The recent availability of commercial platforms for behavioral analysis in zebrafish larvae based on video-tracking technologies has exponentially increased the number of studies analyzing different behaviors in this model organism to assess neurotoxicity. Among the most commonly used assays in zebrafish larvae are basal locomotor activity (BLA) and visual motor response (VMR). However, the effect of different intrinsic and extrinsic factors that can significantly alter the outcome of these assays is still not well understood. In this work we have analyzed in 143 larvae the influence of age (5-8 days post-fertilization), time of day (8:00, 10:00, 12:00, 14:00; 16:00, 18:00, and 20:00 h), and experiment (three experiments performed at different days) on BLA and VMR results (4,004 analyses for each behavior). Results from both behaviors were adjusted to a Random-Effects linear regression model using generalized least squares (GLS), including in the model the effect of the three variables, the second-way interactions between them and the three-way interaction. The results presented in this manuscript show a specific effect of all three intrinsic factors and their interactions on both behaviors, supporting the view that the most stable time period for performing these behavioral assays is from 10:00 am to 04:00 pm, with some differences depending on age of the larva and the behavioral test.

Remotely sensed data play a crucial role in monitoring the El-Niño/La-Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate responses. Since 1980, Earth has witnessed three strong ENSO events (1982–1983, This study aims, 2015-2016). In September 2022, La-Niña entered its third year and was unlikely to continue through 2023. Instead, since 2022 forecasts pointed to a transition from La-Nina to a Neutral phase in summer or late 2023. The onset of the El-Niño occurred around April 2023, and it is anticipated by sophisticated models to be a strong event through the Northern Hemisphere winter (December 2023 - February 2024). The aim of this study is to demonstrate the ability of the combination of two new methods to improve the accuracy of the above claim because El-Niño apart from climate anomalies, significantly impacts Earth’s ecosystems and human societies, regulating the spread of diseases by insects (e.g., malaria and dengue fever), and influencing nutrients, phytoplankton biomass, and primary productivity. This is done by exploring first the previous major El-Niño events in the period January 1876-July 2023. Our calculations show that the ongoing 2023-2024 El-Niño will not be the strongest.

High-rise building machines (HBMs) play a critical role in the successful construction of super-high skyscrapers, providing essential support and ensuring safety. The HBM’s climbing system relies on a jacking mechanism consisting of several independent jacking cylinders. A reliable control system is imperative to maintain a smooth posture of the construction steel platform (SP) under the action of the jacking mechanism. This research introduces three multivariate time series neural network models—namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN)—to predict the posture of HBM. The models take pressure and stroke measurements from the jacking cylinders as inputs, and their outputs determine the levelness of the SP and the posture of the HBM at various climbing stages. The development and training of these neural networks are based on historical on-site data, with the predictions subjected to thorough comparative analysis. All proposed neural network models exhibit the capability to dynamically predict the posture of the HBM during the climbing process, using data from sensors. Notably, the GRU model shows better predictive performance.

This study aimed to compare procedural learning skills between Spanish-speaking preschool children (ages 4 years to 4 years, 11 months) with Developmental Language Disorder (DLD) and their typically developing (TD) peers. Using the Serial Reaction Time task, participants (30 DLD and 30 TD children) respond to visual stimuli in a sequenced manner over four blocks, followed by a random order block. The task assessed reaction time (RT) and accuracy. Results showed a significant interaction between group and block for reaction time (RT) and accuracy, with children with DLD exhibiting longer RTs and accuracy deficits across blocks. In contrast, the TD group showed higher RT efficiency and accuracy in the sequential blocks and as expected, decreased performance in the random block according to the experimental manipulation. Overall, the results of this investigation suggest that there was no implicit learning in the DLD group, as indicated by the SRT task paradigms of procedural memory. These findings align with the Procedural Deficit Hypothesis (PDH), suggesting that linguistic deficits in the TD population may derive from a general dysfunction of the procedural memory system domain in the Spanish context.

In order to obtain the good match between yield strength and low-temperature toughness, the short-time partial austenitization (SPA) process was employed for V-microalloying 0.22C-5.24Mn steel. The initial microstructure after intercritical tempering was dual-phase ferrite and reversed austenite (RA), while the final microstructure was consisted of ferrite, RA and secondary martensite (SM) after subjected to SPA process. (V, Mo)C with disc-like morphology mainly precipitated during intercritial tempering, and the aspect ratio of particle decreased, leading to the appearance of near-spherical morphology. After subjected to SPA process, the resultant multiphase hierarchical microstructure (three layers: out layer of ferrite, interlayer of SM and inner layer RA) enabled high yield strength of 1097 MPa, total elongation of 14%, and impressive impact energy of 33.3 J at -20 °C. The strengthening contribution of (V, Mo)C precipitation was estimated about 108 MPa.

Intracranial compliance (ICC) plays a pivotal role in understanding the underlying mechanisms of various brain disorders and is of great clinical importance. Therefore, addressing the challenges in practical application of ICC is crucial for neurosurgeons. This study explored the significance of ICC assessment considering the time-dependency of specific brain disorders through two distinct approaches: short and large time elapsed (TE) in measuring volume or intracranial pressure (ICP) changes in the ICC equation (∆Volume/∆ICP). Variations in ICC values were observed across various ICC assessment methods and different TE values. Notably, the compensatory response of the brain exhibited non-monotonic and variable changes in a large TE for certain brain disorders, diverging from patterns observed in short TE assessments. Furthermore, the recovery behavior of the brain changed under different brain disorders when exposed to short and long TE conditions. These findings emphasized the dynamic nature of ICC and provided valuable insights into the correct practical assessment of ICC by selecting the appropriate TE, as well as considering the differences in strain rates and loading durations on the brain in different brain disorders. These insights also shed light on the reasons why, despite its clinical significance, ICC monitoring has not yet become a standard component of clinical care, unlike ICP monitoring.

People have long had a problem: the equations of motion that reflect the laws of physics are invariance under time inversion, while there always are irreversible processes for gases compose of microscopic particles. This article solves this problem. The point is that we should distinguish between the concepts of the equation of motion and motion. We also need to distinguish between the concepts of time-inverse motion and reverse motion. The former is anticlockwise, which is a frictional motion, while the latter is clockwise. For the single-particle motions in classical mechanics and in quantum mechanics, we present mathematical expressions for time-inversion motion and reverse motion, respectively. We demonstrate that single-particle motion is irreversible. The definition of the reversibility of two-particle collisions is given. According to the definition, the two-particle collision as a microscopic motion processes is irreversible. Consequently, for a gas consisting of a large number of particles colliding with each other, its movement should be irreversible, unless the condition of detailed balance is met. We give a physical explanation for the detailed balance, which does not concern the meaning of microscopic reversibility. The detailed balance means that after a pair of reciprocal collisions occur, the distribution function of the particles remains unchanged. Therefore, microscopic two-particle collision events are irreversible, but the statistical average of a large number of collision events makes it possible for the macroscopic process of a gas to be reversible. Conclusively, we clarify the microscopic mechanism of the irreversible process of gases.

Smart grids are seeing an increase in renewable energy and an enormous introduction of advanced power electronics, increasing the number of disturbance sources. This requires improving the accuracy of measurement systems capable of acquiring data for use in power system monitoring applications. Rogowski Coils (RCs) and Merging Units (MUs) are being widely deployed in controlling, metering, and monitoring of digital power systems. Because the performance of digital measurement devices has a direct influence on power measurement accuracy, it is critical to perform a laboratory calibration of these measurement instruments before they are installed in any project. To determine the magnitude and phase displacement errors of the RC+MU under normal and abnormal operating conditions, this article presents a comprehensive laboratory digital calibration system constructed from precise, readily available commercial products such as a fluxgate current transducer and a National Instrument. In addition, the analysis of measurement chain is affected by sources of uncertainty is discussed. The tests yield findings that are consistent with the relevant standards.

In the present work, we report on a 3D MOF of {[Pb5(3-OH)(3-NO3)3(6-pmdc)3·H2O}n formula (pmdc = pyrimidine-4,6-dicarboxylate) synthesized by an oven-heated solvent-free procedure. The large connectivity afforded by the three ligands in their coordination to lead(II) ions grows cubic building units characterized by a central Pb atom with an unusual coordination index of 12 and six pmdc ligands occupying the faces. These cubic units are linked one another giving rise to a quite condensed structure that represents an unprecedented topology showing the (4.62)6(43)2(45.610)3(45.68.82)6(46.69)6(612.83) point symbol. The crystalline material has been characterized by routine physico-chemical techniques to confirm its purity and its thermal behaviour has been also studied by thermogravimetric and thermodiffractometric analyses. The solid presents greenish blue photoluminescent emission based on pmdc ligands, as revealed time-dependent density-functional theory (TD-DFT) calculations, by that is substantially more intense than in free H2pmdc ligand according to its improved quantum yield. The emissive capacity of the material is further analysed according to decreasing temperature of the polycrystalline sample, finding that sizeable long-lasting phosphorescence is present.

Long span cable-stayed bridge is the main bridge type with a span of more than 400 meters. It is generally designed as a double tower long-span structure with good spanning capacity and economic performance. Wind resistance safety performance is the main index to control the long-span cable-stayed bridge structure. During the operation of long-span cable-stayed bridge structure, because the service life of the cable is far less than the design life of the structure, the wind resistance performance of the structure will inevitably deteriorate significantly, which will seriously affect the structural service performance of symmetric cable-stayed bridge. Based on symmetric reliability theory, this paper takes flutter and static wind stability of long-span cable-stayed bridge structure as the main design control index, uses positive reliability theory to calculate the reliability index of symmetric cable-stayed bridge structure, uses inverse reliability theory to calculate the safety factor of symmetric cable-stayed bridge structure, and evaluates the wind resistant time-varying performance of long-span cable-stayed bridge structure by comprehensively considering the reliability index and safety factor. Taking a practical project of a long-span cable-stayed bridge as a specific case, the wind resistant time-varying reliability of the bridge during its operation for more than 30 years is analyzed, and the parameter sensitivity is analyzed. The results show that the wind resistance performance of the cable-stayed bridge structure is obviously affected by the cable, and the degradation of the cable performance will have an important impact on the wind resistance time-varying performance of the structure, especially the critical wind speed of the structure has obvious time-varying characteristics. The safety factor and reliability index can objectively evaluate the wind resistance time-varying performance of the long-span cable-stayed bridge structure.

Novel approaches under the extreme conditions of ultra-high gravity of a super massive black hole allow us to assume that the spacetime degrades into the single dimension of time. A unique interpretation of the non-relativistic Schrödinger equation together with the relativistic Klein-Gordon and Dirac equations is presented by swapping the three dimensional space derivatives with the singular time derivative in the D’Alembertian operator. This apparatus reveals a unique time dependent wave equation that results in some implications such as possible time quantization with the intervals of around 3.36 × 10−21 ? which is in the order of uncertainty in time, ∆? = 1.3 × 10−21 ?, calculated from the energy-time version of the Heisenberg uncertainty principle for electrons. As a result of this, an antisymmetric wave function around the edge of the event-horizon zone appears and indicates that the virtual particle-antiparticle pairs of the electron field split into two real components with the spin-up and down states, reconfirming the Hawking radiation. Energy eigenvalues calculated from the application of the Dirac equation satisfies the preservation of energy and charge. The wave equation invoked in this study reduces into the time dependent Dirac equation within the neighborhood of zero-point, ? = 0, insuring the relevance of the evaluations.

This article purposed to examine the impact of integration between Lean accounting (LA) tools & Just in Time (JIT) technique on Cost Reduction (CR) in modern manufacturing environment in Saudi establishments, these two variable moderated by Ethical Standards (ESs). A quantitative methodology was used for realizing the study objectives and answering study questions, the main tool exercised for producing the incipient data was a fully-structured questionnaire with closed ended questions, which was designed and passed around online among a selected sample of (109) managers and accountants in Saudi manufacturing firms. Descriptive and deductive statistics adopted to derive the findings, wherefore the compiled responses analyzed by SPSS & AMOS and the results showed that there is direct and indirect effect of LA tools & JIT technique on CR in Saudi industrial companies, especially when using ESs. These findings could provide decision makers in Saudi manufacturing companies with the acuity of adopting both methods in a combined way to maximize the profits of these firms. The results of this study also suggest that the application of LA tools & JIT technique has a positive effect on the promotion of sustainable manufacture and consequent achievement of high sustainable performance.

This paper presents a V2G strategy to flatten the daily load curve by considering the charging and discharging location of the electric vehicle to help understand the effect of the load generated by charging and discharging of the electric vehicle on the power grid. Based on the estimated share of electric vehicles, the assumption that electric vehicles go to work in the morning and leave in the afternoon was set, and the electric vehicle discharge power was calculated by reflecting both the characteristics of vehicles that go to work in the morning and the SMP1 and SMP2 plans. In addition, the electric vehicle charging power was calculated by reflecting the characteristics of vehicles returning home from work in the afternoon and the TOU rate system. In other words, in order to establish an efficient V2G strategy, a charging/discharging type-specific modeling was developed that considered all the characteristics of an electric vehicle commuting vehicle, and charging and discharging daily load using SMP1 and SMP2 rate plans for morning discharge and TOU1 and TOU2 rate plans for afternoon charging were applied. After calculating the curve, this value was compared and analyzed to evaluate the effect on grid leveling by adding the existing load. The analysis provides an accurate understanding of the impact of EV charging and discharging loads on the power grid according to V2G strategies. In this paper, it is helpful to apply EV charge and discharge management technology to avoid grid overload and minimize grid expansion by leveling the grid daily load curve by applying appropriate SMP and TOU rates through the establishment of an efficient V2G strategy by charge and discharge modeling.

With the targets of petroleum exploration transferred to the deep and ancient strata, abundant oil and gas resources have been found in the Lower Paleozoic and older strata in central and western China. Due to complex evolutionary processes including multiple episodes of hydrocarbon accumulation and ubiquitously accompanied by secondary alterations, significant uncertainties were remained for the generation time and accumulation processes of these revealed petroleum. In this paper, relative pure Re and Os elements existing in the asphaltene fractions of Lower Cambrian solid bitumen collected from the Guangyuan area, western Sichuan Basin, SW China and another Middle–Lower Ordovician heavy oils in the Aiding area of the Tahe oilfield in the Tarim Basin, NW China were successfully obtained by the sample pre-treatments, and Re–Os isotopic analysis was subsequently carried out for the dating of these two petroleum. The Re–Os isotopic composition indicates a generation time of Guangyuan bitumen to locate between 572 Ma and 559 Ma, corresponding to the late Sinian period of Neoproterozoic era. By the means of Re–Os isochron ages, initial 187Os/188Os ratios, and carbon isotopic compositions, the Lower Cambrian bitumen is supposed to be originated from source rocks of the Doushantuo Formation in the Sinian strata and subsequently migrated into the reservoirs of Dengying Formation. These previously reserved petroleum had been transformed to present bitumen by the destruction of reservoirs caused by tectonic uplift. The Re–Os dating results of Middle–Lower Ordovician heavy oil of Tarim Basin supposed that it was formed between 450 Ma to 436 Ma, corresponding to the Late Ordovician–Early Silurian system, and the generated petroleum is likely to migrate into the Middle–Lower Ordovician karst reservoirs to form early oil reservoirs. With tectonic uplift, these oil reservoirs were degraded and reformed to be present heavy oil reservoirs.

Spinal muscular atrophy 5q (SMA) is the most common neuromuscular inherited disease in neonates which is associated with homozygous deletion of exon 7 in the SMN1 gene. Recently established drugs can improve the motor functions of SMA infants when treated in the pre-symptomatic stage. With aim of providing an early diagnosis, newborn screening (NBS) for SMA using real-time PCR assay with dried blood spots (DBS) was performed in Saint-Petersburg from January 2022 through November 2022. Here, 36,140 newborns were screened by GenomeX real-time PCR-based screening test, and three genotypes were determined. Homozygous deletion carriers (4 newborns), heterozygous carriers (772 newborns) and wild-type individuals (35,364 newborns) were identified. All four newborns screened positive for homozygous SMN1 deletion were confirmed by alternate methods. Two of the newborns had two copies of SMN2, and two of the newborns had three copies. We determined Saint-Petersburg spinal muscular atrophy incidence of 1 in 9,035 and SMA carrier frequency of 1 in 47. In conclusion, it can be summarized that providing both timely SMN1 information and confirmation along with SMN2 copy number as part of SMA newborn screening algorithm can significantly improve clinical follow-up, family members testing, and SMA patients' treatment.

The best business relationships are built as relationships between people and organizations that work together to achieve common goals. In the current digital era, B2B collaboration relationships are essential for business success. The response time to customer requests can be a key factor in maintaining these relationships and obtaining a competitive advantage. The use of VBA automation for repetitive tasks can help speed up this process, but it is important that computer operators have medium knowledge about using VBA macros. This work deals with issues related to improving response times to requests received by email, using a macro program for repetitive tasks. The empirical study was conducted on an average of 50 daily emails for order confirmation, with an average writing time of 68 s per email, resulting in a potential reduction in working time for the operator of approximately 1 h/day, only for order confirmation. The results show a significant improvement in working time for computer operators, using VBA buttons for repetitive tasks.

We have shown that according to the predictions of the standard quantum theory, signaling is possible using a postponed compensation Hong-Ou-Mandel interferometer, although it violates Lorentz invariance. We argue that new physics is required to deal with such a paradox. Either quantum principles or relativity needs reassessment. We propose an experiment that can decide whether relativity rules over quantum or vice versa.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating positons of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determine the signal arrival time and then locate the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transfers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

Abstract: This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in years 1984–2018. NDVI is derived from MODIS and Landsat data, temperature and precipitation data are from meteorological stations. Two study sites were selected, representing arid and semi-arid grassland vegetation types, respectively. Various trend estimators including Mann-Kendall (MK) and derivatives were combined for vegetation change analysis at different time scales. Results suggest that temperature and precipitation had negative and positive impacts on vegetation growth, respectively, in both areas. NDVI-to-precipitation correlation was significant but with an apparent time-lag effect that was further investigated. No significant general changes were observed in vegetation along the observed period. Further comparisons between results from corrected and uncorrected data led us to conclude that MODIS and Landsat data with BRDF, topographic and atmospheric corrections applied are best suited for analyzing relationships between NDVI and climatic factors for the 2000-2018 period in grassland at a very local scale, but in the absence of correction tools and information, uncorrected data can still provide meaningful results. Future refinements will include removal of anthropogenic impact, and deeper investigation of time-lag effects of climatic factors on vegetation dynamics.

We describe a recently developed approach to quantify welfare loss in animals, the Cumulative Pain metric. It combines the two most relevant dimensions of negative affective experiences: intensity and duration. The metric enables estimating the time individuals spend in negative affective states of a physical or psychological nature (operationally referred to simply as ‘pain’) of different intensities as the result of one or more challenges (e.g., diseases, injuries, deprivations). A new notation protocol (the Pain-Track) is used in which the duration of the experience is represented along the horizontal axis and intensity is represented by four categories in the vertical axis. Pain experiences are partitioned into temporal segments, where hypotheses for the experienced duration and intensity are proposed based on existing welfare indicators (e.g., neurophysiological, behavioral, anatomical, evolutionary). This structure forces transparency about assumptions and uncertainties, highlights knowledge gaps, and enables estimates to be continuously adjusted. Because the Cumulative Pain metric is based on parameters with a broadly common biological meaning, it provides the much needed interoperability among assessments of animal welfare. It enables comparing the impact of practices and living conditions, policies and interventions, and the calculation of welfare footprints of animal-sourced products using a universal measurement unit.

Covid-19 pandemic affected outcome in ST-elevation myocardial infarction (STEMI) patients in majority of countries. We aimed to assess retrospectively admissions, treatments, complications and mortality of STEMI patients in the northeast of Slovenia in the first (March-May 2020) and the second wave (October-December 2020) of Covid-19 pandemic and compare them with the data from 2019 (March-October). Comparing 2019 and both waves of Covid-19 pandemic we observed nonsignificant differences in the number of STEMI admissions, in baseline characteristics, in the use of primary percutaneous coronary intervention (PCI), either within the first 3 (40.5%* vs 30.2% vs 25%*, *p = 0.074) and 6 hours, nonsignificant differences in TIMI III flow after primary PCI and in hospital complications, except for significant increase in hospital heart failure (23.3% vs 42%, p = 0.015) and mitral regurgitation in the second wave (10% vs 26.9%, p = 0.008) of the pandemic with nonsignificant increase in hospital mortality (8.9% vs 9.4% vs 13.6%) in both waves of the pandemic. We conclude, that with increased severity of Covid-19 pandemic in the second wave there was a longer delay to primary PCI in STEMI patients, resulting in significantly increased hospital heart failure and nonsignificantly increased hospital mortality

Context: Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives: We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time. Methods: The key idea is to use on one side natural language processing solutions to detect problems at service level, extracting words that represent anomalies. Clustering and topic modeling techniques have been used to identify patterns and group them with respect to topics. On the other side time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Results: We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. We have observed a correspondence between anomalies in log files and monitoring data, e.g. an increase in memory usage or machine load. The results are extremely promising. Conclusion: Our model requires to integrate site administrators' expertise in order to consider all critical scenario in the data center and understand results properly.

Recently we started the development of Holographic Dendrogramic Theory (DH-theory). It is based on the novel mathematical representation of the relational event universe (in the spirit of (Smolin, Barbour, Rovelli). Elementary events are represented by branches of dendrograms, finite trees, which are generated from data with clustering algorithms. In this note we study the dynamics of the event-universe generated by the appearance of a new event. Generally, each new event can generate the complete reconstruction of the whole dendrogramic universe. However, we found (via numerical simulation) unexpected stability of this universe. Its events are coupled via the hierarchic relational structure which is relatively stable with respect even random generation of new events. We also observe the regularity patterns in location of new events on dendrograms. In the curse of evolution, the dendrogram’s complexity increases and determine the arrow of time the event universe. We use the complexity measure from particle shape dynamics which was shown to be increase in both direction away from a Janus point and thus determine the arrow of time in symmetrical manner away from a Janus point. The particle shape dynamics theory is a relational theory with close ideological resemblance to DH-theory as both relays on Mach’s principle and Leibniz’s relationalism and his principles. By using the complexity measure on dendrograms and its p-adic string representation, we demonstrate the emergence of time arrow from the p-adic zero-dimensional field, where space and time are absent.

Combining Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility test (AST) of S. aureus. Based on the AI predictive probability, the cases with probabilities between low and high cut-offs are defined as “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. A total 479 S. aureus isolates were collected, analyzed by MALDI-TOF, and AST prediction, standard AST were obtained in a tertiary medical center. The predictions were categorized into the correct prediction group, wrong prediction group, and grey zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For MRSA, larger cefoxitin zone size was found in the wrong prediction group. MLST of the MRSA isolates in the grey zone group revealed that uncommon strain types composed 80%. Amid MSSA isolates in the grey zone group, the majority (60%) was composed of over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity would contribute to suboptimal predictive performance.

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

Whether the universe is a computer simulation, or whether we wish to efficiently model our universe in a computer simulation, there would be benefits to modeling it in a fashion analogous to computer spreadsheet, each lattice cell can be conceived as containing all the mathematical formula necessary to continuously compute its state relative to changes in all its neighboring cells, and by progression, in relation to all the cells of entire space-time lattice. Alternatively, the “real” universe may itself be built on a space cell lattice, an irregular foam of space cells, in which each cell may be conceived as a multidimensional cell of distortable space, the shape of which fully describes (a) the four basic forces (gravity, electromagnetic, strong, weak) observed at that cell of space, and (b) the probability (or weight distribution) of any quantum states overlapping the cell and its neighbors. At an appropriate scale, it would appear that this conceptual model would resolve apparent conflicts between general relativity and quantum physics. It would also provide a new interpretation of Planck’s constant as description of the number of space cell events associated with any set of observable events. If formulae operating at a lattice cell level can be improve our ability to understand and model larger scale phenomena, this would be strong evidence in favor of the theory that mathematics is not just a human invention but rather an inherent feature of space-time itself.

This paper presents a discrete compartmental Susceptible-Exposed-Infected-Recovered/Dead (SEIR/D) model to address the expansion of Covid-19 pandemic. When time passes, the status of the cells is determined by binary rules that update following both a neighbourhood and a delay pattern. The model assumes the environmental parameters have a crucial impact on the expansion of the disease so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids. This proposal shows how the grid architecture, along with an update rule and a neighbourhood pattern is a valuable tool to model the pandemic expansion. This model has already been analyzed in previous works and compared with the corresponding continuous models solved by ordinary differential equations (ODE), coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between Susceptible and Asymptomatic, Asymptomatic and Infected, Infected and Recovered/Dead) may have a crucial impact on both the peak of Infected and the Recovery/Death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and in the case of plant pests.

Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient (r) values. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task.

Conventional mechanical Fourier Transform Spectrometers (FTS) are able to simultaneously measure absorption and dispersion spectra of gas-phase samples. However, they usually need very long measurement times to achieve time-resolved spectra with a good spectral and temporal resolution. Here, we present a mid-infrared dual-comb-based FTS in an asymmetric configuration, providing broadband absorption and dispersion spectra with a spectral resolution of 5 GHz, a temporal resolution of 20 μs, and a total measurement time of a few minutes. We used the dual-comb spectrometer to monitor the reaction dynamics of methane and ethane in an electrical plasma discharge. We observed ethane/methane formation as a recombination reaction of hydrocarbon radicals in the discharge in various static and dynamic conditions. The results demonstrate a new analytical approach for measuring fast molecular absorption and dispersion changes and monitoring fast dynamics of chemical reactions, which can be interesting for chemical kinetic research and particularly for the combustion and plasma analysis community.

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

IoT system becomes hot topic nowadays for smart home, IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circular polarized array rectenna system to harvest enough power from any direction to increase life time of the batteries used in IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node and low power microcontroller are used to operate in lower power modes. The proposed system has an 84.6 days lifetime which is approximately 10 times the lifetime for similar system. On the other hand, the proposed power management circuit operated at 0.3 V DC to boost the voltage to ~3.7V from radio frequency energy harvesting and manage battery level to increase the batteries lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a non-static plan, approve energy consumption and avoiding failure of sensor nodes in smart home.

Objective: SARS-Cov-2 infection or COVID-19 is a global pandemic. From the time of identification to till, multiple clinical symptoms and parameters have been identified by the researchers of various countries and regions regarding the diagnosis and presentations of COVID-19 disease. In this manuscript, we investigated the primary symptoms and basic hematological presentations of SARS-CoV-2 infection among the Bangladeshi patients. Methodology: We have collected the disease history of mild to moderate degree of COVID-19 patients; hematological and biochemical on admission reports of moderate degree COVID-19 patients. All of them were tested positive for SARS-CoV-2 by RT-PCR in different institutes in Bangladesh. Results: According to this study though COVID-19 patients in Bangladesh commonly presented with fever, cough, fatigue, shortness of breath, and sore throat, but symptoms like myalgia, diarrhea, skin rash, headache, Abdominal pain/cramp, nausea, vomiting, restlessness, and a higher temperature of >100⁰F have a greater presentation rate and more frequent than other published studies. CRP and Prothrombin time was found to increase in all the patients. Serum ferritin, ESR, SGPT, and D-Dimer were found increased among 53.85%, 80.43, 44%, and 25% patients respectively. 17.39% of the patients had leukocytosis and neutrophilia. 28.26% of patients presented with lymphocytopenia. 62.52% of patients had mild erythrocytopenia. Conclusion: Despite some similarities, our study has evaluated a different expression in presenting symptoms in the case of COVID-19 patients in Bangladesh. CRP, Prothrombin time, serum ferritin, ESR, SGPT, D-Dimer, erythrocytopenia, and lymphocytopenia can be initial diagnostic hematological findings and assessment for prognosis COVID-19 disease. Also, gender variations have a different scenario of clinical and laboratory appearance in this region.

Since mid-December of 2019, coronavirus disease 2019 (COVID-19) has been spreading from Wuhan, China. As of February 21, total 75,773 confirmed cases worldwide have spread to more than two dozen countries. Transmission of COVID-19 can occur early in the course of infection since SARS-CoV-2 viral loads in asymptomatic patients are similar to that in the symptomatic patients. Therefore, more sensitive diagnostic methods are needed to detect early phase of the infection to prevent secondary or tertiary spreads. Here, we compare the RT-PCR confirmatory test results using two different SARS-CoV-2 viral RNAs from two Korean COVID-19 confirmed cases.RT-PCR method targeting the RdRP gene, which was recommended by WHO guideline, was less sensitive than targeting N genes (as per CDC guideline). Because many countries follow the WHO guideline, our findings may contribute to the early diagnosis of COVID-19.

To understand the mystery of final unification, in our earlier publications, we proposed that there exist three atomic gravitational constants associated with electroweak, strong and electromagnetic interactions. During cosmic evolution, if one is willing to give equal importance to Higgs boson and Planck mass in understanding the massive origin of elementary particles, then it seems quite logical to expect a common relation in between Planck scale and Electroweak scale. Based on these two points, we noticed that, electroweak field seems to be operated by a primordial massive fermion of rest energy 585 GeV. It can be considered as the zygote of all elementary particles and galactic dark matter. H-bar seems to be a characteristic outcome of unified electroweak gravity. Electron rest mass seems to be a characteristic outcome of electroweak and strong gravity. Proton rest mass seems to be a characteristic outcome of electroweak, strong and electromagnetic gravity. Recently observed 3.5 keV photon seems to be an outcome of annihilation of charged baby lepton of rest energy 1.75 keV. Interesting point to be noted is that, Schwarzschild radius of electron is 0.48 nanometer and it needs further investigation with respect to emerging nano-science and technology. Proceeding further, by considering electromagnetic and weak gravitational constants, neutron life time can be understood.

It is unclear which noise exposure time window and noise characteristics during nighttime are most detrimental for sleep quality in real life settings. We have conducted a field study with 105 volunteers wearing a wrist actimeter to record their sleep during seven days, together with concurrent outdoor noise measurements at their bedroom window. Actimetry recorded sleep latency increased by 5.6 minutes (95% confidence interval: 1.6 to 9.6 minutes) per 10 dB(A) increase in noise exposure during the first hour after bedtime. Actimetry assessed sleep efficiency was significantly reduced by 2-3 percent per 10 dB(A) increase in measured outdoor noise (Leq, 1h) for the last three hours of sleep. For subjectively reported sleepiness, noise exposure during the last hour prior to wake up was most crucial with an increase in the sleepiness score of 0.31 units (95% CI: 0.08 to 0.54) per 10 dB(A) Leq,1h. Associations for estimated indoor noise were not more pronounced than for outdoor noise. Considering noise events in addition to equivalent sound pressure levels (Leq) only marginally improved the statistical models. Our study provides evidence that matching the nighttime noise exposure time window to the individual’s diurnal sleep-wake pattern results in a better estimate of detrimental nighttime noise effects on sleep. We found that noise exposure at the beginning and the end of the sleep is most crucial for sleep quality.

Statistics of languages are calculated by counting characters, words, sentences, word rankings. Some of these random variables are also the main “ingredients” of classical readability formulae. Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G – the semantic index G_C, proportional to the number of characters per word, and the syntactic index G_F, proportional to the reciprocal of the number of words per sentence −, G_F is dominant because G_C is, in practice, constant for any author throughout seven centuries of Italian Literature. Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author. For any author, any couple of text variables can be modelled by a linear relationship y=mx, but with different slope m from author to author, except for the relationship between characters and words, which is unique for all. The most important relationship found in the paper is, in author’s opinion, that between the short−term memory capacity, described by Miller’s “7∓2 law”, and the word interval, a new random variable defined as the average number of words between two successive punctuation marks. The word interval can be converted into a time interval through the average reading speed. The word interval is spread in the same of Miller’s law, and the time interval is spread in the same range of short−term memory response times. The connection between the word interval (and time interval) and short−term memory appears, at least empirically, justified and natural, and should further investigated. Technical and scientific writings (papers, essays etc.) ask more to their readers. A preliminary investigation of these texts shows clear differences: words are on the average longer, the readability index G is lower, word and time intervals are longer. Future work done on ancient languages, such as Greek or Latin, could bring us a flavor of the short term−memory features of these ancient readers.

A study of Bianchi Type I cosmological model is undertaken in the framework of creation of particles. To accommodate the creation of new particles, the universe is regarded as an Open thermodynamical system. The energy conservation equation is modified with the incorporation of a creation pressure in the energy momentum tensor. Exact solutions of the field equations are obtained (i) for a particular choice of the particle creation function and (ii) by considering the deceleration parameter to be constant. In the first model the behavior of the solution at late times is investigated. The physical aspects of the model have also been discussed. In the case of the second model we have restricted our analysis to the power law behaviour for the average scale factor. This leads to a particular form for the particle creation function. The behavior of the solution is investigated and the physical aspects of the model have also been discussed for the matter dominated era.

This paper proposes a real-time local positioning system (RT-LPs) utilizing a mobile platform equipped with three anchor nodes placed in a right-angle triangle formation for a real-time locating system (RTLS). After deriving an analytic formula to calculate the target position utilizing the measured distances among anchor nodes and the target node, we find that four parameters have an effect on the position error of the target node. The spacing between anchors is a design parameter that must be large enough to the reduce position error. However, the distance from the anchor node to the target node is an operation parameter that must be small enough to reduce the position error. Additionally, the measured ranges among the anchors and the target node have probabilistic distributions with a mean and variance, which are dominant parameters that have effects on the position error. A comparison study was conducted to determine the effects of the parameters of the target position in both a simulation and an experiment, showing rates of approximately 4% ~ 10%. These findings indicate that our simulation can work properly with the proposed method after assuming that the distance error is a Gaussian model.

With the development of satellite load technology and very-large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. Limited by severe size, weight, and power consumption constraints, a key challenge of on-board SAR imaging system design is to achieve high real-time processing performance. In addition, with the rise of multi-mode SAR applications, the reconfiguration of the on-board processing system is beginning to receive widespread attention. This paper presents a multi-mode SAR imaging chip with SoC architecture based on the reconfigurable double-operation engines and multilayer switching network. We decompose the commonly used extend chirp scaling (CS) SAR imaging algorithm into 8 types of double-operation engines according to the computing orders, and design a three-level switching network to connect these engines for data transition. The CPU is responsible for engine scheduling based on data flow driven with instructions to implement each part of the CS algorithm. Thus, multi-mode floating-point SAR imaging processing can be integrated into a single Application-Specific Integrated Circuit (ASIC) chip instead of relying on distributed technologies. As a proof of concept, a prototype measurement system with chip-included board is implemented, and the performance of the proposed design is demonstrated on Chinese Gaofen-3 stripmap continuous imaging. A chip requires 9.2 s, 50.6 s and 7.4 s for a stripmap with 16,384×16,384 granularity, multi-channel stripmap with 65.536×8192 granularity and multi-channel scan mode with 32,768×4096 granularity and 6.9 W for the system hardware to process the SAR raw data.

Cardiac dynamics are traditionally linked to a left ventricle, right ventricle and septum morphology, a topography that differs from the heart’s 5-century-old anatomic description of containing a helix and circumferential wrap architectural configuration. Torrent Guasp’s helical ventricular myocardial band (HVMB) defines this anatomy and its structure, explains why the heart’s 6 dynamic actions of narrowing, shortening, lengthening, widening, twisting and uncoiling happen. This database raises questions about deductions behind “accepted cardiac mechanics”, and its functional aspects will challenge and overturn them. These suppositions include the LV, RV, and septum description, timing of mitral valve opening, isovolumic relaxation period, reasons for torsion/twisting, untwisting, reasons for longitudinal and circumferential strain, echocardiographic sub segmentation, resynchronization, RV function dynamics, and diastolic dysfunction’s cause and unrecognized septum impairment. Torrent Guasp’s revolutionary contributions may alter future understanding of the diagnosis and treatment of cardiac disease.

The operation of electricity and natural gas transmission networks in the U.S. are increasingly interdependent, due to the growing number of installations of gas fired generators and the penetration of renewable energy sources. This development suggests the need for closer communication and coordination between gas and power transmission system operators in order to improve the efficiency and reliability of the combined energy system. In this paper, we present a co-simulation platform for examining the interdependence between natural gas and electricity transmission networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We analyze the value of day-ahead coordination of power and natural gas network operations and show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and renewable energy sources. Results show that day-ahead coordination contributes to a reduction in curtailed gas during high stress periods (e.g., large gas offtake ramps) and a reduction in energy consumption of gas compressor stations.

In this research, univariate and bivariate statistical methods were applied to rainfall, river and piezometric level datasets belonging to 24 years long time series (1986-2009). These methods, that often are used to understand the effects of precipitation on rivers and karstic springs discharge, have been used to assess, piezometric level response to rainfall and river level fluctuations in a porous aquifer. A rain gauge, a river level gauge and three wells, located in Central Italy along the lower Pescara river valley in correspondence of its important alluvial aquifer, provided the data. The statistical analysis has been used within a known hydrogeological framework, which has been refined by mean of a photo-interpretation and a GPS survey. Water-groundwater relationships were identified following the autocorrelation and cross-correlation analyses; the spectral analysis and mono-fractal features of time series were assessed, in order to provide information on multy-year variability, data distributions, their fractal dimension and the distribution return time within the historical time series. The statistical-mathematical results were interpreted through field work that identified distinct groundwater flowpaths within the aquifer and enabled the implementation of a conceptual model, improving the knowledge on water resources management tools.

Compared with the traditional slow charging loads, random integration of large scale fast charging loads will exert more serious impacts on the security of power network operation. Besides, to maximize social benefits, effective scheduling strategy guiding fast charging behaviors should be formulated rather than simply increasing infrastructure construction investments on the power grid. This paper has analyzed the charging users’ various responses to the elastic charging service fee, introduced the index of charging balance degree to a target region by considering the influence of fast charging loads on power grid. Then, a multi-objective optimization model of the fast charging service fee is constructed, whose service fee can be further optimized by employing fuzzy programming method. Therefore, both users’ satisfaction degree and the equilibrium of charging loads can be maintained simultaneously by guiding EVs to different fast charging stations reasonably. The simulation results demonstrate the effectiveness of proposed dynamic charging service pricing and the proposed fast charging load guidance strategy.

Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of the Northern Territory of Australia. This work developed and tested a time-series, change-point detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. These cycles were classified to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term (> 3 years) and short term (< 3 years) growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.

This study sought to investigate steel frames’ performance with dual lateral loader system (Frame bending + bracings of divergent and convergent) in near and far filed to fault. In order to this, four categories of steel frame with dual system with 8, 10 and 12 story are designed with average formation based on existing seismic regulation in 2800 standard of Iran and tenth chapter of national regulations of construction (planning and performing steel construction). Time history non-linear dynamic analysis under the effect of near and far field earthquakes has been done on plan’s models. Then the maximum of floors’ dislocation, floors’ drift, roof dislocation, base shear and energy curves of frames are shown and compared with each other. All non-linear time history analyses have been accomplished using PERFORM 3D software.

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller’s bandwidth. A variety of input trajectories can be used to drive a controller’s attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system’s flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Whiplash compensation is additionally implemented for comparison. The control method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.

Vibration suppression in a field of frequencies and the creation of band gaps which do not allow the propagation of the waves is studied in this paper by means of microstructures designed through topology optimization. Topology optimization is formulated in frequency domain. Band gap design is based on Floquet-Bloch theory and genetic optimization. The result is appealing in view of modern 3D printing techniques.

Accurate estimation of the state of health (SOH) of batteries for automotive applications, particularly in electric vehicle battery management systems (EV-BMS), is still under study and is highly relevant to ensure battery system availability. This paper proposes a comprehensive SOH estimation method that goes beyond the methods that are based on the estimation of the battery available capacity by the integral of the current, or the estimation of the increase of the internal resistance of the battery. The SOH estimator is based on a partial capacity discharge method and a linear state of charge (SOC) observer based on an equivalent electrical circuit model. This method uses readily available manufacturer data and is designed for real-time applications. The proposed method was tested and validated with three different automotive battery technologies in a Model-In-Loop (MIL) environment with OPAL-RT platform, utilizing voltage and current measurements of pulsed discharge current profiles at temperature-controlled conditions and an electric vehicle’s driving profile.

Pursuing sustainable energy solutions has prompted researchers to focus on optimizing energy extraction from renewable sources. Control laws that optimize energy extraction require accurate modeling, often resulting in time-varying, nonlinear differential equations. An energy-maximizing optimal control law is derived for time-varying, nonlinear, second-order, energy harvesting systems. We demonstrate that sustaining periodic motion under this control law when subjected to periodic disturbances necessitates identifying appropriate initial conditions, inducing the system to follow a limit cycle. The general optimal solution is applied to two point absorber wave energy converter models: a linear model where the analytical derivation of initial conditions suffices and a nonlinear model demanding a numerical approach. A stable limit cycle is obtained for the latter when the initial conditions lie within an ellipse centered at the origin of the phase plane. This work advances energy-maximizing optimal control solutions for nonautonomous nonlinear systems with application to point absorbers. The results also shed light on the significance of initial conditions in achieving physically realizable periodic motion for periodic energy harvesting systems.

Background/Objectives: Prospective cohort studies are useful for studying how biomolecular status affects risk of adverse health outcomes. Less well known is that the longer the follow-up time, the lower the apparent effect due to “regression dilution.” Here we evaluate how follow-up time affects the relationship between serum 25-hydroxyvitamin D [25(OH)D] concen-tration and incidence of stroke and major cardiovascular events (MCEs). Methods: Findings re-garding the relative risk (RR) of stroke and MCEs with respect to serum 25(OH)D concentrations at baseline from prospective cohort studies were plotted against mean follow-up time. Fifteen studies from mainly European countries and the United States were used for stroke, with nine studies for MCEs. Linear regression analyses were performed for follow-up periods of up to 10 years. Results: For stroke, the linear regression fit for 1–10 years is RR = 0.34 + (0.065 × follow-up [years]), r = 0.84, adjusted r2 = 0.67, p <0.001. No significant change was evident for studies with follow-up periods of 10–20 years. For MCEs, the linear fir for 1–8.1 years is RR = 1.61 ‒ (0.074 × follow-up [years]), r = 0.75, adjusted r2 = 0.49, p = 0.03. Discussion: The shorter the follow-up period, the greater the apparent effect of vitamin D in reducing risk of stroke and MCEs. In addition, the apparent effect of higher 25(OH)D concentration found for the shortest follow-up time is more than twice as high as the estimate based on averaging the results for all studies without considering follow-up time. Mechanisms have been found to explain how higher serum 25(OH)D concentrations could reduce risk of stroke and MCEs. Randomized controlled trials have not shown that vitamin D supplementation significantly reduces risk of either stroke or MCE, probably because risk of both outcomes increases rapidly below 15 ng/mL (38 nmol/L) and it is difficult in Western developed countries to enroll enough participants with concentrations that low. Nonetheless, vitamin D’s role in reducing risk of stroke and MCE can be considered causal on the basis of an evaluation of the evidence with respect to Hill’s criteria for causality in a biological system. Conclusions: Serum 25(OH)D concentrations above 20 ng/mL are associated with significantly reduced risk of stroke and MCEs in an apparent causal manner. Raising serum 25(OH)D concentrations to >20 ng/mL should be recommended for everyone likely to be at risk for stroke or MCE and indeed in the general population.

With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.

Re-purposing wastewater is necessary to circumvent water shortage for vegetable farmers in urban areas and to reduce public health hazards associated with using contaminated water for irrigation. In this study, the efficiency of biochar filtration systems in removing contaminants from wastewater was investigated. The hydraulic retention time (HRT) and the cleaning mechanism of coconut biochar filtration systems were examined. The filtration systems were designed with biowastes, specifically coconut biochar, as an adsorbent to remove pollutants from wastewater. Two setups of bed, 40 cm and 30 cm thick, were examined under different HRTs. Fourier Transform Infrared Spectroscopy (FTIR) analysis and Scanning Electron Microscopy (SEM) imaging were used to ascertain the properties of the biochar that enhances the removal of pollutants in wastewater. For both setups, the removal of heavy metals (Lead, Pb and copper, Cu) as well as Total Coliform (TC) was judged satisfactory but appeared ineffective in removing E. coli.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.

Due to the convenience of implementing without additional transmitter, radar communication integrated systems using conventional communication signals have become an effective means of monitoring unmanned aerial vehicles and other aircraft. However, the non-ideal radar signal form causes the strong signal to cover the weak signal, and the clutter suppression is required to expose the target. As the target signal is extremely low, and the performance of conventional clutter suppression methods is limited, the residual clutter remains after the time-varying clutter is suppressed, which leads to many false alarm points on the processed RD spectrum. The two-dimensional distribution of these false alarms in range-Doppler is very similar to the target and difficult to be distinguished, which seriously affects the target detection and tracking. To reduce false alarm points and improve the performance of target detection, the difference of spatial-temporal stationarity between target signal and clutter in a short time is discussed in this paper, a radar false alarm suppression method based on target spatial-temporal stationarity is proposed by using the difference of stationarity between target signal and false alarm points in range, Doppler, energy and azimuth. In this algorithm, to ensure the short-time stationarity of the target, the RD spectrum of sub-frames with short time interval is obtained by using the sliding matching filtering method and extract the peak points. Then, the Mahalanobis distance between the peak points of each sub-frame is used to eliminate false alarm points. Finally, false alarm points are further eliminated through the target tracking and retain the real target information, so as to improve the radar target detection performance. The simulation experiments show that this method can eliminate more than 90% false alarms points while maintaining the detection performance of the target. The processing of actual data in the field experiment shows that the after suppression false alarm by this algorithm can obtain more ideal target detection results, and realize the tracking of the UAV and other aircraft track.

The purpose of this project is to explore the potential of Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), as a tool for making directional predictions for assets. The focus will be on using LSTM for classifying up/down moves in stock indices, stocks, and FX rates (EUR/USD). Various financial ratios, advanced technical indicators, and volatility estimators will be used as features to enhance the predictive power of the model. Incorporating credit spreads and news indicators can also improve the model. The advantage of LSTM is its ability to handle sequences of arbitrary length, which means that longer time frames such as 5-day, 10-day, or 1-week returns can be used for prediction. Before using RNNs, exploratory data analysis (EDA) will be performed to understand the underlying patterns in the data. This may include dimensionality reduction techniques such as autoencoding, self-organizing maps, or decision tree regression. The results of the EDA will be used to guide the selection of features for the LSTM model. It is important to note that long memory and stationarity are common characteristics of time series data, such as interest rates or economic indicators. Power-law autocorrection, Hurst exponent fractional, or Markov processes may be used to model these data. Predicting 5-day, 10-day returns for equities or 1-week, 1-month returns for financial factors presents two challenges. First, a large amount of data is required, typically 7–10 years of data. Second, positive autocorrelation in the 5-day or 10-day returns in equity time series must be isolated.

Recent studies have shown the potential of Transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. This study explores the predictive capabilities of the Informer model compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in flood time series. Among other things, the present work extends its analysis to encompass diverse time series datasets from various locations (>100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the significant superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. Furthermore, it is observed that the structure of time series, as expressed by climacogram, affects the performance of the Informer network.

The adoption of cloud-based supply chain management (SCM) solutions represents a transformative shift in how organizations manage their supply chains, offering significant advantages in terms of cost efficiency, flexibility, real-time data access, and the integration of advanced technologies. This study explores the multifaceted impacts of cloud-based SCM systems, focusing on both their benefits and the challenges encountered during their implementation. Through an in-depth analysis of interview and case study data, the research reveals that cloud-based solutions enable organizations to reduce capital and operational expenditures substantially while providing the scalability necessary to adapt to fluctuating demands. Real-time access to data enhances visibility and decision-making, and the incorporation of advanced technologies such as artificial intelligence and machine learning improves predictive analytics and process optimization. However, the study also identifies several critical challenges associated with the adoption of cloud-based SCM solutions. Key issues include data security concerns, integration difficulties with legacy systems, and the complexities of managing organizational change. Effective implementation requires meticulous planning, comprehensive training, and strategic vendor management to address these challenges successfully. The research highlights the importance of robust security measures, thorough integration strategies, and effective change management practices in overcoming these obstacles. The strategic outcomes of adopting cloud-based SCM solutions include improved operational efficiency, enhanced inventory management, and increased customer satisfaction. Additionally, organizations gain a competitive edge through better supplier coordination and greater responsiveness to market changes. Overall, the study demonstrates that while cloud-based SCM solutions offer substantial benefits, their successful adoption hinges on effectively managing associated challenges and leveraging their transformative potential.

This article provides a detailed analysis of non-invasive techniques for prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state-of-the-art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to enhance fault diagnosis, as well as underscore the crucial role of non-invasive techniques in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

Speech emotion recognition (SER) is a technology that can be applied in distance education to analyze speech patterns and evaluate speakers’ emotional states in real-time. It provides valuable insights and can be used to enhance the learning experience by enabling the assessment of instructors’ emotional stability, a factor that significantly impacts information delivery effectiveness. Students demonstrate different engagement levels during learning activities, and assessing this engagement is an important aspect of controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is the emotional states of their instructors. Accordingly, this research uses deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entails integrating Transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance SER. Feature extraction from audio data used Mel-frequency cepstral coefficients, chroma, Mel spectrogram, zero crossing rate, spectral contrast, centroid, bandwidth, roll-off, and root-mean square, with subsequent optimization processes adding noise to, conducting time stretching, and shifting the audio data. Notably, several Transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The pre-processing and data augmentation methodologies significantly enhanced the precision of the results in that the model achieved accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% on the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio-Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets. Furthermore, it achieved 83% accuracy on another dataset created for this research—the Saudi Higher Education Instructor Emotions dataset. The results demonstrate this model’s considerable accuracy in detecting emotions in speech data across different languages and datasets.

Sepsis is a life-threatening syndrome triggered by infection and accompanied by high mortality, with antimicrobial resistances (AMRs) further escalating clinical challenges. Rapid and reliable detection of causative pathogens and AMRs are key factors for fast and appropriate treatment, in order to improve outcome in septic patients. However, current sepsis diagnostics based on blood culture is limited by low sensitivity and specificity while current molecular approaches fail to enter clinical routine. Therefore, we developed a Suppression PCR-based selective enrichment sequencing approach (SUPSETS), providing a molecular method combining multiplex suppression PCR with Nanopore sequencing to identify most common sepsis-causative pathogens and AMRs using plasma cell-free DNA. Applying only 1 mL of plasma, we target eight pathogens across three kingdoms and ten AMRs in a proof-of-concept study. SUPSETS was successfully tested on first ten clinical samples and revealed comparable results to clinical metagenomics while clearly outperforming blood culture. Several clinically relevant AMRs could be detected additionally. Furthermore, SUPSETS provided first pathogen and AMR-specific sequencing reads within minutes of starting sequencing, thereby potentially decreasing time to results to 11 - 13 hours and suggesting diagnostic potential in sepsis.

Quantum cryptography revolutionizes secure information transfer, providing defense against both quantum and classical computational attacks. The primary challenge in extending the reach of quantum communication comes from the exponential decay of signals over long distances. Addressing this challenge, we have developed Quantum-protected Control-based Key Distribution (QCKD), overcoming transmission range limitations by monitoring signal leakages and rendering leaked quantum states significantly non-orthogonal. This paper reports on the experiments with the QCKD, conducted over a 1,707 km fiber line. We demonstrate advancements in critical components of the protocol, showing its remarkable scalability and adaptability for extended distances and multi-user configurations.

Commuting is a vital travel channel for college students throughout their higher education career; nevertheless, excessive commute time negatively impacts students’ school satisfaction, academic achievement, health, and quality of life. Meanwhile, few studies have explored students’ commute time, covering a large population and scope. Therefore, this study analyzes the dwelling type that influences college and graduate students’ school commute times in the Seoul Metropolitan Area, using data from the 2016 Household Travel Diary Survey. This study uses spatial data on housing development to classify five dwelling types (i.e., new town, housing site development, Seoul, Incheon, and Gyeonggi-do). A multiple linear regression model empirically analyses students’ household and personal characteristics. The results reveal that dwelling type significantly affects students’ commute time. Specifically, Students living in Seoul have a shorter commute to school than those living in new towns. Socioeconomic characteristics have less effect on college and graduate students' school commute time. This study discusses the problem of excessive commuting of college and graduate students in new towns.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. In many instances, utility companies pay for both energy and peak demand. PV systems can effectively reduce energy consumption during their operational periods, while, their contribution during peak hours, particularly in the early morning and late evening, is often limited. To address this issue, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV-battery systems remains a challenge. This study proposes a novel statistical model to optimize PV-battery system sizing for enhanced peak demand reduction. The model aims to flatten 95% of peak demand days up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV-battery system successfully flattens 95% of daily peak demand up to 2000 kW, yielding a financial benefit of $812,648 over 20 years.

The main objective of this this cross-sectional study was to analyze the influence of lifestyle fac-tors (diet, physical activity, sleep), which can affect the concentrations of fecal short-chain fatty acids (SCFAs) and SCFAs potential role in modulating cardiometabolic disease risk by interact-ing with biochemical and body composition parameters. The study comprised 77 healthy, non-obese individuals, aged 30-45 years who were assessed in the light of SCFAs concentration in stool, diet, physical activity level and sleep duration. Moreover, body composition measurement and patients’ biochemical parameters were included in the analysis. We have indicated a signifi-cant negative correlation between several SCFAs [especially acetic acid (AA), isobutyric acid (IBA), butyric acid (BA), isovaleric acid (IVA) and valeric acid (VA)] with BMI, VAT/SAT ratio (visceral to subcutaneous fat ratio) and percentage of fat mass in a group of females enrolled in the study as well as with waist circumference (WC) in case of both sexes included in the study. Moreover, the results of our study acknowledged the importance of a diet in shaping SCFAs pro-file - we noticed the significant negative associations between energy and fat intake and some SCFAs in males [IBA, IVA, VA, isocaproic acid (ICA)]. In terms of fiber intake, we noticed elevat-ed concentrations of vast majority of SCFAs and amount of SCFAs in total in case of both sexes included in the study. These strong correlations reflect the fact that diet shapes the composition of the gut microbiota and SCFAs (main microbial metabolites) are synthesized from dietary fiber. Furthermore, we noticed that in a group of women, the concentration of AA, propionic acid (PA) and ICA as well as the total concentration of SCFAs showed a significant positive association with their sleep duration. We concluded that SCFAs can have a potential role in modulating car-diometabolic disease risk by interacting with adiposity parameters and diet. In addition, this potential direct link between diet and SCFAs may at least partly contribute to sleep improvement.

Background: Delayed graft function (DGF) is common after kidney transplantation from de-ceased donor and may significantly affect post-transplant outcomes. This study aimed to evaluating whether an innovative approach, based on the administration of intravenous prostaglandin-analogue iloprost, could be beneficial in reducing the incidence of DGF occurring after kidney transplantation from deceased donors. Methods: This prospective, randomised (1:1), place-bo-controlled study enrolled all consecutive patients who received a kidney transplantation from a deceased donor from January 2000 to December 2012 and who were treated in the peri-transplant period with the prostaglandin-analogue iloprost at 0.27 μg/min through elastomeric pump (treatment group) or placebo (control group). Results: A total of 476 patients were included : DGF was reported in 172 (36.1%) patients in the entire cohort. The multivariate analysis showed that donor’s age > 70 years (OR 2.50, 95% Confidence Interval (CI) 1.40-3.05, p < .001), cold ischemia time > 24 h (OR 2.60, 95 CI 1.50-4.51, p < .001), donor’s AKI (OR 2.71, 95% CI 1.61-4.52, p = .021) and, above all, recipient’s arterial hypotension (OR 5.06, 95% CI 2.52-10.1, p < .0001) were the strongest risk factors for developing post-transplant DGF. The incidence of DGF was 21.4% in the treatment group and 50.9% in the control group (p< .001). Interestingly, among patients who developed DGF, those who received iloprost had a shorter duration of post-transplant DGF (10.58.3 v s13.46.7, days, p= .016). Conclusions: This study showed that the use of a continuous infusion of iloprost could safely and effectively reduce the incidence of DGF in recipients of deceased donor kidneys, allowing a better graft functionality as well as a better graft survival.

Transportation tasks in nursing are common and shorten the time for direct patient care in times of nursing shortage and an aging society. There is a lack of research on transportation needs, with most studies focusing on overall nursing task analysis without a deeper analysis of transportation tasks. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N=1,830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05 % (n=495) of all transports, followed by medical supplies at 17.32 % (n=317), pharmacotherapy at 14.10 % (n=258) and other other categories like meals or drinks contributing 12.68 % (n=232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15 % of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5 % of all transports. Our economic evaluation spotlighted meals as the most costly transport, with 9,596.16€ p.a. in the observed clinics. Budget-friendly robots would amortize over one year by transporting meals. We support understanding nurses’ transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

Thermal Non-destructive Testing and Evaluation (NDT&E) is crucial in ensuring the quality and safety of industrial materials, components, and structures. It serves as a key tool for assessing their operational reliability, thus enhancing safety in a wide range of industries. There is a growing demand for dependable, swift, remote, and secure inspection and assessment techniques to detect hidden flaws, especially for sustainable solutions prompts adjustments in design and manufacturing standards. Hidden defects often emerge during the service life of these materials and structures due to various stress factors, potentially resulting in catastrophic failures. This study delves into the optimal and dependable experimental method for conducting fast, remote, and secure inspections and assessments of carbon fiber reinforced polymer materials using Infrared Imaging (IRI) as part of Thermal Non-Destructive Testing and Evaluation (TNDT&E). Additionally, it examines the post-processing approach associated with this technique. This perspective also sheds light on the current state-of-the-art infrared imaging methods employed in TNDT&E, emphasizing their strengths and weaknesses about their ability to detect subsurface defects present within the material Most of the methods discussed in previous research primarily focus on the thermal differences in specific areas of a sample using processed thermal images, even though these images come from analyzing a series of images captured over time. This study highlights the latest research in thermal/infrared non-destructive testing and evaluation, along with the related post-processing techniques. It aims to not only show hidden subsurface defects through thermal differences also provides information about how these defects change over time.

Alzheimer's Disease (AD) is a neurodegenerative disease that is common in the elderly. This paper introduces the overview of Alzheimer's disease and the application of relevant signal processing methods in its detection. Signal processing is a technique that converts raw data into meaningful information and is widely used in the medical field. This paper lists common signal processing techniques, including Fourier transform, time-frequency analysis and statistical signal processing, and discusses their applications in the detection of Alzheimer's disease. Fourier transform can convert time domain signals into frequency domain representations, providing an effective tool for the study of EEG in Alzheimer's disease. Time-frequency analysis can perform a combined time and frequency analysis of the signal to help detect the signal characteristics of Alzheimer's disease. Statistical signal processing methods can be used to identify the features of Alzheimer's disease by building mathematical models. Finally, the challenges of Alzheimer's disease detection are discussed, including signal noise, diversity, and insufficient data volume. Through in-depth research and development of signal processing methods, it is expected to improve the accuracy and efficiency of early detection of Alzheimer's disease.

Steel construction is used more often these days as an alternative to the R.C.C. when light weight, high strength, large-span structures with a faster erection are required. Extensive studies have been done by researchers to study the seismic performance of reinforced concrete and steel structures, both in terms of elastic and inelastic behavior. Composite construction is also a recent advancement in the building industry with similar advantages. However, no emphasis has been given to the comparison between the inelastic behavior of steel and composite structures when subjected to lateral loads. This study compares the inelastic behavior of steel and a composite frame designed to have the same plastic moment capacity for structural members. The responses, such as the formation of hinges, story drifts, story displacements, lateral stiffness, ductility, maximum strength, energy dissipated, joint accelerations, and performance points, are compared with the aid of the building analysis and design software ETABS-18. For this, response spectrum analysis, pushover analysis and nonlinear direct integration time history analysis have been performed on both frames. For design and analysis, international codes such as IS 800-2007, IS 875 (Part I, II, IV), IS 1893-2002, AISC 360 (16 &amp; 10) and FEMA 440 have been used. Part of this study also aims at comparing the response of these frames when subjected to near field and far field earthquakes. It can be concluded from the results that the post yield performance of the composite frame is superior to that of the steel frame when seismically excited.

The timely diagnosis of defects at their incipient stage of formation is crucial to extend the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damages, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of Acoustic Emission (AE)-based inspection techniques through the computation of the Time of Arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor Signal-to-Noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative Deep Learning methods are proposed for ToA retrieval, namely a Dilated Convolutional Neural Network (DilCNN) and a Capsule Neural Network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two novel methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations.

n this article a first attempt at formalizing a new approach to the grand unified theory is introduced. This theory named NUVO is based primarily on considering two aspects of inertia. The first aspect is how a mass couples to space, introducing definitions to distinguish massive particles from massless particles. The second aspect is space’s availability to couple to massive particles. In essence these are the two contributions of inertia; mass and space. Postulates, clarifications, and a mathematical structure are introduced. Part 1 covers the physics of the very large (cos- mic), laying ground work for part two which applies the structure to the very small (quantum). The cosmic application of the theory will show a strong correlation to empirical data and correspondence to General Rela- tivity, including; time dilation, length contraction, and orbital advances. Departing from the canonical findings of General Relativity by predicting a quantization of physical units under the effect of acceleration and or gravity.

The neural network technology for real-time cryptographic data protection with symmetric keys (masking codes, neural network architecture and weights matrix) for unmanned aerial vehicles (UAV) onboard communication systems has been developed. It provides hardware and software implementation with high technical and operational characteristics. The development of neural network technology for real-time cryptographic data protection was performed using an integrated approach based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern element base with the possibility of programming for the structure, and computer-aided design of hardware and software tools. The development and implementation of the on-board system of neural network cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; specialization and adaptation of hardware and software to data encryption and decryption. The tabular-algorithmic method of calculating the scalar product has been improved, it provides fast calculation of the scalar product of input data for both fixed and floating-point by bringing to the largest common order of weights and forming tables of macro-partial products for them. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers like 0, -1, 1, and -2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.

Research has consistently found that people with mental illness (known as consumers) experience lower levels of participation in meaningful activities, which can limit their opportunities for recovery support. The aim of this study was to describe the outcomes of participation in a group program designed to address all stages of activity participation, known as Pathways to Participation (P2P). A descriptive longitudinal design was utilized, collecting data at three time points. Outcomes were measured by the Camberwell Assessment of Need Short Appraisal (CANSAS), Recovery Assessment Scale – Domains and Stages (RAS-DS), Behaviour and Symptom Identification Scale (BASIS-24), Living in the Community Questionnaire (LCQ) and time use diaries. All data was analysed using descriptive statistics, and Chi square analyses. Seventeen consumers completed baseline data, eleven contributed post program data and eight provided follow up data. Most were female (63.64%) and had been living with mental illness for 11.50 (± 7.74) years on average. Reductions in unmet needs and improvements in self-rated recovery scores were re-ported, but no changes were identified in either time use or psychosocial health. The findings indicate the P2P program may enable consumers to achieve positive activity and participation out-comes as part of their personal recovery.

This paper considers the solution of the equations for ruin probabilities in infinite continuous time. Using the Fourier Transform and certain results from the theory of complex functions, these solutions are obtained as com- plex integrals in a form which may be evaluated numerically by means of the inverse Fourier Transform. In addition the relationship between the re- sults obtained for the continuous time cases, and those in the literature, are compared. Closed form ruin probabilities for the heavy tailed distributions: mixed exponential; Gamma (including Erlang); Lognormal; Weillbull; and Pareto, are derived as a result (or computed to any degree of accuracy, and without the use of simulations).

Timely crop yield forecasts at national level are substantial to support food policies, to assess agricultural production and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for 2000–2019 period, the relative RMSE for NUTS-2 units are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for the LAU units it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments (such as DIAS or Amazon AWS), where data sets from the Copernicus programme are directly accessible.

The geodesics on a relativistic manifold are given by the well-known equation involving the Christoffel coefficients. This equation can be solved numerically step by step and transposed into a quantization. We study here the effect of this quantization on the Schwarzschild spacetime, more precisely in the Kruskal-Szekeres map.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

The work consisted in identifying possible effects from heavy metals (HMs) pollution due to waste disposal activities in three potentially polluted sites located in Basilicata (Italy), where a release of pollutants with values over the thresholds allowed by the Italian legislation was detected. The potential variations in the physiological efficiency of vegetation have been analyzed through the multitemporal processing of satellite images. In detail, Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) images were used to calculate the Normalized Difference Vegetation Index (NDVI) trend over the years. Then, the multitemporal trends were analyzed using the median of Theil-Sen, a non-parametric estimator particularly suitable for the treatment of remote sensing data, being able to minimize the outlier effects due to exogenous factors. Finally, the subsequent application of the Mann-Kendall test on the trends identified by Theil-Sen slope allowed the evaluation of trends significance and, therefore, the areas characterized by the effects of contamination on vegetation. The application of the procedure to the three survey sites led to the exclusion of the presence of significant effects of HMs contamination on the vegetation surrounding the sites during the years of waste disposal activities.

Electricity distribution network operators face several operational constraints in the provision of safe and reliable power given that investments for network area reinforcement must be commensurate with improvements in network reliability. This paper provides an integrated approach for assessing the impact of different operational constraints on distribution network reliability by incorporating component lifetime models, time-varying component failure rates as well as the monetary cost of customer interruptions in an all-inclusive probabilistic methodology that applies a time-sequential Monte Carlo simulation. A test distribution network based on the Roy Billinton test system is modelled to investigate the system performance when overloading limits are exceeded as well as when preventive maintenance is performed. Standard reliability indices measuring the frequency and duration of interruptions, and the energy not supplied are complemented with a novel monetary reliability index. The comprehensive assessment includes not only average indices but also their probability distributions to adequately describe the risk of customer interruptions. Results demonstrate the effectiveness of this holistic approach as the impacts of operational decisions are assessed from both reliability and monetary perspectives. This informs network planning decisions through optimum investments and consideration of customer outage costs.

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style libre 1 (FSL1) to Dexcom G4 (DG4) over 1 year. Patients (n=21, 43±15 years, BMI 25±5, HbA1c 8.1±1.0%) had severe hypoglycemia and/or HbA1c≥8%. AGP metrics (time-in-range (TIR) 70-180 mg/dL, time-below-range (TBR)<70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50±17 vs. 45±16, P=0.036), and lower TBR<70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], P=0.0007), TBR<54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], P=0.007) and %CV (39±5 vs. 45±8, P=0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR<70mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs “non-responders” warrant to be investigated.

Different approaches for digital workflows have already been presented for their use in palatal plates for newborns and infants. However, there is no evidence on the accuracy of CAD/CAM manufactured orthodontic appliances for this kind of application. This study evaluates trueness and precision provided by different CAM technologies and materials for these appliances. Samples of a standard palatal stimulation plate were manufactured using stereolithography (SLA), direct light processing (DLP) and subtractive manufacturing (SM). The effect of material (for SM) and layer thickness (for DLP) were also investigated. Specimens were digitized with a laboratory scanner (D2000, 3Shape) and analyzed with a 3D inspection software (Geomagic Control X, 3D systems). For quantitative analysis, differences between 3D datasets were measured using root mean square (RMS) error values for trueness and precision. For qualitative analysis, color maps were generated to detect locations of deviations within each sample. SM showed higher trueness and precision than AM technologies. Reducing layer thickness in DLP did not significantly increase accuracy, but prolonged manufacturing time. All materials and technologies met the clinically acceptable range and are appropriate for their use. DLP with 100µm layer thickness showed the highest efficiency, obtaining high trueness and precision within the lowest manufacturing time.

This research takes Cuiwei Village in Qianshan District of Zhuhai City as an example. Through field research, the village is investigated and analyzed, and the street scale, spatial structure, functional characteristic streets, internal commercial distribution forms, and functions of Cuiwei Historic District are studied. analysis. On this basis, based on the convex space method and the line-of-sight analysis method in the space syntax theory, with the help of Depthmap software, the complex street node, that is, the space of the two nodes A and B, is established through the establishment of a visual field model. It includes the analysis of the integration degree of the horizon, the concentration of the horizon, the connection value of the horizon and the spatial characteristics of the historical area, as well as the traditional buildings that are the most representative of the village, namely the Three Kings Temple and the Webster's Mansion. When R=N and radius R=3, observe the changes in the two parameters of the visual integration and visual depth of the two historical buildings. And with the help of Baidu Time Machine photos in different periods to observe and record the changes in the store and the characteristics of people's behavior, and draw conclusions.

The thermoelectric generator (TEG) stands out in many energy harvesters due to its simple structure, small size, etc. However, previous studies rarely probe into the influence of TEG internal resistances on extracting maximum power from TEG, and tracking efficiency is limited. By analyzing the relationship between the tracking efficiency and the TEG internal resistances, a time exponential rate perturbation and observation (P&amp;O) technology is proposed to achieve maximum power point tracking (MPPT) for a wide resistance range of TEG. Using the time exponential rate P&amp;O, the MPPT circuit observes the power change by comparing PMOS on-time and perturbs the power by adjusting NMOS on-time exponentially. The MPPT circuit is implemented in a 110nm CMOS process. The tracking efficiency keeps a high level from 98.9 to 99.5%. The applicable range of the TEG resistance is from 1 to 12Ω, which is enhanced at least 2.2 times.

The underlying physical concept of computing out-of-time-ordered correlation (OTOC) is a significant new tool within the framework of quantum field theory, which now-a-days is treated as a measure of random fluctuations. In this paper, by following the canonical quantization technique we demonstrate a computational method to quantify the two different types of Cosmological auto-correlated OTO functions during the epoch when the non-equilibrium features dominates in Primordial Cosmology. In this formulation, two distinct dynamical time scales are involved to define the quantum mechanical operators arising from cosmological perturbation scenario. We have provided detailed explanation regarding the necessity of this new formalism to quantify any random events generated from quantum fluctuations in Primordial Cosmology. We have performed an elaborative computation for the two types of two-point and four-point auto-correlated OTO functions in terms of the cosmological perturbation field variables and its canonically conjugate momenta to quantify random auto-correlations in the non-equilibrium regime. For both the cases we found significantly distinguishable non-chaotic, but random behaviour in the OTO auto-correlations, which was not pointed before in this type of studies. Finally, we have also demonstrated the classical limiting behaviour of the mentioned two types of auto-correlated OTOC functions from the thermally weighted phase space averaged Poisson Brackets, which we found exactly matches with the large time limiting behaviour of the auto-correlations in the super-horizon regime of the cosmological scalar mode fluctuation.

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map, the Quantization Error (SOM-QE). Shape pairs with perfect geometric mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, longer choice RT for ‘yes’ responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

Fractional-time Schrödinger equation (FTSE) describes the evolution of quantum processes endowed with memory effects. FTSE manifestly breaks all consistency requirements of quantum field theory (unitarity, locality and compliance with the clustering theorem), unless the order of fractional differentiation and integration ( ) falls close to the standard index . Working in the context of the minimal fractal manifold (where , ), we confirm here that FTSE approximates the attributes of gravitational metric and provides an unforeseen generation mechanism for massive fields.

There is a lack of knowledge concerning the effects of ambient heat exposure on morbidity in Northern Europe. Therefore, this study aimed to evaluate the relationships of daily summer-time temperature and heatwaves with cardiorespiratory hospital admissions in the Helsinki metropolitan area, Finland. Methods: Time-series models adjusted for potential confounders such as air pollution were used to investigate the associations of daily temperature and heatwaves with cause-specific cardiorespiratory hospital admissions, during summer months of 2001-2017. Daily number of hospitalizations was obtained from the national hospital discharge register, weather information from the Finnish Meteorological Institute. Results: Increased daily temperature was associated with decreased risk of total respiratory hospital admissions and asthma. Heatwave days were associated with 20.5% (95% CI: 6.9, 35.9) increased risk of pneumonia admissions and during long or intense heatwaves also with total respiratory admissions in the oldest age group (≥ 75 years). There were also suggestive positive associations between heatwave days and admissions due to myocardial infarction and cerebrovascular diseases. In contrast, risk of arrhythmia admissions was decreased 20.8% (95% CI: 8.0, 31.8) during heatwaves. Conclusions: Heatwaves, rather than single hot days, are a health threat affecting the morbidity even in a Northern climate.

Although energy management of a microgrid is generally performed using a day-ahead scheduling method, its effectiveness has been questioned by the research community due to the existence of high uncertainty in renewable power generation, power demand and electricity market. As a result, real-time energy management schemes are recently developed to minimise the operating cost of a microgrid while high uncertainty presents in the network. This paper develops modified particle swarm optimisation (MPSO) algorithms to solve optimisation problems of energy management schemes for a community microgrid and proposes a scheduling approach after taking into consideration high uncertainty to effectively minimise the operational cost of the microgrid. The optimisation problems are formulated for real-time and scheduling approaches, and solution methods are developed to solve the problems. It is observed that the scheduling program demonstrates superior performance in all the cases, including uncertainty in prediction, as compared to the other energy management approaches, although solutions have significant deviations due to prediction errors.

This work describes a new transducer prototype for continuous monitoring both in the structural and geotechnical fields. The transducer is synthetically constituted by a wire of optical fiber embedded between two fiber tapes (fiberglass or carbon fiber) and glued by a matrix of polyester resin. The fiber optical wire ends have been connected to a control unit whose detection system is based on Brillouin optical time-domain frequency analysis. Three laboratory tests were carried out to evaluate the sensor's reliability and accuracy. In each experiment, the transducer was applied to a sample of inclinometer casing sets in different configurations and with different constraint conditions. The experimental data collected were compared with theoretical models and with data obtained from the use of different measuring instruments to perform validation and calibration of the transducer at the same time. Several diagrams allow comparing the transducer and highlighting its suitability for monitoring and maintenance of structures. The characteristic of the transducer suggests its use as a mixed system for reinforcing and monitoring, especially in lifetime maintenance of critical infrastructures such as transportation and service networks, and historical heritage.

Management of crowding at subway platform is essential to improving services, preventing train delays and ensuring passenger safety. Establishing effective measures to mitigate crowding at platform requires accurate estimation of actual crowding levels. At present, there are temporal and spatial constraints since subway platform crowding is assessed only at certain locations, done every 1~2 years, and counting is performed manually Notwithstanding, data from smart cards is considered real-time big data that is generated 24 hours a day and thus, deemed appropriate basic data for estimating crowding. This study proposes the use of smart card data in creating a model that dynamically estimates crowding. It first defines crowding as demand, which can be translated into passengers dynamically moving along a subway network. In line with this, our model also identifies the travel trajectory of individual passengers, and is able to calculate passenger flow, which concentrates and disperses at the platform, every minute. Lastly, the level of platform crowding is estimated in a way that considers the effective waiting area of each platform structure.

The search for periodic signals from blazars has become an actively pursued field of research in recent years. This is because periodic signals bring us information about the processes occurring near the innermost regions of blazars, which are mostly inaccessible to our direct view. Such signals provide insights into some of the extreme conditions that take place in the vicinity of supermassive black holes that lead to the launch of the relativistic jets. In addition, studies of characteristic timescales in blazar light curves shed light on some of the challenging issues in blazar physics that includes disk-jet connection, strong gravity near fast rotating supermassive black holes and release of gravitational waves from binary supermassive black hole systems. However, a number of issues associated with the search for quasi-periodic oscillations (QPOs) in blazars e.g., red-noise dominance, modest significance of the detection, periodic modulation lasting for only a couple of cycles and their transient nature, make it difficult to estimate the true significance of the detection. Consequently, it also becomes difficult to make meaningful inferences about the nature of the on-going processes. In this proceedings, results of study focused on searching for QPOs in a number of blazar multi-frequency light curves are summarized. The time series analyses of long term observations of the blazars revealed the presence of year-timescale QPOs in the sources including OJ 287 (optical), Mrk 501 (gamma-ray), J1043+2408 (radio) and PKS 0219 -164 (radio). As likely explanations, we discuss a number of scenarios including binary supermassive black hole systems, Lense–Thirring precession, and jet precession.

Imaging lidars are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a career to decide the final solution to be implemented. The diversity of state-of-the art approaches to the solution brings, however, a large uncertainty towards such decision. This results often in contradictory claims from different manufacturers and developers. Within this paper we intend to provide an introductory overview of the technology linked to imaging lidars for autonomous vehicles. We start with the main single-point measurement principles, and then present the different imaging strategies implemented 8 in the different solutions. An overview of the main components most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development has been included, 10 in order to discuss some of the problems which still need to be solved before an efficient final implementation.Beyond this introduction, the reader is provided with a detailed bibliography containing both relevant books and state of the art papers.

The article presents the original derivation method of transformations for kinematics with a universal reference system. This method allows to derive transformations that meet the results of the Michelson-Morley and Kennedy-Thorndike experiments only in some frame of reference, e.g. in laboratories moving in relation to a universal frame of reference with small speeds. The obtained transformations are the basis for the derivation of the new physical theory, which has been called the Special Theory of Ether. The generalized transformations can be expressed by relative speeds (26)-(27) or by the parameter δ (v) (37)-(38). Based on conclusions of the Michelson-Morley’s and Kennedy-Thorndike’s experiments, the parameter δ (v) was determined. This allows the transformations to take a special form (81)-(82), which is consistent with experiments in which velocity of light is measured. On the basis of obtained transformations, the formulas for summing speed and relative speed were also determined. The entire article includes only original research conducted by its author.

The aim of this research work was to provide model for predicting stock volatility in Nigeria Stock market. To achieve this, monthly data for Nigerian stock exchange, Exchange rate, Share index and inflation rate was collected for a period of January 1990 to December 2016.The descriptive statistics revealed these variables to exhibit volatility as a characteristics of financial time –varying series. DCC Model was fitted, were the coefficients for all the parameters and that of the correlation-Targeting (rho_21) are both negative and positive and tend very close to 1 and -1, indicating that high persistence in the conditional variances. The Model DCC, satisfied the properties of a good model of conditional mean and variance of the confidential Interval (C.I) of 1 and -1, that is, the conditional variances are finites and their series are strictly stationary. This therefore implies that the Nigerian Stock Exchange, Exchange rate, share index and Inflation rate will experience a non-steady shock in the Stock market. However Each of these variables have different length of recovery (volatility half- life) ranging from 1.5month, 6.5months, 6months to 2,4months for stock exchange, exchange rate, share index and inflation rate respectively. By implication, the volatility of these variables had a long memory, persistence and mean-reverting.

Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments based on simulations. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the performance of the methods using 18 metrics. The results indicate that stochastic and ML methods perform equally well.

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

The purpose of work is to quantify the changes of the organism functional reserves during the successive phases of the human health Space-Time Continuum (STC) on the basis of postulates of reliability complex systems theory. The study is devoted to the problem of age-related human involution, which is evaluated not from the causal, but from the kinetic point of view. The analysis of 10 most important basic life support systems of human body — cardiovascular (CVS), respiratory (RS), nervous (NS), digestive (DS), endocrine (ES), immune (IS), excretory (EXS), brain (BS), musculo-skeletal (MSS), hematopoietic (HS) was carried out. Based on this analysis two levels of ensuring the reliability of organism’s work were revealed: sequential and parallel. The system of logical equations for reduced sequential system is: Ys1 = CVS ∧ RS ∧ BS, where ∧ is the notation for the conjunctions of set elements. The system of logical equations for the reduced parallel system is: Ys2 = NS ∨ DS ∨ ES ∨ IS ∨ HS ∨ EXS ∨ MSS ,where ∨ is the disjunction of the scheme elements. Visualization of human STC changes the concept of the kinetics of age-related changes in the organism and the role of determinants of health as a stable factor accompanying a uniform, smooth transition from the most pronounced functions of the body to their gradual extinction. For human STC is formulated the following regularity kinetics of involutionary processes: after 30 years of age in the human body morphological changes regress in arithmetic progression, and the functions of organs in a geometric one. Assumption of health as a state redundancy of functions is suggested.