Titanium-based metal-matrix composites manufactured by additive manufacturing offer tremendous lightweighting opportunities. However, processing high reinforcement content remains challenging. This study reports an improved manufacturing process for Ti-TiC enabling high reinforcement content and significant fracture strain concurrently: mechanical blending, followed by laser powder bed fusion and a single heat treatment. As-built microstructure shows both un-melted TiC particles and sub-stoichiometric TiC dendrites resulting from a partial dissolution of TiC particles. The heat treatment is shown to fully convert TiC dendrites into equiaxed TiC grains. Reduction of the C/Ti ratio in TiC during the process results in an increase in the reinforcement content, from a nominal 12 vol% to an effective 21.5 vol%. Ti-TiC tensile samples reached fracture strains of up to 1.7%, Young’s moduli of up to 149 GPa and ultimate tensile strengths of up to 827 MPa. Lower TiC initial powder size distributions displayed the best mechanical performance.

This study outlines a 3-step production process for Ti-TiC Metal Matrix Composite (MMC) employing powder Mechanical Blending, Laser Powder Bed Fusion (LPBF), and a heat treatment. A TiC fraction of more than 20 vol% was formed in-situ through the reaction of titanium with carbon during the LPBF process. The manufactured MMC pieces showed a record stiffness and fracture strain combination. The LPBF energy density impacted strongly porosity type. The as-built microstructure displayed an homogeneous distribution of sub-stoichiometric TiC dendrites which were fully converted into equiaxed TiC grains during the heat treatment. The TiC C/Ti ratio was found to be close to 0.5, resulting in an effective reinforcement content nearly twice the one expected for stoichiometric TiC, leading to stronger reinforcement. The mechanical analysis revealed a Young’s modulus of up to 149 GPa and an ultimate tensile strength of up to 770 MPa, indicating a 27% and 34% improvement, respectively, compared to commercially pure Titanium produced by LPBF and heat treated in the same conditions. A fracture strain of up to 2.8% was achieved. The combination of in-situ formation of defect-free TiC reinforcement and subsequent heat treatment enables to reach an exceptional ductility, considering the 20 vol% reinforcement content.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

The traditional understanding of brain function has predominantly focused on chemical and electrical processes. However, new research in fruit fly (Drosophila) binocular vision reveals ultrafast photomechanical photoreceptor movements significantly enhance information processing, thereby impacting a fly's perception of its environment and behaviour. The coding advantages resulting from these mechanical processes suggest that similar physical motion-based coding strategies may affect neural communication ubiquitously. The theory of neural morphodynamics proposes that rapid biomechanical movements and microstructural changes at the level of neurons and synapses enhance the speed and efficiency of sensory information processing, intrinsic thoughts, and actions by regulating neural information in a phasic manner. We propose that morphodynamic information processing evolved to drive predictive coding, synchronising cognitive processes across neural networks to match the behavioural demands at hand effectively.

For this research we are selected three indicators of wheat: yield (t/ha), producer price (USD/t) and share of the harvested area under wheat in relation to the total land area of country (%). The mentioned indicators were analyzed for the period 2006-2021 in thirty-two European countries. The purpose of the research is to determine which of the mentioned indicators achieve average annual growth/decrease rates, what is the correlation between them, how European countries are grouped with their similarities and differences. The research examines the impact of the COVID-19 pandemic on the mentioned indicators in a two time phases: Phase I, before the pandemic (2006-2019); Phase II, during the pandemic (2020-2021). The methodological approach to the research topic involved the application of descriptive statistics, as the method of principal components analysis (abbr. PCA) with graphics. A positive correlation exists only between the yield of wheat and the share of the harvested area under wheat in the total area of the land (0.291). PCA method showed that countries are grouped differently depending on the observed indicator and the analyzed time phase.

Protected and conserved areas are a key area-based strategy to address the biodiversity and climate change crises. Fundamental to understand their performance over time are indicators. The Digital Observatory for Protected Areas (DOPA) was born in 2013 as a set of open access web services and applications to be used to assess, monitor, and report on protected and conserved areas. For over a decade it has delivered over 50 indicators to support policy processes, national and regional governments, and practitioners. DOPA has also developed a versatile and efficient back-end approach that is widely applicable in other area-based conservation contexts. Here, we describe the methods and workflows behind DOPA´s back end and provide examples of policy relevant questions it can answer. We discuss key advantages and limitations of this systematic and replicable approach and explore the use of this back-end architecture to inform progress in area-based conservation targets for the following decades. This approach, embedded in multiple services provided by the Knowledge Centre for Biodiversity of the European Commission (KCBD), can also support the implementation and monitoring of area-based targets of the Kunming Montreal Global Biodiversity Framework at international, regional, and national levels.

The software industry has adopted component-based software development (CBSD) to rapidly build and deploy large and complex software systems with significant savings at minimal engineering effort, cost, and time. However, CBSD encounters issues on security trust, mainly with respect to dependability attributes. A system is considered dependable when it can produce the outputs for which it was designed with no adverse effect on its intended environment. Dependability consists of several attributes that imply availability, confidentiality, integrity, reliability, safety, and maintainability. Dependability attributes must be embedded in a CBSD model to develop dependable component software. Motivated by the importance of these attributes, this paper pursues two objectives: to design a model for developing a dependable system that mitigates the vulnerabilities of software components, and to evaluate the proposed model. The model proposed in this study is labelled as developing dependable component-based software (2DCBS). To develop this model, the CBSD architectural phases and processes must be framed and the six dependability attributes embedded according to the best practice method. The expert opinion approach was applied to evaluate 2DCBS framing. In addition, the 2DCBS model was applied to the development of an information communication technology (ICT) portal through an empirical study method. Vulnerability assessment tools (VATs) were employed to verify the dependability attributes of the developed ICT portal. Results show that the 2DCBS model can be adopted to develop web application systems and to mitigate the vulnerabilities of the developed systems. This study contributes to CBSD and facilitates the specification and evaluation of dependability attributes throughout model development. Furthermore, the reliability of the dependable model can increase confidence in the use of CBSD for industries.

Factors responsible of species distribution were intensively investigated in the past, including response at different spatial scales. The aim of the present research is to review the most relevant factors explaining chironomid species distribution focusing to factors operating at different spatial scales, such as latitude, longitude, altitude, water temperature, dissolved oxygen, nutrients acting at regional, country, water basin extension. Data including chironomid species abundances from different lentic and lotic waters from Italy and other surrounding countries were analyzed with different multivariate methods: partial canonical correspondence analysis and multiple discriminant analysis. Spatial analysis included univariate Moran’I correlograms, multivariate Mantel correlograms and Moran’s Eigenvectors Maps (MEM) calculation. The results confirmed previous conclusions, that is habitat type including different running waters reaches (kryal, krenal, rhthral, potamal) and different lake types is the most significant factor in separating chironomid assemblages and spatial factors act only as indirect influencers..

The spin-torsion theory is a gauge theory approach to gravity that expands upon Einstein's general relativity (GR) by incorporating the spin of microparticles. In this study, we further develop the spin-torsion theory to examine spherically symmetric and static gravitational systems that involve free-falling macroscopic particles. We posit that the quantum spin of macroscopic matter becomes noteworthy on cosmic scales. We further assume that the Dirac spinor and Dirac equation adequately capture all essential physical characteristics of the particles and their associated processes. A crucial aspect of our approach involves substituting the constant mass in the Dirac equation with a scale function, allowing us to establish a connection between quantum effects and the scale of gravitational systems. This mechanism ensures that the quantum effect of macroscopic matter is scale-dependent and diminishes locally, a phenomenon not observed in microparticles. For any given matter density distribution, our theory predicts an additional quantum term, the quantum potential energy (QPE), within the mass expression. QPE induces time dilation, distance contraction, and thus mimics a gravitational well. When applied to cosmology, our theory yields a static cosmological model. The QPE serves as a counterpart to the cosmological constant introduced by Einstein to balance gravity in his static cosmological model. The QPE also offers a plausible explanation for the origin of the Hubble redshift (traditionally attributed to the universe's expansion). The predicted luminosity distance-redshift relation aligns remarkably well with SNe Ia data from the cosmological sample of SNe Ia. In the context of galaxies, QPE functions as the equivalent of dark matter. The predicted circular velocities align well with the rotation curve data from the SPARC (Spitzer Photometry and Accurate Rotation Curves database) sample. Importantly, our conclusions in this paper are reached through a conventional approach, with the sole assumption of the quantum effects of macroscopic matter on large scales, without the need for additional modifications or assumptions.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. However, capturing the shrub biomass accurately by satellite observations is challenging due to the short and sparse features of shrubs. In this study, we presented a framework to estimate shrub biomass at a 10m spatial resolution by integrating ground in-situ data, unmanned aerial vehicle (UAV), Sentinel-2, and Sentinel-1 observations. The pilot study was conducted in the Helan mountains of Ningxia province, China. First, the spatial distribution map of shrubland and other land cover types was generated in 2023. Then, a prediction model of shrub biomass was developed in a Random Forest Regression (RFR) approach driven by different predicted variable datasets based on in-situ measurements, UAV, and satellite images. Finally, the developed model was used to produce the biomass map of shrubland in 2023. The uncertainty was characterized by creating a standard deviation (SD) map using the Leave-One-Out Cross-Validation (LOOCV) method in the shrub biomass estimates. The resultant shrubland distribution map in 2023 has an F1 score of 0.92. The shrub biomass model driven by spectral bands and vegetation indices (R2= 0.62) demonstrated superior performance than that only driven by spectral bands (R2=0.33) or vegetation indices (R2=0.55). The uncertainty of the estimated biomass was lower than 4%, with the lowest values (<2%) happening in the high shrub coverage (>30%) and biomass production (>30kg/m2) regions. Furthermore, the findings show that the magnitude of shrub biomass was affected by the aridity and precipitation significantly in the study region. This study provides a workflow to accurately monitor the biomass of shrublands in a complicated environment based on remote sensing images from multi-platform observations.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to address the network dismantling problem effectively.

Despite soccer popularity, comparatively little scientific information is available concerning the assessment of youth players’ physiological characteristics. The use of medical software integrating wearable sensors to provide accurate and objective data is necessary. In the present study, the objective assessment of physical performance of 128 adolescent soccer players at two timepoints (pre-season and end-season) has been performed. Participants completed a comprehensive test battery including stability, countermovement jumps (CMJ), plyometric jumps (PJ), agility, and quick feet tests. A single Inertial Measurement Unit (IMU) sensor provided quantitative data on various performance metrics. Principal Component Analysis (PCA) reduced the dataset to four principal components (PCs) explaining over 80% of the variance: power (PC1), balance (PC2), speed/agility (PC3), and stiffness (PC4). Paired samples t-test between the two timepoints showed no significant differences in the PCs between the two timepoints, indicating that the training program did not significantly alter the assessed physical attributes. However, subtle individual changes may still inform personalized training adjustments. The study highlights the importance of continuous monitoring and individualized training strategies to optimize athlete performance and reduce injury risk.

Mixture of abramis brama (freshwater bream), carassius carassius (crucian carp), and sander lucioperca (pike perch) scales was used for the preparation of fish scale powder containing about 26.5 wt. % of removed when heating components preferably of organic nature, and 63.5 wt. % of mineral components. Fish scale powder enriched with inorganic components was prepared from washed, dried, and ground fish scale mixture using vibration sieving. Inorganic powders consisting of hydroxyapatite and magnesium whitlockite were obtained via heat treatment of this fish scale powder at 800-1000 oC. Particles of these inorganic powders consisted of sintered grains with dimensions less than 100 nm after heat treatment at 800 oC, less than 200 nm after heat treatment at 900 oC, and 100-500 nm after heat treatment at 1000 oC. Fish scale powder enriched with inorganic components as well as heat-treated inorganic powders consisting of hydroxyapatite and magnesium whitlockite can be recommended for the production of different materials such as ceramics or composites.

Friction is an important subject for sustainability due to problems associated with energy loss. Recent years, surface micro- and nanostructures have attracted much attention to reduce friction; however, suitable structures are still under consideration. Many functional surfaces are present in nature, such as the friction reduction surfaces of snake skins. In this study, we focused on firebrats, Thermobia domestica, living in narrow spaces such as under bark, so their surface frequently contacts with surrounding surfaces. We speculate that their body surface would be adapted to reduce friction. To investigate the firebrat surface functions, firebrat surfaces were observed by using a field-emission scanning electron microscope (FE-SEM) and a colloidal probe atomic force microscope (AFM), respectively. Results of surface observations by the FE-SEM revealed that firebrats are entirely covered with scales, whose surfaces have micro groove structures. Scale groove periods around the firebrat's head are almost uniform within a scale but vary between scales. AFM friction force measurements revealed that firebrat scale reduces friction by decreasing contact area between scales and a colloidal probe. The heterogeneity of groove periods of the scales suggest that it is difficult to fix the whole body in particular rough surfaces and that lead to be "fail-safe".

Given the cross-scale interactions of agricultural ecosystems, it is important to collect ecosystem service data at the multiple spatial scales they operate at. Mapping of ecosystem services helps to assess their spatial and temporal distribution and is a popular communication tool of their availability and value. For example, maps can be used to quantify distance between areas of available ecosystem services and their beneficiaries and how services fluctuate with changes in land use patterns over time, allowing identification of synergies and trade-offs. However, a lack of local context and too large a resolution can reduce the utility of these maps, whilst masking heterogeneities in access due to equity dynamics. This review identifies and summarizes eight main methods of ESS mapping found in the literature—remote sensing, biophysical modelling, agent based modelling, economic valuation, expert opinion, user preference, participatory mapping, and photo-elicitation. We consider what spatial scales these methods are utilized at and the transferability of data created by each method. The analysis concludes with a methodological framework for mapping ecosystem services, intended to help researchers identify appropriate methods for a multi-scale research design. The framework is exemplified with an overview of a research project in Ethiopia.

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic and theranostics assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical as-sessment inventories, but is considered to be a medical discipline and, as such, uses medical in-tervention methods (e.g., pharmacological, ECT; rTMS; tDCS), and therefore is supposed to oper-ate with the language and methods of nomothetic networks. The idiographic assessments are pro-visionally “quantified” into “structured clinical scales” to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other, biological tests (molecular, neuroimaging) are per-formed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI session in pa-tients suffering from the two main spectra of mental disorders: schizophrenia and major depres-sion. The current status of this research program and future implications to promote the devel-opment of psychiatry as a medical discipline are discussed.

Background: Screening for postpartum depression can lower prevalence and ensure that mothers get adequate treatment and support. Yet, few practitioners screen.The present article is a brief review on barriers to screening and two screening scales that are validated for perinatal women. Findings: Despite recommendations from health organizations, most new mothers are not screened for depression. Providers cite lack of time, opening “Pandora’s box,” and lack of resources for mothers who screen positive as reasons for why they do not screen. The Edinburgh Postnatal Depression Scale and the Patient Health Questionnaires are brief screening scales that have been validated for new mothers and are widely available.Conclusions: Screening is the necessary first step to identifying new mothers who are depressed. Practitioners who screen need a clear plan, and to know how to access available community resources, so they will know what to do when a mother screens positive.

Background: Vestibular Neuritis (VN) induces Acute Vestibular Syndrome (AVS). This study aimed to identify predictive factors of VN recovery by healing and/or compensation. Methods: In this longitudinal and prospective study, 40 patients were included. The first assessment (T0) included medical history taking (general and specific), for cardiovascular risk factors (CVRFs), a battery of diagnostic tests, bithermal caloric test (BCT), video Head Impulse Test (VHIT) and skull vibration induced nystagmus (SVIN). All patients also completed a Dizziness Handicap Inventory (DHI). All assessments were repeated 90±15 days (T3) later. Subjective criteria compensation were based on DHI total score and objective criteria compensation on the instrumental test results. Four groups of patients (A, B, C, D) were delineated by combining patients with normal vs abnormal vestibular tests and patients with normal vs abnormal DH. Results: CVRFs were associated with a poorer recovery of symptoms but not the age or BMI. Vestibular tests mainly BCT (canal paresis %), VHIT (lateral semicircular canal gain) and SVINT (nystagmus slow phase velocity) recovered within normal values in 20%, 20%, 26% of cases respectively at T3 for the 40-patient group. Conclusions: Vascular risk factors (hypercholesterolemia) are correlated with patients who do not recover their symptoms either by healing or compensation. The evolution did not show any significant difference between high or low frequency vestibular tests in patients recovering from their symptoms. However, some patients with objective recovery may continue to show persisting subjective symptoms.

Here we deduce the scales of a spherically symmetric, closed system subject to internal repulsive vacuum pressure. Due to periodic boundary conditions of virtual particle loops, it is shown that Ramanujan summation must be physical for the virtual particle case, as to avoid numerical results of either null, very large or infinity. It is thereafter shown that this closed system must be holographic in nature, with mass-energy inversely proportional to the Planck area, up to a yet to be determined geometrical proportionality constant. The validity of this result is strengthened when it is shown that one may obtain the same answer using different methods, either thermodynamic laws or a force balance equation. Implications of this small study is not yet fully understood and is hopefully investigated in subsequent letters.

This study provides designs for a low-cost, easily replicable open source lab-grade digital scale that can be used as a precision balance. The design is such that it can be manufactured for use in most labs throughout the world with open source RepRap-class material extrusion-based 3-D printers for the mechanical components and readily available open source electronics including the Arduino Nano. Several versions of the design were fabricated and tested for precision and accuracy for a range of load cells. The results showed the open source scale was found to be repeatable within 0.1g with multiple load cells, with even better precision (0.01g) depending on load cell range and style. The scale tracks linearly with proprietary lab-grade scales, meeting the performance specified in the load cell data sheets, indicating that it is accurate across the range of the load cell installed. The smallest loadcell tested(100g) offers precision on the order of a commercial digital mass balance. The scale can be produced at significant cost savings compared to scales of comparable range and precision when serial capability is present. The cost savings increase significantly as the range of the scale increases and are particularly well-suited for resource-constrained medical and scientific facilities.

Preserving and restoring biodiversity is becoming a great challenge as we face a world where planetary boundaries will likely be crossed over the following decades. Such challenge needs to consider multiple scales of complexity, both in space and time. A common thread in most cases is the presence of nonlinear phenomena generating shifts among alternative states. These breaking points imply a new perception of risk and different management strategies. A broad range of phenomena affect the preservation of healthy communities and constrain the ways to deal with conservation, from local features associated with habitat loss or facilitation to mesoscale or global network-level ecological complexity and the role played by extreme events. How are these scales connected? How can the emergent properties associated with ecosystem dynamics be exploited? Here a synthesis of ideas is presented, with a complex systems view of the different scales involved, the emergent phenomena separating them, and the universal properties that allow defining simple models on each scale.

When we attempt to define life, we tend to refer to individuals, those that are alive. But these individuals might be cells, organisms, colonies... ecosystems? We can describe living systems at different scales. Which ones might be the best ones to describe different selves? I explore this question using concepts from information theory, ALife, and Buddhist philosophy. After brief introductions, I review the implications of changing the scale of observation, and how this affects our understanding of selves at different structural, temporal, and informational scales. The conclusion is that there is no single ``best'' scale for a self, as this will depend on the scale at which decisions must be made. Different decisions, different scales.

In this paper we introduce the Adomian decomposition method for a class of first order fuzzy dynamic equations on arbitrary time scales for existence of solutions. The results are provided with suitable numerical examples.

If visible matter alone is present in the Universe, General Relativity (GR) and its Newtonian weak field limit cannot explain several pieces of evidence, from the largest to the smallest scales. The most investigated solution is the cosmological model ΛCDM, where GR is valid and two dark components are introduced, dark energy and dark matter, to explain the ∼70% and ∼25% of the mass-energy budget of the Universe, respectively. An alternative approach is provided by modified gravity theories, where a departure of the gravity law from ΛCDM is assumed and no dark components are included. This work presents refracted gravity (RG), a modified theory of gravity formulated in a classical way where the presence of DM is mimicked by a gravitational permittivity ϵ(ρ) monotonically increasing with the local mass density ρ, which causes the field lines to be refracted in small density environments. Specifically, the flatter the system the stronger the refraction effect and, thus, the larger the mass discrepancy if interpreted in Newtonian gravity. RG presented several encouraging results in modelling the dynamics of disk and elliptical galaxies and the temperature profiles of the hot X-ray emitting gas in galaxy clusters and a covariant extension of the theory seems to be promising.

One of the biggest challenges in modern physics is how to unify gravity with quantum theory. There is an absence of a complete quantum theory of gravity, and conventionally it is thought that the effects of quantum gravity occur only at high energies (Planck scale). Here we suggest that certain novel quantum effects of gravity can become significant even at lower energies and could be tested at laboratory scales. We also suggest a few indirect effects of dark energy that can show up at laboratory scales. Using these ideas, we set observational constraints on radio recombination lines of the Rydberg atoms. We further suggest that high-precision measurements of Casimir effects for smaller plate separation could also show some manifestations of the presence of dark energy.

Biological invasions have reached an unprecedented level and the number of introduced species is still increasing worldwide. Despite major advances in invasion science, the determinants of success of introduced species, the magnitude and dimensions of their impact, and the mechanisms sustaining successful invasions are still debated. Empirical studies show divergent impacts of non-native populations on ecosystems and contrasting effects of biotic and abiotic factors on the dynamics of non-native populations; this is hindering the emergence of a unified theory of biological invasions. We propose a synthesis that merges perspectives from population, community, and ecosystem levels. Along a timeline of ecosystem transformation driven by non-native species, from historical to human-modified ecosystems, we order invasion concepts and theories to clarify their chaining and relevance during each step of the invasion process. This temporal sorting of invasion concepts shows that each concept is relevant at a specific stage of the invasion. Concepts and empirical findings on non-native species may appear contradictory. However, we suggest that, when mapped onto an invasion timeline, they may be combined in a complementary way. An overall scheme is proposed to summarise the theoretical dynamics of ecosystems subjected to invasions. For any given case study, this framework provides a guide through the maze of theories and should help choose the appropriate concepts according to the stage of invasion.

Principal Component Analysis (PCA) is a method that identifies common directions within multivariate data and presents the data in as few dimensions as possible. One of the advantages of PCA is its objectivity, as the same results can be obtained regardless of who performs the analysis. However, PCA is not a robust method and is sensitive to noise. Consequently, the directions identified by PCA may deviate slightly. If we can teach PCA to account for this deviation and correct it, the results should become more comprehensible. The methods for doing this and an issue with this are presented.

While some research supports utilizing plain radiography for measuring biomechanical alignment of the spine for prognosis and treatment, there are contrasting viewpoints regarding both the value and utilization of these procedures in conservative care. Evaluation of both conservative and non-conservative approaches to spinal care revealed vast differences in radiographic utilization and interpretation between orthopedic surgeons, primary care physicians, chiropractic physicians, and physical therapists, which may account for the different viewpoints and rationale in the literature. A research summary is provided regarding the various methods of spinal analysis utilized by these professions to help determine if there is any unique diagnostic or prognostic value associated with radiographic utilization that could assist in advancing conservative spinal assessment and treatment. Understanding any unique value provided through biomechanical assessment utilizing plain radiography of the spine established in the literature may help chiropractic physicians determine the appropriate use of radiographic procedures in clinical practice and how to coordinate efforts with other conservative and non-conservative professions to improve spinal healthcare.

The ’social amoeba’ Dictyostelium discoideum, where aggregation of genet- ically heterogeneous cells produces functional collective structures, epitomizes social conflicts associated with multicellular organization. ’Cheater’ populations that have a higher chance – quantified by a positive spore bias – of surviving to the next generation are selectively advantaged. Their spread is thus expected to undermine collective functions over evolutionary times. In this review, we discuss the two main approaches adopted to conceptualize social conflicts in Dictyostelium discoideum: describing spore bias as a property of cell popula- tions (strains), or as a result of individual cell choices during the developmental process. These two points of view are often held equivalent and used inter- changeably. While the population-level view allows for more direct evolutionary inference, however, the cell-level interpretation reveals that such evolutionary predictions may be modified if developmental mechanisms, such as dependence on the environment and intrinsic unpredictability of cell fate choices, are taken into account. We conclude by proposing a set of open questions that in our opinion lie at the core of a multi-scale description of aggregative life cycles, where the formulation of predictive evolutionary models would include cell-level mechanisms responsible for spore bias alongside population-level descriptors of multicellular organization.

A naked human eye can perceive objects down to a millimeter length. While lenses and microscopes have overcome this limit, the human mind still lacks perspective when navigating conventional scales (1), especially in the range that are less palpable to naked human eye (2,3). This problem is particularly acute in the context of science communication, where the conventional scale bar units facilitate little comprehension regarding the perception for factorial size differences (3). Here we aim to bridge the gap of scale factors and perspectives using a universal toolkit of objects, which can help comprehend the relative change in length dimensions up to 13 orders of magnitude difference. We further have demonstrated the use of such a universal object toolkit as a length perceptive scale by illustrating and narrating biological phenomena. The meter to picometer ‘length perceptive scale’ proposed here has the potential to cover majority of length scales present in the biological realm, and is analogous to the time compression methods widely used in explaining cosmos timeline (4). Our toolkit can also be calibrated according to the users need in their scientific communication and illustrations, which will aid the readers’ benefit in understanding the length scale perception of illustrated phenomenon.

Abstract: Recent years demonstrate the increased attention to radon from two scientific directions. After neglecting radon as earthquake precursors in 1990-th it becomes again the subject of earthquake-forecast papers discussions due to growing networks of the radon monitoring in different countries, especially, the technologies of real-time radon measurements where the gamma-spectrometers become the leader of interest as the sources of 222Rn identification. The second fast developing direction is including the radon in the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) models as a source of the boundary layer ionization. And here we encounter with second direction which is not connected with the earthquake forecast problems. It is the role of air ionization by radon as a source of the Global Electric Circuit (GEC) modification. In this publication we try to unite all these problems to present more complex view on radon as important element of our environment.

A monocular vision pose estimation and identification algorithm used on a small spacecraft for future orbital servicing is studied in this paper. A tracker spacecraft equipped with a short-range vision system is proposed to recover the 3D structural model of a space target in orbit and automatically identify its solar panels and main body using only visual information from an onboard camera. The proposed reconstruction and identification framework is tested using structure-from-motion and point cloud identification methods. The Efficient Perspective-n-Points (EPnP) descriptor is used for pose estimation. Triangulated points are used for component segmentation by means of orientation histogram descriptors. Experimental results based on laboratory images of a spacecraft model show the effectiveness and robustness of our approach.

Abrus precatorius is a medicinal herb originated from Southeast Asia and native to India. The Hindustani centre of origin was found to possess lot of variability especially with respect to seed color. However, no previous researchers have focused on studying the variability by including the entire variability of the region. A total of 99 accessions were analyzed for its biochemical components like moisture content, total phenols, antioxidants, ash content, protein content, total monomeric anthocyanin and flavonols. Principal component analysis (PCA) shown that first three components viz., seed color, phenols and antioxidants are major principal components that contribute to 80.86% of total variability in the germplasm. The dendrogram constructed based on the biochemical data and by using the software Darwin version 6.0. formed three main clusters i.e. cluster 1, cluster 2 and cluster 3 which contain 55 accessions, 40 accessions and 1 accession respectively. Seed color was found to be the main criteria based on which the Abrus accessions were grouped. Accessions from different clusters also differ for their mean phytochemical constituents. Germplasm curators / plant breeders can select such elite genotypes and utilize them in crop improvement programmes.

In large-scale parallel computing and communication systems, an interconnect network structure is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. In a graph, the vertices and edges are likely to fail, so we must think about the fault tolerance of a graph. Connectivity is an important parameter in the study of faulty tolerance for a graph. In this paper, we study a special class of connectivity: $m$-component connectivity, this is a natural generalization of the classical connectivity of graphs defined in terms of the minimum vertex-cut. Let $F$ is a vertex set of $G$ $(i.e, F\subseteq V(G))$, if the following conditions are satisfied, we say $F$ is a $m$-component cut: $(1)$ $G-F$ is disconnected; $(2)$ the number of components in $G-F$ is greater than or equal to $m$. In another word, the $m$-component connectivity $c\kappa_{m}(G)$ is defined as $min\{|F|~|~F\subseteq V(G)$ and $F$ is a $m$-component cut$\}$. Determining $m$-component connectivity is still unsolved in most interconnection networks even for small $m'$s. Leaf-sort graph is a special Cayley graph, it has many special properties that are different from other Cayley graphs. So we need to pay attention to some of it's special properties when we study it. In this paper, we can get the values: $c\kappa_{3}(CF_{n})=3n-6$ $(n$ is odd$)$ and $c\kappa_{3}(CF_{n})=3n-7$ $(n$ is even$)$ for $n\geq3$; $c\kappa_{4}(CF_{n})=\frac{9n-21}{2}$ $(n$ is odd$)$ and $c\kappa_{4}(CF_{n})=\frac{9n-24}{2}$ $(n$ is even$)$ for $n\geq4$; $c\kappa_{5}(CF_{n})=6n-16$ $(n$ is odd$)$ and $c\kappa_{5}(CF_{n})=6n-18$ $(n$ is even$)$ for $n\geq5$.

The nuclear factor-ĸB (NF-ĸB) transcriptional system is a major effector pathway involved in inflammatory responses. Previous studies found that a Gardenia decoction (GD) inhibited the expression of NF-κB in a lipopolysaccharide (LPS)-stimulated mouse intestinal injury model. Herein, we hypothesized that geniposide (GE), a component of Gardenia jasminoides Ellis, also exerts anti-inflammatory effects and inhibits NF-ĸB activity in LPS-induced intestinal epithelial cells (IEC-6). IEC-6 cells were stimulated with LPS, following which the effects of GE on NF-ĸB signaling in the IEC-6 cells were examined by western blotting to detect IĸB phosphorylation/degradation. The expression of NF-κB was determined by immunofluorescence assay (IFA). Enzyme-linked immunosorbent assay (ELISA) was used to detect the inhibitory effect of GE on the release of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β) activated by LPS in IEC-6 cells. In addition, the migration ability of IEC-6 cells was observed by the scratch method. These results showed that GE dose-dependently downregulated levels of the proinflammatory cytokines TNF-α, IL-6 and IL-1β that had been upregulated by LPS and suppressed the phosphorylation of IĸB and NF-ĸB induced by LPS. Our findings indicated that GE could reduce LPS-induced NF-ĸB signaling and proinflammatory expression in IEC-6 cells and significantly enhance the migration of IEC-6 cells. Moreover, GE inhibited the expression of NF-κB, nuclear transfer, and transcriptional activity in IEC-6 cells. GE could block the synthesis of inflammatory factors of IEC-6 cells by inhibiting activation of the IĸB/NF-κB signaling pathway induced by LPS.

Quantum chaos is presented as a paradigm of information processing by dynamical systems at the bottom of the range of phase-space scales. Starting with a brief review of classical chaos as entropy flow from micro- to macro-scales, I argue that quantum chaos came as an indispensable rectification, removing inconsistencies related to entropy in classical chaos: Bottom-up information currents require an inexhaustible entropy production and a diverging information density in phase space, reminiscent of Gibbs' paradox in Statistical Mechanics. It is shown how a mere discretization of the state space of classical models already entails phenomena similar to hallmarks of quantum chaos, and how the unitary time evolution in a closed system directly implies the “quantum death” of classical chaos. As complementary evidence, I discuss quantum chaos under continuous measurement. Here, the two-way exchange of information with a macroscopic apparatus opens an inexhaustible source of entropy and lifts the limitations implied by unitary quantum dynamics in closed systems. The infiltration of fresh entropy restores permanent chaotic dynamics in observed quantum systems. Could other instances of stochasticity in quantum mechanics be interpreted in a similar guise? Where observed quantum systems generate randomness, that is, produce entropy without discernible source, could it have infiltrated from the macroscopic meter? This speculation is worked out for the case of spin measurement.

In this perspective, we give an overview of several less explored structural and optical characterisation techniques useful for biomaterials. New insights into the structure of natural fibres such as spider silk can be gained with minimal sample preparation. Electromagnetic radiation (EMR) over a broad range of wavelengths (from X-ray to THz) provides information of the structure of the material at cor- respondingly different length scales (nm-to-mm). When the sample features, such as the alignment of certain fibres, cannot be characterised optically, polarisation analysis of the optical images can provide further information on feature alignment. The 3D complexity of biological samples necessitates that there be feature measure- ments and characterisation over a large range of length scales. We discuss the issue of characterising complex shapes by analysis of the link between the color and struc- ture of spider scales and silk. For example, it is shown that the green-blue color of a spider scale is dominated by the chitin slab’s Fabry-P ́erot type reflectivity rather than the surface nanostructure. The use of a chromaticity plot simplifies complex spectra and enables quantification of the apparent colors. All the experimental data presented herein are used to support the discussion on the structure-color link in the characterisation of materials.

The entropy of the partition generated by an n-tone music scale is proposed to quantify its regularity. The normalized entropy relative to a regular partition, and its complementary, here referred to as the bias, allow to analyze various conditions of similarity between an arbitrary scale and a regular scale. Interesting particular cases are scales having limited bias because their tones are distributed along interval fractions of a regular partition. The most typical case in music concerns partitions associated with well-formed scales generated by a single tone h. These scales are maximal even sets that combine two elementary intervals. Then, the normalized entropy depends on the number of tones as well as the relative size of both elementary intervals. When well-formed scales are refined, several nested families stand out with increasing regularity. It is proven that a scale of minimal bias, i.e. with less bias than those with fewer tones, is always a best rational approximation of log2h.

The Golden ratio is an irrational number that has a tendency to appear in many different scientific and artistic fields. It may be found in natural phenomena across a vast range of length scales; from galactic to atomic. In this review, the mathematical properties of the Golden ratio are discussed before exploring where in nature it has been found; beginning at astronomical scales and progressing to smaller lengths, until reaching those of atomic and quantum physics. In making such a tour across length scales, it is illustrated just how prevalent this single number is within the natural universe.

The electronics industry is a significant contributor to environmental challenges, generating approximately 50 million tons of electronic waste annually and high levels of carbon dioxide emissions throughout the production and disposal of devices. This paper investigates the interplay between reliability and sustainability by analyzing how degraded components affect the environmental footprint of electronic products. Using a board with 215 components as a baseline, we calculate the mean time between failures (MTBF) and compare it to scenarios involving degraded components. Our findings demonstrate that degraded components can reduce MTBF by up to 50%, resulting in higher failure rates, increased electronic waste, and up to 30% more CO2 emissions due to shortened product lifespans. This study underscores the crucial importance of component quality control in the production process to improve both reliability and sustainability. Additionally, the broader economic and environmental impacts of using unchecked components are highlighted, showing their contribution to elevated failure rates, shortened product lifespans, and an estimated 20% increase in e-waste. By adopting a 100% inspection protocol, leveraging advanced technologies, and enforcing stringent material quality controls, manufacturers can significantly enhance the reliability of electronic products while reducing their environmental impact, thus fostering a more sustainable electronics industry.

Biochar application to soil has been proposed as a means for reducing soil greenhouse gas emissions and mitigating climate change. The effects, however, of interactions between biochar, moisture and temperature on soil CO₂ and N₂O emissions, remain poorly understood. Furthermore, the applicability of lab-scale observations to field conditions in diverse agroecosystems remains uncertain. Here we investigate the impact of a mixed wood gasification biochar on CO₂ and N₂O emissions from loess-derived soils using: (1) controlled laboratory incubations at three moisture (27, 31 and 35%) and three temperature (10, 20 and 30°C) levels, and (2) a field study with four cropping systems (continuous corn, switchgrass, low diversity grass mix, and high diversity grass-forb mix). Biochar reduced N₂O emissions under specific temperatures and moistures in the laboratory and in the continuous corn cropping system in the field. However, the effect of biochar on N₂O emissions was only significant in the field, and no effect on cumulative CO₂ emissions was observed. Cropping system also had a significant effect in the field study, with soils in grass and grass-forb cropping systems emitting more CO₂ and less N₂O than corn cropping systems. Observed biochar effects were consistent with previous studies showing that biochar amendments can reduce soil N₂O emissions under specific, but not all, conditions. The disparity in N₂O emission responses at the lab and field scales suggests that laboratory incubation experiments are not reliable for predicting the impact of biochar at the field scale.

Immunoglobulin G (IgG) adopts a modular multidomain structure that mediates antigen recognition and effector functions, such as complement-dependent cytotoxicity. IgG molecules are self-assembled into a hexameric ring on antigen-containing membranes, recruiting the complement component, C1q. To provide deeper insights into the initial step of the complement pathway, we report a high-speed atomic force microscopy study for quantitative visualization of the interaction between IgG and the C1 complex composed of C1q, C1r, and C1s. Results showed that C1q in the C1 complex is restricted regarding internal motion and has a stronger binding affinity for on-membrane IgG assemblages than C1q alone, presumably because of smaller conformational entropy loss upon binding. Furthermore, we visualized a 1:1 stoichiometric interaction between C1/C1q and an IgG variant that lacks the entire CH1 domain in the absence of antigen. In addition to the canonical C1q-binding site on Fc, their interactions are mediated through a secondary site on the CL domain that is cryptic in the presence of the CH1 domain. Our findings offer clues for novel-modality therapeutic antibodies.

Redundancy in ship systems is provided to ensure operational resilience through equipment backups which ensures system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems, hence is provided with good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired black outs are common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensure a more robust maintenance and support platform for efficient ship operations. In this regard hybrid reliability and fault detection analysis were conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of 4 marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate overall low system reliability of less than 70 per cent within the first 24 months of the 78 operational months. Component criticality using reliability importance measures was used to identify all components with more than 40 per cent contribution to sub systems failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Networks classification. The ANN has identified failure patten threshold at about 200kva which can be attributed to overheating, hence establishing link between component failure and generator performance.

Symmetry-breaking is a phenomenon that is observed in various contexts, from the early universe to complex organisms, and is considered a key puzzle in understanding the emergence of life. The presence of enantiomeric amino acids and proteins highlights its critical role. However, the origin of symmetry-breaking has yet to be comprehensively explained, particularly from an energetic standpoint. This article explores a novel approach by considering energy dissipation, specifically lost free energy, as a crucial factor in elucidating symmetry-breaking. Through a thorough thermodynamic analysis applicable to enantiomeric systems at various scales, from elementary particles to aggregated matter like crystals, we present experimental findings that establish a direct correlation between non-equilibrium free energy and energy dissipation during formation processes. Results emphasize the pivotal role of energy dissipation not only as an outcome but as the trigger for symmetry-breaking. This insight suggests that understanding the origins of complex systems, from cells to living beings and the universe itself, requires a lens focused on non-equilibrium processes, ultimately facilitating the formation of non-racemic systems.

In this article, we establish some new generalized inequalities of Hilbert-type on time scales delta calculus which considered as similar formulas for inequalities of Hilbert type proved by Chang-Jian, Lian-Ying and Cheung [7]. These inequalities will proved by applying Hölder’s inequality, chain rule on time scales and the mean inequality. As special cases of our results (when T = N and T = R), we get the discrete and continuous inequalities. Also, we can obtain other inequalities in different time scale, like T = q^Z, q > 1.

Despite their obvious differences, biological systems at different scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are usually obscured by the parcelled terminology employed by various scientific sub-disciplines. To explore these commonalities, this papers a comparative study of diverse applications of the maximum entropy principle, ranging from amino acids up to societies. By presenting these studies under a common language, this paper establishes a unified view over seemingly highly heterogeneous biological scenarios.

The range of rating instruments in depression measurement and the depth of their analytical relevance constitutes a major development in this psychiatric and psychotherapeutic field of mental health. Though the competition is acute amongst these various instruments, the results for the public have been outstandingly positive. A depression rating scale is essentially a psychiatric measuring instrument utilized in the identification and ranking of depression severity within the patient. The scale provides the practitioner, psychiatrist or psychotherapist, with sufficient information to assess the severity of the depression plotted on the scale. Not used as a “diagnostic tool” itself, nevertheless, the depression rating scale does function as an effective device for designating and assigning a behavioral score which may, then, be used in establishing the severity of depression of value in the designation of a diagnosis and treatment formula. In this paper, we will take a close look at the leading depression rating scales and briefly summarize their scope of assessment value in rating depression.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

We define a spinor-Minkowski metric for SL(4,C). It is not a trivial generalization of the SL(2,C) metric and it involves the Minkowskian one. We define 4x4 version of the Pauli matrices and eight 4-component associated generalized eigenvectors that can be regarded as undotted covariant spinors. The 4-component spinors can be grouped into four categories. Each category transforms in its own way. The outer products of pairwise combinations of 4-component spinors can be associated with 4-vectors. Including the dotted covariant, undotted and dotted contravariant forms totally we have sixteen pairs of spinors. Eight of them live in the conjugate space which has no countepart in SL(2,C).

Accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understand their structure-function relationships and electrochemical properties. Current method of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly / partially resolved and unresolved spectra, which is a common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here we used 9 bands from multispectral high-to-medium resolution (10–60 m) satellite data (Sentinel-2) and machine learning to predict the local vegetation plot characteristics at broad extent (approx. 30.000 km2) in terms of plants’ preferences for soil moisture, soil fertility, and pH, mirroring the levels of the corresponding actual soil factors. These factors are believed to be among the most important for local plant community composition. Our results showed that there are clear links between the Sentinel-2 data and plants abiotic soil preferences and using solely satellite data we achieved predictive powers between 26–59% improving to about 70% when habitat information was included as a predictor. This show that plants abiotic soil preferences can be detected quite well from space, but also that retrieving soil characteristics using satellites is complicated and that perfect detection of soil conditions using remote sensing – if at all possible – needs further methodological and data development.

The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.

Analyzing big data poses a great challenge for numerous researchers to explore the data structure. Dimension reduction methods can be used to reduce data dimensionality, taking it from occupying a high-dimensional space to existing in a lower-dimensional space while retaining as much information as possible. Principal Component Analysis is one of the most popular used to reduce the dimensional space. The bootstrap sample is obtained by randomly sampling n times with replacement from the original sample, the method provides easy tool to understand the interactive component and develop the process. There are not enough researches discussed the stability of PCA method using bootstrap method. In this paper, the bootstrap method is used to analyze the stability of PCA results and to estimate the number of PCA in efficient way. The method is used to estimate the number of PCA that needed to classify the data set, and the effectiveness of the discussed techniques is demonstrated through real data sets.

This research explores the depolymerization of waste polymethyl methacrylate (PMMAW) from dental material in fixed-bed semi-batch reactors, focusing on three production scales: laboratory, technical, and pilot. The study investigates the thermal degradation mechanism and kinetics of PMMAW through thermogravimetric (TG) and differential scanning calorimetry (DSC) analyses, revealing a two-step degradation process. The heat flow during PMMAW decomposition is measured by DSC, providing essential parameters for designing pyrolysis processes. The results demonstrate the potential of DSC for energetic analysis and process design, with attention to standardization challenges. Material balance analysis across the production scales reveals a temperature gradient across the fixed bed negatively impacting liquid yield and methyl methacrylate (MMA) concentration. Reactor load and power load variables are introduced, demonstrating decreased temperature with increased process scale. The study identifies the influence of temperature on MMA concentration in the liquid fraction, emphasizing the importance of controlling temperature for efficient depolymerization. Furthermore, the research highlights the formation of aromatic hydrocarbons from the remaining char, indicating a shift in liquid composition during the depolymerization process. The study concludes that lower temperatures below 450 °C favor liquid fractions rich in MMA, suggesting the benefits of lower temperatures and slower heating rates in semi-batch depolymerization. The findings contribute to a novel approach in analyzing pyrolysis processes, emphasizing reactor design and economic considerations for recycling viability. Future research aims to refine and standardize the analysis and design protocols for pyrolysis and similar processes.

Animals’ facial expressions have been widely used as a readout for emotion. Scientific interest in the facial expressions of laboratory animals has centered primarily on negative experiences, such as pain, experienced as a result of scientific research procedures. Recent attempts to standardize evaluation of facial expressions associated with pain in laboratory animals has culminated in the development of “grimace scales”. In the context of laboratory animals, these have been developed and evaluated for mice, rats, rabbits, sheep, and ferrets. The prevention or relief of pain in laboratory animals is a fundamental requirement for in vivo research to satisfy community expectations. However, to date it appears that the grimace scales have not seen widespread implementation as clinical pain assessment techniques in biomedical research. In this review, we discuss some of the barriers to implementation of the scales in clinical laboratory animal medicine, progress made in automation of collection, and suggest avenues for future research.

Mediterranean catchments are characterized by significant spatial and temporal hydrological variability caused by the interaction of natural as well human-induced abiotic and biotic factors. This study investigates the (non-)linearity rainfall-runoff relationship at multiple temporal scales in representative small Mediterranean catchments (i.e., < 10 km2) to achieve a better understanding of the hydrological response. Rainfall-runoff relationship was evaluated in 44 catchments at annual and event –203 events in 12 of these 44 catchments– scales. A linear rainfall-runoff relation was observed at annual scale with higher scatter in pervious than impervious catchments. Larger scattering was observed at event scale, although pervious lithology and agricultural land use promoted significant rainfall-runoff linear relations in winter and spring. These relationships were particularly analysed during five hydrological years in Es Fangar catchment (3.35 km2; Mallorca, Spain) as a temporal downscaling to assess intra-annual variability in which antecedent wetness conditions played a significant role in runoff generation.

In this study, the autofrettage method, which is used to increase the durability of cylindrical parts, was applied to a barrel made of steel material according to the AISI 4340 standard. Assuming that the barrel works continuously under the pressure of 400 MPa, optimum autofrettage was performed. The stresses occurring on the barrel before and after autofretage were calculated. Additionally, analytical equivalent stress values (Von-Mises) formed on the part's wall were compared with numerical analysis results obtained with a commercial finite element program ANSYS®, and their compatibility was examined. As a result of the calculations and analysis performed, it was observed that autofretage has positive effect on increasing the pressure carrying capacity of the barrel. After autofrettage the cylindrical part, it was determined that the Von-Mises stress values formed in the part wall changed and these stresses took the lowest value at a point (elastic-plastic interface). It has been evaluated that this point gives the optimal autofretage pressure value for a certain operating pressure. After the autofrettage application, it was determined that the Von-Mises Stress value in the barrel wall decreased by 25.27 % and the pressure carrying capacity of the barrel increased without any design or dimensional change. It has been seen that the analytical and numerical analysis results are consistent with each other, and it is expected that this study will guide and contribute to the studies to be conducted on increasing the strength or lightening the weight of the parts.

Principal component analysis (PCA) is used to reduce the dimensionalities of high dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods, and their applications for investigating protein dynamics.

As the aging of the world accelerates, clarifying the relationship between cultural differences and ageism is an urgent issue. Therefore, in this study, we conducted a correlation analysis between the six cultural scales of Hofstede et al. [1] and the 10 ageism scales calculated from data on 35,232 people from 31 countries included in the World Values Survey Wave 6 by Inglehart et al. [2]. The results of a partial correlation analysis controlling for economic and demographic factors showed that the cultural scales were correlated with ageism. This is the first study to show that diverse cultural scales are related to multiple dimensions of ageism.

The article adds new policy insights to the way in which formal teaching methods of environmental stewardship education areas are evaluated in Tuvalu. We aimed to answer three questions: 1. What are the formal policies shaping environmental stewardship education in Tuvalu? 2. Are national educational and environmental policies mutually consistent? and 3. Are these national policies consistent with regional and global policies? These questions were addressed using a study of Tuvaluan online-available documentary assessments of national policies, frameworks, and curricula in conjunction with those obtained from the Education Department. Our findings revealed limitations and weaknesses regarding the provisions of environmental stewardship education at the national scale, including misaligned curricula, policy documents, and formal educational provisions with Tuvalu’s national environmental policy. We also found inconsistencies with regional and global policies such as the Pacific Islands Framework for Nature Conservation, the Framework for Resilient Development in the Pacific, and international strategic plans for biodiversity and climate change. These cross-scalar policy weaknesses and limitations highlight the need for the Tuvaluan government to align educational and environmental policies at different scales. We recommend a multi-pronged strategy where modes of informal environmental education can support and complement formal government policymaking on education and the environment.

When undertaking wind assessment around buildings using large eddy simulation (LES), the implementation of the integral length scale at the inlet for inflow generation is controversial, as real atmospheric length scales require huge computational domains. While length scales significantly influence inflow generation in the domain, their effect on the downstream flow field has not yet, been investigated. In this paper, we validate the effectiveness and accuracy of implementing a reduced turbulence integral length scale for inflow generation in LES results at the rooftop of low-rise buildings and develop a technique to estimate the real local length scales using simulation results. We measure the wind locally and calculate the turbulence length scales from the energy spectrum of the wind data and simulation data. According to these results, there is an excellent agreement between the length scale from simulation and measurement when they are scaled with their corresponding freestream/inlet value. These results indicate that a reduced integral length scale can be safely used for LES to provide a reliable prediction of the energy spectrum as well as the length scales around complex geometries. The simulation results were confidently employed to obtain the best location for a wind turbine installation on low-rise buildings.

Species diversity has been shown to be influenced by environmental factors, but the mechanism underlying their relationship remains unclear across spatial scales. Based on field investigation data collected from 3,077 sample plots in temperate forest ecosystems, we compared tree species richness, evenness and dominance at 10 km × 10 km, 30 km × 30 km and 90 km × 90 km spatial scales. Then, we detected the scale dependence of changes in tree species composition on climate, topography and forest structure using variation partitioning and quantified their contribution to tree diversity with gradient boosted models (GBMs) and fitted their relationships. The magnitude of tree richness, evenness and dominance significantly increased with spatial scale. Ecological factors jointly accounted for 24.3%, 26.5% and 38.5% of the variation in tree species composition at the three spatial scales, respectively. Annual mean temperature had a strong impact on tree richness, evenness and dominance and peaked at an intermediate scale. Tree evenness and dominance increased with the variation of temperature but had upper and lower limits. Tree richness obviously increased with annual precipitation on multiple scales and decreased with annual sunshine duration at large spatial scale. Tree richness, evenness and dominance obviously increased with the variation elevation and diameter at breast height at large scales and small scales, respectively. Tree dominance decreased with tree height in a hump at small scale. The dependence of tree diversity on ecological factors increased with spatial scales. Furthermore, different factors exert various controls on tree diversity at different spatial scales, representing a comprehensive mechanism regulating tree diversity.

Three-layer structures based on various multi-component films of III–V semiconductors heavily doped with Fe were grown by pulsed laser sputtering of InSb, GaSb, InAs, GaAs and Fe solid targets. The structures comprising InAsSb:Fe, InGaSb:Fe and InSb:Fe layers with Fe concentrations up to 24 at. % separated by GaAs spacers were deposited on (001) i-GaAs substrates at 200ºC. Transmission electron microscopy showed that the structures have a rather high crystalline quality and do not contain secondary-phase inclusions. X-ray photoelectron spectroscopy investigations revealed significant diffusion of Ga atoms from the GaAs regions into the InAsSb:Fe layers, which has led to the formation of an InGaAsSb:Fe compound with a Ga content up to 20 at. %. It has been found that the ferromagnetic properties of the InAsSb:Fe magnetic semiconductor improve with increasing Sb:As ratio. It has been concluded that the indirect ferromagnetic exchange interaction between Fe atoms occurs predominantly via Sb atoms.

Currently, two-component integrable nonlinear equations from the hierarchies of the vector nonlinear Schrodinger equation and the vector derivative nonlinear Schrödinger equation are being actively investigated. Usually, such two-component integrable nonlinear evolutionary equations are considered, the form of which does not depend on the replacement of one component with another. In this paper, we propose for consideration and usage a new hierarchy of two-component integrable nonlinear equations, which have an important difference from the already known equations. Among the hierarchy equations there are analogues of the two-component nonlinear Schrödinger equation (second equation from hierarchy) and the two-component modified Korteweg-de Vries equation (fourth equation from hierarchy). The third equation of the hierarchy is a combination of the nonlinear Schrödinger equation for one component and the modified Korteweg-de Vries equation for the second component. The equations for the individual components are very different from each other, even if they have the same order. Let us note that even hierarchy equations can be reduced to well-known variants of the scalar derivative nonlinear Schrödinger equations, and odd equations can’t be reduced. To construct the hierarchy equations, we use the monodromy matrix method, first proposed by B.A. Dubrovin. Knowledge of the monodromy matrix makes it possible to construct spectral curves of multiphase solutions, as well as to find stationary equations that these solutions satisfy. The last section presents the simplest solutions in the form of solitons and periodic one-phase waves, as well as spectral curves corresponding to these solutions.

This study aims to develop novel automated computer vision algorithms and systems for component replacement inspection for Printed Circuit Boards (PCBs). The proposed algorithms are able to identify locations as well as sizes of different components. They are object detection algorithms based on keypoints of the target components. The algorithms can be implemented as neural networks consisting of two portions: frontend networks and backend networks. The frontend networks are used for the feature extractions of input images. The backend networks are adopted for producing component inspection results. Each component class can has its own frontend and backend networks. In this way, the neural model for the component class can be effectively reused for different PCBs. To reduce the computation time for the inference of the networks, different component classes can share the same frontend networks. A two-stage training process is proposed for effectively exploring features of different components for accurate component inspection. The proposed algorithm has the advantages of the simplicity for training data collection, high accuracy for defect detection, and high reusability and flexibility for online inspection. The algorithm is an effective alternative for the automated inspection in smart factory with growing demand for product quality and diversification.

The coombs (in Catalan comes) form an integral part of the landscape. These are small valleys, with the deepest and flattest part cultivated for agriculture. They are predominantly found in the most arid regions of Catalonia, particularly around the cities of Tarragona and Lleida. They are generally dry land areas. We propose that these coombs gained significance in the early centuries of the Middle Ages, a period marked by intense erosion. It is important to identify their locations and establish connections with medieval written documents, the settlements that thrived throughout the Middle Ages, archaeological sites such as necropolises, and the valuable data derived from to-ponymy studies. We think that understanding these coombs can greatly contribute to under-standing how the territory was organized in the Middle Ages, the extent of cultivated lands that were established, and the distribution of many settlements. Ultimately, this knowledge can aid in acquiring better understanding of the current landscape.

The aim of this study was to examine whether H2O2, generated in such beverages as tea and coffee, is formed also in infusions of medicinal herbs and propose the possible mechanism of its formation. Infusions of sixteen herbs were studied and the presence of H2O2 was found in all cases. The highest concentrations of H2O2, exceeding 50 µM, were found in the infusions of leaves of Betula and Polygonum, and of the inflorescence of Tilia. These low amounts of H2O2 should not have a harmful effect and may even exert beneficial action, perhaps contributing to the health effects of the herbs. Generation of semiquinone and superoxide radicals was detected in herbal infusions, indicating that oxidation of phenolic compounds is the main source of H2O2 in these infusions. Herbal infusions were cytotoxic to human ovary cancer SKOV-3 and PEO1 cells; this cytotoxicity was compromised by catalase added to the growth medium, demonstrating the contribution of hydrogen peroxide to the cytotoxic action of herbal extracts.

The well-known coupling between the magnetic and electric fields in electromagnetic radiation is revisited in light of the recent introduction of a microscopic theory for the Ørsted magnetic field induction. I argue that, if the Ørsted magnetic field induction does indeed have a microscopic origin rather than the continuum solution offered by the Maxwell-Ampere equation, the "electromagnetic wave"' propagating in vacuum will not have a magnetic component.

In this paper, the quantum theory of the infinite-component Majorana field for the fermionic tower is formulated. This study proves that the energy states with increasing spin are simply composite systems made by a bradyon and antitachyons with half-integer spin. The quantum field describing these exotic states is obtained by the infinite sum of four-spinor operators, which each operator depends on the spin and the rest mass of the bradyon in its fundamental state. The interaction between bradyon-tachyon, tachyon-tachyon and tachyon-luxon has also been considered and included in the total Lagrangian. The obtained theory is consistent with the CPT invariance and the spin-statistics theorem and could explain the existence of new forms of matter not predictable within the standard model.

The growing demand for clean energy transitions has become increasingly significant, with solar energy emerging as one of the most prominent clean energy resources contributing to this effort. However, there remains limited knowledge regarding the economic feasibility of solar project development across different geographic locations and scales. This study introduces a comprehensive economic analysis framework to assess the economic viability of residential and utility-scale solar projects, using California, Tennessee, and Texas as case studies. The economic assessment is conducted through a cost-benefit analysis that adopts a full life-cycle approach, encompassing phases from permitting to demolition for both scales of solar projects. We found that utility-scale solar projects generally offer more favorable investment return ratios compared to residential projects, despite the absence of land acquisition costs and lower initial capital expenditures for the latter. Furthermore, the incorporation of energy storage solutions, such as Battery Energy Storage Systems (BESS), is shown to be essential for improving the efficiency of residential solar energy usage, with efficiency gains of up to 50%. Finally, region-specific strategies, including net metering policies, electricity retail market structures, and the promotion of solar adoption, play a crucial role in enhancing the financial viability of solar projects for both residential and utility scales.

In this paper, the spatial distribution of stony desertification characteristics and its influencing factors in Karst areas in different sampling scales are studied using a grid sampling method based on geographic information system (GIS) technology and geo-statistics, with the rock bareness rate obtained through sampling with 150m × 150m grids in the Houzhai River Basin being taken as the original data and five grid scales (300m × 300m, 450m × 450m, 600m × 600m, 750m × 750m, and 900m × 900m) as the subsample sets. The results show that the rock bareness rate does not vary much from one sampling scale to another while average values of the five sub-samples all fluctuate around the average value of the entire set. As the sampling scale is expanding, the maximum value and the average value of rock bareness rate are decreasing gradually, with a gradual increase in the coefficient of variability. In the scale of 150m × 150m, the areas of minor stony desertification, medium stony desertification, and major stony desertification in the Houzhai River Basin are 7.81 km2, 4.50 km2, and 1.87 km2, respectively. The spatial variability of stony desertification on small scales is influenced by many factors, and that on medium scales is jointly influenced by gradient, rock contents, and rock bareness rate. On large scales, the spatial variability of stony desertification is mainly influenced by soil thickness and rock bareness rate.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

The testing of high-dimensional normality has been an important issue and has been intensively studied in literatures, it depends on the Variance-Covariance matrix of the sample, numerous methods have been proposed to reduce the complex of the Variance-Covariance matrix. The principle component analysis(PCA) was widely used since it can project the high-dimensional data into lower dimensional orthogonal space, and the normality of the reduced data can be evaluated by Jarque-Bera(JB) statistic on each principle direction. We propose two combined statistics, the summation and the maximum of one-way JB statistics, upon the independency of each principle direction, to test the multivariate normality of data in high dimensions. The performance of the proposed methods is illustrated by the empirical power of the simulated data of normal data and non-normal data. Two real examples show the validity of our proposed methods.

In this paper we review dynamics and roughness of bonds in proteins on example of albumin, that is important from the physiological point of view. We have performed computer simulations of albumin chain. Statistics were collected by performing many simulations realizations for each experimental setting. We concentrate on hydrogen bonds, cation-π and π- π interactions and NP contacts. Histograms of hydrogen bonds length are positively skewed in contrary to histograms of interactions and HP contacts that are negatively skewed. Scaling exponents of power spectra of energies of bonds / interactions /contacts are in range -0.2 to -0.5 and significantly differ between various hydrogen bonds or interactions. Varying scaling of such spectra can be used to classify between distinct bonds or contacts. Concerning particular amino-acids, largest amount of HBO H20 bonds are between Glutamate (GLU) amino-acids and water particle, while large amount of HBO bonds are formed with Lysine (LYS). For HP contacts the mayor role plays Phenylalanine (PHE) and Leucine (LEU) amino-acids. From decay curves HBO H2O bonds decays in fastest rate, while HBO bonds and HP contacts at slowest rate. We present as well decay curves of bonds formed by particular amino-acids, that gives interesting results.

Ulcerative colitis has a significant impact on the quality of life for the patients, and can substantially increase the risk of colon cancer in patients suffering long-term. Conventional treatments provide only modest relief paired with a high risk of side effects, while complementary and alternative medicines can offer safe and effective options. Over the past decade, we have shown that American ginseng has anti-oxidant and anti-inflammatory properties that can suppress mouse colitis and prevent colitis associated colon cancer. With the goal of isolating a single active compound, we further fractionated the hexane fraction, and found the most abundant molecule in this fraction was the polyacetylene, Panaxynol. After isolating and characterizing Panaxynol, we tested the efficacy of Panaxynol in the treatment and prevention of colitis in mice and studied the mechanism of action. We demonstrate here that Panaxynol effectively treats colitis in a Dextran Sulfate Sodium mouse model by targeting macrophages for DNA damage and apoptosis. Positive outcomes from this study could take American ginseng one-step further towards becoming a conventional drug for the treatment of colitis, and possibility exploring other autoimmune diseases associated with macrophage dysfunction.

As one of the common techniques for measuring coseismic deformations, optical image correlation techniques are capable of overcoming the drawbacks of inadequate coherence and phase blurring which can occur in radar interferometry, as well as the problem of low spatial resolution in radar pixel offset tracking. However, the scales of the correlation window in optical image correlation techniques typically influence the results, the conventional SAR POT method faces a fundamental trade-off between the accuracy of matching and the preservation of details in the matching window size. This study regards co-seismic deformation as a two-dimensional vector, and develops a new post-processing workflow called VACI-OIC to reduce the dependence of shift estimation on the size of the matching window. This paper takes the coseismic deformations in both the east-west and north-south directions into account at the same time, treating them as vectors, while also considering the similarity of displacement between adjacent points on the surface. Herein, the angular continuity index of the coseismic deformation vector was proposed as a more reasonable constraint condition to fuse the deformation field results obtained by optical image correlation across different matching window. Taking the earthquake of 2021 in Maduo, China, as the study area, the deformation with the highest spatial resolution in the violent surface rupture area was determined (which could not be provided by SAR data). Compared to the results of single-scale optical correlation, the presented results were more uniform (i.e., more consistent with published results). At the same time, the proposed index also detected the strip fracture zone of the earthquake with impressive clarity.

This work develops a new method for vector data augmentation. The proposed method applies the Principal Component Analysis (PCA) and determines the Eigenvectors of a set of training vectors for a Machine Learning (ML) method and uses them to generate the distilled vectors. The training and the PCA distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors the extended set trains a ML classifier to achieve a required accuracy. Hence, the novelty of this study is, the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: Neural Network, Logistic Regression, Support Vector Machine with linear and polynomial kernels. For the purpose of augmentation, we conduct several distillations including nested (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We train the classifiers with three sets of vectors: original vectors; original augmented with vectors distilled by PCA; original augmented with distilled and double distilled, by PCA, vectors. The experimental results are presented in the paper, and they confirm the advantage that the PCA distilled vectors increase the classification statistics of ML methods, if the distilled vectors augment the original training vectors.

This study introduces a new procedure for three-component spectral matching of seismic ground acceleration records. The procedure involves preserving the phase angle and varying the Fourier amplitudes to match the spectra of the recorded records with a provided target spectrum that represents the spectral values of ground motions in three orthogonal directions. Several three-component recorded records were matched against two target response spectra to test the method. An accurate convergence was achieved in the velocity and displacement records. In addition, the scales among the components’ magnitude were maintained, creating realistic three-component ground motions. Such ground motion inputs are important to qualify critical structures sensitive to three-component motions. Our proposed procedure is novel because the present practices are mostly created based on one-component ground motions.

Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-based Read-Across predictions. In this work, we have generated a simple, interpretable, and transferable quantitative-RASAR (q-RASAR) model which can efficiently predict the cytotoxicity of TiO2-based multi-component nanomaterials. The data set involves 29 TiO2-based nanomaterials which contain specific amounts of noble metal precursors in the form of Ag, Au, Pd, and Pt. The data set was rationally divided into training and test sets and the Read-Across-based predictions for the test set were generated using the tool Read-Across-v4.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home. The hyperparameters were optimized based on the training set data and using this optimized setting, the Read-Across-based predictions for the test set were obtained. The optimized hyperparameters and the similarity approach, which yields the best predictions, were used to calculate the similarity and error-based RASAR descriptors using the tool RASAR-Desc-Calc-v2.0 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home. These RASAR descriptors were then clubbed with the physicochemical descriptors and were subjected to features selection using the tool Best Subset Selection v2.1 available from https://dtclab.webs.com/software-tools. The final set of selected descriptors was used to develop multiple linear regression based q-RASAR models, which were validated using stringent criteria as per the OECD guidelines. Finally, a random forest model was also developed with the selected descriptors. The final machine learning model can efficiently predict the cytotoxicity of TiO2-based multi-component nanomaterials superseding previously reported models in the prediction quality.

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional Artificial Neural Network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and Exhaust Gas Temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

Multispectral remote sensing data may contain component images which are heavily corrupted by noise and pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images – component images having relatively high quality and which are similar to the image subject to pre-filtering. Here we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. Examples of denoising of real-life images demonstrate high efficiency of the proposed approach.

Anomaly occurrences in hydraulic machinery may lead to massive systems shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications succeeding the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only the machines and their components are prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig is thoroughly achieved. Firstly, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies to perform two separate stages of fault detection and diagnosis. The two phases are condensed by: the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework is applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of the past decade related work for the two stages separately is successfully conducted in this paper.

Multicriteria decision aid requires a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g. means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensate for this relaxation. The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences (CPP) method in multicriteria problems with data from Likert scales. The CPP is capable of allaying the protests raised and obtaining more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of CPP with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the CPP.

Some studies investigated the relationship between frequency domain analysis of heart rate variability and the cognitive performance at the Stroop task. We propose a combined assessment also of trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac and motor variables based on a principal component analysis. We also assessed the change on this result after an intervention of two months aiming at improving the perception-action link. At baseline, the PCA correctly identified 3 components: one related to cardiac variables, one to trunk motion and one to the performance of Stroop task. After the intervention, only two components were found with trunk symmetry and range of motion, accuracy and time to complete the Stroop task and low frequency heart rate variability aggregated in a single component by PCA. It suggested that this protocol was effective in investigating the embodied cognition and we defined this approach as “embodimetrics”.

State Forest Management Organizations (SFMOs) play a crucial role in the European forest sector, managing almost half the forests in the region. SFMOs are often managed for timber production only whereas, being publicly owned, they should play an important role in providing a vast range of public goods (e.g. soil protection, biodiversity conservation). Their management goals depend on the history and current conditions of the forest sector at a national level, as well as different challenges and the potential for development. Although there is a lack of knowledge about the current performance of SFMOs, there have been recent changes to their management goals and practices in response to the new demands expressed by society (e.g. transparency, social inclusion). The main purpose of this study is to analyse the current situation of SFMOs by clustering them according to indicators that reflect three pillars of the common understanding of sustainable forest management (SFM) concept. With the help of Principal Component Analysis (PCA), we grouped countries according to common characteristics of the forest sector at the national level. Results show three main clusters of SFMOs in Europe. The first cluster has rather small but commercially-oriented forestry unit together with other business activities and a strong focus on public services. The second sees itself as the protector of public interest, rather than commercially-oriented organisations. The third is mainly profit-seeking. The existence of diverse SFMO clusters shows the possibility of different approaches for SFM with a focus on different goals (e.g. profit gaining, public service delivery).

Citrus essential oils (EOs) have various bioactivities like antioxidants, with many applications. Antioxidant activities depend on the chemical compositions of the EOs, which are affected by climate, soil, and geographical region. Thus, investigations on chemical compositions and antioxidant activities of Citrus EOs in different countries are valuable. In this study, we distilled EOs from peels of Indonesian-grown Citrus, including C. nobilis, C. limon, C. aurantifolia, C. amblycarpa, and Citrus spp.Chemical compositions of EOs were analyzed using Gas Chromatography-Mass Spectrometer (GC-MS), whereas the antioxidant activities were determined by employing 2,2-diphenyl-2-picrylhydrazyl (DPPH) method. Furthermore, principal component analysis (PCA) was applied to elucidate the main contributing compounds for antioxidant activity. The results show that all EOs possess unique chemical characteristics, with limonene as the majority constituent. For antioxidant activities, C. limon and C. amblycarpa EOs are the two strongest, IC50 values below 7.00 μL/mL. PCA approach suggests that -terpinene mainly contributes to the high antioxidant activities of C. limon and C. amblycarpa. Moreover, o-cymene, thymol, p-cymene, and α-pharnesene may also be responsible for the antioxidant activity of C. limon EO. These results are valuable information for the applications of Citrus EOs as antioxidant sources.

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.

Introduction and objectives: a close association between metabolic syndrome among subjects with erectile dysfunction has been addressed, since mechanisms underlying metabolic syndrome compromise the blood flow to the penis in numerous ways. This study aims to analyze the relationship between erectile dysfunction and metabolic syndrome in a group of Mexican patients, to study the influence of other morbidity factors on erectile dysfunction, and to define the specific metabolic syndrome components most associated with erectile dysfunction severity. Methods: a descriptive and cross-sectional study was completed in a group of 86 adult Mexican patients with previous diagnosis of erectile dysfunction. Participants were classified as with or without metabolic syndrome. Erectile dysfunction severity, alcohol or tobacco consumption, and depressive behavior were identified through validated questionnaires and compared between both groups, as well as anthropometric, biochemical, and clinical parameters. Results: anthropometric measures, laboratory values, clinical characteristics and the Beck Depression Inventory score were significantly different among both groups. Additionally, more patients affected by severe and moderate erectile dysfunction were identified in the group with metabolic syndrome. Among the metabolic syndrome components, HbA1c >5.7% and fasting glucose >110 mg/dl were significantly associated to the development of erectile dysfunction (p = 0.004 and 0.04, respectively). Conclusions: metabolic syndrome components aggravate erectile dysfunction, particularly the lack of glycemic control manifested by Hb1Ac >5.7% and/or fasting glucose >110 mg/dL. The inclusion of fasting glucose and HbA1c as a complementary biochemical screening among patients with erectile dysfunction should be assessed.

The utilization of biowaste fillers in the development of high-density polyurethane (PU) foams has gained significant attention due to environmental and economic benefits. This study investigates the mechanical properties of PU foams reinforced with biowaste fillers extracted from fish scales (FS) and fish scale derived collagen (FSC). The fish scales and collagen were characterized for their composition and integrated into PU foams at various loadings. Mechanical properties such as tensile strength, hardness, and density were evaluated. ANOVA was used to analyze the mean values. Bonferroni tests were used to identify differences between the filler materials (α=.05). The tensile strength increases with increase in filler content for both FS (59.48 Kpa) and FSC (65.43Kpa). No differences were observed between FS and FSC in tensile strength. Significance differences were observed between the FS and FSC in both hardness and density (p&lt;0.001). The results demonstrated that both fillers enhanced the mechanical properties of PU foams, with collagen-reinforced foams showing superior performance. This suggests that collagen and fish scales can be effective biowaste fillers for developing environmentally friendly PU foams with enhanced mechanical properties.

Abstract: (1) Background: Surgery is the best curative option for Gastric Cancer (GC). However, morbimortality associated with surgery still a significant problem. Thus, it is essential to determine the preoperative surgical risk, to optimize outcomes in higher risk patients. (2) Methods: Patients who underwent curative gastrectomy between January 2010 and December 2013 were included, obtaining a sample of 131 patients, and the acuity of the scales understudy was evaluated. The predictive power of the scales was evaluated through calibration and discrimination. (3) Results and Conclusions: Regarding mortality, the scales demonstrated good calibration, with POSSUM and P-POSSUM obtaining the best O:E ratio. The scales presented good discrimination, with POSSUM showing the best result. Considering morbidity, the scales showed good calibration and discrimination. POSSUM and P-POSSUM seem to have potential in predicting morbidity and mortality when applied to the studied population, but there is a need for further studies in this area.

Background The Delta Neutrophil Index (DNI), a prodiagnostic marker for sepsis, utilizes leukocyte count. Platelet activation, similar to leukocytes, plays a crucial role in host defence against pathogens and may serve as a predictor of sepsis outcome. However, the combined evaluation of Mean Platelet Component (MPC) and DNI in assessing sepsis has been rarely used. Methods To assess the prodiagnostic validity of simultaneous utilization of DNI and MPC in cases of human febrile sepsis, we conducted measurements of cellular indices, including DNI and MPC, as well as molecular biomarkers such as procalcitonin (PCT) and C-reactive protein (CRP). This study was carried out on patients admitted to the emergency department with suspected sepsis. Results Using a cutoff value of 2.65%, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DNI in sepsis were found to be 69%, 73.9%, 77.9%, and 64.1%, respectively. Furthermore, significant differences in DNI and MPC levels were observed between the sepsis and non-sepsis groups (6.7±7.8% versus 2.1±2.2%, P=0.000 and 26.0±1.9 g/dl versus 26.8±1.4 g/dl, P=0.002), respectively. Notably, there was a negative correlation between DNI and MPC, with the strength of the correlation varying based on the cause of sepsis. By setting the cutoff value for DNI at 6.2%, the sensitivity, specificity, and NPV improved to 100%, 80.3%, and 100%, respectively, although the PPV remained at 10.6%. Importantly, the DNI≥6.2 group exhibited a higher incidence of death (10.9%, 5/46) and multiorgan failure (19.6%, 9/46) compared to the DNI<6.2 group (0.0%, 0/46 and 0.0%, 0/167, respectively) (P=0.000 and P=0.000, respectively). Conclusions In our study, DNI demonstrates superior effectiveness compared to other molecular biomarkers such as CRP and procalcitonin in distinguishing febrile septic patients from febrile nonseptic patients and predicting prognosis. Additionally, there exists a negative correlation between MPC and DNI, making MPC a valuable marker for differentiating the etiology of sepsis. These findings hold significant clinical implications as DNI/MPC is a cost-effective and readily applicable approach in various impending sepsis scenarios. Notably, this study represents the first examination of the prodiagnostic validity of simultaneously employing DNI and MPC in human cases of febrile sepsis.

The (benzylthio)acetic acid (HBTA) and some aminopyrimidines, namely 2-aminopyrimidine (2-AP), 5-aminopyrimidine (5-AP), 2-amino-4,6-dimethylpyrimidine (2-A-4,6-DMP) and 2,4,6-triaminopyrimidine (2,4,6-TAP), were successfully embodied as structural units into the construction of a total of four novel supramolecular organic frameworks. The received crystalline solids were inspected by single crystal X-ray diffraction (SC XRD) in order to obtain insight into the structural and supramolecular facets. The SOFs deriving from 2-AP, 5-AP as well as 2-A-4,6-DMP crystallize in the form of true co-crystals (1-3), while the one originating from 2,4,6-TAP crystallizes as salt solvate (4). The SC XRD results indicated the different contents of structural residues present in the asymmetric units of the crystals 1-4 despite the using the same molar ratio of molecular co-formers in each case. The molecular structures of co-crystals 1-3 consist of either one neutral residue of each starting components (1 and 3) or a half nonionized residue of aminopyrimidine ingredient and one neutral residue of acidic specie (2). The asymmetric unit of salt solvate 4 is composed of two ionized residues of each co-former (two 2,4,6-TAP+ cations and two BTA- anions) and one MeOH solvent molecule. The most extensive H-bonding network is observed in the crystal structure of salt solvate 4. The relevant molecular ingredients in co-crystals 1-3 are mainly hold together by the neutral Ocarboxylic–H···Npyrimidine and Namine–H···Ocarboxylic H-bonds. In the case of aggregate 4, the corresponding ionic residues are predominantly sustained by the charged-assisted Npyrimidinium–H···Ocarboxylate and Namine–H···Ocarboxylate hydrogen interactions. The MeOH solvent incorporated into the crystal lattice of adduct 4 is also involved in H-bonding by simultaneously serving as the single donor in OMeOH–H···S and the single acceptor in Namine–H···OMeOH H-bonds afforded the structural diversity within the 2,4,6-TAP+ cations and BTA- anions. Other weaker sets of additional non-covalent contacts existing in the crystal structures of analyzed conglomerates are involved in the self-assembly, stabilization and expansion of total supramolecular organic frameworks. The fact of the formation of non-covalent bonded supramolecular organic frameworks in question is also reflected in corresponding results obtained through elemental analysis (EA), Fourier transform infrared spectroscopy (FT−IR) and thermal analysis (TG/DSC).

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern. It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

Background: Metastasis is a complex process that involves the spread of the tumor to distant parts of the body from its original site. Metastatic dissemination represents the main physiopathology of cancer. Soluble factors such as cytokines have been closely related to breast cancer (BC) metastasis. Bisphenol A (BPA) is an endocrine disrupting chemical compound with estrogenic properties, which exposure in the early stages of neonatal life leads to an increase in the size and weight of breast tumors and cellular changes in the tumoral immune microenvironment. Methods: Thus, we used female BALB/c mice that were exposed neonatally to a single dose of BPA. Once sexual maturity was reached, a mammary tumor was induced injecting 4T1 cells in situ. After 25 days of injection, we evaluated endocrine alterations, cytokine expression, tissue alterations denoted by macro and micro metastases in the lung, and metastasis-induced cell infiltration. Results: BPA neonatal treatment did not show significant endocrine alterations. Nevertheless, BPA induced a great rate of metastasis to the lung associated with higher intratumoral expression of IL-1b, IL-6, IFN-g, TNF-a and VEGF. Conclusions Our data suggest that cytokines are key players in BC metastasis induction, and that BPA is a risk factor to be considered. This knowledge must be considered with the aim of recognizing environmental pollution in the clinical history of patients to possibly counter BC metastases.

Asphalt cement will not last for a long time as the world encounters a diminishment in the crude oil. For sustainable, flexible pavement development, new resources can provide a contribution to replace it partially or entirely. In this study, asphalt was partially and entirely replaced by guayule resin as a bioresource by-product, extracted during the guayule natural rubber production. Crumb rubber modifier (CRM) was used as an asphalt enhancer. The Superpave grading system was followed at high, intermediate, and low temperatures to evaluate such innovative binder for rutting, fatigue, and thermal cracking, respectively, in addition to viscosity. Therefore, the original, short-term aging and long-term aging were simulated using tank, rolling thin film oven, and pressure aging vessel materials. Additionally, component analysis using Fourier-transform infrared spectroscopy was provided to link the rheological properties with the chemical changes. Outcomes showed a relatively much lower viscosity of guayule in the same high-temperature asphalt grade indicating savings in plant energy consumption and reduced environmental emissions. CRM enhanced guayule, but not as much as asphalt, proven by polymeric component migration through liquid binder. This enhancement was reflected in the rheological performance besides other factors. As-received guayule seems to have high oxygen content proven by strong absorption peak intensities of oxidative bonds (e.g., Carbonyl and sulfoxide). Such pre-oxidation was negatively reflected in the intermediate- and low-temperature performance of guayule and guayule-based binders. However, the investigated guayule had potential to compensate for asphalt replacement in the presence of CRM by 23–42% by weight of blend.

In this study, a composite flame retardant was created by combining fish scale (FS), fishery waste, and ammonium polyphosphate (APP), a commercially available flame retardant. The composite flame retardant was added to epoxy resin (EP) to form an EP/APP/FS composite that prevents burns and is environmentally friendly. The use of FS conforms to the concept of circular economy and lowers costs by reducing the consumption of APP. Thermogravimetric analysis (TGA), integral procedural decomposition temperature (IPDT), pyrolysis kinetics, limiting oxygen index (LOI), the Underwriters Laboratories 94 (UL94) flammability test, scanning election microscopy, Raman spectroscopy, and energy-dispersive X-ray spectroscopy were used to determine the thermal properties, flame retardant properties, flame retardant mechanism, char morphology, and composition of the composites. The TGA results indicated that the addition of 40% flame retardant raised the char residue from 16.45 wt% (pure EP) to 36.07 wt%, IPDT from 685.6 °C (pure EP) to 1143.1°C, LOI from 21% (pure EP) to 30%, and UL94 classification from fail (pure EP) to V-0. These results suggest an increase in char residue, which indicates better protection of the polymer matrix material. The improvements in IPDT, LOI, and UL94 classification, which indicate greater thermal stability, lower flammability (from flammable to fireproof), and higher flammability rating (from fail to V-0), respectively, suggest that the composite material has favorable thermal properties and is less inflammable.

Alcohol and substance addiction is a type of brain disease that damages many areas of life of the individual and has psychological components. Since questioning alcohol use alone is not sufficient in the diagnostic evaluation process, self-awareness and the individual's long-term psychological well-being are important in the rehabilitation process. Since investigating alcohol use alone is not sufficient in the diagnostic evaluation process, self-awareness and the individual’s long-term psychological well-being are important in the rehabilitation process. Primary prevention is used to prevent disease in healthy people, and secondary prevention is used for early diagnosis to people at risk. Tertiary prevention is important to prevent recurrence of the disease in the treated person. In addiction, which is a chronic problem, a need has emerged for tertiary prevention, that is, rehabilitation standards. If people are uncertain about the harm perception and the natural result of their behavior, they easily slip into the substance. Therefore, as a positive psychotherapy technique, harm perception and result awareness levels should be clarified. For this reason, scales were developed. The inclusion of new psychology perspectives and therapy techniques, such as positive psychology, in the treatment process requires setting new standards. In this study, it was aimed to develop result awareness and harm perception scales to determine the awareness levels and rehabilitation standards of alcohol and substance addicted individuals, and to compare awareness in normal and addicted groups. The study group consisted of 1134 participants, 41 of whom were addicts. Among the two scales developed in the study, the Uskudar Result Awareness Scale (USRAS) consisting of 25 items and 6 factor, explained 58.4% of the total variance. The Uskudar Harm Perception Scale (USHPS), consisting of 36 items and 10 factors, explained 56.3% of the total variance. Confirmatory factor analysis of the two scales resulted in acceptable goodness-of-fit values. (X2/df<3; RMSEA<.08; NFI>.90; NNFI>.95; CFI>.95; GFI>.90; AGFI>.85). In the comparisons made, the result awareness of the normal group was moderate (X=3,81); whereas the addicted group had a low result awareness (X=3,20); The effect size of the difference between the two groups was found to be high (d=1,45; >0,8). In the other measure, the harm perception of the normal group was found in the low-risk group (X=3,78); the harm perception of the addicted group was found in the moderate-risk group (X=3,43). According to Cohen d calculations, the effect size of the difference between the two groups is high (d=1,43; >0,8). It has been concluded that the two scales that emerged in the research are valid and safe, can be included in the rehabilitation studies and the creation of self-awareness, and their effectiveness should be tested.

A compositional data vector is a special type of multivariate observation in which the elements of the vector are non-negative and sum to a constant, usually taken to be unity. A compositional data does not have zero and only retains relative information. Furthermore, comparisons can only be made between compositional data of the same component. The relevant sample space is the standard simplex. A simplex space is a space that is a generalized form of a triangle. For compositional data, many of the operations defined in Euclidean space are meaningless. Microbiome analyzes have become popular in recent years. Operational Taxonomic Units (OTU) and Amplicon Sequence Variants (ASVs) used in microbiome analysis are one type of compositional data. The microbiome data are counts of different species within a sample and it is compositional. Although there is an order of bacterial abundance within a sample, there is no order of bacterial abundance between samples. Firmicutes and Bacteroidetes, the major phyla in the colon, have been observed in humans worldwide. Gut microbiome analyses often use the Firmicutes/Bacteroidetes ratio and principal coordinates analysis (PCoA). Alpha and beta diversities are used as indicators of bacterial flora diversity. However, misinformation is pervasive in the human microbiome literature and analysis. There is a lot of misunderstanding regarding compositional data and its analysis. An attempt was made to demonstrate how to analyze using the “National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN JMD) (Public Data).” The results showed that PCoA did not work, and principal component analysis (PCA) was useful for analyzing the gut microbiome relative diversity.