Plants grown under fluctuating light impact plant developments compared with those grown under non-fluctuating light conditions. However, our knowledge on the underlying regulatory mechanisms is still quite limited, particularly from the transcriptional perspective. In order to investigate the influence of different light intensity distributions on tomato plant development, we designed three fluctuating light intensity distributions with the non-fluctuating light intensity as control and compared the transcriptional differences after five weeks of treatment. We found plant height and aerial/root weight were significantly reduced under all fluctuating light treatments. Transcriptome analysis revealed that the number of up and down regulated genes had a distinct distribution pattern between different treatments and control. The largest difference between the numbers of down and up regulated genes was found between treatment 1 and 3, reaching to a total of 416 genes. The number and type of the top 20 enriched pathways differed between treatments and control. The largest number of genes enriched was involved in the biosynthesis of secondary metabolites. These results provide insights into the transcriptional regulations of tomato under different light intensity distributions.

This paper presents the preservation property of the average intensity order and the preservation property of the monotonic average intensity classes under distorted distributions. Several sufficient conditions are given to get the preservation properties. It is shown that the imposed conditions are achievable as we examine in some examples. The preservation of the average intensity order and the preservation of the decreasing average intensity class under the structure of a parallel system with independent and identically distributed components' lifetime are made. A lower bound for the average intensity function of a random lifetime with an increasing failure rate in average distribution is derived. The results are applied to some semiparametric models as a particular standard family of distorted distribution.

This paper investigates the randomly stopped sums, minima and maxima of heavy- and light-tailed random variables. The conditions on the primary random variables, which are independent but generally not identically distributed, and counting random variable are given in order that the randomly stopped sum, random minimum and maximum is heavy/light tailed. The results generalize some existing ones in the literature. The examples illustrating the results are provided.

The use of several light-related variables, such as the Secchi disc depth, the euphotic depth, and in particular, the diffuse attenuation coefficient (Kd), is deeply rooted in phytoplankton research, but these are not the most appropriate indicators of the amount of light available for photosynthesis. We argue that the variable of interest for phytoplankton is the mean light intensity in the mixed layer (Im), which represents the mean light to which phytoplankton cells are exposed throughout their life cycle, while being continuously mixed in the mixed layer. We use empirical data collected in different coastal ecosystems in southern Portugal to demonstrate why Im should be the preferred metric instead of the deeply rooted Kd. We show that, although the relationship between Im and Kd is inversely proportional, it is not always strong or even significant. Different Im values can be associated to the same Kd, but distinct Im have different physiological effects of phytoplankton. Therefore, Kd does not capture the amount of light available for photosynthesis, given that, unlike Im, Kd calculation does not consider the depth of the mixed layer. Therefore, we urge phytoplankton researchers to consider the measurement and calculation of Im when evaluating light-related processes in phytoplankton ecology.

The analysis of the current safety standards for electric toys showed that the normative intensity limits for visible light emitted by LEDs integrated in toys are overestimated. These limits were originally set in a scientific article published in 2012 that was adapted into the international safety standard published in 2017, and into the subsequent European and national standards, all published in 2020. The overestimation of the intensity limits results from several errors made in the original article. Accordingly, the current normative intensity limits for visible light used in toys do not protect the children’s eyes against adverse effects of exposure to high intensity LEDs which may compromise their visual health. Updating the safety standards for electric toys using a method based on robust scientific data is recommended to protect children's visual health and ensure their long-term well-being.

The Rényi entropy functional was optimised for the novel findings of this investigation under various conditions. Their research's findings suggest that the complete spectrum of Lévy stable distributions is covered by generalised t-distributions. Additionally, an exposition was undertaken to prove that the Lévy distribution generalizes Tsallisian distribution, rather than the reverse. The current study is a strong generalization of an existing research work in the literature.

Eelgrass meadows have attracted a lot of attention not only for their ability to maintain marine ecosystems as feeding grounds for marine organisms, but also for their potential to store atmos-pheric and dissolved CO2 as blue carbon. This study comprehensively evaluated the bacterial and chemical composition data obtained from eelgrass sediments of different scales along the coast of Japan to investigate the sediment effect on the acclimatization of eelgrass. Regardless eelgrass habitat, approximately 1% Anaerolineales, Babeliales, Cytophagales, and Phycisphaerales were present in the bottom sediment. Sulfate-reducing bacteria (SRB) were present at 3.69 % in eelgrass sediment compared to 1.70 % in bare sediment. Sulfur-oxidizing bacteria (SOB) were present at 2.81% and 1.10% in the eelgrass and bare sediment, respectively. Bacterial composition analysis and linear discriminant analysis revealed that sulfur-oxidizing bacteria detoxified H2S in the eelgrass meadows and that the larger-scale eelgrass meadows had a higher diversity of sul-fur-oxidizing bacteria. Our result indicated that one of the following systems detoxifies the H2S in eelgrass meadows microbial oxidation by sulfur-oxidizing bacteria, oxidation by O2 perme-ated by the physical diffusion of benthos. This study indicated the issues of maintaining sustain-able conservation of the eelgrass bed.

Isothermal interrupted hot compression tests of 5754 aluminum were conducted on Gleeble-3500 thermo-mechanical simulator at temperature 350 °C and 450 °C, strain rate 0.1 s-1 and 1s−1. These tests simulated flat rolling to investigate how softening behaviors respond to controlled parameters, such as deformation temperature, strain rate and delay times. This data allowed the parameters for the hot rolling process to be optimized. The delay times during interrupted compression vary between 5s and 60s. The dynamic softening at each pass and metadynamic recrystallization at the intervals of deformation passes were analyzed in detail. 0.2% offset yield strength is applied to calculate the softening fraction undergoing metadynamic recrystallization. A kinetic model was developed to describe metadynamic recrystallization behaviors of the hot deformed 5754 aluminum alloy. Furthermore, the time constant for 50% recrystallization was expressed as functions related to the temperature and the strain rate. The experimental and calculated results were found to be in close agreement, which verified the developed model.

For 80 years high-intensity focused ultrasound (HIFU) has been a significant area for medical research. It is a non-invasive procedure which causes cell death in a highly localised area at a defined distance from the transducer. Since 2000, the clinical indications have broadened considerable as the technology has improved. This review looks back at the history of HIFU and the current clinical indications.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these popular distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is the upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of a random discrete variable. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these legendary distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of the random discrete variable X. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

Tea plants (Camellia sinensis L.) are economically important horticultural crops for industrial use. The drink (tea) obtained from them is used all over the world, and its functional activity is determined by the polyphenols content and composition. Its accumulation is regulated by the action of many environmental factors. This paper presents data on the different intensity (50, 75, and 100 µmol·m−2·s−1) and duration of light exposure on tea plant in vitro cultures. Changes in morphophysiological characteristics, accumulation of carbohydrates and polyphenols, including their monomeric (phenylpropanoids, flavanols) and oligomeric (proanthocyanidins) forms, were studied. The malondialdehyde and photosynthetic pigments (chlorophyll a and b) contents were also analyzed. In the calli grown in light conditions, chloroplasts and anthocyanoplasts formation was noted, which was especially pronounced under the highest illumination intensity. A significant similarity between the growth of callus cultures, the carbohydrates and polyphenols accumulation at 50 and 75 µmol·m−2·s−1 was established. Which wasn’t characteristic of the calli grown at 100 µmol·m−2·s−1. The stress reaction absence in tea callus cultures has been demonstrated, which may be a consequence of the phenolic antioxidants’ accumulation in them. The obtained data evidence that their potential ability to accumulate primary (carbohydrates) and secondary (polyphenols) metabolites depends on the light exposure intensity and duration.

Pea shoots is a popular vegetable in certain regions of the world due to their unique taste and abundance of health-promoting phytochemicals. The perishable nature and susceptibility of root rot of pea shoots necessitate a new soilless production system located close to the market. This study compared the growth of pea shoots using various cultivation methods in LED plant factory. The results showed that early topping (4 days after transplanting, ET) promoted early harvest compared to later topping (20 days after transplanting, LT) and increased the number of harvested shoots by extending the harvest time to 2.8 times, ultimately resulting in a substantial yield improvement. Moreover, the yield of ET with lower planting density (72 plants m-2, ET-LD) was 8.7% higher than ET with higher planting density (126 plants m-2, ET-HD). Particularly, the average fresh weight of individual pea shoots under ET-LD exceeded that of ET-HD by 48.9%. It is advisable to consider adopting ET-LD for the cultivation of pea shoots in LED plant factory. Based on ET-LD, the yield, nutritional quality, and light use efficiency of pea shoots were further explored at different stages under three levels of light intensity (50, 100, 150 μmol m-2 s-1). Contrasted against the light intensity of 50 μmol m-2 s-1, the average fresh weight per pea shoots, number of harvested, and total fresh yield under light intensity of 150 μmol m-2 s-1 treatment increased by 60.2%, 62.8%, and 165.1%, respectively. Meanwhile, the fresh weight per pea shoots, photosynthetic capacity, soluble sugar and vitamin C levels in leaves, as well as light use efficiency and photon yield, initially increased and then decreased with the extension of the planting period. Among these, soluble sugar, LUE, and PY started to decrease after reaching the maximum value at 60-70 days after transplanting. In conclusion, light intensity of 150 μmol m-2 s-1 with a photoperiod of 16 h d-1 using LEDs, combined with early topping within a planting period of 60-70 days, proves to be suitable for the hydroponic production of pea shoots in LED plant factory.

A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

We describe electron temperature measurements in the SSX MHD wind tunnel using two different methods. First, we estimate T_e along a chord by measuring the ratio of the C__III 97.7 nm to C_IV 155 nm line intensities using a vacuum ultraviolet monochrometer. Second, we record a biasing scan to a double Langmuir probe to obtain a local measurement of T_e. The aim of these studies is to increase the Taylor state lifetime, primarily by increasing the electron temperature. Also, a model is proposed to predict magnetic lifetime of relaxed states and is found of predict the lifetime satisfactorily. Furthermore, we find that proton cooling can be explained by equilibration with the electrons.

Cannabis (Cannabis Sativa L.) is now legally produced in many regions worldwide. Cannabis flourishes under high light intensities (LI); making it an expensive commodity to grow in controlled environments, despite its exceptionally high market value. It is commonly believed that cannabis secondary metabolite levels may be enhanced both by increasing LI and by exposing crops to ultraviolet radiation (UV). However, there is sparse scientific evidence to guide cultivators. Therefore, the impact of LI and UV on yield and quality must be elucidated to enable cultivators to optimize their lighting protocols. We explored the effects of LI, ranging from 350 to 1400 μmol m^-2 s^-1 and supplemental UV spectra on cannabis yield and potency. There were no spectrum effects on inflorescence yield, but harvest index under UVA+UVB was reduced slightly (1.6%) vs. the control. Inflorescence yield increased linearly from 19.4 to 57.4 g/plant and harvest index increased from 0.565 to 0.627, as LI increased from 350 to 1400 μmol m^-2 s^-1. Although there were no UV spectrum effects on total equivalent Δ⁹-tetrahydrocannabinol (T-THC) content in leaves, the neutral form, THC, was 30% higher in UVA+UVB vs. control. While there were no LI effects on inflorescence T-THC content, the content of the acid form (THCA) increased by 20% and total terpenes content decreased by 20% as LI increased from 350 to 1400 μmol m^-2 s^-1. High LI can substantially increase cannabis yield and quality, but we found no commercially-relevant benefits of adding supplemental UV radiation to indoor cannabis production.

In this work, we'll present the power spectra density, auto-correlation function, dentrended fluctuation analysis and multifractal spectrum are performed to the daily light curves of Blazars monitored by Fermi LAT. Some signs of power law are found.

The development of one technology can be portrayed by common methods like Gartner’s hype cycle or S-curve, however, there is no method to characterize the feature distributions of multiple technologies within a specific domain. This study proposes a big data-based method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or causing potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach via comparing historical trends with literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

The effects of rare earth Ce on the microstructure and properties of cast pure copper were investigated through thermodynamic calculations, XRD analysis, mechanical testing, metallographic microscopy, and scanning electron microscopy (SEM). The experimental results demonstrate that the reaction between rare earth Ce and oxygen as well as sulfur in copper exhibits a significantly negative Gibbs free energy value, indicating a strong thermodynamic driving force for deoxidation and desulfurization reactions. Ce is capable of removing trace amounts of O and S from copper. Moreover, the maximum solid solubility of Ce in Cu falls within the range of 0.009% to 0.01%. Furthermore, Ce can refine columnar grains while enlarging equiaxed grains in as-cast copper.Upon addition of rare earth Ce, the tensile strength increased by 8.45%, elongation increased by 12.1%, and microhardness rose from 73.5HV to 81.2HV-an increase of 10.5%. Overall, rare earth Ce has been found to enhance both the microstructure and mechanical properties of cast pure copper.

Blazars show rapid and high amplitude variability. In this work , Fermi daily light curve of 130 sources are analyzed, distribution of daily variability are compared with each other by using Kolmogorov-Smirnov (K-S) test. results show: 1)Some pairs of the distributions are similar; 2) In most cases, the distributions are not Gaussian.

We report on the design, characterization and validation of a spherical irradiation system for inactivating SARS-CoV-2, based on UV-C 275 nm LEDs. The system is designed to maximize ir-radiation intensity and uniformity, and can be used for irradiating a volume of 18 liters. To this aim: (i) several commercially available LEDs have been acquired and analyzed; (ii) a complete optical study has been carried out in order to optimize the efficacy of the system. (iii) The re-sulting prototype has been characterized optically, and tested for the inactivation of SARS-CoV-2 for different exposure times, doses and surface types. Results indicate that a 99.9% inactivation can be reached after 1 minute of treatment with a dose of 83.1 J/m2.

Antisense oligonucleotides (ASOs) are short oligodeoxynucleotides designed to bind to specific regions of target mRNA. ASOs can modulate pre-mRNA splicing, increase levels of functional proteins and decrease levels of toxic proteins. ASOs are being developed for the treatment of several diseases, including motor neuron diseases (MNDs), a group of neurodegenerative disorders characterised by the loss of motor neurons, including spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and spinal and bulbar muscular atrophy (SBMA). The biggest success has been the ASO known as nusinersen, the first effective therapy for SMA, able to improve symptoms and slow disease progression. Another success is tofersen, an ASO designed to treat ALS patients with SOD1 gene mutations. Both ASOs have been approved by the FDA and EMA. On the other hand, ASO treatment in ALS patients with the C9orf72 gene mutation did not show any improvement in disease progression. The aim of this review is to provide an up-to-date overview of ASO research in MND, from preclinical studies to clinical trials and, where available, regulatory approval. We highlight the successes and failures, underline the strengths and limitations of the current ASO research, and suggest possible approaches that could lead to more effective treatments.

Surface initiated atom transfer radical polymerization (SI-ATRP) is one of the most versatile technique to modify the surface properties of material. Recent developed metal free SI-ATRP makes such technique more widely applicable. Herein photo-induced metal-free SI-ATRP of methacrylates, such as methyl methacrylate, N-isopropanyl acrylamide, and N,N- dimethylaminoethyl methacrylate, on the surface of SBA-15 was reported to fabricate organic-inorganic hybrid materials. SBA-15 based polymeric composite with adjustable graft ratio was obtained. The structure evolution during the SI-ATRP modification of SBA-15 was monitored and verified by FT-IR, XPS, TGA, BET, and TEM. The obtained polymeric composite showed enhanced adsorption ability for the model compound toluene in aqueous. This procedure provides a low cost, ready availability, and facile modification way to synthesize the polymeric composites without the contamination of metal.

This article introduces a novel payload structure, which has been experimentally proven to have smaller impact force while the carried internal electronics remains intact. The study describes in details how materials and geometric structures were chosen based on information analysis. It is clarified how possible geometric structures and materials can be chosen to yield optimal efficiency. Finite element method simulations were carried out to investigate the mechanical properties of several geometric structures, which has led to the dodecahedron form. For the frame, Polylactic Acid were chosen as an optimal material. A low air pressure test for parts of the structure, and also the whole structure were carried out with successful results. Drop tests, with force measuring sensors and high speed videos, were carried out for measuring and comparing the maximal impact force of classical payloads and the novel one. Laboratory and real life tests demonstrated that the developed new structure is safer and it is applicable.

Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents’ awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost, and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the IDW method. This approach can also be used in 3 dimensions to quantitatively evaluate the distribution of light intensity distribution. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific and whether it provides residents with a basis for the protection of their rights.

The philosophy of testing statistical hypothesis is a natural consequence and functional extension of mathematical analysis of Probability. Along with the concept of recurrence when applied to random sequences and functions, it leads to the analysis of a priori and posterior which implies testing statistical hypothesis. Testing statistical hypothesis also involves algebraic, functional and dimensional considerations, which are found in the works of Laplace. Aspects of mathematical analysis such as universality of solutions, Laws of Large Numbers, Entropy, Information, and various functional dependencies are the main factors explained in the five properties that lead to implication of testing statistical hypothesis. Various interesting examples with modern scientific significance from genetics, astrophysics, and other areas give methodological access to answers of different problems and phenomena which are involved in the logic of testing statistical hypothesis.

Background Augmented reality (AR) in surgery can offer an enhanced view of reality through the superimposition of computer-generated digital images on the real environment. It allows surgeons to integrate image visualisation, improving operative efficiency, surgical outcomes, surgical training and patient education. This review aims to evaluate the current status of augmented reality in surgery, surgical training and potential future applications. Methods We performed a non-systematic review of available literature from January 2005 to August 2021 by searching PubMed, EMBASE and the Cochrane library using a combination of terms “augmented reality”, “virtual reality”, “surgery”, “simulation” and “training”. Articles considered for this review were identified by relevant search criteria including title, keywords, abstract, and full-text. Conclusions AR technologies present an exciting new trend with multiple potential applications in surgery. Intraoperative AR systems have shown promise in specialties involving fine movement of organs during surgical procedures, including Neurosurgery, Ears, Nose and Throat and Orthopaedic Surgery. AR has also exhibited the potential to enhance surgical training and improve knowledge acquisition; it can foster international collaborations via telesurgery and telepresence. In the near future, AR will likely work in symbiosis with surgeons, serving as a complex computer-human coalition which can improve patient outcomes, patient education and surgical training.

Retrospective information about the climate system and plant ecophysiology are key inputs in climate and Earth system modelling. Dendrochronology provides such information with large spatiotemporal coverage, and stable carbon isotope (¹³C/¹²C) analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules, and, to this day, ¹³C/¹²C variation is attributed to ¹³C discrimination during CO₂ diffusion into leaves and assimilation by rubisco. By contrast, ¹³C discrimination by post-rubisco processes is presumed constant. Here, recently reported results on the first dataset of intramolecular ¹³C discrimination in tree rings were synthesised by variance component analysis. The emerging picture is not consistent with the classical (DR-discrimination-centred) concepts and practices of carbon-isotope dendrochronology. Specifically, leaf and stem post-rubisco discrimination each account for more variation in the data than diffusion-rubisco discrimination, i.e., post-rubisco discrimination is not constant. Furthermore, diffusion-rubisco discrimination is used widely as proxy of leaf intrinsic water-use efficiency (iWUE), a key determinant in the responses of global biogeochemical cycles to climate change. However, since diffusion-rubisco discrimination is a small component of the total data variance, whole-molecule analysis yields confounded iWUE estimates, yet intramolecular analysis will likely offer solutions. Lastly, all currently observed ¹³C/¹²C-climate relationships are attributed to diffusion-rubisco discrimination. However, here, relationships with temperature and radiation derive from leaf-level post-rubisco discrimination while relationships with precipitation derive from stem-level post-rubisco discrimination. That said, advances in mass spectrometry may soon make intramolecular ¹³C/¹²C analysis broadly available taking carbon-isotope dendrochronology to the next level.

Ranked Set Sampling (RSS) is a useful technique for improving the estimator of population mean when the sampling units in a study can be easily ranked than the actual measurement. RSS performs better than simple random sampling (SRS) when the mean of units corresponding to each rank is used. The performance of RSS can be increased further by assigning weights to the ranked observations. In this paper, we propose weighted RSS procedures to estimate the population mean of positively skew distributions. It is shown that the gain in the relative precisions of the population mean for chosen distributions are uniformly higher than those based on RSS. The gains in relative precisions are substantially high. Further, the relative precisions of our estimator are slightly higher than the ones based on Neyman’s optimal allocation model for small sample sizes. Moreover, it is shown that, the performance of the proposed estimator increases as the skewness increases by using the example of lognormal family of distributions.

This work reports measurements of calcified gallstone elemental compositions using laser-induced optical emission spectroscopy. The experimental results support the importance of the magnesium concentration in gallstone growth. Granular stones reveal an increased magnesium concentration at the periphery of the granules, suggesting the inhibition of further growth. Non-granular gallstones reveal lower overall magnesium concentrations but with higher values near the center.

For evaluating probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based upon the Gauss-Laplace law. The latter will be considered here as an element of the newly introduced family of (p,q)-spherical distributions. Based upon a suitably defined non-Euclidean arc-length measure on (p,q)-circles we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly like with elliptically contoured distributions and more general homogeneous star-shaped ones. This is demonstrated at hand of a generalization of the Box-Muller simulation method. En passant, we prove an extension of the sector and circle number functions.

This paper addresses the issues of how we can quantify structural information for nonparametric distributions and how we can detect its changes. Structural information refers to an index for a global understanding of a data distribution. When we consider the problem of clustering using a parametric model such as a Gaussian mixture model, the number of mixture components (clusters) can be thought of as structural information in the model. However, there does not exist any notion of structural information for nonparametric modeling of data. In this paper we introduce a novel notion of {\em kernel complexity} (KC) as structural information in the nonparametric setting. The key idea of KC is to combine the information bias inspired by the Gini index with the information quantity measured in terms of the normalized maximum likelihood (NML) code length. We empirically show that KC has a property similar to the number of clusters in a parametric model. We further propose a framework for structural change detection with KC in nonparametric distributions. With synthetic and real data sets we empirically demonstrate that our framework enables us to detect structural changes underlying the data and their early warning signals.

Recently, research on ultra-high-performance concrete (UHPC) with the characteristics of ultra-high strength, high toughness, and high durability has been actively conducted from materials to actual structural applications. However, research on experimental methods that reflect the unique characteristics of UHPC, which are different from those of normal concrete, is insufficient. To solve the difficulties in applying the compressive creep test method for normal concrete to UHPC and to verify the validity of the recently proposed ASTM C 1856 standard test method, this study performed compressive creep tests on UHPC with variables of specimen size, sustained load intensity, fiber length, and strain measuring method. Two types of cylindrical specimens of ϕ100 × 200 mm and ϕ75 × 150 mm were used for a specimen size variable. The sustained load intensities of 10% to 40% based on the specified compressive strength of 150 MPa were applied. Two types of steel fiber lengths of 13 mm and 19.5 mm in the UHPC mix were used as a variable, and three strain measuring methods of the embedded strain gauge method, the attached strain gauge method, and the strain meter with stud measured the change in length of the UHPC specimens. The experimental results showed that the strain meter method using the standard rod had limitations due to the error in the verification process using the standard rod and the observation error of the measurer, and the sustained load intensity is preferable to 15% to 40% of the actual compressive strength. It was found that it is possible to perform a compressive creep test for UHPC using a specimen with a size of ϕ75 mm or more, and the fiber length did not affect the creep behavior of UHPC up to 19.5 mm. As a result of comparing the UHPC creep test results with the creep prediction model, the B3 model was found to most accurately predict the UHPC creep behavior. The creep coefficient pattern of the B3 model was very similar to the measured results, and well reflected the effects of the mixing ratio and compressive strength of UHPC.

Four decades of snooker rankings are analyzed in terms of prize money distributions. In particular, it is investigated how much these rankings are dominated by the top players, and also by players from the UK. The main conclusions are: (1) with an increasing amount of available prize money, the rankings are less dominated by the top players, and (2) players from the UK have historically earned a significantly larger share of the available prize money than could be reasonably expected based on the actual percentage of UK players.

Congestion pricing has received lots of attention in the scientific discussion. Congestion pricing means that the operator increases prices at the time of congestion and the traffic demand is expected to decrease. In a certain sense, shadow prices are an optimal way of congestion pricing: users are charged shadow prices, i.e., the expectations of future losses because of blocked connections. The shadow prices can be calculated exactly from Howard’s equation, but this method is difficult. The paper presents simple approximations to the solution of Howard’s equation and a way to derive more exact approximations. If users do not react by lowering their demand, they will receive higher bills to pay. Many users do not react to increased prices but would want to know how the congestion pricing mechanism affects the bills. The distribution of the price of a connection follows from knowing the shadow prices and the probability of a congestion state. There is another interesting distribution. The network produces profit to the operator, or equivalently, blocked connections produce a cost to the operator. The average cost rate can be calculated from Howard’s equation, but the costs have some distribution. The distribution gives the risk that the actual costs exceed the average costs, and the operator should include this risk to the prices. The main result of this paper shows how to calculate the distribution of the costs in the future for congestion pricing by shadow prices and for congestion pricing with a more simple pricing scheme that produces the same average costs.

We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure, alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the actual estimation of the noise power. A complete Bayesian study over the model parameters and the scale parameter can be also performed. Numerical experiments show the benefits of the proposed approach.

Purpose: To report the early clinical outcomes of combining intensity‐modulated radiation therapy (IMRT) and intensity‐modulated proton therapy (IMPT) in comparison with IMRT alone in treating the oropharynx cancer (OPC) patients. Materials and Methods: The medical records of 148 OPC patients were retrospectively reviewed, who underwent definitive radiotherapy (RT) with concurrent systemic therapy, from January 2016 till December 2019 at Samsung Medical Center. During the 5.5 weeks’ RT course, the initial 16 (or 18) fractions were delivered by IMRT in all patients, and the subsequent 12 (or 10) fractions were either by IMRT in 81 patients (IMRT only) or by IMPT in 67 (IMRT/IMPT combination), respectively, based on comparison of adaptive re-plan profiles and availability of equipment. Propensity‐score matching (PSM) was done on 76 patients (38 from each group) for comparative analyses. Results: With the median follow‐up of 24.7 months, there was no significant difference in overall survival and progression free survival between groups, both before and after PSM. Before PSM, IMRT/IMPT combination group experienced grade ≥3 acute toxicities less frequently: mucositis in 37.0% and 13.4% (p<0.001); and analgesic quantification algorithm (AQA) in 37.0% and 19.4% (p=0.019), respectively. The same trends were observed after PSM: mucositis in 39.5% and 15.8% (p=0.021); and AQA in 47.4% and 21.1% (p=0.016), respectively. In multivariate logistic regression, grade ≥3 mucositis was significantly less frequent in IMRT/IMPT combination group, both before and after PSM (p=0.027 and 0.024, respectively). AQA score ≥3 was also less frequent in IMRT/IMPT combination group, both before and after PSM (p=0.085 and 0.018, respectively). Conclusions: In treating the OPC patients, with comparable early oncologic outcomes, more favorable acute toxicity profiles were achieved following IMRT/IMPT combination than IMRT alone.

Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts (KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

We give two examples of the appearance of heavy-tailed distributions in applications to social sciences. Among these distributions are the laws of Pareto, Lotka, and some new ones. The examples are illustrated by constructing suitable toy models.

This article presents six probability distributions from the Gamma family with three parameters, for the flood frequency analysis in hydrology. The choice of the Gamma family of statistical dis-tributions was driven by its frequent use in hydrology. In the Faculty of Hydrotechnics, the im-provement of the estimation of maximum flows and including the methodological bases for the realization of a regionalization study with the linear moments method with the corrected pa-rameters was researched, being an element of novelty. The linear moments method is better than MOM because it avoids the choice of skewness depending on the origin of the flows, practiced in Romania. The L-moments method conforms to the current trend for estimating the parameters of statistical distributions. Observed data from hydrometric stations are of relatively short length, so the statistical parameters that characterize them are of a sample that requires correction. The correction of the statistical parameters is proposed, using the method of least squares based on the inverse functions of the statistical distributions expressed with the frequency factor for L-moments. All the necessary elements for their use are presented like, quantile functions, the exact and ap-proximate relations for estimating parameters and frequency factors. A flood frequency analysis case study was carried out for the Ialomita river, to verify the proposed methodology. The per-formance of this distributions is evaluated using Kiling-Gupta and Nash-Sutcliff coefficients.

Estimating the parameters of statistical distributions generally involves solving a system of nonlinear equations or a nonlinear equation, being a technical difficulty in their usual application in hydrology. This article presents improved approximations and, in some cases, new approximations for the estimation with the method of ordinary moments and the method of linear moments, which are useful for the direct calculation of the parameters, because the errors in the approximate estimation are similar to the use of iterative numerical methods. Thirteen statistical distributions of two and three parameters frequently used in hydrology are presented, for which parameter estimation was laborious. Thus, the approximate estimation of the parameters by the two methods is simple but also precise and easily applicable by hydrology researchers. The approximate forms characterized by very small errors are the result of the research conducted within the Faculty of Hydrotechnics to update the romanian normative standards in the hydrotechnical field.

Spatially resolved, line-of-sight measurements of aluminum monoxide emission spectra in laser ablation plasma are used with Abel inversion techniques to extract radial plasma temperatures. Contour mapping of the radially deconvolved signal intensity shows a ring of AlO formation near the plasma boundary with the ambient atmosphere. Simulations of the molecular spectra were coupled with the line profile fitting routines. Temperature results are presented with simultaneous inferences from lateral, asymmetric radial, and symmetric radial AlO spectral intensity profiles. This analysis indicates that we measured shockwave phenomena in the radial profiles, including a temperature drop behind the blast wave created during plasma initiation.

Many systems in nature and laboratories are far from equilibrium and exhibit significant fluc- tuations, invalidating the key assumptions of small fluctuations and short memory time in or near equilibrium. A full knowledge of Probability Distribution Functions (PDFs), especially time- dependent PDFs, becomes essential in understanding far-from-equilibrium processes. We consider a stochastic logistic model with multiplicative noise, which has gamma distributions as stationary PDFs. We numerically solve the transient relaxation problem, and show that as the strength of the stochastic noise increases the time-dependent PDFs increasingly deviate from gamma distributions. For sufficiently strong noise a transition occurs whereby the PDF never reaches a stationary state, but instead forms a peak that becomes ever more narrowly concentrated at the origin. The addition of an arbitrarily small amount of additive noise regularizes these solutions, and re-establishes the existence of stationary solutions. In addition to diagnostic quantities such as mean value, standard deviation, skewness and kurtosis, the transitions between different solutions are analyzed in terms of entropy and information length, the total number of statistically distinguishable states that a system passes through in time.

The likelihood of pre-hypertensive young adults developing hypertension has been steadily increasing over the past few years. Despite the fact that aerobic exercise training (AET) has demonstrated positive results in lowering high blood pressure, the efficacy of different types of AET among pre-hypertensive young adults has not been well-established. The objective of this study was to evaluate the effectiveness of high-intensity interval training (HIIT) and continuous moderate-intensity training (CMT) on blood pressure (BP) of physically inactive pre-hypertensive young adults. 32 adults (age 20.0±1.1 years and BMI 21.5±1.8) were randomly assigned into 3 groups: HIIT, CMT and control (CON). HIIT and CMT groups participated in 5 weeks of AET; while the CON group followed a DASH diet plan only. The HIIT protocol consisted of 1:4 minute work to rest ratio of participants 80%-85% heart rate reserve (HR-reserve) and 40%-60% HR-reserve respectively for 20-minutes, CMT group exercised at 40%-60% of HR-reserve continuously for 20-minutes. In both HIIT and CMT groups, systolic blood pressure (SBP) (3.8±2.8 mmHg, P=0.002 VS 1.6±1.5 mmHg, P=0.011) was significantly reduced. While, significant reductions in the diastolic blood pressure (DBP) (2.9±2.2 mmHg, P=0.002) and mean arterial pressure (MAP) (3.1±1.6mmHg, P<0.0005) were noted only in the HIIT group. No significant differences in SBP (-0.4±3.7 mmHg, P=0.718), DBP (0.4±3.4 mmHg, P=0.714), or MAP (0.1±2.5mmHg, P= 0.892) were observed in the CON group. Both HIIT and CMT decreased the BP in physically inactive pre-hypertensive young adults; however, HIIT yielded more beneficial results in terms of reducing the SPB, DBP and MAP.

Physics behind collimated highly directional nature of lasers, and factors that keep the seven coloured waves that form white light together during their journey from Sun to Earth, in the face of the natural disruptive forces, is not fully understood. Energy levels were measured, in terms of alterations in induced current and voltage, in beams from a red laser, white LED light and the Sunlight before and during their disruption by diffusers (frosted glass for the lasers) and diffractors (diffraction grating for the white light) using a photovoltaic solar cell panel attached to a digital multimeter. Results show that disruption of the beams results in release of extra energy named as ‘Latent Light Binding’ Energy’. It is hypothesized that the ‘binding’ energy keeps laser waves firmly bound together both end-on and side-on enabling laser beams to travel long distances in collimated manner. Likewise, the 7 coloured waves that constitute white light are kept together, probably side-on, in their journey from the Sun to the Earth. The observation that diffraction of sunbeam is associated with increased power generation provides a new lead to improve harnessing of solar energy, where, currently, the focus is mainly on improving efficiency of photovoltaic cell.

Silicon light funnels are three-dimensional subwavelength structures in the shape of inverted cones with respect to the incoming illumination. Light funnel arrays can serve as an efficient absorbing layers on account of their light trapping capabilities associated with the presence of high density complex Mie modes. Specifically, light funnel arrays exhibit broadband absorption enhancement of the of the solar spectrum. In the current study, we numerically explore the optical coupling between surface light funnel arrays and underlying substrates. We show that the absorption in LF array-substrate complex is higher than the absorption in LF arrays of the same height (~10% increase). This, we suggest, imply that a LF array serves as an efficient surface element that imparts additional momentum components to the impinging illumination, and hence optically excites the substrate by near-field light concentration, excitation of traveling guided modes in the substrate and mode hybridization.

In this work, the nearest neighbor distances and Voronoi cell features of the Cu-Ag deposits were analyzed and fitted with Lognormal, Weibull, and Gamma distributions. The nearest neighbor distance distributions of the samples were compared with those of complete spatially random points, showing spatial inhomogenity due to the nucleation exclusion effect. The radial distribution function was calculated, showing both influences from the grain size and the nucleation exclusion effect. Voronoi cells were generated based on the shape of the grains. The size, occupancy, and coordination of the Voronoi cells were examined and fitted. The results show that despite the Cu-Ag deposits seem to be governed by instantaneous nucleation mode, the spatial distribution of the nuclei is more impacted by the nucleation exclusion effect than the Cu-only samples. This behavior is also justified by the grain size distribution generated with Voronoi cell size and occupancy distributions.

The vast array of interconnected microbes across earth’s ecosystems and within holobionts has been called the “Internet of Microbes.” Research into the fundamental microbial properties has demonstrated that bacteria and archaea are masters of energy and information collection, storage, transformation, and dissemination using both “wired” and wireless (at a distance) capacities. These features suggest that microbes are quantum entangled within and between earth’s holobionts, and that specific tools affecting energy and information functions could provide an effective strategy for managing microbial populations. Microbes use four critically important microbial networking functions (sound, light, electricity, and magnetism). This narrative review focuses on microbial management based on two of the four: sound and light (as well as related vibrations). These are examined as: 1) tools for managing microbial populations, 2) tools to support new technologies, and 3) tools for healing humans and other holobionts. Given microbial sensitivity to sound, light, and vibrations, it is critical that microbial populations and microbe-laden holobionts are protected from potentially damaging exposures. We conclude that sound, light, and vibrations are useful therapeutic tools that can help support useful microbial populations and help to address ongoing challenges of holobiont disease.

This work seeks to approach the comparative analysis of the distribution of POFs (polymeric optical fibers) arranged in a random and sorted manner in the manufacture of translucent concretes. The following characteristics were analyzed: compressive strength, flexural tensile strength, capillary water absorption and light transmittance. Portland cement, water, fine aggregate (sand), silica fume, polycarboxylate ether-based superplasticizer and polymeric optical fiber were used in the production of the composites. Except the fiber content, the mixture of all other inputs was kept as a fixed parameter. Groups with the following levels of POFs were produced: 0%, 2%, 3.5% and 5%. The POF content is the sum of the areas of the fiber sections in the composite with the total area of the translucent surface. The results showed that for the analysis of mechanical behavior (flexural tensile and compressive strength) and durability (capillary water absorption), composites with random and sorted fibers within the 2% POF presented to belong to statistically distinct groups. For the other fiber contents, there was no pattern of behavior, that is, it cannot be said that there is a difference within 3.5% or 5% POF between sorted and random fibers in relation to the mechanical and durability characteristics analyzed. As for the light transmittance, it was attributed that the statistical difference between the fiber arrangements only within the 5% POF content can be a consequence of their accumulation in certain points of the material, thus influencing this characteristic measured by a light meter.

In statistics and probability theory, one of the most important statistics is the sums of random variables. After introducing a probability distribution, determining the sum of n independent and identically distributed random variables is one of the interesting topics for authors. This paper presents the probability density functions for the sum of n independent and identically distributed random variables such as Shanker, Akash, Ishita, Pranav, Rani, and Ram Awadh. In order to determine all aforementioned distributions, the problem-solving methods are applied which is based on the change-of-variables technique.

For bounded unit interval, we propose a new Kumaraswamy generalized (G) family of distributions from a new generator which could be an alternate to the Kumaraswamy-G family proposed earlier by Cordeiro and de-Castro in 2011. This new generator can also be used to develop alternate G-classes such as beta-G, McDonald-G, Topp-Leone-G, Marshall-Olkin-G and Transmuted-G for bounded unit interval. Some mathematical properties of this new family are obtained and maximum likelihood method is used for estimating the family parameters. We investigate the properties of one special model called a new Kumaraswamy-Weibull (NKwW) distribution. Parameter estimation is dealt and maximum likelihood estimators are assessed through simulation study. Two real life data sets are analyzed to illustrate the importance and flexibility of this distribution. In fact, this model outperforms some generalized Weibull models such as the Kumaraswamy-Weibull, McDonald-Weibull, beta-Weibull, exponentiated-generalized Weibull, gamma-Weibull, odd log-logistic-Weibull, Marshall-Olkin-Weibull, transmuted-Weibull, exponentiated-Weibull and Weibull distributions when applied to these data sets. The bivariate extension of the family is proposed and the estimation of parameters is given. The usefulness of the bivariate NKwW model is illustrated empirically by means of a real-life data set.

Laser machining by ultra-short (sub-ps) pulses at high intensity offers high precision, high throughput in terms of area or volume per unit time, and flexibility to adapt processing protocols to different materials on the same workpiece. Here we consider the challenge of optimization for high throughput: how to use the maximum available laser power and larger focal spots for larger ablation volumes by implementing a fast scan. This implies the use of high-intensity pulses approaching ∼PW/cm2 at the threshold where tunnelling ionization starts to contribute to overall ionization. A custom laser micromachining setup was developed and built to enable high speed, large area processing and easy system reconfiguration for different tasks. The main components include laser, stages, scanners, control system, and software. Machining of metals such as Cu, Al, or stainless steel, and fused silica surfaces at high-fluence and high-exposure doses at high scan speeds up to 3 m/s were tested for the fluence scaling of ablation volume, which was found to be linear. Modified surfaces are color-classified for their appearance, which is dependent on surface roughness and chemical modification. Such colour-coding can be used as a feedback parameter for industrial process control.

There are four commercial pathways to make grill charcoal from wood. These have been modelled to calculate a carbon intensity (CI) for each. Results are presented along with discussion of the sensitivities: biogenic carbon; quality of the charcoal; classification of products, wastes and residues; and division of burden. Grill charcoal’s CI ranges greatly, depending on the pathway by which it is produced. Wood distillation, a commercial process that once was common but now is rare, has a CI 50+% lower than that of the next-lowest commercial process, Kilns. Earth mounds/pits and external-fuelled Retorts have CIs considerably higher still. The carbon efficiency (CE) of Wood distillation is more than twice that of the next-best, again Kilns. CE is defined as normalised, total carbon emitted. This is not the same as CI, it is not usually considered in studies such as this, yet it is important for climate impact. CE could be a useful measure in assessments (such as this) where biogenic carbon is significant.

In this paper the problem of sampling from uniform probability distributions is approached by means of space-filling curves (SFCs), a topological concept that has found a number of important applications in recent years. Departing from the theoretical fact that they are surjective but not necessarilly injective, the investigation focused upon the structure of the distributions obtained when their domain is swept in a uniform and discrete manner, and the corresponding values used to build histograms, that are approximations of their true PDFs. This work concentrates on the real interval [0,1], and the Sierpinski space-filling curve was chosen because of its favorable computational properties. In order to validate the results, the Kullback-Leibler distance is used when comparing the obtained distributions in several levels of granularity with other already established sampling methods. In truth, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. In this fashion, sequences resulting from this sort of process are not truly random.

With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions on them. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or generate water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modelling covers a wide range of parameters, such as varying wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modelling results were partially validated against direct measurements carried out by a test vehicle. The model outputs showed strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. Dimensional Analysis of the problem has highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface on the perceived precipitation level. The proposed model is used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.

The importance of oxygen in the winemaking process is widely known, as it affects the chemical aspects and therefore the organoleptic characteristics of the final product. Hence, it is evident the usefulness of a continuous and real-time measurements of the levels of oxygen in the various stages of the winemaking process, both for monitoring and for control. The WOW project has focused on the design and the development an innovative device for monitoring the oxygen levels in wine. This system is based on the use of an optical fiber to measure the luminescent lifetime variation of a reference metal/porphyrin complex, which decays in presence of oxygen. The developed technology results in a high sensitivity and low cost sensor head that can be employed for measuring the dissolved oxygen levels at several points inside a wine fermentation or aging tank. This system can be complemented with dynamic modeling techniques to provide predictive behavior of the nutrient evolution in space and time given few sampled measuring points for both process monitoring and control purposes. The experimental validation of the technology has been first performed in a controlled laboratory setup to attain calibration and study sensitivity with respect to different photo-luminescent compounds and alcoholic or non-alcoholic solutions, and then in an actual case study during a measurement campaign at a renown Italian winery.

Labelling techniques such as electron paramagnetic resonance spectroscopy and single-molecule fluorescence resonance energy transfer, allow access to distances in the range of tens of angstroms, corresponding to the size of proteins and small to medium-sized protein complexes. Such measurements do not require long-range ordering and are therefore applicable to systems with partial disorder. Data from spin-label-based measurements can be processed into distance distributions that provide information about the extent of such disorder. Using such information in modelling presents several challenges, including a small number of restraints, the influence of the label itself on the measured distance and distribution width, and balancing the fitting quality of the long-range restraints with the fitting quality of other restraint subsets. Starting with general considerations about integrative and hybrid structural modelling, this review provides an overview of recent approaches to these problems and identifies where further progress is needed.

A number of accidents at sea are primarily caused by low visibility due to sea fog therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. However, the existing estimation systems have significant costs so that it is not viable to install them at various locations, such as small vessels, minor ports, and a number of buoys. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior) which provides the estimation at a low complexity and a low cost. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels, and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in South Korea are used rather than artificially composed images. Considering facilities in oceans which are usually operated in the power limited environment, the processing time performance of PDCP are also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.

In this paper, we derive the distribution of eigenvalues for a 2×2 random confusion matrix stemming from a machine learning (ML) evaluation problem. Additionally, we present the distributions of both the matrix’s trace and the difference between the two traces of random confusion matrices. We also illustrate the application of these derived distributions in calculating the superiority probability of four baseline ML models.

This article analyzes 6 probability distributions from the Generalized Pareto family, with 3, 4 and 5 parameters, with main purpose to identify other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distributions use are presented like, the probability density functions, the complementary cumulative distribution functions, the quantile functions, the exact and approximate relations for estimating parameters, for both methods of parameters estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor river, to numerically present the analyzed distributions. The performance of this distributions is evaluated using relative mean error, relative absolute error and linear moments diagram.

Increasingly measured data in the context of smart cities can be used to develop new and innovative business models to increase efficiency and the value of life. A time-series classification algorithm can support to automatize many different processes such as forecasting services. In order to ensure data security and privacy, Federated Learning trains a global model collaboratively on multiple clients. Having different data-distributions and data-quantities across participating clients, neural networks suffer from slow convergence and overfitting. Based on different data-distributions, data-quantities and number of clients, we develop and evaluate different data-clustering strategies to update global model weights in comparison to the state of the art. We use public time-series data, generate various synthetic datasets and train a Relational-Regularized Autoencoder for classification purposes. Our results show an improvement of model performance concerning generalization.

This work addresses the problem of sampling from Gaussian probability distributions by means of uniform samples obtained deterministically and directly from space-filling curves (SFCs), a purely topological concept. To that end, the well-known inverse cumulative distribution function method is used, with the help of the probit function,which is the inverse of the cumulative distribution function of the standard normal distribution. Mainly due to the central limit theorem, the Gaussian distribution plays a fundamental role in probability theory and related areas, and that is why it has been chosen to be studied in the present paper. Numerical distributions (histograms) obtained with the proposed method, and in several levels of granularity, are compared to the theoretical normal PDF, along with other already established sampling methods, all using the cited probit function. Final results are validated with the Kullback-Leibler and two other divergence measures, and it will be possible to draw conclusions about the adequacy of the presented paradigm. As is amply known, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. That said, sequences resulting from this kind of procedure are not truly random. Even so, and to be coherent with the literature, the expression ”random number” will be used along the text to mean ”pseudo-random number”.

Most existing flexible count regression models allow only approximate inference. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under- and overdispersion). The new method, referred as “balanced discretization”, consists in discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation in terms of the continuous distribution. For illustrative purposes, we have developed the family of balanced discrete gamma distributions which can model equi-, under- and overdispersed count data. This family of count distributions is appropriate for building flexible count regressionmodels because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we have shown that under equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersion, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data is Poisson distributed.

Photobiomodulation (PBM) is a procedure that uses light to modulate cellular functions and bio-logical processes. Over the past decades, PBM has gained considerable attention for its potential in various medical applications due to its non-invasive nature and minimal side effects. This narrative review article examines the existing literature on PBM, encompassing research studies, clinical trials, and technological developments. This review highlights the mechanisms of action underlying PBM, including the interaction with cellular chromophores and the activation of in-tracellular signaling pathways. The evidence from clinical trials and experimental studies to evaluate the efficacy of PBM in clinical practice is summarized with a special emphasis on der-matology. Furthermore, advancements in PBM technology, such as novel light sources and treat-ment protocols, are discussed in the context of optimizing therapeutic outcomes and improving patient care. This narrative review underscores the promising role of PBM as a non-invasive therapeutic approach with broad clinical applicability. Despite the need for further research to develop standard protocols, PBM holds great potential for addressing a wide range of medical conditions and enhancing patient outcomes in modern healthcare practice.

The figures of merit of luminescence intensity ratio (LIR) thermometry for Er3+ in 40 different crystals and glasses have been calculated and compared. For calculations, the relevant data has been collected from the literature while the missing data were derived from available absorption and emission spectra. The calculated parameters include Judd-Ofelt parameters, refractive indexes, Slater integrals, spin-orbit coupling parameters, reduced matrix elements (RMEs), energy differences between emitting levels used for LIR, absolute and relative sensitivities. We found a slight variation of RMEs between hosts as a result of variations in values of Slater integrals and spin-orbit coupling parameters, and we calculated their average values over 40 hosts. The calculations showed that crystals perform better than glasses in Er3+ based thermometry, and we identified hosts that have large values of both absolute and relative sensitivity.

Phenotypes are necessary for genomic evaluations and management. Sometimes genomics can be used to measure phenotypes when other methods are difficult or expensive. Prolificacy of bulls used in multiple-bull pastures for commercial beef production is an example. A retrospective study of 79 bulls aged 2-year-old and older used 141 times in 4-5 pastures across 4 years was used to estimate repeatability from variance components. Traits available before each season’s use were tested for predictive ability. Sires were matched to calves using individual genotypes and evaluating exclusions. A lower cost method of measuring prolificacy was simulated for 5 pastures using the bulls’ genotypes and pooled genotypes to estimate average allele frequencies of calves and of cows. Repeatability of prolificacy was 0.62 ± 0.09. A combination of age-class and scrotal circumference accounted for less than 5 % of variation. Simulated estimation of prolificacy by pooling DNA of calves was accurate. Adding pooling of cow DNA or actual genotypes both increased accuracy about the same. Knowing a bull’s prior prolificacy would help predict future prolificacy for management purposes and could be used in genomic evaluations and research with coordination of breeders and commercial beef producers.

We propose two methods for obtaining estimations on the minimum distance and covering radius of orthogonal arrays. Both methods are based on knowledge about the (feasible) sets of distance distributions of orthogonal arrays with given length, cardinality, factors and strength. New bounds are presented either in analytic form and as products of an ongoing project for computation and investigation of the possible distance distributions of orthogonal arrays with parameters in doable ranges.

Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

Co-integration and Causality was built to conduct studies on causality relation between carbon intensity and coal consumption leading to providing important basis for the transition to a low carbon economy. The EG two-step method was performed to study the relation between carbon intensity and coal consumption of China during 1990-2015 and the co-integration and Granger test was constructed to build up the co-integration and error correction models for analysis of the interaction between carbon intensity and coal consumption. The results showed that in long term there is a stable co-integration relation and a positive correlation between carbon intensity and coal consumption; whereas fluctuations exist in short term and there is a one-way Granger causality of carbon intensity with respect to coal consumption.

More water utilities are adopting Aquifer Storage and Recovery (ASR) to balance long-term water supply and demand. Due to large implementation and operation costs, ASR projects need to be optimized, particularly for energy use, which is a major operating expense. This study examines the relationships among energy use, recharge, and recovery at two ASR projects in the western United States. The major finding is an economy of scale for recovery processes, but not for gravity-fed recharge processes. The economy of scale found is as follows: the energy intensity recovered decreases with volume. This suggests it is more energy-efficient to recover large volumes of water in one interval instead of recovering smaller volumes at more frequent intervals. The H2Oaks recovery process experienced a 78% decrease in energy intensity from 0 to 50,000 m3 recovered, while the Sand Hollow site experienced a 43% decrease in energy intensity from 0 to 50,000 m3 recovered. Statistical analyses of the recovery process showed p values lower than 0.0001, R2 values between 0.43 and 0.57, and a RMSE value between 0.55 and 2.1, indicating the presence of a moderate correlation between energy and volume. This economy of scale has been observed in multiple instances in water and wastewater treatment. This finding not only has applications to ASR but also all recovery or recharge wells, whether or not they are paired with each other. Furthermore, this study confirms the need for more reliable and accessible energy data to fully understand the implications of the energy–water nexus.

The present paper investigates flaw strength distributions established using various flexural tests on batches of SiC bar test specimens: 4-point bending as well as 3-point bending with different span lengths. Flaw strength is given by the elemental stress operating on the critical flaw at fracture of a test specimen. Fracture inducing flaws and their locations are identified by fractography. A single population of pores was found to dominate fracture. The construction of diagrams of p-quantile vs elemental strengths was aimed at assessing the Gaussian nature of flaw strengths. Then, empirical cumulative distributions of strengths were constructed using the normal distribution function. Weibull cumulative probability distributions were derived from the mean and the standard deviation using the first moment equation. Agreement was obtained between both Weibull and normal flaw strengths distributions. The cumulative distributions for the different bending tests were predicted satisfactorily from the flaw strength density function using the elemental strength model. Flaw strength distributions that include potential weaker flaws encountered in larger test pieces are extrapolated using the p-quantile diagrams. Implications are discussed about the pertinence of an intrinsically representative flaw strength distribution, about failure predictions, about size effects, and about the significance of flaw spatial distributions.

In this paper, a novel strategy is adopted in a degradation model to affect the implied lifetime distribution. The multiplicative degradation model is utilized as a postulate in the model. It will be established that the implied lifetime distribution forms a classical mixture model. In this mixture model, time-to-failure is lying with some probabilities between two first passage times of the degradation process to reach two specified levels. Stochastic comparisons in the model under a change in the probabilities are studied. Several examples are provided to highlight the applicability of the results in the cases when typical degradation models are candidate.

Understanding swash zone dynamics is of crucial importance for coastal management as the swash motion, consisting of the uprush of the wave on the beach face and the subsequent downrush, is responsible for driving changes the beach morphology trough sediment exchanges between the sub-aerial and sub-aqueous beach. Improved understanding of the probabilistic characteristics of these motions has the potential to allow coastal engineers to develop improved sediment transport models which, in turn, can be further developed into coastal management tools. In this paper, novel descriptors of swash motions are obtained by combining field data and statistical modelling. Our results indicate that the probability distribution function (PDF) of shoreline height (p(ζ)) and trough-to-peak swash heights (p(ρ)) measured at a high energy, sandy beach were both inherently multimodal. Based on the observed multimodality of these PDFs, Gaussian Mixtures are shown to be the best method to statistically model them. Further, our results show that both offshore and surf zone dynamics are responsible for driving swash zone dynamics, which indicates unsaturated swash. The novel methods and results developed in this paper, both data collection and analysis, could aid coastal managers to develop improved swash zone models in the future.

In this paper we define a continuous wavelet transform of a Schwartz tempered distribution $f \in S^{'}(\mathbb R^n)$ with wavelet kernel $\psi \in S(\mathbb R^n)$ and derive the corresponding wavelet inversion formula interpreting convergence in the weak topology of $S^{'}(\mathbb R^n)$. It turns out that the wavelet transform of a constant distribution is zero and our wavelet inversion formula is not true for constant distribution, but it is true for a non-constant distribution which is not equal to the sum of a non-constant distribution with a non-zero constant distribution.

Cutaneous mycoses are common infections whose treatment has become more complex due to increasing antifungal resistance and the need for prolonged therapies, hindering patient adher-ence and increasing the incidence of adverse effects. Consequently, the use of physical therapies, especially photodynamic therapy (PDT), has increased for the treatment of onychomycosis due to its antimicrobial capacity mediated by the production of reactive oxygen species. This study investigates the in vitro effect of the combination of blue light (448 nm) and red light (645 nm) with terbinafine on the cell viability of human keratinocytes and the production of reactive ox-ygen species. The combination of terbinafine and blue light exhibits a synergistic effect on the production of reactive oxygen species and a significant increase in the expression of superoxide dismutase (SOD), an enzyme involved in the reduction of superoxide ions (O2-) and the genera-tion of hydrogen peroxide (H2O2). Such effect could be useful in clinical practice to improve the response of cutaneous mycoses to pharmacological treatment, reduce their toxicity, and shorten their duration.

Soil nitrogen (N) is a common limiting factor where soil N cycling is a key component of agro-ecosystems. Soil N transformation processes are largely mediated by microbes and understanding bacteria involvement in soil N cycling in agricultural systems has both agronomic and environmental importance. This 2-yr field scale study examined the abundances and spatial distributions of total bacterial community (16s RNA), bacterial involved in nitrification (amoA), and de-nitrification (narG, nirK, and nosZ) and soil physicochemical properties of winter wheat (Triticum aestivum L.) – soybean (Glycene max L.) double cropping with 2-3 weeks of spring grazing (WGS) and without grazing (WS) and of tall fescue (Festuca arundinacea (L.) Schreb.) pasture (TF) managed to near natural conditions with similar grazing. The TF had a significantly higher abundance of 16S rRNA, amoA, narG, nirK, and nosZ genes than the WS and WGS, which had similar levels between themselves. Soil organic matter (OM) and soil pH had a stronger effect on the N-cycling bacteria gene abundance. All bacterial gene concentrations and soil pH showed non-random distribution patterns with 141-186 m range autocorrelation. These results indicate that biological N transformation processes are more important in natural agricultural systems and the abundance of N-cycling bacteria can be manipulated by field scale management strategies.

Two-photon imaging of light stimulus-evoked neuronal activity has been used to study all neuron classes in the vertebrate retina, from the photoreceptors to the retinal ganglion cells. Clearly, the ability to study retinal circuits down to the level of single synapses or zoomed out at the level of complete populations of neurons, has been a major asset in our understanding of this beautiful circuit. In this chapter, we discuss the possibilities and pitfalls of using an all-optical approach in this highly light-sensitive part of the brain.

Tropical cyclones (TC) are dangerous weather events and accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms of theory and forecasting. ERA5 reanalysis is benchmark data set for tropical cyclone studies, yet the maximum wind speed error is very large (68 kts) and still 19 kts after simple linear correction even in the better sampled North Atlantic. Here, we develop an adaptive learning approach to correct the intensity in the ERA5 reanalysis, by optimising the inputs to overcome the problems because of the poor data quality and updating the features to improve the generalisability of the deep learning-based model. Specifically, we use TC knowledge to increase the representativeness of the inputs so that the general features can be learned with deep neural networks in the sample space, and then use domain adaptation to update the general features from the known domain with historical storms to the specific features for the unknown domain of new storms. This approach can reduce the error to only 6 kts which is within the uncertainty of the best track data in IBTrACS in the North Atlantic. The method may have wide applicability, such as extending it to the correction of intensity estimation from satellite imagery and intensity prediction from dynamical models.

Although mountain areas account for approximately one fifth of the terrestrial surface, there has been less research focused on fire in these areas compared to lowlands. Mountain fires have distinct behavior due to dynamic winds interacting with the terrain, which can influence the fireline intensity and propagation. For the sake of fire safety of fire crews, it is essential to know how difficult to control the fire is in the mountain regions, with fireline intensity providing a useful indicator of risk and suppressibility. We studied one of the major disasters, wildfire, in Australia in such a highland by using the Great Pine Tier Fire, which occurred 15^th January in 2019, ending up burning approximately 511.86km². Weather and fire intensity at pseudo weather stations located at key points of fire progression were analyzed by wind vector maps and numerical weather model vertical sounding (NWMVS). Fire propagation was then simulated in Prototype 2, a fire simulator capable of detecting the potential for lateral fire channeling (LFC), and simulating fireline intensity using Australian vegetation sub-models. We found that the synoptic wind appeared to be modified by the interaction with the terrain in windward and the fire intensified the most in its leeward. In practice, the fire moved out of the valley axis and up its sidehill by following the wind which had been modified by local vertex of the curved valley axis before reaching this location.

NMR spectral datasets, especially in systems with limited samples, can be difficult to interpret if they contain multiple chemical components (phases, polymorphs, molecules, crystals, glasses, etc…) and the possibility of overlapping resonances. In this paper, we benchmark several blind source separation techniques for analysis of NMR spectral datasets containing negative intensity. For benchmarking purposes, we generated a large synthetic datasbase of quadrupolar solid-state NMR-like spectra that model spin-lattice T1 relaxation or nutation tip/flip angle experiments. Our benchmarking approach focused exclusively on the ability of blind source separation techniques to reproduce the spectra of the underlying pure components. In general, we find that FastICA (Fast Independent Component Analysis), SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF (Non-Negative Matrix Factorization) are top-performing techniques. We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes. Within the range of noise levels studied, we did not find drastic changes to the ranking of techniques. The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure components are predicted. Our results indicate poor performance of SVD (Singular Value Decomposition) methods, and we propose alternative techniques for matrix initialization. The benchmarked techniques are also applied to real solid state NMR datasets. In general, the recommendations from the synthetic datasets agree with the recommendations and results from the real data analysis. The discussion provides some additional recommendations for spectroscopists applying blind source separation to NMR datasets, and for future benchmark studies. Applications of blind source separation to NMR datasets containing negative intensity may be especially useful for understanding complex and disordered systems with limited samples and mixtures of chemical components.

The Dual-Frequency Precipitation Radar (DPR) on the Core GPM satellite provides spaceborne 3-dimensional observations of the precipitation field and surface rainfall rate with quasi global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and Drop Size Distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR derived products version 07 from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground reference. Four matching techniques between satellite estimates and ground level observations were tested, and best results were found for the so-called optimal comparison approach. Overall, GPM DPR products capture well the variability of the observed DSD at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw are observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, lowest errors were found for radar reflectivity factor and Dm, and highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested and it was found a substantial overestimation of stratiform cases compared to disdrometer observations.

Product innovation is a crucial factor in enterprise survival. Even though there are sources from strategic theory that guides the clear comprehension towards appreciating the nexus between these two variables (product innovation and enterprise survival), there are still many lacunas that should be addressed and filled. Consequently, the need for additional empirical corroboration or support is pertinent. This study aimed at verifying the nexus between product innovation and enterprise survival, and how they are affected by the existence of antecedent variables such as competitive intensity and competitive advantage. In the methodology, this study adopts the conduct of explanatory and cross-sectional investigations through the use of structural equation modelling (SEM) to a sample of selected food and beverages enterprises in Lagos, Nigeria. Regarding the food and beverage enterprises in Lagos, Nigeria, this study discovered that competitive intensity has huge positive implication on product innovation at (0.39; t = 5.69, p < 0.05). This gives numerical evidence that, in the face of more market competition, enterprises will be pressured to adopt the model of costs reduction on products which will enhance the reduction of product prices, and will have significant impact on profit. However, the findings reveal that there is no significance between competitive advantage and product innovation at (0.002; t = 0.203, p > 0.05), and there is no significance between product innovation and enterprise survival at (-0.035; t = -1.583, p > .05). As a result, the food and beverage enterprises should concentrate more on product innovation so that they will be able to stand the intensity of competition. The results emanated from the study is germane as it make significant contribution to literature and the body of knowledge and on strategic management by enlightening that competitive intensity is a necessary inducement for product innovation.

We analyse the light curve in the R-band gathered during the 2015/16 observing season of OJ287. We did a search for QPOs using several methods in wide time domain. No statistically significant periods were found in the high frequency domain both in the ground data and also in K2 observations. In the longer periods domain the Lomb-Scargle periodogram revealed several peaks above the 99\% significance level. The longest one, about 95-day, corresponds to the ISCO period of the more massive SMBH. The 43-day period could be an alias or can be attributed to accretion in the form of two armed spiral wave.

Digital transformation process gained significant research interest over the past years especially when related with achievements in sustainability goals. Even though there exist growing research regarding various aspects of digital transformation and sustainability procedure, a more detailed analysis is needed in different national environments. Proposed study empirically analyzes the Greek managers’ perspective for the relationship between digital management intensity and environmental performance. Almost 60 Greek senior managers from various organizational sectors were interviewed, while a synthetic index already developed from previous studies was used. Results indicate that Greek companies are not fully exploit digital technologies to further develop their environmental practices. Digital transformation strategy is contributing effectively to environmental strategies in the case of reducing emissions of waste and avoid environmental accidents, while in the rest of cases emerging technologies play a less important role and not as a part of an holistic digital strategy.

At the plasma membrane, transmembrane receptors are at the interface between cells and their environment. They allow sensing and transduction of chemical and mechanical extracellular signals. The spatial distribution of receptors and the specific recruitment of receptor subunits to the cell membrane is crucial for the regulation of signaling and cell behavior. However, it is challenging to define what regulates such spatial patterns for receptor localization, as cell shapes are extremely diverse when cells are maintained in standard culture conditions. Bone morphogenic protein receptors (BMPRs) are serine-threonine kinases, which build heteromeric complexes of BMPRI and II. These are especially interesting targets for receptor distribution studies, since the signaling pathways triggered by BMPR-complexes depends on their dimerization mode. They might exist as pre-formed complexes, or assemble upon binding of BMP, triggering cell signaling which leads to differentiation or migration. In this work we analyzed BMPR receptor distributions in single cells grown on micropatterns, which allows not only to control cell shape, but also the distribution of intracellular organelles and protein assemblies. We developed a script called ComRed (Center Of Mass Receptor Distribution), which uses center of mass calculations to analyze the shift and spread of receptor distributions according to the different cell shapes. ComRed was tested by simulating changes in experimental data, showing that shift and spread of distributions can be reliably detected. Our ComRed-based analysis of BMPR-complexes indicates that receptor distribution depends on cell polarization. The absence of a coordinated internalization after addition of BMP suggests that a rapid and continual recycling of BMPRs occurs. Receptor complexes formation and localization in cells induced by BMP might yield insights into the local regulation of different signaling pathways.

$\beta$-decay is one of the key factors to understand the $r$-process and evolution of massive stars. The Gamow-Teller (GT) transitions drive the $\beta$-decay process. We employ the proton-neutron quasiparticle random phase approximation (pn-QRPA) model to calculate terrestrial and stellar $\beta$-decay rates for 50 top-ranked nuclei possessing astrophysical significance according to a recent survey \cite{Nab21}. The model parameters of the pn-QRPA model effect the predicated results of $\beta$-decay. The current study investigates the effect of nucleon-nucleon pairing gaps on charge-changing transitions and the associated $\beta$ decay rates. Three different values of pairing gaps, namely TF, 3TF and 5TF, were used in our investigation. It was concluded that both GT strength distributions and half-lives are sensitive to pairing gap values. The 3TF pairing gap scheme, in our chosen nuclear model, resulted in best prediction with around 80$\%$ of the calculated half-lives within a factor 10 of the measured ones. The 3TF pairing scheme also led to calculation of biggest $\beta$-decay rates in stellar matter.

During the high-speed flight of a vehicle in the atmosphere, surface friction with the air generates aerodynamic heating. The aerodynamic heating phenomenon can create extremely high temperatures near the surface. These high temperatures impact material properties and the structure of the aircraft, so thermal deformation measurement is essential in aerospace engineering. This paper revisits high-temperature deformation measurement using the digital image correlation (DIC) technique under high-intensity blackbody radiation with a precise speckle pattern fabrication and a heat haze reduction method. The effect of speckle pattern on the DIC measurement has been thoroughly studied at room temperature, but high-temperature measurement studies have not reported such effects so far. We found that the commonly used methods to reduce the heat haze effect could produce incorrect results. Hence, we propose a new method to mitigate heat haze effects. An infrared radiation heater was employed to make an experimental setup that could heat a specimen up to 950 ℃. First, we mitigated image saturation using a short-wavelength bandpass filter with blue light illumination, a standard procedure for high-temperature DIC deformation measurement. Second, we studied how to determine the proper size of the speckle pattern under a high-temperature environment. Third, we devised a reduction method for the heat haze effect. As proof of the effectiveness of our developed experimental method, we successfully measured the deformation of stainless steel 304 specimens from 25 ℃ to 800 ℃. The results confirmed that this method can be applied to the research and development of thermal protection systems in the aerospace field.

Knee osteoarthritis (OA) is the most common joint disease worldwide. Exercise therapy has been identified as a first-line treatment option in patients suffering from knee OA. High-intensity interval training (HIIT) is an innovative exercise modality with potential in improving various disease-related outcomes. The purpose of this review is to explore the impact of HIIT on knee OA symptoms and physical functioning. A comprehensive search of scientific electronic databases was conducted to identify the articles on the effects of HIIT on knee OA. Thirteen studies were included in this review. Nine compared the effects of HIIT with those of low-intensity training, moderate-intensity continuous training, or a control group. Three evaluated the effects of HIIT alone. Eight reported a decrease in knee OA symptoms, and eight reported an increase in physical functioning. HIIT was shown to improve knee OA symptoms and physical functioning, but also aerobic capacity, muscle strength, and quality of life with minimal or no adverse events. However, compared to other exercise modalities, no clear superiority of HIIT was found. HIIT is a promising exercise strategy in patients with knee OA: anyway, the actual quality of evidence remains very low, and more high-quality studies are needed to confirm these promising outcomes.

Background: Depending on the phase of the menstrual cycle the athlete is in, some kinetic, physiological and fatigue variables will show differences. The aim of this study is to analyze whether there are changes in these variables over the course of the menstrual cycle. Methods: Eight regular female runners and triathletes performed a maximal treadmill test, as well as a submaximal test (6’ stages at 50%, 60% and 80% of maximal aerobic speed) in each of the phases of the menstrual cycle: bleeding phase (day 2.4 ± 0.7), follicular phase (day 10.4 ± 2.2) and luteal phase (day 21.8 ± 2.1). Running dynamics were measured (using RunScribe) at the end of each test, as were lactate concentration, heart rate and fatigue (evaluated on a scale of 0 to 5). Results: Higher shock (G) values were recorded in the bleeding phase and higher vertical spring stiffness (kN/m) in the follicular phase. The phase of the menstrual cycle had a significant effect on average and peak heart rate, which was significantly higher in the follicular phase. Conclusions: Higher vertical spring stiffness was observed in the follicular phase, as well as higher peak and average heart rate.

Elucidating the nature of dark matter in galactic systems remains one of the important unsolved mysteries of modern cosmology. As a thought experiment, we consider a galaxy model where the light radiating outward from stellar objects produces a gravitational effect larger than Einstein's theory of gravity predicts. Using computer simulations, we observe that this assumption allows the basic rotation curve profiles observed in both dwarf and late-type spiral galaxies to be recreated. It is important to highlight that a separate mass model describing the dark matter halo is not needed or used. This toy model may also lead to insights about the nature of dark energy. If the gravitational effects in the universe are currently dominated by radiated light as this toy model may suggest, the cosmic scale factor would be closely linked to the time-history and spatial distribution of star formation and death rates. An accelerating universe may simply be a manifestation of star death rates exceeding star formation rates in the current epoch.

The increasing enhancement in the modulation accuracy of spatial light modulators has garnered significant attention towards real-time control technology for light fields based on these modulators. It has been empirically demonstrated that this technology possesses a remarkable capability to generate vector beams with arbitrary complex amplitude distributions. Nevertheless, past studies indicate that the generation of only one vector beam at a time has been observed. The simultaneous generation of numerous vector light fields can give rise to several challenges, including compromised picture quality, limited single mode operation, and intricate optical path configurations. In pursuit of this objective, we present a novel methodology that integrates the coding methodology of modified off-axis interferometric holography with the idea of optical superposition. This technique facilitates the concurrent generation of several vector beams. In this study, we present a demonstration of the simultaneous creation of twelve vector beams using a single spatial light modulator (SLM) as a proof of concept. Significantly, this technology has the ability to generate an unlimited quantity of vector light fields concurrently, under the assumption that the resolution of the SLM does not impose any limitations. The findings indicate that the imaging quality achieved by this technology is of a high standard. Furthermore, it is possible to separately control the beam waist radius, topological charge, polarization order, and extra phase of each beam.

The LED light source is an important light source for indoor visible light communication. It has the characteristics of a large divergence angle and a high transmission rate. Therefore, using LED as the light source for visible light full-duplex communication can not only satisfy the lighting requirements but also transmit information at high speed. To analyze the factors that affect the optical power of the indoor single-light source visible light communication uplink receiving end, an indoor visible light communication model was established, and mathematical calculation formulas were derived based on the model establishment, and the maximum movable range formula of the retroreflective end under specific conditions was derived; The reliability of the formula was verified with the help of Zemax simulation software. Theoretical analysis and experimental results show that increasing the movement range of the retroreflective end can be achieved by increasing the lens diameter, reducing the focal length, and increasing the link distance.

In preserving genetic diversity among domestic animal breeds, the strategies emphasizing be-tween-breed diversity may be not optimal by neglecting within-breed variation. The present study aimed to assess the extent of the population subdivision of Mangalica pigs and the contri-bution of migration intensity to their substructure. Genealogy analysis was performed for breeding animals born between 1981 and 2023 examining three colour variations (Blonde, Swal-low-Belly, and Red). The fixation index (FST) was calculated and evaluated by Multidimensional Scaling and Clustering to expose the population substructure. The average FST is 0.04 for the Blonde and 0.05 for the Swallow-Belly and the Red while in the active herds, these parameters are smaller being 0.03 and 0.04, respectively. The migration of individuals affected 61.63% of the Blonde, 75.53% of the Swallow-Belly, and 63.64% of the Red breed herds. No population sub-structure was found in any Mangalica breed which can be explained by the extensive within breed migration among the herds.

Chamois are mountain ungulates (Subfamily Caprinae) which inhabit several medium and high altitude mountain ranges from southern Europe to the Near East. The first findings of Sarcocystis cysts in the musculature of chamois were reported in the 1970s. However, only limited work on the epidemiology of sarcocystosis and identification of the species of Sarcocystis in chamois has been done in the past. The present work was part of a comprehensive study on the parasites of chamois in Germany aiming to provide, for the first time, data on the prevalence and intensity of Sarcocystis infection in native Alpine chamois using histology examination of heart and/or diaphragm tissue samples collected from 216 chamois (40 kids and 176 chamois ranging of one up to 18 years of age). Sarcocysts were detected in either heart or diaphragm of 167/216 chamois (77.3%) with 131 of 183 heart samples and 127 of 215 diaphragm samples testing sarcocyst positive. Of the 181 chamois with both heart and diaphragm available (34 kids and animals up to 18 years of age), sarcocysts were detected in heart and/or diaphragm of 142 animals translating in an overall 78.5% prevalence of Sarcocystis infection (95%CI 72.5% - 84.4%). Sarcocysts were more frequently recorded in heart vs. diaphragm (72.4% vs. 56.4%; p=0.0021), and diaphragm positivity was associated with heart positivity (p=0.0001). Sarcocyst positivity (heart and/or diaphragm) was significantly (p<0.001) lower in kids than in the older chamois (27.1% vs. 88.6%, respectively) but prevalence did not differ between the sexes, regardless of the chamois’ age (p>0.3). Intensity of infection was generally low (<10 sarcocysts per cm² muscle cut) in both heart positive and diaphragm positive animals (94.7% and 93.7%, respectively) with heart yielding higher sarcocyst counts than diaphragm (p<0.001). Both heart and diaphragm sarcocyst counts were significantly (p<0.001) lower in kids than in the older chamois. Sarcocystis infection was demonstrated to be prevalent in chamois in Germany but intensity is apparently low. Further studies are desired to identify the species of Sarcocystis parasitizing the chamois using both phenotypic and molecular characteristics.

Cosmic strings can arise from spontaneous phase transitions during the inflationary period and are describable as disclination topological defects in spacetime. Analytical integration of the geodesic equations for a particle around a cosmic string reveals distinct orbit patterns dependent on the disclination intensity $\beta_{0}$. After its interaction with the cosmic string, the particle's behavior becomes random. To categorize these patterns, we describe the geodesic motion as iterated maps of a discrete parameter. Through the stabilization values of these functions, we correlate the scattering angle and the number of loops for the particle with $\beta_{0}$. Therefore, we interpret the gravitational field mathematically as an instructor of a particle's next position, given its previous one. We find that the radial function in the small displacement limit is similar to the logistic map. This leads us to conjecture the potential of the discrete parameter being a multiple of Planck's time, suggesting a method for the geometric discretization of spacetime around a cosmic string.

To explore the spatial network structure characteristics and driving effects of carbon emission intensity in China's construction industry, the investigation combined the modified gravity model and social network analysis method to deeply analyze the spatially associated network structure characteristics and driving effects of carbon emission intensity in China's construction industry, based on the measurement of carbon emission data of China's construction industry from 2006 to 2017. The results show that the regional differences of carbon emission of construction industry are significant, and the carbon emission intensity of construction industry show a fluctuation trend. The overall network of carbon emission intensity shows an obvious “core-edge” state, the hierarchical network structure is gradually broken. Economically developed provinces generally play a leading role in the network, and play an intermediary role to guide other provinces to develop together with them. Among the network blocks, most of the blocks play the role of “brokers”. The block with the leading economic development has a strong influence on the other blocks. The increase of network density, the decrease of network hierarchy and network efficiency will reduce the construction carbon emission intensity.

In the coded pulse scanning light detection and ranging (LIDAR) system, the number of laser pulses used at a given measurement point changes depending on the modulation and the method of spreading used in optical code-division multiple access (OCDMA). The number of laser pulses determines the pulse width, output power, and duration of the pulse transmission of a measurement point. These parameters determine the maximum measurement distance of the laser and the number of measurement points that can be employed per second. In this paper, we evaluate the performance and characteristics of combinations of modulation and spreading technology that can be used for OCDMA, and study optimal combinations according to varying operating environments.

L\'evy walks represent important modeling tools for variety of real life processes. Their natural scaling limits are known to be described by the so-called material fractional derivatives. So far these scaling limits were derived for spatially homogeneous walks, where Fourier and Laplace transforms represent natural tools of analysis. Here we derive the corresponding limiting equations in the case of position depending times and velocities of walks, where Fourier transforms cannot be effectively applied. In fact, we derive three different limits (specified by the way the process is stopped at an attempt to cross the boundary), leading to three different multi-dimensional versions of Caputo-Dzherbashian derivatives, which correspond to different boundary conditions for the generators of the related Feller semigroups and processes. Some other extensions and generalisations are analysed.