Range Of Variation Research Articles

Effect of Li2O on physical, structural, thermal, optical, and electrical properties of mixed alkali borate glasses containing silver nanoparticles

In this work, mixed alkali borate glasses doped with Li2O were prepared with a molar composition of (75-x) B2O3-15Na2O-8CaO-1V2O5-1AgCl-xLi2O; x = 0, 5, 10, 15, and 20 mol% by using traditional melt-quenching process. X-ray diffraction studies confirmed the amorphous nature of the samples. Upon Li2O doping the glass density, measured at ambient temperature, increases from 2.336to 2.518 g/cm3, while molar volume decreases from 29.637 to 24.337 cm3/mol. The direct and indirect band gap energies determined using UV–Visible absorption analysis was in the range 3.000 –3.285 eV and 2.910–3.218 eV respectively. The DTA measurements indicated that with Li2O addition the thermal stability of the glasses has improved. FTIR study revealed the vibrational modes corresponding to the functional groups present in the glasses. The DC conductivity of the samples (measured at 588 K) was found to increase from 2.394 x 10-4 to 3.987 x 10-4 Ω-1m-1with Li2O content. The temperature dependency of DC conductivity followed Mott’s Small Polaron Hopping (SPH) model at high temperature and Variable Range Hopping (VRH) model at low temperatures. The linear fit of the data to the SPH model yielded the activation energy (W) values in the range 0.152 to 0.693 eV, whereas the density of states at Fermi level evaluated by the VRH model was in the order of 1030 eV-1m-3. The improved electrical conductivity of the prepared Li doped glasses together with their good thermal stability and transparency render them as potential candidates for optoelectronics device applications.

A numerical study of Lamb wave localization in thin plates using a passive co-linear phased array

Lamb waves have become increasingly popular in the field of aerospace vehicle non-destructive testing and evaluation as well as structural health monitoring. These guided waves possess the ability to travel long distances and exhibit a notable inclination to interact with existing damage. This work has numerically explored for the first time the use of a passive co-linear phased array to localize emission sources over a wide range of variables. Three localization methods are explored, namely, reverse beamforming, wavefront curvature ranging, and hyperbolic lateration in a direction comparison without modeling transducers. It was shown that both reverse beamforming and wavefront curvature ranging could localize an emission with <1% error in both range and bearing, while hyperbolic lateration was significantly worse. A relationship between bearing error and bearing was demonstrated, presenting the ability to develop new methods with correction factors that can localize emissions with even greater accuracy.

A Multi-Modal Approach for A Healthier Weight and More Active Lifestyle

Abstract Background Obesity and overweight has been increasing globally and in the UK over the last few years, bringing along a wide array of challenges in health and wellbeing. Not only do they take a heavy toll on those affected, but obesity and overweight-related ill-health is costing the UK National Health Service £6.1 with projected costs to run up to £9.7 billion in 2050. This project aimed to apply artificial intelligence to identify the predictive indicators for obesity and co-create an intervention with public health practitioners and stakeholders. Methods Using health and wellbeing public health population data from the 2015, 2018 and 2022 from a city in the East Midlands, we applied artificial intelligence that uses machine learning techniques to discover insights, find hidden patterns and discover relationships in the data about engaging in physical activity and a healthy diet. The data on dietary choices and physical activity preferences were further analysed using advanced machine learning techniques. Results The outcomes from our machine learning process revealed patterns of physical activity engagement and diet from specific locations, along with a range of demographic variables that influence individuals’ patterns of physical ability and dietary practices. These findings, which will be presented in detail, have been integrated into a comprehensive city map. This map not only showcases opportunities for engaging in a healthy lifestyle but also aids in prioritising interventions in areas of greatest need. Conclusions The AI technology we employed allowed us to parameterise and generalise our findings with the possibility of scaling up the machine learning approach to other similar datasets. The findings facilitated the development of bespoke training for public health officers working with underserved communities, further demonstrating the impact of applying AI to inform the design of public health interventions. Key messages • Applying Artificial Intelligence to public health data has the potential to inform the development of effective interventions and increase precision. • Using AI in public health data can generate new insights into population health and well-being and inform public health policy.

RV-PA uncoupling is a strong predictor of 3-year all-cause mortality in transthyretin amyloid cardiomyopathy

Abstract Background Right ventricle to pulmonary artery coupling (RV-PAc) describes the right ventricle's ability to adjust to an increased afterload which, in case of transthyretin amyloid cardiomyopathy (ATTR-CM), can result from increased left ventricular diastolic stiffness due to fibril deposition. While RV-PAc is a validated prognostic parameter in pulmonary arterial hypertension (PAH), its prognostic relevance in ATTR-CM remains unclear. This study sought to evaluate the prognostic implications of impaired RV-PAc on survival in ATTR-CM. Methods In this two-centre study, RV-PAc was investigated by the echocardiographic surrogate of ratio of tricuspid annular plane systolic excursion to estimated pulmonary arterial systolic pressure (TAPSE/PASP) in 418 ATTR-CM patients, all treated with TTR stabilizer. The primary outcome was all-cause mortality. Receiver operation curves and Youden’s J analysis were applied to identify an optimal cut-off value of RV-PAc ratio. Results Within a median time of 631 (IQR 361-983) days after diagnosis, 14.4% of patients died. RV-PAc ratio at first presentation emerged as a robust marker for risk stratification with a determined optimal cut-off of 0.382mm/mmHg (area under the curve (AUC) 0.746, 95% CI 0.68-0.82). Patients with RV-PAc ratio ≤0.382mm/mmHg (impaired RV-PAc, n=158) exhibited significantly lower survival (HR 4.07, 95% CI 2.26-7.33, p&amp;lt;0.001) within 3 years of follow up than those with RV-PAc ratio &amp;gt; 0.382mm/mmHg. In multivariate Cox regression analysis, incorporating a range of known prognostic variables, reduced RV-PAc was a strong echocardiographic predictor of mortality (Hazard Ratio (HR) 2.99, 95% Confidence Interval (CI) 1.63-5.51, p &amp;lt;0.001). Conclusion Impaired adaption of the RV to increased afterload is associated with worse outcome in ATTR-CM patients. RV-PAc ratio can serve as an echocardiographic predictor for all-cause mortality within three years after diagnosis. Therefore, determination of RV-PAc could improve risk stratification for ATTR-CM patients.Figure_1_Kaplan-Meier curves

A novel classification of dyslipidaemia through the analysis of five million lipid profiles: an unsupervised machine learning approach

Abstract Background Dyslipidemia encompasses a wide range of lipoprotein disorders categorised through two classifications (Fredrickson-Levy [FL] or Sniderman).(1,2) However, both classifications are criticised for relying on incomplete knowledge of lipoprotein metabolism, especially with the emergence of novel treatment options and variations in individual treatment responses.(3) Clustering, an unsupervised machine learning (ML) algorithm that can process a wide range of variables, has the potential to unmask patient groups with distinct molecular profiles and unique therapeutic targets that can inform more effective prevention strategies for cardiovascular disease (CVD).(4) Aim We aimed to use unsupervised ML algorithms to discover intrinsic dyslipidaemia categories from lipoprotein measurements, recognise the necessary components of lipid panels for classification, and analyse the similarities between the newly formed clusters, FL and Sniderman classifications. Methods Lipid profiles of 5,080,248 patients were obtained from the ‘Very Large Database of Lipids’ database. This yielded up to 78 blood components per patient, including at least 31 lipoprotein variables. The analysis involved unsupervised K-means clustering with optimised values for K and the subset of variables, determined in an unsupervised manner using a suitable measure of complexity. We then interpreted our clusters using probabilistic decision trees to provide compact and interpretable representations. Finally, we compared the clusters with Sniderman and FL categories. Results In a completely unsupervised fashion, we identified 14 clusters that could be matched to Sniderman categories. The confusion matrix showed total agreement of 76% (see Figure 1, left panel), relative Cohen’s kappa of 0.78 (the relative version captures accuracy on categories containing smaller numbers of patient profiles) and an accuracy of 96% on the small Type III class. Similar results were observed when matching to FL types. We accurately represented our clusters using probabilistic decision trees of small depth (see Figure 2). We discovered that the data had low intrinsic dimension and a manifold-like structure in which the different clusters could be illustrated (see Figure 1, right panel). Specifically, only 3 variables were needed to obtain our classification: apolipoprotein b, total cholesterol and triglycerides. Conclusion We showed that completely unsupervised ML techniques can uncover dyslipidaemia categories in lipoprotein profiles from a large patient population. The categories largely align with existing classifications based on prior knowledge of lipoprotein metabolism. Furthermore, few lipoprotein variables were required for categorisation (low-dimension data), which could aid in determining which lipoproteins should be measured in a clinical setting. Further analysis of the differences between ML clusters and traditional classifications is needed, which may enhance CVD risk management.

Photoinduced modulation and the effect of CNT loading on field effect transistor characteristics of CNT/ZnO/PVDF composite.

CNT/ZnO/PVDF polymer composite phototransistor is studied for the effect of CNT loading and the photoinduced modulation on its transfer characteristics. XRD study shows that the induced strain in the composite is due to the addition of CNT to the ZnO/PVDF composite. The percentage of β-phase present in PVDF is estimated through Raman spectroscopy and the composite's spectral response is determined by UV-Vis absorbance spectroscopy. From the DC electrical conductivity study it is found that the percolation threshold for the composites is obtained for 0.3 wt% of CNT, and 0.44 wt % of CNT loading makes the composite conductive. On adding 1 wt% of CNT, the electrical conductivity of the ZnO/PVDF composite increases 40 times (~ 0.2 μS/m). The temperature-dependent DC conductivity shows that the conductivity of the composites changes from variable range hopping (VRH) to band conductance upon an increase in CNT loading above the percolation threshold and exhibits a negative temperature coefficient (NTC). Two terminal photoconductivity studies are done to understand the photo enhancement and sensitivity of all the devices. PE hysteresis studies show that the polarization of the composites increases drastically from 0.05 μC/cm2 below the percolation threshold to 10 - 30 μC/cm2 above the percolation threshold of CNT in the composite. To study the effect of interfacial polarization on photoconductivity, the composite is studied in a three-terminal device format using SiO2 as a gate dielectric. A band diagram analysis of the oxide-composite and CNT/ZnO interface is done to understand the mechanism behind the photoinduced field effect on transfer characteristics and the effect of CNT loading. The switching behavior and decay time under UV illumination are studied to understand the effect of CNT loading and photoinduced polarization. The persistent photoconductivity decreases and the charge collection efficiency of the FET increases as the CNT loading increases.

Employment and mental health for adults on probation, 2002-2021.

The 21st century has seen a decline in employment rates in the US at the same time that it has experienced a historically unprecedented rise in the numbers of adults under criminal justice system control. Both low employment and high incarceration have posed serious challenges for public health. Using data from the National Survey on Drug Use and Health from 2002-2021, we estimated employment rates by community supervision status. Variations by sociodemographic subgroups were explored as well as correlations between employment and a range of mental and behavioural health variables. Those on probation were twice as likely as those not to live in poverty. They experienced higher rates of poor mental and behavioural health, including three times the rate of substance use. Employment rates varied little by community supervision status. Health risk factors were associated with more risk and protective factors did less to mitigate risk for those under community supervision. Despite the range of adversities faced by individuals under criminal justice system control, their employment rates are remarkably close to those not. Despite near equivalent involvement in the labour force, this population has substantially poorer health and substantially reduced likelihood of escaping poverty.

Evaluation of the flexural stiffness of a lattice girder composite slab at the construction stage according to the lattice girder shape

Lattice girder composite slabs (LGCS) for use in construction stages are structures in which part of the lattice girder is embedded in precast concrete panels with a thickness of less than 100 mm. they serve as a permanent formwork supporting the casting loads of on-site concrete while constituting a portion of the RC slab. In this study, analytical research was conducted to evaluate the flexural stiffness of LGCS effectively and simply considering a range of variables. Based on the results, an Euler-Bernoulli beam-theory-based equivalent sectional analysis method is proposed. To achieve this, the study extracted the second moments of area (Iexp) from the experimental results of previous researchers and compared them with the second moments of area (Ibeam) calculated using the Euler-Bernoulli beam-theory-based sectional analysis, analyzing how the ratio (Ibeam/Iexp) varies according to the design parameters. Variable analyses over a wide range were conducted by expanding the types and ranges of variables, and the effects of different variables and ranges were analyzed by comparing the second moments of area based on an FE analysis of LGCS (IFEM) with those based on a beam analysis (Ibeam). Furthermore, to apply the equivalent sectional analysis method to LGCS, equivalent second-moment-of-area formulas were proposed through the equivalent neutral axis and equivalent arm length for each sectional element.

Predicting financial performance with intellectual capital using machine learning

Purpose This study aims to apply machine learning techniques to efficiently predict leisure firms’ financial performance. Accurate financial forecasting is crucial in leisure and tourism, greatly affecting firms’ strategic decisions and competitive positioning. This study emphasizes the roles of intellectual capital to offer a nuanced understanding of how these types of capital influence firm success. Design/methodology/approach Using comprehensive firm-level data, this study examines several machine learning algorithms’ predictive capacity across a spectrum of industry sectors (general, manufacturing, service) to identify the most effective model and training dataset. These tools are used to evaluate financial metrics such as return on sales, return on assets and sales growth. A range of variables are incorporated into this process to enhance model accuracy and relevance. Findings Results demonstrate the support vector machine algorithm’s exceptional performance based on a training data set from the service sector in predicting leisure firms’ return on sales and sales growth. This algorithm is thus an efficacious strategic forecasting instrument. The variables significantly affecting firm performance include demand variation; organizational, product and technological innovation; synergistic innovation between multiple domains; salary levels; market strategy; and the number of employees. Originality/value By integrating advanced machine learning techniques with the strategic management of intellectual capital, this study presents a sophisticated approach to predicting leisure firms’ financial performance. Findings enrich the discourse on firm performance forecasting and offer actionable insights into strategic planning and resource allocation for practitioners in the leisure and tourism sectors.

Lifestyle changes and patient-reported outcomes over five years in a sample of people with multiple sclerosis after a single multimodal intensive lifestyle education workshop.

Modifiable lifestyle risk factors for progression of multiple sclerosis (MS) have been increasingly studied. This study employed a single-group design involving a one-off intensive live-in educational workshop on lifestyle modification for people with MS. We aimed to examine changes in a range of clinical and lifestyle variables and quality of life, self-efficacy, physical impact of MS and disability from baseline to 3- and 5-years post-intervention. 95 participants completed the baseline survey. Data included lifestyle risk factors of diet quality, meat and dairy consumption, omega 3 and vitamin D supplementation, physical activity, stress reducing activities, and smoking status, and use of disease-modifying therapies (DMTs). Patient-reported outcomes included health-related quality of life, self-efficacy, physical impact of MS and disability. Generalised estimating equation models were used to account for within-participant correlation over time. Sixty participants (63.2%) provided data at 3- and 5-years. Significant improvements in diet quality, omega 3 supplementation, and non-smoking were seen at both timepoints. Use of DMTs and disability remained unchanged. Mental (8.8- and 6.9-point) and physical (10.5- and 7.3-point) quality of life, and self-efficacy (2.4- and 1.9-point) improved significantly at 3- and 5-years, respectively. Physical impact of MS reduced from baseline to 3-years (-3.7 points) with a trend towards reduction at 5-years (-2.9 points; p = 0.079). Education on lifestyle modification can lead to lifestyle modification and short and long-term improvements in mental and physical wellbeing outcomes. Results suggest potential value in lifestyle modification as an adjunctive component to standard therapy for MS.

Enhancing Surveillance Systems with Cost-Effective Ultrasonic Radar for Object Detection and Tracking

Object detection is critical in surveillance systems because it enables timely identification of unauthorized objects, which is essential for maintaining security and mitigating risks. Existing approaches to surveillance typically suffer from limited coverage, variable detection range, moderate accuracy, high cost, and complex setup requirements. This article presents an approach to object detection using microcontroller-based ultrasonic sensors. Our approach leverages four distinct processing phases to create a radar-like system capable of real-time detection and autonomous decision-making. In this study, we conducted experiments by integrating more than six peripheral modules with the Arduino platform. By evaluating 17 diverse test cases in various scenarios and environments, the approach demonstrated enhanced object tracking and robustness compared to existing methods. The system effectively detects objects within the targeted area, providing precise distance measurements (around 50 cm) and position information (0 to 360 degrees). Moreover, our approach enabled the identification of three risk zones—medium, high-risk, and mild danger—within the critical region, contributing to improved security and risk management. The results reveal the system’s effectiveness in real-time detection, accurate distance estimation, and comprehensive risk assessment. Overall, our study significantly contributes to the advancement of object detection technology by effectively addressing the limitations of existing methods and offering a cost-effective and practical solution. Our research has the potential to monitor objects in surveillance systems across various domains, with significant implications for enhancing security, risk mitigation, and monitoring in industries reliant on effective object detection systems. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 157−171

Mitochondrial Maintenance in Skeletal Muscle.

Skeletal muscle is one of the tissues with the highest range of variability in metabolic rate, which, to a large extent, is critically dependent on tightly controlled and fine-tuned mitochondrial activity. Besides energy production, other mitochondrial processes, including calcium buffering, generation of heat, redox and reactive oxygen species homeostasis, intermediate metabolism, substrate biosynthesis, and anaplerosis, are essential for proper muscle contractility and performance. It is thus not surprising that adequate mitochondrial function is ensured by a plethora of mechanisms, aimed at balancing mitochondrial biogenesis, proteostasis, dynamics, and degradation. The fine-tuning of such maintenance mechanisms ranges from proper folding or degradation of individual proteins to the elimination of whole organelles, and in extremis, apoptosis of cells. In this review, the present knowledge on these processes in the context of skeletal muscle biology is summarized. Moreover, existing gaps in knowledge are highlighted, alluding to potential future studies and therapeutic implications.

The Clash of the Policies: The Joint Effect of EU Cohesion Policy and Common Agricultural Policy on the Public Support for European Integration

AbstractA vital element of the European Union (EU) political system is the idea of further European integration. Whilst most existing works investigate the effects of the Cohesion Policy (CP), only a handful of studies consider the effects of the Common Agricultural Policy (CAP) on citizens' support for European integration. In this contribution, I argue that these two largest EU redistribution policies neutralise each other in their effects on the citizens' support for European integration: the CP improves public support due to its positively perceived redistributive nature and positive externalities, whilst the CAP decreases public support due to due to a limited target audience, somewhat elitarian distribution of resources and bureaucratic complexities. Using regional‐level CP and CAP data for 2017 and Eurobarometer surveys for 2018 and 2019, the empirical findings of multilevel regressions show that the CAP counteracts the positive effects of the CP on citizens' support for further European integration. The findings are robust to including a wide range of control variables, sample size change and alternative specifications of key independent variables. These findings also hint that policy evaluations should not disregard the differences between various policy instruments.

A shallow mantle seismic discontinuity beneath northeast India: Evidence from receiver function analyses

The crust and shallow upper mantle structure beneath the Upper Brahmaputra Valley, Indo-Burma Ranges, and Bengal Basin of Northeast India have been investigated based on receiver function (RF) analysis of teleseismic earthquakes recorded by 11 seismological stations. The study reveals a thin crust (∼35 km) beneath the Brahmaputra Valley (at JORH station) with a surface sedimentary layer of ∼4 km thick. The crustal thickness is observed to increase towards the north in the Himalaya (∼40 km at ZIRO and ITAN) and to the south (up to ∼46 km at KOHI). The crustal thickness near the Tripura fold-belt and Bengal Basin varies within ∼36–40 km. The study reveals the existence of a shallow mantle discontinuity (Hales discontinuity) at a variable depth range of ∼54–78 km characterized by a step increase (∼7.5–11 %) in shear wave velocity observed in the inverted models. The mineralogical phase transformation from spinel to garnet is considered as the origin of this discontinuity. The shallow depth of the discontinuity indicates an increase in upper mantle temperature which conforms to the high geothermal gradient reported in the region. The variation of depth of the discontinuity can be interpreted in terms of the addition of Cr+3 that shifts the spinel-garnet stability field to higher depths whereas Fe+2 shifts it to lower depths. Despite the high temperature in the upper mantle, the observed low Vp/Vs ratio (1.65–1.75) below the Hales discontinuity can be explained by the presence of a high fraction of orthopyroxene.

Lymph node ratio is a prognostic indicator for locally advanced gastric cancer after neoadjuvant immunochemotherapy

ObjectiveThe efficacy of lymph node ratio (LNR) as a prognostic indicator in locally advanced gastric cancer (LAGC) patients underwent radical resection after neoadjuvant immunochemotherapy (NICT) remains to be demonstrated. The objective of the current retrospective study is to investigate the relationship between LNR and survival in patients with LAGC who underwent radical resection after NICT.MethodsA retrospective analysis was performed on 121 cases of LAGC in patients underwent radical resection after NICT between July 2020 and October 2023. The LNR values of the patients were divided into two groups using X-tile software. The first group, designated the low LNR group, comprised patients with LNR values of ≤ 33%. The second group, designated the high LNR group, comprised patients with LNR values of > 33%. The correlation between patient survival rates and a range of clinical and pathological variables was examined.ResultsOverall, 121 patients were enrolled: 108 with low-LNR (LNR ≤ 33%) and 13 with high-LNR (LNR > 33%). A better 2-year overall survival (OS) (88.5% vs. 32.6%; p < 0.001) and progression-free survival (PFS) (80.2% vs. 23.5%; p < 0.001) were observed in patients with low LNR. A similar result was also found in those with non-pathological complete response group (non-pCR), where the 2-year OS was 87.2% vs. 32.6% (p < 0.001), and the 2-year PFS was 77.7% vs. 23.5% (p < 0.001). Compared to the pathologic lymph nodes staging (ypN), LNR exhibited similar prognostic capabilities for OS and PFS. Multivariate analysis indicated that LNR was an independent prognostic factor for both OS (HR 6.258, 95% CI 1.798–21.778; p = 0.004) and PFS (HR 3.431, 95% CI 1.341–8.780; p = 0.010), but not ypN.ConclusionsLNR may serve as a viable indicator for prognostication in LAGC patients treated with NICT.

Exploring climate-reproductive concern: factors influencing hesitancy towards parenthood in the context of the climate crisis

ABSTRACT The uncertainty surrounding the future influences significant life decisions, including the choice to have children, and previous research has raised concerns about how climate change might impact young adults’ reproductive plans. This study, conducted through an online survey, aims to provide insight into factors that distinguish individuals with high climate-reproductive concern. Specifically, we investigate the relationship between different levels of climate-reproductive concern and various psychographic factors such as attitudes towards reproduction and parenthood, the desired number of children, climate anxiety, depressive symptoms, values, environmental concerns (including overpopulation and resource use), pro-environmental behaviors, and a range of socio-demographic variables. Using a sample of 334 childless Americans aged 18 to 44, we found no significant differences across levels of climate-reproductive concern in parenthood attitudes, pro-reproductive attitudes, or the socio-demographic variables except for political leaning. Notable discrepancies existed for anti-reproductive attitudes, the preferred number of children, overpopulation concerns, altruistic values, and pro-environmental behaviors. Climate anxiety emerged as a crucial factor distinguishing between levels of climate-reproductive concern, whereas general depressive symptoms did not play a significant role. Our research provides a nuanced understanding of adults who hesitate to have children due to climate change concerns and offers implications for various stakeholders and research disciplines.

Inheritance of signs of parent species by hybrids of Russet, Yellow and Speckled ground squirrels (&lt;i&gt;Spermophilus&lt;/i&gt;, Sciuridae, Rodentia)

The inheritance of size, color and bioacoustic characteristics of Russet (Spermophilus major), Yellow (S. fulvus) and Speckled (S. suslicus) ground squirrels in hybrids differing in their genetic status were studied. In a hybrid population of Russet and Yellow ground squirrels, 10 individuals of S. major, 10 individuals of S. fulvus and 40 hybrids of S. major × S. fulvus were analyzed; in a hybrid population of Russet and Speckled ground squirrels, 11 individuals of S. major, 11 individuals of S. suslicus and 24 hybrids of S. major × S. suslicus were analyzed. Hybrid individuals different in genetic status have been shown to demonstrate differentiation in the vector space of the variability range of the parental species. At the same time, both a shift in some categories of hybrids towards one of the hybridizing species and a significant deviation are noted. The results obtained indicate an increase in the level of variability in size, color and bioacoustic characters in hybrid populations, associated with the erosion of the divergent hiatus of specific features of the parental forms due to the formation of a differentiated hybrid population. As hybrid individuals with different combinations of parental traits accumulate in the population, the situation arises when a sufficient number of hybrids with an overall combination of parental traits (Hcomb) appear in the hybridogenic population. In the hybrid population of the Russet and Yellow ground squirrels, the proportion of such hybrids, according to our data, is 22.5% (n = 40), and in the hybrid population of the Russet and Speckled ground squirrels it is 12.5% (n = 24). This category of hybrid individuals can be considered as possible material for further microevolutionary events and the process of speciation.

Correlation dimension for temperature and other micrometeorological variables in different ground cover from six biomes of the Amazon basin

Correlation dimension () for embedding dimensions, varying between 3 to 11, were estimated for a 1- or 2-month time series of air temperature and other micrometeorological variables, such as energy fluxes, solar radiation and concentration, in six ecosystems in Amazon and the central region of Brazil. Dc remained within the 1.5 - 3.0 range, for variables locally coupled to ecosystems, and outside the range for variables not locally coupled, such as precipitation and wind velocity. Results indicate that the feature of the variables does not have an important stochastic component. There is a small amount of data available concerning estimates of correlation dimension () of micrometeorological variables. The measurement of for several variables from six Brazilian biomes indicates that this parameter is sensitive to environmental conditions as the rainfall. The obtained results also indicates that the natural processes in the ecosystems remain in a non-chaotic regime. In certain circumstances, as in conditions of high humidity, natural ecosystems may stay inside a limit which is close but lower than chaos. Moreover, an important stochastic component was not observed.

Correlations and Variations Between the Major Biochemical Parameters of the Blood of Hybrid Swine.

In modern animal husbandry, increasing attention is given to mathematical modeling and statistical methods, especially for evaluating commercial hybrids. Our aim was to evaluate the phenotypic and genetic variability of biochemical parameters of blood serum of the 56 hybrid boars (Large White × Landrace × Duroc) raised in feeding stations (Russia) through mathematical modeling. The particular variances and covariances of traits were calculated using the limited maximum likelihood model and the REMLF90 programs. A narrow range of variability was found for major biochemical parameters in relationship with the "FFG-factor" ("fattening period × final live weight × gain"), including the majority of the metabolites (p ≤ 0.05). The highest values of the genetic correlations were observed for the "total protein" parameter with albumins (0.78), globulins (0.94), creatinine (0.99), and enzymes: AST (0.98), ALT (0.80), etc. Phenotypic and genetic relationships showed fairly high correlation coefficients (0.5-0.8). It is important to emphasize that most of the studied amino acids (alanine, arginine, aspartic acid and asparagine, glutamic acid and glutamine, glycine, isoleucine, leucine, serine, threonine, tyrosine, valine) were significantly associated with the "FFG-factor" (p ≤ 0.05). The proposed approach provides reliable data on metabolite variability and correlations.

Impact of Variable Device Structural Changes on Particle Deposition Distribution in Multi-Rotor UAV

The aim of this study was to investigate the effect of structural changes in variable fertilizer application devices on the distribution of particle deposition in UAVs. With the rapid development of drone technology, particularly in particulate spreading, drones have demonstrated significant potential due to their efficiency and precision. This paper evaluates the impact of different variable adjustment modes of the device on particulate deposition distribution through drone spreading experiments and particulate deposition data analysis. In this study, device structure change is the main variable factor, and flight altitude, flight speed and ambient wind speed are single quantitative factors. Experiments were conducted by varying the structure of the device to test the detailed deposition distribution of the device under group a, b, and c structures. Experimental results indicate that by choosing different variable combinations, the spreading device can achieve various fertilizer deposition states to meet regional needs. Among all 27 variable groups, the fertilizer particle deposition data for group b1b2b3 is relatively uniform, with three-quarters of particulate deposition values being 3 g/m2 and the maximum value being 4 g/m2. However, even with a relatively uniform distribution of fertilizer particles, the coefficient of variation for group b1b2b3 remains high (36.5%), with a range of 4.5% to 41%. Under different group adjustments, the particle distribution shows the smallest variability range in group b1b2b3, with a range of 15.71–26.44% and a variability difference of 10.73%. The particle distribution shows the largest variability range in group a1a2b3, with a range of 0.78–35.06% and a variability difference of 34.28%. These research conclusions provide important guidance for the study and practice of drone spreading systems.