This paper investigates the critical factors for balancing environmental sustainability and economic development through the lens of New Structural Economics. Specifically, we utilize the congruence index to access the alignment between local factor endowment structure and firm production input structure. Using a comprehensive manufacturing firm-level pollutant emissions dataset from the Yangtze River Economic Belt (YEB) region in China, we find that firms demonstrating higher congruence exhibit improved economic performance and alignment with comparative advantages. This not only contributes to increased economic efficiency for these firms but also leads to a reduction in pollutant emissions, including wastewater discharge and Chemical Oxygen Demand (COD) emissions. Our study provides valuable insights into achieving mutually beneficial environmental and economic outcomes.

Neurodegeneration is becoming one of the leading causes of death worldwide as the population expands and grows older. There is a growing desire to understand the mechanisms behind prion proteins as well as the prion-like proteins that make up neurodegenerative diseases (NDs) including Alzheimer’s Disease (AD). Both amyloid β (Aβ) and hyperphosphorylated tau (p-tau) proteins behave in ways similar to that of the infectious form of the prion protein, PrPSc, such as aggregating, seeding, and replicating under not yet fully understood mechanisms, thus the designation of prion-like. This review aims to highlight the shared mechanisms between prion-like proteins and prion proteins in the structural variations associated with aggregation and disease development. These mechanisms are largely focusing on the dysregulation of protein homeostasis, self-replication, and protein aggregation, and how this knowledge could contribute to diagnoses and treatments for the given NDs.

An airfoil is the geometric shape of a cross-section of a wing. When it is exposed to the flow of a fluid over its surface, it generates both lift and drag forces through a pressure distribution. Designing an efficient airfoil for aerodynamic applications is a complex challenge in the field of aerodynamic engineering, with the aim of achieving an optimal lift-to-drag ratio. The success of airfoil design hinges on the effective reduction of drag force under specific conditions, while simultaneously attaining the desired lift without encountering significant flow separation, such as boundary layer shedding. Furthermore, the design must adhere to lifting force limitations and meet structural requirements. In the field of airfoil design and optimization, literature review articles provide a valuable resource by offering an overview of the latest advancements in specific areas. These areas encompass a wide range of topics including the design and testing of adaptive geometry wings, such as morphing wing technology as well as the application of shape optimization techniques to enhance airfoil performance. Our goal is to provide researchers with an in-depth review of relevant articles published to date, examining the applied approaches utilized to tackle the complex problem of airfoil design and explore the methodology employed to address it, from a broad perspective on the topic.

A high perovskite activity is sought for use in magnetic applications. In this paper, we present the simple synthesis of (2.5 and 5%) Tellurium-impregnated-LaCoO3 (Te-LCO), Te and LaCoO3 (LCO) by using a ball mill, chemical reduction, and hydrothermal synthesis, respectively. We also explored the structure stability along with the magnetic properties of Te-LCO. Te has a rhombohedral crystal structure, whereas Te-LCO has a hexagonal crystal system. The reconstructed Te was imbued with LCO that was produced by hydrothermal synthesis; as the concentration of the imbuing agent grew, the material became magnetically preferred. According to the X-ray photoelectron spectra, the oxidation state of the cobaltite is one that is magnetically advantageous. As a result of the fact that the creation of oxygen-deficient perovskites has been shown to influence the mixed (Te4+/2-) valence state of the incorporated samples, it is abundantly obvious that this process is of utmost significance. The TEM image confirms the inclusion of Te in LCO. The samples start out in a paramagnetic state (LCO), but when Te is added to the mixture, the magnetic state shifts to a weak ferromagnetic one. It is at this point that hysteresis occurs due to the presence of Te. Despite being doped with Mn in our prior study, rhombohedral LCO retains its paramagnetic characteristic at room temperature (RT). As a result, the purpose of this study was to determine the impacts of RT field dependency of magnetization (M-H) for Te-impregnated LCO in order to improve the magnetic properties of RT because it is a low-cost material for advanced multi-functional and energy applications.

Advanced Nano structural hard coatings are thin materials films that range in thickness from a few nanometers to a few micrometers and are applied to different surfaces to increase their hardness and wear resistance. Physical vapor deposition (PVD) or chemical vapor deposition (CVD) methods are frequently used to deposit these coatings. These coating’s distinctive characteristics result from their microstructure, which is made up of material columns or grains with a diameter of only a few nanometers. Increased hardness, higher wear resistance, and improved tribological properties result from these short dimensions. In applications with significant wear and/or high contact pressures, such as cutting tools, bearings, and engine parts, the usage of sophisticated Nano structural hard coatings is crucial. Advanced Nano structural hard coatings include, 1. Amorphous carbon coatings with high hardness, low friction, and exceptional wear resistance are known as diamond-like carbon (DLC) coatings. DLC coatings are frequently used in a variety of industries, including the automotive, aerospace, and biomedical ones. 2. Coatings made of titanium nitride (TiN): These metallic coatings have high hardness and wear resistance. Cutting tool, machining, and aerospace industries frequently use TiN coatings. 3. Chromium nitride (CrN) coatings: These coatings have great corrosion resistance, good wear resistance, and high hardness. Cutting tools, gears, and bearings are just a few of the objects that use CrN coatings. 4. Tungsten carbide (WC) coatings: These coatings, which have a high degree of hardness and wear resistance, are frequently employed in cutting tools and other applications that require wear resistance. Advanced Nano structural hard coatings provide many advantages over conventional coatings, including better hardness, increased wear resistance, and improved tribological qualities. The automotive, aerospace, and biomedical industries are just a few that could use these coatings. In this paper the author will explain about the design synthesis and applications of advanced Nano structural hard coating in aerospace and automotive applications.

Four decades of proteasome research have yielded extensive information on ubiquitin-dependent proteolysis. The archetype of proteasomes is a 20S barrel-shaped complex that does not rely on ubiquitin as a degradation signal but can degrade substrates with a considerable unstructured stretch. Since roughly half of all proteasomes in most eukaryotic cells are free 20S complexes, ubiquitin-independent protein degradation may coexist with ubiquitin-dependent degradation by the highly regulated 26S proteasome. This article reviews recent advances in our understanding of the biochemical and structural features that underlie the proteolytic mechanism of 20S proteasomes. The two outer α-rings of 20S proteasomes provide a number of potential docking sites for loosely folded polypeptides. The binding of a substrate can induce asymmetric conformational changes, trigger gate opening, and initiate its own degradation through a protease-driven translocation mechanism. Consequently, the substrate translocates through two additional narrow apertures augmented by the β-catalytic active sites. The overall pulling force through the two annuli results in a protease-like unfolding of the substrate and subsequent proteolysis in the catalytic chamber. Although both proteasomes contain identical β-catalytic active sites, the differential translocation mechanisms yield distinct peptide products. Non-overlapping substrate repertoires and product outcomes rationalize cohabitation of both proteasome complexes in cells.

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that describe heterogeneity of the outer most layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicate that aerial LiDAR can be of use in quantifying broad-scale variation in structural diversity across macroscales.

This study investigates the potential of a hybrid nanofluid composed of MoS2 and ZnO nanoparticles dispersed in engine oil, aiming to enhance the properties of lubricant's chemical reaction with the Soret effect on stretching sheet under the influence of applied magnetic field. With the growing demand for efficient lubrication systems in various industrial applications, including automotive engines, the development of novel nanofluid-based lubricants presents a promising avenue for improving engine performance and longevity. However, the synergistic effects of hybrid nanoparticles in engine oil remain relatively unexplored. This research addresses this gap by examining the thermal conductivity, viscosity, and wear resistance of the hybrid nanofluid, shedding light on its potential as an advanced lubrication solution. By analyzing the dispersion stability and interaction mechanisms between nanoparticles and base oil, valuable insights are gained into the fundamental aspects governing the entertainment of hybrid nano lubricants. Overall, the objectives of studying hybrid nano lubricant MoS2+ZnO with engine oil aim to advance the development of more efficient and durable lubrication solutions for automotive engines, contributing to improved reliability, fuel efficiency, and environmental sustainability. Governing non-linear partial differential equations are simplified as ordinary differential equations by utilizing similarity variables. MATLAB Bvp4c technique is used to solve the obtained linear ODE equations. The outputs are presented through graphs and tables for various state variables. The comparative survey in all the graphs is presented for the nanofluid (MoS2/Engine oil) with the hybrid nanofluid (MoS2+ZnO/Engine oil). These graphs show the effect of various parameters M, Q, β, Pr, Ec, Sc, Sr, Kp, Kr and (hybrid nano lubricant parameter). The velocity profile diminished against the values of M, Kp, and β. Temperature profile rising with Ec and Q. reciprocally goes down to Pr. The concentration profile incremented with the value of Sr and decremented with the value of Sc, and Kr. With the increasing value of both f′ (η) and θ(η) increases. But in θ(η) hybrid nanofluid slows down with the higher value of .

Natural astaxanthin is a precious substance obtained from some organisms such as microalgae. This plant has many benefits for humans, so research into its cost-effective and economical production has recently increased. For this purpose, some methods such as the use of different culture media, gene engineering, different stresses, nanoparticles, bio acids, and phytohormones are important. Accordingly, this review study was conducted to demonstrate the effect of the factors mentioned above for the high production of astaxanthin in microalgae, especially Haematococcus pluvialis (H.p).

The potential yield of annual crops is affected by management practices and water and energy availabilities throughout the crop season. The current work aimed to assess the effects of plant population and soil covering on yield components of maize. Field experiments were carried out during 2014-15 and 2015-16 growing seasons at areas grown with oat straw, voluntary plants and bare soil, considering five different plant populations (40,000, 60,000, 80,000, 100,000 and 120,000 plants ha-1) and three sowing dates (15 Sep., 30 Oct., 15 Dec.) for the hybrid P30F53YH in Ponta Grossa, State of Parana, Brazil. Non-impacts of soil covering or plant population on plant height at the flowering phenological stage were observed. Significant effects of soil covering on crop physiological and yield components responses throughout the 2014-15 season were detected. Influence of plant populations on yield components was evidenced, suggesting that from 80,000 plants ha-1 the P30F53YH hybrid performs a compensatory effect among assessed yield components in such a way as to not compromise productivity insofar as plant population increases up to 120,000 plants ha-1. It was noticed a positive trend of yield components and crop final yield as a function of plant density increments.

The Digital Twin is one of the big technology trends of the last decade. In the course of its rapid expansion into the various fields of application, many different definitions emerged, tailored to the respective applications. Taxonomies can cluster the diversity and define application-specific archetypes. This paper presents a characterization of the Digital Twin in the context of structural mechanics and lightweight design. It begins by exploring various definitions of the Digital Twin concept and its application in different industries. The paper further analyzes the product life cycle of structures, dividing it into the design and operational phases and identifying the requirements for a Digital Twin in each phase. The structural design process is examined, including the definition of a structure and the stages of the product development process. Additionally, the paper discusses the operational phase of structures, highlighting the need for continuous monitoring, evaluation, and adaptation through Structural Health Monitoring (SHM) systems. The paper proposes two archetypes of Digital Twins for structures: a structure-designing Digital Twin for the design phase and a structure-monitoring Digital Twin for the operational phase. The findings provide a framework for implementing Digital Twins in structural mechanics, enabling digital design and monitoring of structures for improved performance and maintenance strategies.

The evolution of sentence representation learning, especially with parse tree encoders, has shown remarkable progress. Traditional approaches predominantly rely on recursive encoding of tree structures, which impedes parallel processing capabilities. Additionally, these methods often overlook the significance of dependency tree arc labels. To overcome these limitations, we introduce the Syntax-Enhanced Transformer (SET), incorporating a novel dual-attention mechanism that integrates relation-focused attention alongside traditional self-attention. This design effectively encodes both dependency and spatial positional relationships within sentence dependency trees. Our approach innovatively incorporates syntactic information into the Transformer framework without compromising its inherent parallelizability. The SET demonstrates superior or comparable performance to contemporary methods across various sentence representation tasks, significantly enhancing computational efficiency.

Complex systems can be described as the network of interactions between their components. In directed networks, information, energy and matter flow in dominant directions. Undirected networks, on the other hand, cannot easily capture these asymmetries. The local flow of information depends on the node spin, which is a function of the number of incoming and outgoing links attached to it. And yet, how the heterogeneous association between in-degree and out-degree constraints global dynamics remains poorly understood. Here, we develop a new theoretical model to study the relationship between node directionality and global flow dynamics. Our computational analysis of random and scale-free directed networks indicates that structural diversity constrains the dimensionality of reachable dynamical space. These results suggest how local directionality might be an universal driver of global dynamics in many systems, from social networks, to technological systems to the connectome.

In Uganda, the common bean (Phaseolus vulgaris) is often infested by a complex of insect pests, but the bean stem maggots, aphids, bean leaf beetles, and flower thrips are the most important. Whereas yield losses due to these pests have been established, there is limited information on their population dynamics at different stages of crop growth and their effect on yield and yield components. In order to describe the population dynamics of selected common bean pests at various phases of bean crop growth, and their impact on yield and yield components, a study was carried out in Uganda during 2016 second rains and 2017 first rains in three agro-ecological zones. Bean fly, bean aphids, bean leaf beetles, whitefly, striped bean weevil, leaf hoppers, and caterpillars of Lepidopteran species were the main insects observed. Pesticide spray schedules were imposed to generate different populations of insect pests whose effects on yield and its components were determined. The findings indicate that spray regimes significantly influenced the abundance of bean fly, aphids, whitefly, striped bean weevil, and leaf hoppers but not bean leaf beetles and caterpillars. Additionally, except caterpillars, insect pests were significantly influenced by crop growth stages, but only aphids, whiteflies, bean flies and leaf hoppers exhibited a significant negative relationship with grain yield. Furthermore, yield and yield components varied significantly between spray regimes, agro-ecological zones, and seasons. Our study is important in informing growers on the stage of crop growth at which management tactics such as use of insecticides can be applied for different insect pests.

A subset of patients with schizophrenia experience physio-somatic symptoms reminiscent of chronic fatigue and fibromyalgia. In schizophrenia, these symptoms contribute to impaired quality of life, and are strongly related to neuro-cognitive deficits, and increased IgA responses to tryptophan catabolites. Negative and PHEM (psychosis, hostility, excitation, mannerism) symptoms, psychomotor retardation (PMR) and formal thought disorders, appear to be manifestations of a single trait reflecting overall severity of schizophrenia (OSOS). In this study, 120 patients with deficit schizophrenia (DEFSCZ) and 54 healthy subjects were assessed with the FibroFatigue (FF) rating scale, and the above-mentioned symptom domains as well as neuro-cognitive tests and biomarkers were measured. In DEFSCZ, there were robust associations between the FF score and all above-mentioned symptom domains, and impairments in semantic and episodic memory and executive functions. Furthermore, the FF score loaded highly on an OSOS latent vector (LV), which showed adequate convergent validity, internal consistency reliability and predictive relevance and fitted a reflective model. Soft Independent Modelling of Class Analogy (SIMCA) showed that the FF items discriminated DEFSCZ from controls with an overall accuracy of 100%. Interleukin IL-1β, IL-1 receptor antagonist (sIL-1RA), tumour necrosis factor (TNF)-α and CCL-11 (eotaxin) explained 66.8% of the variance in the FF score and 59.4% of the variance in OSOS. In conclusion, these data show that physio-somatic symptoms are a core component of the phenomenology of DEFSCZ and are largely mediated by neurotoxic effects of activated immune pathways, including aberrations in CCL-11, IL-1β and TNF-α signalling.

In this paper, we aim to address the challenge of airflow interference during fault detection in high-speed train bogies by introducing a flow field and investigating the characteristics of the sound field distribution of critical components under its influence. This approach overcomes the limitation observed in previous studies that ignored the impact of the flow field. Furthermore, we evaluate and compare various layouts for inter-track acoustic sensor arrays. The proposed sensing methods hold significant practical engineering value, and the collected data serves as a valuable reference for acquiring acoustic signals from key bogie components in future research.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

This study employed a TSMC 0.18-µm 50 V process to establish a high-voltage n-LDMOS structure. In the reference device, the number of floating-poly (poly-2) turns was 7, and the width and spacing of each turn was 1 µm. Although increasing the number of turns reduces the peak field, directly reducing or increasing the number of poly-2 turns is a common practice. The present experiment was realized in three steps, namely fixing the number of occupied area adjustment turns, adjusting the width of each turn, and adjusting the distance between turns. The first step was fixing the number of occupied area adjustment turns. The number of turns was increased from 7 to 9, and the number of turns was reduced to 5 and 3 in the reference group. A reduction in the number of turns led to a greater decrease in the peak field and increase in the breakdown voltage despite the area remaining unchanged. A comparison of the use of three turns with that of seven turns revealed that the maximum electric field decreased by 42%, from 4.17e+3 to 2.46e+3 V/cm, and that the breakdown voltage (VBK) increased from 30.2 to 35.84 V. The second step was adjusting the width of each turn from 1 to 1.4, 1.2, 0.8, and 0.6 µm in the reference group. This increase in width reduced the maximum electric field and led to a greater increase in VBK. When the width was increased to 1.4 µm, the maximum electric field decreased by 29% from 1.95e+3 to 1.4e+3 V/cm, and the VBK increased by 183% from 30.2 to 85.6 V. The third step was adjusting the spacing of each turn from 1 to 1.4, 1.2, 0.8, and 0.6 µm in the reference group. The results revealed that a reduction in the spacing led to a greater reduction in the maximum electric field and an in-crease in the VBK. When the pitch was reduced to 0.6 µm, the maximum electric field decreased by 33%, from 1.95e+3 to 1.32e+3 V/cm, and the VBK increased by 345%, from 30.2 to 134.4 V.

Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep-fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determine an analytical solution for the straightened superheater tube under thermal conditions and compare it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep-fatigue life of the reheater header is estimated by considering flexible operation factors and employing the Coffin-Manson and Larson-Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermal-structure analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors.

Multilevel functional principal components analysis (mFPCA) is used to treat dynamical changes in shape in this article. Results of standard (single-level) FPCA are presented here also as a comparison. Monte Carlo (MC) simulation is used to create univariate data (i.e., a single “outcome” variable) that contains two distinct classes of trajectory with time. MC simulation is also used to create multivariate data of sixteen 2D points that represent (broadly) an eye; this data also has two distinct classes of trajectory (an eye blinking and an eye widening in surprise). This is followed-up by an application of mFPCA and single-level FPCA to “real” data consisting of twelve 3D landmark outlining the mouth that are tracked over all phases of a smile. By consideration of eigenvalues, results for the MC datasets find correctly that variation due to differences in groups between the two classes of trajectories variation are larger than variation within each group. In both cases, differences in standardized component scores between the two groups are observed, as expected. Modes of variation are shown to model the univariate MC data correctly, and good model fits are found for both the “blinking” and “surprised” trajectories for the MC “eye” data. Results for the “smile” data show that the smile trajectory is modelled correctly, namely: the corners of the mouth are drawn backwards and wider during a smile. Furthermore, the first mode of variation at level 1 of the mFPCA model show only subtle and minor changes in mouth shape due to sex, whereas the first mode of variation at level 2 of the mFPCA model govern whether the mouth is upturned or downturned. These results are all an excellent test of mFPCA, which show that mFPCA presents a viable method of modelling dynamical changes in shape.

3D facial surface imaging is a useful tool in dentistry and in terms of diagnostics and treatment planning. Between-groups PCA (bgPCA) is a method that has been used to analyse shapes in biological morphometrics, although various “pathologies” of bgPCA have recently been proposed. Monte Carlo (MC) simulated datasets were created here in order to explore “pathologies” of multilevel PCA (mPCA), where mPCA with two levels is equivalent to bgPCA. The first set of MC experiments involved 300 uncorrelated normally distributed variables, whereas the second set of MC experiments used correlated multivariate MC data describing 3D facial shape. We confirmed previous results of other researchers that indicated that bgPCA (and so also mPCA) can give a false impression of strong differences in component scores between groups when there is none in reality. These spurious differences in component scores via mPCA reduced strongly as the sample sizes per group were increased. Eigenvalues via mPCA were also found to be strongly effected by imbalances in sample sizes per group, although this problem was removed by using weighted forms of covariance matrices suggested by the maximum likelihood solution of the two-level model. However, this did not solve problems of spurious differences between groups in these simulations, which was driven by very small sample sizes in one group here. As a “rule of thumb” only, all of our experiments indicate that reasonable results are obtained when sample sizes per group in all groups are at least equal to the number of variables. Interestingly, the sum of all eigenvalues over both levels via mPCA scaled approximately linearly with the inverse of the sample size per group in all experiments. Finally, between-group variation was added explicitly to the MC data generation model in two experiments considered here. Results for the sum of all eigenvalues via mPCA predicted the asymptotic amount for the total amount of variance correctly in this case, whereas standard “single-level” PCA underestimated this quantity.

The analytical method for the calculation of the properties of a bolted connection established by the structural Eurocodes proposes the T-stub as a component for the characterization of the tension and compression zones in moment joints. In this article, a review of the state of the art on the T-stub component is developed, where the works developed since it was initially defined, and from the perspectives of formulation, experimentation and numerical simulation are summarized and discussed. Additionally, possible future lines of work are proposed.

Highly-resolved data on water balance components (like runoff or storage) are crucial to improve water management, e.g., in drought or flood situations. Because regional observations of these components cannot be acquired adequately, applying water balance models is a feasible solution. We developed an innovative approach using the physically-based lumped-parameter water balance model BROOK90 (R version) integrated into a sensor network platform to derive daily water budget components for catchments in the Free State of Saxony. The model is not calibrated but rather uses available information on soil, land use and precipitation only. We applied the hydro response units (HRUs) approach for 6175 small and medium-sized catchments. For the evaluation, model output was cross-evaluated in ten selected head catchments in a low mountain range in Saxony. The mean values of Kling-Gupta efficiency (KGE) for the period 2005-2019 to these catchments are 0.63 and 0.75 for daily and monthly discharge simulations, respectively. The simulated evapotranspiration and soil wetness are in good agreement with the SMAP_L4_GPH product in April 2015-2018. The study can be enhanced by using different data platforms as well as available information on study sites.

Binder jet printed components typically have low overall density in the green state and high shrinkage and deformation after heat treatment. It has previously been demonstrated that, by including nanoparticles of the same material in the binder, these properties can be improved as the nanoparticles can fill the interstices and pore throats between the bed particles. The beneficial effects from using these additive binder particles can be improved by maximising the binder particle size, enabling the space within the powder bed to be filled with a higher packing efficiency. The selection of maximum particle size for a binder requires detailed knowledge of the pores and pore throats between the powder bed particles. In this paper, a raindrop model is developed to determine the critical radius at which binder particles can pass between pores and penetrate the bed. The model is validated against helium pycnometry measurements and binder particle drop tests. It is found that the critical radius can be predicted, with acceptable accuracy, using a linear function of the mean and standard deviation of the particle radii. Percolation theory concepts have been employed in order to generalise the results for powder beds that have different mean particle sizes and size distributions. The results of this work can be employed to inform the selection of particle sizes required for binder formulations, to optimise density and reduce shrinkage in printed binder jet components.

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulates heat balances and gaseous exchanges in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of learning techniques is one such tool that is used to identify the characteristics of seasonal ice variability. This paper utilizes the Sentinel-1 RADAR imagery with a support vector machine learning scheme through Google Earth Engine to classify lead types in the Beaufort Sea.

The identified Urban Heat Island (UHI) phenomenon, coupled with diminished vegetation and anthropogenic heat release, poses a significant environmental challenge for major urban centers. Particularly worrisome is the UHI effect in hot and temperate climates such as the Mediterranean region during the summer season. This exacerbates discomfort and heightens the risks of diseases linked to high temperatures. Additionally, it contributes to environmental consequences, including elevated electricity consumption due to increased demand for cooling [1]. The utilization of Remotely Sensed imagery for estimating Land Surface Temperature has become more prevalent in various applications associated with evaluating urban micro-climates and the Urban Heat Island (UHI) phenomenon. This study presents an algorithm for the automatic mapping of Land Surface Temperature (LST) from Landsat-8 data using a vegetation index-based technique in the Google Earth Engine (GEE) platform. The primary aim is to discern patterns and trends in LST over a 5-year period, from 2017 to 2021. The application was tested in the urban area of Thessaloniki, Northern Greece, which is particularly representative of the Mediterranean urban environment. Computation of Normalized Difference Indices (NDVI, NDBI, NDWI) for the study area was also performed within the GEE platform and correlation analysis was implemented to evaluate the impact of the urban landscape on the distribution of LST.

For the territory of the Republic of Tatarstan (Russia), a spatial model of forest communities was built as a basis for assessing the sequestration potential of ecosystems. Combination of multi-temporal and multi-spectral satellite imagery from the Landsat 8 and Landsat 9 platform as input data and Google Earth Engine cloud platform were used. The set of 292 vegetation indices and metrics computed from the pre-processed imagery, were combined into dataset. The Weka X-Means clustering algorithm was trained and applied to study area. The unsupervised classification was carried out by vegetation classes in the Braun-Blanquet system. The results of unsupervised classification were verified using data from more than 17,000 relevés with geographic references from the Flora database. For automatic classification, the EuroVeg Checklist expert system in the JUICE 7.1 package was used. The proposed methodology for obtaining initial data and unsupervised classification, supported by an automated expert system, made it possible to obtain a picture of the vegetation distribution in the study area with sufficient accuracy, and in the future, it will be used to assess the sequestration potential of the ecosystems of the region under study.

In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website's visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website's source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website's performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors.

Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

The article presents development of the more electric turbofan engine in distributed architecture with a design thrust in the range from 3 to 7.5 and from 7.5 to 30 kN for small and medium-sized unmanned aerial vehicles. The engine subsystems are considered as separate smart modules with a built-in control system, exchanging data via a digital channel with the central engine control and diagnostics unit. The key smart engine units are combined in the following subsystems: starter and turbine generators, oil pumps, actuator of guide vanes, fuel pumps, fuel metering unit, control and diagnostic unit. All pumps and guide vane actuator are electrically driven. Control and monitoring signals are transmitted via a digital bus. Functional and reliability analysis, technical configuration design of each subsystem are presented. Based on analysis of the architecture of distributed control system for gearbox-free more electric engine, different configurations of described subsystems are proposed.

We have an unprecedented ability to map the Earth’s surface as deep learning technologies are applied to an abundance of high-frequency Earth observation data. Simple, free, and effective methods are needed to enable a variety of stakeholders to use these tools to improve scientific knowledge and decision making. Here we present a trained U-Net model that can map and delineate ground mounted solar arrays using publicly available Sentinel-2 imagery, and that requires minimal data pre-processing and no feature engineering. By using label overloading and image augmentation during training, the model is robust to temporal and spatial variation in imagery. The trained model achieved a precision and recall of 91.5% each and an intersection over union of 84.3% on independent validation data from two distinct geographies. This generalizability in space and time makes the model useful for repeatedly mapping solar arrays. We use this model to delineate all ground mounted solar arrays in North Carolina and the Chesapeake Bay watershed to illustrate how these methods can be used to quickly and easily produce accurate maps of solar infrastructure.

This article focuses on the issue of motor oils used in the engines of off-road mobile machinery (NRMM), more specifically tractors. The primary goal of the paper is to determine the appropriate replacement interval for these oils. The physical properties of the examined samples were first determined by conventional instruments. Furthermore, the concentration of abrasive metals, contaminants, and additive elements were measured using an optical emission spectrometer. Lastly, the content of water, fuel, glycol, and the products of oxidation, nitration, and sulphation were determined by using infrared spectrometry. The measured values were compared to the limit values. Based on the processing and evaluation of these analyses, the overall condition of the oils was assessed and subsequently the optimal exchange interval of the examined oils was determined. In addition, a risk analysis of the outage was performed. Due to the high yields of crops, farmers can lose a significant amount of product when a tractor is not functioning during the harvest period. This loss for calculated in the paper.

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

Native reactive electrophile species (RES) are long-recognized regulators of pathophysiology; yet, knowledge surrounding how RES regulate context-specific biology remains limited. The latest technological advances in profiling and precision decoding of RES sensing and signaling have begun to bring about improved understanding of localized RES regulatory paradigms. However, studies in purified systems—prerequisites for gaining structure/function insights—prove challenging. We here introduce emerging chemical biology tools available to probe RES signaling, and the new knowledge that these tools have brought to the field. We next discuss existing structural data of RES-sensor proteins complexed with electrophilic metabolites or small molecule drugs (limited to < 300 Da), including challenges faced in acquiring homogenous RES-bound proteins. We further offer considerations that could promote enhanced understanding of RES regulation derived from three-dimensional structures of RES-modified proteins.

Repetitive nature of the ribosomal DNA (rDNA) gene makes the sequencing of hundreds copies of mammalian 45S rDNA (about 45-kb per copy) extremely difficult and its assembly is often excluded. Increasing evidence shows that 45S rDNA variations (copy number or single nucleotide), structural ribosomal RNA (rRNA) transcript variants, and non-structural rRNA transcripts (sense and anti-sense long noncoding rRNAs that include promoter rRNAs, and rRNA-derived fragments) play essential roles in mammalian development and diseases. Complete pictures of the hundreds copies of 45S rDNA and their rRNA transcripts require further innovation in sequencing techniques that include bioinformatics. The advancements in mammalian rDNA and rRNA sequencings and the discoveries of novel functions of the rDNA variants and rRNA transcripts are discussed here.

Fossils constitute the principal repository of data that allow for independent tests of hypotheses of biological evolution derived from observations of the extant biota. Traditionally, transformational series of structure, consisting of sequences of fossils of the same lineage through time, have been employed to reconstruct and interpret morphological evolution. More recently, a move toward an updated paradigm was fueled by the deliberate integration of developmental thinking in the inclusion of fossils in reconstruction of morphological evolution. The vehicle for this is provided by structural fingerprints – recognizable morphological and anatomical structures generated by (and reflective of) the deployment of specific genes and regulatory pathways during development. Furthermore, because the regulation of plant development is both modular and hierarchical in nature, combining structural fingerprints recognized in the fossil record with our understanding of the developmental regulation of those structures produces a powerful tool for understanding plant evolution. This is particularly true when the systematic distribution of specific developmental regulatory mechanisms and modules is viewed within an evolutionary (paleo-evo-devo) framework. Here, we discuss several advances in understanding the processes and patterns of evolution, achieved by tracking structural fingerprints with their underlying regulatory modules across lineages, living and fossil: the role of polar auxin regulation in the cellular patterning of secondary xylem and the parallel evolution of arborescence in lycophytes and seed plants; the morphology and life history of early polysporangiophytes and tracheophytes; the role of modularity in the parallel evolution of leaves in euphyllophytes; leaf meristematic activity and the parallel evolution of venation patterns among euphyllophytes; mosaic deployment of regulatory modules and the diverse modes of secondary growth of euphyllophytes; modularity and hierarchy in developmental regulation and the evolution of equisetophyte reproductive morphology. More generally, inclusion of plant fossils in the evo-devo paradigm has informed discussions on the evolution of growth patterns and growth responses, sporophyte body plans and their homology, sequences of character evolution, and the evolution of reproductive systems.

The bacterial flagellum is a motility organelle, consisting of a long helical filament as a propeller and a rotary motor that drives rapid filament rotation to produce thrust. Salmonella enterica serovar Typhimurium has two genes of flagellin, fljB and fliC, for flagellar filament formation and autonomously switches their expression at a frequency of 10^-3–10^-4 per cell per generation. We report here differences in their structures and motility functions under high viscosity conditions. A Salmonella strain expressing FljB showed a higher motility than the one expressing FliC under high viscousity. To examine the reasons for this motility difference, we carried out structural analyses of the FljB filament by electron cryomicroscopy and found that the structure is nearly identical to that of the FliC filament except for the position and orientation of the outermost domain D3 of flagellin. The density of domain D3 was much lower in FljB than FliC, suggesting that domain D3 of FljB is more flexible and mobile than that of FliC. These differences suggest that domain D3 plays an important role not only in changing antigenicity of the filament but also in optimizing motility function of the filament as a propeller under different conditions.

The informational re-interpretation of the basic laws of the mechanics exploiting the Landauer principle is suggested. When a physical body is in rest or it moves rectilinearly with the constant speed, zero information is transferred; thus, the informational affinity of the rest state and the rectilinear motion with a constant speed is established. The analysis of the minimal Szilard thermal engine as seen from the non-inertial frame of references is carried out. The Szilard single-particle minimal thermal engine undergoes the isobaric expansion relatively to the accelerated frame of references, enabling the erasure of 1 bit of information. The energy ΔQ spent by the inertial force for the erasure of 1 bit of information is estimated as: ΔQ≅5/3 k_B T ̅, which is larger than the Landauer bound but qualitatively close to it. The informational interpretation of the equivalence principle is proposed: the informational content of the inertial and gravitational masses is the same.

Various crystallite size estimation methods were used to analyze X-ray diffractograms of spherical cerium dioxide and donut-like titanium dioxide anatase nanoparticles aiming to evaluate their reliability and limitations. The microstructural parameters were estimated from Scherrer, Monshi, Williamson-Hall, and their variants: i) uniform deformation model, ii) uniform strain deformation model, and iii) uniform deformation energy density model, and also size-strain plot, and Halder-Wagner method. For that, and improved systematic Matlab code was developed to estimate the crystallite sizes and strain, and the linear regression analysis was used to compare all the models based on the coefficient of determination, where the Halder Wagner method gave the highest value (close to 1). Therefore, being the best candidate to fit the X-ray Diffraction data of metal-oxide nanoparticles. Advanced Rietveld was introduced for comparison purposes. Refined microstructural parameters were obtained from a nanostructured 40.5 nm Lanthanum hexaboride nanoparticles and correlated with the above estimation methods and transmission electron microscopy images. In addition, electron density modelling was also studied for final refined nanostructures, and μ-Raman spectra were recorded for each material estimating the mean crystallite size and comparing by means of a phonon confinement model.

This study evaluates the viability of n-octanol as an alternative fuel in a direct-injection diesel engine, aiming to enhance sustainability and efficiency. Experiments employed varying blends of n-octanol (10%, 30%, and 50% by volume) with pure diesel, analyzing impacts on engine performance and emissions. The methodology involved testing each blend in a single-cylinder engine, measuring engine performance parameters such as brake torque and brake power under full load conditions across an engine speed range from 1400 to 2500 rpm. Comparative assessments of performance and emission characteristics at a constant engine speed of 1700 rpm were also conducted with varying loads. Results indicated that while n-octanol blends consistently improved brake thermal efficiency, they also increased brake specific fuel consumption due to the lower energy content of n-octanol. Consequently, while all n-octanol blends reduced nitrogen oxides (NOx) emissions compared to pure diesel, they also significantly decreased carbon monoxide, hydrocarbons, and smoke opacity, presenting a comprehensive reduction in harmful emissions. However, the benefits came with nuanced trade-offs: notably, higher concentrations of n-octanol (especially the 50% blend) led to a relative increase in NOx emissions as the octanol ratio increased. The study concludes that n-octanol significantly improves engine efficiency and reduces diesel dependence, but optimizing the blend ratio is crucial to balance performance improvements with comprehensive emission reductions.

Heat transfer enhancement and hydrodynamic characteristics of nanofluid use in a micro heat exchanger is investigated for cooling electronic components working in hot climatic conditions. The cooling fluid employed was water and TiO2 nanoparticles at concentrations of 1% and 5%, Reynolds numbers ranged from 400 to 2000, and inlet temperatures ranged between 35ºC and 65ºC. At a nanofluid inlet temperature of 55ºC and a nanoparticle concentration of 1%, the Nusselt number increased by 23% up to 54% as the Reynolds number varied between 400 and 2000. At a nanoparticle concentration of 5%, the percentages which correspondingly enhanced the Nusselt number were 32% and 63%. The temperature of the electronic heating component decreased by 4.6-5.2ºC when the nanofluid concentration was increased from 0 to 5% at a Reynolds number of 400 and a nanofluid inlet temperature of 35ºC. Small increments in pressure drop of about 6% and 13% were observed at nanofluid concentrations of 1% and 5%, respectively. With nanoparticle concentrations of 1% and 5%, a Reynolds number of 2000 and a nanofluid inlet temperature of 35ºC, performance evaluation criterion (PEC) values of 1.36 and 1.45 were obtained. When the nanofluid inlet temperature was increased to 65ºC, the PEC parameter decreased to 1.02-1.10 for both concentrations.

Soil microbial fuel cells (sMFC) are a novel technique that use organic matters in soils as an alternative energy source. External resistance (ER) is a key factor influencing sMFC performance and, furthermore, alters the soil’s biological and chemical reactions. However, little information is available on how the microbial community and soil component changes in sMFC with different ER. Therefore, the effects of anodes of sMFC at different ER (2000 Ω, 1000 Ω, 200 Ω, 80 Ω and 50 Ω) were examined by measuring organic matter (OM) removal efficiency, trace elements in porewater and bacterial community structure in contaminated paddy soil. The results indicated that ER has significant effects on sMFC power production, OM removal efficiency and bacterial beta diversity. Moreover ER influences iron, arsenic and nickel concentration as well in soil porewater. In particular, greater current densities were observed at lower ER (2.4mA, 50Ω) compared to a higher ER (0.3mA, 2000Ω). The removal efficiency of OM increased with decreasing ER whereas it decreased with soil distance away from the anode. Furthermore, principal coordinate analysis (PCoA) revealed that ER may shape the bacterial communities that develop in the anode vicinity but have minimal effect on that of the bulk soil. The current study illustrates that lower ER can be used to selectively enhance the relative abundance of electrogenic bacteria and lead to high OM removal.

This research seeks to solve the multi-faceted problem of waste disposal by analyzing the application of waste plastic and tyre material within non-structural concrete to ensure more sustainability and less environmental degradation. Study focusses on material properties including specific gravity, water absorption and bulk density and characteristics of the concrete that is produced by the utilization of the above waste aggregates including workability, compressive strength, flexural strength and tensile strength. This paper employs secondary data collection and MATLAB in the analysis of the findings pointing to the fact that the mechanical properties reduce with the level of waste content yet emphasizing the green aspect of such materials. Thus, a complex and diverse effect is demonstrated by the life cycle assessments (LCA) for global warming, ozone depletion, terrestrial ecotoxicity, and acidification. Furthermore, the utilization of waste materials decreases the compressive, flexural, and tensile strength, but it provides distinct ecological benefits which prove the importance of proper mix proportions for concrete performance. The outcomes of this research will be useful for further investigation in the application of the concept as well as to call for the development of new ideas for the improvement of bonding of wastes to aggregates in concrete.

Recently, Electric Two-Wheelers ETW are changing the face of the global automotive market. This study focused on selecting proper material and mechanical isolation gap to design a protective enclosure for the battery pack of ETW. The integration of the Failure, Modes, Mechanism and Effect Analysis (FMMEA) method is utilized to develop the interface matrix and the severity index of different components of the enclosure. By analyzing different forces from the road conditions, dynamics during turn, acceleration, and deceleration with the enclosure, it becomes a crucial load-bearing element. Employing Finite Element Modeling (FEM), structural strength using materials like AL6061, Q235, C22000, DC 01 and Teflon are assessed under varying static, dynamic and thermal conditions. Modal analysis is conducted to observe the excitation frequencies where the maximum deformation for the metal enclosure is observed beyond 500Hz. AL6061 material that can withstand the stresses and deformations that are under allowable stress limits with negligible deformation is most preferable material based on the results. Minimal of 2.5 mm gap to be provided in case of metal casing and 10mm in case of Teflon is proven.

In the current study, a two-dimensional numerical study is carried out to investigate the performance of a novel Double-chamber Parallel Flexible Valves micropump, which utilized the electrowetting-on-dielectrics (EWOD) effect to drive the microfluid flow. By observing the flow fields, the internal circulations are seen on both the left and right side of the pump. The generation of the backflow is discussed by tracking the movement of the vortices. Only slight flow fluctuation is seen in the micropump. Based on the simulation outcomes, the structural parameters including the width of the inlet/outlet, the width of the pumping channel and the diverging angle in the micropump are analyzed, the influence of these parameters on the pumping volume and the maximum pressure have been discussed. Eventually, a group of optimal parameter combinations is given according to the results to extend the operating potential of the micropump.

Cancer is a complex disease, considered a major public health problem worldwide. Among the types of cancer, melanoma, and leukemias present high mortality rates in Brazil and worldwide. Currently, conventional cytotoxic treatments cause severe side effects by the non-selectivity between normal cells and cancer cells. Therefore, molecules of natural origin with more efficient anticancer properties and that present fewer adverse effects are of extreme importance for cancer therapy and improvement in patients’ quality of life. This study isolated, identified, and characterized the chemical structure of a new anthraquinone present in the extract of the roots of Senna velutina. In addition, we sought to evaluate the anticancer potential of this molecule against melanoma and leukemic cell lines and identify the pathways of cell death involved. To this end, a novel anthraquinone was isolated from the barks of the roots of S. velutina, analyzed by HPLC-DAD, and its molecular structure was determined by NMR. Subsequently, their cytotoxic activity was evaluated by the MTT method against non-cancerous, melanoma, and leukemic cells. The migration of melanoma cells was evaluated by the scratch assay. By flow cytometry technique, the apoptosis process, caspase-3 activation, analysis of mitochondrial membrane potential, and measurement of ROS were evaluated. In addition, the pharmacological cell death inhibitors NEC-1, RIP-1, BAPTA, Z-VAD, and Z-DEVD were used to confirm the related cell death mechanisms. With the results, it was possible to elucidate the novel compound characterized as 2'-OH-Torosaol I. In normal cells, the compound showed no cytotoxicity in PBMC but reduced the cell viability of all melanoma and leukemic cell lines evaluated. 2'-OH-Torosaol I inhibited chemotaxis of B16F10-Nex2, SK-Mel-19, SK-Mel28 and SK-Mel-103. The cytotoxicity of the compound was induced by apoptosis via the intrinsic pathway with reduced mitochondrial membrane potential, increased levels of reactive oxygen species, and activation of caspase-3. In addition, the inhibitors demonstrated the involvement of necroptosis and CA+ in the death process and confirmed caspase-dependent apoptosis death as one of the main programmed-cell death pathways induced by 2'-OH-Torosaol I. Taken together, the data characterize the novel anthraquinone 2'-OH-Torosaol I, demonstrating its anticancer activity and potential application in cancer therapy.

We used the Taylor-Ulitovsky Technique to prepare nanocrystalline Co2MnGe Heusler alloy glass-coated microwires with a metallic nucleus diameter of 18 ± 0.1 µm and a total diameter of 27.2 ± 0.1 µm. Magnetic and structural studies were carried out to determine the fundamental magneto-structural characteristics of Co2MnGe glass-coated microwires. XRD revealed a well-defined nanocrystalline structure with average grain size about 63 nm, lattice parameter a = 5.62 and a unique mixture of L21 and B2 phases. The magnetization curves for field cooling and field heating (FC-FH) demonstrate a considerable dependence on the applied magnetic field, ranging from 50 Oe to 20 kOe. Internal stresses, originated by the production process, resulted in various magnetic phases, which were responsible for the notable difference of field cooling (FC) and field heating (FH) curves on magnetization dependence versus temperature. Furthermore, the ferromagnetic behavior and expected high Curie temperature together with high degree of L21 ordered makes it a promising candidate for many applications.

The protein C is a small viral protein encoded in an overlapping frame of the P gene in the sub-family Orthoparamyxovirinae. This protein, expressed by alternative translation initiation, is a virulence factor that regulates viral transcription, replication and production of defective interfering RNA, interferes with the host-cell innate immunity systems and supports assembly of viral particles and budding. We expressed and purified full-length and an N-terminally truncated C protein from Tupaia paramyxovirus (TupV) C protein (genus Narmovirus). We solved the crystal structure of the C-terminal part of TupV C protein at a resolution of 2.4 Å and found that it is structurally similar to Sendai virus C protein, suggesting that despite undetectable sequence conservation, these proteins are homologous. We characterized both truncated and full-length proteins by SEC-MALLS and SEC-SAXS and described their solution structures by ensemble models. We established a minireplicon assay for the related Nipah virus (NiV) and showed that TupV C inhibited the expression of NiV minigenome in a concentration-dependent manner as efficiently as NiV C protein. A previous study found that the Orthoparamyxovirinae C proteins former two clusters without detectable sequence similarity, raising the question of whether they were homologous or instead had originated independently. Since TupV C and SeV C are representative of these two clusters, our discovery that they have a similar structure indi-cates that all Orthoparamyxovirine C proteins are homologous. Our results also imply that, strik-ingly, a STAT1-binding site is encoded by exactly the same RNA region of the P/C gene across Paramyxovirinae, but in different reading frames (P or C) depending on which cluster they belong to.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

Functional osseoconductive coatings based on hydroxylapatite (HAp) and applied preferentially by atmospheric plasma spraying to medical implant surfaces are a mainstay of modern implantology. During contact with the hot plasma jet, HAp particles melt incongruently and undergo complex dehydration and decomposition reactions that alter their phase composition and crystallographic symmetry, and thus, the physical and biological properties of the coatings. Surface analytical methods such as laser-Raman and nuclear magnetic resonance (NMR) spectroscopies are useful tools to assess the structural changes of HAp imposed by heat treatment during their flight along the hot plasma jet. In this contribution, the controversial information on the existence or non-existence of oxyapatite, i.e. fully dehydrated HAp as a thermodynamically stable compound is highlighted.

A novel cytoplasmic dye decolorizing peroxidase from Dictyostelium discoideum was investigated that oxidizes anthraquinone dyes, lignin model compounds and general peroxidase substrates like ABTS efficiently. Unlike related enzymes, an aspartate residue replaces the first glycine of the conserved GXXDG motif in Dictyostelium DyPA. In solution, Dictyostelium DyPA exists as a stable dimer with the side chain of Asp146 contributing to the stabilization of the dimer interface by extending the hydrogen bond network connecting two monomers. To gain mechanistic insights, we solved the Dictyostelium DyPA structures in the absence of substrate as well as in the presence of potassium cyanide and veratryl alcohol to 1.7, 1.85, and 1.6 Å resolution, respectively. The active site of Dictyostelium DyPA has a hexa-coordinated heme iron with a histidine residue at the proximal axial position and either an activated oxygen or CN^- molecule at the distal axial position. Asp149 is in an optimal conformation to accept a proton from H₂O₂ during the formation of compound I. Two potential distal solvent channels and a conserved shallow pocket leading to the heme molecule were found in Dictyostelium DyPA. Further, we identified two substrate-binding pockets per monomer in Dictyostelium DyPA at the dimer interface. Long-range electron transfer pathways associated with a hydrogen-bonding network that connects the substrate-binding sites with the heme moiety are described.

In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity to reach 90 % accuracy of the prediction.

The degradation and detachment of the polyamide (PA) layer in thin-film composite (TFC) membranes due to chlorine based chemical cleaning and material difference of PA layer and substrate are two major bottlenecks of forward osmosis (FO) technology. In this study, a new type of FO membranes were prepared by controlling self-polymerization of dopamine (DA) in the aqueous phase and the reaction with trimesoyl chloride (TMC) during interfacial polymerization (IP) process. These membranes were characterized by attenuated total reflection Fourier transform infrared (ATR-FTIR), X-ray photoelectron spectroscopy (XPS), field-emission scanning electron microscopy (FESEM) and water contact angle measurements. The influence of synthesis parameters such as pH of the aqueous phase, reaction time, temperature, and monomer concentrations were systematically investigated. The optimized membrane showed enhanced structure stability in ethanol (7.1 times higher) and chlorine resistance (72.3 times higher) than the conventional Piperazine(PIP)/TMC membrane due to（poly-dopamine）PDA bio-adhesion and polyester groups in the membrane structure. In general, DA/TMC membranes could be an effective strategy to fabricate high-performance FO membranes with excellent structural stability and chlorine resistance.

Herein, we present preparation of solid polymer electrolyte (SPE) comprising of PEO, NaPF₆ and varying fraction of Succinonitrile (SN) by standard solution cast technique. The morphological features and structural properties were studied by the FESEM, XRD, respectively. FTIR was performed to study the interactions between polymer host, salt, and SN. Impedance spectroscopy, Transference number measurements, LSV and CV were used to examine the electrochemical properties. The complex permittivity/conductivity & modulus were studied to understand the dielectric properties by evaluating the dielectric strength, relaxation time, hopping frequency and dc conductivity. Based on the experimental results an interaction mechanism is presented.

This paper presents a groundbreaking approach to real-time image processing in electronic component assembly, enhancing quality control in manufacturing. By capturing images from pick and place machines between component pickup and mounting, defects are identified and addressed in-line, significantly reducing the likelihood of defective products. Leveraging fast network protocols like gRPC and orchestration with Kubernetes, along with C++ programming and TensorFlow, this method achieves an average turnaround time of less than 5 milliseconds. Tested on 20 live production machines, it ensures compliance with IPC-A-610, and IPC-STD-J-001 standards while optimizing production efficiency and reliability.

The roots, leaves, and other parts of American ginseng contain ginsenosides and polyphenols, among other bioactive components. So far, reports on exploring the changes in bioactive components within its leaves after heat treatment have been limited. In the present study, we investigated the effects of far-infrared irradiation (FIR) heat treatment from 160 to 200 ℃ on the deglycosylation and dehydration rates of the bioactive components in American ginseng leaves. As the temperature was increased, the amounts of constant ginsenosides decreased while those of rare ginsenosides increased. After FIR heat treatment of the American ginseng leaves at an optimal 190 ℃, the highest total polyphenolic content and kaempferol content were detected, the antioxidant activity was significantly enhanced, and the amounts of rare ginsenosides F4, Rg6, Rh4, Rk3, Rk1, Rg3, and Rg5 were 41, 5, 37, 64, 222, 17, and 266 times higher than those in the untreated leaves, respectively. Moreover, the radical scavenging rates for 2,2-diphenyl-1-picrylhydrazyl and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), and the reducing power of the treated leaf extract, were 2.17, 1.86, and 1.77 times higher, respectively. Hence, optimal FIR heat treatment at 190 ℃ is an efficient method for producing beneficial bioactive components from American ginseng leaves.

Allergies are a common and increasing health problem affecting millions of people worldwide. In addition to genetic predisposition, this increase in allergic cases is attributed to air pollution, climate change, more time spent indoors, lack of physical activity, and alterations in eating habits. The Mediterranean diet (MD), which includes a lot of fruits and vegetables, whole grains, legumes, nuts, olive oil and fish has been linked to a variety of health benefits, including a lower risk of chronic and allergic diseases. The purpose of this paper is to explore the effects of the dietary components of the MD on food allergies. Electronic databases PubMed, Scopus, Science Direct, and EBSCO were used to conduct this systematic review up to March 2023. 696 studies were initially identified and nine were included (five human and four animal studies). The findings of this review showed an overall beneficial effect between the food components of the MD and food allergies. Although the results are promising, the limited number of studies highlights the need for more research.

The debate about the meaning of life in the literature has been around for a while and portraits a fundamental human need to find a personal path of existence. The meaning of life phenomenon purpose lies in its impact on the psychological wellbeing. Knowing your purpose in life can make it more structured and in balance, therefore more meaningful and comprehensible. However, in the existing body of the academic literature meaning of life is discussed more from the philosophic perspective rather than a practical area, which is not helping to solve the global issues of increasing mental health problems. When social and emotional wellbeing is being affected, it is urgent to apply a practical approach to effectively overcome these states. Introduction of a new, holistic meaning of life model is an attempt to offer a practical solution for society to maintain their mental health through awareness of their life’s purpose. Previously in the pilot study (Dombrovskis, 2017) in a sample group of 100 citizens of Latvia who self-reported being in a crisis situation at the given moment, using qualitive analysis the first component for the model structure was identified – antecedent. In our research antecedent is defined as something that triggers certain action, so in crisis situation person can regain a structure and stability in life. We found 8 different antecedents that trigger behavior of those affected by crisis: achievements; power; success; development; freedom; uniqueness; interesting life and fleeing. To continue to explore other components for our model in the setting of Latvia, this study aims to investigate the demographic and personality related variables that may serve as antecedents and as such become one of component in our model structure. The study used a sample of residents of Latvia representing the total population of 1,110 respondents (633 women, 57%, and 477 men, 43%) who have completed the self-assessment questionnaire Meaning of life scale (MOLS) (online and frontally) designed by the authors on their satisfaction with different factors regarding the meaning of life. Average age of respondents (M=33.88; SD=8,527) varied from 18 to 67. The exploratory and confirmatory analysis confirms the data regarding existence of totally 16 factors: 8 antecedents identified in the pilot study and 8 new antecedents identified in the present study - family relations; level of income; relations with friends/acquaintances; state of health; organization of free time; career growth (achievements); sexual life quality and quality of education. Findings confirm a model’s structure to be complex phenomenon consisting of various components. However, findings suggest that more research should be done to fill the following gaps: 1) identify actions, which are triggered by 16 antecedents; 2) figure out F1, F2 and F3 factor role in the model structure.

In the present work, effect of basic components on the energy pathway of ethylene oligomerization by landmark Chevron-Phillips catalyst has been explored in detail using density functional theory (DFT). Studied factors were chosen considering the main components of Chevron-Phillips catalyst, i.e. ligand, cocatalyst and halocarbon compounds, comprising i) the type of alkyl substituents in pyrrole ligand as methyl, iso-propyl, tert-butyl, and phenyl, as well as the simple hydrogen, and the electronwithdrawing fluoro and trifluoromethyl; ii) the number of Cl atoms in Al-compound (as AlMe2Cl, AlMeCl2 and AlCl3) which indicates halocarbon amount and iii) cocatalyst type as alkylboron, alkylaluminium, or alkylgallium. Besides main ingredients, solvent effect, from toluene or methylcyclohexane, on oligomerization pathway was explored as well. In this regard, the full catalytic cycles for the main product (1-hexene) formation as well as side reactions, i.e. 1-butene release and chromacyclononane formation, were calculated on the basis of the metallacycle based mechanism. Based on results, a modification on the Chevron-Phillips catalyst system, to reach higher 1-hexene selectivity and activity, is suggested.

The COVID-19 virus was first detected in Wuhan, China and its genesis was traced to infected bats, passed through an intermediate host to humans. COVID-19 or “Coronavirus” reached approximately 4 million confirmed cases in the United States by July 2020. The spread of this virus has affected both social and economic affairs on a global scale with more than 15 million confirmed cases worldwide in July 2020. The pandemic has proven a global public health and socio-economic crisis. In addition, the shelter-in-place orders provide an unprecedented opportunity for examining the resulting reduction in greenhouse gases and aerosols on the atmosphere. The Sentinel-5P satellite has shown distinct changes in atmospheric compounds over the COVID-19 epicenter in Wuhan. After Texas, U.S.A. established similar shelter-in-place orders to prevent the spread of the virus, the state’s industrial activity experienced an economic slowdown. This paper quantifies the changes in the atmosphere during the time of the COVID-19 slowdown over major cities in Texas and compares it to similar changes in Wuhan, China using satellite imagery.

ATP-binding cassette (ABC) transporters, acting as cellular "pumps," facilitate solutes translocation through membranes via ATP hydrolysis. Their overexpression is closely tied to multidrug resistance (MDR), a major obstacle in chemotherapy and neurological disorder treatment, hampering drug accumulation and delivery. Extensive research has delved into the intricate interplay between ABC transporter structure, function, and potential inhibition for MDR reversal. Cryo-electron microscopy has been instrumental in unveiling structural details of various MDR-causing ABC transporters, encompassing ABCB1, ABCC1, ABCG2, as well as recently revealed ABCC3 and ABCC4 structures. The newly obtained structural insight has deepened our understanding of substrate and drug binding, translocation mechanisms, and inhibitor interactions. Given the growing body of structural information available for human MDR transporters and their associated mechanisms, we believe it is timely to compile a comprehensive review of these transporters and compare their functional mechanisms in the context of multidrug resistance. Therefore, this review primarily focuses on the structural aspects of clinically significant human ABC transporters linked to MDR, with the aim of providing valuable insights to enhance the effectiveness of MDR reversal strategies in clinical therapies.

Wind energy is becoming increasingly important as a renewable energy source due to its environmental and economic benefits. Wind turbines are key components in wind energy systems, and their performance is critical for efficient power generation. Wind turbine blades are the most critical components as they interact with the wind, and their design has a significant impact on the overall system performance. Therefore, it is essential to optimize the design of wind turbine blades to enhance their efficiency and reduce their costs. This paper presents an aero-structural optimization approach for wind turbine blade design. The optimization aims to maximize the torque generated by the blade while minimizing its mass. The optimization is implemented using DAFoam software for CFD simulation, TACS for FEM simulation, and Mphys under the OpenMDAO framework for fluid-structure interaction between the CFD and FEM. The optimization results show a 6.78% increase in torque and a 4.22% decrease in mass, which demonstrates the effectiveness of the proposed approach. Aerodynamic optimization focuses on maximizing the blade's torque by modifying the blade's shape. The optimization results show that the optimized blade generates more torque than the original blade design. Additionally, structural optimization aims to minimize the blade's mass while maintaining its structural integrity. This is achieved by adjusting the thickness of the blade's cross-section. The proposed aero-structural optimization approach presents an effective solution for the design optimization of wind turbine blades. The approach considers the interaction between the aerodynamic and structural aspects of the blade and optimizes them simultaneously. This leads to an optimized design that is efficient and cost-effective, which is crucial for the widespread adoption of wind energy systems. The results of this study highlight the importance of considering the interaction between the aerodynamic and structural aspects of wind turbine blades and the effectiveness of the proposed optimization approach in enhancing their performance.

The complexity of objects and control systems increases the requirements for mathematical models. The structural identifiability (SI) assessment of nonlinear systems is one of the identification problems. Until now, this problem solves by parametric methods using various approximation methods. This approach is not always effective under uncertainty. We apply an approach to SI estimation based on the analysis of virtual structures. The requirements form for the system input based on the expansion of the excitation constancy property and S-synchronizability. The approach generalization to structural identifiability used in the analysis of systems with symmetric nonlinearity is given. homothety and identifiability conditions for systems with non-symmetric nonlinearities (SNN) obtained. The detectability and recoverability proofed for virtual frameworks (VF) guaranteed the SI estimation under uncertainty. The conditions under which the non-symmetric nonlinearity is hypothetical symmetric nonlinearity obtained. SI estimation examples considered for closed nonlinear systems under uncertainty.

The determination of the three-dimensional structure is a key step for the identification of new targets as well as the rational design of bioactive compounds. Herein, we report the synthesis and the structural elucidation of novel tetrameric macrocycles. Measurements were taken in solid and solution states with the help of X-ray scattering and NMR spectroscopy. The investigations made will help to find diverse applications for this new, promising compound class.

Vibration suppression in a field of frequencies and the creation of band gaps which do not allow the propagation of the waves is studied in this paper by means of microstructures designed through topology optimization. Topology optimization is formulated in frequency domain. Band gap design is based on Floquet-Bloch theory and genetic optimization. The result is appealing in view of modern 3D printing techniques.

We address Multi-Wall Carbon NanoTubes (MWCNTs) for structural health monitoring in adhesive bonds such as in building structures. MWCNT-loaded composites are employed to sense strain changes under tension load using an AC impedance measurement setup. Different weight percentages of 1, 1.5, 2 and 3 wt.% MWCNT are added to the base epoxy resin using different dispersion times, i.e. 5, 10 and 15 minutes. The equivalent parallel resistance of the specimens is measured by applying an alternating voltage at different frequencies. To determine the mechanical as well as sensory properties, the specimens are subjected to a tensile test with concurrent impedance measurement. Using alternating voltage, a higher sensitivity of the impedance reading can be achieved. Employing these sensors in buildings and combining the readings of a network of such devices can significantly improve the buildings’ safety. Additionally, networks of such sensors can be used to identify necessary maintenance actions and locations.

We present a modified version of the Szilard Engine, demonstrating that an explicit measurement procedure is entirely unnecessary for its operation. By considering our modified engine, we are able to provide a new interpretation of Landauer’s original argument for the cost of erasure. From this view, we demonstrate that a reset operation is strictly impossible in a dynamical system with only conservative forces. Then, we prove that to approach a reset yields an unavoidable instability at the reset point. Finally, we present an original proof of Landauer’s principle that is completely independent from the Second Law of Thermodynamics.

Pesticides are among the most widely used synthetic chemicals worldwide to protect crops, but besides being beneficial, they pose several environmental and health issues. The present study evaluates toxic effects of acetamiprid (ACMP) in rat liver and kidney tissues. Rats were exposed to ACMP (21.7 mg/kg b.wt; 1/10th LD50) for 21 days. Oxidative stress generation, apoptotic progression, and structural changes were evaluated via biochemical assays, semi-quantitative PCR, western blotting, and histopathology. ACMP exposure significantly decreased body weight and altered relative organ weight in hepato-renal tissues. Significant oxidative stress was evident by increased oxidative injuries to lipids and proteins and alterations of endogenous anti-oxidative enzymes. The administration of ACMP upregulated mRNA expression of Bax and caspase-3, while downregulated the mRNA expression of Bcl-2 and released cytochrome c into the cytosol of the liver and kidney tissues. Likewise, exposure to ACMP caused severe degenerative changes in the histo-architecture of hepato-renal tissues. The results of the present study confirmed the toxicity of ACMP in rats’ liver and kidney tissues and emphasized the need for strict regulation and more mechanistic understanding to delineate the toxicity of ACMP in mammals.

Compared to other 2D materials, MBenes are at an early stage in both experimental and theoretical approaches. However, the wide range of possible 2D structures leads to novel and challenging properties and their consequent applications. From all the possible stoichiometries, we have performed a theoretical study of orthorhombic and hexagonal M2B2 MBenes in the framework of the Density Functional Theory. We have found that both symmetries of Cr2B2, Fe2B2, and Zr2B2 show a metallic behavior and could be grown under certain conditions, as they are demonstrated to be dynamically stable. Moreover, the values of the magnetic moment, in specific ferromagnetic cases exceeding 2.5 μB/M2B2, make them suitable as robust 2D magnets. All our findings represent an important step in the understanding of MBenes and open several windows to future research in fields like energy conversion and storage, sensing, catalysis, biochemistry, or nanotechnology, among others.

The extracts from the aerial parts of Helianthus salicifolius A. Dietr and Helianthus tuberosus L. collected in June were obtained using carbon dioxide supercritical fluid extraction with water as co-solvent. The antimicrobial activity in vitro of these extracts were determined against the reference species of Gram-positive and Gram-negative bacteria as well as fungi, representing by the yeast species of Candida spp. The following parameters were estimated: minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) or minimum fungicidal concentration (MFC). Both extracts were found to possess antimicrobial activity with MIC = 0.62-5 mg mL-1 for bacteria and MIC = 5-10 mg mL-1 for yeasts, showing bactericidal (MBC/MIC = 2-4) or fungicidal effect (MFC/MIC = 1-2 ). The highest activity was observed against S. aureus ATCC 29213 (MIC = 0.62 mg mL-1 for H. salicifolius extract; MIC = 2.5 mg mL-1 for H. tuberosus extract). Bactericidal effect of both extracts against S. aureus ATCC 29213 was confirmed by time-kill assay. Higher antioxidant activity was found for H. tuberosus extract (EC50 = 0.332 mg mL-1) as compared to that of H. salicifolius (EC50 = 0.609 mg mL-1). The total polyphenol content (TPC) expressed as gallic acid equivalents (GAE) was 13.75 ± 0.50 mg GAE (g of H. salicifolius extract)-1 and 33.06 ± 0.80 mg GAE (g of H. tuberosus extract)-1. There was a correlation between the antioxidant potential of both extracts and TPC but not between antistaphylococcal activity and TPC. The obtained data suggest potential application of these extracts as the natural preparations with the biocidal activity, including those with antistaphylococcal activity. Besides, both extracts may be regarded as potential natural conservants in cosmetics as well as natural preservatives in food.

Accurate estimation of Remaining Useful Life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention based hierarchical transformer (STAR) frame-work. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrate hierarchical encoder-decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms current state-of-the-art models for RUL prediction.

In this article, copper(II) oxide, titanium dioxide and yttrium(III) oxide nanoparticles were added to Group III type base oil formulated with overbased calcium sulfonate. The nano-sized oxides were treated with ethyl oleate surface modification. The tribological properties of the homogenized oil samples were tested on a linear oscillating tribometer. Friction was continuously monitored during the tribological tests. Surface analysis was performed on the worn samples: the amount of wear was determined using a digital optical and confocal microscope. The type of wear was examined with a scanning electron microscope, while the additives adhered to the surface were examined with energy-dispersive X-ray spectroscopy. From the results of the measurements, it can be concluded that the surface-modified nanoparticles worked well with the overbased calcium sulfonate and significantly reduced both wear and friction. In the present tribology system, the optimal concentration of all three oxide ceramic nanoadditives is 0.4 wt%. By using oxide nanoparticles, friction can be reduced by up to 15% and the wear volume by up to 77%. Overbased calcium sulfonate and oxide ceramic nanoparticles together form a lower friction anti-wear boundary layer on the worn surfaces. It is advisable to further investigate the possibility of formulating nanoparticles into the oil.

Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.

The goal of this study was to analyze the forest degradation in the Reserve for San Rafael National Park, Paraguay, during the period 2005-2019. This Reserve is one of the most important forest remnants of the Upper Paraná Atlantic Forest Ecoregion. A multitemporal analysis of degradation was carried out due to the occurrence of three disturbances: forest fires, a twister and illicit crops, using the Continuous Degradation Detection (CODED) algorithm, for which 3 factors were considered: variations due to pixel in the NDFI index values before, during and after every disturbance registered. In this context, the phenomenon with the greatest impact in terms of magnitude of degradation were the forest fires of 2005, being that year at the same time, the one that reported the highest degradation values. Secondly, there are the illicit crops established until the first semester of 2019, and lastly, the twister that occurred in 2017. Our findings demonstrate that CODED algorithm can detect multi-temporal degradation events in a Subtropical Broadleaf Forest, and the post-disturbance regeneration process after every disturbance tends to occur immediately. The response in terms of degradation-regeneration is highly variable, depending of the nature and severity of each disturbance and the vegetation recovery dynamics.

Three-temperature heating systems consist of a heat engine and a heat pump, enabling thus maximum usage of the primary thermal source for the heating of buildings. This analysis has revealed obvious advantages and disadvantages that the combining of thermodynamic systems has in future development, also with respect to environmental and economic issues. It appears that the combination of a Stirling engine or a similar heat drive with a heat pump is especially suitable. In order to analyze the effectiveness of such a system, a comprehensive calculation procedure is used: its basis lies in accounting for all types of energy and their relationship to the original natural resource. The present paper aims to point out that the combination of Stirling engine and a heat pump is a useful solution thanks to the most favorable resultant economic impact if compared to the usage of a diesel, four-stroke gas, or, most commonly used, electric drive.

Abstract: Background: Bangladesh has achieved remarkable progress in reducing maternal mortality, yet postpartum deaths remain a significant issue. Emphasis on quality postnatal care (qPNC) is crucial, as increased coverage alone has not sufficiently reduced maternal morbidity and mortality. Methods: This study included data from the Bangladesh Maternal Mortality Survey of 32,106 mothers who delivered within three years prior to the survey. Descriptive statistics were used to report coverage and components of postnatal care stratified by covariates. Log-linear regression models were used to assess the determinants of quality postnatal care among facility and home births. Results: From 2010 to 2016, postnatal care coverage within 48 hours of delivery by a qualified provider rose from 23% to 47%. 94% of facility births received timely PNC, contrasted with only 6% for home births. Despite the increased coverage, quality of care remained as low as 1% for home births and 13% for facility births. Key factors affecting qPNC utilization included socio-demographic factors, pregnancy complications, delivery method, financial readiness, and mobile ownership. Conclusion: Importantly, deliveries assisted by skilled birth attendants correlated with higher quality postnatal care. The study reveals a significant gap between the coverage and quality of postnatal care in rural Bangladesh, especially for home births. It underscores the need for targeted interventions to enhance qPNC.

Assuming a geometrically closed universe, we predict a value for the cosmic curvature, , a value within current observational bounds. We also propose a thermodynamic heat engine model for the universe, which bypasses the need for an inflaton field. Our model is based on a Carnot Cycle where we have isothermal expansion, followed by adiabatic expansion, followed by isothermal contraction, followed by adiabatic contraction, bringing us back to our original starting point. For the working substance, we focus specifically on the CMB radiation filling the collective voids in the universe. Using this construct, we identify cosmic inflation as the isothermal expansion phase, which lasts just under, . The collective CMB volume we see today only increases by a factor of 5.65 times during this process, and homogeneity and perturbations in the CMB are explained. The singularity problem is avoided and we have a clear mechanism for the work done by the cosmos in causing expansion, and later contraction. For scaling laws with respect to the density parameters in Friedmann’s equations, we will assume a susceptibility model for space, where, , the smeared cosmic susceptibility, decreases with increasing cosmic scale parameter, . Within this framework, we can predict a maximum Hubble volume with minimum CMB temperature for the voids before contraction begins, as well as a minimum volume with maximum CMB temperature when expansion starts. The thermodynamic heat cycle deviates from efficiency in converting heat energy into mechanical energy (expansion) by a minuscule amount, namely, . The significance of this number is not known.

Chikungunya (CHIKV) and Mayaro (MAYV) viruses are arthritogenic alphaviruses that promote an incapacitating and long-lasting inflammatory muscle-articular disease. Despite studies pointing out the importance of skeletal muscle (SkM) in viral pathogenesis, the long-term consequences on its physiology after acute injury and the mechanism of persistence of symptoms are still poorly understood. Combining molecular, morphological, Nuclear Magnetic Resonance Imaging, and histological analysis, we conduct a temporal investigation of CHIKV and MAYV replication in a wild-type mice model, focusing on the impact on SkM composition, structure, and repair in the acute and late phases of infection. We found that viral replication and induced-inflammation promote fast loss of muscle mass and reduction of fibers cross sectional area, by inducing muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin expression, that trigger protein breakdown, resulting in acute SkM fibers atrophy. After temporal reduction of inflammation and clearance of infectious viral particles, SkM atrophy persists until the late phase of infection. The genomic CHIKV and MAYV RNAs were still detected in SkM at the late phase, along with increased levels of chemokines and anti-inflammatory cytokines expression. In agreement with the involvement of inflammatory mediators on induced atrophy, neutralization of TNF and the reduction of oxidative stress using monomethyl fumarate, an agonist of Nrf2, decreases atrogens expression and atrophic fibers, while increasing weight gain in treated mice. Taken together, these data indicate that arthritogenic alphavirus infection could chronically impact body SkM composition and also harm repair machinery, contributing to a better understanding of mechanisms of arthritogenic alphaviruses pathogenesis and with description of potentially new targets of therapeutic intervention.

This study aimed to extract bioactive compounds from apple (Malus domestica) peel waste using supercritical CO2 extraction. The bioactive fractions from apple peels from the three Italian cultivars were extracted applying a temperature of 60 °C and a pressure of 250 bar for 15 min. 20% ethanol was used as a co-solvent. The extraction process was carried out at a flow rate of 2 mL/min and their total polyphenol (TP), anthocyanin (TA), ascorbic acid (AA), and antioxidant activity contents (TAC) were measured. The extracts from the Stark cultivar had the highest levels of polyphenols, anthocyanins, and ascorbic acid, while the Royal Gala cultivar had the highest total antioxidant activity. Chromatographic analyses were also performed to characterize polyphenols. The extracts were then tested for their effect on the mitochondrial NADH-ubiquinone oxidoreductase (Complex I) activity on mitochondria isolated from human embryonic kidney cells (HEK239) cells, with the Stark extracts showing the most positive response in terms of NADH oxidation. These findings highlight the potential of apple peel waste as a source of functional phytocompounds and suggest the Stark cultivar extracts may have pharmacological applications. This study supports the circular bioeconomy by promoting the use of waste products as a valuable resource.

There are not many exhaustive works emphasizing the amount of genetic diversity among the strawberry tree (Arbutus unedo L.) genotypes in Morocco. This work aims to assess the biochemical composition of strawberry tree fruits, as well as to establish the variation of this composition among them. In this study, total phenols, total flavonoids, condensed and hydrolyzable tannins, total anthocyanins and free radical scavenging activity through ABTS were investigated in strawberry tree fruits. Furthermore, qualitative and quantitative analyses of individual phenolic compounds by high-performance liquid chromatography (HPLC) were carried out. Color parameters such as lightness (L*), Chroma (c*) and hue angle (h°) were also investigated. All studied variables showed highly significant differences among all samples with the exception of hydrolyzable tannins and chromatic coordinates. Total phenolics varied from 22.63 ± 1.74 to 39.06 ± 2.44 mg GAE/g dry wt, total flavonoids varied from 3.30 ± 0.60 to 8.62 ± 1.10 mg RE/g dry wt and total anthocyanins ranged between 0.12 ± 0.06 and 0.66 ± 0.15 mg cya-3-glu/100g dry wt. In addition, condensed and hydrolyzable tannins amounts were in the range of 10.41 ± 1.07 - 16.08 ± 1.50 mg TAE/g dry wt and 4.08 ± 2.43 - 6.34 ± 3.47 respectively. Moreover, the IC50 value (ABTS) ranged between 1.75 and 19.58 mg AAE/g dry wt. 17 phenolic compounds were detected in strawberry tree fruits. Gallocatechol and catechin were the most abundant phenolic compound. Matrix of correlations revealed significant positive and negative correlations among variables particularly c*, a* and b*. Principal component analysis showed that the first three components formed than 68% of the total inertia. The following variables gallic acid, protocatechuic, gallocatechin, gallic acid derivative, chlorogenic acid, syringic acid, ellagic acid derivative II, L* and h* were the most involved in the total variance explained. Hierarchical clustering classified samples into one main cluster, with a single branch. The results highlight a high biochemical diversity within studied strawberry genotypes, which is probably more genetically related

The current study compared the goodness of fit and estimation precision of Bayes and maximum likelihood (ML) structural equation modeling across sample sizes. Data consisted of Markov Chain Monte Carlo generated samples of 50, 75, 100, 125, 150, 200, 250, 500, 750, 1000, and 1500 observations. The Bayes and ML methods were used to estimate a structural equation model with 40 parameters, including 12 continuous observed indicators, four latent variables, and three structural paths. Analyses were conducted with the Mplus 8.0 statistical software and were replicated 500 times with each of the 22 conditions (11 sample sizes x 2 estimation methods). Indices of model fit were the Bayes information criterion, the Tucker-Lewis index, the comparative fit index, the root mean square error of approximation, the posterior predictive p-value (for Bayes models), and the ꭓ2 test (for ML models). Results showed that as the sample size increased, both estimation methods exhibited improved model fit and enhanced estimation precision, and both methods performed well with samples of 200 or larger; however, ML demonstrated a marginally superior fit with smaller samples. These findings were not consistent with previous research and suggest that ML remains a better choice with continuous data and small samples.

Evaluation of structural behaviour of a masonry Venetian Church with pointed vault, is presented in this paper through an analysis following the neccessary steps of a monument study. Having a detailed geometric model and the material estimation, the finite element method was used in order the influence of specific structural parts of the structure like masonry butresses and walls connections to the structural behaviour are examined. The comparison of the eigenfrequencies which are estimated by in situ measurements and finite element modal analysis is used to model identification. The response spectrum analysis, the static analysis for subsidence of some parts and the transient analysis of a specific seismic excitation are used to evaluation of the church strength. Comparison of modal analysis results with the structural pathology is helpful to this research about the structural behaviour of the church.

Apoptosis, necroptosis, and autophagy are cellular mechanisms by which cells are programmed to die under a wide range of physiological and developmental stimuli. A multitude of protein mediators of programmed cell death have been identified and apoptosis, necroptosis, and autophagy signals have been found to utilize common pathways elucidating the proteins involved. This narrative review focuses on caspase-dependent and caspase-independent programmed cell death systems. Included studies of caspase-dependent programmed cell death include extrinsic pathway apoptotic mechanisms including phosphatidylserine (PS), FAS (APO-1/CD95), Tumor Necrosis Factor (TNF) Receptor type 1 (TNF-R1) and TNF-Related Apoptosis-Inducing Ligand (TRAIL) and intrinsic or mitochondrial pathway such as Cytochrome C, the Bcl-2 family of proteins and Smac/Diablo. The Bcl-2 family has apoptotic mediators Bcl-2-associated X protein (Bax) and Bcl-2 homologous antagonist/killer (Bak), Bcl-2-Interacting Protein BIM (Bim), Bcl-2 agonist of cell death (Bad), Bid, Bcl-2 adenovirus E1B 19kDa-interacting protein 1 NIP3 (Bnip3), BMF, HRK, Noxa and PUMA and anti-apoptotic proteins such as Bcl-2 itself, Mcl-1, Bcl-w, A1, and Bcl-XL. Moreover, caspase-independent programmed cell death pathways include the mitochondrial pathway with the protein mediators Apoptosis Inducing Factor (AIF), and Endonuclease G, the pathways Necroptosis, and Autophagy. Understanding programmed cell death from those reported in this review could shed substantial light on the processes of biological homeostasis. In addition, identifying specific proteins involved in these processes is mandatory to identify molecular biomarkers as well as therapeutic targets. This knowledge could provide the ability to modulate the programmed cell death response and could lead to new therapeutic interventions in a wide range of diseases.

A La2FeCrO6 (LFCO) double perovskite powders were synthesized by hydrothermal method, which crystallized in an orthorhombic (Pnma) structure and exhibited a spherical morphology with average particle size of 900 nm. Fourier transformation infrared spectroscopy confirmed the octahedral coordination of Fe and Cr ions in the powders. XPS spectra revealed dual oxide states of Fe (Fe2+/Fe3+) and Cr (Cr3+/Cr4+) elements, and oxygen element appeared as lattice oxygen and defect oxygen, respectively. The LFCO powders exhibit ferromagnetic behavior at 5 K with saturation magnetization of 0.31 μB/f.u., coercive field of 8.0 kOe, and Curie temperature of 200 K. Griffiths phase appeared in the powders between 200 K and 223 K. The LFCO ceramics displayed a butterfly-like magnetoresistance (MR)-magnetic field (H) curve at 5 K with MR (5 K, 6 T) value of -4.07%. Temperature dependence of resistivity of the LFCO ceramics demonstrated their semiconducting nature. Electrical transport data were fitted by different conduction models. Strong frequency dispersion dielectric behavior was observed in the LFCO ceramics. A dielectric abnormality observed around 260 K, was ascribed to the jumping of electrons trapped in shallow levels created by oxygen vacancies. The dielectric relaxation behavior exhibited by dielectric loss between 160 K and 260 K, was contributed to the singly-ionized oxygen vacancies.

From the first isolation of the cystovirus bacteriophage Φ6 from Pseudomonas syringae 50 years ago, we have progressed to a better understanding of the structure and transformations of the many parts of the virion. The three-layered virion encapsulating the tripartite double stranded RNA (dsRNA) genome, breaches the cell envelope on infection, generates its own transcripts, and coopts the bacterial machinery to produce its proteins. The generation of new virions starts with a procapsid with a contracted shape, followed by packaging single stranded RNA segments with concurrent expansion of the capsid, and finally replication to reconstitute the dsRNA genome. The outer two layers are then added, and the fully formed virions released by cell lysis. Most of the procapsid structure composed of the proteins P1, P2, P4 and P7 is now known, as well as its transformations to the mature, packaged nucleocapsid. The outer two layers are less well studied. One additional study investigated the binding of the host protein YajQ to the infecting nucleocapsid, where it enhances the transcription of the large RNA segment that codes for the capsid proteins. Finally, we relate the structural aspects of bacteriophage Φ6 top those of other dsRNA viruses, noting the similarities and differences.

Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost-efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of Building Information Modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early-stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with widespread SDO implementation. These preliminary steps of systematic review and industry survey furnish a robust knowledge foundation, enabling the proposal of an intelligent framework for automating early-stage sustainable structural design optimization (ESSDO) within the construction sector. The framework ESSDO addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

The microstructure of the coating during hot-dip galvanizing under industrial conditions of two structural steels, a low-silicon ASTM A36 steel and a high-silicon Q345B steel, both of commercial quality, have been characterized for industrial-relevant times. For both cases it is noted that the formation of the Fe-Zn phases begins in early stages during the heating of the steel, a situation in which all the phases are in the solid state. The characterization was carried out through traditional metallographic techniques including SEM-EDS and DRX equipment. The evolution in time of the microstructure of both steels is examined, being able to observe that the mechanism by which silicon accelerates the formation of Fe-Zn phases in galvanizing is related to the change in the energy of the interfaces caused by a silicon accumulation in the liquid phase in contact with the ζ layer. These results provided us with a deeper understanding of the phase evolution in the hot-dip galvanizing of high silicon steels.

Food and drinks can be contaminated with pollutants such as lead and strontium and this poses a serious danger to human health. For this reason, a number of effective sensors have been developed for the rapid and highly selective detection of such contaminants. TBA, a well-known aptamer developed to selectively target and thereby inhibit the protein of clinical interest -thrombin, is receiving increasing attention for sensing applications, particularly for the sensing of different cations. Indeed, TBA, in the presence of these cations, folds into the stable G-quadruplex structure. Furthermore, different cations produce small but significant changes in this structure that result in changes in the electrical responses that TBA can produce. In this article we produce an overview of the expected data regarding the use of TBA in the detection of lead and strontium, calculating the expected electrical response using different measurement techniques. Finally, we conclude that the TBA should perform better as a detector of strontium rather than lead.

Blatter radicals 1-(3,4-difluorophenyl)- (1a) and 1-(2,4-difluorophenyl)-3-phenyl-1,4-dihydrobenzo[e][1,2,4]triazin-4-yl (1b) were prepared in good yields through oxidation of the corresponding amidrazones using MnO2 in dry CH2Cl2. Cyclic voltammetry showed that both radicals are oxidized and reduced chemically and electrochemically reversibly in accordance with –1/0 and 0/+1 processes. EPR spectroscopy indicated that spin density is mainly delocalized on the triazinyl moiety of the heterocycle. Structure of all paramagnets was unambiguously confirmed by single-crystal X-ray diffraction, and two different 1D chains of alternating radicals were identified. 3,4-Difluorophenyl-derivatives 1a are packed into columns composed of two kinds of alternating centrosymmetric dimers, having comparatively short intermolecular distances. In crystals of 2,4-difluorophenyl-derivative 1b, the parallel arrangement of bicyclic moieties and phenyl rings favors the formation of 1D regular chains wherein the radicals are related by translation parallel to the crystallographic stacking axis. Magnetic susceptibility measurements in the 2–300 K region showed that in crystals of the radicals, strong antiferromagnetic interactions are dominant. Subsequent fitting of the dependence of T on T with 12-membered looped uniform stacks gave the following best-fit parameters: for 1a, g = 2.01 ± 0.05, J/kB = –292 ± 10 K; for 1b, g = 2.04 ± 0.1J/kB = –222 ± 17 K. For comparison, in a nonfluorinated related radical, there are only very weak intermolecular interactions along the columns (J/kB = –2.2 ± 0.2 K). These results illustrate the magnitude of the influence of the fluorine atoms introduced into Blatter radicals on their structure and magnetic properties.

The chelating aluminum complex [2-(Me2NCH2)C2B10H10]AlX2 (X = Br 3, CH3 4) was synthesized using 2-dimethylaminomethyl-o-carboranyl lithium (2) with aluminum tribromide or dimethylaluminum bromide, resulting in a modest yield. Compound 4 was obtained by reacting compound 3 with CH3Li in toluene. All compounds were characterized by infrared (IR) spectroscopy, 1H, 11B, 13C nuclear magnetic resonance (NMR) spectroscopy, and X-ray crystallography. X-ray structural studies of CabNAlBr2 (3) and CabNAlMe2 (4) indicated that the Al atom was located at the center of a distorted tetrahedron. Crystal structures of the CabNAlBr2 (3) [a = 8.9360(3) Å, b = 12.0358(9) Å, c = 14.7730(4) Å, α = β = γ = 90°] and the CabNAlMe2 (4) [a = 8.9551(3) Å, b = 11.9126(9) Å, c = 14.7711(4) Å, α = β = γ = 90°] were obtained. The reactivity of aluminum complexes 3 and 4 with Lewis bases, such as H2O, pyridine, alkylamines, and arylamines, confirmed their rapid decomposition.

In this paper, the research progress on dynamic behaviors of RC structural members is reviewed from experimental, theoretical and numerical perspectives. Firstly, the basic overview, measurement methods, main conclusions and current limitations of available dynamic loading tests are presented. Then the theoretical studies on the dynamic constitutive models of RC materials, the dynamic increase factor (DIF) model for concrete and reinforcing steel and the proposed modified models of dynamic behavior parameter at the structural member level are summarized. Finally, the available modelling approach and the method for incorporating the dynamic effect in numerical simulation of RC structures are reviewed. Moreover, a brief introduction is made to the dynamic hysteretic model established on the experimental data, which provides an alternative approach to consider the dynamic effect beside the commonly used DIF method. This paper provides valuable reference for experimental studies and numerical simulations on the dynamic behaviors of RC structures, and also puts forward some issues that need to be solved in the future research works..

A total internal reflection photonic crystal fiber (PCF) based on hexagonal core is proposed for gas sensing in a specific wavelength range. The higher sensitivity and lower confinement loss were realized by the structure of the proposed PCF consists of two layers with circular holes rotated hexagonally around a core region and six slotted air-hole in the cladding based on numerical analysis. The simulation results show that the enhancement of the relative sensitivity has been done by enhancing the diameter of the hexagonal shape air-hole cladding (d1) and the hexagonal arranged holes around the central solid core (d0) in this design. Also, the confinement loss has been reduced by enhancing the ratio of length to width of slotted holes (l/w) and decreasing the cladding air-hole diameters (d1). As the refractive index increases, the wavelength shifts toward the long wavelength. And in a certain temperature range, the transmission characteristics of the device does not change with the temperature. The results are helpful for designing high performance PCF for gas sensing applications.

Rationale: The 14-3-3 protein family is known to interact with many proteins in non-cardiac cell types to regulate multiple signaling pathways, particularly those relating to energy and protein homeostasis; and the 14-3-3 network is a therapeutic target of critical metabolic and proteostatic signaling in cancer and neurological diseases. Although the heart is critically sensitive to nutrient and energy alterations, and multiple signaling pathways coordinate to maintain the cardiac cell homeostasis, neither the structure of cardiac 14-3-3 protein interactome, nor potential functional roles of 14-3-3 protein-protein interactions (PPIs) in heart has been explored. Objective: To establish the comprehensive landscape and characterize the functional role of cardiac 14-3-3 PPIs. Methods and Results: We evaluated both RNA expression and protein abundance of 14-3-3 isoforms in mouse heart, followed by co-immunoprecipitation of 14-3-3 proteins and mass spectrometry in left ventricle. We identified 52 proteins comprising the cardiac 14-3-3 interactome. Multiple bioinformatic analyses indicated that more than half of the proteins bound to 14-3-3 are related to mitochondria; and the deduced functions of the mitochondrial 14-3-3 network are to regulate cardiac ATP production via interactions with mitochondrial inner membrane proteins, especially those in mitochondrial complex I. Binding to ribosomal proteins, 14-3-3 proteins likely coordinate protein synthesis and protein quality control. Localizations of 14-3-3 proteins to mitochondria and ribosome were validated via immunofluorescence assays. The deduced function of cardiac 14-3-3 PPIs is to regulate cardiac metabolic homeostasis and proteostasis. Conclusions: Thus, the cardiac 14-3-3 interactome may be a potential therapeutic target in cardiovascular metabolic and proteostatic disease states, as it already is in cancer therapy.

The present study utilizes a metal inert gas welding (MIG) to make a dissimilar weld joint of different stainless steel grades AISI 304, 314, 316L, 420 and a standard structural steel S355MC to estimate the correlation of a microstructure and the mechanical properties. The microstructure of the base metals (BM), the heat affected zone (HAZ), the fusion zone (FZ) and the weld seam were analyzed using optical microscopy. Optical microscopy did not reveal any presence of weld defects such as porosity or cracks. The analysis of microstructure showed that both the austenitic and martensitic stainless steel weld structures contain some retained delta ferrite and coarse Me23C6 carbides in the HAZ, while the FZ exhibits delta ferrite and some retained austenite. The hardness profiles revealed difference between austenitic and martensitic steel welds that the later showed extremely high values in the HAZ (~500 HV/0.1) which causes fracture in this zone. The welds of all austenitic steel grades withstood tensile test, showing the average tensile strength of 472 MPa with fracture observed in the base metal zone. It made clear that the use of a filler rod 308LSI is suitable only for the austenitic stainless and structural steel dissimilar welds, and not appropriate for martensitic-structural steel welds. The achieved results revealed that the higher hardness of the martensitic phase in the HAZ of AISI 420 is closely related with the formation of untempered coarse martensitic structure and higher carbon content.

The Coronavirus Disease 2019 (COVID-19) is a new viral infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). Genomic analyses have revealed that SARS-CoV-2 is related to Pangolin and Bat coronaviruses. In this report, a structural comparison between the Sars-CoV-2 Envelope and Membrane proteins from different human isolates with homologous proteins from closely related viruses is described. The analyses here reported show the high structural similarity of Envelope and Membrane proteins to the counterparts from Pangolin and Bat coronavirus isolates. However, the comparisons have also highlighted structural differences specific of Sars-CoV-2 proteins which may be correlated to the cross-species transmission and/or to the properties of the virus. Structural modelling has been applied to map the variant sites onto the predicted three-dimensional structure of the Envelope and Membrane proteins.

Questions: Elevation, biodiversity, and forest structure are commonly correlated, but their relationships near the positive extremes of biodiversity and elevation are unclear. We asked 1) How does forest structure vary with elevation in a high biodiversity, high topographic complexity region? 2) Does forest structure predict vascular plant biodiversity? 3) Is plant biodiversity more strongly related to elevation or to forest structure? Location: Great Smoky Mountains National Park, USAMethods: We used terrestrial LiDAR scanning (TLS) to characterize vegetation structure in 12 forest plots. We combined two new canopy structural complexity metrics with traditional TLS-derived forest structural metrics and vascular plant biodiversity data to investigate correlations among forest structure metrics, biodiversity, and elevation. Results: Forest structure varied widely across plots spanning the elevational range of GRSM. Our new measures of canopy density (Depth) and structural complexity (σDepth) were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Vascular plant biodiversity was negatively correlated with elevation, and more strongly positively correlated with vegetation structure variables. Conclusions: The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms.

The study of vegetation community and structural change has been central to ecology for over a century, yet how disturbances reshape the physical structure of forest canopies remains relatively unknown. Moderate severity disturbance including fire, ice storms, insect and pathogen outbreaks, affects different canopy strata and plant species, which may give rise to variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits with known linkages to ecosystem functioning. We used lidar-derived canopy structural measured within a machine learning framework to detect and differentiate among various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that disturbance agents such as fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, pathogen and insect infestation, and senescence altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, indicating that this commonly quantified canopy feature is insufficient to characterize many moderate severity disturbances. Rather, measures of canopy structure, including those that describe multidimensional change, are needed to characterize disturbance at moderate severities because structural changes from these events are spatially and quantitatively variable. Our findings suggest that standard disturbance detection methods, such as optical based remote sensing platforms, may currently be limited in their ability to detect, differentiate, and characterize disturbance. Further, we conclude that a more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structure change will improve the modeling, detection, and prediction of functional implications of moderate severity disturbance.

The Zika virus (ZIKV) is a mosquito-borne Flavivirus and can be transmitted through an infected mosquito bite or through human-to-human interaction by sexual activity, blood transfusion, breastfeeding or perinatal exposure. After the 2015-2016 outbreak in Brazil, a strong link between ZIKV infection and microcephaly emerged. ZIKV specifically targets human neural progenitor cells, suggesting that proteins encoded by ZIKV bind and inactivate host cell proteins leading to microcephaly. Here, we present a systematic annotation of interactions between human proteins and the seven non-structural ZIKV proteins corresponding to a Brazilian isolate. The interaction network was generated by combining tandem-affinity purification followed by mass spectrometry with yeast two-hybrid screens. We identified 150 human proteins, involved in distinct biological processes, as interactors to ZIKV non-structural proteins. Our interacting network is composed of proteins that have been previously associated with microcephaly in human genetic disorders and/or animal models. This study builds on previously published interacting networks of ZIKV and genes related to autosomal recessive primary microcephaly to generate a catalog of human cellular targets of ZIKV proteins implicated in processes related to microcephaly in humans. Collectively, this data can be used as a resource for future characterization of ZIKV infection biology and help create a basis for the discovery of drugs which may disrupt the interaction and reduce the health damage to the fetus.

The evaluation of geometric defects is necessary in order to maintain the integrity of structures over time. These assessments are designed to detect damages of structures and ideally help inspectors to estimate the remaining life of structures. Current methodologies for monitoring structural systems, while providing useful information about the current state of a structure, are limited in the monitoring of defects over time and in linking them to predictive simulation. This paper presents a new approach to the predictive modeling of geometric defects. A combination of segmented from point clouds are parametrized using the convex hull algorithm to extract features from detected defects, and a stochastic dynamic model is then adapted to these features to model the evolution of the hull over time. Describing a defect in terms of its parameterized hull enables consistent temporal tracking for predictive purposes, while implicitly reducing data dimensionality and complexity as well. In this study, 2D point clouds analogous to information derived from point clouds were first generated over simulated life-cycles. The evolutions of point cloud hull parameterizations were modeled as stochastic dynamical processes via autoregressive integrated moving average (ARIMA) and vectorized autoregression (VAR) and compared against ground truth. The results indicate that this convex hull approach provides consistent and accurate representations of defect evolution across a range of defect topologies and is reasonably robust to noisy measurements, however assumptions regarding the underlying dynamical process play a significant the role in predictive accuracy. The results were then validated on experimental data from fatigue testing with high accuracy. Longer term, the results of this work will support finite element model updating for predictive analysis of structural capacity.