the environment, trust in institutions; risk and time preferences; pro-social behavior and socio-demographics in Spain and the UK. We found that there is significant public support for agricultural soil quality protection and improvement in Spain and the UK. We also found that the support does not vary significantly under uncertainty of success of policies aiming at achieving soil protection. However the individual’s reasons for supporting agricultural soil quality protection and improvement are found to depend on the level of uncertainty and country. Hence, promoting public support for soil protection needs to be tailored according to the level of general public’s perceived uncertainty and geographic location.

With the advent of real-time MRI, the motion and passage of cerebrospinal fluid can be visualized without gating and exclusion of low-frequency waves. This imaging modality gives insights into low-volume, rapidly oscillating cardiac-driven movement as well as sustained, high-volume, slowly oscillating inspiration-driven movement.Inspiration means a spontaneous or artificial increase in the intrathoracic dimensions independent of body position. Alterations in thoracic diameter enable the thoracic and spinal epidural venous compartments to be emptied and filled, producing an upward surge of cerebrospinal fluid inside the spine during inspiration; this surge counterbalances the downward pooling of venous blood toward the heart.Real-time MRI, as a macroscale in vivo observation method, could expand our knowledge of neurofluid dynamics, including how astrocytic fluid preloading is adjusted and how brain buoyancy and turgor are maintained in different postures and zero gravity.Along with these macroscale findings, new microscale insights into aquaporin-mediated fluid transfer, its sensing by cilia and its tuning by nitric oxide will be reviewed. By incorporating clinical knowledge spanning several disciplines, certain disorders—congenital hydrocephalus with Chiari malformation, idiopathic intracranial hypertension and adult idiopathic hydrocephalus—are interpreted and reviewed according to current concepts, from the basics of the interrelated systems to their pathology.

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

Exact and soliton type solutions have great importance in propagation of surface waves, fluid dynamics, optics, and many other elds of nonlinear sciences. In this study, the explicit and exact soliton type solutions for two space-time fractional Equal- Width (FEW) equations with conformable derivative are procured via the hyperbolic function approach. The wave type solutions are represented in some hyperbolic and trigonometric functions.

The measurement of sP4 often varies due to different laboratory methodologies and individual canine characteristics. In this investigation, sP4 outcomes obtained from a commercial POC analyzer, designed for efficient sP4 assessment in bitches, were compared with results derived from CMIA, the reference laboratories in Thailand. Thorough documentation encompassed various parameters: mean, standard deviation (SD), 95% confidence interval (CI), minimum and maximum sP4 concentration values. Additionally, we meticulously recorded the Pearson’s correlation coefficient, Lin’s concordance correlation coefficient, and the bias correction factor. Interestingly, there was no significant difference (p &gt; 0.05) in the means obtained by the POC and CMIA. The Pearson’s correlation coefficient between POC and CMIA stood at 0.957, with Lin’s concordance correlation coefficient for POC recorded as 0.949. Furthermore, the bias correction factor was established at 0.991. This investigation followed established CMIA guidelines, modified to incorporate the mean and 95% CI as criteria for optimal breeding time using the POC analyzer. In conclusion, the commercial POC immunologic analyzer emerges as a valuable tool, aiding in precisely determining the optimal timing for natural mating or artificial insemination in bitches.

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

In this paper, we considered two phenomena in acoustically excited aqueous solutions of a strong electrolyte. These are the well-known Debye ionic vibrational potential (IVP), and radiofrequency anisotropy we discovered earlier , apparently, for the first time. Since both occur due to the accelerated motion of the solution, we have tried to combine them in one simple model. We have established that for a polarized UHF radio wave passed through a NaCl aqueous solution excited by an acoustic pulse the rotation angle of its vector E is proportional to the integral of the square of the observing IVP over time. An equivalent electrical circuit simulating the observed phenomena has been proposed and tested for physical feasibility. Several arguments are given in favour of the fluid-gyroscopic mechanism of RF anisotropy-related effects. We also found out that the IVP is practically independent of the vibrational velocity for frequencies below 10 kHz and it tends to zero at zero frequency. The latter is consistent with the law of conservation of energy but contradicts the incomplete existing theory.

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

Intermittent fasting (IF) has been indicated as a valuable alternative to the classical caloric restriction dietary regimen for lowering body weight and preventing obesity-related complications such as metabolic syndrome and type II diabetes. However, is it effective? In this review article, we analyzed over 50 clinical studies in which IF, conducted by alternate day fasting (ADF) or time-restricted feeding (TRF), was compared with the caloric restriction approach. We evaluated the different roles of IF in treating and preventing human disorders such as metabolic syndrome, type II diabetes, and some types of cancer, as well as the usefulness of IF in reducing body weight and cardiovascular risk factors such as hypertension. Furthermore, we explored the cellular pathway targeted by IF to exert their beneficial effects by activating effector proteins that modulate cell functions and resistance to oxidative stress. In contrast, we investigated the concerns for human health related to the adoption of IF dietary regimen, highlighting the profound debate on weight loss regimens. We have examined and compared several clinical trials to formulate an updated conception regarding IF and its therapeutic potential.

Abstract: Experiments conducted to date to verify the relativistic gravitational time dilation have limited scientific significance because the basic postulate of Einstein’s relativistic GR that the same time (t0) is measured in all non-inertial frames of reference, because all non-inertial frames of reference must be equal, has not yet been verified by time measurements. According to Einstein's relativistic GR, a correlation between time and different gravitational potentials must be the result of a relative relation between non-inertial frames of reference and must not the result of any other physical influence. An experiment that can differentiate between a causal correlation (relative relation) and an apparent correlation (relativistic relation) between time and gravitational potentials is still outstanding. The expressions "relative" and "relativistic" are often used as synonyms. However, Einstein's GR is a relativistic theory. In the case of general relativity, relativistic means that time depends only on the relative relation between non-inertial frames of reference and not on other physical factors. The author proposes an easy-to-realize experiment that is able to distinguish between a causal correlation (relative relation) and an apparent correlation (relativistic relation) between time and gravitational potentials, which can confirm or falsify Einstein's relativistic GR. .

This paper introduces the exponential time differencing (ETD) technique as a numerical method to solve efficiently American vulnerable options pricing. We address several challenges, including removing cross-derivative terms through appropriate transformations, treating early-exercise opportunities using the penalty method, and substituting fixed boundary conditions with corresponding one-sided finite differences. The proposed method is shown to be both accurate and efficient through numerical experiments, which also compare the results with existing methods and analyse the numerical stability and convergence rate.

To study the coupling mechanism and dynamic response of the end-face movable gear trans-mission system under complex excitation, a specific configuration of end-meshing movable gear reduction mechanism was used to achieve predetermined rigid thrust transmission and mis-matched gear meshing functions, which solved the inherent defects of traditional harmonic gear mechanism thin-wall flexible wheel easy to be damaged by fatigue. Considering the phenomenon of elastic deformation of live teeth accompanied by significant changes in meshing characteristics in the transmission process of end-meshing harmonic reducer, the influence of dynamic meshing parameters, live tooth deformation, time-varying stiffness of tooth meshing, and time-varying backlash on nonlinear dynamic performance was explored, as well as the mechanism of mul-ti-parameter coupling on transmission performance. The nonlinear dynamics model of the end-meshing harmonic reducer is established to solve the chattering prediction problem. Finally, a comprehensive test bed for the transmission system of harmonic reducer with meshing type with adjustable characteristic end was built to verify the correctness of the theoretical model and provide the theoretical and technical basis for exploring the optimal parameter selection of pas-sive vibration suppression problem.

(1) Background: There is a risk of Aflatoxin B1(AFB1) pollution in yellow rice wine[1],threatening human life and health, the risk is a major focus of government attention and social concern.The current detection technology cannot meet the needs of large-scale testing due to the disadvantages of high false positive, cumbersome operating procedures or high cost.Therefore, it is urgent to study and establish a high-sensitivity and high-fast detection method for AFB1 to provide technical support for yellow rice wine quality supervision and risk assessment.; (2) Methods: In this study, three-factor and three-level orthogonal experiments were carried out for oscillation time, reaction temperature and tomography time to determine the best reaction conditions for pretreatment.AFB1 detection was carried out by aflatoxin time-resolved fluorescence immunochromatography technology，Through the ratio of the T signal value of the detection line to the C signal value of the quality control line and the natural logarithmic value of the standard solution concentration[2], a rapid detection technology of time-resolution fluorescence immunochromatography is established.The technology was methodically assessed, the actual yellow rice wine samples were tested, and the results were compared with the HPLC method; (3) Results: The results show that the optimal reaction conditions for pretreatment are: the sample oscillation time is 20min, the sample reaction temperature is 37°C, and the sample chromatography time is 6min. In the actual sample detection of yellow rice wine, The time-resolved fluorescence immunochromatography detection technology established in this study detects AFB1. The detection limit is 0.3 μg·kg-1,The linear range is 0.8-12.0μg·kg-1,The linear equation of the corresponding standard curve y=-0.1913x+0.5206 (R2=0.9948),The standard deviation is 0.029.The results of the in-batch accuracy and precision of this method show that the addition and recovery rate is 77.9% - 105.7%,The coefficient of variation is in 4.5% - 10.3%; Inter-batch accuracy and precision test results show that the addition recovery rate is75.9% - 85.8%,the coefficient of variation is in 8.1% - 13.9%,explain that the detection technology has good accuracy and precision.Compared with HPLC, the relative error is less than 10%,It shows that aflatoxin time-resolution fluorescence immunochromatography technology is in good agreement with the test results of national standard methods; (4) Conclusions:he rapid detection technology of time-resolution fluorescence immunochromatography of AFB1 in yellow rice wine is easy to operate with fast detection speed, high sensitivity, good repeatability and stability, and has broad application prospects.

Infant hepatitis B vaccine coverage in China is high, with over 95% of infants immunized; however, high vaccine coverage can often mask low timeliness. The vaccination interval between the second and third dose is not clearly defined by immunization guidelines in China. This retrospective cohort study assessed the time interval distribution of hepatitis B vaccination among a cohort of randomly selected live births from Centers for Disease Control and Prevention across four provinces or municipalities in China between January 2017 and December 2021. Among the infants analyzed, 163,224 received the first dose of hepatitis B vaccine with 146,905 (90.0%) and 135,757 (83.2%) infants receiving the second and third doses, respectively. A total of 132,577 (90.2%) infants received the second dose between 28–61 days after the first dose. Of the 119,437 (88.0%) infants that completed the hepatitis B series between 61–214 days after the second dose, 87,067 (64.1%) infants were vaccinated with the third dose between 151–180 days after the second dose. The time interval distribution for the three doses of hepatitis B vaccine varied across the four provinces or municipalities (P&lt;0.001). Of the 58,077 infants who completed the hepatitis B vaccine series, 36,377 (62.6%) infants used the same type of hepatitis B vaccine for all three doses. Overall, the timeliness of hepatitis B vaccination for infants was lower than expected, with regional disparities observed. This highlights the need for improved timeliness through the introduction of a defined timeframe for the last two doses of vaccine, and training for obstetricians and related personnel.

In this paper, we explore the use of superluminal ionization fronts to accelerate and amplify electromagnetic radiation. These fronts are defined as optical boundaries between two regions of a gas, the neutral region and the plasma region, characterized by two different values of the refractive index. For that reason, the front velocity is not necessarily related with the motion of material particles, such as neutral atoms, ions and electrons, which can stay at rest. The fronts can therefore become superluminal without violating causality. In recent years, different experimental configurations, such as the flying-focus, showed that it is possible to create superluminal fronts in the laboratory. These fronts can easily be described theoretically in a special reference frame, called the time-frame, which is used here. In this frame, superluminal fronts reduce to time-refraction, a process that is symmetrical to the well-known optical refraction. We show that propagation through such fronts can lead to considerable frequency shift and energy amplification of probe laser beams. This could eventually be used to develop new sources of turnable radiation.

Discrete-time architectures offer a distinct advantage over their continuous counterparts, as they can be seamlessly implemented on embedded hardware without the necessity for discretization processes. Yet, because of the difficulty of ensuring Lyapunov difference expressions, their designs, which are based on quadratic Lyapunov-based frameworks, are highly complex. As a result, various existing continuous-time results using adaptive control methods to deal with system uncertainties and coupled dynamics in agents of a multiagent system cannot be directly applied to the discrete-time context. Furthermore, compared to their continuous-time equivalent, discrete-time information exchange based on periodic time intervals is more practical in the control of multiagent systems. In this paper, we first introduce a discrete-time adaptive control architecture designed for uncertain scalar multiagent systems without coupled dynamics as a preliminary result. We then introduce another discrete-time adaptive control approach for uncertain multiagent systems in the presence of coupled dynamics. Our approach incorporates observer dynamics to manage unmeasurable coupled dynamics, along with a user-assigned Laplacian matrix to induce cooperative behaviors among multiple agents. Our solution includes Lyapunov analysis with logarithmic and quadratic Lyapunov functions for guaranteeing asymptotic stability with both controllers. To demonstrate the effectiveness of the proposed control architectures, we provide an illustrative example.

The coronavirus (COVID-19) pandemic limitations may negatively affect children and youth in terms of health behaviors, and it might be especially challenging for children who are diagnosed with neurodevelopmental disorders such as children and youth diagnosed with Autism Spectrum Disorder (ASD). The present study assessed alterations in physical activity, screen time, and sleep duration of ASD children and the prevalence of meeting the 24-hour movement guidelines during the COVID-19 outbreak. Forty-six Arab Israeli mothers of children with ASD were surveyed by an online cross-sectional survey. As reported by the mothers, the results show a significant decrease in physical activity, a significant increase in screen time, and a significant increase in sleep duration during the COVID-19 pandemic. Moreover, the proportion of the sample who gained the physical activity and screen time recommendations lessened while the percentage of children who met the sleep duration guidelines increased. The prevalence of ASD children who achieved the overall 24-hour movement guidelines was very small during the COVID-19 outbreak. The outcomes extend the body of knowledge regarding the negative effects of the COVID-19 pandemic on children diagnosed with neurodevelopmental disorders and highlight the need for instant healthcare and interventions and programs for children with ASD.

The emergence of the Internet of Things (IoT) has led to the deployment of various types of sensors in many application fields, including environment monitoring, smart cities, health, industries, and others. The increasing number of connected devices has led to the creation of massive quantities of data that need to be analyzed. Typically, this data is ordered by time, as a time series. In this context, this paper presents a time series prediction model based on Recurrent Neural Networks in order to predict one step ahead. Result obtained through five Internet of Things monitoring datasets, showed that the Recurrent Neural Network obtained better performance that the prediction methods, ARIMA and SVM.

Real-time tracking is one of the most challenging problems in computer vision. Most Transformer-based trackers usually require expensive computational and storage power, which leads to the inability of these robust trackers to achieve real-time performance in resource-constrained devices. In this work, we propose a lightweight tracker, AnteaTrack. To localize the target more precisely, we develop a scaling-invariant max-filtering operator employing a sliding window combined with local max-pooling, which filters out the suspected target from the feature and performs an augmented representation while suppressing the background. For a more tight bounding box, we employ Pixel-Shuffle to increase the resolution of the feature map and get a more fine-grained representation of the target. In addition, AnteaTrack can run in real-time at 47 frames per second(FPS) on the CPU. We tested AnteaTrack on 5 datasets, and a large number of experiments have shown that AnteaTrack provides the most efficient solution compared to the same type of CPU real-time trackers. The code will be available at https://github.com/cnchange/AnteaTrack.

Due to climate change, the sugar content of grapes in Hungary has increased so the high alcohol content alone can make wines disharmonious. In most vintages, this phenomenon is only a problem for early-ripening varieties. In order to prevent and treat this difficulty, we have been carrying out experiments in grape canopy management for four years with the aim of delaying ripening and thus reducing the sugar content of the grapes. The experiments were set up on an early (Pinot noir) and a late (Welshriesling) variety; two treatments (leaf re-moval - LR and short topping - ST) were applied and compared to untreated control in the years 2019-2022. Our results showed that must sugar yield was significantly reduced in all four years and for both of cultivars, while the other measured parameters (yield, acidity, pH, Botrytis infection) were not or lightly affected.

Background: Pneumocystis pneumonia (PCP) commonly affects immunocompromised individuals, whereas in immunocompetent persons, it occurs relatively rarely, and in most cases the Pneumocystis infection is detected as an asymptomatic colonization. The present study aimed to establish the prevalence of Pneumocystis jirovecii infection in human hosts with different immune status (immunocompromised and immunocompetent), using molecular diagnostic methods, and to compare their diagnostic value with that of classical staining methods. Methods: We used the collected to this moment data from a prospective study on the prevalence of pneumocystosis among the Bulgarian population. Clinical specimens (including throat secretion, induced sputum, tracheal aspirate, and bronchoalveolar lavage collected from 220 patients suspected of PCP (153 immunocompetent and 67 immunocompromised patients) were examined with staining microscopic methods and real-time PCR for detection of P. jirovecii. Results: DNA of the pathogen was detected in 38 (17%) specimens (32 immunocompromised patients and 6 immunocompetent subjects). From all 220 clinical samples examined by staining methods, only in five (2%) P. jirovecii cysts were detected by the Gomori's stain. All patients with PCP were treated with trimethoprim-sulfamethoxazole, but in ten of them (HIV- positive patients) the disease was with fatal outcome. Conclusions: This study is the first for the country including the main available laboratory methods for diagnosis of human pneumocystosis in Bulgaria. Regarding the etiological diagnosis of PCP, in our study the sensitivity of real-time PCR was higher compared to the staining methods. The choice of a method for sample collection and examination has an important role in the efficiency of the laboratory diagnostics.

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measures and 24-hour predictions from 9 models provided by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM predictions for the following 24 hours. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multi-layer perceptrons (MLPs), which are commonly employed in time series forecasting tasks. Irrespective of the chosen memory cell type, our results demonstrate that the proposed framework consistently outperforms the CAM models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Additionally, we investigate the impact of outliers on the overall model performance.

Longer operating time in radical prostatectomy may escalate the risk of perioperative complications. Various factors like cancer extent, procedure’s level of difficulty, habitus and previous surgeries may lengthen robotic assisted radical prostatectomy (RARP) and therefor compromise outcomes. Objective: this study investigates the influence of operating time on outcomes after RARP in real life settings in a monocentric single surgeon study. Methods: 500 consecutive patients who underwent RARP between April 2019 and August 2022 were included. Patients were divided in three groups. Short (n=157; 31.4%) under or equal to 120 minutes, average (n=255; 51%) between 121 and 180 minutes, long (n=88; 17,6%) above 180 minutes console-time. Demographic, baseline and perioperative data were analyzed and compared between groups. Univariate logistic regression was completed to investigate the association between console-time and outcomes and to predict factors which may prolong surgery. Results: hospital stay and catheter days were significantly longer in group 3 with medians 6 and 7 days (p<0.001 and <0.001, respectively). Those findings were confirmed in univariate analysis, p=0.012 for Catheter days and p<0.001 for Hospital stay. Moreover, major complications were higher in patients with longer procedures p=0.008. Prostate volume was the only predictor for prolonged console time (p=0.005). Conclusion: RARP is a safe procedure and most patients will be discharged uneventfully. Yet longer console-time is associated with longer hospital stay, catheter days and major complications. Caution has to be taken in large prostate to avoid longer procedures, which may prevent postoperative advert events.

The phycobilisomes (PBS) of cyanobacteria and red algae are the primary light-harvesting an-tennas, absorbing solar energy and transporting it to photosynthetic reaction centres with ex-traordinary efficiency and rate. The mechanism of energy transfer in PBS should be investigated in conjunction with biological structural information, as the functions of proteins result from structures. Here, we report the energy transfer study in PBS from a thermophilic cyanobacte-rium Thermosynechococcus vulcanus NIES 2134 (T. 2134), with the Cryo-EM model resolved at near-atomic-resolution recently. The time-resolved fluorescence spectroscopy of PBS with the sub-picosecond resolution was discovered at 77K. Deconvolution of the fluorescence decay curve was then used to reveal the energy transfer channels and the associated transfer rates. Expert for the fluorescence lifetimes of terminal emitters, four time-components, i.e., 9 ps, 13 ps, 23 ps, and 55 ps, were recognised in the energy transfer in PBS. The energy transfer dynamics in PBS were further analysed by combining the cryo-EM structure and the spectral property. The findings aid our understanding of the energy transfer mechanisms in PBS.

Hydrogel microneedles are a promising technology for the delivery of different types of medicines locally and painlessly, as well as ISF extraction. As the hydrogel microneedles are inserted into the tissue, they swell and release drugs. To improve the effectiveness of this technology in delivering medicine at controlled and desirable doses and intervals, a deep understanding of the mechanism of drug delivery inside the microneedles is required. In this work, drug diffusion inside a tapered microneedle is investigated using numerical simulation. The microneedle is divided into many small elements, and the mass transfer equation of meloxicam is solved in each element over time. The skin is simulated as the sink in the microneedle surface for drug absorption. Simulations are performed for different sizes of microneedles. For a microneedle with a height of 500 µm and a base diameter of 250 µm, the drug completely penetrates the skin within 3.2 seconds. The rate of drug diffusion from the tip of the microneedle is higher than diffusion from the side area near the microneedle base. The obtained data demonstrate that in addition to the height and the base diameter, the microneedle’s aspect ratio, h/d, also affects the time of drug diffusion. We present a nonlinear equation to predict the time of complete drug diffusion as a function of the microneedle geometrical parameter, including the height and base diameter. The proposed equation calculates the total drug diffusion time with an error of less than 7% for all studied cases. Predicting drug diffusion patterns inside microneedles can be helpful in the biomedical field, especially in the drug-controlled release system for the optimization of drug delivery.

The electron of spin −1/2 is a Dirac fermion of a complex four-component spinor field. Though it is effectively addressed by relativistic quantum field theory, an intuitive form of the fermion still remains lacking and it is often described by the so-called Dirac belt trick. In this novel undertaking, the fermion is examined within the boundary posed by a recently proposed MP model of a hydrogen atom into 4D space-time. Its physicality and transformation to Dirac fermion of four-component spinor is unveiled consistent with Dirac belt trick. The outcomes are compatible with Dirac field theory and other associated features like, SO(3) group to SU(2) transition, quantized Hamiltonian, non-relativistic wave function and Feynman diagrams. The model though speculative could become important towards defining the fundamental state of matter and its field theory subject to further investigations.

Background: The first few minutes after birth are the most dangerous for the survival of an infant. Babies in neonatal intensive care units are either under heated or overheated, and hypothermic infants remain hypothermic or develop a fever. As a result, special attention must be paid to monitoring and maintaining the time of recovery from hypothermia states. Despite numerous studies, only a few have examined the transition from neonatal hypothermia and associated risk factors in depth. Method: A retrospective observational study was conducted to track axillary temperatures taken at the time of neonatal intensive care unit admission, which were then tracked every 30 minutes until the newborn's temperature stabilized. All hypothermic neonates admitted to the neonatal intensive care unit between January 2018 and December 2020 was included in the study. Temperature data were available at birth and within the first three hours of admission for 391 eligible hypothermic neonates. The effect of factors on the transition rate in different states of hypothermia was estimated using a multi-state Markov model. Result: The likelihood of progressing from mild to severe hypothermia was 5%, while the likelihood of progressing to normal was 34%. The average time spent in a severe hypothermia state was 48, 35, and 24 minutes for three different levels of birth weight, and 53, 41, and 31 minutes for low, moderate, and normal Apgar scores, respectively. Furthermore, the mean sojourn time in a severe hypothermia state was 48, 39, and 31 minutes for three different levels of high, normal, and low pulse rate, respectively. Conclusion: For hypothermic survivors within the first three hours of life, very low birth weight, low Apgar, and high pulse rate had the strongest association with hypothermia and took the longest time to improve/recover. As a result, there is an urgent need to train all levels of staff dealing with maintaining the time of recovery from neonatal hypothermia.

Increasingly measured data in the context of smart cities can be used to develop new and innovative business models to increase efficiency and the value of life. A time-series classification algorithm can support to automatize many different processes such as forecasting services. In order to ensure data security and privacy, Federated Learning trains a global model collaboratively on multiple clients. Having different data-distributions and data-quantities across participating clients, neural networks suffer from slow convergence and overfitting. Based on different data-distributions, data-quantities and number of clients, we develop and evaluate different data-clustering strategies to update global model weights in comparison to the state of the art. We use public time-series data, generate various synthetic datasets and train a Relational-Regularized Autoencoder for classification purposes. Our results show an improvement of model performance concerning generalization.

The measurements of the proton electromagnetic form factors in the time-like region using the initial state radiation technique are reviewed. Recent experimental studies have shown that initial state radiation processes at high luminosity electron-positron colliders can be effectively used to probe the electromagnetic structure of hadrons. The BABAR experiment at the B-factory PEP-II in Stanford and the BESIII experiment at the $\tau$-charm factory BEPC-II in Beijing have measured the time-like form factors of the proton using the initial state radiation process $e^{+}e^{-}\to pbar{p}\gamma$. The two kinematical regions where the photon is emitted from the initial state at small and large polar angles have been investigated. In the first case the photon is in the region not covered by the detector acceptance and is not detected. The Born cross section and the proton effective form factor have been measured over a wide and continuous range of the the momentum transfer squared $q^2$ from threshold up to ~42 (GeV/c)$^2$. The ratio of electric and magnetic form factors of the proton has been also determined. In this report, the theoretical aspect and the experimental studies of the initial state radiation process $e^{+}e^{-}\to p\bar{p}\gamma$ are described. The measurements of the Born cross section and the proton form factors obtained in these analyses near the threshold region and in the relatively large $q^2$ region are examined. The experimental results are compared to the predictions from theory and models. Their impact on our understanding of the nucleon structure is discussed.

In a safety-critical system typically not all provided services have the same criticality, which we call mixed-criticality systems. Criticality arithmetic, also called SIL arithmetic, is an approach to lower the development effort of a service by providing redundancy with tasks that are developed for a lower criticality level. In this paper we present ATMP-CA, which is a derivation of the multi-core scheduler ATMP. ATMP-CA is able to take into account the knowledge about the use of criticality arithmetic. ATMP-CA has a modified core allocation and procedure for utility optimisation, considering the context of the replicated tasks. We conducted experiments that show that ATMP-CA is able to provide the services using criticality arithmetic, while the reference schedulers were not.

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.

An outbreak of winter disease, complicated by severe respiratory syndrome, occurred in January 2020 in a high production dairy cow herd located in a hilly area of the Calabria region. Of the 52 animals belonging to the farm, 5 (9.6%) died with severe respiratory distress, death occurring 3-4 days after the appearance of the respiratory signs (caught and gasping breath). Microbiological analysis revealed absence of pathogenic bacteria whilst Real-time PCR identified the presence of RNA from Bovine Coronavirus (BCoV) in several organs: lungs, small intestine (jejunum), mediastinal lymph nodes, liver and placenta. Since being the only pathogen identified, BCoV was hypothesized to be the cause of the lethal pulmonary infection. Like the other CoVs, BCoV is able to cause different syndromes. Its role in calfhood diarrhoea and in mild respiratory disease is well known: we report instead the involvement of this virus in a severe and fatal respiratory disorder, with symptoms and disease evolution resembling that of Severe Acute Respiratory Syndromes (SARS).

Huge filaments with scales from several hundred megaparsecs to gigaparsecs are detected in the distribution of galaxies and clusters, quasars, gamma-bursters. The hypothesis on the nature of the huge filaments as regions of space-time deformation is proposed. An anisotropic deformation of the local region is described by the strain tensor, it depends on the velocities of matter. Galaxies get an extra velocity in the region, which leads to the formation of filamentary structures. The class of exact solution of the GR equations is constructed by introducing the special definition of the Christoffel symbols as function of the velocity of matter. With a definition of these symbols, the motion matter equation turns into identity. For the sake of simplicity, an ideal fluid is considered.

COVID-19 is fast spreading around the globe in a highly contagious manner. In this article we have described that after prolonged interventions the percentage growth curve for COVID-19 cases showed a flattened nature, after prolonged volatility in number of COVID-19 cases. The stability in the growth curve was continuously maintained from 18 April, 2020 to 29 April, 2020. The significance of this sustained stable curve on survival & doubling time has been discussed. One significant part of the study reflected that the doubling time of COVID-19 cases showed a negative correlation with the percent increase in COVID-19 cases (R=0.301). It is plausible that if such negative correlation is maintained with further flattening of the growth curve at a lower level, it may influence the infective ability of nCoV-19 & ultimately have a positive evolutionary implication on the spread of COVID-19 among the population.

Adaptable or adapted? Whether it is a question of physical, biological or even economic systems, this problem arises when all these systems are the location of matter and energy conversion. To this interdisciplinary question we propose a theoretical framework based on the two principles of thermodynamics. Considering a finite time linear thermodynamic approach, we show that non-equilibrium systems operating in quasi-static regime are quite deterministic as long as boundary conditions are correctly defined. The Novikov-Curzon-Ahlborn approach [1,2] applied to non-endoreversible systems then makes it possible to precisely determine the conditions for obtaining characteristic operating points. As a result, power maximization principle (MPP), entropy minimization principle(mEP), efficiency maximization, or waste minimization states are only specific modalities of system operation. We show that boundary conditions play a major role in defining operating points because they define the intensity of the feedback that ultimately characterizes the operation. Armed with these thermodynamic foundations, we show that the intrinsically most efficient systems are also the most constrained in terms of controlling the entropy and dissipation production. In particular, we show that the best figure of merit necessarily leads to a vanishing production of power. On the other hand, a class of systems emerges which, although they do not offer extreme efficiency or power, have a wide range of use and therefore marked robustness. It therefore appears that the number of degrees of freedom of the system leads to an optimization of the allocation of entropy production.

In a previous paper\cite{simplified}, a variation of the Collision-time Statistics method was applied to identify the relevant perturbers for line broadening under the action of a constant magnetic field. As discussed, that version was simplified and inadequate for low magnetic field and/or large perturber mass (ions). The purpose of the present work is to augment the previous work, so that such cases, corresponding to the Larmor radius exceeding the maximum impact parameter, can be handed efficiently. The results may also be used to construct analytic, i.e. impact/unified models under the usual assumptions in these models.

The purpose of this paper is to investigate the effect of a varying moment on the rotation angle of a large tethered satellite that is orbiting a planet. Two different types of orbits were investigated: a simple circular orbit and an elliptical orbit. Cases with zero and non-zero initial angular rotation velocity were investigated as well. This investigation will assist satellite docking missions. The large rigid tethered satellite is a futuristic concept, and this investigation is meant to assist possible docking missions to the satellite. To simplify the problem, the rotation is constrained to the orbital plane.

This paper explores an event-based version of quantum mechanics which differs from the commonly accepted one, even though the usual elements of quantum formalism, e.g., the Hilbert space, are maintained. This version introduces as primary element the occurrence of micro-events induced by usual physical (mechanical, electromagnetic and so on) interactions. These micro-events correspond to state reductions and are identified with quantum jumps, already introduced by Bohr in his atomic model and experimentally well established today. Macroscopic bodies are defined as clusters of jumps; the emergence of classicality thus becomes understandable and time honoured paradoxes can be solved. In particular, we discuss the cat paradox in this context. Quantum jumps are described as temporal localizations of physical quantities; if the information associated with these localizations has to be finite, two time scales spontaneously appear: an upper cosmological scale and a lower scale of elementary "particles''. This allows the interpretation of the Bekenstein limit like a particular informational constraint on the manifestation of a micro-event in the cosmos it belongs. The topic appears relevant in relation to recent discussions on possible spatiotemporal constraints on quantum computing.

It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.

Quantum effects arising from manifestly broken time-reversal symmetry are investigated using time-dependent perturbation theory in a simple model. The forward time and the backward time Hamiltonians are taken to be different and hence the forward and backward amplitudes become unsymmetrical and are not complex conjugates of each other. The effects vanish when the symmetry breaking term is absent and ordinary quantum mechanical results such as Fermi Golden rule are recovered.

L-glutamate is one the most important excitatory neurotransmitter at central nervous system level and it is implicated in several pathologies. So, it is very important to monitor its variations, in real time in animal models’ brain. Amperometric biosensors have been used because of their very high temporal and spatial resolution, and because suitable for a short to mid-term implantation. The present study aimed to develop and characterize a new glutamate biosensor design that exploits the selectivity of Glutamate Oxidase (GluOx) for l-glutamate, and the capability of a small molecule as propylene glycol (PG) to influence and extend the stability and the activity of enzyme. Different designs were evaluated by modifying the main components in their concentrations to find the most suitable design. Moreover, enzyme concentrations from 100 U/ml up to 200 U/ml were verified and different PG concentrations (1%, 0.1% and 0.05%) were tested. The most suitable selected design was Pt_c/PPD/PEI(1%)₂/GlutOx₅/PG(0.1%) and it was compared to the same already described design loading PEDGE, instead of PG, in terms of over-time performances. PG has proved to be capable of determining an over-time stability of the glutamate biosensor in particular in terms of linear region slope (LRS) up to 21 days.

Introduction Pharmacovigilance may detect safety issues after marketing of medications, and this can result in regulatory action such as direct healthcare professional communications (DHPC). DHPC can be effective in changing prescribing behaviour, however the extent to which prescribers vary in their response to DHPC is unknown. This study aims to explore changes in prescribing and prescribing variation among GP practices following a DHPC on the safety of mirabegron, a medication to treat overactive bladder (OAB). Methods This is an interrupted time series study of English GP practices from 2014-2017. NHS Digital provided monthly statistics on aggregate practice-level prescribing and practice characteristics (practice staff and registered patient profiles, Quality & Outcomes Framework indicators, and deprivation of the practice area). The primary outcome was monthly mirabegron items as a percentage of all OAB drug items. The exposure was a DHPC issued by the European Medicines Agency in September 2015. Variation between practices in mirabegron prescribing before and after the DHPC was assessed using the systematic component of variation (SCV). Multilevel segmented regression with random effects quantified the change in level and trend of prescribing after the DHPC. Practice characteristics were assessed for their association with a reduction in prescribing following the DHPC. Results This study included 7,408 practices. During September 2015, 88.9% of practices prescribed mirabegron and mirabegron composed a mean of 8.2% (SD 6.8) of OAB items. Variation between practices was classified as very high and the median SCV did not change significantly (p=0.11) in the 6 months after the September 2015 DHPC (12.4) compared to before (11.6). Before the DHPC, there was a monthly trend of 0.294 (95%CI, 0.287, 0.301) percentage points increase in mirabegron percentage. There was no significant change in the month immediately after the DHPC (-0.023, 95% CI -0.105 to 0.058) however there was a significant reduction in trend (-0.036, 95% CI -0.049 to -0.023). Higher numbers of registered patients and patients aged ≥65 years, and practice area deprivation were associated with having a significant decrease in level and slope of mirabegron prescribing post-DHPC. Conclusion Variation in mirabegron prescribing was high over the study period and did not change substantively following the DHPC. There was no immediate prescribing change post-DHPC, although the monthly growth did slow. Knowledge of the degree of variation in and determinants of response to safety communications may allow those that do not change prescribing to be provided with additional supports.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

This paper deals with the problem of real-time management of Smart Grids. For this sake, the energy management is integrated with the power system through a telecommunication system. The use of Multiagent Systems leads the proposed algorithm to find the best-integrated solution, taking into consideration the operating scenario and the system characteristics. The proposed technique is tested with the help of an academic microgrid, so the results may be replicated.

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

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

Wildfires are important natural drivers of forest stands dynamics, strongly influencing on their natural regeneration and ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns over the period 2000–2022 in the Middle Volga region of the Russian Federation on the base of remote sensing time series, considering the impact of cli-matic factors on forest fires. The temporal trends were assessed with the Mann-Kendall nonpara-metric statistical test and Theil-Sen’s slope estimator using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment indicated a high overall accuracy (> 84%) and F-score value (> 82%) for forest burnt area detection as evaluated against 581 test sites of ref-erence data. The results revealed that the fire occurrences in the region were mainly irregular with the highest frequency of 7.3 over a 22-year period. The total forest BA was about 280 thousand ha, which equals to 1.7% of the land surface area or 4.0% of the total forested area under study in the Middle Volga region. The coniferous forest stands are the most fire-prone ecosystems accounting for 59.0 % of the total BA; deciduous stands accounts for 25.1%; and insignificant fire occurrences were registered in young forests and shrub lands. On a seasonal scale, temperature generally has a greater impact on the BA than precipitation and wind speed.

This paper consists of two parts. In part I some new relations for a field theory with time intervals are derived. One concept of field theory evaluated is complementarity, another is field operators both defined within a time interval description. Part II includes specific results and commentary. Discussed are time interval dependent wave propagation surfaces for star source emission waves and derived is a metric propagation surface area requirement. The results allow to consider one same field that like gravitation within General Relativity applies to both non zero and zero mass. The associated field energy is space time dependent for non zero mass, and is related to a space time dependent metric tensor for zero mass wave particles. Defined is internal energy transfer where wave particle numbers increase linearly and mass and momentum diminish, decrease inversely with the distance from the wave emission source. The commentary are applications related to cosmological overall volume and temperature dependence.

This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

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

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

Failure characteristics induced by unloading disturbance and the corresponding mechanical mechanism of the coal seam floor are important theoretical bases for water-bursting prevention from the floor of the coal seam and rock burst alarm in deep mining. However, the existing two-dimensional ground-pressure-control theory based on shallow mining cannot sufficiently guide deep-mining practices. In this study, the redistribution of mining-induced stress field in rocks surrounding the longwall face and mechanical behaviors of strata in deep mining are investigated through a combination of numerical simulation, physical simulation, and field measurement. Results demonstrate that mining-induced stress fields in the floor of the longwall face differ in space and time. Vertical stress unloading from top to bottom of the floor and horizontal stress unloading are relatively low. A concentration zone of high horizontal stress exists at stope boundaries. The critical yield load of rock stratum in the floor is determined through thin plate yield theory. Under the combined effect of concentrated high horizontal and vertical resilience stresses, strata in the floor fracture from seam to seam if the load increases to the minimum critical buckling value. Fractured strata slide along the fracture surface, which leads to floor heave. The stope floor shows evident time-delay progressive failure characteristics. The stress shell in the stope floor in deep mining is found to be a sensitive mechanical parameter that produces three-dimensional ground-pressure behavior in the floor. This ground-pressure behavior in the stope floor is controlled by the existence of the corresponding stress shell and effects induced by its space–time evolution. This study provides theoretical basis for the dynamic control of a hazard-inducing environment in engineering and minimizing or altering disaster-occurrence conditions during the construction engineering of the coal seam floor.

Solar radiation corresponds to fundamental parameters for solar photovoltaic (PV) technology. Reliable solar radiation prediction became valuable to design solar PV systems, performance, operatively efficient planning, safety operation, grid dispatch and financial characteristics. However, high quality ground-based solar radiation measurements are scarce, especially for very short-term time horizon. Most of the existent studies trained the machine learning (ML) model used dataset with time horizon of 1-hour or day, very fewer studies have been reported using a dataset with a 1-mitute time horizon. In this study, a comprehensive evaluation of nine ensemble learning algorithms (ELA) is performed to estimate the solar radiation in Santo Domingo with a 1-minute time horizon dataset collected from a local weather station. The ensemble learning evaluated included seven homogeneous ensembles; Random Forest (RF), Extra Tree (ET), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGBM), Histogram-based Gradient Boosting (HGB) and heterogeneous ensemble named as Voting and Stacking. RF, ET, GB, HGB were combined to develop Voting and Stacking and Linear Regression (LR) was adopted in the second layer of the Stacking. Five technical metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) were used as criteria to determine the prediction quality of the developed ensemble algorithms. Comparison of the results indicates that the HGB algorithm offers superior prediction performance among the homogeneous ensemble learning and overall, the Stacking provides the best accuracy with metric values of MSE=3218.27, RMSE= 56.73, MAE=29.87, MAPE=10.60, R2=0.964.

This research presents a comprehensive bibliometric and sentiment analysis of nanocomposite literature from 1990 to 2024. Employing cutting-edge computational methods, the study delves deep into the progression of microstructural characterization, electrical properties, and mechanical behaviors of nanocomposites. The analysis utilizes advanced Boolean search strategies from the Scopus database, ensuring a thorough extraction of thematic content. The results explore various themes and insights, shedding light on trends evident in the Scopus database, particularly the prominence of research on nanocomposite microstructure, electrical attributes, and mechanical performance. The paper also offers textual analytics and bibliometric data, showcasing critical collaborative efforts and influential studies. Significant discoveries encompass the evolution of research language, shifts in thematic focus, and global contributions, providing a comprehensive perspective on the current landscape of nanocomposite research and its dynamic evolution. Moving forward, the "State-of-the-Art and Gaps Extracted from Results and Discussions" section of the paper delves into the most recent advancements in nanocomposite research. It delves into various types of nanocomposites, with a particular emphasis on their microstructural characteristics, electrical properties, mechanical dynamics, and the application of nanocomposite films. This section identifies key research themes, traces historical progress, and highlights emerging trends while underscoring the significance of collaboration and the influence of pivotal studies that have shaped the field. Lastly, in the "Literature Review Guided by Artificial Intelligence" section, the paper introduces a revised approach for researching nanocomposites through AI-guided techniques. This approach prioritizes articles published in 2023 based on citation frequency. Here, the focus is on exploring the relationship between nanocomposites and their electrical properties, emphasizing their fundamental interactions and impact on electrical characteristics. Various nanocomposite systems are covered, highlighting their composition, structure, and functionality. Findings from recent studies are integrated to provide a comprehensive overview of the current state of knowledge in this area. Notably, the sentiment analysis, anchored by an average sentiment score of 0.638771, underscores a positive trajectory in academic discourse, emphasizing the growing recognition of the potential of nanocomposites. The bibliometric exploration maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While this study is thorough, it acknowledges its limitations and advocates for broader database inclusion in future research endeavors. This work elucidates the prevailing trends in nanocomposite research, emphasizing the indispensable role of computational tools in comprehending this vast wealth of information.

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here we use joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical input across the country, Sentinel-1 12-day backscatter composites were created after incidence angle normalization, and Sentinel-2 NDVI images were smoothed to yield dekadal cloud-free composites. An optimized random forest classifier predicted the 8 crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types are largest. Furthermore we showed that the concept of classification confidence derived from the random forest classifier provided insight in the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

It is expected that scholars will continue to strengthen the depth and breadth of theoretical research on RIS, so as to provide a higher theoretical upper bound for the engineering application of RIS. While making breakthroughs in academic research, great progress has been made in engineering application research and industrialization promotion. This article will provide an overview of RIS engineering applications, mainly including the typical features, typical classifications, and typical deployment scenarios of RIS. Then the challenges and candidate solutions of the RIS are systematically and deeply analyzed, involving the cascade channel decoupling for solving the RIS beamforming, the influences and solutions of RIS regulation constraints, RIS system architecture of network controlled mode, the integrated channel regulation and information modulation, TDD mechanism used for RIS. Future trends and challenges are also provided.

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

Durability of concrete requires a dense microstructure which can be achieved by using self-compacting concrete (SCC). Both calcined clay (CC) and rice husk ash (RHA) are promising supplementary cementitious materials (SCMs) that can partially replace cement, but their use in SCC is critical due to their higher water demand and specific surface area (SSA) compared to cement. Empirical method of SCC design was adopted considering the physical properties of both CC and RHA. The influence of partially substituting cement at 20 vol-% with binary and ternary blends of CC and RHA were investigated. The fresh properties of SCC were investigated using a variety of tests. The time dependent workability was monitored by plunger method and flow resistance determined based on the rheological measurements of SCC. The evolution of the hydrate phases of the binder in SCC was determined by thermogravimetric analysis, while the durability was evaluated by rapid chloride migration test. Cement partial replacement with 20 vol-% CC has no significant effect on SCC fresh, time dependent, compressive strength and durability properties. 20 vol-% RHA on the other hand requires higher dosage of SP to achieve self-compactability and increased the viscosity of SCC. Its workability retention is only up to 30 min after mixing and exhibited higher flow resistance. It consumes more CH and improves compressive strength and chloride resistance of SCC. The ternary blending with CC and RHA yielded better fresh SCC properties compared to the binary blend with RHA, while an improved chloride penetration resistance could be achieved compared to binary CC blend.

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low field NMR (LF-NMR) relaxation characteristic data of various sample oils were analyzed. We also suggest a new method for prediction size of edible oil molecules using LF-NMR relaxation time distributions. According to the relative molecular mass, carbon chain length, transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid are displayed, reflecting the composition information of different oils. To study the correlation between the rotation correlation time and molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation, and molecular dynamics of complex fluids. Measurements of grease in the free fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured by low-field nuclear magnetic resonance.

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real- time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Human detection from Unmanned Aerial Vehicles (UAV) is gaining popularity in the field of disaster management, crowd counting, people monitoring. Real time human detection from UAV is a challenging task, because of many constraints involved. This study proposes a system for real time detection of humans on videos captured from UAVs addressing three of these constraints namely, flying height, computation time and scale of viewing. The proposed method integrated an android application with a binary classifier based on Haar-features to automatically detect human / non-human class from UAV images. The video frames were parsed and detected humans from image frames were geo-localized and visualized on Google Earth. The performance was evaluated for geo-localization accuracy, computation time and detection accuracy, considering human coverage – pixel size relationship for various heights and scale factor. Based on flying height - human size relationship and tradeoff between detection accuracy vs computation time, the study came up with optimal parameters for OpenCV’s cv2.cascadeClassifier. detectMultiScale function. This paper establishes a strong ground for further research relating to real time human detection from UAV.

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

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

The investigation of the spatiotemporal propagation characteristics of the "meteorological-agricultural-hydrological" drought system, under diverse climatic conditions, is crucial for the development of a robust drought warning system and the effective implementation of proactive drought prevention and resilience strategies. To achieve this, the current study utilizes the Soil and Water Assessment Tool (SWAT) model to simulate key components of the watershed water cycle, such as evaporation, surface water, soil water, and groundwater. Specifically, the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Nonlinear Joint Hydrological Drought Index (NJHDI) are employed to characterize meteorological drought, agricultural drought, and hydrological drought, respectively. By analyzing the correlation between these types of drought, the propagation characteristics of the "meteorological-agricultural-hydrological" drought system are elucidated using the rigorous strongest correlation coefficient method. The Yellow River Basin (YRB) is chosen as the case study for this research. Results showed that (1) The propagation time from meteorological to agricultural drought exhibited distinct seasonal characteristics, with durations of 5-6 months in spring, 2-3 months in summer, 3-5 months in autumn, and 6-8 months in winter. Compared to 1961-1990, the propagation time increased in spring and summer but decreased in autumn and winter during 1991-2010 for most YRB regions. (2) The agricultural to hydrological drought propagation showed no clear seasonal differences but increased over time. Specifically, zone C (arid/semi-arid with moderate temperatures) had shorter propagation time of 1-5 months, while zones B (transitional plateau to mid-latitude) and E (semi-arid/semi-humid temperate continental climate) experienced longer propagation time of 7-12 months. (3) Despite the extended timescales, agricultural-hydrological drought correlation was weaker than meteorological-agricultural linkage. This is because meteorological deficits directly reduce soil moisture, rapidly inducing agricultural drought. However, groundwater sustaining baseflow during agricultural drought delays streamflow deficits, prolonging the agricultural-hydrological propagation time.