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.

Time series forecasting has been a challenging area in the field of Artificial Intel-ligence. Various approaches such as linear Neural Networks, Recurrent Linear Neural Networks, Convolutional Neural Networks and recently Transformers have been attempted for the time series forecasting domain. Although, Trans-former-based architectures have been outstanding in the Natural Language Pro-cessing domain, especially in autoregressive language modeling, the initial at-tempts to use transformers in the time series arena have met with mixed success. One of the recent papers demonstrated that modeling the multi-variate time se-ries problem as independent channels on a transformer architecture produced lower mean square and mean absolute errors on benchmarks, however, another recent paper claimed that a simple linear neural network out-performs previous approaches including transformer-based designs. We investigate this paradox in detail comparing the linear Neural Network and Transformer based designs, provide insights to why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear Neural Network based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on majority of the popular benchmarks.

The aim of the study was to assess the impact of two lengths of Nordic walking (NW) training interventions combined with time restricted eating (TRE) on improving body composition parameters, lipid profile, and levels of selected adipokines in women with disrupted body mass. Overweight and obese women (n=55, age: 21-85) were recruited. Four groups were selected: 6 weeks (SG6, n=13) and 12 weeks intervention (SG12, n=13); and two control groups: CON6 (n=13); and CON12 (n=13). The training sessions took place 3 times a week (60 minutes each) and were conducted outdoor under the supervision of professional coach. The training intensity was determined individually. The extended NW program combined with TRE induced a significant weight reduction in SG12 by 1.96 kg (p=0.010) and fat tissue by 1.64 kg (p=0.05). The proposed interventions did not affect LBM, TBW [kg], VFA and lipid profile. The LDL/HDL ratio changed with small size effect. Leptin concentration differed between groups (p=0.006), but not over time. For resistin, the differentiating factor was time (p=0.019, with lower results observed after the intervention). The change in leptin concentration was negatively correlated with its baseline concentration. Extended to 12 weeks, this intervention allows for an improvement in body composition. Neither 6 nor 12 weeks of training and fasting affected the lipoprotein profile.

Dynamic time history analysis is a powerful and effective method to study the seismic response of tunnels, the dynamic time history to analyze the seismic response of the tunnel in the typical subway in this paper. The analysis results show that the overall levelling of the tunnel will not affect the tunnel too much, and the seismic response of the tunnel is mainly related to the relative displacement of the ground around the tunnel. The analysis results show that the internal force of the tunnel and the tunnel inclination have a good linear relationship, and the tunnel inclination can be used to measure the magnitude of the seismic response of the tunnel.

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. .

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and caused by the changes in magnetic field states in solar active regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares ranging from electronic communication disruption to radiation exposure-based health risks to the astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MINIROCKET), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations SEQuence Learner (Mr-SEQL), and Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the on the Space Weather ANalytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over 9 years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the true skill statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MINIROCKET, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics.

This work introduces a new architecture for a time-domain feedback-based beamformer designed to meet real-time processing demands. The primary goal of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture is a unique aspect that refines localization precision. We conduct an in-depth analysis highlighting their respective benefits and limitations to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

Time and space are fundamental concepts for characterizing and understanding our world. Despite of continuous curiosity and extensive studies, the nature of time and space remains elusive, and the underlying quantum structure is unclear. Here we propose a molecular model based on energy space decomposition to incorporate time and space synthetically in a unified framework. In the proposed model, the multipole expansion of action mapped to energy modules in scalar, vector, and matrix forms, results in the generators of time, space, and a material framework, cloud. And the invariance of time, space and cloud transformation corresponds to the conservation of energy, momentum, and mass, respectively. The classification of energy modules reveals a periodicity of energy elements, which constructs a hierarchical world in a molecular framework with dynamics represented by energy transformations. Here the role of time and space could switch from each other, and motion is attributed to the coupling of particle with intermediate bosons, corresponding to energy transformations among different energy modules. The application on Bohr model demonstrates that this picture provides a consistent classical interpretation to the foundation of quantum mechanics, and suggests an atomic model for gravitation.

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.

The present state of research in Computational Fluid Dynamics (CFD) is marked by an ongoing process of refining numerical methods and algorithms with the goal of achieving accurate modeling and analysis of fluid flow and heat transfer phenomena. Remarkable progress has been achieved in the domains of turbulence modeling, parallel computing, and mesh generation, resulting in heightened simulation precision when it comes to capturing complex flow behaviors. Nevertheless CFD faces a significant challenge due to the time and expertise needed for meticulous simulation setup and intricate numerical techniques. To surmount this challenge, we introduce paint2sim—an innovative mobile application designed to enable on-the-fly 2D fluid simulations using a device’s camera. Seamlessly integrated with OpenLB, a high-performance Lattice Boltzmann-based library, paint2sim offers accurate simulations. The application leverages the capabilities of the Lattice Boltzmann Method (LBM) to model fluid behaviors accurately. Through a symbiotic interaction with the open-source OpenCV library, paint2sim can scan and extract hand-drawn simulation domains, affording the capability for instant simulation and visualization. Notably, paint2sim can also be regarded as a digital twin, facilitating just-in-time representation and analysis of 2D fluid systems. The implications of this technology extend significantly to both fluid dynamics education and industrial applications, effectively lowering barriers and rendering fluid simulations more accessible. Encouragingly, the outcomes of simulations conducted with paint2sim showcase promising qualitative and quantitative results. Overall, paint2sim offers a groundbreaking approach to mobile 2D fluid simulations, providing users with just-in-time visualization and accurate results, while simultaneously serving as a digital twin for fluid systems.

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<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.

For colloids of NaYF4:Yb3+/Tm3+ nanoparticles in DMSO by the method of time-resolved lumi-nescence spectroscopy with nanosecond pulsed excitation at a wavelength of 975 nm the photo-physical processes that determine the course of kinetic curves have been revealed. It has been found that the luminescence rise time decreases with an increase of the concentration of activators and sensitizers due to increase of the efficiency of energy transfer from sensitizers to activators. Cross-relaxation of the excited states of activators provides a decrease of the lumi-nescence decay time with an increase of the concentration of activators and a constant concentration of the sensitizer. There was no correlation between the time of luminescence decay with the change of the concentration of sensitizers and constant concentration of activators due to the competition of the processes of energy back transfer from activators to sensitizers and the "feeding" of activators by excitations coming from remote sensitizer ions.

The Vaal Dam catchment, which is the source of potable water for Gauteng province, holds various human activities including mining, urbanization, agricultural and industrial activities coupled with high fluxes of nutrients from different areas within the catchment. This situation led to an enhancement of Harmful Algal Blooms (HABs) in the dam reservoir. Phosphorus and Nitrogen are the limiting nutrients for HABs in many eutrophic aquatic systems when their concentrations are raised in such waters. In this study, multi-time-series plot types and scutter plots were analysed to reveal the relationship between HABs proxy chlorophyll-a (Chl-a) and the potential nutrients. Also (Chl-a) data extracted from landsat-8 satellite images were visualised to show the spatial distribution of HABs in the reservoir. The results showed that the Vaal Dam HABs productivity is a function of the total phosphorus (TP) and the organic nitrogen (KJEL_N) levels, which were positively correlated with chlorophyll-a (Chl-a) concentration. The long-term analysis of Chl-a in-situ data (1986 – 2022) measured at one point within the reservoir showed an average of 11.25μg/l but at some stochastic dates, the Chl-a concentration jumped to very high values reaching the maximum value of 452.8μg/l. Such high values are associated with high records of TP and KJEL_N concentrations at the same dates which clearly reveals their effect on the productivity levels. The decadal time series and its trend analysis showed that the average productivity level of Chl-a increased during the studded period from 4.75μg/l in the first decade (1990 – 2000) to 10.51μg/l in the second decade (2000 – 2010) reaching 16.7 during the last decade (2010 – 2020). The increasing averages of the decadal values are associated with the increasing decadal average of its driving factors, TP from 0.1043 to 0.1096 to 0.1119mg/l for the three decades, respectively and KJEL_N from 0.80mg/l in the first decade to 1.14mg/l in the last decade. The decadal average of temperature showed no significant increase, 17.9 °C in the first decade and 18 °C in the last decade which suggests that temperature is not the only enhancing factor of the HABs productivity in the Vaal Dam. The satellite data analysis during the last decade revealed that the HABs spatial dynamics is a function of the dam geometry and the levels of the discharges from its two feeding rivers. Higher concentrations were observed where the reservoir is meandering and within the areas of restricted water circulation.

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.

This pioneering study presents an in-depth biomechanical examination of soccer's diving header, aiming to quantify its impact on ball speed enhancement (BSE) and effective offensive range (EOR). Despite the diving header's widespread acclaim and historical significance, there remains a dearth of scientific scrutiny into its biomechanical intricacies. Employing an innovative research design featuring a static hanging ball at varied offensive distances and heights, the study replicates diverse header scenarios. The results of 3D motion quantification have shown that a physically excellent player (identified through the maximal standing long jump test) could reach an EOR around 2.64 times his body height. Furthermore, the study unveils that proficient players could attain BSE surpassing 9 m/s, signifying the diving header's heightened efficacy compared to traditional heading techniques, which could only result in 4.5 m/s. Correlation analyses unveil noteworthy relationships, highlighting the pivotal role of head speed at impact and the influence of minimizing speed-drop and temporal disparities for amplified effectiveness. Considerations for optimizing diving header execution are introduced, emphasizing the necessity for targeted training programs. Despite acknowledged limitations inherent to its pilot nature, this exploration furnishes foundational knowledge to guide subsequent research and practical applications, providing valuable insights into soccer training and skill development through a biomechanical lens.

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.

A question of physical importance is whether finite-energy spatiotemporally localized (i.e., pulsed) generalizations of monochromatic accelerating Airy beams are feasible. For luminal solutions, this question has been answered affirmatively by Saari, Valdmann, Piksarv, Valta-Lukner and Saari, and Kaganovsky and Heyman within the framework of the paraxial geometry. The time-diffraction technique introduced by Porras recently has been motivated by Besieris and Shaarawi in terms of the Lorentz invariance of the equation governing the narrow angular spectrum and narrowband temporal spectrum paraxial approximation and has been used to derive finite-energy spatiotemporally confined subluminal, luminal, and superluminal Airy wave packets. The goal in this article is to provide exact finite-energy broadband spatiotemporally localized Airy solutions (a) to the scalar wave equation in free space; (b) in a dielectric medium moving at its phase velocity; (c) in a lossless second order temporally dispersive medium.

This study compared cortical responses to speech in preschoolers with typical language development (TLD) and Developmental Language Disorder (DLD), and in adults. We investigated whether top-down language effects modulate speech perception in an adult-like manner. We compared cortical mismatch responses (MMR) during passive perception of speech differing in linguistic complexity in three groups of participants: preschoolers with TLD (n=11), preschoolers with DLD (n=16) and adults (n=20). We also measured children’s phonological skills and investigated if they are associated with the cortical responses. MMR results indicated top-down language effects in adults, with enhanced cortical discrimination of lexical stimuli but not of non-words. In preschoolers, the TLD and DLD groups did not differ on the MMR measures and no top-down effects were detected. Moreover, we found no association between MMRs and phonological awareness skills, even though the DLD group’s phonological skills were significantly lower. Our findings suggest that top-down language modulations on speech discrimination may not be present during early childhood, and that children with DLD may not exhibit cortical speech perception deficits. The lack of association between phonological and MMR measures indicates that further research is needed to understand the link between language skills and cortical activity in preschoolers.

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.

Enteric viruses are the major cause of gastroenteritis and enteric hepatitis worldwide, but in some areas like Saudi Arabia little data is known about their presence in water sources. The available information from clinical samples is not enough to figure their actual prevalence. The aim of this study was to gather information for the first time in Saudi Arabia on the presence of Norovirus (NoV) genogroup GI and GII, hepatitis A virus (HAV) and hepatitis E virus (HEV) in water. For this purpose, thirteen monthly samples were collected in lake Wadi Hanifa and surrounding wells from December 2014 to November 2015. Viruses were detected and quantified by Real-time RT-qPCR. Despite HEV findings were anecdotic, our results highlight interesting behaviors of the other viruses. There was a higher prevalence of noroviruses in Wadi Hanifa samples than in well water samples (46.43% vs12.5% of NoV GI; 66.67% vs8.33% of NoV GII). On the contrary, similar levels of HAV positivity were observed (40.48% in surface water vs 43.06% in well water). Also, a strong influence of flooding events on HAV and NoV GI occurrence was observed in both surface and well water samples, being NoV GII apparently not affected.

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.

Herein, bamboo-laminated lumber for furniture was coated with waterborne acrylic paint. The effects of different painting techniques and quantities on the drying rate, smoothness, hardness, adhesion and wear resistance of the paint film were investigated. Then, the thermal properties of the paint were analysed using thermogravimetry and differential scanning calorimeter to examine the film-forming mechanism. The results showed that painting technique had no effect on the drying time, flatness, hardness and adhesion of the paint film, but it influenced the wear resistance. Herein, the films’ flatness showed little variation with different painting methods (one-layer primer one-layer topcoat, one-layer primer two-layer topcoat, two-layer primer one-layer topcoat and two-layer primer two-layer topcoat). The drying time for primer surfaces/solids was 8min/8.5 min, while it was 6.5/7 min for topcoats. The paint films exhibited grade B hardness and grade 0 adhesion and the better wear resistance when using one-layer primer one-layer topcoat at 51.24 mg·100 r−1. The amount of paint applied significantly affected the drying time and flatness of the paint film. As the quantity increased, the drying time of the paint’s surfaces and solids also increased, whereas the film’s flatness initially increased before decreasing. The film’s optimal adhesion, hardness and wear resistance values were grade 0, grade B and 51.24 mg·100 r−1, respectively, when 80 g/m2 of paint was applied. Thermal analysis of the primer and topcoat showed that water decomposition occurred at 100℃ and thermal decomposition of organic components occurred at 350℃. The primer and topcoat exhibited the same weight loss, but primer’s thermal loss rate was higher than that of topcoat. Consequently, the topcoat displayed better thermal stability than the primer.

In this paper, we investigate an exact Universe which is observational viable and filled with binary mixture of perfect fluid and cosmological constant $\Lambda$. Owning the non-uniform expansion of cosmos, we have considered redshift drift $\dot{z} = -(1+z)H_{0}+H(z)$ and performed statistical test to obtain the best fit value of model parameters of derived Universe with its observed values. Here $H_{0}$ and $z$ denote the present value of Hubble constant and redshift respectively. We estimate the best fit values of Hubble constant and density parameters are $H_{0} = 68.58 \pm 0.84$ km/s/Mpc, $(\Omega_{m})_{0} = 0.26 \pm 0.010$ and $(\Omega_{\Lambda})_{0} = 0.71 \pm 0.025$ by bounding the derived model with latest observational Hubble data (OHD) while with joint Pantheon data and OHD, its values are $H_{0} = 71.93 \pm 0.58$ km/s/Mpc, $(\Omega_{m})_{0} = 0.272 \pm 0.06$ and $(\Omega_{\Lambda})_{0} = 0.74 \pm 0.09$. The analysis of deceleration parameter and jerk parameter show that the Universe in derived model is compatible with $\Lambda$CDM model.

A novel manufacturing process is presented for producing Nano powders and thin films of CuCoO₂ (CCO) material. This process utilizes three cost-effective synthesis methods: hydrothermal, sol-gel, and solid-state reactions. The resulting delafossite CuCoO₂ samples were deposited onto transparent substrates through spray pyrolysis, forming innovative thin films with a Nano crystal powder structure. Prior to the transformation into thin films, CuCoO₂ powder was first produced using a low-cost approach. The precursors for both powders and thin films were deposited onto glass surfaces using a spray pyrolysis process, and their characteristics were examined through X-ray diffraction, scanning electron microscopy, HR-TEM, UV-visible, and Electrochemical impedance spectroscopy (EIS) analyses was conducted to determine the conductivity in transversal direction of this groundbreaking material for solar cell applications. On the other hand, the sheet resistance of the samples was investigated using the four probe-method to obtain the sheet resistivity and them calculate the in plane conductivity of the samples. We also investigated the aging characteristics of different precursors with varying durations. The functional properties of CuCoO2 samples were explored by studying chelating agent and precursor solution aging periods. Resuming, this study thoroughly discusses the synthesis of delafossite powders and their conversion into thin films, which hold potential as hole transport layers in transparent optoelectronic devices.

The frequency synthesizer is a critical component in Cs beam clock technology. In this paper, we present a demonstration of a direct microwave frequency-synthesis chain for a cesium-beam atomic clock, which utilizes frequency multiplication and a dual-phase-locked loop mode. A detailed analysis of the frequency-synthesis chain is conducted, and a mathematical model is established. The phase settling time and system stability are simulated, measured, and verified. The experimental results for the phase settling time align with the simulation outcomes. The phase settling time can be adjusted within the range of 644.5 µs to 1.5 ms, and the absolute phase noise values are -63.7 dBc/Hz, -75.7 dBc/Hz, -107.1 dBc/Hz, and -122.5 dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively. Additionally, the Ramsey fringes are detected, and the Allan deviations of the 10 MHz output from the cesium-beam atomic clock are measured to be 2.99×10−12 at 1s and 8.02×10−14 at 10,000 s.

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.

Background: The patients with cancer, especially pancreas cancer, are frequently associated with thrombosis which is one of causes for poor outcome, and hypercoagulability exists in cancer patients. Hypercoagulability is considered to be caused by thrombin burst. Methods: Activated partial thromboplastin time (APTT), small amount of tissue factor induced FIX activation (sTF/FIXa) assays and thrombin time (TT) using clot waveform analysis (CWA) were performed in 138 patients with malignant neoplasm including pancreas cancer and 66 non-cancer patients. Results: Thrombosis was frequently associated in pancreas cancer and was observed in stage I. CWA-APTT showed that the peak times and heights were markedly long and heigh, respectively, in cancer patients, and the peak times were significantly longer in pancreas cancer patients than benign pancreas diseases, and the 1st DPH was significantly higher in pancreas cancers than other cancers or benign pancreas diseases. CWA-sTF/FIXa showed that the peak times and heights were longer and higher, respectively, in patients with cancer than those without cancers, and that the 1st DPH was significantly higher in pancreas cancers than in other cancers. CWA-TT showed that the peak times were significantly shorter in cancer patients than in healthy volunteers and that the peak heights were significantly higher in cancer than in benign pancreas diseases. Conclusions: Cancer patients including pancreas cancer were frequently associated with thrombosis due to hypercoagulability with thrombin burst detected by CWA.

The use of the voice of the customer as a main input to guide decision making towards customer centricity strategies has become a necessity for companies. This research proposes a structured method of textual processing using the KDD (Knowledge Discovery Databases) methodology applied to the tweets of users of Colombian public sector companies, through the analysis of temporal sentiments and topic modeling to identify the areas in which actions should be taken to improve the perception of service. To fulfill such purpose, tweets from January to June 2022 are processed, followed by a temporal analysis of the evolution of the sentiment based on 3 enriched dictionaries; after, the LDA (Linear Discriminant Analysis) algorithm is implemented to find the areas with ailment for the user, in addition to propose a method to homologate the CIER (Comisión de Integración Energética Regional) survey. Finally, metrics are detailed to follow up the perception of the service. It is concluded that for the Acueducto the topic with the highest number of complaints is related to "Water truck request", for Enel "Servide Outages" and for Vanti: " Case solution and request information". Also, the homologation of 3 of the 5 pillars on which the CIER survey is based is presented.

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.

We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. Compared to time series with normal densities, such time series are associated with rare events. Hence, employing Gaussian statistics drastically underestimates the occurrence of their rare events. The central objective of this study is to investigate the effects of rare events in the probability densities of time series from the point of view of quantum measurements. For this purpose, we first model the non-Gaussian behavior of time series using the multifractal random walk (MRW) approach. Then, we examine the role of the key parameter of MRW, λ, which controls the degree of non-Gaussianity, in quantum potentials derived for time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for rare events. Thus, rare events can generate a potential barrier in the high-frequency region of the quantum potential, and the effect of such a barrier becomes prominent when the system transverses it. Finally, as an example of applying quantum potential beyond the microscopic world, we compute quantum potentials for the S&amp;P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.

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.

We demonstrate that the conductivity of graphene on thin-film polymer substrates can be accurately determined by reflection-mode air-plasma-based THz time-domain spectroscopy (THz-TDS). The phase uncertainty issue associated with reflection measurements is discussed, and our implementation is validated by convincing agreement with graphene electrical properties extracted from more conventional transmission-mode measurements. Both the reflection and transmission THz-TDS measurements reveal strong nonlinear and instantaneous conductivity depletion across an ultra-broad bandwidth under relatively high incident THz electrical field strengths.

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.

Although total nett CO₂ atmospheric flow can be estimated with reasonable accuracy, the contributing gross fluxes between the atmosphere and the earth's surface are poorly understood. This paper presents a method, driven by the objective of simplicity, by which the global outflow and inflow of CO₂ between atmosphere and a globally equivalent "mixing reservoir" can be estimated, using the isotopes ¹⁴C and ¹³C as tracers. It has been asserted that the isotopic carbon in CO₂ cannot be directly used as a tracer in flow studies because it is not subject to the Revelle factor. Evidence is provided showing that this view is mistaken. The model contains 7 key parameters which are used to create synthetic records of Δ¹⁴C and d¹³C spanning 200 years or more, including during the period of atmospheric weapons testing and its decay known as the "bomb pulse". By optimising the fit between these computed values and the historical records of d¹³C and Δ¹⁴C, all seven key parameters are determined. The effective "mixing reservoir" is thereby determined to have a size around six times that of the atmosphere, with global outflux rising from 39.7 GTC yr-1 in 1750 to 58.9 GTC/yr in 2020, this figure probably not including annually cycled carbon.

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 minutes allows us to form a multivariable timeseries that serves two-ways: a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

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.

Vegetation status assessment is crucial for agricultural monitoring and management. Vegetation indices derived from high resolution image time series can be used to derive key phenological parameters for annual crops. In this work, we propose a procedure for the estimation of these parameters and their associated uncertainties. The approach uses Bayesian inference through Markov Chain Monte Carlo in order to obtain the full joint posterior distribution of the phenological parameters given the satellite observations. The proposed algorithm is quantitatively validated on synthetic data. Its use on real data is presented together with an application to real-time within season estimation allowing for phenology forecasting.

The Thermocontextual Interpretation (TCI) is proposed here as an alternative to existing interpretations of physical states and time. Prevailing interpretations are based on assumptions rooted in classical mechanics. Logical implications include the determinism and reversibility of change, and an immediate conflict. Determinism underlies causality, but causality implies a distinction between cause and effect and an arrow of time, conflicting with reversibility. Prevailing interpretations also fail to explain the empirical irreversibility of wavefunction collapse without untestable and untenable metaphysical implications. They fail to reconcile nonlocality and relativity without invoking superdeterminism or unexplained superluminal correlations. The Thermocontextual Interpretation defines a system’s state with respect to its actual surroundings at a positive ambient temperature. The TCI bridges existing physical interpretations and thermodynamics as special cases, which define states either with respect to an absolute-zero reference or with respect to a thermally equilibrated reference. The TCI defines system time as a complex property of state spanning both reversible mechanical time and irreversible thermodynamic time, and it distinguishes between system time and the reference time of relativity and causality, as measured by an observer’s clock. And, the TCI provides a physical explanation for nonlocality, consistent with relativity, without hidden variables, superdeterminism, or “spooky action.”

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.

Background: Jerusalem artichoke (Helianthus tuberosus L.) is tolerant to salinity stress and has high economic value. The salt tolerance mechanisms of Jerusalem artichoke are still unclear. Especially in the early stage of Jerusalem artichoke exposure to salt stress, the plant physiology, biochemistry and gene transcription are likely to undergo large changes. Elucidating these changes may be of great significance to understanding the salt tolerance mechanisms of it. Results: We obtained high-quality transcriptome from leaves and roots of Jerusalem artichoke exposed to salinity (300 mM NaCl) for 0 h, 6 h, 12 h, 24 h and 48 h, with 150,129 unigenes and 9023 DEGs (Differentially Expressed Genes). The RNA-seq data were clustered into time-dependent groups (nine clusters each in leaves and roots); gene functions were distributed evenly among the groups convergence. KEGG enrichment analysis showed the genes related to plant hormone signal transduction were enriched in almost all treatment comparisons. Under salt stress, genes belongs to PYL (abscisic acid receptor PYR / PYL family), PP2C (Type 2C protein phosphatases), GH3 (Gretchen Hagen3), ETR (ethylene receptor), EIN2/3 (ethylene-insensitive protein 2/3), JAZ (Genes such as jasmonate ZIM-domain gene) and MYC2 (Transcription factor MYC2) had extremely similar expression patterns. The results of qPCR of 12 randomly selected genes confirmed the accuracy of RNA-seq. Conclusions: Under the impact of high salinity (300mM) environment, Jerusalem artichoke in the seedling stage was difficult to survive for a long time, and the phenotype was severe in the short term. Based on the expression of genes on the time scale, we found that the distribution of gene functions in time is relatively even. Upregulation of the phytohormone signal transduction had a crucial role in the response of Jerusalem artichoke seedlings to salt stress, the genes of abscisic acid, auxin, ethylene, and jasmonic acid had the most obvious change pattern.

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.

The paper describes a method of solving some stochastic processes using generating functions. A general theorem of generating functions of a particular type is derived. A generating function of this type is applied to a stochastic process yielding polynomial time algorithms for certain partitions. The method is generalized to a stochastic process describing a rather general linear transform. Finally, the main idea of the method is used in deriving a theoretical polynomial time algorithm to the knapsack problem.

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.

Pattern recognition has long been regarded as key role for crime prevention and reduction. Crime analysts and policy makers can formulate effective strategies and allocate resources with reference to spatial and temporal pattern of crime. In order the combat and prevent severe crime in New York City (NYC), this study analyzed Felony Crime data of NYC in previous 5 years (2015 2020) and discovered criminal hotspots pattern and temporal patterns with open criminal complaint data provided by New York Police Department (NYPD). This study adapt a human computer interactive appraoch to draw patterns from crime data, whereas computations and visualization are performed by Python libraries, and human to inform the decision of visualization methods, computational parameters and direction of this exploratary analysis. Density based clustering algorithms, Grid Thematic Mapping and Density Heatmap are displayed to identify hotspots and demonstrates their associations with spatial features. Timeline analysis on moments of crime occurance demonstrates seasonality where crimes are mostly commited, while aoristic analysis showed hours of day when crime is mostly committed considering their timespan. Lastly, 3D visualization improved recognition of the displacement of hotspot over time, and suggested long term hotspots in NYC in 3 D visualization. This inform strategic plans for police deployment.

The prevailing interpretations of physics are based on deeply entrenched assumptions, rooted in classical mechanics. Logical implications include: the denial of entropy as a fundamental physical property, and the inability to explain irreversible change, random quantum measurements, or nonlocality without untestable and implausible metaphysical implications. We propose a conceptual model that is based on empirically justifiable assumptions and consistent with observations. The WYSIWYG Conceptual Model (WCM) assumes no hidden properties: “What You can See Is What You Get.” The WCM contextually defines a system’s state with respect to its actual ambient background, and it extends existing models of physical reality by defining entropy and exergy as objective contextual properties of state. The WCM establishes the irreversible dissipation of exergy and the Second law of thermodynamics as a fundamental law of physics, it recognizes physical randomness, and it provides a physical explanation for nonlocality, consistent with Special Relativity, without hidden variables, superdeterminism, or “spooky action.”

In the last decades droughts, deforestation and wildfires have become recurring phenomena that have affected both human activities and natural ecosystems in Amazonia. The time an ecosystem requires to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, the factors influencing and controlling the recovery time and its spatiotemporal patterns at the regional scale are still poorly understood. In this study, we combined forest growth model, remote sensing and field plots, to map Amazonia-wide (300-ha resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our simulated results indicate that AGB decreases by 4%, 19% and 46% in forests disturbed by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: 1) the recovery time of the forests takes longer in the southeastern part of the basin, and, 2) as droughts and wildfires become more frequent – since the intervals between the disturbances is getting shorter than forest regeneration – potentially causing a long-lasting damage in these fragile ecosystems and a permanent degradation.

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).

The quantified residence time distribution (RTD) provides a numerical characterization of mixing in the continue casting tundish, thus allowing the engineer to better understand the metallurgical performance of the reactor. This paper describes a computational fluid dynamic (CFD) modelling study for analyzing the flow pattern and the residence time distribution in a five-strand tundish. Two passive scalar transport equations are applied to separately calculate the E-curve and F-curve in the tundish. The numerical modelling results are compared to the water modelling results for the validation of the mathematical model. The volume fraction of different flow regions (plug, mixed and dead) and the intermixing time during the ladle changeover are calculated to study the effects of the flow control device (FCD) on the tundish performance. The result shows that a combination of the U-baffle with deflector holes and the turbulence inhibitor has three major effects on the flow characteristics in the tundish: i) reduce the extent of the dead volume; ii) evenly distribute the liquid streams to each strand and iii) shorten the intermixing time during the ladle changeover operation.

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.

This paper aims to determine the insecticidal activity of Panyawan (Tinospora rumphii) leaf extract against American cockroach (Periplaneta americana). A quantitative experimental research design was employed in the study. Cockroach strains were collected through trapping and hand catch methods. Panyawan leaf was collected and extracted to achieve seven different concentrations. The concentrations and controls were tested against the strains through the standard contact glass jar bioassay. Results revealed that the shortest knock-down time was achieved by the positive control at 1.08 minutes while the Extracts of Panyawan leaf reported a 1.42 to 20.25 minutes knock-down time. Probit Regression reported that the lethal concentration 50 of Panyawan leaf extract was 15.836%. One-Way ANOVA revealed that there is a significant difference in the knock-down times of cockroaches when exposed to different Panyawan leaf extract concentrations, negative and positive controls (p<0.01). Homogeneous subsets derived from Post Hoc Test using Tukey’s Honest Significant Difference reported that there is no significant difference between positive control and Extract 100% (p>0.01).

The Planning and implementation of construction projects are difficult processes and are burdened with many risk elements. The budget spread over time, which is developed on the basis of the schedule, presents the expected distribution of costs throughout the duration of the works, which during the implementation of the project is subject to constant changes resulting from time, cost and organizational factors. Managing construction contracts requires managers to be able to analyze on an ongoing basis the variances of production costs - from the values calculated in the offer cost estimate and assumed in the Budgeted Cost of Work Scheduled. The article attempts to analyze the emerging time and cost deviations using proprietary T/S and T/C monitoring, based on simple indicators of the EVM method. An example of construction of a multi-family housing development was used to study the variances of planned and incurred costs.

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.

Planning and implementation of construction projects are difficult processes and are burdened with many risk elements. The budget spread over time, which is developed on the basis of the schedule, presents the expected distribution of costs throughout the duration of the works, which during the implementation of the project is subject to constant changes resulting from time, cost and organizational factors. Managing construction contracts requires managers to be able to analyze on an ongoing basis the variances of production costs - from the values calculated in the offer cost estimate and assumed in the Budgeted Cost of Work Scheduled. The article attempts to analyze the emerging time and cost variances using proprietary C-S and S-C monitoring, based on simple indicators of the EVM method. An example of construction of a multi-family housing development was used to study the variances of planned and incurred costs.

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 aim of the study was to determine the presence Fusarium species and mycotoxins in winter wheat grain in Poland. Grain samples from different locations in Poland in 2009 and 2010 were analysed for the content of biomass of Fusarium species and mycotoxins. In 2009 biomass of F. graminearum and F. poae was present in all samples, F. culmorum in 82% of samples, F. avenaceum in 55% of samples. F. sporotrichioides, F. tricinctum and F. equiseti were found only in individual samples. F. langsethiae was not detected. In 2010, five Fusarium species were detected with the exception of F. sporotrichioides. The highest content of biomass was found for F. graminearum followed by F. avenaceum, F. poae and F. langsethiae. The amount of F. culmorum biomass was very low. The most frequently occurring species was F. poae and F. graminearum. In 2009, deoxynivalenol was detected in all samples. In 2010, the average content of deoxynivalenol was lower than in 2009. Nivalenol was detected at very low concentration in both years. Significant correlations between content of F. graminearum biomass and deoxynivalenol concentration in grain and between content of F. poae biomass and nivalenol concentration in grain in 2009 were found. The most important finding of this study was that main Fusarium species infecting wheat kernels in Poland in both years was F. graminearum. The amount of biomass of F. graminearum was the highest in both years. It was present in the most samples. The other frequently detected species was F. poae, which in 2010 appeared in more samples than F. graminearum. However, the amount of F. poae biomass was lower. F. culmorum, species that was previously dominating as wheat pathogen in Poland, was found less frequently than F. graminearum. The amount of biomass of this species was the lowest in 2010.

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.

A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this paper, focusing on the problem of weak feature learning ability when dealing with continuous data. In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer functions, the contrastive divergence is designed in hidden layer training process to raise convergence speed, an improved dropout strategy is then implemented in unsupervised training to realize features learning by de-cooperating between the units, and then the network is fine-tuned using back propagation algorithm. Besides, hyper-parameters are analysed through stability analysis to assure the network can find the optimal. Finally, the experiments on Lorenz chaos series, CATS benchmark and other real world like CO2 and waste water parameters forecasting show that cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than other methods.

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.

Time irreversibility, i.e. the lack of invariance of the statistical properties of a system under time reversal, is a fundamental property of all systems operating out of equilibrium. Time reversal symmetry is associated with important statistical and physical properties and is related to the predictability of the system generating the time series. Over the past fifteen years, various methods to quantify time irreversibility in time series have been proposed, but these can be computationally expensive. Here we propose a new method, based on permutation entropy, which is essentially parameter-free, temporally local, yields straightforward statistical tests, and has fast convergence properties. We apply this method to the study of financial time series, showing that stocks and indices present a rich irreversibility dynamics. We illustrate the comparative methodological advantages of our method with respect to a recently proposed method based on visibility graphs, and discuss the implications of our results for financial data analysis and interpretation.

The relationship between economic growth, growth volatility and financial sector development continues to attract attention in the theoretical and empirical literature. Over time, some studies hypothesize that finance has a causal linear relationship with growth. Recently several other authors contradict this claim and argue that the relationship that exists between finance and growth is nonlinear. We investigate these claims for Nigeria for the period between 1970 and 2015, using semi-parametric econometric methods, Hansen sample splitting techniques and threshold estimator. We observed no evidence of ‘Too much finance’ as claimed by many researchers in recent times. We show that the relationship between financial development and economic growth is U-shaped. This is equally true for the relationship between financial development and growth volatility. We also discuss policy implications of our findings and recommend financial innovations and decentralization of stock exchanges to boost access to financial services, in addition, improved regulation to enhance financial market efficiency.

In biology molecules and macromolecules like sugars, proteins, DNA, and RNA are of utter importance. Detecting their presence as well as their conformation is still a challenge in many cases. It is well known that the vibrational states of such molecules lie from the infrared to the TeraHertz range. Spectroscopy can be used to detect such compounds and probe their conformation. Still, terahertz spectroscopy on biosample is a challenge for two main reasons: water absorption; and the small size of the samples. The sample volume is smaller than the cube of the TeraHertz wavelength; the light matter interactions are thus extremely reduced. In this paper, we present the design, fabrication, characterization and the first typical uses of a biophotonic device aiming at increasing light matter interaction to enable terahertz spectroscopy of minute samples on a broad band (0.2–2 THz). We demonstrate time domain spectroscopy experiments on few µl samples showing the validity of our approach.

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.

The static formation temperature (SFT) is required to determine the thermophysical properties and production parameters in geothermal and oil reservoirs. However, the SFT is not easy to be obtained by both experimental and physical methods. In this paper, a mathematical approach to predicting SFT based on a new model describing the relationship between bottom hole temperature (BHT) and shut-in time was proposed. The unknown coefficients of the model were derived from least squares fit by Particle Swarm Optimization (PSO) algorithm. Besides, the ability to predict SFT based on a few BHT data (such as first 3, 4, or 5 ones of a data set) was evaluated. The accuracy of the proposed method to predict SFT was testified with a deviation percentage less than ±4% and high values of regression coefficient R² (>0.98). The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells.

We present a novel method for interpolating univariate time series data. The proposed method combines multi-point fractional Brownian bridges, a genetic algorithm, and Takens’ theorem for reconstructing a phase space from univariate time series data. The basic idea is to first generate a population of different stochastically interpolated time series data, and secondly, to use a genetic algorithm to find the pieces in the population which generate the smoothest reconstructed phase space trajectory. A smooth trajectory curve is hereby found to have a low variance of second derivatives along the curve. For simplicity, we refer to the developed method as PhaSpaSto-interpolation, which is an abbreviation for phase-space-trajectory-smoothing stochastic interpolation. The proposed approach is tested and validated with a univariate time series of the Lorenz system, five non-model data sets and tested against a cubic spline interpolation and a linear linear interpolation. We find that the criterion for smoothness guarantees low errors on known model and non-model data. Finally, we interpolate the discussed non-model data sets, and show the corresponding improved phase space portraits. The proposed method is useful for interpolating low-sampled time series data sets for, e.g., machine learning, regression analysis, or time series prediction approaches.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

The existence of semantic inaccuracy §2 articles by A. Einstein “On the electrodynamics of moving bodies”, illustrating the principle of the relativity of simultaneity. In a sentence “The observers co-moving with the moving rod would thus find that the two clocks do not run synchronously while the observers in the system at rest would declare them synchronous”, the preposition “not” must be moved and placed before the word “synchronous”, at the end of the sentence. The criterion for synchronism of the clocks on the rod is the time it takes for the beam to travel from clock A to clock B and back, and for observers on the rod it will be the same both when the rod is at rest and when the rod is moving. Otherwise, observers on the rod would have had at their disposal an experiment that would allow them to determine whether the rod is moving or at rest, which contradicts the first postulate of the Special Theory of Relativity. The inaccuracy is found unchanged in modern editions in English and Russian, and can become a source of difficulties when teaching the basics of the Special Theory of Relativity based on primary sources, as well as make it difficult to understand the author’s logic when studying independently. Nobel laureate R. Feynman, having expanded and modified A. Einstein's thought experiment, eliminated the inaccuracy described in the article.

The development of mineral resources in China has gradually entered the stage of deep mining. Rock burst, collapse and other ground pressure disasters caused by deep high stress have become major hidden dangers restricting mine safety production. Microseismic monitoring technology is an important means of ground pressure risk prediction. In this paper, the wavelet coefficient threshold denoising method in time-frequency domain, STA/LTA method, AIC method, Skew and Kurtosis method are studied, and the automatic P-phase onset time picking model based on noise reduction and multiple detection indexes is established. Through the effect analysis of microseismic signals collected by microseismic monitoring system of coral Tungsten Mine in Guangxi, automatic P-phase onset time picking is realized. the reliability of the P-phase onset time picking method proposed in this paper based on noise reduction and multiple detection indexes is verified. The picking accuracy can still be guaranteed under the severe signal interference of background noise, power frequency interference and manual activity underground mine, Which is of great significance to the data processing and analysis of microseismic monitoring.

Since its initial development in the 1970’s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the de facto approach for the calculation of accurate data for general electron-atom/ion/molecule collision and photoionization processes. The use of a non-orthonormal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist their high cost prohibit their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor which is designed specifically for long-term stormwater in-field monitoring. Its low cost ($23.50 USD), enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. The laboratory results show a strong linear relationship (R2 > 0.99) between the sensor readings and the commercial hand-held turbidimeter (Thermo Scientific AQ4500) results of the solution. In the field, the low-cost sensor measurements were statistically significantly correlated (p < 0.01) to a standard high-cost commercial turbidity sensor (GreenSpan TS-1000). Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the Green-Span sensor.

Special and general relativity (SR/GR) describe nature “subjectively”, that is, from the perspective of just one observer at a time (one class of observers, to be exact). Mathematically, SR/GR are correct. I show: (1) Physically, SR/GR have an issue. There is always just one active perspective—despite the Lorentz covariance of SR, the general covariance of GR, and coordinate-free formulations. This is a fact because SR/GR do not consider a unique 4D vector “flow of proper time” τ for each object. The vector is only disclosed by Euclidean relativity (ER). Because of this constraint, SR/GR lack a holistic view of nature. The issue shows itself in mysteries. Some mysteries (time’s arrow, Hubble tension) are solved by ER. Other mysteries (accelerating expansion of space, entanglement) require concepts (dark energy, non-locality) that are obsolete in ER. Still, SR/GR work well for observers because the Lorentz factor and gravitational time dilation are correct. (2) ER describes nature “objectively”, that is, from the perspectives of all objects at once. An object’s (!) proper space d₁, d₂, d₃ and proper time τ span 4D Euclidean spacetime (ES), where d₁, d₂, d₃ and d₄ = cτ are treated the same. ER provides a holistic view because ES is absolute. An observer’s reality is created by projecting ES orthogonally to his proper space and to his proper time. The SO(4) symmetry of ES is incompatible with waves. This is fine because waves and particles are subjective concepts construed by observers. I conclude: The master reality ES (described by ER) is beyond each observer’s reality (described by SR/GR).

In this paper, a proof similar to the Pusey-Barrett-Rudolph (PBR) theorem is given to prove that time in quantum theory is not epistemic but has ontological reality. For this, we first discuss how time can be encoded as an intrinsic property of a quantum system. We prepare a quantum state containing time as an intrinsic property and show by a PBR-type proof that time cannot be an epistemic notion. In fact, the PBR theorem implies an ontic notion of time. Indeed, if the quantum state is a physical property, then quantum state reduction is a physical process involving information destruction. In this case, quantum probabilities are intrinsic probabilities without epistemic origin, and they generate a genuinely new sequence of events. This novelty introduced by quantum probabilities can be interpreted as time.

The process of establishing secure communication between devices in the Internet of Things (IoT) can be approached by using a One-time-Pad (OTP) protocol. We propose using a known secure multiparty sum protocol for generating a One-time-Pad key using true (physical) random numbers in each device (party). We implemented the proposal using ZeroC-Ice, a middleware for distributed computing. The protocol security properties were analyzed under the assumptions of the Dolev-Yao threat model.

Landsat and Sentinel-2 acquisitions are among the most widely used medium-resolution optical data adopted for terrestrial vegetation applications, such as land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. When combined, both data archives provide over 40 years, and counting, of continuous and consistent observations. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of cloud-, snow-, and shade-free observations at the pixel level is lacking and should be explored individually for each study to correctly parametrize subsequent analyses. However, data exploration is time and resource consuming, thus is rarely performed a-priori. Consequently, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present precomputed data on the daily 1982‑2023 availability of usable Landsat and Sentinel-2 acquisitions across the globe, sampled at a pixel-level in a regular 0.18°-point grid. The dataset comprises separate Landsat- and Sentinel‑2‑specific data records, and is accompanied by a pixel-specific growing season information to facilitate data exploration. The dataset was derived based on freely available 1982-2023 Landsat surface reflectance (Collection 2) and Sentinel-2 top-of-the-atmosphere reflectance (pre‑Collection-1 and Collection-1) scenes from 2015 through 2023, following the methodology developed in the recent study on data availability over Europe [1]. Growing season information was derived based on 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6) [1]. As such, the dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much needed a-priori information aiding identification of appropriate methods and challenges for terrestrial vegetation analyses at local to global scale.

Lumpy skin disease (LSD) is a viral disease of cattle and water buffalo characterized by cutaneous nodules, biphasic fever and lymphadenitis. LSD is endemic in Africa and Middle East but in recent years has spread in different Asian countries. Drop of animal productions, vaccination campaign, surveillance costs and trade restrictions have significant impact on the economy of affected countries. The disease is well characterized in cattle while little is known in buffaloes and no experimental studies have been conducted in this species. Six buffaloes and two cattle were inoculated with an Albanian LSD virus (LSDV) field strain. Over a period of 42 days animals were clinically monitored and sampled for serological and virological analysis. Only two buffaloes showed clinical sign, fever, skin nodules and lymphadenitis. All samples tested in real time PCR (blood, swabs, biopsies and organ) gave negative results. Three buffaloes seroconverted at day 39 and 42 post-inoculation, detected by ELISA, but all sera were negative in serum neutralization test. Cattle showed more severe clinical signs. Moreover, viraemia and virus shedding were proven by positive PCR results and antibodies were detected by both ELISA and serum neutralization test. These findings demonstrated the limited susceptibility of buffaloes compared to cattle once experimental infected with LSDV. Further studies are needed to investigate the epidemiological role of the buffaloes.

We review our theory of interdependence applied to autonomous human-machine teams, with which we have made several generalizations based on theory that have led to new discoveries, compared to social science which is struggling to break free of its validation crisis, but also has no generalizations to new discoveries with human teams, let alone to human-machine teams. In this invited article, based around the stability of a team, of our discoveries, we review the uncertainty between a team's structure and its performance, our first finding. We generalize that finding to deception, vulnerability, innovation and fittedness. From there, our next generalization is to the quantum likeness of interdependence and quantum mechanics; we move on to Shannon holes, knowledge, and conclude with the value of debate and the need for boundaries to contain debate, to reduce interference, and to protect states of interdependence. After the introduction, we extend our model in Section 2 to energy and time, and we close with a brief review of innovation and future plans.

The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. This data covers significant fuels such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic, influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision-making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trend for electricity generation using data from 2015 to 2022. Several time-series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For Natural Gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5,936.203. The seasonal Naïve (SNaïve) model outperforms others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models' performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from the study help policymakers strategically allocate resources, plan infrastructure development and support economic growth.

Soil nitrite (NO2‒) is an important reactive intermediate in many nitrogen transformation processes, but it is unstable under acidic conditions. Canonical extraction method of soil NO2‒ with potassium chloride (KCl) solution greatly underestimates its concentration. In order to reflect the concentration more accurately, we optimized the extraction method of soil NO2‒ in this study. Moreover, soil ammonium (NH4+) and nitrate (NO3‒) were also systematically investigated to achieve efficient extraction of soil inorganic nitrogen. The results showed that un-buffered KCl significantly underestimated soil NO2‒ concentration compared to DIW. The highest recovery of NO2‒ was obtained by extracting with DIW at 10 min of oscillation for three soils. Compared with DIW, the concentration of NH4+ and NO3‒ in soil extracted from KCl solution increased significantly. Furthermore, the soil inorganic nitrogen content of extracts stored at 4°C for one day were closer to directly measurements of fresh samples than the other storage methods. Overall, the recommend analysis method for soil NO2‒ was extracted by DIW, oscillated for 10 min, and filtered by a 0.45 µm filter, and soil NH4+ and NO3‒ were extracted with KCl solution and oscillated for 30 min. The extract should be stored at 4°C and analyzed within 24 hours.

Penrose’s singularity from 1965 predicted that in the center of the black hole space-time curvature is infinite and consequently gravity force there is infinite. In 2014 NASA measured universal space has Euclidean shape. This means that stellar objects cannot curve universal space and that the space-time singularity model has no physical existence. In the center of black holes energy density of superfluid space is so low that the electromagnetic properties of space are changed. This causes electromagnetic forces between the nucleus and orbiting electrons to become unstable. In the center of a black hole, atoms are falling apart into elementary particles. Black holes are rejuvenating systems of the universe, they transform old matter into fresh energy in the form of elementary particles. Astrophysical jets are the outcome of this process.

Background and objectives: Although intravenous tissue plasminogen activator (rt-PA) has been shown to be effective in the treatment of acute ischemic stroke (AIS) only a small proportion of stroke patients receive this drug. Low administration rate is mainly due to delayed presentation of patients to the emergency department (ED) or lack of a stroke team/unit in most of the hospitals. So, the aim of this study is to analyze ED time targets and the rate of rt-PA intravenous administration after the initial admission of patients with AIS in an ED from a traditional healthcare center (without a neurologist or stroke team/unit). Methods: To analyze which factors, influence administration of rt-PA we split the general sample (n=202) in two groups: group No rt-PA (n=137) and group rt-PA (n=65) based on performing or no intravenous thrombolysis. Results: Analyzing ED time targets for all sample we found that the median onset-to-ED door time was 180 minutes (IQR, 120-217.5 minutes), door-to-physician time was 4 minutes (IQR, 3-7 minutes), door-to-CT time was 52 minutes (IQR, 48-55 minutes), and door-in-door-out time was 61 minutes (IQR, 59-65 minutes). ED time targets such as door-to physician time (p=0.245), door-to-CT time (p=0.219), door-to-door-out time (p=0.24), or NIHSS at admission to Neurology department (p=0.405), or NIHSS after 24 h (p=0.9) did not have a statistically significant effect on administration or no rt-PA treatment in patients included in our study. Only highest Door-to-CT time was statistically significantly correlated with death outcome. Conclusion: In our study, rt-PA administration rate was higher than expected. Statistically significant correlation between ED time targets was found between lower Onset-to-ED door time with administration of rt-PA treatment (p<0.001) and between highest Door-to-CT time and death outcome.

As a new type of gear, bevoloid gears have the advantages of compensating axial error, smooth transmission, and eliminating turning error, and they are widely used in applications requiring high transmission accuracy and stability. However, research on calculating the time-varying mesh stiffness of bevoloid gears is still limited, and there is an urgent need to propose a method that can calculate the stiffness of bevoloid gears quickly and accurately. This paper first establishes the bevoloid gear tooth profile expressions, then assumes a pair of bevoloid gears meshing with the same rack and derives the contact line equations of parallel axis bevoloid gear pairs, and analyze the contact process of bevoloid gears. We propose an analytical algorithm that uses the slicing method to calculate the stiffness of helical gears, straight bevoloid gears, and helical bevoloid gears, change the parameters of helical bevoloid gears respectively, and analyze the influence of different parameters on stiffness. Finally, the finite element method is used to verify the analytical method, and the correctness of the analytical calculation results is verified, and the errors are analyzed.

Discussing quantum theory foundations, von Neumann noted that the measurement process should not be regarded in terms of a temporal evolution. A reason for his claim is insurmountability of the gap between reversible and irreversible processes. The time operator formalism that goes beyond such a gap is adequate framework for elaboration of the measurement problem. It considers signals to be stochastic processes, whether they correspond to variables or distribution densities. Signal processing like that utilizes statistical properties to perform its tasks, which is the definition of statistical signal processing. A hierarchy of the measurement process is indicated by crossing between states and devices, which implies an evolution in the temporal domain. The concept has generalized to an open system by the use of duality in frame theory.

Persons with stroke-induced aphasia (PWAs) are often impaired in tense/time reference (TR) production. It is not clear, however, whether PWAs’ impaired TR production is due to TR-related encoding or retrieval deficits. This study aims at disentangling TR-related encoding deficits from TR-related retrieval deficits in aphasia. Two sentence completion tasks (SCTs) tapping production of past and future reference were administered to eight Greek-speaking PWAs, eight Russian-speaking PWAs, six Italian-speaking PWAs, seven English-speaking PWAs and four groups of language-, age- and education-matched healthy controls. SCT 1 tapped TR-related encoding processes and TR-related retrieval processes to a similar extent. SCT 2 predominantly tapped TR-related retrieval processes. All four control groups performed at ceiling. Three Greek-speaking PWAs, one Russian-speaking PWA, three Italian-speaking PWAs and two English-speaking PWAs showed between-task dissociations. A double dissociation emerged, as some Greek-, Russian- and English-speaking PWAs performed better on SCT 1 than on SCT 2, whereas other Greek- and Italian-speaking PWAs performed worse on SCT 1 than on SCT 2. It will be shown that the experimental design employed here has the potential to disentangle TR-related encoding deficits from TR-related retrieval deficits, as both PWAs with selective TR-related encoding deficits and PWAs with selective TR-related retrieval deficits were identified.

This paper aims to discuss the relationship between financial development and inflation in Kenya using time series data between 1973 and 2021. The short- and long-run impact of financial development on inflation remains an important issue in the empirical and theoretical literature especially for country specific, but has yet to receive significant research consideration. For this purpose, the paper uses an ARDL analysis method, which is considered an advanced analytical model. Empirical findings confirmed that financial development has a significant long-run impact on inflation in Kenya. On the contrary, results confirmed that there is no significant short-run effect of financial development on inflation. In addition, there is significant long-run and short-run negative relationship between interest rate and inflation. The results indicate significant long-run Granger and ARDL causality from financial development to inflation. The main policy implications of the empirical results of this paper suggest supervision of financial sector must be directed in a way that would stimulate a stable and a moderate inflation rate. In particular, both financial institution and government should enhance the infrastructures of the financial market and promote the utilisation of financial services.

Recently, we demonstrated the validity of anomalous numbers law, known as Newcomb-Benford´s law, at the mammalian neuromuscular transmission, considering different extracellular calcium concentrations . The present work continues to examine how alterations in extracellular solution modulate the first digit law in the context of the spontaneous release of acetylcholine from the neuromuscular junction. We investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. The analysis showed that the interval between the miniature potentials at high potassium concentrations follows Newcomb-Benford´s law. Also, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.

Basil (Ocimum basilicum L.) is one of the important medicinal and aromatic plants. Light intensity is an indispensable factor for plants due to its effect on photosynthesis and physiological processes. Here, we investigated the impact of light intensities and harvesting times on the photosynthesis of green and purple basil. The experiment involved subjecting the plants to three different sunlight levels for 12 days: complete – 100%, 50%, and 30% sunlight. In addition, we scrutinize the harvesting time. The highest levels of photosynthetic and protective pigments were detected under full sunlight conditions in the purple basil harvested at noon. The highest levels of soluble and storage carbohydrates were recorded in the purple basil grown under full sunlight and harvested in the early morning. In contrast, their lowest levels were obtained in plants grown under 30% sunlight and harvested at noon time. Under all light treatments, the maximum quantum yield of photosystem II (FV/FM) was detected at 4 a.m. in both basil varieties; it decreased at noon and increased again at 5 p.m. Non-Photochemical Quenching (NPQ) got most elevated in the green variety under all light intensities at noon. However the highest NPQ was detected in the purple variety at 8 a.m. The NPQ was lowest in both basil varieties during the early morning and afternoon. Full sunlight at noon caused temporary photoinhibition and reduced car-bohydrates while enhancing pigment concentration and photo-protective mechanisms in basil plants.

This paper introduces a novel approach to addressing the challenge of accurately timing short distance runs, a critical aspect in the assessment of athletic performance. Electronic photoelectric barriers, although recognized for their dependability and accuracy, have remained largely inaccessible to non-professional athletes and smaller sport clubs due to their high costs. A comprehensive review of existing timing systems reveals that claimed accuracies beyond 30 milliseconds lack experimental validation across most available systems.To bridge this gap, a mobile, camera-based timing system is proposed, capitalizing on consumer-grade electronics and smartphones to provide an affordable and easily accessible alternative. By leveraging readily available hardware components, the construction of the proposed system is detailed, ensuring its cost-effectiveness and simplicity. Experiments involving track and field athletes demonstrate the proficiency of the proposed system in accurately timing short distance sprints. Comparative assessments against a professional photoelectric cells timing system reveal a remarkable accuracy of 62 milliseconds, firmly establishing the reliability and effectiveness of the proposed system. This finding places the camera-based approach on par with existing commercial systems, thereby offering non-professional athletes and smaller sport clubs an affordable means to achieve accurate timing.In an effort to foster further research and development, open access to the device's schematics and software is provided. This accessibility encourages collaboration and innovation in the pursuit of enhanced performance assessment tools for athletes.