The disc brake rotors are designed to withstand both, Maximum possible deceleration (emergency braking) and a series of frequent braking cycles. A effective rotor design and superior heat dissipating material provide better performance during the braking mechanism. In this experiment, modified ventilated disk brake rotors are developed with holes and slots and the stress, deformation, heat flux and temperature distribution has been analysed. Designing of the Finite element models of the rotor are created with SolidWorks and simulated using ANSYS. Structural and thermal characteristics are compared with a reference disk brake rotor of the motorcycle (TVS Apache RTR180). It is found that the modified rotors outperform the conventional one in terms of stress generation, temperature distribution and deformation. Furthermore, this Analysis helps us to find out the best suited material for one of the proposed designs. This experiment provides us an insight of the structural and thermal characteristics of the geometrically modified rotor that can be used to upgrade and outperform the current disc brake rotor used in the motorcycle (TVS Apache RTR180).

Temperature is a critical factor that influences the proliferation of pathogens in hosts. The impact of temperature on pathogens is commonly explored in controlled and constant temperatures. Experiments under varying environmental temperature are becoming more frequent, however, testing every temperature scenario à la carte is unachievable. One example of this is the human pathogen Vibrio parahaemolyticus (Vp) in oysters. Here, a predictive model was developed for predicting the growth of Vp in oysters under varying ambient temperature. The model was fitted and evaluated against data from experiments studying growth and inactivation of Vp in oysters at eight constant temperatures. Once evaluated, Vp dynamics in oysters were estimated at different post-harvest varying temperature scenarios affected by water and air temperature, and different ice treatment timing. The model performed adequately under varying temperature, reflecting that (i) increasing temperature, particularly in hot summers, favors a rapid Vp growth in oysters, resulting in a very high risk of gastroenteritis in humans after consumption of a serving of raw oysters, (ii) pathogen inactivation due to day/night oscillations, and more evidently, due to ice treatments, (iii) ice treatment is much more effective limiting risk of illness when applied immediately onboard compared to dockside. The model results to be a promising tool for improving the understanding of the Vp-oyster system and support studies on public health impact of pathogenic Vp associated with raw oyster consumption. Although robust validation of the model predictions is needed, initial results and evaluation show the potential of the model to be easily modified to match similar systems where the temperature is a critical factor shaping the proliferation of pathogens in hosts.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

In CCS projects, the transportation of CO₂ by ship can be an attractive alternative to transportation using a pipeline, particularly when the distance between source and disposal location is large. However, the energy consumption of the liquefaction process can be significant, making the selection of an energy-efficient design an important factor in the minimization of operating costs. Since the liquefaction process operates at low temperature, its energy consumption will vary with ambient temperature, which could be a factor that influences the trade-off point between pipelines and shipping in different geographic locations. A consistent set of data showing the relationship between energy consumption and cooling temperature is therefore potentially useful to CCS system modelling. This study compares the performance of a wide range of CO₂ liquefaction schemes. It applies a methodical approach to the optimization of process operating parameters and studies performance across a range of operating temperatures. A set of data for the minimum energy consumption cases is presented. The main findings are that open-cycle CO₂ processes often offer minimum energy consumption; NH₃ based schemes often offer better performance at higher ambient temperatures; and that for the cooling temperature range 15 to 50 °C, the energy consumption for the best performing liquefaction process rises by around 40%.

In this article, we investigate both the Schrödinger and continuity equations in the presence of nonlocal potential that arising from electron-electron interaction and local one in the context of minimal uncertainty in momentum within commutative and noncommutative frameworks. Furthermore, the Frahn–Lemmer potential type is used. Interestingly, the combined effects of both the phase-space noncommutativity and nonlocality on the current density while considering a minimal uncertainty in momentum are examined. We find that the current density, as conventionally defined, does not fulfil the condition of current conservation, thus, a new definition of the current density that encompasses the aforementioned contributions is given. Thereafter, it is shown that the computed current using the new definition of the current density satisfies the current conservation. Noting that using both the $★$product and linear Bopp-Shift, the noncommutativity is inserted.

The dispersal potential of crown-of-thorns starfish (CoTS) larvae is important in understanding both the initiation and spread of population outbreaks, and is fundamentally dependent upon how long larvae can persist while still retaining the capacity to settle. This study quantified variation in larval survivorship and settlement rates for CoTS maintained at three different chlorophyll concentrations (0.1, 1.0 or 10.0 µg.L^-1), achieved by varying densities of single-celled flagellate phytoplankton, Proteomonas sulcata. Based on the larval starvation hypothesis we expected that low to moderate algal concentrations would significantly constrain both survival and settlement. CoTS larvae were successfully maintained for up to 50 days post-fertilization, but larval survival differed significantly between treatments. Survival was greatest at intermediate (1.0 µg.L^-1) chlorophyll concentrations, and lowest at highest (10.0 µg.L^-1) chlorophyll concentrations. Rates of settlement were also highest at intermediate (1.0 µg.L^-1) chlorophyll concentrations and peaked at 22 days post-fertilization. Peak settlement was delayed at low chlorophyll concentrations, probably reflective of delayed development, but there was no evidence of accelerated development at high chlorophyll concentrations. CoTS larvae were capable of settling 17-43 days post-fertilization, but under optimum conditions with intermediate chlorophyll concentrations, peak settlement occurred at 22 days post-fertilization. Moderate increases in nutrient concentrations and algal densities may increase the number of CoTS that effectively settle, but are unlikely to influence dispersal dynamics.

This paper investigates portfolio optimization methodologies and short-term investment strate-gies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from January 2020 to November 2023. We employ high frequency data and utilize the Kurtosis Mini-mization methodology, alongside other optimization strategies, to construct and evaluate port-folios under different rebalancing frequencies. The empirical analysis reveals that cryptocurren-cies exhibit significant volatility, skewness, and kurtosis, necessitating sophisticated portfolio management techniques. We find that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, delivering optimal returns to investors, particularly in shorter-term investment horizons. We demonstrate the diversification benefits of integrating cryptocurrencies into multi-asset portfolios and emphasize the importance of regular portfolio rebalancing in the volatile cryptocurrency market. Our findings offer insights for portfolio man-agers and investors seeking to optimize their investment outcomes in the cryptocurrency market, highlighting the importance of risk management, diversification, and dynamic portfolio man-agement strategies.

Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

In the context of the minimally extended varying speed of light (meVSL) model, both the absolute magnitude and the luminosity distance of type Ia supernovae (SNe Ia) differ from those predicted by general relativity (GR). Utilizing the Pantheon+ data, we examine the plausibility of various dark energy models under meVSL. Both ωCDM and CPL parameterization dark energy (DE) models exhibit indications of cosmic variations in the speed of light at the 1-σ level. For Ωm0=0.30,0.31, and 0.32 with (ω0,ωa)=(−1,0), the 1-σ range of c˜˙0/c˜0(10−13yr−1) is (-8.76 , -0.89), (-11.8 , 3.93), and (-14.8 , -6.98), respectively. Meanwhile, the 1-σ range of c˜˙0/c˜0(10−12yr−1) for CPL dark energy models with −1.05≤ω0≤−0.95 and 0.28≤Ωm0≤0.32, is (-6.31 , -2.98). The value of c˜ at z=3 can exceed that of the present by 0.2∼3 % for ωCDM models and 5∼13 % for CPL models. Additionally, for viable models except for the CPL model with Ωm0=0.28, we find −25.6≤G˜˙0/G˜0(10−12yr−1)≤−0.36. For this particular model, we obtain an increasing rate of the gravitational constant within the range 1.65≤G˜˙0/G˜0(10−12yr−1)≤3.79.

This paper investigated reliability modeling for systems subject to dependent competing risks considering that the variation of failure threshold is not considered in most studies on competing failure reliability. Firstly, the variation of degradation quantity under shocks was analyzed, and the variation of threshold was considered on this basis. Secondly, the cumulative degradation under the influence of the random shock process was analyzed. The attractive property of copula is symmetry. Then, A linear Wiener process model is applied to model performance degradation failure, and a multi-performance-degradation correlated-competition model based on a Copula function is constructed, which considers the correlated competition between multiperformance degradation failures. Lastly, the micromotor system is used to analyze the applicability of the proposed model for bivariate instances. demonstrating the rationality and effectiveness of the proposed model.

The article concerns problems related to the assessment of reactive power in power networks with non-linear, fast-varying loads, such as electric arc furnaces, rolling mill drives, etc. The operation of this type of loads is characterized by the introduction of interharmonic currents (including higher harmonics) into the power supply network and a relatively low power factor. Rapid changes in the RMS value of the current also cause voltage fluctuations and the related phenomenon - flicker. Therefore, there is a need to evaluate the selection of power of compensating devices taking into account the random nature of load changes and the distortion of current and voltage waveforms, in particular interharmonic components, the impact of which has not been fully investigated so far. To analyze the random nature of load changes, autocorrelation functions were used, which allowed for the estimation of the expected values of the arc furnace current distortion coefficient (based on the recorded waveforms). In order to determine the parameters of reactive power compensating devices, and in particular capacitor banks, an autocorrelation function in the exponential-cosine-sine form was used, which meets the conditions of differentiation. The article contains comparative results of calculations of the reactive power of capacitor banks determined using different methods.

Understanding the spatially varying effects of demographic factors on the spatio-temporal variation of intestinal parasites infections is important for public health intervention and monitoring. This paper presents a hierarchical Bayesian spatially varying coefficient model to evaluate the effects demographic factors on intestinal parasites morbidities in Ghana. The modeling relied on morbidity data collected by the District Health Information Management Systems. We developed Poisson and Poisson-gamma spatially varying coefficient Models. We used the demographic factors, unsafe drinking water, unsafe toilet and unsafe liquid waste disposal as model covariates. The models were fitted using the Integrated Nested Laplace Approximations (INLA). The overall risk of intestinal parasites infection was estimated to be 10.9 per 100 people with a wide spatial variation in the district-specific posterior risk estimates. Substantial spatial variation of increasing multiplicative effects of unsafe drinking water, unsafe toilet and unsafe liquid waste disposal occurs on the variation of intestinal parasites risk. The structured residual spatial variation widely dominates the unstructured component, suggesting that the unaccounted-for risk factors are spatially continuous in nature. The study concludes that both the spatial distribution of the posterior risk and the associated exceedance probability maps are essential for monitoring and control of intestinal parasites.

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO₂ emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

A pump station is a very important flood control facility for mitigating inundation of urban lowland areas. It is not easy to secure a site to increase the capacity of a pump station in an urban area because of various limitations or to maximize the discharge capacity of the pump sump. Adding a facility to improve the pump capacity of a pump station may affect the flow characteristics, such as unexpected increases in the flow velocity and vorticity, and cause severe problems with operating the pump station. To solve those problems, anti-vortex devices (AVDs) have been developed and adopted for appropriate design standards. The Korean design criteria for AVDs are based on experience and foreign standards because not enough data on the AVD are available. In this study, the flow in the sump was numerically simulated at various AVD heights to collect information on improving the design and efficiency of a pump station. Consequently, an appropriate height for the AVD and changes in the flow pattern and vortex in the pump sump were determined and compared with 12 cases of inflow conditions with respect to the vertical location.

This paper explores the views of a group of young men aged 16–18, studying towards a Level 1 diploma in music in the North East area of England. It uses the conceptualization of “social haunting” to focus our discussions about how Level 1 learners articulated their experiences in the context of their learning journeys and perceived marginalization. The research took place at a large Further Education (FE) college and included a series of five “research encounters” with the group of Level 1 learners. These encounters included a range of activities, resulting in a variety of data being collected, including several activity worksheets, structured and unstructured focus group interviews, and questionnaires. This paper focusses on the outcomes of a single research activity that focused on exploring the topic of marginalization. We present a discussion on social haunting as one of the emerging findings from this research encounter, outlining how the reality of the social and cultural legacy of the UK’s industrial past can shape today’s youth and their futures. We will talk about the young men’s views of marginalization, their school-to-work transitions, the formation of masculinity in post-industrial communities and the role of Further Education in it. The paper concludes by discussing some insights for practice and future research.

We will apply Pascal’s hydrostatic pressure law to black holes and also to the Hubble sphere. Haug has recently demonstrated how hydrostatic pressure in water can be utilized to determine the Planck length. One can conceptualize the energy in the Hubble sphere as a type of superfluid, and if this is correct, then Blaise Pascal’s hydrostatic pressure formula may also be applicable to such a superfluid, even within the Hubble sphere. By employing the same method on the Hubble sphere and considering it as a Schwarzschild black hole with the critical Friedmann mass, we can estimate the Planck length based on its hypothetical hydrostatic pressure.

While Neural Radiance Fields (NeRF) are gaining increasing interest in various domains as innovative methods for novel view synthesis and image-based reconstruction, their potential application in the realm of Cultural Heritage remains unexplored. Purpose of this paper is to assess the effectiveness of applying NeRF to sets of images of digital heritage objects and sites. The study’s findings demonstrate that NeRF could be valuable when used in combination with or as a comparison to other well-established techniques such as photogrammetry, to expand the possibilities of preserving and presenting heritage assets with enhanced visual fidelity and accuracy. Particularly, NeRF show promising results in improving the rendering of translucent and reflective surfaces, objects with homogeneous textures, and elements with intricate details. In addition, we demonstrate that, when considering the same set of input images (with known camera poses), reducing the image quality or the number of images results in significantly less information loss with NeRF compared to photogrammetry. This suggests that NeRF is preferentially suited for scenarios involving sparse information or low-quality photos or videos, which could be especially valuable in risky or challenging situations.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

Dirac's Large Number Hypothesis (LNH), proposed in 1937, has captivated the scientific community with its exploration of profound correlations between cosmic and atomic scales. This hypothesis delves into the intricate interplay between the infinitesimally small and the vastly large, offering potential insights into the fundamental nature of the universe. As we continue to uncover the mysteries of the cosmos, the LNH oscillates on the precipice of scientific acceptance—neither fully validated nor entirely dismissed. In this review paper, we embark on a comprehensive journey through Dirac's LNH, shedding light on its theoretical underpinnings, its implications for universe models, and its resonance with the Anthropic Principle. We delve into the possibilities of variable gravitational constants and continuous mass creation, inviting further exploration into the intricacies of our cosmic symphony. By embracing the ongoing quest for understanding, we endeavor to unravel the profound harmonies that connect the infinitesimal with the infinite, contributing to the symphony of knowledge in theoretical physics and cosmology.

Atmospheric corrosion of aluminum aircraft structures occurs due to numerous reasons. A typical phenomenon leading to corrosion is the deliquescence of contaminants such as salts due to changes in relative humidity (RH) caused by aircraft operation at different altitudes and climate zones. Currently, corrosion of aircrafts is controlled by scheduled inspections. In contrast, the present contribution aims for a continuous monitoring approach by using the acoustic emission (AE) method to detect and further evaluate atmospheric corrosion. The AE method is frequently used for corrosion detection at typically immersion-like conditions or for corrosion types where stress-induced cracking is involved. However, it has not yet been demonstrated for atmospheric corrosion at unloaded aluminum structures. To address this question, the present investigation uses small droplets of a corrosive sodium chloride (NaCl) solution to induce atmospheric corrosion on aluminum alloy AA2024-T351. Operating conditions of an aircraft are simulated by a controlled variation in RH. In addition, videos of the corrosion site are recorded to visually observe the corrosion process. Pitting corrosion is generated and clearly measurable AE signals are detected. An automatic video processing algorithm looking for sudden changes on the corrosion site mainly detects hydrogen bubbles formed when aluminum reacts with aqueous solutions. A clear correlation between the observed pitting corrosion, the AE and the hydrogen bubble activity and the RH, i.e., the electrolyte present at the aluminum surface, is found. Thus, the findings demonstrate the applicability of the AE method for monitoring atmospheric corrosion of aluminum aircraft structures by today’s measurement equipment. Numerous potential effects that can cause measurable AE signals are investigated and discussed. Among these, bubble activity is clearly considered to be the most emissive one.

Trash mulches are very effective in preventing soil erosion; reduce sediment transport rate, runoff rate and increasing infiltration. The study was carried out with the objectives to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions by using rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching in soil loss reduction. The quantity of mulch was taken as, 6 t/ha, 8 t/ha and 10 t/ha, three rainfall intensities viz. 11cm/h, 13cm/h and 14.65cm/h at 0%, 2% and 4% land slopes were selected. The duration of rainfall was fixed (10 minutes) for every mulch treatment. The total runoff volume was found to be varying with different mulch rates for particular rainfall input and land slope. The runoff distribution pattern was observed to be increasing with the increase in land slope. The average sediment concentration (SC) and outflow was found to be increasing with the increasing land slope, but SC and outflow decreased with increasing mulch rate for particular land slope and rainfall intensity. The SOR (SOR) for no mulch treated land was higher as compared to trash mulch treated lands. Mathematical relationships were developed for relating SOR, SC, land slope and rainfall intensity for a particular mulch treatment. It was observed that values of SOR and average SC had a good correlation with rainfall intensity and land slope for each mulch treatment. The correlation coefficients of developed models were found to be more than 90%.

In many practical applications, it is more convenient to characterize the quality of production processes or service operations throughout the count of nonconformities. In the context of SPC, nonconformities are usually assumed to appear according to the binomial probability model. The conventional way for monitoring nonconformities involves Shewhart-type control procedures based on both constant and time-varying sample sizes. In this article, an EWMA scheme is proposed for monitoring the fraction of nonconforming items with time-varying sample sizes. The proposed control chart is referred to as the EWMAG-B and can be easily adapted to work with a constant sample size by fixing it at a needed value. By means of simulation, it was found out that the EWMAG-B chart outperforms the conventional p control chart in Phase II while detecting changes in the process level is wanted.

Single particle tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein-Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use change detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.

A new label-free molecular probe for luminescent nucleotide detection in neutral aqueous solution is presented. Phosphate-containing molecules such as nucleotides possess vital role in cell metabolism, energy economy, and signaling. Thus the monitoring of nucleotide concentration and nucleotide related enzymatic reactions is of high importance. Two component lanthanide complex formed from Tb(III) ion carrier and light harvesting antenna, readily distinguishes nucleotides containing different number of phosphates and enable direct detection of enzymatic reactions converting nucleotide triphosphate (NTP) to nucleotide di/monophosphate or the opposite. Developed sensor enables the detection of enzymatic activity with a low nanomolar sensitivity, as highlighted with K-Ras and apyrase enzymes in there hydrolysis assays performed in high throughput screening compatible 384-well plate format.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Over the last few decades, advancements in astrophysics have been closely linked to the development of powerful machine-learning models that can accurately classify celestial bodies. At the same time, however, many astronomical datasets are filled with new features collected by increasingly powerful telescopes. These features can cause overfitting, clouding predictive abilities, and dampening the ability of many models to classify images. Therefore, motivation to design more efficient models has skyrocketed—aiming to optimize for lower run times and high accuracies, even with fewer provided features. In our project, we seek to optimize a convolutional neural network model using a technique known as wavelet analysis. This technique allows us to pick the key features of an astronomical image and accentuate niche details, saving time and boosting accuracy. We applied it to the MiraBest Dataset, a dataset compiled from the FIRST sky survey using a virtual telescope. In the end, after training our neural network on the original images and the five filters (approximation, horizontal, vertical, diagonal, and combined), we found that with fewer features and less overfitting, the vertical Daubechies-family wavelet filter outperformed the original runs with the unaltered images by over 10%. Our findings suggest that wavelet analysis can help harvest the most valuable features in images of celestial bodies–leading to enhanced predictions in astronomical applications and perhaps bolstering modern astrophysical theory.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

To accurately identify high-quality functional wood with superior mechanical properties and enhance its durability, it is imperative to understand the relationships between the physical and mechanical attributes of diverse wood types. Presently, there exist issues related to accuracy, efficiency, and differentiation in studying these performance correlations. The Liang Kleeman (L-K) information flow theory, a causative analytical approach rooted in fundamental physics principles, offers swift computational capabilities. Furthermore, the coefficient of variation in L-K information flow quantitatively gauges the intensity of causal effects. Therefore, this article adopts a causal artificial intelligence perspective and introduces a wood performance analysis method based on L-K information flow.This study focuses on four Chinese fir varieties, including fast-growing Chinese fir (YKS), red heart Chinese fir (CSH, XXH), and iron heart Chinese fir (XXT). By computing and comparing variation coefficients of L-K information flow for various physical properties and their impact on mechanical properties, causal relationships between these properties are elucidated. These findings are then applied to predict wood mechanical properties, achieving a remarkable maximum accuracy of 90%.In the future, these findings will prove invaluable for the selection of high-quality wood and for improving the manufacturing process to prolong the service life of functionalized wood.

Quarry waste fines and eggshells are unavoidable wastes which relentlessly contribute to environmental load and pollution. Although a number of studies have suggested various methods for recycling, these wastes remain underutilized due to some technical constraints. In addition, no study has yet been explored on the possibility of combining quarry waste fines (QWF) and eggshell powder (ESP) for tobermorite synthesis. Tobermorite is the main component which primarily provides strength to the autoclaved aerated concrete products. For this purpose, this study seeks to evaluate the potential of QWF- ESP mix at 10%, 15% and 20% amounts of cement, respectively. The XRF, XRD, and TGA–DTA techniques were used to characterize the waste materials while physical and mechanical property tests and XRD analysis were performed on the autoclaved samples. It was found that QWF contains 53.77% SiO2, while ESP contains 97.8% CaO which are key components for tobermorite synthesis. This study also revealed that the mixture with only 10% cement has the highest compressive strength among the QWF-ESP samples. Furthermore, the formation of tobermorite in the samples was confirmed through XRD analysis. Hence, the hydrothermal curing of QWF –ESP can be further developed to produce functional tobermorite – bearing materials.

The prospects of processing blast furnace and steelmaking sludge using Waelz process in a laboratory rotary kiln is shown. The influence of varying thermal treatment modes, furnace atmosphere and type of reducing agents on the level of zinc reduction from sludges was analyzed. In general, the blast furnace sludge contains a high portion of iron (approx.48 wt. %) and can be reused as a charge after satisfactory zinc reduction. It was found that N- atmosphere and high content of the graphite or coke oven reducing agent in combination with high temperature can reduce the content of Zn in the sludge to 0.08 wt. % at 1200 °C for mixture of steelmaking and blast furnace sludge. A significant reduction in the Zn content to 0.66 wt. % occurs at 1100 °C. The content and type of reducing agent play an important role; graphite has shown a better reducing ability compared to coke oven dust. When nitrogen is used, zinc is reduced even without an additional reducing agent, since the carbon contained in the sludge is made use of for the reduction. In an air atmosphere, without the use of a reducing agent, there was no reduction in the Zn content.

Salinity, resulting from climate change and excessive mineral fertilization, burdens farmers and negatively impacts soil and water ecosystems in the Northeastern Nile Delta. Organic and biological approaches are crucial for addressing these issues. This study examined the effects of individual and combined inoculations with cyanobacteria, yeast, and Arbuscular Mycorrhizal Fungi (AMF), with or without K-humate, on enhancing salinity tolerance, increasing yield, and improving crop quality of wheat and faba bean. The aim was to reduce NPK mineral fertilizer application rates to 25% of recommended doses. An economic evaluation was also conducted. In a preliminary laboratory experiment, the interactive effects of these microbiomes on the production of plant antioxidant enzymes and soil enzymes were investigated under salinity stress. Results showed that co-inoculation, especially with K-humate, yielded superior outcomes compared to individual inoculations. The laboratory experiment findings were confirmed by a field trial conducted in saline-alkaline soil in the Northeastern Nile Delta region. The study found that the integrated inoculation of cyanobacteria, yeast, and mycorrhizae with K-humate, along with 25% mineral NPK fertilization enhanced microbial activity, including AMF colonization, spore density, and total bacteria, cyanobacteria, and yeast counts in the plant rhizosphere. It also increased soil enzyme activity, improving soil fertility compared to individual or mineral control treatments. This integration resulted in wheat yields of 2.77 tons/ha and faba bean yields of 4.75 tons/ha. Crop quality improved, with higher levels of nitrogen, potassium, phosphorus, and protein in wheat grains and faba bean seeds. Microbiomes inoculation increased potassium uptake over sodium, enhancing the potassium/sodium ratio and salt stress tolerance. This approach reduces reliance on costly mineral fertilizers by up to 75%, enabling economically viable cultivation in marginal lands. These sustainable practices optimize resource utilization, maximize economic efficiency, and preserve natural resources.

This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) Manufacturing Company B's Electricity consumption data and meteorological data in Naju, Jeollanam-do, South Korea are collected and preprocessed. (2) The preprocessed data proposes the Prophet model in the first step for seasonality and event handling prediction. (3) In the second step, seven multivariate data are experimented with GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15-minute intervals. (4) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The experimental results demonstrate that the proposed method outperforms the conventional Prophet model by more than 23 times and the modified GRU model by more than 2 times in terms of MAPE.

Aim: the main aim of this study is to compare the onset of effectiveness of mental nerve block with lingual infiltration and inferior alveolar nerve block on patients visiting dental hospital for extraction of mandibular anterior teeth. Materials and methods: The study was set in a clinical setting. This was a randomized controlled double-blind cross study, where 25 subjects received two treatments at an interval of at least 1 week in a within a dental hospital.Inferior alveolar nerve block and lingual nerve block injection is given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered for the right side. Mental nerve block along with lingual infiltration of the corresponding tooth was given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered on the left side.Visual analogue scale (VAS) was used in this study, which consists of a scale with range of 0-10 pain severity on which 0 indicated absence of pain and 10 the worst pain possible. The patients were asked to give scores corresponding to his/her pain, after IANB, mental nerve block and lingual infiltration and these scores were recorde.Results: From the results we can see that patients gave scores between 3-4, stating that they had moderate pain during extraction after administration of IANB (54%) (graph 2) and VAS scores of 0-1 were given by patients, which is suggestive of no pain during extraction after administration of Mental nerve block and lingual nerve infiltration (62%)Conclusion: Within the limits of the study we can conclude that mental nerve block along with lingual infiltration is an effective way to completely and quickly anaesthetise the central incisor to the 1st premolar region.

The feasibility of replacing the toxic chlorobenzene antisolvent with the environmentally friendly anisole in the fabrication of planar triple cation perovskite solar cells was explored. The successful integration of anisole not only ensures comparable device performance but also contributes to the development of more sustainable and green fabrication processes for next-generation photovoltaic technologies. Nevertheless, to ensure the possibility to achieve well-functioning unencapsulated devices, at any time of the year, with changed temperature and relative humidity, several batches with different cesium concentration were realized. We found that 10% of CsI in the precursor solution makes devices with low hysteresis index and sustained performance stability over a 90-day period both with cholorobenzene and anisole antisolvent. However the use of anisole as a green antisolvent ensures enhanced performance.

Different coffee drying technologies face complex task in ensuring an acceptable final seed moisture content. Drying technology of agricultural commodities aims to maintain and improve the quality of the agricultural products themselves. In addition to maintaining quality, especially from other bacterial attacks, it can last for a long time before further processing. Coffee commodities are promising for the welfare of coffee farmers. Drying technology plays a major role in determining the quality of coffee. Various drying models are applied including the traditional model that until now is still applied, direct drying under solar radiation. However, this drying process is less hygienic and requires a large area. Thus, one of the drying technologies that can accelerate drying time is to vary the air flow in the drying chamber so that the coffee can be determined to reduce the water content with a certain temperature. In this study, it was found that the moisture content of coffee beans dried using a dryer on air flow with variations of velocity in solar collector 1 (DB1) air velocity of 1.0m/s obtained the final mass = 732.249 gr, solar collector 2 (DB2) with air velocity 1.5 m/s obtained the final mass = 774.70 gr, solar collector 3 (DB3) with air velocity 2.0 m/s obtained the final mass = 855.10 gr, solar collector 4 (DB4) with air velocity 2.5 m/s obtained the final mass = 745.79 gr, and solar collector 5 (DB5) with air velocity 3.0 m/s obtained the final mass = 786.40 gr. Water Content DB1=12.0%, DB2=13.6%, DB3=18.5%, DB4=12.9% and DB5=15.2%. The time required for 25 hours with a maximum radiation of 586.9 w/m2, total heat utilized from solar radiation in 3 days =16.663128 MJ/m2. from the initial weight of 1500gr coffee bean samples and dried in five solar collectors with parallel simultaneously. The evaporated water content DB1=51.18%, DB2=48.35%, DB3=42.96%, DB4=50.28% and DB5=47.57%. Based on this research, it was found that the most optimum quality of coffee was by using solar collector 2 (DB2), namely with air flow of 1.5 m/s with average temperature in the drying box DB2=43.68°C, with the quality content of the coffee, Protein content=12.2%, Carbohydrate=22.8% and Free Fatty Acid (ALB) content=0.05%.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with > 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by >1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and finally identified six main ridership patterns. We then developed a new built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis result revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type station, and centrality was only associated with employment-oriented hybrid type station. These results could inform the coordinated development of rail transit and land use, and the renewal and enhancement of the built environment in the pedestrian catchment area surrounding metro stations.

The present study examines the long-term care service awareness, needs, and usage intention of elderly male homosexuals in Taiwan and their ideal long-term care service model. This study of five elderly male homosexual subjects aged 66 to 73 years was intended as a preliminary exploration. Interviews were used to collect data. The study determined that the five subjects had high awareness of long-term care services, as they had actual experience of long-term care services or even participated in care service staff training to obtain certificates. Some even had experience in applying for home care services and experienced problems during use. The subjects perceived that they were very likely to require long-term care services in the future and tended to use home care services if they required long-term care services. Due to their personal experiences, the subjects had negative awareness of long-term care services and worried that long-term care service staff had poor attitudes toward homosexuals. The subjects considered the most important aspects of long-term care to be basic medical care and lifestyle care. However, they worried that long-term care staff would delay or refuse to provide such services due to the subjects’ sexual orientation or stereotypes, and they were concerned above all about the “friendly attitude” of long-term care staff. They hoped that long-term care staff were friendly toward homosexuals and did not discriminate against them, and they felt that it would be more appropriate for homosexual long-term care staff to provide assistance. In terms of vision, while preferring organizations with homosexual employees, the subjects worried that they would be stigmatized and discriminated against . With regard to ideal long-term care services, while considering institutions with homosexual staff to be ideal, the subjects also worried that these would be labeled as institutions that were dedicated to homosexuals, potentially resulting in discrimination. Therefore, they hoped that the sexuality sensitivity of long-term care staff could be improved and that they would undergo professional continuing education to learn about homosexuals, the situation and care needs of homosexuals, and care techniques for homosexuals.

Introductions- Liveable housing environments face the menace of global climate change. In-frastructure (including buildings and houses) continuously experiences significant impacts ex-acerbated by natural variability in climate. The study addressed how climate change impacts the resilience of residential buildings, increased maintenance frequency, and the wellbeing and comfort of residents in UK residential buildings. Design/Methodology/Approach: This study used deductive reasoning and an empirical epis-temological methodology as a data-collecting basis to evaluate primary information obtained via a questionnaire. Regression analysis was ultimately employed to analyse the collected da-ta. Result/Discussions: The findings show a significant relationship between climate change and the wellbeing of the occupants of UK residential buildings. Also, the results show that social wellbeing is more important to the occupants than mental and physical wellbeing. It reveals that residential buildings in the UK suffer the cost of maintenance due to continuous reduc-tion in the building fabric's resilience to the impacts of climate change; for instance, a recent increase in rainfall/storms results in unprecedented flooding, which continues to damage the UK residential building fabrics.

Individuals with BPD grapple with unstable relationships, marked by extreme emotional swings and interpersonal conflicts. Emotional instability, a distorted self-image, and impulsivity further complicate matters, often leading to self-harm and suicide attempts as desperate coping mechanisms. Genetics and adverse childhood experiences contribute to BPD's development, shaping brain function and emotional regulation. Diagnosis involves meticulous assessments, paving the way for personalized treatments like dialectical behavior therapy and, in severe cases, hospitalization. Early intervention, professional support, and societal understanding are pivotal for positive outcomes, and challenging mental health stigma.

In order to the effect of hydrated phase change materials on suppressing coal spontaneous combustion, the thermogravimetric experiment and reaction activation energy analysis experiment were used to explore the changes of combustion characteristic, parameters characteristic temperature and activating energy of gas coal, long flame coal, meagre coal, and lean coal before and after adding hydrated phase change materials. The research results showed that hydrated phase change materials increased the coal samples’characteristic temperature point and have effective inhibitory effects on different stages of oxidation process.However, the effect was best at low temperatures, as hydrated phase change materials undergo phase change and absorb heat when heated at low temperatures, isolating coal from contact with oxygen. The activating energy increased by 1.138 KJmol-1～23.048 KJmol-1 and the mass loss reduced by 1.6%～9.3% after the inhibition of the coal sample,indicating that the oxidation rate of various coal samples can be slowed down and the spontaneous combustion can be suppressed by using hydrated phase change materials.At the same time, this material can also reduce the combustibility index of meagre coal and lean coal, as well as the comprehensive combustion index and maximum loss rate of long flame coal and gas coal.

This review explores the transformative impact of sensor technologies in early disease detection. It highlights recent advancements in nanotechnology-based sensors, particularly focusing on their applications in identifying disease biomarkers at early stages. The integration of these sensors with artificial intelligence for predictive analytics in healthcare is also discussed. Applications in cancer, infectious diseases, and chronic disease management underscore their diverse utility. Challenges in sensor technology adoption and future directions, including multiplexed sensors and wearable devices, are examined. This review underscores sensors' potential in enhancing disease management and patient outcomes.

Legumes develop root nodules that harbour endosymbiotic bacteria, rhizobia. These rhizobia convert nitrogen to ammonia by biological nitrogen fixation. A thorough understanding of the biological nitrogen fixation in legumes and its regulation is key to develop sustainable agriculture. It is well known that plant hormones affect nodule formation; however, most studies are limited to model legumes due to their suitability for in vitro, plate-based assays. Specifically, it is almost impossible to measure the effects of exogenous hormones or other additives during nodule development in crop legumes such as soybean as they have huge root system in soil. To circumvent this issue, the present research develops suitable media and growth conditions for efficient nodule development under in vitro, soil free conditions in an important legume crop, soybean. Moreover, we also evaluate the effects of all major phytohormones during soybean nodulation under identical conditions. This versatile, inexpensive, scalable and simple protocol provides several advantages over previously established methods. It is extremely time-and resource-efficient, does not require special training or equipment, and produces highly reproducible results. The approach is expandable to other large legumes as well as for other exogenous additives.

In the present study. At first, some properties of r-fuzzy soft β-closed sets are introduced on fuzzy soft topological spaces based on the paper Aygunoglu et al. (Hacet. J. Math. Stat. 2014, 43, 193-208). Also, we define the closure and interior operators with respect to the classes of r-fuzzy soft β-closed and r-fuzzy soft β-open sets, and investigate some of their properties. In addition, the concept of r-fuzzy soft β-connected sets is introduced and characterized with help of fuzzy soft β-closure operators. Thereafter, some properties of a fuzzy soft β-continuity are studied. Furthermore, we introduce and study the concepts of fuzzy soft almost (weakly) β-continuous functions, which are weaker forms of a fuzzy soft β-continuity. Also, the relationships between these classes of functions are specified with the help of some illustrative examples. Finally, we explore new types of fuzzy soft functions called, fuzzy soft β-irresolute (strongly β-irresolute, β-irresolute open, β-irresolute closed and β-irresolute homeomorphism) functions, and discuss some properties of them. Also, we show that fuzzy soft strongly β-irresolute → fuzzy soft β-irresolute → fuzzy soft β-continuity, but the converse may not be true.

To quantify the time-varying reproduction number (Rt) for Malaysia using the COVID-19 incidence data., we used data the from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository. Day 1 was taken from the first assumed local transmission of COVID-19. Data was split into four intervals: a) Interval 1: from Day 1 to Day 10 MCO 1, b) Interval 2: from Day 1 to Day 10 MCO 2, c) Interval 3: from Day 1 to Day 10 MCO 3 and d) Interval 4: from Day 1 to Day 10 MCO 4. We estimated the Rt using the EpiEstim package. The means for Rt at Day 1, Day 5 and Day 10 for all MCOs ranged between 0.665 to 1.147. The average Rt gradually decreased in MCO 1 and MCO 2. However, Rt increased in MCO 3 before stabilized around 0.8 in MCO 4. MCO 1 and MCO 2 which were stricter coincide with the gradual reduction of Rt. However, the more relaxed MCO 3 and MCO 4 correspond to a slight increase in the Rt before it stabilized.

Barley is rich in phenolic compounds, providing health benefits and making it a valuable addition to a balanced diet. However, most studies focus on these compounds at barley's final maturity, neglecting their synthesis during grain development and its impact on barley quality for food applications. This study investigates phenolic profiles during grain development in four hull-less barley genotypes with different grain colors, specifically bred for food applications. The objectives were to determine the phenolic profile and identify the optimal maturity stage for maximum phenolic content and antioxidant capacity. Using UPLC-MS/MS and in-vitro antioxidant capacity assays, results show that total phenolic compounds decrease as grain matures due to increased synthesis of reserve components. Flavan-3-ols, phenolic acids, and flavone glycosides peaked at immature stages, while anthocyanins peaked at physiological maturity. The harvest stage had the lowest phenolic content, with a gradient from black to yellow, purple, and blue genotypes. Antioxidant capacity fluctuated during maturation, correlating positively with phenolic compounds, especially bound phenolic acids and anthocyanins. These findings suggest that early harvesting of immature grain can help retain bioactive compounds, promoting the use of immature barley grains in foods. To support this market, incentives should offset costs associated with decreased grain weight.

This work presents a numerical study on atmospheric sound propagation over rough water surfaces. A method to generate pseudorandom sea profiles consistent with sea wave spectra is presented. The proposed method is suited to capture the logarithmic nature of the energy distribution of the waves. Sea profiles representing fully developed seas for sea states 2, 3, 4 and 5 are generated from the Elfouhaily et al. (ECKV) sea wave spectra. Excess attenuation caused by refraction and surface roughness is predicted with a parabolic equation (PE) solver. A novel method to estimate equivalent effective impedance based on PE predictions at different sea states is presented. Parametric expressions using acoustic frequency and significant wave height are developed for effective surface impedances. In this work, sea surface roughness is on a scale comparable to the acoustic wavelength. Under this condition, the acoustic scattering is primarily incoherent. This work shows the limitations of using an equivalent surface impedance in such incoherent scattering cases.

In this work we developed two new polyfunctional hybrid systems, in which the presence of Fc redox "antennas" on peptide scaffolds allows a modulation of their electronic properties. Specifically, we synthesized two helical hexapeptides containing four Aib (α-amionoisobutyric acid) and two L-Dap (2,3-diamino propionic acid) residues. L-Dap side chains were then functionalized with Fc moieties. The structures of the two 310 helical peptides, namely Z-Aib-L-Dap(Fc)-Aib-Aib- L-Dap(Fc)-Aib-NH-iPr and Z-Aib-L-Dap(Fc)-Aib- L-Dap(Fc)-Aib-Aib-NH-iPr, were investigated by X-ray diffraction, 2D-NMR, CD and IR spectroscopies. Due to the helical conformation, in Z-Aib-L-Dap(Fc)-Aib-Aib- L-Dap(Fc)-Aib-NH-iPr the Fc groups are located on the same face of the helix, in Z-Aib-L-Dap(Fc)-Aib- L-Dap(Fc)-Aib-Aib-NH-iPr on opposite faces. Surprisingly, two bands were found in DPV for Z-Aib- L-Dap(Fc)-Aib- L-Dap(Fc)-Aib-Aib-NH-iPr, indicating electrostatic interaction between the Fc groups despite their longer reciprocal distance with respect to that in Z-Aib- L-Dap(Fc)-Aib-Aib- L-Dap(Fc)-Aib-NH-iPr. CD experiments at different concentrations evidenced aggregation for Z-Aib- L-Dap(Fc)-Aib-L-Dap(Fc)-Aib-Aib-NH-iPr even at high dilution, thus suggesting that the Fc-Fc electrostatic interaction could be of intermolecular nature.

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

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.

Hydrogen used as an energy carrier can provide an important route to the decarbonization of energy supplies. However, realizing this opportunity requires a significant increase in both production and transportation capacity. Part of the increase in transportation capacity could be provided by the shipping of liquid hydrogen, but this introduces an energy-intensive liquefaction step into the supply-chain. The energy required for liquefaction can be reduced by developing improved process designs, but since all low-temperature processes are affected by the available heat-sink temperature, local ambient conditions will also affect the energy penalty. This work studies how the energy consumption associated with liquefaction varies with heat-sink temperature through the optimization of design parameters for a typical next-generation hydrogen liquefaction process. The results show that energy consumption increases by around 20%, across the cooling temperature range 5 to 50 °C. Considering just the range 20 to 30 °C there is a 5% increase, illustrating the significant impact ambient temperature can have on energy consumption.

In this paper, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.

The purpose of this paper is to investigate the effect of a varying moment on the rotation angle of a large tethered satellite that is orbiting a planet. Two different types of orbits were investigated: a simple circular orbit and an elliptical orbit. Cases with zero and non-zero initial angular rotation velocity were investigated as well. This investigation will assist satellite docking missions. The large rigid tethered satellite is a futuristic concept, and this investigation is meant to assist possible docking missions to the satellite. To simplify the problem, the rotation is constrained to the orbital plane.

This work focuses on short-term forecasting of PV output power. The multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with good success. Their performances in forecasting PV power have been compared with other algorithms but not with themselves. The study aims to compare performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions. The performance of MLPNN, CNN and kNN are being compared using solar PV (hourly) energy yield data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in south Africa that may be prone to extreme weather conditions. The kNN algorithm was found to have RMSE value of 4.95% and MAE value of 2.74% at its worst performance, and RMSE value of 1.49% and MAE value of 0.85% at its best performance. It outperformed the others by a good margin and kNN could serve as a fast, easy, and accurate tool to forecast solar PV output power.

Long-term, multi-decade research on planted tree survival in urban settings is sparse. One understudied urban environment is highway rights-of-way (ROW), lands adjacent to high-speed, unsignalized roadways. We conducted a re-inventory of tree planting cohort in northern Illinois, U.S. on a 48 km-long highway near Chicago which were 10-, 21-, and 30-years old to evaluate long-term patterns of survival and diversity. Using each randomly selected planting site along the highway as a unit of observation and analysis, we compared the number of trees documented in record drawing to the number of trees currently alive to determine percent survival. We evaluated 224 planting sites which originally contained 2,944 trees and collected data about the planting site location. For the oldest cohort, 26% of trees were still alive in 2018 (median survival by species = 16%, Q1 = 0%, Q3 = 48%), while 31% of the 21-year-old cohort (med. = 6%, Q1 = 0%, Q3 = 47%) and 86% of the 10-year-old cohort were still alive (med. = 85%, Q1 = 74%, Q3 = 96%). The survival of the 21- and 30-year-old cohort matches urban tree survival estimates by other researchers, while the 10-year-old survival is higher than expected. The only planting location characteristic that significantly affected survival was traffic islands (areas between the highway and entrance/exit ramps). Species with low drought tolerance were less likely to be alive for the 10-year-old cohort. Waterlogging tolerant species were more likely to be alive in the 10-year-old cohort. Since some species in the 21- and 30-year-old cohorts had very low survival, the tree species richness and diversity s in study areas declined between the initial record drawings and reinventory. This study demonstrates the challenges of maintaining long-term survival and diversity in the highway ROW and emphasizes the importance of species selection.

Abstract: In the Western of Mekong Delta belong to Vietnam a amount of alluvium sediments flows from upstream China to Vietnam, they are going from the river branches to in the Mekong Delta, then get out the Western Sea of Vietnam. This sediments accumulated gradually elevation the new land. The increase elevation of the land creates a fertile coastal plain where mangrove forests with a rich ecosystem of plants and animals. Over time, these forests change, with different plant species succeeding each other. This aims of this study to finding plant species in the forest, classification of forest types based on ecological regions, assessement the biodiversity of tree species, and compare the abundance communities, measuring the growth of the forest in these regions. In 2023, a comprehensive survey was conducted by using a systematic approach. The number of survey plots have done depend on the density of the forest, Base on the width of the forest range, the number of survey plots from 500m to 1,000m set up 1-2 plots, sub region set up from 10 to 15 plots. In total, 68 plots have done established in the erea, the area of plot is 100 square meters (10m x 10m). Using the statistical software in forestry to survey and analysis data. The result provided a complete picture of the tree species composition, distribution, and community structure characteristics in each ecological sub-region. The result of survey showed in the sub-region 1 have seven species as Sonneracia ovate, Rhzophora apiculata, Excoecaria agallochha, Avicennia officinalis, Avicennia alba, Agricerasvcoculatum, Bruguiera gymnorhiza. In the sub region 2 have eleven species as Sonneracia caseolaris, Annona reticulata, Lumnitzera littorea, Lumnizera racemosa, Rhizophora apiculata, Excoecaria agallocha, Avicennia officinalis, Avicennia alba, Hibiscus tillacerus, Bruguiera gymorhiza, Aericeras comiculatum. In the sub region 3 have eight as Sonratia caseolaris, Rhizophora mucronata, Rhizophora apiculata, Excoecaria agallocha, Avicennia marine, Avicennia alba, Bruguiera gymorhiza. In the region 4 have ten species as Sonneracia caseolaris, Sonneracia ovate, Lumnizera racemosa, Rhizophoa apiculata, Excoecaria agallocha, Avicennia marina, Avicennia officinalis, Aviecnnia alba, Bruguiera gymorhiza, Bruguiera cylindrica The total species of the mangrove forest in the Western Mekong Delta have 16 species from 11 plant families have been identified. Among these species have 6 dominant species include Avicennia oficinali),Avicennia alba, Rhizophora apiculata, Excoecaria agallocha, Someratia caseolaris, and Bruguiera yipamoriza. Two species easy grow on the new land are Avicennia officinalis Avicennia alba this findings show they can develope on the original new land for the shore of the Western Mekong Delta. The surveyed results also calculated the average of the height, diameter (D1.3), canopy, health of the nature mangrove tree for each sub region and total region. From these results showed the division of foresty structure, the structure of height, diameter (D1.3), canopy, vitality of the sub region and total region in the Western Mekong Delta.

Abstract: Early life gut microbiota have been increasingly recognized as major contributors to short and/or long-term human health and diseases. Numerous studies have demonstrated that human gut microbial colonization begins at birth but continues to develop a succession of taxonomic abundances for two to three years until the gut microbiota reaches adult-like diversity and proportions. Several factors, including gestational age (GA), delivery mode, birth weight, feeding types, antibiotic exposure, maternal microbiome and diet influence the diversity, abundance and function of the early life gut microbiota. Gut microbial life is essential for assisting with the digestion of food substances to release nutrients, exerting control over pathogens, stimulating or modulating the immune system and influencing many systems such as the liver, brain, and endocrine system. Microbial metabolites play multiple roles in these interactions. Furthermore, studies provide evidence supporting that imbalances of the gut microbiota in early life, referred to as dysbiosis, are associated with specific childhood or adult disease outcomes, such as asthma, atopic dermatitis, diabetes, allergic diseases, obesity, cardiovascular diseases (CVD) and neurological disorders. These findings support that the human gut microbiota may play a fundamental role in the risk of acquiring diseases that may be programmed during the early life stage. In fact, it is critical to explore the role of the human gut microbiota in early life. In this review, we summarize the general understanding of the colonization and development of the gut microbiota in early life, highlighting the recent findings regarding the relationship between the gut microbiota composition and their metabolites, and immune functions, which could significantly influence long-term health and disease. We then review known pathophysiological interactions of the early gut microbiome with a number of well characterized diseases and pose potential etiological mechanisms.

Using DCC-GARCH and EGARCH model, this paper finds that since 1990, the relationship between crude oil prices and the US dollar index is time-varying, demonstrating a process of “very weak correlation—negative correlation—enhanced negative correlation—weakening negative correlation”, but the existing research does not provide enough reasonable explanation. Therefore, this paper proposed a “key mediating factors” hypothesis which points out that whether there is a common “key mediating factor” is important source of the time-varying relationship between two assets. We argue that market trend and financial market sentiment undertook the role of “key mediating factor” during the period of “2002 to the financial crisis” and “financial crisis to 2013”, while other periods lack the “key mediating factors”.

The chlorophyll is one of the most important natural pigments used extensively in the food industry. Two important factors for the production of chlorophyll are the use of plants rich in chlorophyll and efficiency of extraction method. Present investigation was performed to compare the extraction of photosynthetic pigments by using solvents of different chemical nature. The purslane plants with different growth behavior viz. Scrollable and standing were grown under shade and sunshine stress condition. Different solvents including diethyl ether, 5% ethanol, pure acetone, 20% acetone, pure methanol and 10% methanol were used to extract chlorophyll and carotenoids from the purslane plant. The results indicated that stress, growth type and different solvents had a significant effect on the extraction of chlorophyll and carotenoids. Different trend was observed in extraction rate for chlorophylls and carotenoids. Among the solvents, pure methanol was the best for extraction of chl a. Methanol and acetone were appropriate solvents to achieve the highest amount of chlorophyll from plant tissues. Among different solvents, pure methanol for chl a, pure acetone and methanol for carotenoids were best solvent for purslane plant with a growing type scrollable of under shade.

Abstract. Introduction: BMD study gives partial information on bone health in patients with TSH suppression therapy due to Differentiated Thyroid Cancer (DTC). TBS, a new parameter of bone microarchitecture is propose for bone quality study. This study analyze their long-term use in patients with DTC. Methods: BMD was measured by DXA and TBS with iNsigth software in 145 postmenopausal DTC patients. Vertebral Fractures (VFs) with a semi-quantitative x-ray method. Results: BMD at the end study was not different from initial. TBS decreased from 1.35±0.1 to 1.27±0.1 (p=0.002). PTH, Osteocalcin, and BAP were increased, suggesting increased bone remodeling. There was an increase in osteoporosis and osteopenia (40.6% and 16.5% to 46.6% and 18.6%); 31% had partially degraded and 15.1% degraded TBS changed to 48,9% and 24,8% at the end study. VFs in 30 patients, was not different in age, BMI, calcium and alcohol ingestion, smoking, radioiodine, or thyroid parameters vs. without VFs. Odds ratio for VFs increased with osteopenia (OR 2.63). Combination of TBS plus TBS did not improve discrimination. Conclusions: TBS was lowered while BMD did not differ. Percentage of osteoporosis and osteopenia and of partially degraded and degraded TBS was higher at the end study. Predominant discordance rate was found in partially degraded microarchitecture, in which the proportion of osteopenia was higher than normal or osteoporotic patients. AUC of TBS plus BMD did not improve discrimination. TBS, radioactive iodine, and sedentary lifestyle were the main differential data for patients with DTC and VFs.

It is not easy to dynamically analyze thermal bridges that require multidimensional analysis in building energy simulations, which are mostly one-dimensional platforms. To solve this problem, many studies have been conducted and, recently, a study was conducted to model the thermal bridge based on the data by approaching this in a similar way to steady-state analysis, showing high accuracy. This was an early-stage study, which is only applicable when the indoor temperature is constant. By extending this study, a thermal bridge model that can be applied even when the indoor temperature changes over time is proposed and validated. Since the governing equation, the heat diffusion equation, is linear, the key idea is to create and apply two thermal bridge transfer function models by expressing the heat flow entering the room as a linear combination of the transfer function for indoor temperature and the transfer function for outdoor temperature. For the proposed thermal bridge model, the NRMSE of the model itself showed a high accuracy of 99.9%, and in the verification through annual simulation using the model, the NRMSE showed an accuracy of 88.8%.

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

Grazing exclusion has been proved to be one of the main measures for rehabilitating degraded arid steppes. However, the effect of this management practice on plant species diversity and composition is ambiguous, specially under prolonged droughts. Concurrently considering the responses of individual plant species, diversity of functional groups, α-diversity, and β-diversity (and its components) may be crucial to the holistic understanding of grazing exclusion effects on plant communities under drought conditions. Here, we investigated the response of these diversity measures to short-term sheep exclusion under severe drought episode in arid steppes of Alfa-grass (Stipa tenacissima) with a long evolutionary history of livestock grazing. Individual species responses were tested based on species occurrence and abundance in either grazed or grazing-excluded steppes, in addition, we used indicator species analysis to assess the strength of the association between plant species and management type. Likewise, α-diversity, abundance- and incidence-based β-diversity, as well as the functional groups’ diversities were quantified using Hill Numbers and compared between the two management types. Sheep grazing exclusion enabled the recovery of various Alfa-steppe indicator species and improved the size of regional species pool, overall α-diversity, and the diversity of therophytes. This management practice decreased the abundance-based β-diversity and the nestedness-resultant fraction of the incidence-based β-diversity at the local scale, while at the landscape scale increased the abundance-based β-diversity and its balanced variation fraction and reduced the incidence-based β-diversity and its turnover component. Furthermore, protection from grazing altered β-diversities scaling patterns by maintaining higher balanced variation in species abundance at large spatial scale and greater abundance-gradient in species composition at the fine-scale. Our results suggest that the implementation of short-term grazing exclusion in degraded arid steppes would be the appropriate management practice for vegetation restoration and plant diversity conservation during prolonged drought periods.

The mathematical structure underlying a sentence is made of two independent processing units in series. The conditional probability density function of sentence length (measured in words) recordable in the extended short–term memory (E–STM) buffer made of CF cells is Gaussian. The number of sentences that authors of Italian and English Literatures have written is compared to that theoretically available to them. The multiplicity factor α describes this comparison. α>1 is more likely than α<1 and often α≫1, therefore the same sentence pattern is reused many times. These texts are easier to read. Very few novels show α<1. In these cases, there are enough diverse sentence patters to convey meaning, but most of them are not used. Texts are more difficult to read, as measured by a universal readability index GU. A mismatch index IM synthetically describes the process. α, GU. and IM increase with year of publication. Future work will concern other literatures – including ancient ones as Greek and Latin – to confirm what, in our opinion, is general feature of human mind.

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic re-mapping, updating the map is recommended. However, in case of incorrect estimation of robot pose, updating the map can lead to errors that prevent the robot localisation and jeopardize map accuracy. In this paper, we propose a safe LIDAR-based occupancy grid map updating algorithm for dynamic environments taking into account the uncertainties in the estimation of the robot’s pose. The proposed approach allows robust long-term operations as it can recover the robot’s pose, even when it gets lost, to continue the map update process providing a coherent map. Moreover, the approach is robust also to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

We investigated the role of the human medio-temporal complex (hMT+) in the memory encoding and storage of a sequence of four coherently moving RDKs by applying repetitive transcranial magnetic stimulation (rTMS) during an early or late phase of the retention interval. Moreover, in a second experiment we also tested whether disrupting the functional integrity of hMT+ during the early phase impaired the precision of the encoded motion directions. Overall, results showed that both recognition accuracy and precision were worse in middle serial positions, suggesting the occurrence of primacy and recency effects. We found that rTMS delivered during the early (but not the late) phase of the retention interval was able to impair not only recognition of RDKs, but also the precision of the retained motion direction. However, such impairment occurred only for RDKs presented in middle positions along the presented sequence, where performance was already closer to chance level. Altogether these findings suggest an involvement of hMT+ in the memory encoding of visual motion direction. Given that both position sequence and rTMS modulated not only recognition but also precision of the stored information, these findings are in support of a model of visual short-term memory with a variable resolution of each stored item, consistent with the assigned amount of memory resources, and that such item-specific memory resolution is supported by the functional integrity of area hMT+.

Virtual reality (VR) describes a family of technologies which immerse users in sensorily-stimulating virtual environments. Such technologies have increasingly found applications in the treatment of neurological and mental health disorders. Depression, anxiety, and other mood abnormalities are of concern in the growing elderly population – especially those who reside in long-term care facilities (LTCFs). The transition from the familiar home environment to the foreign LTCF introduces a number of stressors that can precipitate depression. However, recent studies reveal that VR therapy (VRT) can promote positive emotionality and improve cognitive abilities in the elderly, both at home and in LTCFs. VR thus holds potential in allowing elderly individuals to gradually adapt to their new environments – thereby mitigating the detrimental effects of place attachment and social exclusion. Nevertheless, while the current psychological literature is promising, the implementation of VR in LTCFs faces many challenges. LTCF residents must gain trust in VR technologies, care providers require training to maximize the positive effects of VRT, and decision makers must evaluate both the opportunities and obstacles in adopting VR. Here, we concisely review the implications of depression related to place attachment in LTCFs, and explore the potential therapeutic applications of VR.

This study reports multivariate statistical techniques applied including cluster analysis to evaluate and classify the river pollution level in Taiwan, and principal component analysis-multiple linear regression (PCA-MLR) to identify the possible pollution source. Water quality and heavy metal monitoring data from Taiwan Environmental Protection Administration (EPA) was evaluated for 14 rivers in the four regions of Taiwan. The Erren River was classified as the most polluted River in Taiwan. Biochemical oxygen demand, ammonia, and total phosphate concentration in this river were the highest of the 14 rivers evaluated. In addition, heavy metal levels of the following rivers exceeded the Taiwan EPA standard limit: lead - in the Dongshan, Jhuoshuei, and Xinhuwei Rivers; copper - in the Dahan, Laojie, and Erren Rivers; and manganese - in all rivers. Water pollution in the Erren River was estimated to originate 72% from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research showed that PCA-MLR and the cluster analysis model accomplished our study objectives and will be helpful tools to evaluate water quality in rivers and we suggest that the continuous monitoring should be conducted to monitor water pollution from anthropogenic activities.

An entrepreneur is a business-able physical person who performs activities to gain the profit and who is registered according to law. Starting from the national and theoretical and legal solutions and court practice from comparative law, the authors analyze the concept and the legal position of an individual entrepreneur noticing the problems and inconsistencies in legal regulations. The authors of the work make a few conclusions and suggestions: 1) terminology is not coordinated with legal terminology from comparative law. In our law, the legal term is “entrepreneur”, which is a too wide and unspecified term because in economic profession this term represents the genus term for individual and collective entrepreneurship; 2) analyze all forbidden activities for entrepreneurs, judge the reasons pro et contra and work on eliminating prohibitions and favouring legal entities; 3) set by law the bankruptcy of an individual entrepreneur i.e. the individual bankruptcy of a physical person; 4) work on passing a separate legislation in the field of the individual entrepreneurship, especially on passing and changing the laws which would regulate handicrafts (including old crafts and jobs of home industry), free professions as well as agricultural activity.

Brain-stimulation reward, also known as intracranial self-stimulation (ICSS), is a commonly used procedure for studying brain reward function and drug reward. In electrical ICSS, an electrode is surgically implanted into the medial forebrain bundle in the lateral hypothalamus or the ventral tegmental area in the midbrain. Operant lever responding leads to the delivery of electrical pulse stimulation. The alteration in the stimulation frequency-lever response curve is used to evaluate the impact of pharmacological agents on brain reward function. If a test drug induces a leftward or upward shift in the ICSS response curve, it implies a reward-enhancing or abuse-like effect. Conversely, if a drug causes a rightward or downward shift in the functional response curve, it suggests a reward-attenuating or aversive effect. A significant drawback of electrical ICSS is the lack of cellular selectivity in understanding the neural substrates underlying drug reward versus aversion. Excitingly, recent advancements in optical ICSS (oICSS) have facilitated the development of at least three cell type-specific oICSS models – dopamine-, glutamate-, and GABA-dependent oICSS. In these new models, a comparable frequency-rate response curve has been established and employed to study the substrate-specific mechanisms underlying brain reward function and a drug's rewarding versus aversive effects. In this review article, we summarize recent progress in this exciting research area. The findings in these new behavioral models have not only increased our understanding of the neural mechanisms underlying drug reward and addiction but have also introduced a novel experimental approach in preclinical medication development for treating substance use disorders.

Refractive surgery is an increasingly popular procedure to decrease spectacle or contact lens dependency. The two most commonly used surgical techniques to correct myopia is Photorefractive keratectomy (PRK) and Femtosecond- Lasik (FS-LASIK)There are few publications that gathers such a long term follow up of both surgical techniques (2) Methods It has been performed a retrospective non-randomized study 509 PRK eyes and 310 FS-LASIK surgeries were followed for 10 years for the treatment of myopia and compound myopic astigmatism. Patients were followed up three months, one year, 2 years, 5 and 10 years. The safety index of both procedures was defined as a quotient between the postoperative BCVA (Best Corrected Visual Acuity) and the preoperative BCVA. The predictability is calculated as difference between the expected spherical equivalent and the achieved spherical equivalent. The efficacy index was calculated as a quotient between postoperative UCVA divided by the preoperative BCVA (3) Results. The results were: a safety index higher than 100% (109%) and an efficacy index of 82.4% after 10 years of PRK surgery in both groups. FS-LASIK was the safest surgery after 10 years and the most efficacy technique although in this case there were no statistically significant differences (4) Conclusions. All these data demonstrated better indexes for FS-LASIK

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

In this paper we study the initial boundary value problem for logarithmic Higher Order Wave equation. Introducing the Logarithmic Sobolev inequality and using the combination of Galerkin method, we consider the theorem of existence of a global weak solution to problem for the initial boundary value problem of the logarithmic wave equation. By constructing an appropriate Lyapunov function, we obtain the decay estimates of energy for logarithmic Higher Order Wave equation. The proof of the main theorem is given.

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

Literature pertaining to youth development has identified the importance of understanding the physical, intellectual and emotional needs of adolescent youth. The purpose of this study was to compare the use of a ‘traditional’ and ‘progressive’ coaching style to train a general male youth population to improve sprint and jump performances, whilst assessing enjoyment to comment on long term application. Maximal sprint times, sprint kinematics, unilateral jump distances and repetitive tuck jump scores were measured alongside anthropometric variables to characterise performance. Results revealed significant (p>0.05) pre/post differences in anthropometric variables across all maturation groups, and each of the maturational levels displayed a tendency to favour a particular coaching or control condition. Pre-PHV groups responded most effectively to the progressive style of coaching, displaying improvements in horizontal jump performances, and -0.7 to -2.7% improvements in all sprint times, despite also showing the largest increase in tuck jump scores (25.8%). The circa-PHV group produced their greatest improvements in the traditional intervention, as displayed through significant improvements (p<0.05) in 20m sprint times and dominant-leg horizontal jump performance, whilst also revealing the greatest deterioration in tuck jump scores (14.2%). Post-PHV displayed the greatest improvements in the control setting, suggesting the natural benefits gained through adolescent development were greater than the influence of the training interventions. In conclusion, it is suggested that matching coaching strategies and delivery techniques to the period of biological maturation may have implications for both performance and athlete safety.

Various environmental challenges, related to oil palm commodity has became a major environmental challenge to oil palm production. The aim and objective of this study is to analyze the actual water footprint of oil palm based on root water uptake under varying crop age and soil type. The research was conducted in Pundu Village, Central Kalimantan. The methodology adopted in carrying out this study consists of various stages which includes observing soil moisture, rainfall, and water table, ETo, root water uptake and oil palm water footprint. The highest rate of water consumption was the 13 years oil palm on spodosol soil type with an average daily rate of 3.73 mm/day. The lowest evapotranspiration was represented by the 7th year oil palm on spodosol with an average rate of 3.07 mm/day. The total water footprint value obtained was between 0.56 – 1.14 m3/kg for a variety of plants with various age and soil types. It can be deduced that the water footprint value of oil palm vary for different crop age and soil types on temporal scale. The study also presented that the source of green water from the first root zone of oil palm deliver the highest contribution for oil palm root water uptake.

This paper investigates the efficacy of YOLOv8 variants for vehicle detection and license plate detection within smart parking applications, emphasizing performance under varying ambient lighting conditions. The proposed system is to seize full video frames, extracts regions of interest containing vehicles, and feeds them into separate, pre-trained YOLOv8 models – one dedicated to vehicle detection and another for license plate detection. Four YOLOv8 variants, nano, small, medium, and large, are evaluated. As a pre-processing step, the images are processed with the help of OpenCV and Pillow libraries to adjust the luminosity and increase the images’ DPI so that they would be easy to perceive by the Tesseract OCR engine. Sixteen potential combinations arise from pairing the four YOLOv8 models for vehicle and license plate detection tasks. To identify the most suitable combinations, we employ the Multi-Criteria Decision-Making method, specifically Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. This analysis considers 4 critical metrics: Precision, mean average precision at 95% Intersection of Union threshold, Recall and Total Inference Time. The objective is to achieve an optimal balance between high accuracy and real-time processing. Following the selection of optimal YOLOv8 combinations through TOPSIS analysis, we assess their performance under varying ambient light intensity (measured in lux). This evaluation aims to identify the most robust model combinations that ensure accurate vehicle and license plate recognition across the diverse lighting conditions encountered in real-world environment.

Synthetic fiber ropes are frequently used as mooring lines in floaters supporting Floating Offshore Wind Turbines (FOWTs) due to their various advantages. Damages in these lines can lead to the disruption of the FOWTs normal operation and thus their early detection is crucial. Currently, this damage detection problem has been investigated exclusively for FOWT chain mooring lines. This study focuses on the damage detection in FOWT synthetic mooring lines under varying environmental-operational conditions (EOCs) via two types of vibration-based Structural Health Monitoring methods: those based on Multiple Models and those based on a single Functional Model. The methods are based on multivariate AutoRegressive or on scalar Transmittance Function (TF) AutoRegressive with eXogenous input models. All methods are evaluated through a Monte Carlo study involving 390 simulations with a 10MW wind turbine mounted on the semi-submersible OO-Star wind floater and two damage scenarios (10%, 14% stiffness reduction) to a single mooring line. The results using acceleration signals from only two measuring positions on the mooring line, indicate the great potential for robust SHM in FOWTs synthetic mooring lines with the TF model-based methods successfully detecting the considered early stage damage scenarios, even for EOCs not encountered in the methods training.

Mini-drones can be used for a variety of tasks, such as weather monitoring, package delivery, search and rescue, and recreation. Their uses are mostly restricted to outside locations with access to the Global Positioning System (GPS) and/or similar systems since their usefulness, safety, and performance substantially rely on ubiquitously accurate positioning and navigation. Indoor localization is getting better, thanks to technologies like Visual Simultaneous Localization and Mapping (V-SLAM). However, more advancements are still required for mini-drone navigation applications with greater safety standards. In this research, a novel method for enhancing indoor mini-drone localization performance is proposed. By merging Oriented Rotated Brief SLAM (ORB-SLAM2), Semi-Direct Monocular Visual Odometry (SVO), and an Adaptive Complementary Filter, the suggested strategy improves V-SLAM approaches (ACF). The findings demonstrate that, when compared to other widely-used indoor localization algorithms, the suggested methodology performs better at estimating location under various situations (low light, low texture, and dynamic environments).

The capacitated team orienteering problem with time windows (CTOPTW) is a NP-hard combinatorial optimization problem. In the CTOPTW, a set of customers is given each with a profit, a demand, a service time and a time window. A homogeneous fleet of vehicles is available for serving customers and collecting their associated profits. Each vehicle is constrained by a maximum tour duration and a limited capacity. The CTOPTW is concerned with the determination of a preset number of vehicle tours that begin and end at a depot, visit each customer no more than once while satisfying the time duration, time window and vehicle capacity constraints on each tour. The objective is to maximize the total profit collected. In this study we propose an iterated local search (ILS) algorithm to deal with the CTOPTW. ILS is a single solution based meta-heuristic that successively invokes a local search procedure to explore the solution space. A perturbation operator is used to modify the current local optimum solution in order to provide a starting solution for the local search procedure. As different problems and instances have different characteristics, the success of the ILS is highly dependent on the local search procedure, the perturbation operator(s) and the perturbation strength. The basic ILS uses a single perturbation operator and the perturbation strength remains the same during the optimization process. To address these issues, we use three different perturbation operators and a varying perturbation strength which changes as the algorithm progresses. The idea is to assign a larger perturbation strength in the early stages of the search in order to focus on exploring the search space. The perturbation strength is gradually decreased so that we focus more on exploitation. The computational results show that the proposed ILS algorithm is able to generate high quality solutions on the CTOPTW benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both the solution quality and computational time. Moreover, the proposed ILS produces 21 best known results and 5 new best solutions.

The land subsidence occurring in goafs after coal mining is a protracted process. The accurate prediction of long-term land subsidence in goafs relies heavily on the availability of long-term monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on Interferometric Synthetic Aperture Radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No.2 mine in Shanxi Province, China. Initially, coal faces with same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation of each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No.2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.

Background. Carotid artery disease is prevalent among patients with coronary heart disease. The concomitant severe lesions in the carotid and coronary arteries may necessitate either simultaneous or staged revascularization, involving coronary bypass and carotid endarterectomy. However, there is presently a lack of consensus on the optimal choice of surgical treatment tactics for patients with significant stenoses in both carotid and coronary arteries. The aim of the current study was to compare the 30-day and long-term outcomes of coronary and carotid artery revascularization surgery based on the simultaneous or staged surgical tactics. Material and Methods. The single-center, non-randomized retrospective study involved 192 patients with concurrent coronary artery disease and carotid artery stenosis ≥70%, of whom 106 patients underwent simultaneous intervention (CABG+CEA), and 86 patients underwent staged CABG/CEA. The mean time between stages ranged from 1 to 4 months (mean 1.88±0.9 months). Endpoints included death from any cause, nonfatal stroke, nonfatal myocardial infarction (MI), and major adverse cardiovascular events (MACE) (death + nonfatal MI + nonfatal stroke) within 30 days after the last intervention and in the long-term follow-up period (median follow-up - 6 years). Results: The 30-day all-cause mortality, incidence of postoperative nonfatal MI, nonfatal stroke, and MACE did not exhibit differences between the groups after single-stage and staged interventions. However, the overall risk of postoperative complications (adjusted for the risk of any complication per patient) (OR 2.214, 95% CI 0.214-0.954, p=0.034), as well as the duration of ventilatory support (p=0.004), were elevated in the group after simultaneous interventions compared with the staged intervention group. However, this difference did not result in an increased incidence of death and MACE in the group after simultaneous interventions. In the long-term follow-up period, there were no significant differences observed when comparing simultaneous or staged surgical tactics in terms of overall survival (54.9% and 62.6% in groups 1 and 2, respectively, P log-rank = 0.068), nonfatal stroke-free survival (45.6% and 33.6% in groups 1 and 2, respectively, P log-rank = 0.364), nonfatal MI-survival (57.6% and 73.5% in groups 1 and 2, respectively, P log-rank = 0.169), and MACE-free survival (7.1% and 30.2% in groups 1 and 2, respectively, P log-rank = 0.060). Risk factors associated with an unfavorable outcome included age, smoking, BMI, LV EF, and atherosclerosis of lower limb arteries. Conclusions: This study revealed no significant difference in the impact of simultaneous CABG+CEA or staged CABG/CEA on the incidence of death, stroke, MI, and MACE over a 30-day and long-term follow-up period. Although the immediate results indicated an increased risk of a complicated course (attributable to overall complications) after simultaneous CABG+CEA compared with staged CABG/CEA, this did not lead to an increase in fatal complications. Therefore, performing both tactics is deemed safe and eligible after a meticulous postoperative risk assessment.

Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

The continuous energy transformation processes in heating, ventilation and air conditioning systems of buildings are responsible for 36% of global final energy consumption. Tighter thermal insulation requirements for buildings have significantly reduced heat transfer losses. Unfortunately, this has little effect on energy demand for ventilation. On the basis of the First and the Second Law of Thermodynamics, the concepts of entropy and exergy are applied to the analysis of ventilation air handling unit (AHU) with a heat pump in this paper. This study aims to develop a consistent approach for this purpose, taking into account the variations of reference temperature and temperatures of working fluids. An analytical investigation on entropy generation and exergy analysis are used, when exergy is determined by calculating coenthalpies and evaluating exergy flows and their directions. The results show that each component of the AHU has its individual character of generated entropy, destroyed exergy and exergy efficiency variation. However, the evaporator of heat pump and fans have unabated quantities of exergy destruction. The exergy efficiency of AHU decreases from 45-55% to 12-15% when outdoor air temperature is within the range of –30°C…+10°C, respectively. This helps to determine conditions and components of improving the exergy efficiency of the AHU at variable real-world local climate conditions. The presented methodological approach could be used in the dynamic modelling software and contribute to a wider application of the Second Law of Thermodynamics in practice.

The new class of mRNA vaccines is studied extensively, leading to many novel prospects based on the pivotal success of vaccines against the SARS-Cov-2 virus. Many novel mechanisms of immune response discovered have led to the possibility of mutation-resistant vaccines by combining multiple conserved epitopes as antigens to manipulate the T-cell and B-cell responses; these antigens can also come from different organisms, providing protection against numerous diseases and of most tremendous significance is the utility of mRNA vaccines in preventing and treating more than 100 autoimmune disorders. This is a significant humanitarian breakthrough given the ease and faster and low cost of mRNA vaccines, being a chemical entity. This paper provides a prospective analysis of mRNA vaccines with much broader applications than anticipated when these vaccines entered treating SARS-Cov-2 infections.

Coronavirus infection disease -2019 (COVID-19) was a global pandemic with high mortality and morbidity, that lead to an increased health burned all over the world. Although the virus affects mostly pulmonary tract, cardiovascular implications were often among COVID-19 positive patients, and are predictive for poor outcomes. Increased values of myocardial biomarkers such as Troponin I or NT-proBNP were proven to be risk factors for respiratory failure (26). Although the risk of acute coronary syndromes (ACS) was greater in acute-phase of COVID-19, there were lower rates of hospitalization for ACS, due to patient’s hesitation for presenting at the hospital (22). Hospitalized ACS patients with COVID-19 infection, had a prolonged symptom-to-first medical contact time, and longer door-to-balloon time. The mechanisms of myocardial injurie in COVID -19 patients are not still not clear, most often is incriminated: the down-regulation of ACE2 inhibitors, endothelial disfunction, pro-coagulant status, increased levels of pro-inflammatory cytokines. The aim of this paper is to evaluate the long-term outcomes of COVID-19 survivors that presented an acute myocardial infarction by reviewing existing data.

This article presents an analysis of monthly precipitation totals based on the Global Precipitation Climatology Centre database and monthly mean temperatures based on National Oceanic and Atmospheric Administration data for 377 catchments located around the world. Data sequences covering 110 years from 1901 to 2010 were analysed. The long-term sequences of precipitation and temperature were used to assess the variability of climate extremes, referred to here as polarisation. Measures of polarisation used in the natural sciences are discussed. A simple measure of polarisation was presented and applied to long-term sequences of monthly precipitation totals and monthly mean temperatures. Due to the nature of the proposed polarisation measure, other characteristics of precipitation and temperature sequences are also presented as a background for the discussion of the polarisation index. The study showed the existence of trends related to the polarisation of temperature and precipitation phenomena. As a result of the analysis, trends of polarisation factors in the area of precipitation and temperature were identified in 11 catchments out of the 377 analysed catchments. The trend analysis used Mann-Kendall tests at a level of significance of 5%. The Pettitt test was used to determine the point of trend change for precipitation and temperature data. The whole investigation is supported by rich graphical analyses, and the results are presented in tabular form.

This brief commentary challenges the use of the term "energy deficiency" in nutrition and metabolism, providing a critical examination of its scientific and philosophical aspects. It emphasizes the need to consider rare diseases associated with actual energy deficiency, which extend beyond the scope of everyday tiredness or fatigue. By analyzing energy balance and its complexities, this critique underscores the oversimplification associated with the term and its potential for misleading implications. It highlights the dynamic nature of energy metabolism and the intricate mechanisms involved in maintaining energy equilibrium, including the impact of rare genetic and physiological abnormalities. In addition to the discussion on energy balance, this commentary explores the manifestation of rare diseases that disrupt energy production, utilization, or hormonal regulation. Conditions such as mitochondrial diseases, glycogen storage diseases, adrenal insufficiency, and Prader-Willi syndrome are examined, shedding light on their profound impact on individuals' energy levels and overall health. The distinct time courses, underlying mechanisms, and clinical implications of protein deficiency, energy deficiency, and vitamin C deficiency are also compared, further emphasizing the complexity of energy metabolism and its relationship with various nutrient deficiencies. To foster a more comprehensive understanding of energy metabolism and enhance clarity in communication within the field, the commentary proposes the adoption of alternative terminology, such as "energy flux," to capture the multifaceted nature of energy balance more accurately. By reevaluating the terminology employed, researchers and healthcare professionals can better convey the intricate dynamics of energy metabolism and address the unique challenges faced by individuals with rare diseases causing actual energy deficiency. In conclusion, this commentary serves as a thought-provoking exploration of the concept of energy deficiency in nutrition and metabolism. It highlights the limitations of the term in capturing the complexities of energy balance, particularly in the context of rare diseases. By broadening the discussion to include these rare conditions, it encourages a more comprehensive understanding of energy metabolism and calls for precise and nuanced terminology to facilitate effective communication and advancements in the field.

Microbial communities play a key role in sustainable agriculture. However, we still need more in-formation, to understand the complex response of the microbial community to long-term organic farming, which aims to reduce synthetic fertilizer and pesticide use in order to produce sustainably and improve soil quality. We have assessed the long-term effect of two organic cropping systems and a conventional system on the microbial soil community structure using high-throughput se-quencing analysis. We analyzed the link between these communities and changes in soil properties and crop yield. Results showed that the crop yield was similar among the three cropping systems. Soil properties, such as total organic carbon, nitrogen, ammonium, magnesium and boron, influ-enced changes in the bacterial community structure. A linear discriminant analysis effect size (LEfSe) showed different bacteria and fungi as key microorganism of each of the three different cropping systems, in addition, our results reflected that fungal community were more sensitive than bacteria to cropping system. This research provides an insight about changes occurred in soils, especially in microbial communities considering the effect of that changes in crop yield which were remained stable among the different cropping systems.

In general, remote sensing has proven to be a powerful tool in the overall understanding of natural and anthropogenic phenomena. Satellites have become useful tools for tasks such as characterization, monitoring, and the continuous prospecting of natural resources. This research aims to analyze spatial dynamic and destructive on coral reefs area and correlation between live coral reduction and population on small islands. Landsat MSS, TM, ETM, and OLI-TIRS are used to spatial analyze of coral reef dynamics from 1972 to 2016. The image processing includes gap-filling, atmospheric correction, geometric correction, image composite (true color), water column correction, unsupervised classification, reclassification, accuracy assessment. The statistical analysis identifies the relationship between dynamic population data with a reduction of live coral, namely Principal Component Analysis (PCA) and Multiple Regression Analysis. The effect of the population shows a positive correlation with the reduction in the area of live coral, although it is significant. The fact is the practice of coral destruction on an island; it is usually not only caused or carried out by residents who live on the island but also carried out by other residents of different islands.

Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes are located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes are expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. Hox clusters are irreversibly elongated along the force direction. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can ‘explain’ this Empirical Rule. In a spontaneous mutation where the cluster fastening is dismantled, a minimal pulling force can automatically shift the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of Temporal Collinearity in Drosophila.

We study the short−term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and modern languages. To model the short–term capacity, we have considered the number of words per interpunctions, the “word interval” , because this parameter can model how the human mind memorizes “chunks” of information. Since can be calculated for any alphabetical text, we can perform experiments − otherwise impossible − with ancient readers by studying the literary works they used to read. The “experiments” compare the of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of their similar short–-term memory capacity) and by defining an “overlap index”. We also define a population of universal readers who can read any the New Testament written in alphabetical language. More than 50% of the readers of specific languages overlap with the universal readers with probability . Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

The role of pregnancy in multiple sclerosis (MS) is of importance because many patients with MS are young women in the childbearing age who require information to inform their reproductive decisions. Pregnancy is now well-known to be associated with fewer relapses of MS and reduced activity of autoimmune encephalomyelitis (EAE). However, in women with multiple sclerosis, this benefit is not always sufficient to protect against a rebound of disease activity if disease modulating therapy is ceased for pregnancy. There is reason to be concerned that use of assisted reproductive therapies can be associated with relapses of MS. It is thought that the beneficial effects of pregnancy are due to the pregnancy-associated changes in the maternal immune system. There is some evidence of this in human studies and studies of EAE. There is also evidence that having been pregnant leads to better long-term outcome of MS. The mechanism for this is not fully understood but it could result from epigenetic changes resulting from pregnancy or parenthood. Further studies of the mechanisms of the beneficial effects of pregnancy could provide information that might be used to produce new therapies.

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

Tobacco use is bad for your health in many ways, not just your lungs. It affects many other parts of your body, including your nervous system. This abstract looks at the link between smoking and the nervous system and shows how smoking affects the nerve system in specific ways. There are a lot of different diseases and risks that can come from smoking. By weakening blood vessels and making it easier for blood clots to form, smoking makes the chance of having a stroke much higher. Using tobacco is also linked to a drop in brain function and memory problems, as well as a higher chance of neurodegenerative diseases like Alzheimer's disease and dementia. Also, people who smoke have a higher chance of getting multiple sclerosis, which is a long-term autoimmune disease that affects the central nervous system. It can also cause peripheral neuropathy, which can damage nerves and cause tingling, pain, and stiffness in the limbs. Nicotine, the substance that makes tobacco addicting, changes the brain's reward circuits. This makes it hard to stop smoking because it leads to addiction. In addition to these effects on the body, smoking also has effects on the mind. It makes mood disorders like sadness and anxiety more likely, making the symptoms worse and hurting general health. Understanding the effects of smoking on the brain is important for improving overall health and getting people to stop smoking. Public health efforts, education, and targeted interventions are needed to make people aware of these risks and help them make choices about smoking that are based on accurate information. By looking at how smoking affects the brain, we can try to make people's lives better and protect their and their communities' well-being.

The astroclimatic conditions characterize the quality of astronomical site. The Terscol Observatory (coordinates 430 16 ‘29’’N, 420 30’ 03’’E) was founded over 42 years ago in 1980. The paper presents the results of analysis of long-term variations (1980-2022) of astroclimatic parameters as total cloud cover, precipitable water vapor, and wind speed at 200mbar level above the Terscol Observatory using ECMWF-ERA5 atmospheric reanalysis data. Long-term trends of the astroclimatic parameters are study statistical significantly trend analysis method using Mann–Kendall and Sen’s slope to estimate the magnitude of the changes. In result the site show not significant decreases of 200-hPa wind speed and increases of precipitable water vapor, but significant (99 per cent confidence level) long-term changes of total cover cloud above the Terscol Observatory.

Objectives: To examine the long term impact of COVID-19 on sleep patterns and development of sleep disorders. Methods: Using the centralized Massachusetts General Brigham (MGB) Research Patient Data Registry (RPDR), SARS-CoV2 positive patients were surveyed about their sleep patterns before and after the viral infection. Information related to co-morbid conditions and medications were obtained through chart review. Results: Two hundred and forty five completed surverys were analysed. Average age was 53.3 ± 16.3 years, and participants were predominantly Non-Hispanic White (84.1%) and female (74.3%). Average BMI (kg/m2) was 29.9 ± 6.9, and a greater proportion was non-smokers (63.2%). After COVID-19, there was an increase in the percentage of participants reporting difficulty initiating (31 ± 46% vs. 39 ± 49%, P=0.01), and maintaining sleep (43 ± 49% vs. 57 ± 49%, P<0.001), and use of sleep aids (24 ± 43% vs. 30 ± 45% P=0.003) with an incidence rate of 24.3%, 37.4%, and 12.3% respectively. In addition, there was an increase in daytime fatigue and the need for napping (58 ± 49% vs. 36 ± 48%, P <0.0001) with an incidence of 8% and 23% respectively. The sleep symptoms persisted beyond 12 months among 28% of the participants and were predominantly seen among women. Conclusions: Infection with SARS-CoV2 has negative effects on sleep, and a significant proportion of adults experience insomnia and daytime sleepiness beyond 12 months after recovering from the initial infection.

The prospective study enrolled 144 patients after surgical treatment of biliary atresia in early infancy. We analyze the immediate effectiveness of the surgery and the age-related structure of complications in the up to 16-year follow-up. The immediate 2-year survival rate after the surgery constituted 49.5%. At the time of this writing, 17 of the patients have celebrated their 10th birthdays with good quality of life and no indications for LT. The obtained results underscore the critical importance of surgical correction of BA by Kasai surgery during the first 60 days of life and subsequent dynamic follow-up of the patients for the purpose of the early detection and timely correction of possible complications.