The differential transformation, an approach based on Taylor's theorem, is proposed as convenient for finding exact or approximate solution to the initial value problem with multiple Caputo fractional derivatives of generally non-commensurate orders. The multi-term differential equation is first transformed into a multi-order system and then into a system of recurrence relations for coefficients of formal fractional power series. The order of the fractional power series is discussed in relation to orders of derivatives appearing in the original equation. Application of the algorithm to an initial value problem results in a reliable and expected outcome.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

Many studies discuss carbon-based materials because of the versatility of the carbon element. They present different sorts of understandings fairly at convincing and compelling levels. A gas-state carbon atom converts into its various states depending on the conditions of processing. The electron transfer mechanism in the gas-state carbon atom is responsible for its conversion to various states, namely, graphite, nanotube, fullerene, diamond, lonsdaleite and graphene. The shape of energy responsible to transfer electron from the sides (east- and west-poles) of its atom is like parabola. That energy is linked to states (from filled state to nearby unfilled state) where exerted force to relevant poles of transferring electron is remained neutral. So, the mechanism of originating different states from a gaseous carbon atom is under the involvement of energy at first, which is not the case for atoms executing their confined inter-state electron-dynamics where force is involved at first. Structure evolved in graphite-, nanotubes- and fullerene-states have respectively one-dimensional, two-dimensional and four-dimensional atoms. Moreover, the associated energy curve is a parabola, indicating the transfer of electrons under neutral exertion of forces to their relevant poles. The graphite structure under only attained-dynamics of atoms is also developed but in two-dimension. Here, binding energy between graphitic carbon atoms is engaged under the influence of a small difference available between their involved forces along opposite poles. Structural evolution in diamond, lonsdaleite and graphene atoms involve potential energy of electrons required to undertake infinitesimal displacements under orientationally-controlled exerting forces to their relevant poles. In this study, the growth of diamond is found to be south to ground where atoms bound ground to south. Thus, diamond atoms merge for a tetra-electron ground to south topological structure. Lonsdaleite atoms merge for a bi-electron ground to just-south topological structure. The growth of graphene was just-north to ground; however, the binding of atoms was ground to just-north showing tetra-electrons ground to just-north topological structure. Glassy carbon exhibits layered-topological structure which successively binds tri-layers of gas-, graphite- and lonsdaleite-state atoms in repetitive manner. Orientating pair of electrons of each atom of below gas layer and above lonsdaleite layer enter from the rear side and front side respectively to undertake another clamping of unfilled energy knots belonging to each atom of intermediate graphitic layer. Different carbon atoms develop amorphous structures when they bind under frustrating amalgamation. Hardness of carbon-based materials was also sketched in the light of force-energy behaviors of different state carbon atoms. Here, structure evolution in each carbon state atom explores its own science.

The USDA-FSIS considers mechanically-tenderized beef as ‘non-intact’ and a food safety concern because of the potential for translocation of surface E. coli O157:H7 into the interior of the meat that may be cooked ‘rare or medium-rare’ and consumed. We evaluated 14 potential spray interventions on E. coli O157:H7-inoculated lean beef wafers (~10⁶ CFU/cm² , n=80) passing through a spray system (18 sec dwell time, ~40 PSI) integrated into the front end of a Ross TC-700MC tenderizer. Inoculated and processed beef wafers were stomached with D/E neutralizing broth and plated immediately, or were held in refrigerated storage for 1-, 7-, or 14 days prior to microbial plating. Seven antimicrobials that showed better performance in preliminary screening on beef wafers were selected for further testing on beef subprimals in conjunction with blade tenderization. Boneless top sirloin beef subprimals were inoculated at ~2 x 10⁴ CFU/cm² with a four-strain cocktail of Escherichia coli O157:H7 and passed once, lean side up, through an integrated spray system and blade tenderizer. Core samples obtained from each subprimal were examined for the presence/absence of E. coli O157:H7. Absence of E coli O157:H7 translocated into core samples correlated with the ability of the antimicrobials to reduce bacterial levels on the surface of beef prior to blade tenderization.

Flow and transport processes in soil and rock play a critical role in agricultural non-point source pollution (ANSP) loads. In this study, we investigated the ANPS load discharged into rivers from an irrigation district in the Tibetan Plateau, and simulated ANPS load using a distributed model involving detailed descriptions of flow and ANPS transport and transformation processes in the soil and rock. Experiments were conducted for two years to measure soil water content and nitrogen concentrations and the quality and quantity of lateral flow in the rock and at the drainage canal outlet during the highland barley growing period. A distributed model, in which the subsurface lateral flow was described using a step-wise method, was developed to simulate flow and ammonium nitrogen and nitrate nitrogen transport. Sobol’s method was used to evaluate the sensitivity of simulated flow and transport processes to model inputs. The results showed that, with a 21.2% increase of rainfall and irrigation in the highland barley growing period, the average NH4+-N and NO3--N concentrations in the soil layer decreased by 10.8% and 14.3%, respectively, due to increased deep seepage. Deep seepage of rainfall water accounted for 0–52.4% of total rainfall, whereas deep seepage of irrigation water accounted for 36.6–45.3% of total irrigation. NH4+-N and NO3--N discharged into the drainage channel represented 19.9–30.4% and 19.4–26.7% of the deep seepage, respectively. The mean Nash-Sutcliffe coefficients, root mean square errors, and cumulative deviations between the measured and simulated flow rates and NH4+-N and NO3--N concentrations at the surface drainage canal outlet were 0.694, 0.081, and 0.242, respectively, indicating that the proposed method can effectively describe the hydrological and ANPS pollution migration in the plateau irrigation zone. The Sobol’ sensitivity analysis results demonstrated that subsurface lateral flow had the most important first order and total effect on the simulated flow and NH4+-N and NO3--N concentrations at the surface drainage outlet.

DNA double-strand breaks (DSBs) are deleterious lesions that are generated in response to ionizing radiation or replication fork collapse that can lead to genomic instability and cancer. Eukaryotes have evolved two major pathways, namely homologous recombination (HR) and non-homologous end joining (NHEJ) to repair DSBs. Whereas the roles of protein-DNA interactions in HR and NHEJ have been fairly well defined, the functions of small and long non-coding RNAs and RNA-DNA hybrids in the DNA damage response is just beginning to be elucidated. This review summarizes recent discoveries on the identification of non-coding RNAs and RNA-mediated regulation of DSB repair

This paper presents the latest developments in microCT, both globally and locally, for supporting the additive manufacturing industry. There are a number of recently developed capabilities which are especially relevant to the non-destructive quality inspection of additive manufactured parts; and also for advanced process optimization. These new capabilities are all locally available but not yet utilized to their full potential, most likely due to a lack of knowledge of these capabilities. The aim of this paper is therefore to fill this gap and provide an overview of these latest capabilities, showcasing numerous local examples.

Common genetic variants of the fat mass and obesity associated (FTO) gene are strongly associated with obesity and type 2 diabetes. FTO is ubiquitously expressed, but appears to have tissue-specific roles. Earlier studies have focused on the role of hypothlamic FTO in the regulation of metabolism. However, it appears that FTO plays a role in the regulation of metabolism in a tissue-specific manner. Recent studies suggest that expression of hepatic FTO is regulated by metabolic signals such as nutrients and hormones and altered FTO levels in liver affects glucose and lipid metabolism. This review outlines recent findings on hepatic FTO in the regulation of metabolism, with particular focus on hepatic glucose and lipid metabolism. It is proposed that abnormal activity of hepatic signaling pathways involving FTO links metabolic impairments such as obesity, type 2 diabetes and nonalcoholic fatty liver disease (NAFLD). Therefore, a better understanding of these pathways may lead to therapeutic approaches to treat these metabolic diseases by targeting hepatic FTO. The overall goal of this review is to place FTO within the context of hepatic regulation of metabolism.

The Hedgehog (Hh) signaling pathway plays an important role in embryonic and postnatal vascular development and in maintaining the homeostasis of organs. Under physiological conditions, Sonic Hedgehog (Shh), a secreted protein belonging to the Hh family, regulates endothelial cell growth, promotes cell migration, and stimulates the formation of new blood vessels. The present review highlights recent advances made in the field of Shh signaling in endothelial progenitor cells (EPCs). The canonical and non-canonical Shh signaling pathways in EPCs and endothelial cells (ECs) related to homeostasis, Shh signal transmission by extracellular vesicles (EVs) or exosomes containing single-strand non-coding miRNAs, and impaired Shh signaling in cardiovascular diseases are discussed. As a promising therapeutic tool, the possibility of using the Shh signaling pathway for the activation of EPCs in patients suffering from cardiovascular diseases is further explored.

Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

Congenital Pouch Colon (CPC) is a rare anorectal anomaly common to North Western India specifically Rajasthan. Despite efforts to understand the clinical genetic makeup of CPC, no attempt on identifying non-coding RNAs was done. We have earlier reported CPC's rare variants from whole exome sequencing across 18 affected samples in a total of 64 subjects. A Smith-Waterman algorithm was used to infer a couple of lncRNAs from WES samples of CPC with predictions from the Noncode database. Further screening and quantification using PCR, we ascertained interactions using Micro Scale Thermophoresis (MST). We report the role of lnc-EPB41-1-1 shown to be promiscuously interacting with KIF13A substantiating their role in regulation.

A nonrenewable resource extraction game model is analyzed in a differential game theory framework with random duration. If the c.d.f. of the final time is discontinuous, the related subgames are differentiated based on the position of the initial instant with respect to the jump. We investigate properties of optimal trajectories and of imputation distribution procedures if the game is played cooperatively.

Depending on human body conditions and environmental conditions, it is sometimes difficult to conduct subject experiments. In such cases, it is effective to use a thermal manikin. There are few studies that investigate the effect of the non-uniform and asymmetric outdoor thermal environment on the mean skin temperature. The purpose of this study is to clarify the influence of the non-uniform and asymmetric thermal radiation of short-wavelength solar radiation in an outdoor environment on the calculation of the mean skin temperature. The skin temperature of the front of the coronal surface, which was facing the sun and where the body received direct short-wavelength solar radiation, and the skin temperature of the rear of the coronal surface, which was in the shadow and did not receive direct short-wavelength solar radiation were respectively measured. The feet, upper arm, forearm, hand and lower leg, which are susceptible to short-wavelength solar radiation in a standing posture, had a noticeable difference in skin temperature between sites in the sun and in shade. The mean skin temperature of sites facing the sun was significantly higher than the mean skin temperature of those in the shade.

Preconception lifestyle modification and reducing several known risk factors may have an influence on the future pregnancy outcome. The aim of the study was to analyse health behaviour and personal values, as well as to assess the relationship of these factors in women without children, in pregnant ones and in women who had already delivered babies. The questionnaire survey included Health Behaviour Inventory (HBI), Personal Value List (PVL) and the sociodemographic data and was conducted in 538 women. These women were divided into 3 groups: women who recently delivered (n = 235), pregnant women (n = 121) and childless women (n = 182). Pregnant women demonstrated a significantly higher level of declared health behaviours and also they rated higher subscales values "positive mental attitude" and "health practices" in comparison to women who recently delivered and to childless women. In all tested groups the highest rated personal value was "a successful family life", while the most appreciated symbol of happiness was "love and friendship". Our results suggest that the system of values and the perception of happiness symbols may influence women's health behaviours. Positioning “health” in the hierarchy of personal values as the most important one may facilitate introduction of healthy behaviours. This in turn could reduce several adverse pregnancy outcomes that are potentially modifiable with changing preconception health attitudes. Our results also identify several unanswered questions and highlights areas where new research is needed.

This paper focuses on evaluating airline service quality from the perspective of passengers view. Until now a lot of researches has performed in airline service quality evaluation in the world but a little research has been conducted in Iran, yet. In this research a framework for measuring airline service quality in Iran is proposed. After reviewing airline service quality criteria, SSQAI model was selected because of its comprehensiveness in covering airline service quality dimensions. SSQAI questionnaire items were redesigned to adopt with Iranian airlines requirements and environmental circumstances in the Iran's economic and cultural context. This study includes fuzzy decision-making theory, considering the possible fuzzy subjective judgment of the evaluators during airline service quality evaluation. Fuzzy TOPSIS have been applied for ranking airlines service quality performances. Three major Iranian airlines which have the most passenger transfer volumes in domestic and foreign flights, were chosen for evaluation in this research. Results demonstrated Mahan airline has got the best service quality performance rank in gaining passengers' satisfaction with delivery of high quality services to its passengers, among the three major Iranian airlines. IranAir and Aseman airlines placed in the second and third rank, respectively, according to passenger's evaluation.Statistical analysis have been used in analyzing passenger responses. Due to abnormality of data, Non-parametric tests were applied. To demonstrate airline ranks in every criterion separately, Friedman test was performed. Variance analysis and Tukey test were applied to study the influence of increasing in age and educational level of passengers' on degree of their satisfaction from airline's service quality. Results showed that age has not significant relation with passenger satisfaction of airlines, however increasing in educational level demonstrated a negative impact on passengers' satisfaction from airline's service quality.

This paper proposes a game-theoretic framework for quantifying effects of strategic bidding behavior of load aggregators (LAs) on wholesale electricity market equilibrium. The independent system operator (ISO) employs double-sided auction design to settle transactions in the wholesale electricity market. Generating companies (GENCOs) and LAs submit their bids to the ISO in the economic model of supply function equilibrium. Under the smart grid paradigm, a LA participates in the electricity market on behalf of end-consumers to minimize their total payment for purchasing electricity. In this paper, bi-level programming (BLP) method is employed to determine optimal bidding strategy of self-interested market participants. In the upper level, the profit of intended market participant is maximized and in the lower level, the ISO clears the market via a bid-based security constrained economic dispatch (SCED). Also, competition of GENCOs and LAs is modeled as a non-cooperative game. To examine the effects of strategic bidding behavior of LAs on market equilibrium, the proposed model is applied to the IEEE 9-bus and the IEEE 30-bus test systems. According to numerical results, to allow the LAs to behave strategically along with the GENCOs makes the former better off and the latter worse off, while the net effect of this on total social welfare turns out to be case-contingent.

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.

In order to improve the performance of the draft tube in hydraulic turbine, a multi–objective optimization method for the draft tube is developed by combining the design of experiment (DOE), the radial basis function (RBF) and the non–dominated sorting genetic algorithm (NSGA–II) in this paper. The geometrical design variables of the median section in the draft tube and the cross section in its exit diffuser are considered as design parameters in this optimization, which objective function is to maximize the pressure recovery factor (Cp) and minimize the energy loss coefficient (ζ). The limited numbers of design matrix required for the shape optimization of the draft tube is generated by optimal Latin hypercube (OLH) method of the DOE technique, of which performances are evaluated through computational fluid dynamic (CFD) numerical simulation. For reducing of the computational consumption, the approximate model is used based on the RBF. The Pareto optimal solutions are finally performed using the NSGA–II for obtaining the best geometrical parameters of the draft tube. The optimization result of the draft tube shows a marked performance improvement over the original, which verifies the theoretical validity and feasibility of the proposed method in this paper.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

The reservoir water level fluctuation zones (RWLFZs) and the natural riparian zones (NRZs) are two riparian ecosystems with the dramatically opposite hydrological rhythms which notably influence the plant guilds. However, little is known about the discrepancy of functional traits and niche characteristics of plant guilds in the RWLFZ and NRZ under different flooding rhythms. Here the RWLFZs (degraded riparian zones) and NRZs were chosen in the Xiangxi River Basin of the Three Gorges Reservoir (TGR) to investigate the variation in plant community structure, the spatial patterns of diversity, key functional traits and niche characteristics of dominant plant populations. The results showed that a total of 78 vascular plant species were identified in the riparian zones of Xiangxi River Basin. Most of the dominant species were annual herbs in the two riparian ecosystems and the percentage of annual dominant species increased from 65.79% in the NRZ to 67.34% in the RWLFZ. Compared with the NRZ, the specific leaf area, vegetation coverage and the aboveground biomass in the RWLFZ significantly increased by 74%, 27% and 19.6% respectively, while the water use efficiency of the RWLFZ decreased by 59.6% and there was no significant difference in the net photosynthetic rate between them, which showed that annuals in the RWLFZ adopt the R adaptation strategy with fast-growing, short lifespan and quickly acquiring and investing resources by altering leaf morphology such as expanding leaf area. The Simpson dominance index of RWLFZ was significantly higher than that of NRZ. Thus counter-seasonally hydrological alterations had the significant effects on functional traits of dominant species in the RWFLZ. Moreover, the highly adaptable and widely distributed species with larger niche breadths and high important values usually had a higher niche overlap value in the RWLFZ than in the NRZ, which showed that the competition in the plant community after experiencing anti-seasonal flooding was much more intraspecific than interspecific and would facilitate the expansion of species niches. And the distribution of the plant community was mainly driven by elevation (flooding depth), flooding duration and TK in the WLFZs. Our findings imply that the species with large niche breadths and high important values should be prioritized in ecological restoration efforts in the newly formed hydro-fluctuation zone of the TGR.

We introduce a novel algebraic structure, termed algebra B, which generalizes Lie algebras through the definition of a non-associative and unital algebra. We demonstrate that algebra B retains essential properties such as bilinearity and antisymmetry, and satisfies both the Jacobi and Malcev identities; this occurs when the product operations of Lie and Malcev algebras are derived from the product operation defined in our algebra. By examining the connections between algebra B, Malcev algebras, and Lie algebras, we establish that algebra B effectively generalizes these structures. Furthermore, we show that algebra B can generate complex entities with a novel structure distinct from those currently known. Our findings lay the groundwork for future investigations into the practical applications and further theoretical development of this new algebraic framework.

MicroRNAs (miRNA) exert regulatory actions via base-pairing with their binding sites on target mRNAs. Cooperative-binding, i.e., synergism, among binding sites on a mRNA is biochemically well-characterized. We studied whether this synergism is reflected in global relationship between miRNA-mediated regulatory activity and miRNA-binding-site count on the target mRNAs, i.e., leading to a non-linear relationship between the two. Recently, using our own and public datasets, we have enquired into miRNA regulatory actions: first, we analyzed the power-law distribution pattern of miRNA binding sites; second, strikingly, mRNAs for core miRNA regulatory apparatus proteins have extra-ordinarily high binding site counts, forming self-feedback-control loops; third, we revealed that tumor suppressor mRNAs generally have more sites than oncogene mRNAs; and fourth, we characterized enrichment of miRNA target mRNAs in translationally less-active polysomes relative to more-active polysomes. In these four studies, obvious correlation was observed qualitatively between the extent to which a mRNA is miRNA-regulated and its binding sites count. This paper summarizes the used datasets. We also re-analyzed the correlation by comparative linear and non-linear regression analyses. The results support a non-linear, instead of linear, relationship between the two parameters, conceivably a transcriptome-level reflection of cooperative-binding/synergism among miRNA binding sites on a target mRNA.

This paper presents data supporting the IC Regimen for potentiating the effectiveness of lorlatinib in treating ALK positive cancers, and specifically non-small cell lung cancer. Lorlatinib is an effective treatment for ALK driven non-small cell lung cancer and other ALK driven cancers. Lorlatinib inhibits ALK kinase and achieves good brain tissue levels, a common site for metastases in lung cancers. However, resistance to lorlatinib usually supervenes and the cancer awakens and starts growing again, resistant to lorlatinib. This paper analyses data indicating that adding two common generic drugs, itraconazole and cilostazol, to lorlatinib treatment may delay resistance development. Itraconazole is marketed worldwide as a generic antifungal drug that also inhibits Hedgehog signaling, CYP3A4, and the p-gp efflux pump. Cilostazol is a generic anti-thrombosis, phosphodiesterase 3 inhibiting drug that carries minimal bleeding risk. Cilostazol may enhance lorlatinib by deprivation of trophic growth factors supplied by platelets. Itraconazole may enhance lorlatinib effectiveness by reducing or stopping a Hedgehog centered amplifying feedback loop with ALK kinase. The combination of metastatic non-small cell lung cancer being a low-survival disease and the general safety itraconazole plus cilostazol augmentation, make a clinical trial of this trio worthwhile.

Objective. To test the hypothesis that a session of unilateral tSMS over the dominant primary motor cortex (M1) in healthy subjects produces a transient decrease in manual dexterity. Methods. A triple-blinded randomized control trial was conducted in 40 healthy participants who were allocated to real- or sham-tSMS group. Each participant underwent one session of tSMS and were assessed before, immediately after, 15 minutes, and 40 minutes after stimulation. Outcome measures were Finger Tapping Test (FTT), 5-seconds FTT, nine Hole Peg Test, and the hand grip assessment. Results: Significant differences were found between groups 15 minutes after stimulation in favor of the tSMS group in the FTT (2.2 ± 0.99; CI= .18-4.22; d= .84p=.034), and 5-second FTT (4.2 ± 1.98; CI= .199-8.2, d= .68; p=.04). In the intragroup analysis, a significantly lower number of taps 15 minutes after stimulation was observed in sham group (3.3 ± 1.01; CI= .47-6.13; d= .42; p=.015), but not in the real stimulation group. Conclusion: A unilateral session of tSMS in the M1 of the dominant hemisphere of healthy individuals does not reduce manual dexterity or grip strength. This technique could have benefits on the motor performance of the arm contralateral to the stimulated motor cortex

Isokinetic testing of the knee joint is the “gold standard” in strength assessment in sport especially in soccer, where it is part of pre-season screening to establish norms in case of injuries. There are many devices on the market that make it difficult to use of normative data. Isokinetics literature in females’ soccer players is scarce in comparison to males. Furthermore, evaluation protocols strongly differ from each other, using different ranges of speeds, movement patterns and output data. Thus, it is difficult for the researcher and the clinician to choose a correct protocol for isokinetic evaluation in female soccer players. The indexes of isokinetic performance that are widely accepted are the hamstring/quadriceps (H/Q) ratio and dominant/non-dominant (D/ND) ratio as reference. In this review are highlighted some common features of isokinetic testing in female soccer players of different levels of qualification. From this study, it emerges that, for a reliable test, 3 reps at the speed of 60°/sec in knee flexion/extension is preferable due to the high reliability and that an optimal H/Q ratio in healthy female soccer players of different level of qualification and age is 50%, while the interlimb differences (D/ND) should be below 10%.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Counter-current spontaneous imbibition of water is a critical oil-recovery mechanism. In the laboratory, the Amott test is a widely used technique to assess efficacy of brine imbibition into oil-saturated core plugs. The classic Amott-cell methodology was designed to estimate ultimate oil recovery, but not the recovery dynamics that provides essential information about the imbibition mechanisms. Retention of oil droplets at the external core surface and delayed onset of oil production are the two key artifacts of the classic Amott experiment. This retention, referred to here as the ``external-surface oil holdup effect'', often results in stepwise recovery curves that obscure true dynamics of spontaneous imbibition. To address these holdup drawbacks of the classic Amott method, we modified the Amott cell and experimental procedure. For the first time, using water-wet Indiana limestone cores saturated with brine and mineral oil, we showed that our improvements of the Amott method enabled accurate and reproducible measurements of oil-recovery dynamics. We used the Generalized Extreme Value (GEV) statistics to describe oil-production histories from water-wet heterogeneous limestone cores with finite initial water saturations, for the first time. We demonstrated that our four-parameter GEV model accurately described the recovery dynamics, and that optimal GEV parameter values systematically reflected the key characteristics of the oil-rock system, such as oil viscosity and rock permeability. These findings gave us a more fundamental understanding of spontaneous, counter-current imbibition mechanisms and insights into what constitutes a predictive model of counter-current water imbibition into oil-saturated rocks with finite initial water saturation.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

A study analyzed financial statements in the beer sectors of Italy and Spain during 2019-2021 using compositional data analysis (CoDa) to gauge pre- and post-pandemic financial health and aid sector-level decision-making. Geometric averages were employed to mitigate statistical issues, offering more accurate insights than arithmetic averages. The exploratory, quantitative methodology calculated key financial ratios and profitability metrics. Results indicated satisfactory short-term solvency. Returns and margins remained subpar despite pandemic policies. Geometric averages proved essential in portraying sector health accurately, underscoring the need for their use in classic brewing sector ratios. The study also showcased CoDa's efficacy in diagnosing sectoral financial health for informed decision-making. Furthermore, a web analysis revealed varying transparency levels in communicating non-financial indicators, with Spain excelling in waste, water, and energy disclosures, while Italy prioritized energy and waste with less transparency.

World Health Organization recommends adjusting the salt intake as a part of the nine global targets to reduce premature mortality from non-communicable chronic diseases as priority and most cost-effective intervention. In 2006. the main aim of the Croatian Action on Salt and Health was to decrease salt intake for 16% because of its critical intake and consequences on human health. We have organized educative activities for increasing awareness on salt harmfulness, defined food categories of prime interest, collaborate with industries and determine salt intake (24h urine-sodium excretion). It was determined that the proportion of salt in ready-to-eat baked bread should not exceed 1.4%. In the period 2014 –2022, salt in the semi-white bread was reduced 14%, in bakery 22% and in largest meat industry in Croatia 25%. Awareness of the harmfulness of salt on health increased from 65.3% in 2008 to 96.9% in 2023 and salt intake was reduced 15.9%-1.8 grams/day (22.8% men, 11.7% women). In last 18 years a significant decrease in salt intake was achieved in Croatia, awareness of its harmfulness increased, collaboration with food industry established and regulatory documents launched. However, salt intake is still very high underlying the need for continuation of efforts and even stronger activities.

Due to the extraordinary mechanical properties of ultra-high-performance concrete (UHPC), the shear stirrups in UHPC beams could potentially be removed. The aim of this study is to determine the effect of beam height and steel fiber volume content on the shear behavior of non-stirrup UHPC beams under a larger shear span-depth ratio (up to 2.8). Eight beams were designed and fabricated, including six non-stirrup UHPC beams and two comparing stirrup-reinforced normal concrete (NC) beams. The experimental results demonstrated that the volume content of steel fiber could be a crucial factor affecting the ductility, cracking strength, and shear strength of non-stirrup UHPC beams and altering their failure modes. Additionally, the height of the beam had a considerable effect on its shear resistance. This study revealed that the French standard formulae were more accurate for the UHPC beams with larger shear span-depth ratios and PCI-2021 formulae greatly overestimate the shear capacity of UHPC beams with larger shear span-depth ratios. Xu’s formulae were more accurate for the steel fiber-reinforced UHPC beams with larger shear span-depth ratios.

Cardiomyopathies (CMPs) are a group of myocardial disorders that are characterized by structural and functional abnormalities of the heart muscle. These abnormalities occur in the absence of coronary artery disease (CAD), hypertension, valvular disease, and congenital heart disease. CMPs are an increasingly important topic in the field of cardiovascular diseases due to the complexity of their diagnosis and management. In 2023, the ESC guidelines on cardiomyopathies were first published, marking significant progress in the field. The growth of techniques such as cardiac magnetic resonance imaging (CMR) and genetics has been fueled by the development of multimodal imaging approaches. For the diagnosis of CMPs, a multimodal imaging approach, including CMR, is recommended. CMR has become the standard for non-invasive analysis of cardiac morphology and myocardial function. This document provides an overview of the role of CMR in CMPs, with a focus on tissue mapping. CMR enables the characterization of myocardial tissues and the assessment of cardiac functions. CMR sequences and techniques, such as late gadolinium enhancement (LGE) and parametric mapping, provide detailed information on tissue composition, fibrosis, edema, and myocardial perfusion. These techniques offer valuable insights for early diagnosis, prognostic evaluation, and therapeutic guidance of CMPs. The use of quantitative CMR markers enables personalized treatment plans, improving overall patient outcomes. This review aims to serve as a guide for the use of these new tools in clinical practice.

Non-coding RNAs have been described as crucial regulators of gene expression and guards for cellular homeostasis. Some recent papers are focused on vault RNAs, one of the classes of non-coding RNA, and their role in cell proliferation, tumorigenesis, apoptosis, cancer response to therapy, and autophagy, which made them potential therapy targets in oncology. In the human genome, four vault RNA paralogues can be distinguished. They are associated with vault complexes, considered the largest ribonucleoprotein complex. The protein part of these complexes consists of a major vault protein (MVP) and two minor vault proteins (vPARP and TEP1). The name of the complex, as well as vault RNA, comes from the hollow barrel-shaped structure that reminds a vault. Their sequence and structure are highly evolutionary conserved and show many similarities in comparison with different species, but vault RNAs show various roles. Vaults were discovered in 1986, and their functions remained unclear for many years. Although not much is known about their contribution to cell metabolism, it has become clear that vault RNAs are involved in various processes and pathways associated with cancer progression and modulating cell functioning in normal and pathological stages. In this review, we discuss known functions of the human vault RNAs in the context of cellular metabolism, emphasizing processes related to cancer and cancer therapy efficacy.

Non-pharmaceutical interventions (NPIs), including social distancing, wearing personal protective equipment, and lockdown measures, have been at the forefront of outbreak control in nursing homes. We used a mixed methodology to assess which NPIs nursing homes in the canton of Geneva, Switzerland, followed for their staff and residents during the first wave of the pandemic, between March 1, 2020 and June 1, 2020. For the qualitative component, we interviewed the attending physicians and/or director of each nursing home. Based on in-vivo codes, NPIs for nursing home workers and residents in each nursing home were thematically classified as: maximally restrictive, moderately restrictive, and minimally restrictive. In the quantitative component, we calculated incident rate ratios (IRR) for infection between the three levels of COVID-19-related measures taken in these nursing homes. We found an equal distribution of maximally (n=4), moderately (n=4), and minimally (n=4) restrictive NPIs. The extent of restriction did not show to be significantly associated with the cumulative incidence of COVID-19 cases among residents (maximally restrictive IRR = 3.90, 95%CI 0.82-45.54, p = 0.184; moderately restrictive IRR = 3.55, 95%CI 0.75-41.42, p = 0.212 ; minimally restrictive IRR = reference). Variabilities in NPIs adopted by nursing homes, and the number of COVID-19 cases appear to be randomly affected.

In this communication, we present an innovative approach leveraging advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques, specifically the Non-Negative Matrix Factorization (NMF) method, to analyze downward and upward light spectra collected by Hyperspectral Ocean Color Radiometer (HyperOCR, HOCR) sensors in the water column. Our work focuses on the development of a robust and efficient tool for unraveling the structure and activities of natural microbial assemblages in the ocean. By applying the NMF method to HyperOCR data, we successfully extracted five spectral signatures, representing unique patterns in the data. These signatures were instrumental in predicting the abundances of various microbial components, including bacteria, heterotrophic nanoflagellates, and picoeukaryotes, showcasing the potential of ML and AI in advancing oceanographic studies. To validate the methods, the study area included a shallow coastal area under the influence of freshwater inflow and an open offshore area with a depth of 100 m. The study sites in coastal and offshore waters (Kaštela Bay and Stončica Vis, respectively) had significantly different hydrographic and microbiological characteristics. Kaštela Bay had lower temperatures and salinity than the site on Vis. We have proved by application of different AI and ML methods with specific HOCR sensors prediction of the structure of the microbial community.

Heterogeneous systems of limited capacity have general applications in manufacturing, but also for logistic or service systems, due to the differences in server or workstation performance or work assignment, in close relationship with system flexibility, where saturation and blocking are ordinary situations of systems with high demand and limited capacity, so accurate loss quantification is essential for performance evaluation. Multi-class systems of limited capacity have been studied much less than parallel homogeneous systems (Erlang models). In this context, accurate models for parallel heterogeneous ordered-entry systems are developed: without any prior queue, M/Mi/c/c, and with a k capacity queue M/Mi/c/c+k. These new matrix models give an exact state formulation, and their accuracy is verified through discrete event simulation and comparison with literature results. Also, the effect of queue capacity is studied in relationship with the pattern of service rates. Next, the heterogeneous recirculating system model is also developed with good approximation results. Finally, the proposed models are applied to evaluate systems with non-exponential service times, through a new hybrid methodology by combining the Markovian model and the Monte Carlo Method (MCM) for normal or lognormal service times that also yield useful good approximations to the simulated system.

During the COVID-19 pandemic, the circulation of non–SARS-CoV-2 respiratory viruses changed throughout the world. This study aimed to analyze the effect of respiratory syncytial virus (RSV), metapneumovirus (MPV), rhinovirus (RV), and parainfluenza (PIV)-3 single infection, dual co-infection, and viral load on cytokine and chemokine expression in 300 Mexican patients with ARIs negative for SARS-CoV-2. Molecular detection and viral load were performed by RT-qPCR. Cytokine and chemokine expression was determined in samples positive for single RSV, MPV, RV, or PIV-3 infection, and in the most frequent dual co-infections, by a cytokine panel using a magnetic-bead-based multiplex immunoassay. Of the patients positive for viral infection, 91.7% had a single infection and 8.3% had dual co-infections. In this study, RSV was the respiratory virus with the highest prevalence, and the most common dual co-infection was RV+RSV. On the other hand, RV infections had the highest viral loads compared to the other viruses detected. FGF basic, MIP-1α IL-8, IP-10, MCP-1, IFN-γ, and RANTES showed differential expression between single infections and dual co-infections compared to healthy patients. We report, for the first time, the circulation of non-SARS-CoV-2 viruses in the south of Mexico and the cytokine and chemokine profiles associated with these viruses.

This paper investigates the constructability of angles multiples of 3 within the framework of Euclidean geometry. It makes a significant contribution by presenting the first geometric construction for all such angles, offering a rigorous solution to a longstanding geometric problem. The paper reaffirms the efficacy of Euclidean geometry in providing precise constructions and robust proofs for these angles, demonstrating the enduring strength of Euclidean principles from classical to modern times. The presented workflow goes beyond Euclidean geometry to examine non-Euclidean methods, particularly analytical approaches, revealing misconceptions that compromise the genetic and geometric rigor of Euclidean principles. The paper exposes incongruities when algebraic proofs related to angle constructability are applied to the Euclidean system, emphasizing the misalignment of fundamental geometric concepts. A notable result in the paper is the construction of a angle, introducing the “ angle chord” as a novel geometric property. This property challenges assumptions made by non-Euclidean methods and highlights the nuanced geometric properties crucial for rigorous constructions. The paper refutes the fallacy of relying solely on algebra for solutions to angles multiples of , emphasizing the necessity of embracing Euclidean geometry for geometric discoveries. The paper underscores the merits and resilience of Euclidean geometry, showcasing its independence and depth across historical and modern perspectives. The newly presented geometric construction not only resolves a longstanding question but also emphasizes the intrinsic strength and uniqueness of Euclidean principles in contrast to alternative methodologies.

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Auto-mated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96, on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

Diamond-like carbon (DLC) films have a promising tribological application in green precision manufacturing, but their toughness represents a serious issue in the way of their application perspective. The film thickness is on the micrometer scale and makes conventional methods unsuitable for its toughness characterization. The absence of an accepted method for characterizing the DLC toughness has hindered the related research on the development of toughening methods and understanding the mechanism. This work intends to explore a relatively simple and acceptable characterization method to measure the film toughness. Moreover, toughening mechanisms of DLC films are proposed during the characterization to reveal the toughening mechanism. The toughness can be fully characterized through combining impact toughness and scratch toughness. The impact toughness can be obtained by observing the size of the indentation and the crack state around the indentation in the impact experiment. The scratch toughness could be assessed by combining the scratch morphology and Crack Propagation Resistance (CPRS). The improvement of film toughness should be carried out from two aspects: preventing microcrack initiation and suppressing crack propagation. Appropriate doping of non-carbide metal particles can effectively improve the toughness of the film, and there are seven main factors affecting the toughening of the film. Five toughening mechanisms of non-carbide metals from film preparation to test stage are revealed during the investigation of the change patterns.

DFT-D3 calculations based on structural X-ray diffraction data obtained for 3-oxo-camphorsulfonyl imine (1), camphorsulfonyl chloride (2) and seven camphor sulfonimines (O2SNC10H13NR, L1-L7) from which L2 (R=4-OHC6H4), L4 (R=4-ClC6H4) and L6 (R=3,5-(CH3)2C6H3) are new, provide information into the intra and inter molecular interactions with concomitant elucidation of the supramolecular arrangement of the compounds. The DFT D3 calculations performed in small clusters of 2 or 3 molecular units reproduce the interactions observed by X-ray analyses showing that as a general trend the structural arrangement of the molecules is driven by electronic rather than by packing parameters. In all compounds the self-assembly of 3D structures involves the sulfonyl imine group ( NSO2) either to establish hydrogen bonds, through the oxygen atoms or non-classic oxygen-aliphatic hydrogen or non-bonding interactions (NBI) which also involve the sulfonyl oxygens. Interestingly, the camphor sulfonimine compounds (L2, L3) having protic groups (R=C6H4X: X=OH, L2 or X=NH2, L3) at the aromatic imine substituents (=NR) present an extra π-π stacking which is absent in the other compounds aromatic derivatives. The X-ray analysis shows that all the reported camphor sulfonimine compounds display the E configuration with respect to the imine substituent (R). The study of the redox behavior of the compounds by cyclic voltammetry allows an insight into the solution properties of the compounds and the rationalization of the molecular interactions that stand in the solid and solution states. Camphor sulfonimine compounds (L) display appropriate binding atoms to coordinate transition metals. The herein results show that monodentate coordination through the nitrogen atom of the sulfonimine 5-membered ring to the {Ag(NO3)} metal center is favored. When this imine nitrogen atom is not itself involved in the Organic Framework DFT-D3 calculations shows that the complexation does not affect the non-covalent interactions that are reproduced in the MOF structure.

Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional and non-functional. Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual government of requirement classes is a timewasting task, and it needs intensive effort. Automatic requirement classification techniques through advanced Machine Learning (ML) strategies are started to be developed to address this problem. Basically, software requirements are generated as natural language explanations and therefore requirement classification may be handled as a particular Natural Language Processing (NLP) problem. From ML point of view, classification strategies require dataset to be used in the training/learning phase. For this goal, we generated a unique Turkish dataset having collected the requiremenst from real-world software projects with 4600 samples. Of these requirements, 3000 and 1600 were labeled as functional and non-functional, respectively. The data set has been evaluated using (i) traditional machine learning algorithms, (ii) deep learning algorithms, and (iii) transformer models respectively. As a result BERTurk was found to be the most successful algorithm to discriminate functional and non-functional requirements in terms of 95% f-score metric.

In many industrial, automotive, and aerospace applications, electro-mechanical systems are subjected to random vibration excitations, and the most critical components are required to undergo qualification tests to verify their suitability. Measured field data are commonly considered as reference for the synthesis of random stationary signals used as shaker input excitations in laboratory tests. For the most popular procedures of random-control testing, the user sets the input profiles in terms of power spectral density (PSD) associated with randomized phases generated by the shaker controller to finally provide the physical motion. As a result, the overall probability distribution of the test signal tends toward Gaussian, whereas many real-life random excitations prove non-Gaussian due to distinctive bursts and peaks. The quantitative estimate of the number and amplitudes of peaks present in a certain signal is usually made through the statistical parameter known as kurtosis. The so-called kurtosis control methods presented in the literature are conceived to perform qualification tests with random and non-Gaussian vibration excitations. In this paper, two novel algorithms able to synthesize shaker input signals for random-control testing with prescribed PSD and kurtosis value are proposed, and the results of their application are comparatively discussed to assess their effectiveness and potentialities in different kinds of qualification testing, including accelerated fatigue-life tests.

The emergence of drug-resistant strains of M. tuberculosis (Mtb) and the phenomenon of phe-notypic tolerance to drugs challenge the goal of tuberculosis (TB) elimination worldwide. By using the ex vivo cultures of alveolar macrophages obtained from lung tissues of TB patients after intensive antimicrobial chemotherapy before surgery, different subpopulations of multi-drug-tolerant Mtb with a spectrum of phenotypic and growth features were identified in the same TB lesions. Our results indicative of not only passive mechanisms generating nonheritable resistance of Mtb to antibiotics, which are associated mainly with a lack of Mtb growth, but also some active mechanisms of Mtb persistence, such as cell wall and metabolic pathway remod-eling. In one of the subpopulations, non-acid-fast Mtb have undergone significant repro-gramming with the restoration of acid-fastness, lipoarabinomannan expression and replication in host cells of some patients after withdrawal of anti-TB drugs. Our data indicate the universal stress protein Rv2623 as a clinically relevant biomarker of Mtb that have lost acid-fastness in human lungs. The studies of Mtb persistence, dormancy, and resumption and the identification of biomarkers characterizing these phenomena are very important to develop vaccines and drug regimens with the individualized management of patients for overcoming the re-sistance/tolerance crisis in anti-TB therapy.

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructure that has to withstand extreme events. Since there is evidence of increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques, (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are compared. Discrepancies due to the parameter estimation methods may reach up to 45 % of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in design levels up to 40%). Despite the underlying large methodological uncertainties, there is a robust tendency for increased DLLs in the Oberland region towards the end of this century based on the (non-)stationary models.

in order to enhance the separation performance and to reduce the heat loss of the transmembrane for membrane distillation, the thermal efficiency and hydrophobicity of the membrane distillation should be simultaneously enhanced. In this work, a PVDF/PET hydrophobic/hydrophilic membrane is prepared by non-solvent phase inducing method. Nanosized silica aerogel (SiAG) with high porosity is added to the composite membranes. The modifying effects and operating conditions on permeate flux and thermal efficiency in direct contact membrane distillation (DCMD) are investigated. Furthermore, the latent heat of vaporization and the heat transfer across the membranes are compared for SiAG addition, which indicates that the composite PVDF@SiAG/PET membranes display a great potential for distillation-separation application with high heat efficiency.

Objectives: Studies on the association between non-steroidal anti-inflammatory drugs (NSAIDs) and major adverse cardiovascular events (MACE) in the Arabian Gulf are scarce. The aim of this study was to evaluate the association between NSAIDs use and MACE in acute coronary syndrome (ACS) patients in the Arabian Gulf region. Methods: Data was analyzed from 3,007 consecutive patients diagnosed with ACS admitted to 29 hospitals in four Arabian Gulf countries from January 2012 to January 2013 as well as being on prior NSAIDs use during the index admission. MACE included stroke/transient ischemic attacks (TIAs), myocardial infarction (MI), all-cause mortality and readmissions for cardiac reasons. Results: The overall mean age of the cohort was 62 ± 12 years and 9.6% (n = 290) of the patients were on prior NSAID use during the index admission. At 12-months follow-up, after adjusting for confounding factors, those on NSAIDs were significantly more likely to have had MACE (adjusted OR (aOR), 1.89; 95% confidence interval (CI): 1.44-2.48; p < 0.001). Specifically, the higher event rates observed were stroke/TIA (aOR, 2.50; 95% CI: 1.51-4.14; p < 0.001) and readmissions for cardiac reasons (aOR, 2.09; 95% CI: 1.59-2.74; p < 0.001) but not MI (aOR, 1.26; 95% CI: 0.80-1.99; p = 0.320) and all-cause mortality (aOR, 0.79; 95% CI: 0.46-1.34; p = 0.383). Conclusions: NSAIDs use was associated with significant stroke/TIA events as well as readmissions for cardiac reasons. However, NSAIDs were not associated with increased MI or all-cause mortality rates in ACS patients in the Arabian Gulf.

Proteins in biological systems function at the interface of single molecule and bulk chemistry and thus provide novel insights into the basic physical chemistry of scaling and emergent phenomena. For example, a binary mechanical model based on the chemistry of muscle contraction unifies molecular mechanics and thermodynamics and provides an explicit solution to the Gibbs paradox. Using the same model system, here I show that chemical activities of molecular states have no effect on chemical kinetics or energetics. Specifically, while the concentration or number of molecules in a molecular state is widely thought to contribute to reaction free energies, here I show that it is not the physical presence of molecules that pushes a reaction forward but the number of microstates, , accessible in a chemical state that pulls a reaction toward equilibrium with an entropic force down an entropic funnel. With the derivation of an entropic contribution to chemical kinetics, I develop a novel chemical kinetic formulation that fully describes the chemical thermodynamics of both equilibrium and non-equilibrium reactions in terms of an a priori system reaction energy landscape.

Based on the Generalized bracket, or Beris-Edwards, formalism of non-equilibrium thermodynamics, we have recently proposed [Stephanou et al. Materials, 13, 2867 (2020)] a new differential constitutive model for the rheology of entangled polymer melts and solutions. It has amended the shortcomings of a previous model that was too strict in the values of the convective constraint release parameter for the model not to violate the second law of thermodynamics and has been shown capable of predicting a transient stress undershoot (following the overshoot) at high shear rates. In this work, we wish to further examine this model’s capability of predicting the rheological response of industrial polymer systems by extending it to its multiple-mode version. The comparison against industrial rheological data (High-Density Polyethylene resins), as compared against available experimental data in (a) Small Amplitude Oscillatory shear, (b) start-up shear, and (c) start-up uniaxial elongation, is noted to be good.

The uneven immunogenicity of the attenuated tetravalent dengue vaccine has created the diffi-culty to achieve a balanced protection against all four serotypes of dengue virus (DENV). To overcome this problem, non-replicative-based vaccines have come into focus as their immuno-genicity is adjustable. This approach is excellent for multivalent vaccines but commonly faces the issue of low immunogenicity. In this present study, we developed a non-replicating dengue vaccine composing of UV-inactivated dengue-2 virus (UV-DENV2) and DENV-2 NS1-279 protein encapsidated within nanoparticles. This vaccine candidate was administered in the presence of BCG cell wall cytoskeleton (BCG-CWS) as an adjuvant. We revealed, here, that encapsidated immunogens with BCG-CWS exerted potent activities on both B and T cells and elicited Th-1/Th-2 responses in mice. This was evidenced by strong neutralizing antibody activity with an unde-tectable antibody enhancing activity in a group of mice receiving encapsidated immunogens with BCG-CWS. Importantly, BCG-CWS significantly augmented antibody-mediated comple-ment-fixing activity, strongly stimulated the antigen-specific polyfunctional T cell responses as well as activated mixed Th-1/Th-2 responses specific to DENV2- and NS1-279 antigens. In conclu-sion, BCG-CWS potently adjuvanted the inactivated DENV-2 and subunit DENV immunogens. The mechanism of adjuvanticity remains unclear. This study revealed the potential use of BCG-CWS in vaccine development.

Background and Objectives: Elders’ needs are rarely examined beforehand, and thus, although technology-based tools can enhance self-management, acceptability rates are still low. This study aimed to examine and compare self-reported needs, priorities, and preferences of elders with heart failure (HF), diabetes mellitus (DM), and chronic obstructive pulmonary disease (COPD) toward technology use to enhance self-management. Materials and Methods: A convenience sample of 473 participants over 60s (60.5% females), diagnosed with HF (N=156), DM (N=164), or COPD (N=153) was recruited. They were administered a questionnaire about the usefulness of technology in general and in specific areas of disease management. Results: Most participants (84.7%) admitted that technology is needed for better disease management. This was equally recognized across the three groups both for the overall and specific areas of disease management (in order of priority: ‘Information’, ‘Communication with Physicians &amp; Caregivers’, and ‘Quality of Life &amp; Wellbeing’). Sociodemographic differences were found. Cell phones and PCs were the devices of preference. The four common features prioritized by all three groups were related to ‘information about disease management’ (i.e., monitoring symptoms, reminders for medication intake, management and prevention of complications), whereas the fifth one was related to ‘communication with physicians and caregivers (i.e., in case of abnormal or critical signs). The top disease-specific feature was also monitoring systems (of respiratory rate or blood sugar or blood pressure, oxygen), whereas other disease-specific features followed (i.e., maintaining normal weight for HF patients, adjusting insulin dose for DM patients, and training on breathing exercises for COPD patients). Conclusions: Elders in these three samples seem receptive to technology in disease management. mHealth tools, incorporating both common and disease-specific features and addressing different chronic patients, and being personalized at the same time, could be cost-saving and useful adjuncts in routine clinical care to improve self-management.

CGT is composed of fiber residues, leaves, dust particles, soil and others derived during the ginning and yarn spinning process at processing industries,. In the cotton spinning industrial clusters, farmers are using CGT) as one of the alternative roughage feed for their sheep, mainly during forage shortages in the summer months. To have baseline information on farmers using gin and factors driving them to choose CGT as a roughage source, which need to be identified for future planning on the usage of CGT in sheep feeding. Considering the above facts, the present study was undertaken to assess socio-personal characteristics, managemental practices associated with farmers using cotton gin in the feeding of sheep and also identify the factors driving the choice of the CGT as the primary source of roughage in the cotton spinning industry cluster of Tamil Nadu. For this, a survey among 80 sheep farmers was carried out using a pre-tested in-terview schedule. The collected data was analysed using descriptive statistics and logit regres-sion. The results indicated that the majority of male, aged, and large-land farmers were involved in practicing CGT feeding during the summer and also indicated the non-availability of green fodder during the summer season. The coarse type of CGT preferred more than the fine type of CGT trash in that area due to quality perception and price. The replacement level of CGT as roughage ranged from 33 to 75% of the total roughage requirement per day. About 88 percent-age of farmers were highly satisfied with the results of using CGT and they also expressed that the presence of foreign particles and dust were the major problems with using CGT. The choice of CGT as primary roughage among sheep farmers was primarily influenced by selective farming contexts, namely landholding, access to labour, and feeding practices of other livestock with cotton gin. Further, research has to be focused on to improve the quality of CGT in the future, as it is being utilised largely by sheep farmers.

This study presents a novel approach to analyzing the present value of total profit for non-participating and participating insurance policies in order to determine the optimal profitability of non-participating and participating insurance policies based on applying the approach used in operations research to the field of finance. As such, a comprehensive insurance product evaluation model was developed using both mathematical models and numerical analysis to evaluate the demand for non-participating and participating life insurance policies in response to changes in interest rates. The findings indicate that non-participating life insurance policies offer greater solvency for insurance companies compared to participating policies. The study also highlights the significance of spontaneous and induced demand in determining the total profit of both types of policies. The study concludes that life insurance companies should focus on generating spontaneous consumer demand, reducing induced demand, and implementing the optimal pricing strategy to achieve maximum profits.

In this work,the wave behaviour propagation of single-walled fluid conveying carbon nanotubes (SWCNT) under longitudinal magnetic fields and elastic foundations is studied, based on the nonlocal strain gradients theory(NSGT). With considertion of surface effect, the governing differential equation are obtained utilising Timoshenko beam theory and Hamilton's variational principle. The influence of small-scale parameters, fluid density, magnetic flux, surrounding elastic medium and surface effects on wave behaviour characteristics of carbon nanotubes are discussed in detail. The numerical results illustrated that with the magnetic flux increase，the phase velocities of the carbon nanotube will increase. When the fluid effects inside the carbon nanotube, the wave frequencies of the system reduces with increase in the non-local coefficient, while promotes with increase in the strain gradient coefficient. In addition, fluid density, surrounding elastic medium and surface effects have meaningful influence on the phase velocity of the system.

Background. There are scant data about the effectiveness of high flow nasal cannula (HFNC) oxygen therapy in patients hospitalized with severe acute respiratory failure (ARF) in non-intensive medical wards, particularly regarding the effect on arterial oxygenation compared to conventional oxygen therapy (COT) and non-invasive ventilation (NIV) or continuous positive airway pressure (CPAP). Methods. In a retrospective observational study, oxygenation parameters were measured before and immediately after HFNC initiation in 37 consecutive patients hospitalized in a geriatric ward in 2017. Results. HFNC was used as escalation therapy for untreatable hypoxia after failure of NIV/CPAP (n=18) or COT (n=19). Twenty-two patients died, 2 were transferred to the intensive care unit, while 13 were discharged alive. A “do not intubate” status was identified in 17 of the 22 deceased patients. Partial pressure of oxygen (pO2, p< 0.0001), oxygen saturation (SO2, p< 0.0001), pO2/fraction of inspired oxygen ratio (p=0.004) and peripheral SO2 measured by pulse oximetry (p< 0.0001) significantly increased soon after HFNC application. Oxygenation improvements were greater after escalation from NIV/CPAP and in patients discharged alive. Conclusion. HFNC significantly improved oxygenation in severe ARF after failure of COT or NIV/CPAP and may be particularly suitable for older patients hospitalized in non-intensive medical wards.

Immune checkpoint blockade (ICB) has revolutionized the prognosis of several advanced-stage solid tumors. However, its success has been far more limited in hematological malignancies and is mostly restricted to classical Hodgkin lymphoma (cHL) and primary mediastinal B cell lymphoma (PMBCL). In patients with non-Hodgkin lymphoma (NHL), response to PD-1/PD-L1 ICB monotherapy has been relatively limited, although some subtypes are more sensitive than others. Numerous predictive biomarkers have been investigated in solid malignancies, such as PD-L1 expression, tumor mutational burden (TMB) and microsatellite instability (MSI), among others. This case-illustrated review aims to appraise the current knowledge on PD-1/PD-L1 ICB efficacy in lymphoid malignancies, when used either as monotherapy or combined with other agents, and describes potential biomarkers of response in this specific setting.

The purpose of this work was to evaluate a novel methodology developed by Digital Blood Corporation (DBC) to calculate critical blood values using four non-invasive measured values as input. The values obtained using a point-of-care testing (POCT) device were utilized for comparison and reference. Radial arterial blood was collected for the POCT comparator analysis using the Abbott i-STAT^® device. The non-invasive methodology from DBC requires four parameters to be directly measured: temperature, hemoglobin, pO2, and pCO2. Subsequently, sodium, potassium, chloride, ionized calcium, total carbon dioxide, pH, bicarbonate, and oxygen saturation are calculated using an algorithm. The agreement between the POCT and DBC’s methodology was analyzed using Bland-Altman difference plots. For a second data set, pO2 and pCO2 values collected with the POCT were used as input for DBC’s algorithm to test its robustness. Data from 37 healthy ambulatory individuals, mean age: 42.4 + 13 years; range: 18-64 years, were included in the primary analysis. In the case of the non-invasive gained four input values the greatest variation between POCT and DBC’s approach was observed for pO2 and consequently for algorithm values that depend upon pO2 precision. Replacing transcutaneous pO2 and pCO2 with POCT values demonstrates the principal ability of DBC’s algorithm to predict the additional 8 blood values in sufficient agreement with a standard POCT device in healthy patients. The algorithm developed by DBC appears to be robust in the case of healthy patients but does need the four measured input values with preciseness comparable to a POCT device to give reliable and clinically relevant results. The present study thus serves as a proof of concept to facilitate future study and further development of this methodology into a non-invasive device.

This research is aimed to develop the method for damage degree evaluation in metals and alloys during their deformation and destruction based on acoustic emission (AE) technique. Experimental studies were carried out on tensile specimens of high-strength steel with simultaneous registration of AE signals in tested material. It was shown that the time-dependent AE primary parameters, such as the AE signal maximum amplitudes and AE activity, do not have a clear relationship with the material damage degree. This fact makes it difficult to use standard methods for assessing the damage degree of acoustic signal sources, including defects. It was proposed to use the Kolmogorov-Smirnov criterion to differ the processes of metal plastic deformation and crack propagation during the rupture. The possibility of estimating the material damage degree by high-energy AE hits share was shown. Besides, a particular expression was obtained for evaluating the damage degree for the material under study.

In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

Background Community health workers (CHWs) drawn from the general population are an important human resource in health care systems, preventing non-communicable diseases (NCDs) and contributing to an increase in healthy life expectancy in Japan. Thus, we have developed the COmmunity health workers perceptual and behavioral Competency Scale for preventing Non-communicable diseases (COCS-N) to measure CHWs’ competence in preventing NCDs. The purpose of this study is to examine individual and community factors affecting CHWs’ COCS-N scores. Methods Municipal public health nurses and other public health professionals are responsible for training and supporting CHWs in Japan. Therefore, the existence of CHWs and their willingness to participate in the study were confirmed with the municipalities, who were asked to distribute the self-administered questionnaire to CHWs where consent was obtained (N = 6,480). Variables used included demographic characteristics, COCS-N scores, and individual- and community-related factors. Logistic regression analysis was used to assess associations between variables. Results A total of 3,120 people completed the questionnaire, a valid response rate of 48.1%. The respondents’ mean age was 67.0 years (standard deviation = 9.0), and 88.0% were female. Comparison of the high- and low- competence groups in terms of NCD prevention based on COCS-N scores identified 13 factors associated with significant differences, including years spent working as a CHW (p < 0.001), subjective sense of health (p = 0.005), European Health Literacy Survey Questionnaire (HLS-EU-Q47) scores (p < 0.001), and community commitment scale (CCS) scores (p < 0.001). Logistic regression analysis revealed that HLS-EU-Q47 scores (odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.02–1.03) were a significant individual factor, while CCS scores (OR: 1.14, 95% CI: 1.11–1.16) were a significant community factor. Conclusions We found that the COCS-N score was associated with the individual factors overall health literacy (HL), perceptions of HL, and subjective sense of health, and with the community factor CCS scores. These results suggest that strengthening individual factors such as HL and subjective sense of health, and community factors such as sense of community is an effective strategy for increasing CHWs’ competence in preventing NCDs.

This article develops duality principles and related convex dual formulations suitable for the local optimization of non-convex primal formulations for a large class of models in physics and engineering. The results are based on standard tools of functional analysis, calculus of variations and duality theory. In particular, we develop applications to a Ginzburg-Landau type equation.

We introduce the notion of p-adic magic contraction on p-adic Hilbert space. We derive p-adic Halmos dilation, p-adic Egervary N-dilation, p-adic von Neumann inequality and p-adic Sz.-Nagy dilation for p-adic magic contraction.

In this work, we analysed human isolates of nontyphoidal Salmonella enterica subsp. enterica (NTS), which were collected from salmonellosis cases in Armenia from 1996 to 2019. This disease became a leading food-borne bacterial infection in the region, with the younger age groups especially affected. The isolates were characterised by serotyping, Enterobacterial Repetitive Intergenic Consensus (ERIC-PCR) typing, and whole genome sequencing (WGS). The main serotypes were S. Typhimurium, S. Enteritidis, and S. Arizonae. ERIC-PCR indicated a high degree of clonality among S. Typhimurium strains, which were also multidrug-resistant and produced extended spectrum beta-lactamases. During the study period, the frequency of S. Typhimurium and S. Arizonae isolations were decreasing, but with the increase of S. Enteritidis and other NTS. A total of 42 NTS isolates were subjected to WGS and explored for virulence-related traits and the corresponding genetic elements. Some virulence and genetic factors were shared by all NTS serotypes, while the main differences were attributed to the serotype-specific diversity of virulence genes, SPIs, virulence plasmids, and phages. The results indicated the variability and dynamics in the epidemiology of salmonellosis and a high virulence potential of human NTS isolates circulating in the region.

This study aimed to apply different complexity-based methods to surface electromyography (EMG) in order to detect neuromuscular changes after realistic warm-up and stretching procedures. Sixteen volunteers conducted two experimental sessions. They were tested before, after a standardized warm-up, and after a stretching exercise (static or neuromuscular nerve gliding technique). Tests included measurements of the knee flexion torque and EMG of biceps femoris (BF) and semitendinosus (ST) muscles. EMG was analyzed using the root mean square (RMS), sample entropy (SampEn), percentage of recurrence and determinism following a recurrence quantification analysis (%Rec and %Det) and a scaling parameter from a detrended fluctuation analysis. Torque was significantly greater after warm-up as compared to baseline and after stretching. RMS was not affected by the experimental procedure. In contrast, SampEn was significantly greater after warm-up and stretching as compared to baseline values. %Rec was not modified but %Det for BF muscle was significantly greater after stretching as compared to baseline. The a scaling parameter was significantly lower after warm-up as compared to baseline for ST muscle. From the present results, complexity-based methods applied to the EMG give additional information than linear-based methods. They appeared sensitive to detect EMG complexity increases following warm-up.

In this work, we have developed a non-ordinary state-based peridynamic model for multiple crack initiation and propagation due to compression-compression fatigue load. In each loading cycle, the fatigue loading is redistributed among the peridynamic solid body, leading to the progressive fatigue damage initiation and propagation in an autonomous fashion. The proposed fatigue model parameters are firstly validated by 3D numerical benchmark tests, and then it is applied to simulate widespread fatigue damage evolution of the aircraft wing corner box. The modified constitutive damage model has been implemented into the peridynamics framework at finite strain. Furthermore, the criterion algorithm from multiple initiation to propagation is discussed. It is shown that the numerical results obtained from peridynamics simulations are in general agreement with those from the experiment data. The comparison of experimental and numerical results indicates that the proposed non-ordinary state-based peridynamics fatigue model has the ability to capture the multiple crack initiation and propagation and other features of the aluminium alloy material.

In remote sensing applications, one of the key points is the acquisition, real-time pre-processing and storage of information. Due to the large amount of information present in the form of images or videos, compression of this data is necessary. Compressed sensing (CS) is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, using a minimal number of non-adaptive linear measurements. After this CS process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose in this work, a CS reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by the augmented Lagrangian which avoids the difficult problem of non-linearity and non-differentiability of the regularization terms. The proposed algorithm, called denoising compressed sensing by regularizations terms (DCSR), will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising, and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach allows to gain up to 25% in terms of denoising efficiency, and visual quality using two metrics: PSNR and SSIM.

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.

In a wide range of lymphoid neoplasms, the process of malignant transformation is associated to somatic mutations in B cells that affect the epigenetic machinery. Consequential alterations in histone modifications contribute to disease-specific changes in the transcriptional program. Affected genes commonly play important roles in cell cycle regulation, apoptosis-inducing signal transduction and DNA damage response, thus facilitating the emergence of malignant traits that impair immune surveillance and favor the emergence of different B-cell lymphoma subtypes. In the last two decades, the field has made a major effort to develop therapies that target these epigenetic alterations. In this review, we discuss which epigenetic alterations occur in non-Hodgkin B-cell lymphoma. Furthermore, we aim to present in close to comprehensive manner the current state-of-the-art in the preclinical and clinical development of epigenetic drugs. We focus on therapeutic strategies interfering with histone methylation and acetylation as these are most advanced in being deployed from the bench-to-bedside and have greatest potential to improve the prognosis of lymphoma patients.

SETMAR is a protein lysine methyltransferase that is involved in several DNA processes, including DNA repair via the non-homologous end joining (NHEJ) pathway, regulation of gene expression, illegitimate DNA integration, and DNA decatenation. However, SETMAR is an atypical protein lysine methyltransferase since in anthropoid primates, the SET domain is fused to an inactive DNA transposase. The presence of the DNA transposase domain confers to SETMAR a DNA binding activity towards the remnants of its transposable element, which has resulted in the emergence of a gene regulatory function. Both the SET and the DNA transposase domains are involved in the different cellular roles of SETMAR, indicating the presence of novel and specific functions in anthropoid primates. In addition, SETMAR is dysregulated in different types of cancer, indicating a potential pathological role. While some light has been shed on SETMAR functions, more research and new tools are needed to better understand the cellular activities of SETMAR and to investigate the therapeutic potential of SETMAR.

This research will examine the computational methods to calculate the nonlinear optical process of second harmonic generation (SHG) that will be hypothesized to be present during lithium ion insertion into silicon nanowires. First it will be determined whether the medium in which SHG is conveyed is non-centrosymmetric or whether the medium is inversion symmetric where SHG as a part of the second-order nonlinear optical phenomenon does not exist. It will be demonstrated that the main interaction that determines SHG is multiphoton absorption on lithium ions. The quantum harmonic oscillator (QHO) is used as the background that generates coherent states for electrons and photons that transverse the length of the silicon nanowire. The matrix elements of the Hamiltonian which represents the energy of the system will be used to calculate the probability density of second-order nonlinear optical interactions which includes collectively SHG, sum-frequency generation (SFG) and difference-frequency generation (DFG). As a result it will be seen that at varies concentrations of lithium ions (Li+) within the crystallized silicon (c-Si) matrix the second-order nonlinear optical process has probabilities substantial enough to create second harmonic generation that could possibly be used for such applications as second harmonic imaging microscopy.

Despite identical genetic constitution, a cancer cell population can exhibit phenotypic variations termed as non-genetic/non-mutational heterogeneity. Such heterogeneity – a ubiquitous nature of biological systems – has been implicated in metastasis, therapy resistance and tumour relapse. Here, we review the evidence for existence, sources and implications of non-genetic heterogeneity in multiple cancer types. Stochasticity/ noise in transcription, protein conformation and/or external microenvironment can underlie such heterogeneity. Moreover, the existence of multiple possible cell states (phenotypes) as a consequence of the emergent dynamics of gene regulatory networks may enable reversible cell-state transitions (phenotypic plasticity) that can facilitate adaptive drug resistance and higher metastatic fitness. Finally, we highlight how computational and mathematical models can drive a better understanding of non-genetic heterogeneity and how a systems-level approach integrating mathematical modelling and in vitro/in vivo experiments can map the diverse phenotypic repertoire, and identify therapeutic vulnerabilities of an otherwise clonal cell population.

Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver–pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver–pickup, also termed as transmitter-receiver (T-R) sensor is designed with a small mean radius of 1 mm. Besides, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution , with a limit of 0.2 μm and selected as 0.25 mm. Experiments have been out on the voltage imaging of an aluminium sheet with 7 machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10 % for tilt angles up to 60 degrees.

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

In this work, quantum dynamics of a spin-0 particle under the effects of Lorentz symmetry violation in the presence of Coulombtype non-electromagnetic potential $(S(r) ∝ \frac{1}{r})$ is investigated. The non-electromagnetic (or scalar) potential is introduced by modifying the mass term via transformation $M → M + \frac{η_c}{r}$ in the relativistic wave equation. The linear central potential induced by the Lorentz symmetry violation is a linear radial electric and constant magnetic field and, analyze the effects on the spectrum of energy and the wave function

Non-viral gene carriers because of their limited side effects, biocompatibility, simplicity and taking the advantages of electrostatic interactions have shown noticeable potential in gene delivery. The low transfection rate of non-viral vectors under physiological conditions is a significant issue. Here, the aim of this study was to investigate the efficacy of hydrophilic and hydrophobic groups on gene carriers such as two synthesized amphiphilic polymer of dextran-stearic acid-spermine (DSASP) with verified lipid and amine conjugations that associated with Fe3O4 superparamagnetic nanoparticles to promote the target delivery and decrease the transfection time using static magnetic field. Our findings illustrate that magnetic nanoparticles are spherical with positive surface charges and superparamagnetic behaviors. The DSASP–pDNA/MNPs offered a strong pDNA condensation, protection against DNase degradation, significant cell viability in HEK 293T cells and. Although conjugations of spermine play a critical role in transfection efficiency, amphiphilic polymer with more derivatives of stearic acid showed better transfection yields. Therefore, DSASP amphiphilic magnetic carriers offer new insights for gene delivery due to the amine contents and ameliorate the uptake of complexes via cell membrane based on its hydrophilic surface.

Set theory faces two difficulties: formal definitions of sets/subsets are incapable of assessing biophysical issues; formal axiomatic systems are complete/inconsistent or incomplete/consistent. To overtake these problems reminiscent of the old-fashioned principle of individuation, we provide formal treatment/validation/operationalization of a methodological weapon termed “outer approach” (OA). The observer’s attention shifts from the system under evaluation to its surroundings, so that objects are investigated from outside. Subsets become just “holes” devoid of information inside larger sets. Sets are no longer passive containers, rather active structures enabling their content’s examination. Consequences/applications of OA include: a) operationalization of paraconsistent logics, anticipated by unexpected forerunners, in terms of advanced truth theories of natural language, anthropic principle and quantum dynamics; b) assessment of embryonic craniocaudal migration in terms of Turing’s spots; c) evaluation of hominids’ social behaviors in terms of evolutionary modifications of facial expression’s musculature; d) treatment of cortical action potentials in terms of collective movements of extracellular currents, leaving apart what happens inside the neurons; e) a critique of Shannon’s information in terms of the Arabic thinkers’ active/potential intellects. Also, OA provides an outer view of a) humanistic issues such as the enigmatic Celestino of Verona’s letter, Dante Alighieri’s “Hell” and the puzzling Voynich manuscript; b) historical issues such as Aldo Moro’s death and the Liston/Clay boxing fight. Summarizing, the safest methodology to quantify phenomena is to remove them from our observation and tackle an outer view, since mathematical/logical issues such as selective information deletion and set complement rescue incompleteness/inconsistency of biophysical systems.

The novel coronavirus (COVID-19) was first reported in the U.S. on December 29, 2019 and has spread rapidly throughout the country, affecting individuals with varying severity due to their risk status. According to the Centers for Disease Control and Prevention, it is estimated that 45.4% of US adults are at higher risk for complications from coronavirus disease because of cardiovascular disease, diabetes, respiratory disease, hypertension, or cancer. In this study, we developed a mathematical model to assess the impact of a COVID-19 vaccine among low and high risk groups. Numerical simulations shows vaccinating both low and high risk groups simultaneously, rather than prioritizing the vaccine on high risk group only, further reduces the daily mortality. The result supports the need for an aggressive vaccination program, regardless of whether individuals are within the low or high risk population.

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

Defect detection in ferromagnetic substrates is often hampered by non-magnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of non-magnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multi-frequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3 % for an extended lift-off range of up to 10 mm.

In this paper we analyze and propose new method and algorithm of selecting the optimal labor time as a function of skills following our main references Mirrlees (1971), Saez (2001) and Stancheva (2014). The optimal labor time is a situation when the utility function of individual reaches a maximum. One of the main differences with Saez (2001) is our algorithm for creation the skill distribution

Hearing impairment is an immensely diagnosed genetic cause, 5% of the total world population effects with different kind of congenital hearing loss (HL). In third-world countries or countries where consanguineous marriages are more common the frequency rate of genetic disorders are at its zenith. Approximately, the incidence of hearing afflictions is ostensibly 7-8:1000 individuals whereas it is estimated that about 466 million peoples suffer with significant HL, and of theses deaf cases 34 million are children’s up to March, 2020. Several genes and colossal numbers of pathogenic variants cause hearing impairment, which aided in next-generation with recessive, dominant or X-linked inheritance traits. This review highlights on syndromic and non-syndromic HL (SHL and NSHL), and categorized as conductive, sensorineural and mixed HL, which having autosomal dominant and recessive, and X-linked or mitochondrial mode of inheritance. Many hundred genes involved in HL are reported, and their mutation spectrum becomes very wide. Mapping of pathogenic genes in consanguinity family is facilitated to understand the disease history. Review presents the bases of HL and also focused on various genetic factors that cause deafness like the basics of genetic inheritance, and classic and well-characterized inherited factors of it. It also overviews the application of linkage analysis, SNPs genotyping and whole exome sequencing methods, in mapping and identification of new locus, causative genes and their variants in families inherited with HL. Conclusively, this review supports researchers in understanding the location of chromosome, the causative genes and specific locus which causing deafness in humans.

Eddy current based approaches have been investigated for a wide range of inspection applications. Dodd-Deeds model and the truncated region eigenfunction expansion (TREE) method are widely applied in various occasions, mostly for the cases that the sample is relatively larger than the radius of the sensor coil. The TREE method converts the integral expressions to the summation of many terms in the truncated region. In a recent work, the impedance of the co-axial air-cored sensor due to a plate of finite radius was calculated by the modified Dodd-Deeds analytical approach proposed by authors. In this paper, combining the modified analytical solution and the TREE method, a new finite region eigenfunction expansion (FREE) method is proposed. This method involves modifying its initial summation point from the first zero of the Bessel function to a value related to the radius of the plate, therefore makes it suitable for plate with finite dimensions. Experiments and simulations have been carried out and compared for the verification of the proposed method. Further, the planar size measurements of the metallic circular plate can be achieved by utilising the measured peak frequency feature.

A vast majority of the countries is under the economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, which is an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We have considered an autoregressive model with a non-linear time trend component that approximately converted into the linear trend using the spline function. The spline function split the COVID-19 series into different piecewise segments between respective knots and fitted the linear time trend. First, we obtain the number of knots with its locations in the COVID-19 series and then the estimation of the best-fitted model parameters are determined under Bayesian setup. The results advocate that the proposed model/methodology is a useful procedure to convert the non-linear time trend into a linear pattern of newly coronavirus case for various countries in the pandemic situation of COVID-19.

Hyper spectral imaging (HSI) is a technique that is used to obtain the spectrum for each pixel in the image. It helps in finding objects and identifying materials etc. Such an identification is very difficult using other imaging techniques. It allows the researchers to investigate the documents without any physical contact. Nowadays detection of unequal Ink mismatch based on HSI has shown vast improvement in distinguishing the inks. Detection of unequal Ink mismatch is an unbalanced clustering problem. This paper used K-means Clustering for ink mismatch detection. K-means Clustering find same subgroups in the data based on Euclidean distance. This paper demonstrates performance in unequal Ink mismatch based on HSI.

In a recent reformulation of the Marrucci-Ianniruberto constitutive equation for the rheology of entangled polymer melts in the context of non-equilibrium thermodynamics, rather large values of the convective constraint release parameter \beta_{ccr} had to be used in order not to violate the second law of thermodynamics. In this work, we present an appropriate modification of the model which avoids the splitting of the evolution equation for the conformation tensor into an orientation and a stretching part. Then, thermodynamic admissibility dictates simply that \beta_{ccr}≥ 0, thus allowing for more realistic values of \beta_{ccr} to be chosen. Moreover, and in view of recent experimental evidence for a transient stress undershoot (following the overshoot) at high shear rates whose origin may be traced back to molecular tumbling, we have incorporated in the model additional terms accounting, at least in an approximate way, for non-affine deformation through a slip parameter \xi. Use of the new model to describe available experimental data for the transient and steady-state shear and elongational rheology of entangled polystyrene melts and solutions shows close agreement. Overall, the modified model proposed here combines simplicity with accuracy, which renders it an excellent choice for managing complex viscoelastic fluid flows in large-scale numerical calculations.

In this work, we investigate the weak deflection angle of light from exact black hole within the non-linear electrodynamics. First we calculate the Gaussian optical curvature using the optical spacetime geometry. With the help of modern geometrical way popularized by Gibbons and Werner, we examine the deflection angle of light from exact black hole. For this desire, we determine the optical Gaussian curvature and execute the Gauss-Bonnet theorem on optical metric and calculate the leading terms of deflection angle in the weak limit approximation. Furthermore, we likewise study the plasma medium's effect on weak gravitational lensing by exact black hole. Hence we expose the effect of the non-linear electrodynamics on the deflection angle in the weak gravitational field.

In the present study, a mathematical model of non-steady partial differential equation from the process of oxygen mass transport in the human pulmonary circulation is proposed. Mathematical modelling of this kind of problems lead to a non-steady partial differential equation and for its numerical simulation, we have used finite differences. The aim of the process is the exact numerical analysis of the study, wherein consistency, stability and convergence is proposed. The necessity of doing the process is that, we would like to increase the order of numerical solution to a higher order scheme. An increment in the order of numerical solution makes the numerical simulation more accurate, also makes the numerical simulation being more complicated. In addition, the process of numerical analysis of the study in this order of solution needs more research work.

In this article, we analyze the weak gravitational lensing in the context of Einstein-non-linear Maxwell-Yukawa black hole. To this desire, we derive the deflection angle of light by Einstein-non-linear Maxwell-Yukawa black hole using the Gibbons and Werner method. For this purpose, we obtain the Gaussian optical curvature and implement the Gauss-Bonnet theorem to investigate the deflection angle of Einstein-non-linear Maxwell-Yukawa black hole. Moreover, we derive the deflection angle of light in the presence of plasma medium. We also analyze the graphical behavior of deflection angle by Einstein-non-linear Maxwell-Yukawa black hole in the presence of plasma as well as non-plasma medium.

Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socio-economic development and sustainable agricultural planning in the region.

The structures of recently discovered primarolides A and B suggest their non-enzymatic formation from a common 2-formylbenzophenone precursor. This hypothesis is based on the experimentally proven facile conversion of pestalone (also a 2-formyl-benzophenone) either into the isomeric lactone pestalalactone or the structurally related isoindolinone pestalachloride A. In a related fashion, the racemic isoindolinone natural product mariline A is supposed to biosynthetically originate from the corresponding keto-aldehyde and an aniline, as experimentally supported by model studies. Due to the close structural relationship with known systems, it appears highly probable that primarolides A and B were generated under the fermentation conditions from a massarinin-related 2-formylbenzophenone (proprimarolide) by reaction either with aniline or a nucleophilic catalyst, respectively. Suberoylanilide hydroxamic acid (SAHA), used as an additive during the fermentation, is supposed to act both as a source of aniline and as a nucleophilic catalyst.

Management of multiple primary cancers, a not so infrequent event in oncology practice, is a critical issue due to the lack of literature . In this study, we reported the case of a patient with metachronous double metastatic non small cell lung cancer (NSCLC) and pancreatic ductal adenocarcinoma (PDAC) who received gefitinib in combination with gemcitabine plus nab-paclitaxel and with mFOLFOX6 in first and second line, respectively. She achieved a progression free survival and an overall survival (OS) of 28 months for NSCLC and PFS-1 and OS of 20 and 13 months, respectively for PDAC. Moreover, the combination of gefitinib and chemotherapies treatments displayed a good safety profile. Given the insignificant frequency of this case, we performed a molecular characterization of both neoplasms with the aim to investigate the existence of particular activated pathways and/or similar immunological mutations. it is interesting to note that two neoplasms shared a commune mutation of B7-H3 gene, with the consecutive impairment of its expressed protein. In both PDAC and NSCLC, the expression of this protein was associated with a worse survival. Since B7-H3 is an anti-apoptotic protein, the reduction of its expression or function should justify a pro-apoptotic activity with a putative justification of the long survival of the patient considered in this report.

Gastroenteritis cases associated with non-toxigenic strains of Vibrio parahaemolyticus have been reported in many countries, suggesting the contribution of novel virulence factors. One candidate is zonula occludens toxin (Zot), which increases the intestinal permeability by other bacteria. Recently we identified prophages belonging to the Inoviridae family encoding putative Zot-like toxins in Chilean strains. Based on this information we performed sequence-based analyses of these toxins, followed by phylogenetic and structural analyses using computational tools. Our results showed that Zots found in Chilean V. parahaemolyticus strains are grouped into three different phylogenetic clusters, sharing two conserved motifs (Walker A and B) in their N-terminal region. These motifs are also conserved in Zots from the human pathogens Vibrio cholerae, Neisseria meningitidis and Campylobacter concisus. Although Zots of V. parahaemolyticus do not possess the FCIGRL sequence responsible for the effects produced by V. cholerae, they do possess a conserved secondary structure within their C-terminal region with Zots proteins able to disrupt the intestinal barrier, which is interesting since it has been suggested that the structure and not the Zot sequence would be responsible for the biological effects. This preliminary study provides the basis to study the function of Zots found in V. parahaemolyticus on the intestinal barrier and their possible role as a virulence factor.

Non-destructive testing of metallic objects that may contain embedded defects of different sizes is an important application in many industrial branches for quality control. Most of these techniques allow defect detection and its approximate localization, but very few give enough information for its 3D reconstruction. Here we present a hybrid laser – transducer system that combines remote laser-generated ultrasound excitation and non-contact ultrasonic transducer detection. This fully non-contact method gives access to separating scan areas on different object’s faces and defect details from different angles/perspectives can be analysed. This hybrid system can analyse the whole object’s volume data and allow a 3D reconstruction image of the embedded defects. As a novelty for the signal processing improvement, we use a 2D apodization window filtering technique, applied along with the synthetic aperture focusing algorithm in order to remove the undesired effects of side lobes and wide-angle reflections of propagating ultrasound waves, thus, enhancing the resulting 3D image of the defect. We provide both qualitative and quantitative volumetric results with high accuracy and resolution compared with conventional techniques.

Developed countries have shown a time trend towards a younger age at menarche (AAM), which is associated with increased risk of later obesity and non-communicable diseases. We aimed to assess whether a time trend in AAM with associated disease risk is also present in Mexico. For this, we used data of 30,826 women from the Mexican National Health Survey (2000). Linear and log binomial regression was used for nutritional and disease outcomes, whereas Welch-ANOVA was used to test for a time trend. AAM decreased over time (p < 0.001), with a maximal difference of 0.99 years between the 1920s (13.6 years) and 1980s (12.6 years). AAM (in years) was negatively associated with weight (β = -1.01 kg; 95 % CI -1.006, -1,004) and BMI (β = -1.01 kg/m2; -1.007, -1.006), and positively with height (β = 0.18 cm; 0.112, 0.231). AAM was associated with diabetes (RR = 0.95; 0.93, 0.98) and hypercholesterolemia (RR = 0.93; 0.90, 0.95), but not with hypertension, breast cancer or arthritis. In Mexico, AAM decreased significantly during the 20th century. AAM was inversely associated with adult weight and BMI, and positively with height. Women with a later AAM had a lower risk of diabetes and hypercholesterolemia.

Among the most important variables in the design of falling film microreactors (FFMRs) is the liquid film thickness as well as the gas/liquid interfacial area, which dictate the mass and heat transfer rates. In a previous work conducted in our lab the characteristics of a free-falling Newtonian liquid film have been studied and appropriate correlations have been proposed. In this work the geometrical characteristics of a non-Newtonian shear thinning liquid, flowing in an inclined open microchannel, have been experimentally investigated and design correlations that can predict with reasonable accuracy the features of a FFMR have been proposed. The test section used was an open μ-channel with square cross section (Wo=1200 μm) made of brass which can be set to various inclination angles. The liquid film characteristics were measured by a non-intrusive technique that is based on the features of a μ-PIV system. Relevant CFD simulations revealed that the volume average dynamic viscosity over the flow domain is practically the same as the corresponding asymptotic viscosity value, which can thus be used in proposed the design equations. Finally, a generalized algorithm for the design of FFMRs, containing non-Newtonian shear thinning liquids, is suggested.

Fault line detection timely and accurately when single-phase-to-earth fault occurs in resonant grounding system is still a focus of research. This paper presents a new approach for fault detection based on data fusion and it has non-artificial setting. Firstly, the fault criterion for interphase difference energy ratio and time-frequency correlation coefficient of each line is proposed. Subsequently, the paper establish a coordinate system with the interphase difference energy ratio as X axis and the time-frequency correlation coefficient as Y axis, and it uses the Euclidean distance algorithm to get the characteristic distance of each line by fusing two-dimensional information. Finally, comparing the sound distance and the fault distance of each line to discriminate the fault line. Electromagnetic Transients Program (EMTP) simulation results and adaptability analysis have confirmed the effectiveness and reliability of the proposed scheme.

Water soluble polymers have gained an increasing interest in enhanced oil recovery (EOR) processes, especially as polymer flooding. Despite the fact that the flow of polymer in porous medium has been a research subject for many decades with numerous publications, there are still some research areas that need progress. The prediction of polymer injectivity remains elusive. Polymers with similar shear viscosity might have different in-situ rheological behaviors and may be exposed to different extent of mechanical degradation. Hence, determining polymer in-situ rheological behavior is of great significance for defining its utility. In this study, an investigation of rheological properties and mechanical degradation of different HPAM (partially hydrolyzed polyacrylamide) polymers was performed using Bentheimer sandstone outcrop cores. Results show that, HPAM in-situ rheology is different from bulk rheology measured in rheometer. Specifically, shear thickening behavior occurs at high rates, and near-Newtonian behavior is measured at low rates in porous media. This deviates strongly from measurements in the rheometer. Polymer molecular weight and concentration influence its viscoelasticity and subsequently its flow characteristics in porous media. Exposure to mechanical degradation by flow at high rate through porous media leads to significant reduction in shear thickening and thereby improved injectivity. More importantly, the degraded polymer maintained in-situ viscosity at low flow rates indicating that improved injectivity can be achieved without compromising viscosity at reservoir flow rates. This is explained by reduction in viscoelasticity. Mechanical degradation also leads to reduced residual resistance factor (RRF), especially for high polymer concentrations. For some of the polymer injections, successive degradation (increased degradation with transport length in porous media) was observed. The results presented here may be used to optimize polymer injectivity.

The commitment of the EU to Corporate Social Responsibility (CSR) is projected in the EU law about annual reporting by businesses. Since EU member states further develop this framework by their own domestic laws, annual reporting with CSR information is not unified and just partially mandatory in the EU. Do all European businesses report CSR information and what public declaration to society do they provide with it? The main dual purpose of this paper is identifying the parameters of this annual reporting duty and studying the CSR information provided by the ten largest Czech companies in their annual statements for 2013-2017. Based on legislative research and the teleological interpretation, the current EU legislative framework with Czech particularities is presented and, via a case study exploring 50 annual reports, the data about the type, extent and depth of the CSR is dynamically and comparatively assessed. It appears that, at a minimum, large Czech businesses satisfy their legal duty and e-report on CSR in a similar extent, but in dramatically different quality. Employee matters and adherence to international standards are used as a public declaration to society more than the data on environmental protection, while social matters and R&D are played down.

Vegetation has a significant influence on velocity distribution and turbulent energy in a confluence channel. A laboratory measurement with ADV was used to investigate the flow through a Y-shaped confluence channel partially covered with rigid vegetation on its inner bank. In this study, the flow velocities in cases with and without vegetation were measured by the ADV in a Y-shaped confluence channel. The results clearly show that the existence of non-submerged rigid plants has changed the internal flow structure, that the velocity in the non-vegetated area is greater than in the vegetated area, and that there is a large exchange of mass and momentum between the vegetated and non-vegetated areas. The velocity on both sides is significantly reduced when vegetation is present. In the vicinity of tributaries, due to the presence of vegetation, the high-velocity area moved rapidly to the middle of the non-vegetated area, and the secondary flow phenomenon disappeared. In the mainstream, when vegetation was present, circulation disappeared, and the degree of lateral mixing decreased. The presence of vegetation caused a great change in the internal flow structure and made the flow in non-vegetated areas more intense.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.