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

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

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

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

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

Big Data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia's healthcare system, IBM and Walmart's Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining Big Data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors.

Background: Potential calcium-related adverse events of vitamin D supplement use have not been addressed in large-scale, real-world data so far. Methods: Leveraging data from the UK Biobank, encompassing 445,493 individuals aged 40-69, we examined associations of high 25-hydroxyvitamin (25[OH]D) levels ≥100 nmol/L and vitamin D supplementation with hypercalcemia (plasma calcium >2.6 mmol/L), kidney stones, and atherosclerosis assessments (pulse wave arterial stiffness index and carotid intima-medial thickness). Regression models were comprehensively adjusted for 49 covariates. Results: Approximately 1.5% of the participants had high 25(OH)D levels, 4.3% regularly used vitamin D supplements, and 20.4% reported regular multivitamin use. At baseline, the hypercalcemia prevalence was 1.6%, and 1.1% were diagnosed with kidney stones during follow-up. High 25(OH)D levels were neither associated with calcium-related adverse events nor atherosclerosis assessments. Vitamin D and multivitamin supplementation were associated with an increased prevalence of hypercalcemia (odds ratios and 95% confidence intervals: 1.46[1.32-1.62] and 1.11[1.04-1.18], respectively) but were neither associated with atherosclerosis nor future kidney stones. Conclusion: High 25(OH)D levels observable in routine care were not associated with any adverse outcome. Vitamin D users have a slightly higher prevalence of hypercalcemia, possibly due to co-supplementation with calcium, but without higher atherosclerosis prevalence or risk of kidney stones.

Carbapenem-resistant Acinetobacter baumannii (CRAB) infections represent the fourth-leading cause of death attributable to antimicrobial resistance globally, but a standardized therapy is still lacking. In this narrative review we focused the antibiotic treatment of CRAB infections in view of newly β-lactam agents (NBLs) like sulbactam/durlobactam and cefiderocol, discussing the main hot points such as the superiority of combination treatment over monotherapy and the best antibiotic partner to use in combination. Sulbactam/durlobactam seems to be the best candi-date to replace current back-bone agents. Cefiderocol, could play a crucial role in combina-tion-regimen. Due to toxicity and the PK/PD limitations, colistin (or polymyxin B) should be used as an alternative agent (when no other options are available). Tigecycline (or minocycline) and fosfomycin could represent suitable partners for both NBLs. Randomized clinical trials (RCTs) are needed to better evaluate the role of NBLs in CRAB infection treatment and to compare the ef-ficacy of tigecycline and fosfomycin as partner antibiotics. Synergism should be tested between NBLs and “old” drugs (rifampicin and trimethoprim/sulfamethoxazole). Huge efforts should be made to accelerate pre-clinical and clinical studies on safer polymyxin candidates with lung im-proved activity, as well as on the i.v. rifabutin formulation.

Background/Aim: Baricitinib (BAR) is the first oral selective Janus kinase inhibitor approved in Europe for rheumatoid arthritis (RA). Real-world data are still needed to clarify its long-term benefit/risk profile. This study aimed to evaluate the effectiveness, persistence, adherence, and safety of BAR in a real-world setting. Methods: An ambispective study was conducted between October 2017 and December 2021 in RA patients starting BAR. Effectiveness was evaluated by change from baseline in the Disease Activity Score using 28-joint counts-C reactive protein (DAS28CRP), and achievement of low disease activity/remission. Drug persistence was evaluated by Kaplan-Meier analysis. Adherence was estimated by medication possession ratio (MPR) and the 5-item Compliance Questionnaire for Rheumatology. Safety was assessed by global incidence proportion and adjusted incidence rates of adverse events. Results: 61/64 recruited patients were finally analyzed, 83.6% female, 78.7% seropositive, mean age of 58.1 (15.4) years, and disease duration of 13.9 (8.3) years. 32.8% were naïve to biologics and 16.4% received BAR on monotherapy. After a median exposure to BAR of 12.4 (6.6-31.2) months (range 3.1-51.4), a significant change in DAS28CRP was observed (difference -1.2, p=0.000). A total of 70.5% and 60.7% patients achieved low disease activity or remission, respectively and 50.8% (31/61) patients remained on BAR during follow-up with a median persistence of 31.2 (9.3-53.1) months. The average MPR was 0.96 (0.08) and all patients were “good adherents” by questionnaire. 21.3% patients discontinued baricitinib due to toxicity. Conclusion: In our real-world practice, BAR demonstrated effectiveness, large persistence, high adherence to treatment and acceptable safety profile.

Background: Dual disorders (DD) involve the coexistence of substance use disorder (SUD) with another mental illness, often from psychotic and affective categories. This study explores the ef-fectiveness of brexpiprazole, a third-generation antipsychotic, in individuals diagnosed with schizophrenia spectrum disorder (DSM-V) and comorbid AUD/SUD (DSM-V). Methods: Eighteen enrolled patients underwent psychometric assessments at baseline and one month into brexpiprazole treatment administered at a mean dosage of 2 mg/day. Results: Brexpiprazole demonstrated significant reductions in psychopathological burden (PANSS total score, p=0.004), improved CGI total score (p=0.042), and decreased MOAS total score (p=0.012). Quality of life improved in SF-36 subscales (p<0.05). Substance craving reduction, though observed, did not reach statistical significance. Conclusions: This study provides initial evidence supporting brexpiprazole's efficacy and safety with positive effects on psychopathology and quality of life, emphasizing the need for tailored, comprehensive treatment strategies in this complex patient population.

Investigating the network response to node removal and the efficacy of the node removal strategies are related and fundamental questions in network science. Research studies proposed many node centralities based on the network structure ranking node to remove. The random walk (RW) on networks describes a stochastic process in which a walker travels among nodes. RW can be a model of transport, diffusion, and search on networks, and an essential tool for studying the importance of network nodes. In this manuscript, we propose four new measures of node centrality based on RW. Then, we compare the efficacy of the new RW node removal strategies to network dismantle with effective node removal strategies from the literature, such as betweenness and closeness node removal over synthetic and real-world networks. We evaluate the network dismantle along node removal using the size of the largest connected component (LCC). We find that, hence the betweenness nodes attack is the best strategy overall, the new node removal strategies based on RW show the highest efficacy in peculiar network topology. Specifically, RW strategies based on covering times emerge as the most effective strategy on a synthetic lattice network and two real-world road networks. Our results may be useful in selecting the best node attack strategies in a specific class of networks and in building more robust network structures.

In this paper, we classify the function space of all real-valued functions on R denoted as F(R,R) into 28 distinct blocks. Each block contains elements that share common features in terms of the cardinality of their sets of continuity and differentiability. Alongside this classification, we introduce the concept of the Connection, which reveals a special relationship structure between four well-known real-valued functions in real analysis: the Cantor function, Dirichlet function, the Thomae function, and the Weierstrass function. Despite the significance of this field, several perspectives remain unexplored.

The paper presents the methods of assessing the technical condition of varistor surge arresters used in laboratory tests and in operation - performed without disconnecting the arresters from the network. The analysis of diagnostic methods was supplemented with the results of measurements of the leakage current of arresters coming directly from production and used in the power industry. Among the available methods of evaluating the technical condition of arresters, mainly indicator solutions (temperature, operation counter) and using the measurement of selected parameters of the leakage current are used. In the latter, the method of determining the resistive component of the leakage current, determined on the basis of the analysis of voltage and current waveforms, or only the arrester current, has become widespread. In this type of measurements, current clamps are used in operation, and additionally in voltage measurements from voltage transformers, where you have to take into account the fundamental, additional sources of errors discussed in the article. These errors and dispersion resulting from the production technology may fundamentally hinder the proper assessment of the technical condition, hence it is so important to properly recognize the listed basic sources of measurement uncertainty. In addition, the analysis should take into account three factors related to external conditions - temperature, voltage applied to the arrester and the content of higher harmonics in the supply voltage for which appropriate methods have been provided to determine the active component of the leakage current for reference conditions.

Background: In schizophrenia (SZ), social cognition mediates the relationship between neurocognition and social functioning. Although people with major depressive disorder (MDD) also exhibit cognitive impairments, which are often prolonged, little is known about the role of social cognition in MDD. Methods: Using data obtained through an internet survey, 210 patients with SZ or MDD were selected using propensity score matching based on their demographic information and illness duration. Social cognition, neurocognition, and social functioning were evaluated using the Self-Assessment of Social Cognition Impairments, Perceived Deficits Questionnaire, and Social Functioning Scale, respectively. The mediation effects of social cognition on the relationship between neurocognition and social functioning were examined in each disease group. Invariances of the mediation model across the two groups were then analyzed. Results: The SZ and MDD groups had mean ages of 44.49 and 45.35 years, contained 42.0% and 42.8% women, and had mean illness durations of 10.76 and 10.45 years, respectively. In both groups, social cognition had significant mediation effects. Configural, measurement, and structural invariances across the groups were established. Conclusion: The role of social cognition in patients with chronic depression was similar to that in schizophrenia. Social cognition could be a common endophenotype for various psychiatric disorders.

Objective: To perform a systematic review of real-world outcomes for anti-CGRP-mAbs. Methods: Following the PRISMA guidelines, we searched PubMed for real-world data of Erenumab, Galcanezumab, Fremanezumab, or Eptinezumab in patients with migraine. Results: We identified 104 publications (73 retrospective), comprising 8 pharmaco-epidemiologic and 63 clinic-based studies, 30 case reports and 3 other articles. None of the clinic-based studies provided follow-up data over more than one year in more than 200 patients. Findings suggest reductions in health insurance claims and days with sick-leave as well as better treatment adherence with anti-CGRP-mAbss. Effectiveness, reported in 59 clinic-based studies, was comparable to randomized controlled trials. A treatment pause was associated with an increase in migraine frequency and switching to another antibody resulted in a better response in some of the patients. Adverse events and safety issues were addressed in 70 papers including 22 single case reports. Conclusion: Real-world data on anti-CGRP-mAbs are limited by retrospective data collection, small patient numbers and short follow-up periods. The majority of papers seem to support good effectiveness and tolerability of anti-CGRP-mAbs in the real-world setting. There is an unmet need for large prospective real-world studies providing long-term follow-up of patients treated with anti-CGRP-mAbs.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO6 vehicles on the exhaust emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterise the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0-37.9%, 44.3-48.3%, 46.9-50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO6 replacement fleet scenario would lead to reductions ranging from 10.0-10.4%, 4.0-4.2%, and 6.0-6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways than on rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies.

Urban accessibility represents one of the great challenges of the contemporary city, which is required to adopt sustainable development models in line with the UN Agenda 2030 objectives, recently confirmed by the health emergency. Urban accessibility and walkability are topics closely related to those aiming at a livable, healthy and inclusive city, based on a system of high-quality public spaces and on a network of services and infrastructures. However, these principles collide with the fragmentation of many urban contexts, built following vehicular accessibility needs. Within this framework, the city of Cagliari represents an interesting case study as it is affected by the disposal of public properties which appear as “enclaves” in the historic urban fabric. This research aims to evaluate if and in which terms the abandoned assets can facilitate the development of the 15-minutes city, as a city reducing the need to move over a certain time and space and therefore granting a more equal access to urban services to a wide range of citizens. This is done by proposing indexes defined as porosity, crossing and attractiveness, which constitute a combined index to improve the pedestrian accessibility in the “central places” of the contemporary city, where the walkability can also become a possible “free choice” for a new healthy lifestyle. These indexes were calculated for the most significant large disused public buildings in the historic center to guide future scenarios towards a 15 minutes city.

Background. To determine the effectiveness and safety of meropenem in routine clinical practice, in terms of clinical and microbiological response. Methods. A real-world, observational, descriptive, longitudinal study with daily monitoring of clinical history records was conducted on all patients who were medically prescribed meropenem during a period between October 2015 and March 2016 at a university hospital in Bucaramanga (Colombia). Results. The study evaluated 84 patients with an average age of 63.2 years, mostly older adults with multiple comorbidities, of whom 54.8% were men. A positive clinical or microbiological response was obtained in 98.8% of the patients. At the end of the treatments, significant improvements in dysthermia (0% vs 29% at the beginning, p = 0.000), tachycardia (13% vs 47%, p = 0.049) and leukocytosis (39% vs 15% at the beginning, p = 0.008) were evidenced. The improvement in the indicator that combines all the SIRS criteria was also significant (p = 0.000). The treatment was well tolerated, although we identified some non-serious and expected adverse reactions. Conclusions. Generic meropenem proved to be effective and well tolerated for different types of infection in routine clinical practice. The results are consistent with the findings of the clinical studies with the innovator drug.

This paper investigates the impact of price and real exchange rate volatility on Foreign Direct Investment (FDI) inflows in a panel of 10 Latin American and Caribbean countries, observed between 1990 and 2012. Both price and exchange rate volatility series are estimated through the Generalized Autoregressive Conditional Heteroscedasticity model (GARCH). Our results, obtained employing the Fixed Effects estimator, confirm the theory of hysteresis and option value, in so far it is found a statistically significant negative effect of exchange rate volatility on FDI. Price volatility, instead, turns out to be positive but insignificant. Moreover, we show that human capital and trade openness are key for attracting foreign capital. From the policy perspective, our analysis suggests the importance of stabilization policies as well as the one of government credibility in promoting trade openness and human capital formation.

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

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

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

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

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

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

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

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

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

This study aims to evaluate the outcomes of neoadjuvant chemotherapy with immunotherapy in patients with non-small cell lung cancer (NSCLC) within a real-world context. We analyzed medical records from January 2022 to August 2023, focusing on individuals aged 18 and older diagnosed with resectable NSCLC who received neoadjuvant chemoimmunotherapy prior to surgical intervention. The cohort comprised 56 patients, predominantly smokers (95%) and male (74%), with 80% presenting at stage III. Of the participants, 44 underwent surgery, with 95% receiving lobar resection. Clinical assessments via PET-CT imaging revealed an 86% rate of response or disease stabilization, while pathological evaluations showed complete and major pathological responses in 61% of cases. This real-world data supports the safety and efficacy of incorporating immune checkpoint inhibitors in the neoadjuvant treatment of NSCLC, followed by surgical resection.

Property valuation services using artificial intelligence (AI) have been developed, with more than 20 services available in Japan. However, since their algorithms and training data are not publicly available, the extent of differences in the AI property valuations among these services is not clear. This study focuses on the five services handling a lot of properties and uses a sample of 4,295 property valuations for 859 condominium units in six popular residential areas in Tokyo. (1) Multiple comparison tests of the AI property valuations among each service are conducted to confirm their statistical significance and to examine the degree of the difference and variation. (2) The business models of each service are compared to examine the factors contributing to these differences and variations. The results showed that there was a variation of approximately 0.15 in the coefficient of variation between the high and low valuation groups, and the service providers in the high valuation group guided the users to their sales, while those in the low valuation group focused on attracting users as their business models. The contribution of this study is to indicate the possibility that the business models may lead to the differences in the AI property valuations.

A multicenter retrospective analysis of 408 patients diagnosed with onychomycosis and attended at three tertiary care Spanish hospitals was conducted to assess the effectiveness and safety of a combined treatment with ciclopirox 8% hydroxypropyl chitosan (CPX 8% HPCH) nail lacquer and oral antifungal agents (terbinafine, itraconazole, and fluconazole). Data were extracted and analyzed using the EHRead® technology based on natural language processing and machine learning and using SNOMED CT terminology. The median age was 51 years with a ratio male: female close to 1. Repeated nail trauma was the most common risk factor (7.1%). More than half of patients (56.6%) underwent fungal culture. Terbinafine was used in 67.7% of patients, itraconazole in 20.8%, and fluconazole in 11.5%. Treatment synchronicity distribution revealed that 59.1% of patients started concomitant treatment with CPX 8% HPCH and oral antifungals, 27.9% had initial oral antifungals, and 13% had initial nail lacquer. The response to treatment (positive response 15.7%, presumed positive 59.8%) was unrelated to treatment synchronicity or type of antifungal agent. Erythema (5.6%), diarrhea (4.9%) and fever (4.2%) were the most frequently registered potential adverse events. The present findings provide valuable insights for physicians paving the way for better management of patients with onychomycosis.

Serum neurofilament light chain (sNfL) levels have been proposed as a biomarker of the clinical activity, disability progression, and response to treatment of people with multiple sclerosis (PwMS); however, questions remain about its implementation in clinical practice. Ocrelizumab (OCR) has proven effective to improve clinical and radiological outcomes and to reduce sNfL levels. This real-life study followed the sNfL levels of 30 PwMS treated for 12 months with OCR and evaluated the usefulness of this biomarker for the short-term prognosis, considering expanded disability status scale (EDSS), annualized relapse rate (ARR), radiological activity, and NEDA-3 values. OCR reduced the ARR in 83% of PwMS and the radiological activity in 80%. Their EDSS was maintained, while NEDA-3 was achieved in 70% at 12 months. OCR produced an early reduction in sNfL levels (at 3 months). At baseline, greater MRI-evaluated radiological activity was associated with higher sNfL levels. sNFL levels over the first 12 months of treatment did not predict a suboptimal response or sustained control of the disease. Longer-term studies are needed to explore the predictive usefulness of sNfL levels in PwMS treated with high-efficacy drugs

Analyzing BRCA1/2 tumor pathogenic variants (TPVs) in epithelial tubal/ovarian cancers (EOC) has become an essential part of the diagnostic workflow in many centers to guide treatment options and genetic cascade testing. However, there is no standardization of testing procedures, including techniques, gene assays, or sequencers used, and data on the execution of tumor tests remains scarce. Therefore, we evaluated characteristics of BRCA1/2 tumor testing in advanced-stage EOC with real-world national data. Pathology reports of patients diagnosed with EOC in 2019 in the Netherlands were obtained from the Dutch Pathology Registry (PALGA), and data regarding histological subtype and BRCA1/2 tumor tests were extracted. A total of 999 patients with advanced-stage EOC were included. Tumor tests were performed for 502 patients (50.2%) and BRCA1/2 TPVs were detected in 14.7%. Of all tests, 48.6% used hybrid capture techniques and 26.5% used PCR-based techniques. More than half of the tests (55.0%) analyzed other genes in addition to BRCA1/2. Overall, this study highlights the heterogeneity in the execution of BRCA1/2 tumor tests. Despite a lack of evidence of quality differences, we emphasize that adequate reporting and internal and external quality monitors are essential for high-quality implementation and execution of reliable BRCA1/2 tumor testing, which is crucial for identifying all patients with BRCA1/2 TPVs.

The expansion of Open World Video Games (OWVGs) has seen the emergence of multiple new fictional worlds. This type of video game is characterized by total immersion in a world built for the players entertainment. What videogames developers want when players enter an OWVG is that they could feel like if they were open a door to an entirely new world and leave the real world behind. These virtual worlds manifest as a complement, an alternative, or the successor to the physical universe, and in some way continue the philosophical tradition of possible worlds begun by Leibniz. Accustomed to ‘surviving’ in hostile environments, millions of people continued to improve their skills in the midst of the COVID-19 pandemic while locked in their homes. Once the pandemic seems to start to be left behind, the paper analyzes the virtual worlds created by the video games and the relations between the pandemic and these fictional worlds.

The connectivity level of last-generation vehicles is constantly on the rise. The combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the optimization of the route and speed trajectory. In this framework, this paper focuses on vehicle eco-driving optimization in a connected environment. The virtual test rig of a premium segment passenger car was used for generating the simulation scenarios. The benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario were assessed. The Reference Scenario is a pre-defined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem is formulated and solved by means of a global optimization algorithm, i.e., Dynamic Programming (DP). The optimization framework includes information coming from the surrounding environment, e.g., traffic lights state, speed limits, distance to travel, etc. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis is performed on the bi-objective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.

In this article, we introduce the concept of soft intervals, soft ordering and sequences of soft real numbers, and some of their structural properties are studied. The notion of soft Lebesgue measure on the soft real numbers has been introduced. Also, a correspondence relationship has been established between the soft Lebesgue measure and the classical Lebesgue measure. Furthermore, we have studied some exciting results and relations between the soft Lebesgue measure and the Lebesgue measure of soft real sets.

Purpose: To meet the challenges of the precision medicine era, quality assessment of shared sarcoma care becomes pivotal. The MDT approach is the most important parameter for succesfull outcome. Because of all MDTs disciplines surgery is the key step to render sarcoma patients disease free, defining the spectrum of a sarcoma surgeon is critical. To the best of the authors knowledge, a comprehensive interoperable digital platform to assess the scope of sarcoma surgery and the experience of a sarcoma surgeon in its full complexity is lacking. Methods: A web-based real-world data (RWD) registry on sarcoma surgery has been created to assess the clinical exposure, tumor characteristics, and surgical settings and techniques applied for both resections and reconstructions of sarcomas and thereby the surgical exposure of an individual surgeon over time. Results: During 10 years, there were 723 sarcoma board/MDT meetings discussing 3130 patients. A total of 1094 patients underwent 1250 surgical interventions on mesenchymal tumors by one single sarcoma surgeon. These included 615 deep soft tissue tumors (197 benign, 102 intermediate, 281 malignant, 27 simulator, 7 metastasis, 1 blood), 116 superficial soft tissue tumors (45 benign, 12 intermediate, 40 malignant, 18 simulator, 1 blood) and 519 bone tumors (129 benign, 112 intermediate, 182 malignant, 18 simulator, 46 metastasis, 14 blood and 18 sequelae of 1st treatment). Detailed types of resections and reconstructions were analyzed. Conclusion: A web-based RWD sarcoma surgeon registry with transparent real-time descriptive analytics is feasible and enables large scale definition of the surgical complexity and ultimately quality of sarcoma care.

More than 179,000 individuals have fallen ill of the Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus, which first emerged in China less than four months ago in December 2019. As of today, there exist no approved treatments against COVID-19. Vaccines are being developed, but they will take time, at least one year, to reach the population. Drug repositioning represents a fast track because already approved medicines have been broadly tested through multiple trials. We developed a repositioning strategy that mostly leads to candidates that are commonly used. The advantages are that they will facilitate proof of concept in humans through a “real-world evidence” approach and should be rapidly available to the population. We focus on the established research results that the unfolded protein response (UPR) and autophagy pathways of the host cells are essential to the life cycle of previously known coronaviruses. We performed the relevant bioinformatics analysis to understand and confirm if SARS-CoV-2 may interact with these druggable pathways. Based on these considerations, we prioritized two additional druggable pathways, which are important to the viral life cycle and tightly connected to UPR/autophagy signaling: the mitochondrial permeability transition pores (MPTP) and NLRP-3 inflammasome pathways. These four important pathways are perturbed in most major common diseases and have therefore been targeted by numerous broadly prescribed drugs. We have identified 97 approved drugs that are known to modulate these four identified pathways, and which represent, therefore, interesting repositioning candidates. Although it is indisputable that these drugs should never be used for immediate self-medication against COVID-19, we notice that some of them could also be prescribed to individuals who have COVID-19 comorbidities (e.g., hypertension). It is debated if these comorbidities are linked to the pathology itself (e.g., hypertension) or the drugs used to treat the pathology (e.g., sartans). Therefore, relevant preclinical tests and massive electronic health records (i.e., real-world evidence) must be used to pre-screen them and check the COVID-19 prognosis of individuals taking these drugs.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Quorum Sensing (QS) and Machine Learning (ML) hybrid systems represent a groundbreaking innovation in synthetic biology, offering unprecedented control and adaptability in microbial gene regulation and metabolic processes. QS, a microbial communication mechanism, is crucial for coordinating gene expression in response to population density, impacting behaviors such as biofilm formation, virulence, and resource optimization. However, traditional QS systems are constrained by their reliance on static, pre-programmed feedback loops, limiting their flexibility in dynamic, complex environments. This review highlights how integrating advanced ML algorithms—such as reinforcement learning and deep learning—into QS systems can overcome these limitations by enabling real-time data processing, predictive modeling, and dynamic feedback control. Through these innovations, QS-ML systems can autonomously adjust gene expression and metabolic outputs, making them more efficient and scalable in applications ranging from pathogen control to precision medicine and industrial biomanufacturing. Key case studies illustrate the successful deployment of QS-ML systems to combat antimicrobial resistance, optimize bio-production, and enhance therapeutic precision in cancer and immune modulation. Despite the clear advantages, challenges remain in data integration, system robustness, and regulatory oversight. Addressing these hurdles through interdisciplinary collaboration and developing scalable, multi-omics data platforms will be critical for advancing QS-ML systems from experimental settings to real-world applications. This review underscores the transformative potential of QS-ML systems in revolutionizing synthetic biology, with profound implications for personalized medicine, sustainable biomanufacturing, and environmental health.Quorum Sensing (QS) and Machine Learning (ML) hybrid systems represent a groundbreaking innovation in synthetic biology, offering unprecedented control and adaptability in microbial gene regulation and metabolic processes. QS, a microbial communication mechanism, is crucial for coordinating gene expression in response to population density, impacting behaviors such as biofilm formation, virulence, and resource optimization. However, traditional QS systems are constrained by their reliance on static, pre-programmed feedback loops, limiting their flexibility in dynamic, complex environments. This review highlights how integrating advanced ML algorithms—such as reinforcement learning and deep learning—into QS systems can overcome these limitations by enabling real-time data processing, predictive modeling, and dynamic feedback control. Through these innovations, QS-ML systems can autonomously adjust gene expression and metabolic outputs, making them more efficient and scalable in applications ranging from pathogen control to precision medicine and industrial biomanufacturing. Key case studies illustrate the successful deployment of QS-ML systems to combat antimicrobial resistance, optimize bio-production, and enhance therapeutic precision in cancer and immune modulation. Despite the clear advantages, challenges remain in data integration, system robustness, and regulatory oversight. Addressing these hurdles through interdisciplinary collaboration and developing scalable, multi-omics data platforms will be critical for advancing QS-ML systems from experimental settings to real-world applications. This review underscores the transformative potential of QS-ML systems in revolutionizing synthetic biology, with profound implications for personalized medicine, sustainable biomanufacturing, and environmental health.

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

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

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

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

In this paper, we propose the definition of block tensors and the real representation of tensors. Equipped with the simplification method, i.e., the real representation along with the M-P inverse, we demonstrate the conditions that are necessary and sufficient for the system of dual split quaternion tensor equations (A*NX,X*SC)=(B,D) when its solution exists. Furthermore, the general expression of the solution is also provided when the solution of the system exists and we use a numerical example to validate it in the last section. To the best of our knowledge, it is the first time that the aforementioned tensor system has been examined on dual split quaternion algebra. Additionally, we provide its equivalent conditions when its Hermitian solution X=X* and η-Hermitian solutions X=Xη* exist. Subsequently, we discuss two special dual split quaternion tensor equations. Last but not least, we propose an application for encrypting and decrypting two color videos and we validate this algorithm through a specific example.

This paper reveals some properties of the cumulative entropy of consecutive k-out-of-n:F system's lifetime. First, we introduce a method for calculating the cumulative entropy of their lifetime and explore its preservation properties using established stochastic orders. Ad- ditionally, we obtain useful bounds that can used in situations where the distribution function of the system's component lifetimes are complicated or systems have large components. To support practical applications, we present two nonparametric estimators for the cumulative entropy of these systems. The efficiency and performance of these estimators are illustrated with simulated datasets and further validated with real data sets.

A new treatment option for cluster headache (CH) prevention is needed. Monoclonal antibodies (mABs) against calcitonin gene-related peptide (CGRP) ligands are used as a preventative treatment for migraine. Considering the CGRP’s role in the CH attack’s ignition and upkeep, fremanezumab and galcanezumab have been evaluated for CH preventative treatment. However, only high-dose (300 mg) galcanezumab was proven for episodic CH prevention. We herein report 3 cases of migraine and comorbid CH with previous failures of preventive treatments. The 2 cases were treated with fremanezumab and the one with non-high-dose galcanezumab. All 3 cases showed good results not only on migraine but also on CH attacks. Our report suggested the efficacy of CGRP-mABs for CH prevention. Our cases differed from the cases in the phase 3 trials of CGRP-mABs for CH prevention in the following 2 points. First, the patients had both migraine and comorbid CH. Second, the combined use of CGRP-mABs with preventative drugs for CH, such as verapamil and/or prednisolone, was performed. Future accumulation of real-world data may prove the efficacy of CGRP-mABs for CH prevention.

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

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

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

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

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

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

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

The Hwangdo tidal flat is an intertidal landform located in Cheonsu Bay, Taean-gun, Chungcheongnam-do, on the west coast of Korea. The topographical characteristics of the semi-enclosed bay on the eastern side of the study area include waterways, sandbars, small islands, and tidal flats. In this study, data were acquired from tide gauges installed on the Hwangdo Bridge, and the height of the ellipsoid was measured using a real-time kinematics global positioning system (RTK-GPS). Digital elevation model (DEM) using Matrice 300 drone data were also acquired after processing. The geochemical sediment characteristics in the study area were analyzed, together with tidal-flat exposure-time characteristics based on environmental factors. Sediment data (n = 107) collected from October 25 to 28, 2022 (Korean local time) were used to classify sediment particles according to Folk and Ward (1957). Sedimentary facies ranged from coarse sand (sand:mud ratio = 9:1) to sandy silt (sZ) followed by muddy sand (mS) and slightly gravelly sandy mud ((g)sM). Total organic carbon (TOC) in the surface sediments were also characterized based on a particle-size analysis. The mean change in tidal height measured at Hwangdo Bridge during the sampling period was ~7.53 m (minimum: –3.86 m, maximum: +3.67 m). Based on the Boryeong's sea level measurement tide data in 2022, the tidal area in the drone images ranged from 6.362 m² (0.006 km²) at DEM +4.0 to 4,841,078 m² (4.841 km²) at DEM –4.0 m, indicating an increase in the tidal-flat area according to the tidal level of up to ~800-fold. The daily average exposure time was 9.0 h (minimum: 1.5 h, maximum: 17.9 h). Based on the results of multivariate analysis using exposure times and a geochemical dataset, four groups were identified: upper, middle, and lower intertidal zones and regions with a relatively high organic-matter concentration. A determination of the main characteristics of the Hwangdo tidal flat according to their spatial distribution showed that, among various environmental factors, changes in the sand or clay sediment composition were determined by community factors. The results of this study demonstrate the four statistically processed groups of marine environmental characteristics in the Hwangdo tidal flats. Changes in sedimentary patterns, rather than in the exposure time, accounted for the differences in the sediment compositions of the upper, middle, and lower stations, a response that is expected to continue.

Knowledge of permafrost’s ice and unfrozen water content is critical for predicting the permafrost behavior during ice–water–ice transition. This is especially relevant when ice and permafrost are melting in many regions under the influence of global warming. It is well-known that only a part of the formation's pore water turns into ice at 0 ^oC. After the further lowering the temperature, the water phase transition continues, but at gradually decreasing rates. Thus, the porous space is filled with ice and unfrozen water. The laboratory data show that frozen formations' mechanical, thermal, and rheological properties strongly depend on the moisture content. Hence, porosity and temperature are essential parameters of permafrost. In this paper, it is shown how, and by combining research in three fields: (1) geophysical exploration, (2) numerical modeling, and (3) temperature logging, it is possible to estimate in-situ the porosity of permafrost. To demonstrate the procedure, five examples of numerical modeling (where all input parameters are specified) are given. This investigation is the first attempt to analyze the permafrost’s porosity in situ quantitively.

The aim of this study was to compare the effectiveness of propofol-based sedation and midazo-lam sedation in same-day bidirectional endoscopy (BDE) in children. The charts of children (≤15 years old) who had undergone same-day BDE were retrospectively reviewed. Demographic data, indications, sedatives and their dosages, clinical outcomes, endoscopic findings, adverse events, and patient time requirements were compared in cases with propofol-based and midazolam se-dation. A total of 91 children [51 boys, mean age 13 years (range 9-15)] were enrolled. Propofol alone or in combination with midazolam and/or pentazocine was given in 51 (propofol-based sedation group) while midazolam alone or in combination with pentazocine was given in 40 (midazolam sedation group). The mean doses of propofol, midazolam and pentazocine were 96 mg (range 40–145 mg), 4.1 mg (range 3–5 mg) and 7.5 mg in the propofol group while the mean doses of midazolam and pentazocine were 6.2 mg (range 4–10 mg) and 15 mg in the midazolam group, respectively. The total procedure times and endoscopic findings between the two groups were similar, but the median patient time requirement in the propofol group was significantly shorter than that of the midazolam group (7.3 h vs. 8.4 h, P<0.001). No adverse events occurred in either group. Propofol-based sedation shortened patient time requirements in same-day BDE compared with midazolam sedation in children.

Within the framework of the previously proposed formulation of the quantum theory of gravity in terms of world histories, it was suggested that the universe has its own mass. This quantity is analogous to the mass of a particle in relativistic mechanics. The mass of the universe is a distribution of non-zero values of gravitational constraints, which arises and changes in time as a consequence of the initial conditions for fundamental dynamic variables. A formulation of the Euclidean quantum theory of gravity is also proposed to determine the initial state, which can be the source of the universe’s own mass. Being unrelated to ordinary matter, the distribution of its own mass affects the geometry of space and forms a dedicated frame of reference. The existence of selected reference systems is taken into account by the corresponding modification of the system of quantum gravitational constraints. A variant of such a modification of the Wheeler-De Witt equation is the operator representation of gravitational constraints, which, together with the state of the universe, determines the parameters of the reference system in the form of a distribution of the spinor field on a spatial section.

This study presents the developments regarding the time-domain-induced polarisation method, as a supporting tool for resistivity soundings during investigations of coastal detrital aquifers that are salinised by marine intrusion. The interpretation of resistivity measurements in such aquifers, which have variable hydrochemistry and lithology, involves uncertainties owing to the presence of low-resistivity lithologies, such as clays. To reduce these uncertainties, the use of other geophysical parameters is necessary; hence, this study focuses on induced polarisation, since it can be measured simultaneously with resistivity. In detail, we propose the determination of induced polarisation using 1D techniques, while developing a different algorithm for processing the induced polarisation data. The aim is to extend the results of this phenomenon, using instead of chargeability, the concepts of polarisability and decay time, which are extracted from the decay curve, given that they represent more intrinsic properties of the various analysed subsurface media. We present results obtained by applying this methodology to a Quaternary aquifer of the Costa del Sol in the SE Iberian Peninsula (in the province of Almería) during two different campaigns, one before and one after winter (i.e., in October and February, respectively). The results reveal the position of the saline front during each campaign, while reflecting the seasonal movement of the marine intrusion.

Inspired by recent developments in wave propagation and scattering experiments with parity-time (PT) symmetric materials, we discuss reciprocity and representation theorems for 3D inhomogeneous PT-symmetric materials and indicate some applications. We start with a unified matrix-vector wave equation which accounts for acoustic, quantum-mechanical, electromagnetic, elastodynamic, poroelastodynamic, piezoelectric and seismoelectric waves. Based on the symmetry properties of the operator matrix in this equation, we derive unified reciprocity theorems for wave fields in 3D arbitrary inhomogeneous media and 3D inhomogeneous media with PT-symmetry. These theorems form the basis for deriving unified wave field representations and relations between reflection and transmission responses in such media. Among the potential applications are interferometric Green’s matrix retrieval and Marchenko-type Green’s matrix retrieval in PT-symmetric materials.

In this work, the hypothesis that the universe is made up of 4-dimensional spheres of space, whose diameter is the Planck length, allows us to calculate most of the constants used in current physical theories. Calculated constants include: elementary charge, fine structure constant, electron mass, Planck constant, gravitational constant, electrical constant, Boltzmann constant, mass and charge of up and down quarks, muon mass, and Higgs boson. All of these depend on the speed of light and the Planck length, which shows that all the constants are related and not due to chance.

Screen viewing time is the total time spent by a child on any digital/electronic device. The objective of the present study was to determine the incidence and predictors of excessive screen viewing time in children in Ujjain, India. This cross-sectional, community-based study was conducted through house-to-house survey by using the three-stage cluster sampling method in 36 urban wards and 36 villages of Ujjain district, India. Excessive screen viewing time was defined as screen viewing for &gt; 2 h/day. The prevalence of excessive screen viewing time was 17.83%. Risk factors identified using the multivariate logistic regression model were: age (OR: 1.5, P &lt; 0.001); mobile phone use before bedtime (OR: 3.17, P = 0.008); parents’ perception about the child habituated to screen (OR: 14.03, P &lt; 0.001); television in bedroom (OR: 48.69, P &lt; 0.001); morning mobile screen viewing time (OR: 9.27, P &lt; 0.001); not reading books other than textbooks (OR: 9.71, P &lt; 0.001); and lack of outdoor play for &gt;2 h (OR: 4.20, P &lt; 0.001). Presence of eye pain was a protective factor for excessive screen viewing time (OR: 0.12, P = 0.011). The study identified multiple modifiable risk factors for excessive screen viewing time.

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further break down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

This paper describes a polynomial time algorithm for solving graph isomorphism and automorphism. We introduce a new tree data structure called Walk Length Tree. We show that such tree can be both constructed and compared with another in polynomial time. We prove that graph isomorphism and automorphism can be solved in polynomial time using Walk Length Trees.

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches. A simple code for the adaptive control algorithm used here can be downloaded from: https://github.com/alexscheinker/ES_adaptive_optimization

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a simple statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

The necessity of the dark energy and dark matter in the present universe could be a consequence of the antimatter elimination assumption in the early universe. In this research, I derive a new model to obtain the cosmic horizon radius and the potential cosmic topology utilising a new construal of space geometry inspired by large-angle correlations of the cosmic microwave background (CMB). A version of the Big Bounce theory is utilised to avoid the Big Bang singularity and inflationary constraints, and to tune the initial conditions of the curvature density. The mathematical derivation of a positively curved universe governed by only gravity revealed two cosmic horizon solutions. Although the positive horizon is conventionally associated with the evolution of the matter universe, the negative horizon solution could imply additional evolution in the opposite direction. This possibly suggests that the matter and antimatter could be evolving in opposite directions as distinct sides of the universe, as in the visualised Sloan Digital Sky Survey. The cosmic horizon radius is found to be accountable for the universal space curvature. By implementing this model, we find a decelerated stage of expansion during the first 10 Gyr, which is followed by a second stage of an accelerated expansion; potentially matching the tension in Hubble parameter measurements. In addition, the model predicts a final time-reversal stage of spatial contraction leading to the Big Crunch of a cyclic universe. The predicted density is 1.14. Other predictions are (1) a calculable flow rate of the matter side towards the antimatter side at the accelerated stage; conceivably explaining the dark flow observation, (2) a time-dependent spacetime curvature over horizon evolution, which could influence the galactic rotational speed; possibly explaining the high speed of stars, and (3) evolvable spacetime internal voids at the accelerated stage, which could contribute in continuously increasing the matter and antimatter densities elsewhere in both sides respectively. These findings may indicate the existence of the antimatter as a distinct side, which influences the evolution of the universe instead of the dark energy or dark matter.

We employ another approach to quantize electromagnetic fields in the coordinate space, instead of the mode (or Fourier) space, such that local features of photons can be efficiently, physically, and more intuitively described. To do this, coordinate-ladder operators are defined from mode-ladder operators via the unitary transformation of systems involved in arbitrary inhomogeneous dielectric media. Then, one can expand electromagnetic field operators through the coordinate-ladder operators weighted by non-orthogonal and spatially-localized bases, which are propagators of initial quantum electromagnetic (complex-valued) field operators. Here, we call them QEM-CV-propagators. However, there are no general closed form solutions available for them. This inspires us to develop a quantum finite-difference time-domain (Q-FDTD) scheme to numerically time evolve QEM-CV-propagators. In order to check the validity of the proposed Q-FDTD scheme, we perform computer simulations to observe the Hong-Ou-Mandel effect resulting from the destructive interference of two photons in a 50/50 quantum beam splitter.

The Aβ cascade and alternations of biomarkers in neuro-inflammation, synaptic dysfunction and neuronal injury followed by Aβ have progressed. But the question is how to use the biomarkers. Here, we examine the evidence and pathogenic implications of protein interactions and the time order of alternation. After the deposition of Aβ, the change of tau, NfL and NG is the main alternation and connection to others. The neuro-inflammation, synaptic dysfunction and neuronal injury function is exhibited prior the structural and metabolic changes in the brain following Aβ deposition. The time order of such biomarkers compared to the tau protein is not clear. Despite the close relationship between biomarkers and plaque Aβ deposition, several factors favor one or the other. There is an interaction between the proteins that CSF SNAP-25, VILIP-1 and YKL-40 can predict the brain amyloid burden. The Aβ cascade hypothesis could be the pathway, but not all subjects are converted to AD, even with very high elevated Aβ. The interaction of biomarkers and the time order of change require further research to identify the right subjects and right molecular target for precision medicine therapies.

The study of order patterns of three equally spaced values $x_t,x_{t+d},x_{t+2d}$ in a time series is a powerful tool. The lag $d$ is changed in a wide range so that differences of frequencies of order patterns become autocorrelation functions. Similar to a spectrogram in speech analysis, four ordinal autocorrelation functions are used to visualize big data series, as for instance heart and brain activity over many hours. The method applies to real data without preprocessing, outliers and missing data do not matter. On the theoretical side, we study properties of order correlation functions and show that the four autocorrelation functions are orthogonal in a certain sense. An analysis of variance of a modified permutation entropy can be performed with four variance components associated with the functions.

A highly dirt resistant paint for building facades without chemicals harmful to nature and environment, is developed which resolves the unattractive disfigurement of building walls caused by dirt. The experimentation is scientifically and statistically planned with the aid of computer programming. It consists of a sequence of phases which include the selection of appropriate raw materials, adopting of Basic Language computer programming to generate a target population of paint formulations. The average PVC percentage is computed using theory and found to be 54.98% for the target population of 543143 paint formulations hence verifies the literature results. Experimentation and statistical analysis are performed to compare the classical conventional agitator with latest lab equipment like Nano mill and it is concluded that Nano mill performs better on the average than conventional agitator in preparation of paint formulations. Hence the sample of paint formulations is prepared on Nano mill and tested in laboratory using advanced available technology for the analysis and comparison of paint properties to determine the best paint formulation. The results are analyzed using Analysis Of Variance Technique (ANOVA) and it is concluded that the paint formulation named “O3” has the highest dirt resistance on the average. The final selected formula O3 is compared with three other competitor paints in market under natural environment for a period of almost one year. A regression model is also constructed to study the effect of environmental factors like time, temperature and humidity on dirt resistance of paints. It is found that O3 formulation is the best environment friendly which performs equally well with one competitor paint and has higher dirt resistance than two other competitor paint formulations containing harmful chemicals. The regression model of dirt resistance on variables including time, temperature and humidity shows that these factors are significantly affecting the dirt resistance of a given paint at 5% level of significance. 95.34% variation in the dirt resistance of a given paint is due to and explained by the given factors. The regression model is useful to predict the average dirt resistance of a given paint with a certain level of confidence. The project exemplifies the work of an applied research from conceptualization to successful commercialization for the paint industry.

The current work investigates how adding a battery of optimal capacity to a grid-connected photovoltaic (PV) system can improve its economic feasibility. Also, the effect of different parameters on the feasibility of the PV system was evaluated. The OBC was determined for different saving targets of the annual electricity consumption of the chosen building. For this aim, real electricity consumption data of a residential building in Landskrona, Sweden, was used as energy consumption profile. Solar World SW325XL, which is a monocrystalline solar panel, was selected as PV panels. The calculations were performed under the metrological and economic conditions of southern Sweden. Different working parameters (WP)were considered (prices of the battery, feed-in tariffs, and saving targets). The performed calculations show that the optimal battery capacity (OBC), in which the payback time (PBT) of the system is maximized, strongly depends on the WP. The proper selection of the battery can considerably increase the economic feasibility of the PV system in southern Sweden. However, in some cases, using battery can have a negative impact on the PBT of the system. The results show that the electricity price, the module price, the inverter price, and the inverter lifetime have the highest effect on the PBT.

Reducing sedentary behaviours can help prevent non-communicable diseases, particularly among young adolescents with long term illnesses or disabilities (LTID). Much of young people’s voluntary sedentary time is related to screen-time behaviours (STBs) such as TV viewing, playing computer games, and using the computer for other activities. Although public health data on adolescents’ STB is growing, information about adolescents with LTID is currently lacking in a European context. The purpose of this study is to compare time on STBs between adolescents with and without LTID in European Countries through the HBSC 2013/14 study. Young adolescents (n = 61,329; boys 47.8%) from 15 European countries reported the time spent on TV viewing, playing computer games, and using the computer for other purposes on weekdays and the weekend. STBs were dichotomised based on international recommendations of less than 2 h per day, and Chi-square tests of independence were performed to investigate differences. STB time was combined to produce a sum score as dependent variable in multiple analysis of covariance with age and family affluence as covariates. There were statistically significant differences in computer gaming among boys and other computer use among girls for both weekdays and weekends, whereby adolescents with LTID reported higher use. In addition, both boys and girls with LTID spent more time on STBs than their same sex peers without LTID (Boys, F = 28.17, p < 0.001; Girls, F = 9.60, p = 0.002). The results of this study indicate a need for preventive strategies to address high levels of STB among young adolescents with LTID and reduce the risk of poor health outcomes associated with higher levels of sedentary behaviour.

Bijective Analysis of 4D Minkowski space-time model confirms that 4D space-time where time would be 4th dimension of space does not exist. In 4D manifold, time is the duration of a physical event which runs in space. An event does not have the duration in time, duration itself is time. It means that any given physical phenomena can only be symmetric in space; not in time.

Prolonged sedentary behaviour (SB) has shown to be detrimental to health. Nevertheless, population levels of SB are high and interventions to decrease SB are needed. This study aimed to explore the effect of an individualized consultation intervention aimed at reducing SB and increasing breaks in SB among college employees. A pre-experimental study design was used. Participants (n=36) were recruited at a college in Massachusetts, USA. SB was measured over 7 consecutive days using an activPAL3 accelerometer. Following baseline measures, all participants received an individualized SB consultation which focused on limiting bouts of SB >30 minutes, participants also received weekly follow-up e-mails. Post-intervention measures were taken after 16 weeks. Primary outcome variables were sedentary minutes/day and SB bouts >30 minutes. Differences between baseline and follow-up were analyzed using paired t-tests. The intervention did not change daily sedentary time (-0.48%; p>0.05). The number of sedentary bouts >30 minutes decreased significantly by 0.52 bouts/day (p=0.015). In this study a consultation based SB intervention was successful in reducing number of bouts >30 minutes of SB. However, daily sedentary time did not reduce significantly. These results indicate that consultation-based interventions may be effective if focused on a specific component of SB.

Flower bud formation and flowering in chrysanthemum occur under short day conditions (SD), but the molecular basis for the switch to reproductive growth is less well understood than in model plants. Here, a spontaneous mutant able to flower under long days is described. In an attempt to reveal the pathway(s) involved in the formation of flower buds under contrasting daylengths, transcriptome sequencing was carried out in plants grown both under SD and under long day conditions (LD). A number of differentially transcribed genes involved in the various known flowering pathways were identified. Both circadian clock genes and CmFTL3 were up-regulated under SD, thereby inducing floral bud formation and flowering. The gibberellin (GA) signalling pathway-related genes GA20ox and GID1 were up-regulated in the mutant under LD, while the catabolic gene GA2ox and GAI was down-regulated, thereby inducing the transcription of CmFTL1, SOC1 and LFY. The GA content of the leaf was higher in the mutant than in the wild type under LD. When treated with GA, the mutant flowered earlier under both SD and LD, but there was no other detectable phenotype difference between the two lines. The indication was that the photoperiod pathway majorly regulates flower bud formation and flowering time in chrysanthemum under SD. GA signalling pathway only plays a subsidiary role for flowering. However, the GA signalling pathway predominated for flowering under LD.

Performing optimisation and scale-up studies of crystallisation systems requires accurate and computationally efficient mathematical models. The assumption of the ideal mixing conditions in batch reactors typically produce inaccurate results while the computational expense of CFD models is still prohibitively high. Therefore, in this work, a new intermediary approach is proposed that takes into account the non-ideal mixing conditions in the reactor and requires less computational resources than full CFD simulations. Starting with the Danckwerts concept of the intensity of segregation, an analogy between its application to chemical reactions and the kinetics of the crystallisation phenomena (such as nucleation and growth) has been made. As a result, the modified kinetics expressions have been derived which incorporate the effect of non-idealities present in stirred reactors. This way, based on the experimental measurements of the mixing time using the Laser Induced Fluorescence (LIF) technique, computationally more efficient mathematical models can be developed in two ways: (1) the accurate semi-empirical correlations are available for standard mixing configurations with the most often used types of impellers, (2) CFD simulations can be utilised for estimation of the mixing time; in this case it is necessary to simulate only the mixing process. The benefits offered by the LIF experimental technique have been demonstrated and some frequent problems in its application analysed. The mixing time results for configurations with and without baffles for three types of impellers and four different rotational speeds have been presented. The false shorter mixing times in the non-baffled configurations have been observed and this phenomena explained by the existence of two segregated zones in the reactor and confirmed by additional experiments. The precise measurements in these cases have been shown as difficult using the LIF technique, particularly for higher rpms. The experimental data has been compared to the preliminary simulation results obtained from the Smoothed Particle Hydrodynamics method and the standard k-ε turbulence model with the modest success. The shortcomings of the SPH model have been recognized and the directions for the future work discussed.

Due to a simple crane limited capacity; there is an urgent need to use high capacity cranes such as tower cranes. However, with regard to their high expense, we have to take into consideration selecting what type of cranes to be utilized which has been discussed by many researchers. In this research, a proposed technique was created to select the suitable type of crane and best place for crane erection, in addition to minimum radius for requested crane in order to minimize cost. To fulfill that target, a computer program is designed to numerate these problems, demonstrating an example explaining how to apply program and the result donated best place.