The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

Thrombin (factor IIa) and factor Xa (FXa) are key enzymes at the junction of the intrinsic and extrinsic coagulation pathways and are the most attractive pharmacological targets for the development of novel anticoagulants. Twenty non-amidino N²-thiophencarbonyl- and N²-tosyl anthranilamides 1-20 and six amidino N²-thiophencarbonyl- and N²-tosylanthranilamides 21-26 were synthesized and evaluated prothrombin time (PT) and activated partial thromboplastin time (aPTT) using human plasma at concentration 30 μg/mL in vitro. From these results, compounds 5, 9, and 21-23 were selected to study the further antithrombotic activity. The anticoagulant properties of 5, 9, and 21-23 significantly exhibited a concentration-dependent prolongation of in vitro PT and aPTT, in vivo bleeding time, and ex vivo clotting time. These compounds concentration-dependently inhibited the activities of thrombin and FXa and inhibited the generation of thrombin and FXa in human endothelial cells. In addition, data showed that 5, 9, and 21-23 significantly inhibited thrombin catalyzed fibrin polymerization and mouse platelet aggregation and inhibited platelet aggregation induced U46619 in vitro and ex vivo. N-(3'-Amidinophenyl)-2-((thiophen-2''-yl)carbonyl amino)benzamide (21) was most active.

In a recent paper, Nicolas Gisin suggests that by conducting physics with intuitionistic rather than classical mathematics, rich temporality - that is, passage and tense, and specifically, the future’s openness - can be incorporated into physics. Physics based on classical mathematics is tenseless and deterministic, and that, so he holds, renders it incongruent with experience. According to Gisin, physics ought to represent the indeterminate nature of reality, and he proposes that intuitionistic mathematics is the key to succeeding in doing so. While I share his insistence on the reality of passage and tense, and on the future being real and open, I argue that the amendment he offers does not work. I show that, its attunement to time notwithstanding, intuitionistic mathematics is as tenseless as classical mathematics, and that physics is bound to remain tenseless regardless of the math it employs. There is much to learn about tensed time, but the task belongs to phenomenology and not to physics.

The exact solutions of some conformable time fractional PDEs are presented explicitly. The modified Kudryashov method is applied to construct the solutions to the conformable time fractional Regularized Long Wave-Burgers (RLW-Burgers, potential Korteweg-de Vries (KdV) and clannish random walker's parabolic (CRWP) equations. Initially, the predicted solution in the finite series of a rational form of an exponential function is substituted to the ODE generated from the conformable time fractional PDE by using wave transformation. The coefficients used in the finite series are determined by solving the algebraic system derived from the coefficients of the powers of the predicted solution.

Background/Objectives: to analyze the effects of vaccination against SARS-CoV-2 on the incidence, lethality and mortality from COVID-19, in the city of São Paulo, in the period 2020-2022. Methods: this is a population-based time series study, with official public data, relating to the period from 2020 to 2022. The population data were extracted from the Municipal Health Department of São Paulo, according to the geographic division of the Regional Health Coordination (CRS). The Prais-Winsten regression model was used, calculating the percentage variation of daily change (DPC). This procedure made it possible to classify the trend of epidemiological rates as increasing, decreasing or stationary. Results: the city of São Paulo recorded that 17% of its total population was infected at least once by the SARS CoV-2 virus, with a fatal outcome in 0.4% of its total population. When comparing the differences between the CRS, it was observed that the North region had a higher lethality rate at the beginning of the pandemic, decreasing significantly over the period. The Central region, in 2021, showed an increasing trend different from the other regions. Regarding mortality, the North, Center and West coordinators showed decreasing trends in all periods analyzed, unlike the East, Southeast and South coordinators, which in 2020 showed stationary trends. Conclusions: there was a reduction in the epidemiological outcomes of COVID-19 after the mass application of vaccination against SARS-CoV-2. Strategies to increase vaccination coverage should be implemented in all regions of the city of São Paulo, especially in those with high social vulnerability.

This paper introduces the novel concept of Time-Traveling Turing Machines (TTTMs) — Turing Machines with the ability to read symbols that have been explicitly sent from their future computational state. We delve into their properties and behavior, particularly emphasizing the importance of maintaining a self-consistent computational timeline. We present theorems demonstrating the universality of these machines and the challenges in achieving faster computations than traditional Turing Machines.

The 3GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1AS based time synchronization mechanism and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1AS based synchronization model of INET so that it works over UDP/IP. With that it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Futhermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.

Introduction: The purpose of this study was to determine the normative values of muscles’ contractile properties, lateral symmetry, and the impact of aging on muscle contractility in gymnastics. Materials and Methods: A survey of 81 athletes from different disciplines was undertaken: MAG (n = 26), WAG (n = 28), and RG (n = 27). The athletes’ average age was 15.41 ± 5.03 years. We try to establish the normative values for contraction time (muscle pairs) in gymnastics for Slovenian athletes for ten skeletal muscles. Results: Athletes’ age affects contractility in a negative way. Differences between the duration of contractility and age were found in all disciplines: m. BF (r = 0.48, p < 0.001), m. TB (r = 0.37, p < 0.013), m. ES (r = 0.17, p < 0.025), m. VL (r = 0.36, p < 0.00) and m. VM (r = 0.40, p < 0.000) at a statistical significance of p < 0.05. Conclusions: A comparison between the left and right sides shows little asymmetry in WAG and that the occurrence of injuries is in the middle range. In RG we see a bigger deviation, which could trigger the emergence of pain or injury in m. BF (13%) and m. GL (14.5%), but in MAG the deviation is greater in m. BB (12%), m. BF (11%) and m. GM (13%).

The anomaly detection task is very important in computer science. And there are a lot of anomaly detection methods. Different from some thresholding methods, some unsupervised methods could make us get more accurate and faster result, which is the object of the project. In this paper, I tried to use EWMA and some other methods in two datasets: Webank time consuming indicators dataset and AIOps Challenge dataset. The paper consists nine parts: background of the project, related work, description of algorithms, implementation details, experimental setup and data sets used, experimental results and discussion, future directions, reference and meeting notes.

There is a long standing ‘two times problem’ in that a satisfactory reconciliation between the time of physics and that of psychology has not been realized. A partial solution to the past/present/future phenomenon has been successfully given by the Hartle information gathering and processing system (IGUS) view. That model IGUS robot is enhanced here for the entire ‘two times problem’ to deal with not only the temporal experiences of the flow of time but also those of manifest time. A dualistic robot is proposed which has a veridical system of temporal experiences that are compatible with various spacetime cosmologies. It also has an illusory system of corresponding temporal experiences. This dualistic IGUS robot was made possible by discovering temporal experience within the brain that correspond to those of physics. The dualistic theory suggests that the veridical system, as a result of evolution, begets the illusory system to enhance behavioral adaptation. Thus, there is just one fundamental physical time which the brain does, indeed, possess and then enhances with illusory counterparts. Therefore, there should no longer be a need to reify illusory temporal experiences as modern spacetime cosmologies tend to do. Physical time already resides within human time.

Divergences have become a very useful tool for measuring similarity (or dissimilarity) between probability distributions. Depending on the field of application a more appropriate measure may be necessary. In this paper we introduce a family of divergences we call gamma-divergences. They are based on the convexity property of the functions that generate them. We demonstrate that these divergences verify all the usually required properties, and we extend them to weighted probability distribution. We investigate their properties in the context of kernel theory. Finally, we apply our findings to the analysis of simulated and real time series.

We propose a geographical time-space model extending time-space relief cartography introduced by Mathis and L’Hostis [,,,]. The novelty of the model resides in the use of cones to describe the terrestrial surface instead of graph faces, and in the use of curves instead of broken segments for edges. The approach lies a the intersection of two domains involving graphic representation: cartography, and three dimensional computer graphics. We implement the model on the Chinese space. The Chinese geographical time-space of the reference year 2006 is produced by the combination and the confrontation of the fast air transport system and of the 7.5 times slower road transport system. Slower, short range flights are represented as curved lines above the earth surface with longer length than the geodesic, in order to account for a slower speed. The very steep slope of cones expresses the relative difficulty of crossing terrestrial time-space, as well as the comparably extreme efficiency of long-range flights for moving between cities. Finally, the whole image proposes a coherent representation of the geographical time-space where fast city to city transport is combined with slow terrestrial systems that allow to reach any location.

In this paper, we present a relapse based demonstrating way to deal with investigate various arrangement MTC information. A commonplace use of this displaying approach incorporates three stages: first, define a model that approximates the connection between quality articulation and trial factors, with boundaries consolidated to address the exploration premium; second, utilize least-squares and assessing condition methods to gauge boundaries and their relating standard blunders; third, register test insights, P-qualities and NFD as proportions of factual criticalness. The benefits of this methodology are as per the following. To begin with, it tends to the exploration interest in a particular, precise way, and maximally uses all the information and other important data. Second, it represents both orderly and irregular varieties related with the information, and the consequences of such examination give not just quality explicit data applicable to the exploration objective, yet additionally its dependability, in this way helping agents to settle on better choices for subsequent investigations. Third, this methodology is truly adaptable, and can undoubtedly be stretched out to different sorts of MTC considers or other microarray explores by detailing various models dependent on the test plan of the investigations.

Nowadays, security is a top priority. In fact, biometrics uses cutting-edge technologies to identify terrorists and criminals. But the practice of distinguishing humans based on intrinsic physical or behavior traits goes back thousands of years. With the widespread use of computers in the late 20th century, new possibilities for digital biometrics emerged and new technologies were generously used. Among these, we remember high resolution security video cameras and drones. So, the aim of the present project is to study and explain the features of these technologies, especially the ones of the the Phantom 4 Pro+ aircraft and analyze its operating methods in order to identify human faces during live streaming of videos. For this purpose, it will be used Paul Viola and Michael Jones’ face detection algorithm, which includes Haar features and cascade classifiers to identify faces, eyes and ears of an individual.

The influence of the film thickness and the substrate’s refractive index on the surface mode at the superstrate is an important study step that may help clearing some of the misunderstandings surrounding their propagation mechanism. A single sub-wavelength slit perforating a thin metallic film is among the simplest nanostructure capable of launching Surface Plasmon Polaritons on its surrounding surface when excited by an incident field. Here, the impact of the substrate and the film thickness on surface waves is investigated. When the thickness of the film is comparable to its skin depth, SPP waves from the substrate penetrate the film and emerge from the superstrate, creating a superposition of two SPP waves, that leads to a beat interference envelope with well-defined loci which are the function of both the drive frequency and the dielectric constant of the substrate/superstrate. As the film thickness is reduced to the SPP’s penetration depth, surface waves from optically denser dielectric/metal interface would dominate, leading to volume plasmons that propagate inside the film at optical frequencies. Interference of periodic volume charge density with the incident field over the film creates charge bundles that are periodic in space and time.

The preparation of antibodies in the powder form without changing their physicochemical properties may enable their use in new drug delivery system therapies or for non-refrigerated storage. The variable domain of the heavy chain of heavy chain (VHH) antibodies is more suited for this purpose than conventional antibodies because of their high thermal stability and ability to refold. In this report, the fine droplet drying (FDD) process was selected as the powderization technique because of its favorable features, such as mild drying conditions and the generation of uniform particle sizes. The aggregation, binding, particle and in vitro inhalation properties of the prepared VHH antibody powder (VHHp) were evaluated. The amount of aggregated VHH antibody present in VHHp depended on the flow temperature during the FDD process, with higher temperatures yielding a high aggregation ratio. In contrast, no significant difference in binding activity was observed between each VHHp preparation and the native VHH antibody. Analysis of VHHp samples revealed that the particles were uniformly spherical with a single-micron size. VHHp showed a fine inhalation property in the inhalation property test. These findings suggest that the FDD process affords various VHH antibody powder formulations, including pharmaceutical formulations.

We study Hadamard's variational formula for simple eigenvalues under dynamical and conformal deformations. Particularly, harmonic convexity of the first eigenvalue of the Laplacian under the mixed boundary condition is established for two-dimensional domain, which implies several new inequalities.

In this paper, we explore the hypothesis that topological defects formed in the quark-gluon plasma (QGP) of the early universe, specifically flux tubes and domain walls, could serve as stable dark matter candidates. Theoretical analysis of the QCD vacuum structure reveals that these defects can form due to nontrivial topological configurations, with the Lagrangian density incorporating topological terms. We model the formation and stability of flux tubes and domain walls, estimating their energy densities and dynamics within the QGP. Flux tubes, with energy per unit length approximately given by $\mu \approx \frac{\sigma}{g_s^2}$, and domain walls, characterized by a surface energy density $\sigma_{\text{DW}} \sim \Lambda_{\text{QCD}}^3$, exhibit unique gravitational signatures. These signatures include distinct gravitational lensing patterns due to their linear and planar mass distributions, making them detectable through astrophysical surveys. Furthermore, their minimal interaction with conventional matter aligns well with observational constraints on dark matter properties. We discuss potential experimental approaches for detecting these defects, including high-energy collider experiments and detailed analysis of gravitational lensing data. Our findings suggest that topological defects in the QGP provide a compelling and novel solution to the dark matter problem, offering a new direction for both theoretical investigation and experimental verification.

The aim of this paper is to investigate whether multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists for the early diagnosis of retinitis pigmentosa (RP). In a case study, we use Spectral Domain Optical Coherence Tomography (SDOCT), which is a non-invasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculate generalized Renyi point-centered dimensions, which is considered a measure of inhomogeneity of data, to prove that it increases in the patients with RP with respect to the control group.

The Ames/quantitative structure-activity relationship (QSAR) International Challenge Projects held during 2014–2017 and 2020–2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), Matthews correlation coefficient (MCC), F1 Score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, 109 models, the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology.

The receptor-type protein tyrosine phosphatases (RPTPs) are involved in a wide variety of physiological functions which are mediated via their diverse extracellular regions. They play key roles in cell-cell contacts, bind various ligands and are regulated by dimerization and other processes. Depending on the subgroup they have been described as everything from ‘rigid rods’ to ‘floppy tentacles’. Here we review current experimental structural knowledge on the extracellular region of RPTPs and draw on AlphaFold structural predictions to provide further insights into structure and function of these cellular signalling molecules.

Monoclonal antibodies (mAbs) are an important treatment option for COVID-19 caused by SARS-CoV-2, especially in immunosuppressed patients. However, this treatment option can become ineffective due to mutations in the SARS-CoV-2 genome, mainly in the receptor binding domain (RBD) of the spike (S) protein. In the present study 7950 SARS-CoV-2 positive samples from the Uppsala and Örebro regions of central Sweden collected between March 2022 and May 2023 were whole-genome sequenced using next-generation sequencing, mainly with the Nanopore sequencing method. Pango lineages were determined and all single nucleotide polymorphism (SNP) mutations that occurred in these samples were identified. The dominant sublineages changed over time and mutations conferring resistance to currently available mAbs became common. Notable ones are R346T and K444T mutations in the RBD that confer significant resistance against tixagevimab and cilgavimab mAbs. Further, mutations conferring a high-fold resistance to bebtelovimab, such as the K444T and V445P mutations, were also observed in the samples. This study highlights that resistance mutations have over time rendered currently available mAbs ineffective against SARS-CoV-2 in most patients. Therefore, there is a need for continued surveillance of resistance mutations and the development of new mAbs that target more conserved regions of the RBD.

Detecting, especially locating local degradations at an incipient stage, is very important for mission-critical high voltage rotating machines. One particular challenge of the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially made defects. These defects include turn-to-turn short, surface creepage, loose coils, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that the FDR can identify and locate both structure and insulation degradations of both shielded and unshielded objects with good sensitivity. This makes the FDR a comprehensive tool for fault diagnosis and aging assessment.

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed following 24 hours. High doses of KYNA (i.e., 20-40 μg/2 μl) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μl) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. The study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

A microwave-photonics method has been developed for measuring distributed acoustic signals. This method uses microwave-modulated low coherence light as a probe to interrogate distributed in-fiber interferometers, which are used to measure acoustic-induced strain. By sweeping the microwave frequency at a constant rate, the acoustic signals are encoded into the complex microwave spectrum. The microwave spectrum is transformed into the joint time-frequency domain and further processed to obtain the distributed acoustic signals. The method is first evaluated using an intrinsic Fabry Perot interferometer (IFPI). Acoustic signals of frequency up to 15.6 kHz were detected. The method was further demonstrated using an array of in-fiber weak reflectors and an external Michelson interferometer. Two piezo-ceramic cylinders (PCCs) driven at frequencies of 1700 Hz and 3430 Hz were used as acoustic sources. The experiment results show that the sensing system can locate multiple acoustic sources. The system resolves 20 nε when the spatial resolution is 5 cm. The recovered acoustic signals match the excitation signals in frequency, amplitude, and phase, indicating an excellent potential for distributed acoustic sensing (DAS).

Study of RNA-protein interactions and identification of RNA targets are among the key aspects of understanding the RNA biology. Currently, various methods are available to investigate these interactions, in particular, RNA pulldown assay. In the present paper, a method based on the HaloTag technology is presented that is called Halo-RPD (HaloTag RNA PullDown). The proposed protocol uses plants with stable fusion protein expression and the MagneBeads magnetic beads to capture RNA-protein complexes directly from the cytoplasmic lysate of transgenic A. thaliana plants. The key stages described in the paper are as follows: 1) preparation of the magnetic beads 2) tissue homogenization and collection of control samples 3) precipitation and wash of RNA-protein complexes; 4) evaluation of protein binding efficacy; 5) RNA isolation; 6) analysis of the obtained RNA. Recommendations for better NGS assay designs are provided.

Cryonotothenioidea is the main group of fishes that thrive in the extremely cold Antarctic environment, thanks to the acquisition of peculiar morphological, physiological and molecular adaptations. We have previously disclosed that IgM, the main immunoglobulin isotype in teleosts, display typical cold-adapted features. Recently, we have analyzed the gene encoding the heavy chain constant region (CH) of the IgT isotype from the Antarctic teleost Trematomus bernacchii (family Nototheniidae), characterized by the near-complete deletion of the CH2 domain. Here, we aimed to track the loss of the CH2 domain along notothenioid phylogeny and to identify its ancestral origins. To this end, we obtained the IgT gene sequences from several species belonging to the Antarctic families Nototheniidae, Bathydraconidae and Artedidraconidae. All species display a CH2 remnant of variable size, encoded by a short Cτ2 exon, which retains functional splicing sites and therefore is included in the mature transcript. We also considered representative species from the three non-Antarctic families: Eleginopsioidea (Eleginops maclovinus), Pseudaphritioidea (Pseudaphritis urvillii) and Bovichtidae (Bovichtus diacanthus and Cottoperca gobio). Even though only E. maclovinus, the sister taxa of Cryonotothenioidea, shared the partial loss of Cτ2, the other non-Antarctic notothenioid species displayed early molecular signatures of this event. These results shed light on the evolutionary path that underlies the origins of this remarkable gene structural modification.

This paper presents findings of a study on continuous feed-in management and continuous synthetic inertia contribution with wind turbines. A realistic case study, based on real measurements, is outlined. A wind turbine feeds into a weak feeder, such that its power has to be adapted to the permissible loading of this feeder. At the same time the wind turbine is to provide inertia to the grid by applying the previously published variable inertia constant controller. It is discussed that optimal grid utilisation and simultaneous inertia contribution are mandatory for the frequency control in power systems that are heavily penetrated with renewable energies. The study shows that continuous feed-in management can be combined well with continuous inertia provision. There are hardly any negative consequences for the wind turbine. The benefits for the grid are convincing, both in terms of increased system utilisation and in terms of provided inertia. It is concluded that wind turbines can enhance angular stability in a power system to a larger extent than conventional power plants.

Differences between the quantum mechanical and relativistic concepts of time observed in GPS satellites are accounted for by applying the equivalence principle to the transitioning electron of an atomic clock. This allows a differential equation of motion to be derived for the electron in the Minkowski space between electron shells. By applying Hamilton’s principle we transform the differential equation into a relativistically correct integral equation of motion, the time integral of a Lagrangian. This newly derived equation accounts for the abstract mathematics of the more familiar non-relativistic model by means of a physical interpretation. Abstract rotations in Hilbert space are replaced by real rotations of particle field geometries in Minkowki space that describe the electron transition and emission of a photon. Because the properties of energy are universal the same integral equations are able to be used to describe the time evolution of galaxies despite vast differences in lifetime. The comparison of gravitational and electromagnetic energy exchanges by means of radial and transverse fields leads to an alternative to the standard model that is based on a field model. It explains the observed properties of galaxies as a superposition of neutrinos localized within the horizon of a black hole with spins aligned. The particle properties of free neutrinos are accounted by means of a classical field geometry; specifically, a spin that is oriented in space with rotational inertia due to energy. A test of the standard model is proposed based on differing views for flavor oscillation.

Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and it achieved high attention in recent industrial research. Our paper focuses on the problem of time series segmentation and presents the first in-depth research about transfer learning for deep-learning based time series segmentation on the example of industrial end-of-line pump testing. In particularly, we investigate if the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models in respect to accuracy and training speed. The benefit is most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative Transfer Learning were observed as well. Thus, the research emphasizes the potential, but also the challenges of industrial Transfer Learning.

Efficient use of the operating room (OR) is crucial for any hospital. One of the major inefficiencies in the OR is surgical cases not starting or finishing on time as scheduled. When a case is delayed, it affects all subsequent cases in that OR. This study uses discrete choice analysis to determine the significant factors, including team familiarity, that influence OR case on-time start and finish. A case is considered on-time if the documented procedure start and finish times are no more than 10 minutes after the scheduled start and finish times. The analysis uses surgical case data from a large tertiary referral hospital and academic center in Greenville, South Carolina. The case data includes all surgical cases (15,091) performed during regular workdays in 2013. Two binary logit models are developed: one for case on-time start and one for case on-time finish. Results indicate that higher team familiarity between surgeon and anesthesiologist, surgeon and circulating nurse, surgeon and scrub nurse, and surgeon and CRNA improve the likelihood of an OR case on-time start and on-time finish. This finding indicates that the OR scheduling staff in the study hospital make a concerted effort to schedule the surgical teams with members who have worked well together in the past.

This paper proposes a novel three-phase voltage source inverter dead-time compensation strategy for accurate compensation in wide current regions of the inverter. In particular, an analysis of the output voltage distortion of the inverter, which appears as parasitic components of the switches, has been conducted for proper voltage compensation in the low current region, and an on-line compensation voltage controller has been proposed. Also, a new trapezoidal compensation voltage implementation method using the current phase is proposed to simplify realizing the trapezoidal shape of the three-phase compensation voltages. Finally, when the proposed dead-time compensation strategy is applied, the maximum phase voltage magnitude in the linear modulation voltage regions is defined to achieve smooth operation even at high modulation index. Simulations and experiments were conducted to verify the performance of the proposed dead-time compensation scheme.

The aim of the present work is to explore a different non-traditional approach to higher dimensional spacetime which differs from the usual approaches such as large extra space dimensions, warped extra space dimensions and multiple extra time dimensions. This approach will be referred to as "Dynamic" higher dimensional spacetime. By this term will be introduced a new kind of higher dimensional spacetime (more than the usual 3+1 dimensions), which behaves dynamically in the sense that time is not a fixed dimension but is the extra plus one dimension to the spatial dimensions of the phenomenon we are studying. For a higher dimensional phenomenon, the lower dimensional time will behave as space and the new time will be a plus one dimension, meaning if we study a 3-dimensional phenomenon then time is the usual 4th dimension but if we are studying a 4-dimensional phenomenon then its time will be an extra 5th dimension and for this phenomenon the 4th dimension will behave as space. All spatial dimensions are indistinguishable from each other and behave in the exact same way, which means that the important parameter is not the dimension we are studying (for example the 4th or the 5th) but the number of dimensions in which a phenomenon can interact and how higher dimensional phenomena interact with lower ones. This non-traditional approach to higher dimensional spacetime may appear confusing at first but will become clearer in the rest of the paper. For this reason, we will first associate the time dimension with propagation relative to space dimensions and change orthogonal to them in order to give the reader a better intuition. Following that, we will describe how higher - dimensional phenomena would appear to us, the 3 - dimensional observer and try to set some guidelines on how we should mathematically model higher - dimensional phenomena in order for us to make accurate predictions of their behaviour. Then we will express the general framework of a "Dynamic" higher - dimensional spacetime and turn our gaze to 4+1 – dimensional phenomena. We will explore how a mass term arises for 4 – dimensional waves by generalizing the frequency – speed – wavelength relation of 3D waves and give a basic description on a possible way to describe a 4+1 – dimensional disturbance in a scalar Energy Field in order to make accurate predictions on our 3+1 dimensional plane. In this framework we will derive the Klein – Gordon equation by generalizing the wave equation and ignoring all lower than 4+1 - dimensional disturbances and compere our mass term findings with the Klein – Gordon equation mass term and with results from gravity – induced quantum interference experiments. Lastly, we will describe how a "Dynamic" 5 - dimensional spacetime would appear in a 3D observer and examine whether our findings are compatible with quantum mechanics and the special theory of relativity.

This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying

The absolute time and relative time are defined in terms of the 4-D Euclidean space. Our universe is the 3-D x₁x₂x₃ quantized photon space which follows the absolute time simultaneity when the universe moves along the absolute time axis of ct. The length expansion of Dx = gDx₀ is derived under the condition of the absolute time (ct) simultaneity. From the similarity between this length expansion of Dx = gDx₀ and the energy increasing of E = gE₀, it is assumed that the energy is proportional to the particle size of Dx. The extension of this assumption to the 4-D Euclidean space gives the new definition that the particle energy (E) is the 4-D volume. Then, the particle mass energy is defined as E= mc² = cDtDV = gE₀. The masses of the elementary particles are originated from the 4-D warped volume of the photon space because the particle is the warped photon space with the velocity of v < c. Therefore, the Higgs boson concept in the standard model (SM) is not needed in the present 3-D quantized space model (TQSM). The scalar boson with the spin of zero, photon with the spin of 1 and graviton with the spin of 2 are the two-boson states. Therefore, the observed Higgs boson is reinterpreted as the two-boson state of the scalar boson with the spin of zero. The cosmic muon observation and twin paradox are explained by using the absolute time and relative time. The relative time is the observed time in the twin paradox and cosmic muon observation. In the twin paradox, a person who travels the long distance is more aged than a person on the earth in terms of the relative time ages because of the space and time conversion effect (STCE effect) of the moving space distance (x). But twins are in the same ages in terms of the absolute time without STCE effect. The fast-moving cosmic muon has the expanded half-life from the time expansion of the relative time. Also, the quantum entanglement and Pauli exclusion principle are explained. The quantum base of the photon space line connects two entangled particles. Two particles and quantum base system is fluctuated along the absolute time axis by the time clicking when one particle is measured. Another particle is instantly selected by the time clicking. This is called as the quantum entanglement. The photons which are the flat photon space with the constant speed of c along the space axis and absolute time axis have the 4-D photon velocity of c_eff = 2^0.5 c. Total 10-D Euclidean space including three 3-D quantized spaces and one absolute time axis is required for the electric charges (EC), lepton charges (CC) and color charges (CC) of the elementary particles. The 3-D photon space is very stiff along the absolute time (ct) axis and very soft along the space axes. The Coulomb force through the photons (2EM waves) of the space fluctuations is much stronger than the Gravitational force through the gravitons (G waves) of the time fluctuations between two electrons.

A previous related paper dealing with the density relaxation of polystyrene (PS) has shown that the equilibrium relaxation time (eq) has a purely exponential temperature dependence (ETD) below ≈ 100ºC. Such an ETD is now also confirmed based upon available dielectric-spectra data for PS. By combining the ETD behavior of eq (or aT) at low temperatures with a VFTH behavior at higher temperatures (based mainly on available recoverable-shear-compliance data), a composite correlation for eq (or aT) is developed which is continuous with continuous slope at a crossover temperature which is found to be 99.22ºC, where eq = 92.15 sec. This composite representation is shown to describe (without any adjustable parameters) available independent data for the segmental-relaxation time over a finite range both above and below Tcrossover (i.e. the glass-transition temperature).

If "quantization is an art" then it can be greatly rened by adopting cyclic time formalism. In past papers we have proven the effectiveness of a formulation of physics based on cyclic relativistic time. Now we can demonstrate in a general way, by using theorems of Geometric Quantization, that the Poisson brackets of intrinsically cyclic time dynamics directly imply the ordinary canonical commutation relations and the other Dirac's rules of canonical Quantum Mechanics. In other words, according to our result, canonical quantization is an implicit way of imposing intrinsically cyclic time dynamics without explicitly saying that time is a cyclic dimension.

The study of precipitation amount at different time scales constitutes an important issue in climate research and risk assessment. When dealing with daily totals, a frequent but sometimes underestimated problem is at what time the observation day begins. The choice of different starting times may lead to incompatibility between stations and incorrect identification of extreme events. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting time of the observation day is analyzed. The most widely used adjustment methods (1day and uniform shift) and two methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics and percentile distributions. As test series, the precipitation amount collected from 9 a.m. local time (09 LT) on the previous day to 09 LT on the target day (9-9 datasets) of the Padua and nearby stations in the period 1993-2022 have been selected. Results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless the station. But, for the periods in which reanalysis data are not available, “1day” method, which shifts the daily amount back one calendar day, and “unif” method, which distributes uniformly the daily total from a 2-day moving window surrounding the target date, can be considered valid alternatives. On the other side, concerning the precipitation statistics, the reanalysis-based methods are not the best option, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. Nevertheless, the uniform method provides a larger deviation from the original daily series. The use of the series of a station nearby the target one, which is mandatory in case of missing data, gives similar or better results than applying any adjustment method to the 9-9 series. General conclusions can hardly be drawn as they depend on the method and station. For the Padua dataset, the analysis was repeated at monthly and seasonal resolution. In general, the adjustment series show the most relevant changes in the precipitation statistics in summer and less temporal alignment with the original series in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentiles distribution, analyzed for all the methods and stations, indicates that any adjustment method underestimates the percentile values, except ERA5. Only Legnaro, the station most correlated with Padua, gives results like ERA5.

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and Explainable Artificial Intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand value changes, in the feature vector or the predicted value, can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.

Time evolution operators of a strongly ionizing medium are calculated by a time-dependent unitary transformation (TDUT) method. The TDUT method has been employed in quantum mechanical system composed of discrete states. This method is especially helpful for solving molecular rotational dynamics in quasi-adiabatic regimes because the strict unitary nature of the propagation operator allows us to set the temporal step size large; a tight limitation on the temporal step size ($\delta t <<1$) can be circumvented by the strict unitary nature. On the other hand, in a strongly ionizing system where the Hamiltonian is not Hermitian, the same approach cannot be directly applied because it is demanding to define a set of field-dressed eigenstates. In this study, the TDUT method was applied to the ionizing regime using the Kramers-Henneberger frame, in which the strong-field-dressed discrete eigenstates are given by the field-free discrete eigenstates in a moving frame. Although the present work verifies the method for a one-dimensional atom as a prototype, the method can be applied to three-dimensional atoms, and molecules exposed to strong laser fields.

We propose a novel tensor-based formalism for inferring causal structures from time series. An information theoretical analysis of transfer entropy (TE), shows that TE results from transmission of information over a set of communication channels. Tensors are the mathematical equivalents of these multi-channel causal channels. A multi-channel causal channel is a generalization of a discrete memoryless channel (DMC). We consider a DMC as a single-channel causal channel. Investigation of a system comprising three variables shows that in our formalism, bivariate analysis suffices to differentiate between direct and indirect relations. For this to be true, we have to combine the output of multi-channel causal channels with the output of single-channel causal channels. We can understand this result when we consider the role of noise. Subsequent transmission of information over noisy channels can never result in less noisy transmission overall. This implies that a Data Processing Inequality (DPI) exists for transfer entropy.

Accurate knowledge of flow rates is essential for hydraulic systems, as it allows for calculating hydraulic power when combined with pressure measurements. This data is helpful in applications such as predictive maintenance. However, most flow rate sensors in fluid power systems operate invasively, disrupting the flow and producing inaccurate results, particularly for transient flow conditions. A common approach is calculating the flow rate using the pressure difference along a pipeline based on the Hagen-Poiseuille law. However, this method is limited to laminar, steady, incompressible flow. This paper presents a novel soft sensor with an analytical model for transient, compressible pipe flow based on two pressure signals, thus no actual volumetric flow rate sensor is required. The model is derived by solving the fundamental fluid equations in the Laplace domain and converting the solution back to the time domain to obtain a physical representation. The relationship between the pressure difference signal and flow rate is derived using the four-pole theorem. The resulting analytical solution, including a convolution integral with a weighting function, shows high accuracy for transient and steady flows. This new equation enables the development of a soft sensor capable of non-invasive flow rate measurements for various pipe flow conditions.

Technological advancements and evolving government regulations are driving the adoption of construction equipment powered by renewable energy sources, aiming to significantly reduce emissions. Electrifying non-road mobile machinery (NRMM), particularly self-propelled Rough-Terrain Variable Reach Trucks (RTVRT) with telescopic booms, presents stability challenges. Replacing diesel engines with electric motors and battery packs shifts the centre of gravity, necessitating precise load capacity determinations via load charts to ensure safe operation. This paper introduces a virtual model developed using multi-physic modelling with Bond Graph methodology. The model encompasses crucial components, including the chassis, rear axle, telescopic boom, attachment fork, and wheels, requiring three-dimensional treatment for spatial dynamics resolution. An illustrative example, supported by experimental data, demonstrates the model's capabilities. Special focus is placed on calculating ground wheel reaction forces and hydraulic self-leveling of the attachment fork. Numerical results validate the BG-3D simulation model of the telehandler with 20-Sim as a promising tool for estimating stability limits with satisfactory precision and predicting dynamic behavior across various operating conditions. Moreover, the paper explores potential extensions of the model, such as integrating the virtual vehicle model with a platform of variable inclination in subsequent phases which aims to evaluate telehandler longitudinal and lateral stability, aligning with the ISO 22915 standard and operator safety.

Traditional urban design often overlooks the synchronisation of human and ecological connectivities, typically favouring corridors for ecological continuity. Our study challenges this convention by introducing a computational design approach, Meta-Connectivity, leveraging the deep generative models performing cross-domain translation to integrate human-wildlife landscape connectivity in urban morphology amidst the planetary urbanisation. Utilising chained Pix2Pix models, our research illustrates a novel meta-connectivity design reasoning framework, combining landscape connectivity modelling with conditional reasoning based on deep generative models. This framework enables the adjustment of both human and wildlife landscape connectivities based on their correlative patterns in one single design process, guiding the re-materialisation of urban landscapes without the need for explicit prior ecological or urban data. Our empirical study in East London demonstrated the framework's efficacy in suggesting wildlife connectivity adjustments based on human connectivity metrics. The results demonstrate the feasibility of creating an innovative urban form in which the land cover guided by the connectivity gradients replaces the corridors based on simple geometries. This research thus presents a methodology shift in urban design, proposing a symbiotic approach to integrating disparate yet interrelated landscape connectivities within urban contexts.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

The evolutionary transition from single-celled to multicellular individuality requires organismal fitness to shift from the cell-level to the cell group. This reorganization of fitness occurs by allocating the two components of fitness, survival and reproduction, between two specialized cell types in the multicellular group – soma and germ, respectively. How does the genetic basis for such fitness reorganization evolve? One possible mechanism is the co-option of life-history genes present in the unicellular ancestors of a multicellular lineage. Single-celled organisms must regulate their investment in survival and reproduction in response to environmental changes, particularly decreasing reproduction to ensure survival when stressed. Such stress response life-history genes can provide the foundation for the genetic basis for cellular differentiation in multicellular lineages. The regA gene family in volvocine green algae provides a model system to study how this co-option occurs. We discuss the origin and evolution of the volvocine regA gene family, including regA – the gene that controls somatic cell development in the model organism Volvox carteri. We hypothesize that the co-option of life history trade-off genes is a general mechanism involved in the transition to multicellular individuality, making volvocine algae and the regA family a useful template for similar investigations in other lineages.

The lactic acid bacteria (LAB) have great potential to produce homoexopolysaccharides (HoPS), have been the subject of extensive research efforts, given their health benefits and physicochemical properties. The HoPS functional properties are determined by structural characteristics of varied molecular weights, types of glycosidic linkages, degrees of branching and chemical composition. The dextransucrases (DSases) are responsible of the synthesis of a kind of HoPS (dextran polymers), which are among the first biopolymers produced at industrial scale with applications in medicine and biotechnology. The concept of glycodiversification opens additional applications for DSases. In that sense the design and characterization of new DSases is of prime importance. Previously, we described the isolation and characterization of a novel extracellular dextransucrase (DSR-F) encoding gene. In this study, from DSR-F, we design a novel chimeric dextransucrase DSR-F-∆SP-∆GBD-CBM2a, where DSR-F-∆SP-∆GBD is fused to the carbohydrate-binding module (CBM2a) of the β-1-4 exoglucanase/xylanase Cex (Xyn10A) of Cellulomonas fimi ATCC 484. This dextransucrase variant is active and without alteration in its specificity. The DSR-F-∆SP-∆GBD-CBM2a is purified by cellulose affinity chromatography for the very first time. Our results indicate that new hybrids and chimeric DSases with novel binding capacity to cellulose can be designed to obtain glyco-biocatalysts from renewable lignocellulosic materials.

Subsurface drainage systems remove excess water from the soil profile thereby improving crop yields in poorly drained farmland. Knowledge of the position of the buried drain lines is important: 1) to improve understanding of leaching and offsite release of nutrients and pesticides, and 2) for the installation of a new set of drain lines between the old ones for enhanced soil water removal efficiency. Traditional methods of drainage mapping involve the use of tile probes and trenching equipment. While these can be effective, they are also time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has focused on the use of time-domain ground penetrating radar (GPR), with variable success depending on local soil and hydrological conditions and the central frequency of the specific equipment employed. The objectives of this study were 1) to test the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) for subsurface drainage mapping, and 2) to evaluate the performance of a 3D-GPR with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor (DUALEM) in-combination. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth. The EMI sensor simultaneously provides information about the apparent electrical conductivity (EC_a) for different soil volumes, corresponding to different depths. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3-D GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to limited penetration depth (PD) of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of EC_a data measured by the DUALEM sensor could be a useful proxy to explain the success achieved by the 3D-GPR in finding the drain lines. The high attenuation of electromagnetic waves in highly conductive media limiting the PD of the 3D-GPR can explain the findings obtained in this research.

This chapter discusses stochastic processes known as "hidden Markov models". In these models, there are states or phases. In applications to statistical process control, for example, the states could correspond to "in control" or "out of control". In applications to the market and the economy, there are states of the market conventionally called "Bull" and "Bear", for up and down markets, resp. The states of the economy are often referred to as recession, recovery, expansion, contraction. Questions arise as to how many such states to use, and whether the nature of the states detected by statistical methods correspond to conventional notions about the phases of the market or the economy. The models considered involve a bivariate time series {Yt, St}; where Y is observed and S is the state, or label. "Hidden" means that the state is unobserved and must be inferred. For example, Yt may be the rate of return (ROR) of the S&P 500 index in month t and St may be 1 or 0, for Bull or Bear for that month. One definition of Bull and Bear is that St = 1 if the ROR Yt > 0 and St = 0; otherwise. In that case, St is directly derivable from the data. In other cases, the labels are unobserved and then are estimated from the observed process by more complicated means. The act of estimating St for each t, that is, labeling each time point of a time series with the name of its estimated state, is called time series segmentation, sequences of 0s or 1s being called segments. Thus a segment (regime, epoch) is a sequence of time points with the same label. Some definitions of phases are in terms of patterns; others are obtained in terms of fitting models in which the states are explicit elements of the model, albeit unobservable. Patterns are considered first, and then hidden state models. Use of model selection criteria for choosing between alternative models is illustrated. For example, it was found that for the stock market use of two states (corresponding to Bull and Bear) is indeed better than three states or a single state.

Time is what we measure with clocks. With clocks, we measure the duration of material change tuning in space. Material change runs in space only, time, as duration enters existence when measured. Time is an emergent physical reality that enters existence when measured by the observer. No measurement means no time. The motion does not require time. Motion requires only space and the physical object that moves in space, which is time-invariant. There is no physical past; there is no physical future. Past and future exist only in the human mind. Humans, we experience time-invariant space as NOW. The entire universe exists and develops into NOW.

In superconductors gauge U(1) symmetry is spontaneously broken. According to Goldstone theorem this breaking of a continuous symmetry establishes the existence of the Bogoliubov phase mode while the gauge-invariant response also includes the amplitude fluctuations of the order parameter. The latter, which are also termed ’Higgs’ modes in analogy with the standard model, appear at the energy of the spectral gap 2Δ when the superconducting ground state is evaluated within the weak coupling BCS theory and are therefore damped. Previously we have shown that in the time-dependent Gutzwiller approximation (TDGA) Higgs modes appear inside the gap with a finite binding energy relative to the quasiparticle continuum. Here we show that the binding energy of the Higgs mode becomes exponentially small in the weak coupling limit converging to the BCS solution. On the other hand, well-defined undamped amplitude modes exist in strongly coupled superconductors when the interaction energy becomes of the order of the bandwidth.

Monkeypox (mpox) caused by the monkeypox virus (MPXV) is currently the most common orthopoxvirus (OPXV) zoonotic disease. Since 2022, a multi-country outbreak of mpox has been reported with atypically rapid human-to-human transmission. The present work aimed to investigate the frequency of MPXV and OPXV DNA detection in different clinical materials taken at acute and convalescent phases of the infection in Bulgarian patients. A total of 181 clinical samples from 42 Bulgarian patients with possible mpox infections were tested. Screening for MPXV DNA (F3L gene) and OPXV DNA (rpo18 gene) were performed by real-time PCR assays. Of all tested patients six (14%) were confirmed with mpox infection. MPXV DNA was detected in 23/181 (12,71%) and OPXV DNA in 20/181 (11,05%) clinical samples. All confirmed patients were men who have sex with men, aged 23 to 44 (at an average age of 35,17±7,91 years) and two of them were HIV positive. The commonest clinical features of patients were fever (5/6), vesicular-pustular rash: perianal (6/6), penile (4/6), and limbs (4/6). MPXV DNA was found with the greatest frequency in specimens of vesicle contents and nasal/oropharyngeal swabs in 6/6 and 5/6. OPXV DNA mainly was detected in vesicle contents (6/6), nasal/oropharyngeal swabs (4/6), and urine (4/6). In the collected follow-up samples, MPXV DNA was detected in the highest percentage in the specimen's vesicle contents (6/6) and urine (6/6) and not demonstrated in samples of feces and saliva. The laboratory surveillance of MPXV is critical to contain the outbreak and to elucidate the new fast transmission of the virus.

Traversal time in tunneling effect for ultrasonic waves in tapered waveguides is derived considering its analogy with quantum and electromagnetic waves tunneling. If, as traversal time, the so called phase time is considered, the ultrasonic wavepacket shows the equivalent in acoustics of superluminality i.e. the derived velocity , crosses the limit of bulk transverse ultrasonic waves in the medium of the waveguide that is the equivalent of c in the quantum and electromagnetic cases. Graphs clearly illustrating this so called Hartman effect are obtained confirming the experimental results in the three different fields.

Fractal analysis of time series aims to extract the hidden or complex structures issued from the associated real data, such as scaling laws, non-stationary, dynamics, and so on. In the present work, the air quality index and its components are considered as time series owing fractal structures. To understand their underlying dynamics, wavelet multifractal approach is applied starting with a wavelet multifractal spectrum test to confirm the multifractality of the underlying data. Next, a new model is introduced improving the classical minmax one, provided with a statistical measure for the adequacy of the new model. The theoretical findings are applied to the north west Tabuk region of Saudi Arabia for environmental and climate data related to pollutants and factors involved in the estimation of the air quality index.

Children's screen time may affect their growth and development. However, different aspects of children and their parents that affect children's screen time has not been investigated in the Japanese context. This study aimed to explore the relationship between relevant factors affecting children's screen time based on their sleep, difficulties, and parental control, among Japanese elementary and junior high school students. A cross-sectional survey was conducted among parents in Japan. Data on screen time duration, parent-child background, strengths and difficulties, sleep variables, and parental control types were collected from 225 households. Path analysis revealed that high Strengths and Difficulties Questionnaire (SDQ) scores (β = 0.167, p = 0.007), sleep duration (β = -0.282, p &lt; 0.001), and parental control (β = -0.205, p &lt; 0.001) were significantly related to children's screen time. Additionally, late parental bedtime negatively correlated with children's sleep duration (β = -0.326, p &lt; 0.001). The findings suggest that children's difficulties, sleep duration, and parental control are associated with children's screen time. This study, together with previous research, provides comprehensive insights to design interventions to decrease screen time in children in the Japanese context.

This paper presents a method for producing a gravity field through an electromagnetic field. The electromagnetic field can produce a positive or negative gravity field. The relationship between gravity force and electromagnetic is unknown. Former studies by theoretical physics predict the relationship between these two forces. Based on the general balance theory in six dimensions of space and time, the electromagnetic field was studied from this viewpoint. Three-dimensional waves oscillate in the fourth dimension and electromagnetic waves distort space-time. Aggregating these distortions can behave like a positive or negative gravity field. Consequently, the production of energy is possible from gravitational fields.

The G2 gas cloud motion data and the scarcity of observations on the event horizon-scale distances have challenged the comprehensiveness of the central supermassive black hole model. In addition, the Planck Legacy 2018 release has preferred a positively curved early Universe with a confidence level higher than 99%. This study investigates the impact of the background curvature and its evolution over the conformal time on the formation and morphological evolution of central compact objects and the consequent effect on their host galaxies. The formation of a galaxy from the collapse of a supermassive gas cloud in the early Universe is modeled based on interaction field equations as a 4D relativistic cloud-world that flows and spins through a 4D conformal bulk of a primordial positive curvature considering the preference of the Planck release. Owing to the curved background, this scenario of galaxy formation reveals that the core of the galaxy undergoes a forced vortex formation with a central event horizon leading to opposite vortices (traversable wormholes) that spatially shrink while evolving in the conformal time. It indicates that the galaxy and its core are formed in the same process whereas the surrounding gas clouds form the spiral arms due to the frame-dragging induced by the fast-rotating core. It demonstrates that the accretion flow onto the central supermassive compact object only occurs at the central event horizon of the two opposite vortices while their other ends eject the relativistic jets. This can elucidate the relativistic jet formation and explain the G2 gas cloud motion if its orbit is around one of the vortices but at a distance from the central event horizon. The gravitational potential of the early curved bulk could contribute to galaxy formation while the present spatial flatness deprives the potential of bulk which could lead to galaxy quenching. The formation of a galaxy and its core simultaneously could explain the growth of the supermassive compact galaxy core to a mass of ~10⁹ M_⊙ at just 6% of the current Universe age.

This article presents the results of reviewing the predictive capacity of Google trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn Matthei in 2013, Sebastián Piñera and Alejandro Guillier in 2017, and Gabriel Boric and José Antonio Kast in 2021 were reviewed. The time series analysed were organised on the basis of relative searches between the candidacies, assisted by R software, mainly with the gtrendsR and forecast libraries. With the series constructed, forecasts were made using the ARIMA technique to check the weight of one presidential option over the other. The ARIMA analyses were performed on 3 ways of organising the data: the linear series, the series transformed by moving average and the series transformed by Hodrick-Prescott. The result indicates that the method offers optimal pre-dictive ability.

The distributed acoustic sensing (DAS) has great potential for monitoring natural-resource reservoirs and borehole conditions. However, the large volume of data and complicated wavefield add challenges to processing and interpretation. In this study, we demonstrate that seismic interferometry based on deconvolution is a convenient tool for analyzing this complicated wavefield. We extract coherent wave from the observation of a borehole DAS system at the Brady geothermal field in Nevada. Then, we analyze the coherent reverberating waves, which are used for monitoring temporal changes of the system. These reverberations are tirelessly observed in the vertical borehole DAS data due to cable or casing ringing. The deconvolution method allows us to examine the wavefield at different boundary conditions. We interpret the deconvolved wavefields using a simple 1D string model. The velocity of this wave varies with depth, observation time, temperature, and pressure. We find the velocity is sensitive to disturbances in the borehole related to increasing operation intensity. The velocity decreases with rising temperature, which potentially suggests that the DAS cable or the casing are subjected to high temperature. This reverberation can be decomposed into distinct vibration modes in the spectrum. We find that the wave is dispersive, and the the fundamental mode propagate with a large velocity. The method can be useful for monitoring borehole conditions or reservoir property changes. For the later, we need better coupling than through only friction in the vertical borehole to obtain coherent energy from the formation.

The Albufera of Valencia is a coastal lagoon located in the western area of the Mediterranean Sea, in the Iberian Peninsula. It has an area of 23.1 km2 and an average depth of only 1 m, with a maximum depth of 1.6 m. This lagoon is the rest of an original and more extensive wetland of about 220 km2, mostly dedicated to rice cultivation nowadays. Surface water is supplied through several main and many secondary canals for a total of 64 water entry points and three exit points to the sea. It is difficult to evaluate the renewal rate due to the lack of reliable measurements of the inflow or outflow, as well as continuous measurements. Between 1988 and 2018 several procedures have been used, synthesizing in this work the result, in which it is observed a decrease of the inflow in these thirty years, and therefore the residence time is increasing. There is a temporal variation during the year of renewal influenced by rainfall and cultivation periods. Likewise, it is observed that the natural hydrological zoning of the lagoon causes a spatial heterogeneity with small areas with more renewal, practically weekly, and large extensions with little renewal during months. It is impossible to know this information if flow measurements are not taken in each of the main watercourses.

We have shown in previous work that the equivalence of the Heisenberg and Schrödinger pictures of quantum mechanics requires the use of the Born and Jordan quantization rules. In the present work we give further evidence that the Born--Jordan rule is the correct quantization scheme for quantum mechanics. For this purpose we use correct short-time approximations to the action functional, initially due to Makri and Miller, and show that these lead to the desired quantization of the classical Hamiltonian.

Algaecides are chemicals that cause serious health problems. Conventional paints contain algaecides to improve the algae resistance on the paint film. Present research has suggested an environmental friendly paint formulation that focuses on developing algae resistance without having algaecides. In this research, Algae growth on newly developed paint is modeled by incorporating dirt resistance of paint and natural phenomena including humidity, temperature and time respectively. The fitted Model revealed explained variation of 59.65% in the average algae growth, of which, Dirt Resistance, Humidity and temperature and some of their interactions play significant role in this variation. Model suggests that the proposed newly developed paint without algaecides is more resilient to algae growth and significantly decreased the average algae growth rate by 0.53% as compared to conventional paints. Keeping the effect of all other factors constant, if dirt resistance of paint (D_c value) increases by one percent, average algae growth decreases by 12.98%; when temperature increases by 1^oC, average algae growth decreases by 22.4%; a positive unit change in the joint linear effect dirt resistance, temperature and humidity caused a decrease in average algae growth by 0.0031%. It was also observed that the individual effect of humidity variable was inversely related with average algae growth. However the combination of humidity and temperature, humidity and dirt resistance, humidity and time, and the quadratic effect of humidity were found to increase the average algae growth rate. The cubic effect of temperature variable by one degree centigrade resulted in decrease of average algae growth by 0.000907%.

Landslide is a sliding movement of rock mass, debris and soil along the slope under the action of gravity. Small Baseline Subset (SBAS) is an established method for the investigation and monitoring of landslide moments. This study focuses on monitoring the long-temporal displacement of mountainous terrain in Danba County, Sichuan Province via SBAS technique, based on 31 scenes of L-band ALOS/PALSAR data from Feb. 2007 to Oct. 2010.The results show that the largest velocity rates in LOS direction are ±120 mm/yr and maximum accumulated displacement is up to -300, which indicates fast movement of the mountainous terrain during the observation time. These results get good consistency against the results of previous study. This demonstrates the strong potential of SBAS technique for monitoring the landslides geohazard.

In nonlinear self-consistent system, Lorenz system (Lorenz equations) is a classic case with chaos solutions which are sensitively dependent on the initial conditions. As it is difficult to get the analytical solution, the numerical methods and qualitative analytical methods are widely used in many studies. In these papers, Runge-Kutta method is the one most often used to solve these differential equations. However, this method is still a method based on explicit time scheme, which would be the main reason for the chaotic solutions to Lorenz system. In this work, numerical experiments based on implicit time scheme and explicit scheme are setup for comparison, the results show that: in implicit time scheme, the numerical solutions (counterexamples) are without chaos; for an original volume, the volume shrinks exponentially fast to 0 in common.

Speech enhancement technology seeks to improve the quality and intelligibility of speech signals degraded by noise, particularly in telephone communications. Recent advancements have focused on leveraging deep neural networks (DNN), especially U-Net architectures, for effective denoising. In this study, we evaluate the performance of a 6-level skip-connected U-Net constructed using either conventional convolution activation blocks (CCAB) or innovative global local former blocks (GLFB) across different processing domains: temporal waveform, short-time Fourier transform (STFT), and short-time discrete cosine transform (STDCT). Our results indicate that the U-Nets can receive better signal-to-noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) when applied in the STFT and STDCT domains, with comparable short-time objective intelligibility (STOI) scores across all domains. Notably, the GLFB-based U-Net outperforms its CCAB counterpart in metrics such as CSIG, CBAK, COVL, and PESQ, while maintaining fewer learnable parameters. Furthermore, we propose domain-specific composite loss functions, considering the acoustic and perceptual characteristics of the spectral domain, to enhance the perceptual quality of denoised speech. Our findings provide valuable insights that can guide the optimization of DNN designs for causal speech denoising.

A time interval only description necessarily needs its own independent definitions, without one moment time coordinates to it. In this paper derived are time interval only set properties, including commutation relations, addition, multiplication and derivatives. Time interval only set commutation relations, consistent with one moment time set properties, are introduced by defining correspondence relations for these sets. Time development and equilibrium within the time interval description depend on the well-known ‘mean velocity theorem’ and the time interval version of the Legendre transform. In contrast, usually applied is only the traditional vector and one moment time coordinate kinematic approach from Newton’s laws. The main results of this paper are correspondences and time interval only set commutation relations. All other properties are derived from these results, while for the time interval only set, different from the one moment time set the canonical property is not valid. The time interval only description can be applied assuming spherical symmetric star-source radiation propagation, with finite time intervals for realistic measurement events. From time development with (a-)symmetry of time intervals, and from Curie’s principle, change can be described with a set theoretic approach. Time interval only set properties include the time interval only version of Noether charges and structure constants. A time interval only description for wave propagation, including group-velocity, for zero and non-zero mass wave particles, relates to the properties of the time interval only set. A second result in terms of the time interval only description is propagation wave energy equivalence with gravitation energy, derived from time interval only set commutation relations. A time interval approach, however including one moment time description dependence, was introduced in earlier publications of this author. In part 1 investigated are the time interval only set and its properties, a forthcoming part 2 to application of the time interval only description to specific groups of stars as emission sources.

Currently Single Board Computers (SBCs) are sufficiently powerful to run Real-Time Operating Systems (RTOSs) and applications with real-time attributes and requirements. SBCs serve as a foundation in Industrial Internet of Things (IIoT). The NXP Semiconductors produces a series of SBCs based on ARM-processors for a variety of industrial applications. The continuous increase in real-time data generated by IoT devices adds further research issues about the efficiency of such systems and applications. The purpose of this research was to investigate the timing performance of an NXP TWR-K70F120M device with μClinux OS on running concurrently tasks with real-time features and constraints. A custom-built multithreaded application with specific compute-intensive sorting and matrix operations was developed and applied to obtain measurements in specific timing metrics, including task’s execution time, threads waiting time, and response time, under different threads variations. The performance of this device was additionally benchmarked and validated against favorite platforms, a Raspberry Pi4 and BeagleBone AI SBCs. The experimental results showed that this device stands well both in terms of timing and efficiency metrics. Execution times were quite lower than the others, by approximately 56% in the case of two threads, and by 29% in the case of thirty-two threads configurations.

This paper presents sufficient conditions for almost global stability of nonlinear switched systems consisting of both stable and unstable subsystems. Techniques from the stability analysis of switched systems have been combined with the multiple Lyapunov density approach - recently proposed by the authors for the almost global stability of nonlinear switched systems composed of stable subsystems. By using slow switching for stable subsystems and fast switching for unstable subsystems lower and upper bounds for mode-dependent average dwell times are obtained. In addition to that, by allowing each subsystem to perform slow switching and using some restrictions on total operation time of unstable subsystems and stable subsystems, we have obtained a lower bound for an average dwell time.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

Multi-gap Resistive Plate Chamber has been attracting increasing attention and has been studied in recent decades because of its good efficiency, high time resolution and relatively low cost. It has become a new standard technology for the Time of Flight system in particle and high energy physics experiments. In this paper, we introduce the operation principle and structure of the MRPC detector and review the applications on the Time of Flight system in several famous experiments, such as STAR, CBM and SoLID. The performances including time resolution and particle identification are discussed in detail. Some recent advances and perspectives with regard to the future development of the next generation MRPC are also outlined.

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use time series analysis method for accurately measuring the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply the dynamic time warping (DTW) method and compare its performance with that of the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and trends of time series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

After developed the formulation of a "general relativity" in C⁴ [2], we proceed with the formulation of a Hamilton-Jacobi equation in C⁴. We argue that in this consideration, the usual problems of the ADM formalism, do not exist, due to the complex time as it exists in our consideration. Specically, we can derive a suitable dispersion relation in order to work with and nd a generalised super Hamiltonian

Manzamines are complex polycyclic marine-derived β-carboline alkaloids with reported anticancer, immunostimulatory, anti-inflammatory, antibacterial, antiviral, antimalarial, neuritogenic, hyperlipidemia and atherosclerosis suppression bioactivities, putatively associated with inhibition of glycogen synthase kinase-3, cyclin-dependent kinase 5, and vacuolar ATPases. We hypothesized that additional and yet undiscovered molecular targets might be associated with Manzamine A (MZA) reported pharmacological properties. We report herein for the first time to our knowledge that MZA inhibited a 90kDa ribosomal protein kinase S6 (RSK1) when screened against a panel of 30 protein kinases. Furthermore in vitro RSK kinase assays demonstrated a 10-fold selectivity in potency of MZA against RSK1 versus RSK2. MZA’s differential binding and selectivity toward the two isoforms is also supported by computational docking experiments. Specifically, the RSK1-MZA (N- and C-termini) complexes appear to have stronger interactions and preferable energetics contrary to the RSK2-MZA ones. In addition, our computational strategy suggests that MZA binds to the N-terminal kinase domain of RSK1 rather than the C-terminal domain. RSK is a vertebrate family of cytosolic serine-threonine kinases that act downstream of the ras-ERK1/2 (extracellular-signal-regulated kinase 1/2) pathway, which phosphorylates substrates shown to regulate several cellular processes including growth, survival and proliferation. Consequently, our findings have lead us to hypothesize that MZA and the 80 currently known manzamine-type alkaloids isolated from several sponge genera, may have novel pharmacological properties.

The process of sexual reproduction in eukaryotes starts when gametes from two different sexes encounter each other. Paramecium, a unicellular eukaryote, undergoes conjugation and uses a gametic nucleus to enter the sexual reproductive process. The molecules responsible for recognizing mating partners, hypothetically called mating-type substances, are still unclear. We have identified an O3-type mating substance polypeptide and its gene sequence using protein chemistry, molecular genetics, immunofluorescence, RNA inter-ference, and microinjection. The O3-type substance is a polypeptide found in the ciliary membranes, located from the head to the ventral side of cells. The O3-type substance has a kinase-like domain in its N-terminal part located outside the cell and four EF-hand motifs that bind calcium ions in its C-terminal part located inside the cell. RNA interference and immunofluorescence revealed that this polypeptide positively correlated with the expression of mating reactivity. Microinjection of an expression vector incorporating the O3Pc-MSP gene induced additional O3 mating type in the recipient clones of different mating types or syngen. Phylogenetic analysis indicates this gene is widely present in eukaryotes and exhibits high homology among closely related species. The O3Pc-MSP gene had nine silent mutations compared to the complementary mating type of the E3 homologue gene.

The origin of neurodegenerative diseases is linked to the abnormal folding of disease-associated proteins. The traditional amyloid hypothesis has been a focal point of research, but recent findings suggest a more complex picture. This review explores the role of physiological conformation of aggregation-prone domain (also known as prion-like low complexity domain) and multiple co-misfolded proteins in neurodegenerative diseases. We highlight the importance of targeting multi-heterotypic misfolded proteins for effective treatments and emphasize the irreversible neurodegeneration stemming from the loss of physiological folding in prion-like disease-causing proteins underscores the criticality of refolding. Additionally, we introduce the concept of ReproFold Therapeutics, a novel approach aimed at restoring the cellular prion-like folding of disease-causing proteins to rebuild normal cross-β interactomes and mitigate neurodegeneration. Baicalein, a potential drug candidate for this approach, shows promise in vitro and in animal models. Moreover, prion-like low complexity domains implicated in neurodegeneration are also associated with other disorders such as diabetes, cancers, and mental illnesses. Understanding the core pathology of neurodegenerative diseases and the broader implications of prion-like LC domains could revolutionize healthcare approaches and policies worldwide.

Diversity-generating retroelements (DGRs) are prokaryotic systems providing rapid modification and adaptation of target proteins. In phages, the main targets of DGRs are receptor-binding proteins that are usually parts of tail structures and the variability of such host-recognizing structures enables phage adaptation to changes on the bacterial host surface. Sometimes, more than one target gene containing a hypermutated variable repeat (VR) can be found in phage DGRs. The role of mutagenesis of two functionally different genes is unclear. In this study, several phage genomes that contain DGRs with two target genes were found in the gut virome of healthy volunteer. Bioinformatics analysis of these genes indicated that they encode proteins with different topology; however, both proteins contain the C-type lectin (C-lec) domain with a hypermutated beta-hairpin on its surface. One of the target proteins belongs to a new family of proteins with a specific topology: N-terminal C-lec domain followed by one or more immunoglobulin domains. Proteins from the new family were named tentaclins after TENTACLe+proteIN. The genes encoding such proteins were found in the genomes of prophages and phages from the gut metagenomes. We hypothesized that tentaclins are involved in binding either to bacterial receptors or intestinal/immune cells.

Intrinsic disorder accounts for the flexibility of protein loops, molecular building blocks that are largely responsible for the processes and molecular functions of the living world. While loops likely represent early structural forms that served as intermediates in the emergence of protein structural domains, their origin and evolution remains poorly understood. Here, we conduct a phylogenomic survey of disorder in loop prototypes sourced from the ArchDB classification. Tracing prototypes associated with protein fold families along an evolutionary chronology revealed ancient prototypes tended to be more disordered than their derived counterparts, with ordered prototypes developing later in evolution. This highlights the central evolutionary role of disorder and flexibility. While mean disorder increased with time, a minority of ordered prototypes exist that emerged early in evolutionary history, possibly driven by the need to preserve specific molecular functions. We also revealed percolation of evolutionary constraints from higher to lower levels of organization. Percolation resulted in trade-offs between flexibility and rigidity that impacted prototype structure and geometry. Our findings provide a deep evolutionary view of the link between structure, disorder, flexibility and function, as well as insights into the evolutionary role of intrinsic disorder in loops and their contribution to protein structure and function.

The Apetala2/ethylene response factor superfamily refers to a group of transcription factors that share a conserved AP2 DNA binding domain. These factors have been found to have different roles in plant responses to both biotic and abiotic stresses. Samples of hexaploid wheat, tetraploid pasta (or durum wheat), and diploid wheat progenitors were selected. The 29 dehydration-responsive element binding transcription factors in these samples were downloaded from NCBI GenBank for six different countries: Iran, China, Italy, France, Afghanistan, and Azerbaijan. The AP2 domain sequences were identified from the dehydration-responsive element binding transcription factors using PROSITE, ProDom, and SMART software. Next, all sequences were aligned using Multalin and Jalview software. The aligned sequences were then analyzed to identify amino acid locations, types, and frequencies. The multiple alignments showed that approximately 76% of the amino acid residues in the AP2 domains are conserved. According to the amino acid analysis, alanine, serine, and glutamic acid are the most abundant amino acids found in three motifs. The performed phylogenetic analysis illustrates the role of geographical effects on the transcription factor sequences of bread wheat in the Middle East. Significant differences were found between Iranian and Chinese transcription factor sequences. Moreover, genetic variation was observed in the transcription factors of Italian sequences found in pasta and wheat progenitors. Motif structures play a critical role in the domain organization of wheat proteins to enhance the characteristics of assorted metabolic pathways. The structure of the AP2 domain was analyzed by several programs, I-TASSER for instance, to identify the α-helix, β-sheets, and the regions of some significant amino acids in the 3-D model.

This paper presents results of the magnetic dynamics study (the microwave power absorptions at the fixed frequencies, during magnetic field sweeping) in samples of Y3Fe5O12 single crystals in the form of plates and spheres of various sizes, at frequencies exceeding 30 GHz, in magnetic fields up to 18 kOe, at room temperature and T=77 K. It was found, that in this case, the inhomogeneity’s of the magnetic state manifested itself in the Y3Fe5O12 samples in as 2D local phase separation regions. These 2D phase separation regions formed inside domain walls representing superlattices with size’s 700 – 900 Å. Landau diamagnetism or de Haas van Alphen oscillations observed in the 2D phase separation regions at room temperature and T=77 K, depending on the shape and size of the studied plates and spheres.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

Since the detection of the first COVID-19 patient, 2 years have passed, during which more than 287,862,000 people fell ill globally, of which about 1.9% died. Implementation of SARS-CoV-2 control programs required efforts from almost all countries. An important direction in the fight against COVID-19 was the formation of herd immunity, the main tool for managing the pandemic. Study goal: to assess the seroprevalence of antibodies (Abs) to SARS-CoV-2 nucleocapsid (Nc) and receptor binding domain (RBD) in the St. Petersburg population during the COVID-19 pandemic. Materials and methods. A longitudinal cohort randomized monitoring study of Ab seroprevalence (SARS-CoV-2 Nc, RBD) was organized and conducted according to a unified methodology developed by Rospotrebnadzor with the participation of the St. Petersburg Pasteur Institute. For this purpose, a cohort of 1000 volunteers was formed who participated in all five stages of seromonitoring. The cohort was divided into 7 age groups: 1-17; 18-29; 30-39; 40-49; 50-59; 60-69; 70; and older (70+) years. Seropositivity levels (Nc, RBD) were assessed by quantitative and qualitative enzyme immunoassays. During the 2nd year of monitoring, some volunteers were vaccinated with the GamCOVIDVac (84%) or EpiVacCorona (11.6%) vaccines approved in Russia. Statistical processing was carried out using the Excel 2010 software package. Confidence intervals for shares and percentages (95% CI) were calculated using the method of A. Wald and J. Wolfowitz with adjustment (A. Agresti, B.A. Coull). The statistical significance of differences was calculated by z-test, using the appropriate online calculator (p<0.05), unless indicated. Results. There was a trend towards: an increase in Nc seropositivity in stages 1-3 of seromonitoring, with a decrease in stages 4-5 among children and adults. The share of RBD seropositive steadily increased during all 5 stages of seromonitoring. The most frequently found were low anti-RBD Abs levels (22.6-220 BAU/ml). High Ab levels were recorded statistically significantly less frequently. Asymptomatic forms were observed in 84-88% of SARS-CoV-2 seropositive volunteers. By the 5th stage of monitoring, this indicator significantly decreased to 69.8% (95% CI: 66.1-73.4). The monitoring revealed a statistically significant increase in anti-RBD Abs, alongside a statistically significant decrease in the proportion of Nc seropositive. This dynamic was especially characteristic of persons vaccinated with GamCOVIDVac. Conclusion. Prior to the use of specific vaccines, a seroprevalence of anti-Nc Abs was noted. After the introduction of the GamCOVIDVac vaccine in adults, a decrease in the level of anti-Nc Abs was noted due to an increase in the proportion of RBD seropositive persons.

Applications in security and electronic surveillance require combination of excellent magnetic softness with good mechanical and anti-corrosive properties and low dimensionality. We overviewed the properties of soft magnetic glass-coated microwires and different post-processing making them quite attractive for electronic article surveillance and security applications. We studied the magnetostatic interaction between the microwires and routes to tune the non-linear hysteresis loops by using the arrays of different types of amorphous microwires. The presence of neighbouring microwire (either Fe or Co-based) significantly affects the hysteresis loop of the whole microwires array. In a microwires array containing magnetically bistable microwires we observed splitting of the initially rectangular hysteresis loop with a number of Barkhausen jumps correlated with the number of magnetically bistable microwires. Essentially, non-linear and irregular hysteresis loops have been observed in mixed arrays containing Fe and Co-rich microwires. The observed non-linearity in hysteresis loops allowed to increase the harmonics and tune their magnetic field dependencies. Non-linear hysteresis loops have been also observed upon devitrification of amorphous microwires. On the other hand, several routes allowing to tune the switching field by either post-processing or modifying the magnetoelastic anisotropy have been reviewed. Observed unique combination of magnetic properties together with thin dimensions and excellent mechanical and anti-corrosive properties provide excellent perspectives for the use of glass-coated microwires for security and electronic surveillance applications.

A simplified nonlinear dispersive system of BBM-type, initially derived by D. Mitsotakis, is employed here in order to model the generation and propagation of surface water waves over variable bottom. The simplification consists in applying the so-called Boussinesq approximation. Using the finite element method and the FreeFem++ software, we solve numerically this system for three different complexities for the bathymetry function: a flat bottom case, a variable bottom in space, and a variable bottom both in space and in time. The last case is illustrated with the Java 2006 tsunami event. This article is designed rather as a tutorial paper even if it contains the description of completely new adaptation techniques.

There is much interest in effective copper chelators to correct copper dyshomeostasis in neurodegenerative and oncological diseases. In this study, a recombinant fusion protein for expression in E. coli cells was constructed from glutathione-S-transferase (GST) and the N-terminal domain (ectodomain) of human high affinity copper transporter CTR1 (hNdCTR1), which has three metal-bound motifs. Several biological properties of the GST-hNdCTR1 fusion protein were assessed. It was demonstrated that in cells, the protein was prone to oligomerization, formed inclusion bodies and displayed no toxicity. Treatment of E. coli cells with copper and silver ions reduced cell viability in a dose- and time-dependent manner. Cells expressing GST-hNdCTR1 protein demonstrated resistance to the metal treatments. These cells accumulated silver ions and formed nanoparticles that contained AgCl and Ag0. In this bacterial population, filamentous bacteria with length about 10 μm were often observed. The possibility for the fusion protein carrying extracellular metal binding motifs to integrate into the cell’s copper metabolism and its chelating properties are discussed.

Motivated by diffraction experiments on the $\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$ reconstructed Si(111) due to deposition of rare earth elements (Dy, Tb) and silicide formation we analyse the splitting and non-splitting of superstructure spots. For this purpose, we model diffraction patterns for one dimensional structures generated by the binary surface technique and use supercell models to keep the analysis simple. Diffraction pattern are calculated in the framework of the kinematical diffraction theory and they are analyzed as a function of the domains and domain boundaries. Basic properties of the diffraction pattern are analyzed for model systems of a two-fold and a three-fold periodicity. The rules derived from these calculations are applied to the "real-world" system of Si(111)-($\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$)-RESi$2_{}$ (RE = Dy or Tb). Depending on the combination of domains and domain boundaries of different types a plethora of different features are observed in the diffraction patterns. These are analyzed to determine the sizes of both domain boundaries and domains from experimentally observed splitting of specific superstructure spots.

The paper deals with two new properties of the linear time-optimal control problems with real non-positive eigenvalues of the system. They are the foundation of a new method of synthesizing the time-optimal control without the need to describe the switching hyper-surfaces. The so called “axes initialization” and the synthesis technique are illustrated on the already classical example of the time-optimal control of a double integrator.

The current work reveals the fine tuning between stability zones and randomness of GI/M/1 Pointwise Stationary Fluid Flow Approximation (PSFFA) model of the non-stationary D/M/1 queueing system. More specifically, this clearly provides more insights into developing a contemporary PSFFA theory that unifies non-stationary queueing theory with chaos theory and fields in both theoretical physics and chaotic systems. This opens new grounds for stability analysis of non-stationary queueing systems. A notable application of GI/M/1 queueing model to achieve ultra-low latency of autonomous driving service is highlighted. Concluding remarks associated with future avenues of research are given.

Today, there is a time series analysis that has gained significant popularity across various industries to provide effective solutions for any specific challenges. Artificial intelligence logic is generally used to solve time-series problems with the help of various frameworks that are available for the development of AI-based applications. Currently, there is a significant framework in Python that is driven by its comprehensive libraries and their practical implementations. Time series analysis can be built with the help of some specific models that give results to differentiate the efficiency and domains of great work. This paper focuses on three diverse libraries of Python to predict the Gulf stock exchange market. These libraries are prediction models that are helpful in predicting future values. These models utilized include SARIMA (Seasonal Auto-Regressive Integrated Moving Average), Neural Prophet Model, and Fb Prophet model. Each model is trained using the provided datasets, which are followed by evaluation parameters such as MSE, RMSE, recall, MDAPE, accuracy, precision, etc. The main purpose of using these three models for stock market prediction is to find the better-suited models. In the past few decades, the stock market industry has seemed to have promising growth after 2022 onwards. This paper will help us make a significant decision about model usage for addressing such complex challenges. Especially, the research findings indicate that all three models demonstrate in different domains. SARIMA can perform well in overall prediction tasks, while the Prophet model works great with smaller datasets that contain seasonality trends.

The paper deals with one new property of the linear time-optimal control problems with real eigenvalues of the system, no matter of the number of eigenvalue multiplicity. This property unveils a prospective of synthesizing the time-optimal control without the need of describing the switching hyper-surfaces. The already chrestomathy example of the time-optimal control of a double integrator is resolved by the new technique and compared with the classical synthesis.

A correct system design can be systematically obtained from a specification model of a real-time system that integrates hybrid measurements in a realistic industrial environment, this has been carried out through complete Matlab / Simulink / Stateflow models. However, there is a widespread interest in carrying out that modeling by resorting to Machine Learning models, which can be understood as Automated Machine Learning for Real-time systems that present some degree of hybridization. An induction motor controller which must be able to maintain a constant air flow through a filter is one of these systems and it is discussed in the paper as a study case of closed-loop control system. The article discusses a practical application of ML methods that demonstrates how to replace such closed loop in industrial control systems with a Simulink block generated from neural networks to show how the proposed procedure can be applied to derive complete hybrid system designs with artificial neural networks (ANN). In the proposed ANN-based method to design a real-time hybrid system with continuous and discrete components, we use a typical design of a neural network, in which we define the usual phases: training, validation, and testing. The generated output of the model is made up of reference variables values of the cyber-physical system, which represent the functional and dynamic aspects of model. They are used to feed Simulink/Stateflow blocks in the real target system.

In this research article we solve the problem of synchronization and anti-synchronization of chaotic systems described by discrete and time-delayed variable fractional order differential equations. To guarantee the synchronization and anti-synchronization of these systems, we use the well-known PID control theory and the Lyapunov-Krasovskii stability theory for discrete systems of variable fractional order.We illustrate the results obtained through simulation with examples, in which it can be seen that our results are satisfactory, thus achieving synchronization and anti-synchronization of chaotic systems of variable fractional order with discrete time delay.

Deep learning methods have been widely applied to time series classification tasks. Although deep learning methods perform excellently on these tasks, they require much training data. Labeling time series data for training is very time-consuming and labor-intensive. Active learning can be used to select the most informative data for labeling in time series classification tasks to save human labeling efforts. In this paper, we propose a new active learning (AL) framework for time series data. First, a temporal classifier for pixel-level temporal classification tasks is designed. Next, We propose an effective active learning method to select informative time series samples for labeling, which combines representativeness and uncertainty. For representativeness, We use the K-shape method to cluster time series data. For uncertainty, we construct an auxiliary deep network to evaluate the uncertainty of unlabeled data. The features of the middle hidden layer with rich temporal information of the classifier will be fed into the auxiliary deep network, which is equipped with a self-attention mechanism to utilize the temporal dependencies of time series data fully. Then, we define a new loss function with the aim of improving the deep model’s performance. Finally, the proposed method in this paper was evaluated on two temporal remote-sensing image datasets. The results demonstrate a significant advantage of our method over other approaches to time series data. Code is available at https://github.com/Fighting-Golion/time_series_active_learning

The P versus NP problem is a fundamental question in computer science. It asks whether problems whose solutions can be quickly verified can also be quickly solved. Here, "quickly" refers to computational time that grows proportionally to the size of the input (polynomial time). While the problem's roots trace back to a 1955 letter from John Nash, its formalization is attributed to Stephen Cook and Leonid Levin. Despite extensive research, a definitive answer remains elusive. Closely tied to this is the concept of NP-completeness. If a single NP-complete problem could be solved efficiently, it would imply that all problems in NP can be solved efficiently, proving that P equals NP. This work posits that MONOTONE ONE-IN-THREE 3SAT, a notoriously difficult NP-complete problem, can be solved efficiently, thereby establishing the equivalence of P and NP.

Biopolymer gels have gained tremendous potential for therapeutic applications due to their biocompatibility, biodegradability, and ability to adsorb and bind biological fluids, making them attractive for drug delivery and therapy. The versatility of biopolymer gels in theranostic backgrounds, with a focus on integrating imaging features and facilitating real-time monitoring of drug delivery. Different methods of delivery are explored imaging agents into biopolymer gels, including encapsulation, surface functionalization, nanoparticle encapsulation and layer-by-layer assembly techniques. Integration of Agents and Real-Time Monitoring Drug Delivery We summarize the synthesis methods, general properties, and mechanisms of biopolymer gels functionality, demonstrating their broad applications as multimodal systems for imaging-based therapeutics. These techniques not only enable multiple imaging but also provide signal enhancement and imaging a targeted facilitates, increasing the diagnostic accuracy and therapeutic efficacy. In addition, current techniques for incorporating imaging agents into biopolymer gels are discussed, and their role in precise drug delivery and monitoring.

The definition of UTC in 1972 balanced the needs of the time and frequency community for a universal time scale that was traceable to the definition of frequency in the SI system of units with the needs of the astronomical community for a time scale that was closely linked to UT1, a time scale related to the rotation of Earth. The balance between these two considerations has changed very significantly since 1972, and there is widespread agreement that the method of relating UTC to UT1 should be changed by increasing the maximum tolerance between the two scales to more than the current limit of 0.9 s. I propose to realize this decision by means of an algorithmic adjustment process based on the variation in the length of the day over the previous centuries. I use these historical data to extrapolate the length of the day for a century into the future, and to compute a periodic rate adjustment to UTC based on this extrapolation. The adjustment process will not use time steps. The magnitude of the periodic rate adjustment will be re-considered every century, but the basic adjustment algorithm will not change. I suggest a maximum tolerance between UT1 and UTC that is large enough to absorb the irregular and unpredictable variations in the length of the day with respect to the long-term average increase in this value but plays no role in determining the parameters of the adjustment process that is proposed. I also propose that the adjustment be implemented at 12 UTC on 1 January since that is closest to a universal holiday in all time zones.

Computational animation of spatiotemporal models for 3-D shape and motion is considered as core of dynamic real-time vision. This is based on sets of features evaluated from multi-focal image streams. Subjects are defined as special objects capable of sensing environmental parameters and of initiating own actions in combination with stored knowledge. Object/subject recognition and scene understanding are achieved on different levels. Multiple objects are tracked individually for perceiving their actual state (‘here and now’). Fast saccadic jumps in gaze direction allow flexible concentration on objects or parts of actual interest. By analyzing motion of relevant objects/subjects over a larger time scale on the level of state variables and documenting them in the ‘scene tree representation’ (computer graphics), the situation with respect to decision making is assessed. Various behavioral capabilities of subjects are represented on an abstract level for characterizing their potential behaviors. These are generated by stereotypical feed-forward and feedback control applications on a separate level close to the actuator hardware with corresponding methods. This dual representation on one level for decision making and one on the implementation level allows for flexibility and easy adaptation or extension. Results are shown for road vehicle guidance based on three cameras on a gaze-controlled platform.

We develop a renormalizable model of Quantum General Relativity (QGR). We do this by building on earlier work with quantum electrodynamics (QED). Working with the Feynman path integral (FPI) approach to QED, we generalize the paths from varying in space to varying in space and time. This gives the standard results plus dispersion in time of order attoseconds or left. Over longer times, the dispersion in time averages out, but at attosecond times it should be detectable with current technology. As an unexpected but welcome side-effect, the approach caused the usual loop integrals to be finite. Since the usual UV divergences are significantly harder to renormalize in QGR than in QED, and since using paths in time plus space is definitely more in keeping with the general spirit of GR, we here apply the same approach to QGR. We show that with an appropriate choice of gauge (trace-reversed de Donder gauge) we can extend the earlier results to include QGR. Sub-attosecond measurements of individual gravitons are not within our reach, but at least the ultraviolet divergences in QGR are contained in the same way. We have therefore a renormalizable model of QGR

Obesity poses significant challenges in rural communities worldwide due to limited resources and healthcare access. Practical, innovative, and culturally sensitive approaches are essential. Time-Restricted Eating (TRE), aligning food intake with circadian rhythms, emerges as a promising strategy. By restricting eating to specific time windows, TRE synchronizes with the body's internal clock, offering potential metabolic health benefits. This review examines TRE's potential in rural obesity management, highlighting its simplicity and scalability. While TRE may address rural health disparities, cultural considerations and challenges such as undernutrition must be acknowledged for effective implementation. Overall, TRE shows promise in improving metabolic health and well-being in rural populations.

Rainfall, or more generally the precipitation process (flux), is a clear example of chaotic variables resulting from a highly nonlinear dynamical system, the atmosphere, represented by a set of physical equations such as the Navier-Stokes equations, energy balances and hydrological cycle among others. As a generalization of the Euclidean (ordinary) measurements, chaotic solutions of these equations are characterized by fractal dimensions, which are non-integer values that represent the complexity of variables like the precipitation. However, observed precipitation is measured as an aggregate variable over time, thus physical analysis of the observed fluxes is very limited. Therefore, this review aims to go through the different approaches used in the identification and analysis of the complexity of the observed precipitation, taking advantage of its geometry footprint. To address the review, it ranges from classical perspectives of fractal-based techniques to new perspectives at temporal and spatial scales as well as for classification of climatic features, including monofractal dimension, multifractal approaches, Hurst exponent, Shannon entropy and time scaling in intensity-duration-frequency curves.

Tungsten and its alloys are considered as a key component in high temperature environment. Self-passivating W-Si-xY alloys with different Y contents made from the powders milled for different times were prepared by mechanical alloying (MA) and spark plasma sintering (SPS). Their microstructures and oxidation resistance were investigated. It is found that the oxidation resistance of the alloys with low Y contents (0Y and 2Y) made from 20 h-milled powders is worse than that of the alloys made from 4 h-milled powders. It is because the protective WO3/SiO2 composite layer is discontinuous. While the alloys with high Y contents (4Y and 6Y) made from 4 h and 20 h-milled powders show good oxidation resistance. Even, the thickness of the oxide layer of the alloys with 4Y and 6Y made from 20 h-milled powders after oxidation at 1000 ℃ for 2 h is 84.0 μm and 70.7 μm, less than that made from 4 h-milled powders. This is due to the size and distribution of W5Si3, Y2Si2O7 aggregates and W-Y-O melt. The synergistic effects of Y contents and milling time on the microstructures and oxidation resistance of the alloys are revealed.