Digital filtering is a fundamental technique in digital signal processing, which operates on a digital sequence without any information on how the sequence was generated. This paper proposes a methodology for designing the equivalent of digital filtering for neuromorphic samples, which are a low power alternative to conventional digital samples. In the literature, filtering using neuromorphic samples is done by filtering the reconstructed analog signal, which is required to belong to a predefined input space. We show that this requirement is not necessary, and introduce a new method for computing the neuromorphic samples of the filter output directly from the input samples, backed by theoretical guarantees. We show numerically we can achieve a similar accuracy compared to the conventional method. However, given that we bypass the analog signal reconstruction step, our results show significantly reduced computation time for the proposed method and good performance even when signal recovery is not possible.

In a recent paper, we have shown how gravity and information can be related. The relation is based on the thermodynamic extension of the gravitational system under consideration using the holographic principle. Using this idea, we were able to derive the Gullstrand-Painlevé (GP) metric. In this short paper, we re-derive the GP metric using information. However, we now extend the spacetime to 5D with the fifth dimension being time, that is, the (3+1)D spacetime is now promoted to (3+1+1)D spacetime. We also comment on the possible Braneworld interpretation of this model.

On the basis of the golden ratio and the compasseses, every angle was divided into three equal parts. In general, it has been proven that the problem of triangulation of an angle cannot be solved on the basis of the golden ratio and the expanding of the angle. The relationship of golden relativity with Pi number and Euler's number has not been investigated to solve impossible problems. According to the investigation of events in six-dimensional space-time, with the simultaneous movement of two arms of the compasses based on the golden ratio, every angle was divided into three equal parts. The growth rate based on the golden ratio is the key to solving the most intractable mathematical and physical problems.

Raman spectroscopy is a type of inelastic scattering that provides rich information about a sub-stance based on the coupling of the energy levels of their vibrational and rotational modes with incident light. It has been applied extensively in many fields. As there is an increasing need for remote detection of chemicals in planetary exploration and anti-terrorism, it is urgent to develop a compact and easily transportable fully automated remote Raman detection system for trace detection and identification of information with high-level confidence about the target’s compo-sition and conformation in real-time and for real field scenarios. Here, we present an unmanned vehicle-based remote Raman system, which includes a 266 nm air-cooling passive Q-switched nanosecond pulsed laser of high-repetition frequency, a gated ICMOS, and an unmanned vehicle. This system obtains good spectral signals from remote distances ranging from 3 m to 10 m for simulating realistic scenarios, such as aluminum plate, woodblock, paperboard, black cloth, and leaves, and even for detected amounts as low as 0.1 mg. Furthermore, a CNN-based algorithm is implemented and packaged into the recognition software to achieve fast and more accurate de-tection and identification. This prototype provides a proof-of-concept for an unmanned vehicle with accurate remote substance detection in real-time, which can be helpful for remote detection and identification of hazardous gas, explosives, their precursors, and so forth.

Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data for studying land cover and land use change (LCLUC) over the past four decades. However, the availability of cloud-, shadow-, and snow-free observations varies spatially and temporally due to climate and satellite data acquisition schemes. Spatio-temporal heterogeneity poses a major issue for some time-series analysis approaches, but can be addressed with pixel-based compositing that generates temporally equidistant cloud-free or near-cloud free synthetic images. Although much consideration is given to methods identifying the ‘best’ pixel value for each composite, determining the aggregation period receives less attention and is often done arbitrary, or based on expert intuition. Here, we evaluated data compositing windows ranging from five days to one year for 1984-2021 Landsat and 2015-2021 Sentinel‑2 time series across Europe. We considered separate and joint use of both data archives and analyzed spatio-temporal availability of composites during each calendar year and pixel-specific growing season. We reported mean annual composites’ availability investigating differences among biogeographical regions, checked feasibility of pan‑European analyses for three LCLUC applications based on annual, monthly and 10-day composites, and analyzed the shortest feasible compositing window ensuring ≥50% temporal data availability and interpolation of the remaining composites for individual years and across a variety of medium- and long‑term time windows. Our results highlighted low data coverage in the 1980s, 1990s, and in 2012, as well as spatial variability in data availability driven by climate and orbit overlaps, which altogether impact spatio-temporal consistency of medium- and long-term time series, limiting feasibility of some LCLUC analyses. We demonstrated that prior to 2011 monthly composites ensured overall 50-62% data coverage for each calendar year, and ~75% afterwards, with further increase to ~82% when Landsat and Sentinel-2 were combined. Temporal consistency of monthly composites was overall low and temporal interpolation augmenting up to 50% missing data each year and across a time window of interest, ensured feasibility of analyses. Applications based on shorter than monthly composites were challenging without joining Landsat and Sentinel‑2 archives after 2015, and beyond the Mediterranean biogeographical region. Using pixel-specific growing season data typically boosted data availability in most geographies and diminished most of the latitudinal differences, but feasibility of complete time series with sub-monthly compositing windows was still restricted to the most recent years, and required data interpolation. Overall, our analyses provided a detailed assessment of Landsat and Sentinel-2 data availability over Europe, and based on selected application examples, highlighted often lacking spatio-temporal consistency of time series with sub-monthly compositing windows and long-time periods, which might hinder feasibility of some LCLUC applications.

This prospective study aimed to evaluate the ability of point-of-care Doppler ultrasound measurements of carotid-corrected flow time and changes in corrected carotid flow time (FTc) induced by volume expansion to predict fluid responsiveness in patients undergoing robot-assisted gynecological surgery in a modified head-down lithotomy position. FTc was measured using Doppler images of the common carotid artery before and after volume expansion. The stroke volume index at each time point was recorded using the MostCare instrument. Fluid responsiveness was defined as a stroke volume index ≥10% increase after volume expansion. Half (50%) of the 52 patients responded positively. The areas under the receiver operating characteristic curves measured to predict fluid responsiveness for the corrected carotid flow time and changes in the FTc were 0.82 [95% confidence interval:0.705–0.937; P < 0.0001] and 0.67 (95% confidence interval:0.520–0.815; P < 0.05), respectively. The optimal cut-off value for the FTc and the change in FTc were 356.5 ms and 19.5 ms, respectively. FTc is a more reliable predictor of fluid responsiveness than FTc induced by volume expansion in gynecological patients undergoing robot-assisted laparoscopic surgery in the modified head-down lithotomy position.

This study is a two-phase hybrid study on the behavioral activities of undergraduate students during the COVID-19 pandemic lockdown. A questionnaire survey of students during the whole school period and a semistructured interviews with 13 undergraduates of different majors were conducted. First, we found that compared with primary and secondary school students, undergraduates showed many problems with self-time management. Second, the behavior of undergraduates at home may result from multiple factors, such as past experience, professional identity and behavioral habits. When secondary school provided a relatively relaxed learning environment or secondary school teachers placed more emphasis on continuous effort, and when students had a strong sense of the social value of their undergraduate major, students worked harder and achieved more during the pandemic. Otherwise, it was easier for students to slack off and be passive during the pandemic. Altogether, this study deepens our understanding of undergraduates' self-management of free time, and the differences in the values underlying different behaviors and the formation process of various behaviors.

Retailers must have accurate sales forecasts to operate their businesses efficiently and effectively and to remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision-making for inventory management, purchasing, and other operational decisions. In this study we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study's findings, we used the M5 forecasting competition's openly accessible and well-established dataset. The results obtained from the DeepAR models, which were trained on various combinations of features including time, price, events, and IDs, suggest that individually only the features corresponding to IDs improve the baseline model. However, when all the features are used together, the best performance is achieved, indicating that the individual relevance of each feature is emphasized when the information is given jointly. Comparing the model with features to the model without features, there is an improvement of 1.76\% for MRMSSE and 6.47\% for MMASE.

In this contribution, time course studies were performed toward oxidation of different peroxidase substrates in the presence of silver nanozymes. In this regard, two common peroxidase substrates, TMB and DAB, were selected as model chromogens. The process of the oxidation reaction was probed by recording the absorbance of the colored products using UV-Vis spectrophotometer. Thereafter, the nanozymatic activity was calculated and used as an index for comparing the performance of the silver nanozymes against different substrates in a certain reaction time. The time course curves, revealed that the maximal activity of the silver nanozymes were achieved after a very short time of 3.0 min toward TMB oxidation while regarding DAB, the maximal activity was observed after 25.0 min which is 6.5-fold slower than that of the TMB oxidation. The difference between the DAB and TMB can be assigned to their different reaction pathways and different reactivity. In fact, the oxidation of DAB has occurred via n-electron irreversible pathway while the TMB oxidation has occurred through a simple 2-electon reversible pathway. Besides, DAB is a high stable and less relative compound compared of TMB, resulting different oxidation times, different affinity for binding to nanozyme active nodes which lead to different nanozymatic activities.

Objective: To study the factors affecting the time of umbilical cord shedding in premature infants.Methods: A total of 151 premature infants who were born in the obstetrics department of a Class III hospital in Hefei from November 2020 to June 2021 were selected as subjects. All premature infants and their families met the complete inclusion criteria. A total of 33 related factors from 3 aspects, including the birth situation of premature infants, the condition of premature infants in hospital and the condition of pregnant mothers, were collected for univariate analysis and multiple Logistic regression analysis. The relevant factors affecting the umbilical cord shedding time of premature infants were screened through the univariate analysis results, and the main factors affecting the umbilical cord shedding time of premature infants were screened through the multiple Logistic regression analysis results.it was found that the weight, gestural age, admission age, whether the premature infants were combined with NRDS, the use of antibiotics, the duration in the warm box, the initial temperature in the warm box, the duration of light therapy, the degree of Apgar for 1 minute, the maternal education level had an effect on the time of umbilical cord shedding of premature infants (P < 0.05). Through Logistic multiple regression analysis, it was screened that the duration of premature infants in the incubator was the main factor affecting the umbilical cord shedding time of premature infants (P < 0.05).Conclusion: Factors such as premature infants' weight, gestural age, admission age, whether they are combined with NRDS, antibiotic use, duration in the incubator, initial temperature in the incubator, duration of phototherapy, Apgar level of 1 minute, and maternal education level will affect the umbilical cord shedding time of premature infants. Among them, duration in the incubator is the main factor affecting the umbilical cord shedding time of premature infants.

Background: Although the use of regular replacement therapy including emicizumab for severe hemophilia has been spread, the assessment of the hemostatic ability using routine activated partial thromboplastin time (APTT) is still difficult in patients being treated with emicizumab. Methods: The hemostatic ability in patients treated with FVIII concentrate or emicizumab was evaluated by APTT, thrombin time (TT) and a small amount of tissue factor induced FIX activation assay (sTF/FIXa) using a clot waveform analysis (CWA). Results: FVIII activities based on a CWA-TT were significantly higher than those based on a CWA-APTT or chromogenic assay. FVIII activities based on the three assays in plasma without emicizumab were closely correlated, and those in plasma with emicizumab based on a CWA-TT and chromogenic assays were also closely correlated. The CWA-APTT and CWA-TT showed different patterns in patients treated with FVIII concentrates from those treated with emicizumab. In particular, the CWA-TT in patients treated with FVIII concentrate showed that the peak heights were significantly higher in platelet-rich plasma than in platelet-poor plasma. In plasma with approximately 16% of FVIII activity based on APTT assay from patients treated with FVIII concentrate, the peak height on the CWA-sTF/FIXa showed a higher hemostatic abilitythan normal plasma. Conclusions: The CWA-TT can measure the FVIII activity in patients treated with emicizumab. Although routine APTT evaluations demonstrate a low hemostatic ability in patients treated with FVIII concentrate, the CWA-TT and CWA-sTF/FIXa show hypercoagulability in those patients.

GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and maintenance of the global coordinate framework. Long Short-Term Memory (LSTM), a deep learning model has been widely applied in the field of high-precision time series prediction especially when combined with Variational Mode Decomposition (VMD) to form the VMD-LSTM hybrid model. To further improve the prediction accuracy of the VMD-LSTM model, this paper proposes a dual variational modal decomposition long short-term memory (DVMD-LSTM) model to effectively handle the noise in GNSS time series prediction. This model extracts fluctuation features from the residual terms obtained after VMD decomposition to reduce the prediction errors associated with residual terms in the VMD-LSTM model. Daily E, N, and U coordinate data recorded at multiple GNSS stations between 2000 and 2022 are used to validate the performance of the proposed DVMD-LSTM model. The experimental results demonstrate that compared to the VMD-LSTM model, the DVMD-LSTM model achieves significant improvements in prediction performance across all measurement stations. The average RMSE is reduced by 9.86%, and the average MAE is reduced by 9.44%. Furthermore, the average accuracy of the optimal noise model for the predicted results is improved by 36.50%, and the average speed accuracy of the predicted results is enhanced by 33.02%. These findings collectively attest to the superior predictive capabilities of the DVMD-LSTM model, thereby enhancing the reliability of the predicted results.

Time series (TS)-based predictions are made by examining the behaviour of historical data to forecast future values. Multiple industries; such as stock market trading, power load forecasting, medical monitoring, and intrusion detection; frequently use it. Several variables; such as the performance of other markets as well as the economic situation of a country which affect its market performance; significantly affect the prediction of stock market prices. Therefore, this present study uses numerous variables; such as the opening, lowest, highest, and closing prices; to predict the indices of the stock market of the Kingdom of Saudi Arabia (KSA). Successfully accomplishing an investment goal largely depends on choosing the right stocks to buy, sell, or maintain. The project's output is the projected closing prices (regression) over the next seven days, which helps investors make the best decisions. This present study used exponential smoothing (ES) to remove noise from the input data obtained from the Saudi Stock Exchange, or Tadawul, before using a multivariate long short-term memory (LSTM) deep learning (DL) algorithm to forecast stock market prices. The proposed multivariate LSTMDL model had satisfactory prediction rates of 97.49% and 92.19% for the univariate model. Therefore, it can be used to effectively predict stock market prices. The results also indicate that DL as well as multiple sources of information can be used to accurately predict stock markets.

This manuscript presents a system that can generate a 2D grid map and a 3D voxel model of an environment by fusing multiple sensors integrated into a robot. The fusion of these sensors is done by utilizing Robot Operating System (ROS) and some of its libraries. The sensors that are fused in the robot are a Light Detection and Ranging (LIDAR), Inertial Measurement Units (IMU), wheel encoders, and a Time-of-Flight camera. With the fusion of the point cloud data information obtained with the Time-of-Flight camera, the laser information of the LIDAR sensor, in addition to the odometry generated with the other sensors, a 2D grid map and a 3D model of two different environments were created, thus testing the performance of the system.

In principle, modular or integral character of manufacturing lines depends on topological designs of products and determined operation tasks. On the other hand, in specific situations there is an articulated need for modular design in smart manufacturing systems, since modular layouts are a crucial step towards agile production via smart manufacturing. The aim of this paper is to explore how the modular layout relates to manufacturing lead time (MLT) and to operational complexity of smart manufacturing systems. For this purpose, topologically different models of alternative process layouts were simulated and tested, while MLT values were obtained using Tecnomatix Plant Simulation. Obtained positive findings of this research could be useful not only in selection of the most suitable process design from the alternative ones, but especially in deepening knowledge and better understanding of the concept of optimal network modularity.

As a result of scientific technological advancements in medical diagnostics, treatment modalities of non-communicable and communicable diseases, and palliative care approaches, the life expectancy of women is soaring than men across the globe, due to the longer telomere length and other protective factors. The main symptoms of menopause broadly be divided into psychological somatovegetative and urogenital. Neglected menopausal symptoms can lead to debilitating comorbidities which may end up in mortalities. Fortunately, modern treatment and therapeutic formalities based on pharmaceutical preparations, as well as lifestyle- modifications have provided long-lasting relief for the pre-menopausal woman. This can be done as per her preference if she is knowledgeable, resourceful, and cheerful. Menarchae is the “ alpha” and menopause is the “omega” phase in the reproductive lifecycle of any woman. Reproductive health is essentially an integral part of women’s health. In this 4^th industial revolution era information technology and digital science go hand in hand to improve the quality of life of populations across the globe irrespective of their educational and professional status. Health literacy among disadvantageous sub populations increased. Positive attitudes, perception and evidence-informed knowledge on adolesecent girls about not only ‘period pains’ but menopausal symptoms as an inevitable phase in a woman’s reproductive health may make a better sense at the dawn of evidence based medicine even in a low middle income country like Sri Lanka. Hence, this timely communication strengthens by, Do not fear when menopause is near: The Importance of a Preparatory Time and Space to face the consequences of Menopaus. How to forget the fact that alpha and the omega are inevitable phases in woman’s reproductive health trajectory. Moreover, the importance of a preparatory time and space well in advance preferably in peri-menopausal time to prevent and control the inevitable health consequences. Moreover, to invest in menopause for a brighter future in the modern personalized medicine era based on global scientific evidence.

In this paper, MgAl Layered double hydroxides (LDH) were synthesized by co-precipitation method using a colloid mill and characterized by XRD, IR and SEM. The LDHs was repeatedly tested and compared with different bearing lifetime tester in lithium base grease, and it was found that the environmental-friendly LDHs had greater performance than the traditional antioxidant, and it was the development direction of a new generation of environmental-friendly antioxidants in the future. By adding LDHs into large electric shovel greats (GRK-A) in open-pit coal mine, it can be seen by PDSC evaluation that the service lifetime of grease is extended by 20% while the overall performance of grease is not affected. With the increase of LDHs addition, the grease sample gets the greater activation energy, the stronger thermal oxidation and decomposition resistance. Comparing the energy storage modulus and flow transition index at different temperatures, it can be seen that adding the right amount of LDHs needs close attention for the system oxidation resistance and viscoelasticity. For the electric shovel grease system, the best oxidation resistance and rheological properties can be achieved by adding 2% of LDHs. The rheological viscosity-temperature curves show that the grease samples with different ratios of solid LDHs have better low-temperature properties than the mine grease.

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself did not get the same attention by researchers. That is why in this article, the authors present a pub-licly available multivariate time series dataset which was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anomalies in the workpiece the dataset can be ap-plied for anomaly detection. By using a convolutional autoencoder as a first model good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learn-ing. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics like anomaly detection and transfer learning.

In this research article, we are going to study the concept of the theory of relativity and after extracting the essential information, we will try to find a relation between the theory of relativity and the results we are obtaining through contemporary physics experiments. It has been investigated that traveling in time is not a dream anymore and time can pass at a different rate for some people.

The athletic performance and safety of racehorses is influenced by hoof-surface interactions. This intervention study assessed the effect of eight horseshoe-surface combinations on hoof acceleration patterns at impact and foot-off in 14 galloping Thoroughbred racehorses retired from racing. Aluminium, barefoot, GluShu (aluminium-rubber composite) and steel shoeing conditions were trialled on turf and artificial (Martin Collins Activ-Track) surfaces. Shod conditions were applied across all four hooves. Tri-axial accelerometers (SlamStickX, range ±500 g, sampling rate 5000 Hz) were attached to the dorsal hoof wall (x: medio-lateral, medial=positive; y: along dorsal hoof wall, proximal=positive; z: perpendicular to hoof wall, dorsal=positive). Linear mixed models assessed whether surface, shoeing condition or stride time influenced maximum (most positive) or minimum (most negative) accelerations in x, y and z directions, using ≥40,691 strides (significance at p&lt;0.05). Day and horse-rider pair were included as random factors and stride time as a covariate. Collective mean accelerations across x, y and z axes were 22–98 g at impact and 17–89 g at foot-off. Mean stride time was 0.48 ± 0.07 s (mean ± 2 s.d.). Impact accelerations were larger on turf in all directions for forelimbs and hindlimbs (p≤0.015), with the exception of forelimb z-minimum, and in absolute terms maximum values were typically double minimum values. Surface-type affected all foot-off accelerations (p≤0.022), with the exception of hindlimb x-maximum; for example, there was an average increase of 17% in z-maximum across limbs on the artificial track. Shoe-type influenced all impact and foot-off accelerations in forelimb and hindlimb datasets (p≤0.024), with the exception of hindlimb impact y-maximum. Barefoot hooves generally experienced the lowest accelerations. Stride time affected all impact and foot-off accelerations (p<0.001). Identifying factors influencing hoof vibrations on landing and hoof motion during propulsion bears implication for injury risk and racing outcomes.

In experimental physics, we understand time as the duration of material changes that run into space. This fact is the standpoint for our model of time-invariant space in which causality is only a principle and not a physical actuality, and entropy runs only in space and not in time. Time is merely a duration of entropy that is increasing in time-invariant space. Time is entering existence when measured by the observer. In time-invariant space, motion happens only in space and not in time which means that also in open timelike curves motion happens only in space and not in time.

An approximate calculation of the spatial characteristics on finite range is required, so one quantitative continuum represents the accumulation of infinite great quantities is artificially divided it into smaller and camparable parts in which calculus operation can be applied .This operation is defined as Theorem 1 in which infinity is not involved, there is a camparable finity is constantly (forever) approaching and not reaching infinity, and only staying within a finite range. Theorem 1 can exist in this paper as a new mathematical basis for physics. Because the essence of all physical quantities is size comparison, and the size comparison relation of matter can only be space/time, so relation formula space/time is the only expression of the concept of matter, all physical quantities are applicable to this expression, each different physical quantity is a multi-dimensional representation of this expression. A new mass energy formula is aslo derived from this paper.

All the arguments of a wavefunction are defined at the same instant implying a notion of simultaneity. In a somewhat related matter, certain phenomena in quantum mechanics seem to have non-local causal relations. Both concepts are in contradiction with special relativity. We propose to define the wavefunction with respect to the invariant proper time of special relativity instead of standard time. Moreover, we shall adopt the original idea of Schrodinger suggesting that the wavefunction represents an ontological cloud-like object that we shall call ‘individual fabric’ that has a finite density amplitude vanishing at infinity. Consequently, measurement can be assimilated to a confining potential that triggers an inherent non-local mechanism within the individual fabric. It is formalised by multiplying the wavefunction with a localising gaussian as in the GRW theory but in a deterministic manner.

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that usually the differences between a candidate series and its neighbour series are analysed instead of directly the candidate series, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e. composite reference series or pairwise comparisons. In composite reference series the inhomogeneities of neighbour series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the ACMANT homogenization method, and tested in large, commonly available monthly temperature test datasets.

The purpose of this study was to explore the association of cognition with hazardous drinking and alcohol related disorder in persons with bipolar disorder (BD). The study population included 1,268 persons from Finland with bipolar disorder. Alcohol use was assessed through hazardous drinking and alcohol related disorder including alcohol use disorder (AUD). Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Alcohol related disorder diagnoses were obtained from the national registrar data. Participants performed two computerized tasks from the Cambridge automated neuropsychological test battery (CANTAB) on tablet computer: the 5-choice serial reaction time task, or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association between RT-test and alcohol use was analyzed with log-linear regression, and eβ with 95% confidence intervals (CI) are reported. PAL first trial memory score was analyzed with linear regression, and β with 95% CI are reported. PAL total errors adjusted was analyzed with logistic regression and odds ratios (OR) with 95% CI are reported. After adjustment for age, education and housing status, hazardous drinking was associated with lower median and less variable RT in females while AUD was associated with a poorer PAL test performance in terms of the total errors adjusted scores in females. Our findings of positive associations between alcohol use and cognition in persons with bipolar disorder are unique.

The concept of space time had been the subject of debate for so long, here another version will be discussed in the form of space and time fields where a new concept of energy constraining can explain the interactions between those fields. This model comes in three parts : energy constraining , where the evolution of the quanton and its different transitions are discussed, the second part , energy fields, their degrees of freedom and the third part electromagnetic waves as relativistic quantons and the generic form of Maxwell equations in terms of space and time fields. This work shows that the origin many of the physical phenomena can be traced back to the quanton based world .

Three different approaches show that, contrary to a longstanding conviction older than 160 years, the advance of Mercury perihelion can be achieved in Newtonian gravity with a very high precision by correctly analysing the situation without neglecting Mercury's mass. General relativity remains more precise than Newtonian physics, but Newtonian framework is more powerful than researchers and astronomers were thinking till now, at least for the case of Mercury. The Newtonian formula of the advance of planets' perihelion breaks down for the other planets. The predicted Newtonian result is indeed too large for Venus and Earth. Therefore, it is also shown that corrections due to gravitational and rotational time dilation, in an intermediate framework which analyzes gravity between Newton and Einstein, solve the problem. By adding such corrections, a result consistent with the one of general relativity is indeed obtained. Thus, the most important results of this paper are two: i) It is not correct that Newtonian theory cannot predict the anomalous rate of precession of the perihelion of planets' orbit. The real problem is instead that a pure Newtonian prediction is too large. ii) Perihelion's precession can be achieved with the same precision of general relativity by extending Newtonian gravity through the inclusion of gravitational and rotational time dilation effects. This second result is in agreement with a couple of recent and interesting papers of Hansen, Hartong and Obers. Differently from such papers, in the present work the importance of rotational time dilation is also highlighted. Finally, it is important to stress that a better understanding of gravitational effects in an intermediate framework between Newtonian theory and general relativity, which is one of the goals of this paper, could, in principle, be crucial for a subsequent better understanding of the famous Dark Matter and Dark Energy problems.

The emergence of the COVID-19 outbreak has caused a pandemic situation in over 210 countries. Controlling the spread of this disease has proven difficult despite several resources employed. Millions of hospitalization and deaths have been observed, and thousands of cases daily with many measures in place. Due to the complex nature of COVID-19, we proposed a system of time-fractional equations to understand the transmission of the disease better. Nonlocality involved in the model has made fractional differential equations appropriate for modeling the behavior. However, solving these types of models is computationally demanding. Our proposed generalized compartmental COVID-19 model incorporates effective contact rate, transition rate (from exposed quarantine and recovered to susceptible and infected quarantined individuals), quarantine rate, disease-induced death rate, natural death rate, natural recovery rate, recovery rate of quarantine infected for a holistic study of the coronavirus disease. A detailed analysis of the proposed model is carried out, including the existence and uniqueness of solutions, local and global stability analysis of the disease-free equilibrium analysis, and sensitivity analysis. Furthermore, numerical solutions of the proposed model are obtained with the generalized Adam-Bashforth-Moulton method developed for the fractional order model. Our analysis and solutions profile show that each of these incorporated parameters is very important in controlling the spread of COVID-19, especially quarantining exposed and infected individuals and the effective contact rate. Based on the results with different fractional order, we observe that there seems to be a third or even fourth wave of the spike in cases of COVID-19, which is what is happening right now in many countries.

The fast and flexible characteristics of badminton determine the necessity of its lunge, and fast lunge will have an important impact on the results of the game in actual combat. In lunge evaluation, the contact time to the ground is a key indicator. This article selects two middle-aged male badminton amateurs with similar body shape and age， but different skill levels as the research objects to carry out comparative experiments. The microseismic equipment is used to record the floor vibration which is caused by the running of badminton amateurs in sports. The microseismic signals of lunge are processed and analyzed by MATLAB software. And we evaluate the pro and cons of various time to touch the ground from amateurs with different skill levels. The research found that 1. Microseismic technology can detect the contact time to the ground. 2. High-level badminton amateurs have obvious advantages in the footwork contact time. Microseismic technology has a certain feasibility as a tool for evaluating the footwork contact time of badminton.

The designs of two large arenas of different epochs are discussed, which are the most famous antique amphitheater the Roman Colosseum (Italy) and the modern Gazprom Arena stadium (St. Petersburg, Russia). 3D-computer models of the arenas have been built and evacuation of people has been simulated in the Sigma FS software (Russia). The arena designs are analyzed in terms of organizing pedestrian evacuation, a comparative analysis is made, and the specific characteristics affecting the evacuation time are established.

The acquisition of adverbs in French as L2 (FSL) by learners with different L1s, at various levels of proficiency, has been extensively studied within various SLA projects (ESF, Interfra etc.). In the generativist tradition, it has been noted that “high category adverbs” such as those related to Mood-evaluative, Mood-epistemic and Tense are present early in FSL data. In the functionalist school, the early presence of time adverbs and scope particles in learner varieties has been analysed in relation with the informational and syntactic structures observed in these varieties and in terms of their contribution to temporal reference and other semantic domains in discourse.

Chilling injury (CI), which causes seed browning in pepper, may arise following long-term cold storage, and is a major cause of postharvest losses. To explore potential strategies of minimizing the associated postharvest losses, the present study investigated the optimal pepper harvest time that could reduce levels of seed browning, in addition to the relationship between fruit maturity and seed browning. Fruit harvested 15 days after flowering (DAF) were sensitive to cold storage at 4°C and exhibited 100% seed browning (CI index, 4.0); in contrast, the seed browning rate of fruit harvested 35 DAF was 10% (CI index, 0.4) within 7 d of cold storage. Seed antioxidant activity was higher in seeds harvested at early stages (15 DAF to 20 DAF) than in seeds harvested at later stages (40 DAF to 50 DAF) at the beginning of storage. Pericarps of fruit harvested at 50 DAF exhibited the highest antioxidant activity. Lipoxygenase, catalase, and peroxidase activity, and the expression levels of cell wall-related genes, pectin methylesterase-like protein, and endo-β-1,4-glucanase were higher in seeds of immature fruit harvested 15 DAF than in seeds of mature fruit harvested 35 DAF. The endosperm separated from the seed coat in fruit harvested 35 DAF and the seeds did not brown under low-temperature storage. The lack of seed browning observed in mature fruit under low-temperature storage could be attributed to physical protection provided by the seed coat rather than cold stress resistance conferred by antioxidants.

Flowering time is a critical stage for crop development as it regulates the ability of plants to adapt to an environment. To understand the genetic control of flowering time, a genome wide association study (GWAS) was conducted to identify the genomic regions associated with the control of this trait in durum wheat (Triticum durum Desf.). A total of 96 landraces and 288 modern lines were evaluated for days to heading, growing degree days, and accumulated day length at flowering across 13 environments spread across Morocco, Lebanon, Mauritania, and Senegal. These environments were grouped into four pheno-environments based on temperatures, day length and other climatic variables. Genotyping with 35K Axiom array generated 7,652 polymorphic SNPs in addition to 3 KASP markers associated to known flowering genes. In total, 34 significant QTLs were identified in both landraces and modern lines. Some QTLs had strong association with already known regulatory photoperiod genes, Ppd-A and Ppd-B and vernalization genes Vrn-A1, and Vrn3. However, these loci explained only 5 to 20% of variance for days to heading. Seven QTLs overlapped between the two germplasm groups in which Q.ICD.Eps-03 and Q.ICD.Vrn-17 consistently affected flowering time in all the pheno-environments, while Q.ICD.Eps-11 and Q.ICD.Ppd-12 were significant only in two pheno-environments and the combined analysis across all environments. These results help clarify the genetic mechanism controlling flowering time in durum wheat and show some clear distinctions to what is known for common wheat (Triticum aestivum L.)

A delayed crosslinked polymer gel was developed for indepth water control in mature oilfields. The thermal gelation behavior of nonionic polyacrylamide (NPAM) and PEI was investigated, and sodium citrate (NaCit) was selected as a new retarder to prolong the gelation time. The gelation performance of NPAM/PEI gel system can be adjusted by the variation of NPAM or PEI concentration, and a quadratic model was developed by statistical analysis, which predict the gelation time of NPAM/PEI gel system. The obtained model showed high significance and good reliability as suggested by the F-ratio of 175.16 and high adjusted R-square value (0.9732). The decrease of the initial pH value of gelling solution leads to the weaker gel viscosity and longer gelation time due to the protonation of amine groups on the PEI chains. Increasing temperature resulted in higher gel viscosity but shorter gelation time. The addition of NaCit showed a good delayed gelation effect on the NPAM/PEI gel system, and the gel system in the presence of NaCit exhibited a good compatibility with injected and formation water. A dense three-dimensional structure was observed in matured NPAM/PEI/NaCit gel, and it could keep stable below 150℃. The gel system could effectively reduce the permeability (>95%) and restricted the flow of water after matured in natural cores.

The vertebrate anterior-posterior (A-P = craniocaudal) axis is evidently made by a timing mechanism. Evidence has accumulated that tentatively identifies the A-P timer as being or involving Hox temporal collinearity. Here, I focus on the two current competing models based on this premise. Common features and points of dissent are examined and a common model is distilled from what remains. This is an attempt to make sense of the literature.

In this study, based on our previous study, we examined the mathematical properties, especially the stability of the equilibrium for our proposed mathematical model. By means of the results of the stability in this study, we also used actual data representing transient booms and resurgent booms, and conducted parameter estimation for our proposed model using Bayesian inference. In addition, we conducted a model fitting to five actual data. By this study, we reconfirmed that we can express the resurgences or minute vibrations of actual data by means of our proposed model.

Polo is an equestrian team sport consisting of four players per team, with level of play determined by cumulative player handicap (-2 to + 10 goals), with a higher handicap denoting a better player. There is minimal literature investigating Polo players’ physical attributes, hence the understanding of the physical characteristics that may contribute to an improved handicap are unknown. This study sought to identify the relationship between pertinent strength measures (left and right hand grip strength; absolute and relative isometric mid-thigh pull) and reaction time in Polo handicap in 19 New Zealand Polo players, and ascertain whether handicap could be predicted by these measures. Correlation coefficients were expressed using R values, accompanying descriptors and 90% confidence intervals (C.I.). Variance explained was expressed via the R2 statistic, and statistical significance set at p < 0.05. Right hand grip strength, isometric mid-thigh pull values were found to significantly correlate to and explain variance within Polo player handicap (all moderate to large correlations; p< 0.05). Whereas left hand grip strength (R: 0.380; 90% C.I. -0.011 to 0.670) and reaction time (0.020; -0.372 to 0.406) were non-significant, moderate and trivial correlates and predictors of handicap respectively. Practically, these findings highlight the differing roles between rein and mallet hands of Polo players and emphasise the importance of a strong and stable platform when riding and striking the ball. Lack of association with reaction time may be explained in part by higher handicapped Polo players employing a more proactive approach to the game.

This study compared the performance of five Family Generalized Auto-Regressive Conditional Heteroscedastic (fGARCH) models (sGARCH, gjrGARCH, iGARCH, TGARCH and NGARCH) in the presence of high positive autocorrelation. To achieve this, financial time series was simulated with autocorrelated coefficients as ρ = (0.8, 0.85, 0.9, 0.95, 0.99), at different time series lengths (as 250, 500, 750, 1000, 1250, 1500) and each trial was repeated 1000 times carried out in R environment using rugarch package. And the performance of the preferred model was judged using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results from the simulation revealed that these GARCH models performances varies with the different autocorrelation values and at different time series lengths. But in the overall, NGARCH model dominates with 62.5% and 59.3% using RMSE and MAE respectively. We therefore recommended that investors, financial analysts and researchers interested in stock prices and asset return should adapt NGARCH model when there is high positive autocorrelation in the financial time series data.

In this paper, we study the usage of machine learning models for sales time series forecasting. The effect of machine learning generalization has been considered. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking technics, we can improve the performance of predictive models for sales time series forecasting.

A university canteen is a queueing system characterised by non-stationary time of arrival with limited resources where the arrival rate is time dependent and has different pattern of arrival for different time interval. This means at certain time of the day, the arrival rate is much higher than other time and for a university canteen, the arrival rate of customer during the lunchtime is much higher and the food (resources) is limited. Non-stationary time dependent queueing system is not easily modelled mathematically hence such queueing systems are modelled using simulation tools such as ARENA. In order to model a non-stationary time dependent queueing system with limited resources and solve queueing problems using ARENA, researchers have to depend on their knowledge and experience in identifying the appropriate and relevant parameters for the system and make modifications to these parameters of the system to solve queueing problems by means of trial and error. Hence, this research work explores the potentials of applying a systematic problem solving tool, TRIZ to help users to make better decisions in deriving solutions to improve a non-stationary time dependent queueing system with limited resources. A case study was carried out to minimize the waiting time of the customers at the cafeteria of the Faculty of Engineering, Universiti Putra Malaysia (UPM), which has queueing problems for years during lunchtime. TRIZ was applied in this case study and the results showed that TRIZ can assist researchers to derive a solution model that lead to shorter waiting time without incurring additional cost and resources.

The morphology and the activity of a submarine caldera, Avyssos, at the northern part of Nisyros volcano in the South Aegean Sea (Greece), has been studied by means of remotely operated underwater vehicle dives. The recorded time series of temperature and conductivity over the submarine volcano have been analyzed in terms of the Generalized Moments Method. The findings of the mathematical analysis shed light on the volcanic activity, but also on the morphology (shape) of the submarine volcano. The conductivity time series indicates the volcano is at rest in agreement with other types of observations. On the other hand, temperature fluctuations, which in general describe a multifractal process, show that the submarine caldera operates as an open system that interacts with its surroundings. This type of analysis can be used as an indicator for the state of activity and the morphological structure (closed or open system) of a submarine volcano.

In recent years, governments applied intelligent transportation system (ITS) technique to provide several convenience services (e.g., garbage truck app) for residents. This study proposes a garbage truck fleet management system (GTFMS) and data feature selection and data clustering methods for travel time prediction. A GTFMS includes mobile devices (MD), on-board units, fleet management server, and data analysis server (DAS). When user uses MD to request the arrival time of garbage truck, DAS can perform the procedure of data feature selection and data clustering methods to analyses travel time of garbage truck. The proposed methods can cluster the records of travel time and reduce variation for the improvement of travel time prediction. After predicting travel time and arrival time, the predicted information can be sent to user’s MD. In experimental environment, the results showed that the accuracies of previous method and proposed method are 16.73% and 85.97%, respectively. Therefore, the proposed data feature selection and data clustering methods can be used to predict stop-to-stop travel time of garbage truck.

The second law of thermodynamics states the increase of entropy, ΔS > 0, for real processes from state A to state B at constant energy from chemistry over biological life and engines to cosmic events. The connection of entropy to information, phase-space and heat is helpful, but does not immediately convince observers of the validity and basis of the second law. This gave grounds for finding a rigorous, but more easily acceptable reformulation. Here we show using statistical mechanics that this principle is equivalent to a force law ⟨⟨f⟩⟩> 0 in systems where mass centres and forces can be identified. The sign of this net force - the average mean force along a path from A to B - determines the direction of the process. The force law applies to a wide range of processes from machines to chemical reactions. The explanation of irreversibility by a driving force appears more plausible than the traditional formulation as it emphasizes the cause instead of the effect of motions.

This paper provides a comparative study between microwave tomography and synthetic time-reversal imaging techniques as applied to ground penetrating radar (GPR) surveys. The comparison is carried out by processing experimental data collected at a controlled test site, with various types of buried targets at given subsurface depths and representative soil conditions. It is shown that the two techniques allow us to obtain complementary information about position, depth and size of the targets from a single GPR survey.

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of a an assigned output. Like first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the following outputs: mean temperature, max temperature and min temperature, all considered daily. Like second example, we use the daily mean temperature measured at the weather station “Chiavari Caperana” in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, ARIMA and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples.

The focus of research has mainly been on the life cycle approach to project development, while limited attention has been given to the ageing construction companies that make all these activities/processes happen. This paper examines the organisational factors determining the sustainability and growth of building and civil engineering contractors in South Africa. The paper explores whether there are specific organisational factors that aid the sustainable development and growth of contractors in the South African construction industry. The paper analyses data gathered from face-to-face interviews conducted with four established and twelve upgraded and successful building and civil engineering contractors listed on the Construction Industry Development Board’s (cidb) Register of Contractors. The aim of the study is to identify the organisational factors critical for sustainable contractor development and growth in South Africa. The study found that organizational factors responsible for the sustainable development and growth of construction contractors include the size of the founding team members, management capabilities, strategic decisions made by founders/leaders and the ability of the company to constantly evolve, adapt and respond effectively to threats/challenges whenever they surface, amongst other findings. Capacity to generalise the results of the study to the large cidb registered contractor group is limited by the smallness of the sample size. Future research should make use of a larger sample size to obtain more general findings. The results of the study imply that the founding team size, experience, entrepreneurial and managerial capabilities, capacities and organisational structures that enable the efficient response of companies to external and internal challenges are key components of the definable arrangements that will support the sustainability and growth of construction organisations. The paper is of value to government departments and agencies such as the cidb and contracting firms in the construction industry.

In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.

Air pollution has created a lot of problems in the developed and developing countries. To avoid or reduce these problems, constant monitoring of the air should be ensured. The conventional techniques is costly because it requires a lot of money and time consuming. Biomonitoring has been the alternative method. Moss, lichens and plants are biomonitors available to entrap air pollutants. The aim of this paper is to discuss one of the ways of monitoring air pollution – Moss bag technique. To do this, types, choice, preparation, handling of bags after preparation of moss were discussed. From the literatures consulted, it was discovered that there were differences in the techniques used by the researchers. In all, the use of mosses with emphasis on the employment of moss bag have proved to be a powerful tool in airborne particulate and toxic elements. To conclude, developing countries should focus more on this technique because it will reduce cost of air monitoring.

This study explores the application of multi-real option valuation integrated with Security Token Offerings (STO) as an innovative approach to real estate project financing, using the Aspen Resort development as a case study. Traditional Discounted Cash Flow (DCF) methods often fall short in capturing the dynamic nature of long-term real estate projects, particularly in adapting to market uncertainties and changes. This research addresses these limitations by employing a multi-real option model, which allows for flexibility in decision-making by evaluating various investment phases through the binomial lattice framework. The analysis demonstrates that the STO-based project financing (STO-PF) model, when applied to the Aspen Resort, provides superior financial outcomes and strategic advantages compared to conventional DCF methods. Key findings reveal that the STO-PF model enhances liquidity, democratizes investment access, and offers improved risk management through digital platforms. The results showcase how integrating STOs with real option models quantifies the project’s expanded net present value (ENPV), thereby optimizing investment decisions under volatile market conditions. This study not only contributes theoretically to the literature on STO financing but also provides practical implications for developers seeking flexible and innovative financing solutions in real estate. Future research should explore the applicability of STOs across diverse market conditions and regulatory environments, including the integration of emerging technologies such as AI and blockchain for enhanced financial modeling.

Increasingly large numbers of people are using digital weight-loss services (DWLSs) to treat overweight or obesity. Although it is widely agreed that digital modalities improve access to care in general, obesity stakeholders remain concerned that many DWLSs are not comprehensive or sustainable enough to deliver meaningful health outcomes. This study adopted a mixed-methods approach to assess why and after how long patients tend to discontinue Austral-ia’s largest DWLS, a program that combines behavioural and pharmacological therapy under the guidance of a multidisciplinary care team. We found that in a cohort of patients who commenced the Eucalyptus DWLS between January and June 2022 (n=5604), mean program adherence was 171.2 (±158.2). Inadequate supply of a patient’s desired Glucose-like peptide-1 receptor agonist medication was the most common reason for discontinuation (43.7%), followed by program cost (26.2%), result dissatisfaction (9.9%), and service dissatisfaction (7.2%). Statistical tests revealed that ethnicity and age both had a significant effect on patient adherence. These findings suggest that DWLSs have the potential to improve access to comprehensive, continuous obesity care, but that care models need to improve upon the one observed in the Eucalyptus Australia DWLS to maximise program adherence.

(1) Background: Most patients with mycosis fungoides (MF), a form of cutaneous T-cell lym-phoma (CTCL), develop relapsed/refractory (R/R) disease following front-line systemic therapy. This report describes treatment patterns and outcomes from the subpopulation with R/R MF. (2) Methods: This observational, retrospective, cohort study analysed patient records (1984–2016) from 27 clinical sites in Eurpe. Outcomes included treatments received, response to first-, second- and third-line treatment, overall survival (OS), and progression-free survival (PFS). (3) Results: Of 104 patients with MF, 100 received second-line and 61 received third-line therapy. Median (range) times from start of first-line therapy to first R/R MF and from first to second R/R MF were 11.2 (0.3–166.5) and 13.5 (0.0–174.6) months, respectively. Second-and third-line treatment options varied and comprised systemic therapies (85% and 79% of patients, respectively), radio-therapy (32% and 34%, respectively) and topical therapies (48% and 36%, respectively). Median (95% confidence interval [CI]) OS from the diagnosis of first R/R MF was 11.5 (6.5–not reached [NR]) years and was higher with non-chemotherapy (NR) versus chemotherapy (6.5 years); es-timated median PFS (95% CI) from the time of first R/R MF was 1.3 (1.0–2.1) years. (4) Conclu-sion: High rates of R/R disease were observed after second- and third-line treatments in this re-al-world cohort, with longer median OS in patients receiving non-chemotherapy versus chemo-therapy. Following the standard management of MF and using recently approved targeted therapies can help improve patient outcomes in advanced-stage MF.

International trade has been one of the most significant economic activities among countries, and its contributions towards a country’s economic growth and sustainable development have been acknowledged. In recent times, international trade and global trade networks have received preeminent attention due to the sharp growth in trade volumes and its contributions to poverty reduction and general welfare. Despite the growth in global trade volumes, there are worrying concerns about the unprecedented changes in international trade patterns and growing imbalances in trade in recent times. Whereas China’s trade volumes have more than tripled over the last decades and have maintained the largest trade surplus spot ahead of Germany, other major trade surplus countries such as Japan, the USA, etc. have become trade deficit countries with their trade volumes increasing at a much slower pace. In view of the growing imbalances in global trade volumes, we use an augmented gravity model of trade that incorporates social network measurement, the real effective exchange rate which is a measurement of international competitiveness, as well as the Linder theory of trade, to investigate the factors that determine the export performance of countries. Using data from 51 countries for 41 years, our results indicate that the GDP of both the home country and the partner country affects the home country’s exports positively. Similarly, the real effective exchange rate REER, trade openness OPEN and dummy variables BORDER, ENGLISH, and EU have produced positive and statistically significant coefficient estimates, and these are in line with our theoretical expectations.

Physics is imagined in the human brain which was formed in Darwin's Evolution by responding to certain threats for the survival of our evolutionary ancestors. The struggle to survive prevented us from wasting resources by paying attention to parts of reality that had no survival impact. These parts are of unknown scope and dimension. Can we negotiate this Darwin's barrier? As to reality within our reach, we suffer from imbalance between information absorption capacity and information analysis capacity which resulted in ignoring the overflow of information to match human capacity to make sense of it. Also James Clerk Maxwell suggested that reality has small scale phenomena beyond our human reach. Since we have no brain other than what Darwin equipped us with, this barrier looked unsurmountable. But lo and behold: much as we have built vehicles to carry us further than our biological legs, so we now build artificial intelligence machines that carry us beyond our Darwinist barrier. Physics may transform to building AI capability to ride on. This capability may require a more universal geometry and material representation to fit AI inference engines. Suggesting Depth Unbound Reality Architecture (DURA). AI connects raw data to its conclusion potential, by-passing a storied theory of nature. The objective of physics ahead may be to build theory-replacement AI machines.

Reliability analysis techniques are customary standard tools that are used for evaluating the performance of different equipment and devices in order to minimize their downtime. To predict the reliability, life data from a sample that is satisfactorily representative of the equipment should be fitted to the suitable statistical distribution. The parameterized distribution may be used to estimate essential characteristics such as failure rate; and probability at a precise time, as well as system reliability. In the current study, Weibull++/ALTA software package is used as a novel tool to fit the available data set to estimate the best fitted probability density function (PDF) using Maximum Likelihood (MLE) for parameter estimation. The determined distributions are then assessed using goodness-of-fit test to define how well it fits the available data set. There are multiple methods for determining goodness-of-fit. Weibull distributions and their special cases’ parameters have an effect on life times.

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

An expansion method based on time fractional Sine-Gordon equation is implemented to construct some real and complex valued exact solutions to the Korteweg-de Vries and modified Korteweg-de Vries equation in time fractional forms. Compatible fractional traveling wave transform plays a key role to be able to apply homogeneous balance technique to set the predicted solution. The relation between trigonometric and hyperbolic functions based on fractional Sine-Gordon equation allows to form the exact solutions with multiplication of powers of hyperbolic functions.

The three dimensional conformable time fractional Kadomtsev-Petviashvili and the conformable time fractional modified Kawahara equations are solved by implementing the Kudryashov's procedure. The corresponding wave transformation reduces both equations to some ODEs. Balancing the nonlinear and the highest order derivative terms gives the structure of the solutions in the finite series form. The useful symbolic tools are used to solve the resultant algebraic systems. The solutions are expressed in explicit forms.

This study introduces a cascade nonlinear proportional integral retarded (CNPIR) controller for the first time in the literature, aiming to achieve real-time precise control of a double-tank liquid level system. It begins by outlining the characteristics of de-lay-based systems and detailing the dynamic behavior of the liquid-level system. The study evaluates the system's performance by developing and analyzing the mathe-matical models of both the feedforward proportional-integral (FF-PI) controller and the CNPIR controller. Two different control techniques were employed, with real-time tests conducted to assess the system's axis movement performance. The FF-PI controller was used as the initial control technique, while the CNPIR controller was introduced to enhance system performance during trajectory tracking. Experimental results demon-strated that the CNPIR method significantly improved trajectory tracking response, reduced overshoot, and achieved better steady-state error under external disturbances compared to the FF-PI controller. Thus, the experimental study, highlights the effec-tiveness of the proposed control design and its superiority over some existing classical methods.

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

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

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

A common fault in turbomachinery is rotor--casing rub. Shaft vibration, measured with proximity probes, is the most powerful indicator of rotor-stator rub. However, in machines such as aeroderivative turbines, with increasing industrial relevance in power generation, constructive reasons prevent the use of those sensors, being only acceleration signals at selected casing locations available. This implies several shortcomings in the characterization of the machinery condition, associated with a lower information content about the machine dynamics. In this work we evaluate the performance of the Continuous Wavelet Transform to isolate the accelerometer signal features that characterize rotor-casing rub in an aeroderivative turbine. The evaluation is carried out on a novel rotor model of a rotor flexible casing system. Due to damped transients and other short-lived features that rub induces in the signals, the Continuous Wavelet Transform proves being more effective than both Fourier and Cepstrum Analysis. This creates the chance for enabling early fault diagnosis of rub before it may cause machine shutdown or damage.

Stock price prediction is challenging due to global economic instability, high volatility and complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examine the influence of sentiment analysis indicator in predicting stock price. Our results are in two-fold. Firstly, we present a lexicon-based sentiment analysis approach for identifying sentiment features and thus, evidence the correlation between the sentiment indicator and stock price movement. Secondly, we propose the use of GRUVader, an optimal gated recurrent unit networks for stock market prediction. Our findings suggest stand-alone models struggle when compared with AI-enhanced models. Thus, our paper made further recommendations on latter systems.

In underwater clustering and benchmark networks, nodes need to reduce the rate and energy consumption of acoustic communication while ensuring synchronization accuracy. In large-scale networks, the improvement of the efficiency of existing network time synchronization often relies on optimization of topological structures, and the improvement in efficiency within local areas is limited. This paper proposes a method to synchronize underwater time using the probability graph model. The method utilizes the positional and motion status information of sensor networks to construct a factor graph model for distributed network synchronization. By simplifying the marginal probability density function of the system clock difference, it can quickly calculate the clock difference parameters of nodes, thereby effectively improving the synchronization efficiency. Experimental results show that the method can complete global time synchronization within a cycle while achieving a clock difference correction accuracy higher than seconds, significantly optimizing the synchronization period and efficiency, and reducing the energy consumption of acoustic communication.

Background/objective: Weight regain after bariatric surgery has serious health consequences, and disordered eating behaviors (DEB) may be involved in it. We compared DEB symptoms after bariatric surgery between patients with low vs. high ratio of weight regain (RWR) and investigated associations between DEB symptoms with weight regain and time since surgery. Meth-od: Ninety-four patients who had undergone laparoscopic Roux-en-Y gastric bypass or sleeve gastrectomy and had not attended follow-up with the multidisciplinary healthcare team for at least one year were recruited. RWR was calculated with respect to maximal weight loss by the nadir weight achieved after surgery. They were allocated into two groups according to the RWR, defined as high RWR (≥20%) or low RWR (

The human brain, a highly complex dynamical system, exhibits various states of consciousness—such as wakefulness, sleep, and altered states—each characterized by distinct patterns of neural activity. To capture the dynamical properties of these states, a range of complexity measures is utilized, with a primary focus on Statistical Complexity (SC) and Lempel-Ziv complexity (LZc), and supplemented by Approximate Entropy (ApEn) and Kolmogorov Complexity (KC). These measures are applied to both simulated data, generated through logistic maps and Multivariate Autoregressive (MVAR) models, and intracranial depth electrode recordings from patients. The results demonstrate that these complexity measures effectively capture intricate dynamics of the brain across different states. Specifically, SC captures the structural complexity and information processing within the system, reflecting organized and predictive neural behavior by accounting for temporal correlations in the data. In contrast, LZc is more sensitive to randomness, measuring the diversity and unpredictability of patterns within the data. This distinction allows SC to serve as a more reliable indicator of organized information processing, while LZc highlights the variability in neural signals. Notably, the study reveals that states of higher consciousness are associated with greater complexity, supporting the entropic brain hypothesis. This research contributes to the ongoing efforts to quantify consciousness through mathematical frameworks and offers insights into the neural correlates of different states of awareness.

Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels. As a part of the sustainable planning and effective management of water resources, it is crucial to assess the existing as well as future groundwater level (GWL) condition. In the current study, an attempt was made to model and forecast GWL using artificial neural networks (ANN) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA incorporating exogenous variables (ARIMAX) were adopted as the time series models. Kushtia district in Bangladesh was selected as the case study area, and GWL data of five monitoring wells in the study are used to demonstrate the modeling exercise. Rainfall was taken as the exogeneous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. The performance of each time series and ANN model was assessed based on various model evaluation criteria. It was evident from the results that the multivariate ARIMAX model (SSE of 15.361) performed better than the univariate ARIMA model with an SSE of 17.217 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the time series and ANN models, it was found that the ANN-based model outperformed the time series models with the enhanced forecasting accuracy (SSE of 9.894). Results also exhibit a significant correlation coefficient value R of 0.995 (ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area. Thus, the ANN approach was not only carried out for model building and simulation but also to provide a valuable tool for managing water resources amidst changing environmental conditions.

Procrastination among students in higher education is a widely recognized problem, with significant effects on academic performance and personal well-being. This descriptive study aims to assess the level of procrastination among nursing students in Bengaluru and determine the association between procrastination and selected demographic variables, such as age, gender, and parental education. A total of 100 nursing students from Sneha College of Nursing participated in the study. Data were collected using a structured questionnaire and a 16-item procrastination scale. The results revealed that 55% of the students had low levels of procrastination, while 45% exhibited high levels. However, no significant association was found between procrastination and demographic variables like age, gender, religion, year of study, or parental education. The findings suggest that while procrastination is a common issue among nursing students, demographic variables may not be reliable predictors of procrastination behavior. These results call for interventions to improve time management and reduce procrastination among nursing students to enhance their academic outcomes.

Rift Valley Fever (RVF), is a viral zoonotic disease predominant in East Africa and transmitted by Aedes mosquitoes carrying the virus. Using the systematic literature review approach, the present study evaluated machine learning techniques and time series approaches to find literature on the impact of climatic changes on RVF outbreaks published between 1930 and 2024. The literature search involved databases including PubMed, PLOS ONE, JSTOR, Web of Science, Google Scholar, and SCOPUS (Kenmoe et al., 2023).The results show that most of the articles were published between 2018 and 2022, and most of the articles were from United States, France, and Kenya. We conducted a detailed review of the articles using the PRISMA 2020 flow chart, screening and qualifying 10,015 articles. Some articles revealed significant gaps in both internal and external validation. Therefore, future research should focus on developing multi-disciplinary models that incorporate climatic condition, geographical, biological, and social factors.

Faba bean is grown globally for human consumption and animal feed and is a significant rotation crop in cereal dominant areas in many countries including Australia. It is sensitive to several abiotic stresses, such as drought, frost and high temperatures, especially at the reproductive stage. However, little is known of the upper and lower temperature limits for successful podding. Experiments were conducted over two years at Breeza and Narrabri in north-western New South Wales to assess the impact of temperature on flowering and pod set of three genotypes; Doza, Cairo and IX1148f under field conditions. Flowering nodes were tagged from designated plants in the field and scored for podding, field temperatures were recorded. Optimum maximum temperature for podset ranged from 23.7-26.1 °C and the equivalent minimum temperature was 3.2-3.8 °C. Optimum average daily temperature for podset ranged from 12.7-13.9 °C. The strongest correlations between temperature and podding were observed at minimum temperatures, indicating that low temperatures are more important than high temperatures in determining yield in faba bean.

Mediterranean diet has been widely suggested to exert significant beneficial effects on endothelial oxida-tive status and cardiometabolic health. Greek Orthodox monasteries, due to their specific nutritionaland sartorial habits, comprise a population which strictly adheres to nutritional patterns with restricted eating and a plant-based subset of the Mediter-ranean diet, often accompanied by profound hypovitaminosis D. Time –restricted eating is also adopted from a large part of general lay Greek population for health promoting reasons, without restrictions on an-imal product consumption, as imposed by Orthodox religious fasting. However, comparative effects of these nutritional patterns on oxidative stress markers remain scarce. The present study attempted to evaluate the effects of Christian Orthodox fasting (COF) in a group of vitamin D –deficient and overweight Orthodox nuns from Central and Northern Greece, compared to the implementation of TRE, 16:8 dietary regimen in a cohort of adult women from the general population from the same region, with regard to markers of endothelial oxidative status. A group of 50 women from two Orthodox monasteries in Northern Greece and one group of 50 healthy lay women were included. During enrollment a detailed recording of dietary habits was performed, along with a scientific registry of demographic and anthropometric characteristics (via bioimped-ance).Orthodox nuns followed a typical Orthodox fasting regimen[daily feeding window (8 am–4 pm)] whereas lay women followed a TRE 16:8 regimen with the same feeding time-window with recommenda-tion to follow a low-fat diet, without characteristics of the Mediterranean diet. We included a complete biochemical analysis, as well as calciotropic profiles [Calcium-Ca, Albumin, Parathyroid hormone-PTH, 25-hydroxyvitamin D- 25(OH)D] as well as and markers of TAC (trichloroacetic acid), (glutathione) GSH and thiobarbituric acid reactive substances (TBARS) concentrations, as markers of oxidative status. All groups were comparable at baseline for calcium, PTH and 25(OH)D concentrations, with no significant differences between groups. Orthodox nuns manifested a lower median GSH compared to con-trols (6.0 vs. 7.2, p 0.04) and a higher median TAC (0.92 vs. 0.77, p <0.001). TBARS comparisons showed no significant difference between the two groups. No significant associations of oxidative status with 25(OH)D, PTH and markers of glucose homeostasis were evident. Results of this small pilot study indicate that both dietary regimens have advantages over oxidative markers compared to each other, with increased TAC in the groups of Orthodox Nuns after a 16th week period of COF compared to a 16:8 TRE and increased GSH concentrations in the lay women group. Future randomized trials are required to investigate superiority or non-inferiority between these dietary patterns, in the daily clinical setting.

In the Euclidean form of the theory of gravity, where there is no dedicated time parameter, a generalized canonical form of the principle of least action is proposed. On its basis, the quantum principle of least action is formulated, in which the “dynamics” of the universe in the Origin is described by the eigenvector of the action operator - the wave functional on the space of 4D Riemannian geometries and configurations of matter fields in some compact region of 4D space. The corresponding eigenvalue of the action operator determines the initial state for the world history of the universe outside this region, where the metric signature is Lorentzian and thus the time parameter exists. The boundary of the Origin region is determined by the condition that the rate of change of the determinant of the 3D metric tensor is zero on it. A modification of quantum theory is proposed that eliminates the uncertainty of the sign of the Hilbert-Einstein action in the Euclidean region of the Origin. On this basis, we formulate a minimum principle for the eigenvalue of the action operator, similar to the principle of minimum energy for the ground state in quantum mechanics. It has been suggested that in the initial state the universe contains a certain distribution of its own mass, which is not directly related to the fields of matter.

In contemporary society, digital technology permeates every aspect of our routines. This study assessed students' comprehension of the environmental impacts of prolonged screen time, specifically during binge-watching sessions. It investigated awareness of carbon emissions from electronic device usage, binge-watching habits, and influencing factors such as content preferences, duration, and frequency. The study also explored potential interventions to mitigate environmental impacts. Using a mixed-methods approach, data were collected from 525 higher education students (150 male, 375 female) through self-designed standardized questionnaires. The analysis involved Percentage, t-test, ANOVA, Multiple Correlation, and coding processes. Results indicated that 66.67% of students had a high level of understanding regarding the environmental impact of binge-watching, while only 4.76% were highly aware of the carbon emissions from extended screen time. Additionally, 23.81% exhibited high levels of binge-watching habits and behavior. Gender disparities were significant; females demonstrated a deeper understanding of the environmental impacts (t=4.08, p < α) and showed different binge-watching habits compared to males (t=3.16, p < α). However, no significant gender difference was found in awareness of carbon emissions (t=0.28, p > α). A notable difference (t=12.98, p < α) was observed between students' understanding of environmental impacts and their awareness of carbon emissions. The study also revealed a weak positive correlation (R=0.36) and significant differences [F(2, 1572) = 320.10, p < 0.05, 0.01] in understanding the environmental impacts, awareness of carbon emissions, and binge-watching behaviors. Factors influencing binge-watching and potential regulatory measures were also identified.

With LiDAR (Light Detection and Ranging) time series being used for various applications, optimal realisation of a common geodetic datum over many epochs is a highly important prerequisite with direct impact on accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches ranging from pragmatic to more rigorous solutions are applied to an 8 year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, we obtain relative datum discrepancies in the range of 1-2 cm for the complete time series. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method.

This research paper aims to evaluate and model the performance of rice husk gasifier cook stoves for household energy use, a byproduct of rice milling. The study aims to address environmental issues and promote the use of rice husk as a renewable energy source and pest repellent in stoves. The study compared insulated and non-insulated gasifier stoves using rice husk gasification. The study compared the start-up time of insulated and non-insulated gasifier stoves, where the insulated stove showed a lower start-up time, but there was no significant difference at a 5% probability level. The temperature change also was not significant, as heat transfer and temperature change started when the husk was ignited. The burner stoves had different size and volume. The maximum operated or flame temperature was 1,190.30oC at room or minimum temperature of 27.33 oC, whereas the average temperature was 758.916oC for the non-insulated type of gasifier stove. The insulated gasifier stove also observed maximum and average temperatures of 518.30 oC and 289.014oC respectively at the same room temperature. The operating time of stoves directly influences the fuel consumption rate, specific gasification rate, and combustion zone rate of stoves. The fuel consumption rate has a significant effect on the type of gasifier stove, with the maximum rates being 3.820 and 2.099 kg/hr. The time to consume rice husks to gasify them depends on the density of the rice husk, reactor volume, and fuel consumption rate. The average specific gasification rate for insulated and non-insulated stoves is 106.45 and 120.63 kg/m2h, respectively. The fuel or rice husk holding capacity of each type of stove is significant at a 5% level, with the amount of rice husk and biochar produced being 1.18kg and 0.27kg for insulated stoves, and 2.511kg and 0.688kg for non-insulated stoves, respectively. The performance of gasifier stoves varies based on the bio char conversion rate, with non-insulated stoves producing a higher bio char mass per rice husk. Multiple regression analysis was used to model the relationship between time elapse with temperature and heat energy. The Squirrel-2020 data logger was used to measure the temperature produced by burning rice husks in the reactor of the stove as well as related generated and losses of heat. A relationship between the heat energy produced and the operating time of gasifier stoves was conducted to estimate the model. The cubic polynomial model was found to be the best predictive model fit for all variables of this study.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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