Abstract: The VSI represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with Irritable Bowel Syndrome (IBS) and other Chronic Inflammatory Bowel Diseases (IBD). However, an Italian version of the instrument is not yet available for the reference population. This study utilized a national sample of 500 individuals, including healthy controls and patients with Ulcerative Colitis and Crohn's Disease, to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, the research applied a questionnaire and the VSI through an online format to 500 participants. Data analysis revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach’s α = 0.95) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with other measures of anxiety and the severity of gastrointestinal symptoms, confirming its role in enhancing the understanding and management of IBS and IBD in Italy.

This paper aims to explore the relationship between airfoil deformation and airfoil aerial-aquatic adaptation. The form of “flexible skin and rigid drive structura” holds immense potential for various applications. In this study, we utilize the B-spline method to parametrically describe the airfoil shape. An airfoil deformation parameterization method is proposed based on the isogeometric method. This method allows us to depict airfoil deformation as the position and displacement of the driving point, enabling an expression of the flexible skin's deformation driven by the internal structure. We utilize the Xfoil application and Gaussian regression method to elucidate the relationship between driving point position and displacement and the airfoil's dynamic performance. Additionally, we employ the Sobol method to assess the sensitivity of the driving point variables to the airfoil's dynamic performance. Our findings indicate that the attack angle of the airfoil exhibits the highest sensitivity index to the airfoil's dynamic performance. Furthermore, variables of the driving point situated at the leading and trailing edges of the lower surface significantly impact the airfoil lift coefficient, those located at the mid-section of the upper surface have a considerable effect on the airfoil drag coefficient.

Advanced lithography requires highly sensitive photoresists to improve lithographic efficiency, and it is critical yet challenging to develop high-sensitivity photoresists and imaging strategies. Here, we report a novel strategy for ultra-high sensitivity using hexafluoroisopropanol (HFIP)-containing fluoropolymer photoresists. The incorporation of HFIP, with its strong electrophilic property and electron-withdrawing effect of the fluorine atoms, significantly increases the acidity of the photoresist after exposure, enabling imaging without conventional photoacid generators (PAGs). The HFIP-containing photoresist has been evaluated by electron beam lithography to achieve a trench of ~40 nm at an extremely low dose of 3 μC/cm2, which shows sensitivity enhancement by ~10 times to the commercial PAGs involved system, revealing its high-sensitivity and high-resolution features. Our results demonstrate a new type of PAGs and a novel approach to higher-performance imaging beyond conventional photoresist performance tuning.

The design complexity of the new generation of civil aero-engines results in higher demands on engines’ components, higher component temperature, higher heat generation, and, finally, critical thermal management issues. This paper will propose a methodological approach to create physics-based models for heat loads developed by sources as well as a systematic sensitivity analysis to identify the effects of design parameters on the thermal behaviour of civil aero-engines. The ranges and levels of heat loads generated by heat sources (e.g., accessory gearbox, bearing, pumps, etc.) and heat absorption capacity of heat sinks (e.g., engine fuel, oil, and air) are discussed systematically. The practical research challenges for thermal management system design and development for the new and next generation of turbofan engines will then be addressed through a sensitivity analysis of the heat load values as well as the heat sinks flow rates. The potential solutions for thermal performance enhancements of propulsion systems will be proposed and discussed accordingly.

Introduction/Aim: Central sensitivity to thyroid hormones refers to the sensitivity of the hypothalamic-pituitary-thyroid (HPT) axis to changes in circulating free thyroxine (fT4). The relationship between fT4 levels and iodine intake is complex. The aim of the present study was to assess central sensitivity to thyroid hormones in pregnancy against iodine intake. Materials & Methods: One thousand one hundred two blood and urine samples were collected from pregnant women (with a mean age+SD of 30.4+4.6 years) during singleton pregnancies; women with known/diagnosed thyroid disease were excluded. Specifically, TSH & fT4 and 24-hour urinary iodine excretion (UI) were measured in each trimester and at two months postpartum. The Thyroid Feedback Quantile-based Index (TFQI) was calculated to assess central sensitivity to thyroid hormones. For the statistical analysis of TFQI by time period, analysis of variance (ANOVA) was used, while Pearson's correlation was used to assess TFQI versus UI. Results: The mean TFQI index ranged from -0.06 (first trimester) to -0.07 (two months postpartum), while the corresponding UI was 141 and 172 μg/L, respectively. The TFQI-UI correlation was negative (Pearson r: -0.325, p: 0.04 and r: -0.399, p: 0.023), for UI values over 250 μg/L, in the first and the third trimester of pregnancy, respectively. Discussion: TFQI is a new index reflecting central sensitivity to thyroid hormones. Lower TFQI indicates higher sensitivity to thyroid hormones. In our sample TFQI was inversely related to iodine intake, in the first trimester of pregnancy (the critical period of organogenesis) and the third trimester (the critical period of fetal growth). Thus, the observed changes in TFQI may reflect different ways of central action of thyroid hormones, according to the phase of pregnancy. The negative association between maternal TFQI (indicative of central sensitivity to thyroid hormone) and urine iodine levels during the third trimester of pregnancy may reflect the combined influence of maternal and fetal thyroid function. Our results are intriguing and have the potential to enhance our comprehension of the changes in the HPT axis’ function via variations in central sensitivity to thyroid hormones and its interplay with nutritional iodine status during pregnancy.

This paper investigates five different GARCH models (GARCH, EGARCH, IGARCH, Component GARCH (CGARCH) and GJR-GARCH) along with six distributions (Normal, Student’s t, GED and their skewed forms), which are used to forecast the volatility for the Bulgarian Stock index SOFIX. We use a GARCH model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data covers the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at forecasting the volatility of SOFIX and which distribution is most appropriate. The results, based on BIC and AIC information criteria, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student t-distribution is preferred for forecasting. With the help of 5000 simulations, it is estimated that the chances of Index SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.

Background: The modified Duke index derived from coronary computed tomography angiography (CCTA) was designed to predict cardiovascular outcomes based on the severity of coronary ste-nosis. However, it does not take into consideration the presence or severity of peri-coronary in-flammation. The peri-coronary fat attenuation index (FAI) is a novel imaging marker determined by CCTA which reflects the degree of inflammation in the coronary tree in patients with coronary artery disease. Purpose: To assess the association between the modified Duke index assessed by CCTA, cardiovascular risk factors and peri-coronary inflammation in the coronary arteries of pa-tients with coronary artery disease. Methods: One hundred seventy-two patients who underwent CCTA for typical angina were assigned into two groups based on the modified Duke index: group 1 – patients with low index ≤ 3 (n=107) and group 2 – patients with high index > 3 (n=65). Demo-graphic, clinical and CCTA data were collected for all patients, and FAI analysis of coronary in-flammation was performed. Results: Patients with increased values of the modified Duke index were significantly older compared to those with a low index (61.83 ± 9.89 vs. 64.78 ± 8.9; p = 0.002). No differences were found between the two groups in terms of gender distribution, hypertension, hypercholesterolemia or smoking history (all p > 0.5). The FAI score was significantly higher in patients from group 2, who presented a significantly higher score of inflammation compared to the patients in group 1, especially at the level of the right coronary artery (FAI score 20.85 ± 15.80 vs. 14.61 ± 16.66; p = 0.01 for the right coronary artery, 13.85 ± 8.04 vs. 10.91 ± 6.5; p = 0.01 for the circumflex artery, and 13.26 ±10.18 vs. 11.37 ± 8.84; p = 0.2 for the left anterior descending artery). CaRi-Heart® analysis identified a significantly higher risk of future events among patients with a high modified Duke index (34.84 % ± 25.86 % versus 16.87 % ± 15.80 %; p

The emotion of disgust protects individuals against pathogens, and it has been found that during pregnancy, it is elevated. Physiological mechanisms discussed in relation to these changes include immune markers and progesterone levels. The aim of this study is to assess the association between steroids and disgust in pregnancy. In a sample of 179 pregnant women, we have analyzed blood serum steroid concentrations and measured disgust sensitivity by text-based questionnaires in the first and third trimester. We found positive correlations between disgust sensitivity and the levels of C19 steroids, including testosterone, but also its precursors in the Δ5 pathway (androstenediol, DHEA, and their sulfates) and the Δ4 pathway (androstenedione). Furthermore, positive correlations between disgust sensitivity and 5α/β-reduced C19 steroid metabolites were consistently present in both trimesters. In the first trimester, we found a positive association between disgust sensitivity and 17-hydroxypregnanolone as well as some estrogens. In the third trimester, we have observed positive associations between disgust sensitivity and cortisol as well as immunoprotective Δ5 C19 7α/β-hydroxy-steroids. Our study has thus shown that disgust sensitivity is positively correlated with immunomodulatory steroids, and in the third trimester, steroids may reflect potential maternal anxiety-related symptoms.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

It is imperative to conduct experimental studies on the seepage behavior of gas-bearing coal under cyclic dynamic loading conditions. This paper focuses on the evolution of coal permeability under the combined effects of dynamic loading, static loading, and gas adsorption. The principal conclusions are as follows. (1) As the frequency and amplitude of dynamic loading increase, the development of pore and fissure structures within the coal body becomes increasingly pronounced during dynamic loading cycles, resulting in a gradual rise in permeability. Notably, as the coal approaches its yielding stage, the permeability can increase by up to 47%. (2) The permeability curve is divided into four regions: the compaction reduction zone, the oscillation zone, the gradual recovery zone, and the abrupt increase zone of failure. Ultimately, in the failure phase, the permeability surges dramatically, potentially reaching 4 to 5 times the initial permeability. (3) When the static load stage is constant, the rate of change in permeability of the coal under dynamic loading decreases with increasing adsorption amount. When the adsorption amount is constant, the rate of change in permeability of the coal under dynamic loading increases with the increase in static load stress stage, with the maximum increase reaching 75.2%. It can be concluded from the rate of change in permeability and the dynamic loading sensitivity coefficient that the permeability of the coal is highly sensitive to cyclic dynamic loading, with increased sensitivity associated with larger static load stages and decreased sensitivity with greater adsorption amounts.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Weathering and erosion transport minerals and organics toward the sea or lake bottoms over geologic time. The finest solids are deposited in lower, less turbulent areas, such as lake bottoms and continental shelves. They sometimes stack up to thicknesses of kilometers, and begin compacting. These sediment sections are called shales, and as initially deposited in water, shales can have porosities up to 50-80{\%} water, As they are buried, many alteration products from oil to slate are produced due to overburden and temperature increasess, making them important to study. A preceeding paper \cite{1} showed that pressure solution is the primary mechanism for porosity reduction, with possible mechanical compaction at shallow depths. Without naming the mineral(s) involved, it postulated that the greater the product of the water and pore interfaces, the faster the reaction would proceed. This term is $ \varphi^{4m/3}(1-\varphi)^{4n/3}$ , where $\varphi$ is porosity and m and n are numbers close to unity. The large exponents, {\em 4/3}, recognize that the reaction occurs at the molecular scale at which the surfaces are rough. A second term, $\exp^{(-E/RT)}$ , indicates that the reaction is impeded by a quantum energy barrier, E, with diminished impeding power as increased available thermal energy, represented by the absolute temperature, T, becomes available at greater depths in the Earth. These two factors combine to allow porosity $\varphi$ to reduce with time, or equivalently for the fraction of solids, $(1-\varphi)$, to increase with time,$\left. \frac{\partial(ln(1-\varphi) ) }{\partial t } \right |_{\sigma} = (\varphi )^{4m/3}( 1-\varphi )^{4n/3}Ae^{-E/(RT)}$. { \em Here it is shown that this equation can cover quantitatively any actual deposition rates which may have been experienced by the six sections studied, the actual deposition rates being unknown for these cases. Hence a time-depth depositional history for any new shale section, known in detail, would allow determination of the parameters m, n, E and a lumped proportionality constant A. This was accomplished by showing that, for a wide range of depositional rates, r, the range of E for any of the studied sections is small compared to laboratory measurements of quartz solution\cite{2}, 24+-15kJ/mol. Results were obtained over this range of r's using the previously determined m and n, and porosity and temperature profiles. The presently existing porosity profiles necessarily incorporate any overpressure or underpressure conditions that may have existed in the past or currently, as the net difference between overburden and pore pressure is a primary driving force for pressure solution. $SiO_2$ usually comprises 20-50{\%} of shales. In conclusion, pressure solution of quartz can account for vertical compaction of shales quantitatively in the studied examples. Separately, a quasi-eqivalent method for discussing reduced porosities in Macron 2 versus Macron 1 as increased vertical stress, rather than additional horizontal stress, is illustrated. Experiments are suggested to clarify the pressure solution mechanism. The role of horizontal forces is discussed. From a global point of view, shale compaction by pressure solution is a quantified step in the cycling of continental rocks, beginning with rifting.

Background: Gender stereotypes and biases can influence perceptions of competence and suitability for certain medical specialties. Empathy is an important component of an optimal doctor-patient relationship. The scope of our study is to verify the correlation between “empathetic" behavioral style with attention and sensibility to the gender differences of patients of the medical students. Methods: An online survey was conducted using the Jefferson Scale for Empathy (JSE), to measure empathy in the context of health professions education and patient care; and the Nijmegen Gender Awareness in Medicine Scale (N-GAMS), which measures attitudes and assumptions about gender. A total of 234 students (52.1% M; mean age: 22.68±2.48) from Medicine and Surgery at the University of Salerno (Italy) participated, of whom n° 175 were third-year students and n° 59 were fifth-year students. Results: The JSE score shows that male students have higher scores (77.29±12.38) than females (75.91±10.39). Conversely, female students have higher perspective-taking (JSE Factor 1: Male= 51.26±11.84; Female= 52.72±11.88). In addition, third-year students are much more empathetic than their fifth-year colleagues (JSE Score: 3°ys= 77.35±11.46; 5°ys= 74.49±11.33). With reference to the GS, we found that male students have greater sensitivity than their female colleagues (GS: Male= 2.67±0.68; Female= 2.37±0.48); conversely, they have higher scores of gender stereotype towards patients (GRIP: Male= 2±0.88; Female= 1.59±0.61) and doctors (GRID: Male= 1.88±0.89; Female= 1.64±0.63). Also, the third-year students compared to their fifth-year colleagues are characterized by greater gender sensitivity (GS: 3°ys= 2.57±0.62; 5°ys= 2.42±0.57), less bias with respect to patients (GRIP: 3°ys= 1.77±0.79; 5°ys= 1.90±0.78) but, on the other hand, show more stereotypes and prejudices towards doctors (GRID: 3°ys= 1.77±0.81; 5°ys= 1.75±0.74). Conclusions: Humanistic attitudes, including empathy, should be instilled, and evaluated among trainees as an essential part of their postgraduate medical education.

.Bacground: Sepsis remains a major health challenge worldwide, characterized by a dysre-gulated host response to infection, leading to high mortality and morbidity in intensive care units (ICUs). The Fibrosis 4 (FIB-4) index, originally developed to assess liver fibrosis in hepatitis C patients, has recently been explored for its potential prognostic value in sepsis patients. Method: This study retrospectively analyzed 309 sepsis patients admitted to the Internal Me-dicine and Anaesthesia ICUs between December 12, 2021, and December 15, 2023, to inves-tigate the relationship between FIB-4 levels, Acute Physiology and Chronic Health Evaluation (APACHE), and Sequential Organ Failure Assessment (SOFA) and clinical outcomes. Results: The study found that higher FIB-4 measurements were statistically significantly asso-ciated with increased 28-day mortality, with a cut-off value of 4.9 providing a sensitivity of 54.92% and specificity of 74.25%. Logistic regression analysis indicated that elevated Conclusion:FIB-4 levels were a significant predictor of early mortality, suggesting that the FIB-4 index could serve as a valuable prognostic tool in assessing the severity and prognosis of sepsis patients.

Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak and accumulated rainfall-based damage are likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index (SWRI24−hr−S), derived from normalising the existing 24-hourly water resources index (WRI24−hr−S) in literature, to monitor flash flood risks on an hourly scale. The proposed SWRI24−hr−S is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V) and peak (Q). The comprehensive result analysis establishes the practical utility of SWRI24−hr−S in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data is available, offering crucial information for flood risk monitoring, assessment, and the development of effective mitigation strategies.

The study presents a novel approach of programing pain inhibition in chronic pain patients based on the hypothesis that pain perception is modulated by dysfunctional dorsal medial nucleus tractus solitarii (dmNTS) reflex arcs that produce diminished baroreflex sensitivity (BRS) resulting from a conditioned response. This study tested whether administration of noxious and non-noxious electrical stimuli synchronized with the cardiac cycle resets BRS, reestablishing pain inhibition. 30 pain-free normotensives controls (NC) and 32 normotensives fibromyalgia (FM) patients received two, ≈8 minute-epochs of cardiac-gated, peripheral electrical stimuli. Non-painful and painful electrical stimuli were synchronized to the cardiac cycle as the neuromodulation experimental protocol (EP) with 2 control conditions (CC1, CC2). BRS, heart-rate-variability (HRV), pain threshold and tolerance, and clinical pain intensity were assessed.Reduced BRS in FM at baseline increased by 41% during two, ≈8 minute-epochs of stimulation. Thresholds in FM increased significantly during the experimental protocol (all Ps<0.001) as did HRV. FM levels of clinical pain significantly decreased by 35.52% during the experimental protocol but not during control stimulations (P<0.001). Baroreceptor training may reduce FM pain by BRS-mediated effects on intrinsic pain regulatory systems and autonomic pain modulation. These processes seem to be linked by classical and operant conditioning.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

:. Background and Objectives: Pulmonary embolism, PE, incidence had been increasing in the last ten years. Computed thoracic pulmonary angiography, CTPA, had a major role in confirming PE di-agnosis.The main purpose of the study was to establish the prognostic value of a CTPA parameter: pulmonary artery obstruction index, PAOI, for PE risk assessment . A second objective of the re-search was to investigate the predictive accuracy of biomarkers (D-dimer, and cardiac Troponin T, c-TnT ), and PAOI, in 7-day mortality. The third objective of the study was the relationship between PAOI, and these biomarkers, in different etiologies of PE. Materials and Methods: The study com-prised 109 patients with PE, hospitalized, and treated between February 2021, and August 2022. They had different etiologies: deep vein thrombosis, DVT; persistent atrial fibrillation, AF; chronic obstructive pulmonary disease, COPD, exacerbation; Covid; and cancers. The investigations were the following: clinical examination; D-dimer testing, as a mandatory method for PE suspicion( values ≥ 500 µg/L were highly suggestive for PE); c-TnT , as a marker of myocardial injury(values ≥ 14 ng/L were abnormal); CTPA, with right ventricle dysfunction, RVD, signs, and PAOI. Treat-ments were according to PE risk: systemic thrombolysis (in high risk , and hemodynamic instability); low weight molecular heparins, LWMH, after systemic thrombolysis, or in high risk PE, with he-modynamic stability; direct oral anticoagulants, DOAC (in low, and intermediate risk). Results: The relationship PAOI- D-dimer, and PAOI- c-TnT, had very strong statistical correlation, for the entire sample, and for DVT, AF, COPD, Covid subgroups ( Rho = 0.815-0.982). PAOI had a high predic-tive accuracy for intermediate , and high risk together (area under curve, AUC=0.948), and high risk PE (AUC= 0.993). For 7-day mortality, D-dimer , and c-TnT had high predictive accura-cy(AUC=0.921, and 0.979). PAOI had statistical significance for 7-day mortality in Covid (p=0.045), and cancer (p=0.038) patients. Conclusions: PAOI was an important tool for PE risk as-sessment. D-dimer , and c-TnT were valuable predictors, for 7-day mortality in PE. PAOI, and D-dimer; PAOI, and c-TnT were strongly correlated, for the entire PE sample, and for DVT, AF, COPD, and Covid patients.

The detection of Fasciola eggs in ruminant fecal samples typically occurs around 15 weeks post-infection during patent infections. The recent introduction of coproantigen ELISA (cELISA) diagnostics, which capture antigenic enzymes in fecal material, holds promise for monitoring Fasciola fecal egg count (FFEC) to implement control programs, particularly targeted treatments. This study compared two study groups, those with positive FFEC (FFEC+ve) and negative FFEC (FFEC-ve), to assess the sensitivity of Flukefinder sedimentation and a cELISA kit. This study reports that the coproantigen ELISA detection limit was 4.5 eggs per gram (epg) for a 100% positivity rate. Additionally, a moderate, statistically significant positive correlation (r²=0.716, p-value < 0.01) and an odds ratio of 1.96 (p-value < 0.01) were observed between FFEC and Fasciola coproantigen from cELISA. This implies that for every single unit increase in FFEC, there is a 96% likelihood of obtaining a higher optical density (OD) reading from the cELISA test. The results suggest that cELISA can complement fecal sedimentation tests, serving as a valuable tool for detecting cattle with high FFEC. This capability facilitates targeted treatment strategies, thereby aiding in the control and prevention of the spread of fasciolosis within the studied area.

Cysteine and its derivatives, including H2S, can influence bacterial virulence and sensitivity to antibiotics. In minimal sulfate media, H2S is generated under stress to prevent excess cysteine and, together with incorporation into glutathione and export into the medium, is a mechanism of cysteine homeostasis. Here, we studied the features of cysteine homeostasis in LB medium, where the main source of sulfur is cystine, whose import can create excess cysteine inside cells. We used mutants in the mechanisms of cysteine homeostasis and a set of microbiological and biochemical methods, including real-time monitoring of sulfide and oxygen, determination of cysteine and glutathione, and expression of the Fur, OxyR, and SOS regulons genes. During normal growth, the parental strain generated H2S when switching respiration to another substrate. The mutations affected the onset time, intensity and duration of H2S production, cysteine and glutathione levels, bacterial growth and respiration rates, and induction of defense systems. Exposure to chloramphenicol and high doses of ciprofloxacin increased cysteine content and GSH synthesis. A high inverse relationship between log CFU/ml and bacterial growth rate before ciprofloxacin addition was revealed. The study points to the important role of maintaining cysteine homeostasis during normal growth and antibiotic exposure in LB medium.

Background: The Advanced Lung Cancer Inflammation Index (ALI) is a novel, comprehensive index that integrates inflammation and nutritional status, incorporating parameters such as albumin, body mass index (BMI), and the neutrophil-to-lymphocyte ratio (NLR). Unlike traditional inflammation biomarkers, this study aims to evaluate the prognosis of IPF patients by considering both inflammation and nutritional status. Method: A total of 102 patients with IPF were included in the study. Clinical data were obtained from the patients’ medical records. NLR and ALI score were calculated based on data collected at the initiation of antifibrotic treatment using the following formula: Neut/Lym and albumin × BMI / NLR. Results: IPF subject subgroups in terms of ALI based on gender–age–physiology (GAP) stages (1,2,3), FVC (median split, <70% vs ≥70%), DLCO (<51% vs ≥51%), 6-Minute Walk Test (6MWT) (<350 vs ≥350) and CCI (≤1 vs >1). ALI showed significant difference depending on the categories of GAP stage, FVC, DLCO, and 6MWT but these differences were not observed based on the charlson comorbidity index (CCI) categories (sırasıyla p=0.000, p=0.000, p=0.000, p=0.001, p=0.233). Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the sensitivity and specificity of ALI, albumin, NLR, and BMI in determining the mortality risk in IPF patients. ALI demonstrated a sensitivity of 63.6% and a specificity of 98.9% at a threshold of 11.2 (AUC = 0.945, 95% CI 0.892–0.998, p < 0.000). Conclusion: We determined that ALI levels are significantly associated with disease severity and mortality in patients with IPF.

Earth's global surface temperature shows variability on an extended range of temporal scales and satisfies an emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature of the observed temperature fluctuations, but an inherent property of the temperature response to radiative forcing, and a principle that links the fast and slow climate responses. It provides a bridge between the decadal- and centennial-scale fluctuations in the instrumental temperature record, and the millennial-scale equilibration following perturbations in the radiative balance. In particular, the emergent scale invariance makes it possible to infer equilibrium climate sensitivity (ECS) from the observed relation between radiative forcing and global temperature in the instrumental era. This is verified in ensembles of Earth system models (ESMs), where the inferred values of ECS correlate strongly to estimates from idealized model runs. For the range of forcing data explored in this paper, the method gives best estimates of ECS between 2.3 and 3.4 K.

In the paper the verification of forecasts of precipitation conditions measured by the standardized precipitation index SPI is presented. For the verification of categorical forecasts a contingency table was used. Standard verification measures were used for the SPI value forecast. The 30 day SPI moved every 10 days by 10 days was calculated in 2013-2015 from April to September on the basis of precipitation data from 35 meteorological stations in Poland. Predictions of the 30 day SPI were created in which precipitation was forecasted in the next 10 days (the SPI 10-day forecast) and 20 days (the SPI 20-day forecast). Both for the 10 and 20 days, the forecasts were skewed towards drier categories at the expense of wet categories. There was a good agreement between observed and 10-day forecast categories of precipitation. Less agreement is obtained for 20-day forecasts – these forecasts evidently “over-dry” the assessment of precipitation anomalies. The 10-day SPI value forecast accuracy is acceptable, whereas for the 20-day forecast is unsatisfactory. Both for the SPI categorical and the SPI value forecast, the 10-day SPI forecast is reliable and the 20-day forecast should be accepted with reservation and used with caution.

Recent analyses of cosmic microwave background surveys have revealed hints that there may be a non-trivial running of the running of the spectral index. If future experiments were to confirm these hints, it would prove a powerful discriminator of inflationary models, ruling out simple single field models. We discuss how isocurvature perturbations in multi-field models can be invoked to generate large runnings in a non-standard hierarchy, and find that a minimal model capable of practically realising this would be a two-field model with a non-canonical kinetic structure. We also consider alternative scenarios such as variable speed of light models and canonical quantum gravity effects and their implications for runnings of the spectral index.

Expansins are cell wall (CW) proteins that mediate the CW loosening and regulate salt tolerance in a positive or negative way. The transcription of Populus trichocarpa expansin A6 (PtEXPA6) was downregulated upon prolonged duration of salt stress (48 h) after a transient increase induced by NaCl (100 mM). The role of PtEXPA6 in salt tolerance was determined by transferring the PtEXPA6 gene into the hybrid species Populus alba × P. tremula var. glandulosa clone 84K (84K) and Populus tremula × P. alba INRA '717-1B4' (717-1B4). The PtEXPA6-transgenic poplars of 84K and 717-1B4 showed a greater reduction in photosynthesis, stem height and diameter growth under salt stress and a reduced capacity for ROS scavenging by antioxidant enzymes, which is associated with excessive Na+ accumulation in roots and shoots. NMT flux recordings showed that the radial translocation of Na+ salt into the root xylem and real-time Na+ translocation from roots to leaves were greater in PtEXPA6-transgenic poplars than in wild-type poplars. Analysis of comparative contractility and comparative extensibility of intact root tips indicated that PtEXPA6 increased the CW loosening in the transgenic poplars of 84K and 717-1B4. Noteworthy, the PtEXPA6-promoted CW loosening was shown to facilitate radial and longitudinal transport of Na+ in transgenic poplars. We conclude that overexpression of PtEXPA6 leads to CW loosening that facilitates radial translocation of Na+ into the root xylem and subsequent Na+ translocation from roots to leaves, resulting in excessive Na+ accumulation and consequently reduced salt tolerance in transgenic poplars.

Climate change is challenging the agricultural sector worldwide. The pressure exerted by the altered thermal conditions drives the zonal shift for various cultivations. This study presents the multiyear analysis of the Growing Degree Days (GDD) index over the Northern Mediterranean Basin (NMB), focused on the high-value vine cultivation derived from the E-OBS dataset. To handle this extensive environmental dataset, R-language scripts and packages were applied, along with the capabilities of the GIS software. The investigated time period spans from 1969 to 2018, and the performed analysis indicates a broad shift/expansion in areas of GDD exceeding 2000 heat units. The extracted results specify the viticultural areas where the GDD index surpasses the suitable values for vine cultivation, owing to the increase of the thermal stress risk. In addition to mapping areas with GDD ≥ 2000, spatial calculations of the surface of areas meeting this criterion per country in the study area were made. In the northern parts of the NMB, the rise of the GDD index values indicates the establishment of vine plantations in the future.

Aim: To investigate inflammation indices and erythropoietin (EPO) levels for their potential role in distinguishing polycythemia vera (PV) from secondary polycythemia (SP), and to compare different parameter combinations in terms of diagnostic accuracy. Methods: This retrospective cohort was created from patients assessed for polycythemia from January 2020 to December 2023. PV diagnosis was made according to the 2016 WHO criteria (n=145). Those who did not fulfill the criteria were defined as having SP (n=84). Results: NLR, PLR and SII were significantly higher in the PV group (p

Purpose: The purpose of this study is to apply the Composite Radiographic Index modified (CRIm) in CBCT scans of patients with Medication related osteonecrosis of the jaws (MRONJ) in order to detect its validity, and corelate the radiologic findings with the clinical staging of the disease. Methods: This study included 43 patients with MRONJ diagnosis and CBCT scans performed in the School of Dentistry and approved by the Research Ethics Committee. Clinical staging for every patient (0-3) was provided according to AAOMS 2022 guidelines. In cases of patients with both jaws affected, each jaw was evaluated independently resulting to 52 CBCT scans. The 52 CBCT scans were assessed under standardized viewing conditions by 2 observers who evaluated independently the following 8 radiologic features: lytic changes, sclerosis, periosteal reaction, sequestration, non-healing extraction sockets and other findings (sinus implication, inferior alveolar canal involvement, jaw fracture). The CRIm index was constructed to assess the extent of osseous changes. The radiologic features were scored as absent (0), localized/single (1), extensive/multiple (2), yielding a theoretical range of 0–12. For statistical analysis Fisher’s exact test and Spearman’s correlation coefficient were performed. Results: A statistically significant association of clinical stage with lytic changes, sequestration and inferior alveolar canal involvement was found. Also, a statistically significant correlation of clinical stage with CRIm index classification was obtained. Conclusions: The CRIm index tends to increase as the clinical stages of MRONJ advance, suggesting a correlation between the clinical stage of MRONJ and the CRIm index.

COPD is currently the fourth leading cause of death in the world. Globally, due to continued exposure to COPD risk factors and an aging population, the burden of COPD is expected to increase in the coming decades. The BODE (body mass index, airflow obstruction, dyspnea, and exercise capacity) index is a practical and multidimensional predictor for prognosis of COPD, and better than FEV1.We used the database of Kaohsiung Chang Gung Memorial Hospital medical center, Taiwan to analyze the correlation between BODE index, healthcare resource utilization, and Charlson comorbidity index (CCI). This retrospective study to collect COPD patients with complete BODE index data who had undergone a 6-minute walk examination in our hospital from January 2015 to December 2016. The medical cost and comorbidities database were analyzed from January 1, 2015, to August 31, 2017. Of 396 patients, 382 (96.5%) were male, with an average age of 71.3 ± 8.4 years. There was a significant association between the BODE index and the CCI of COPD patients (p < 0.001). Healthcare resource utilization was positively correlated with the BODE index during the 32 months of retrospective clinical outcomes: positively correlated with the number of hospitalizations (p<0.001), hospitalization days (p<0.001), hospitalization expenses (p=0.005), and total medical expenses (p=0.024), respectively. Our findings provide the crucial information for clinician to predict medical burden and comorbidities in patients with COPD by using BODE index.

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, Fourier Granger Causality, and Fourier Toda Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

Sensory Processing Sensitivity (SPS) is a trait describing inter-individual differences in sensitivity to environments, both positive and negative ones. SPS has attracted growing societal interest. However, (neuro)scientific evidence is lagging behind. We critically discuss how to measure SPS, how it relates to other theories of Environmental Sensitivity and other temperament and personality traits, how SPS interacts with environments to influence (a)typical development, what the underlying aetiologies and mechanisms are, and its relation to mental disorders involving sensory sensitivities. Drawing on the diverse expertise of the authors, we set an agenda for future research to stimulate the field. We conclude that SPS is a heritable, evolutionarily conserved trait, linked to increased risk for psychopathology and stress-related problems in response to negative environments, as well as to greater benefits (e.g., intervention responsivity, positive mood) in positive environments. We need advances in objective assessment of SPS, understanding mechanisms, differentiating it from (seemingly) related mental disorders, to exploit the potential of SPS to improve mental health, preserve human capital, and prevent adverse effects.

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants which have been shown to be sensitive to human steps and sounds. A methodology is proposed to use plants as human emotion detectors. Electrical signals from plants are tracked and labeled based on video data. Labeled data are then used for classification. MLP, biLSTM, MFCC-CNN, MFCC-ResNet, and additionally, Random Forests, 1-Dimensional CNN, and biLSTM without windowing models are set using a grid search algorithm with cross-validation. Finally, best-parameterized models are trained and used with the test set for classification. The performance of this methodology is measured with a case study with 54 participants, who were watching an emotionally charged video, as ground truth their facial emotions were simultaneously measured using face emotion analysis. The Random Forest model shows the best performance, particularly in recognizing high-arousal emotions, achieving an overall weighted accuracy of 55.2% and demonstrating high weighted recall in emotions such as fear 61.0% and happiness 60.4%. The MFCC-ResNet model offers decently balanced results, with Accuracy(MFCC-ResNet)=0.318 and Recall (MFCC-ResNet)=0.324. With the MFCC-ResNet model fear and anger are recognized with 75% and 50% recall respectively. Thus, using plants as an emotion recognition tool seems worth investigating, addressing both cost and privacy concerns.

Abstract: Understanding the reliability and sensitivity of jump testing is essential to determine the neuromuscular progress of athletes and make informed decisions. This study aimed to assess the reliability and sensitivity of countermovement jump (CMJ) and drop jump (DJ) tests metrics in female volleyball players. Sixteen semi-professional female volleyball players participated in the study. Intrasession and intersession reliability of CMJ and DJ metrics were evaluated using a randomized cross-over design. Dual force platform was used to collect data, and several dependent variables were calculated using forward dynamics. Intraclass correlation coefficients (ICC), coefficients of variation (CV) and minimum difference (MD) were calculated. The third attempt presented the highest values for both test, and changes in jump strategy in DJ. CMJ is consistent across sessions, while DJ also shows intersession differences (p<0.05) in 11 out of 15 variables. Most CMJ variables exhibit excellent reliability while DJ variables have lower reliability and higher variability. Concentric and eccentric impulses for DJ showed excellent reliability. Most CMJ variables showed excellent reliability and variability, DJ had lower reliability, higher variability, and lower sensitivity. These findings provide valuable information when assessing athletes' performance and highlight the reliability, sensitivity, and variability in jump performance for CMJ and DJ tests.

Achieving effective control over microbial contamination necessitates the precise and concurrent identification of numerous pathogens. In this research, we have devised a remarkably sensitive duplex droplet digital PCR (dddPCR) reaction system to simultaneously detect Pseudomonas aeruginosa (P. aeruginosa) and Pseudomonas fragi (P. fragi). Employing comparative genomics, we identified four genes of P. fragi. By specific analysis, RS22680 gene was selected as the detection target of P. fragi and LasR gene was chosed as P. aeruginosa, which were applied to construct a dddPCR reaction. In terms of specificity, sensitivity and anti-interference ability, the constructed dddPCR detection system was verified and analyzed. The assay showed excellent sensitivity and applicability, as evidenced by a limit of detection of 100 CFU/mL. When the concentration of natural background bacteria in milk or fresh meat was 100 times that of the target detection bacteria, the method was still capable of completing the absolute quantification. In the simulation of actual sample contamination, P. aeruginosa could be detected after 3 h of enrichment culture, and P. fragi could be detected after 6 h. The established ddPCR detection system exhibits exceptional performance, serving as a foundation for the simultaneous detection of various pathogenic bacteria in food products.

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: Body Mass Index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 11.90 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33) but increased risk in the BFP model (OR: 1.66). The BMI model demonstrates exceptional predictive ability (AUC=0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions:BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

Groundwater flow and transport models are essential tools for assessing and quantifying the mi-gration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol’s High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analysed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity and hydraulic head calibration metrics. Input parameters in-clude aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductiv-ities, and boundary inflows and conductances. VARS, HDMR and graphical methods agree to identify the most influential parameters which for most of the outputs are the hydraulic conduc-tivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations.

Abstract: Doppler renal resistive index (RRI) has been used for several decades as a potential indi-cator for renal disease and progression. In recent years, RRI has emerged as a useful prognostic indicator for a range of co-morbidities in patients who are acutely unwell and as a potential marker for sub-clinical acute kidney injury (AKI) and post procedural AKI risk. Despite this, the clinical and theoretical determinants of RRI are poorly understood and remain contentious. Method: A narrative review of studies exploring the theoretical, experimental, and clinical evidence for factors affecting RRI. No date restrictions were set. Findings: Both experimental and clinical data confirm that RRI is markedly affected by a range of systemic and renal determinants and is influenced closely by the underlying risk factors and sys-temic vascular changes that lead to progressive renal damage. Discussion: This paper provides an overview of the key haemodynamic determinants of renal re-sistive index and aims to clarify their relative importance. With reference to a simplified theoretical model, evidence from experimental and clinical studies is reviewed. Pathophysiological factors and patient characteristics that influence RRI are explored to provide a better understanding of why RRI values change in the presence of common or complex pathologies. Conclusion: By gaining a better understanding of the complex systemic and renal factors that influ-ence RRI, ultrasound users will be better prepared to understand and interpret changes in RRI across a range of patient presentations.

Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

A novel characterization method for discrete saw filters vibrational sensitivity is presented. The proposed approach allows the characterization of filters under vibrations and the extraction of a behavioural model. Filters are assumed to be transducers so that external induced vibrational energy is partially transformed in a undesired simultaneous amplitude and phase modulation of the input RF signal. When the filter is mechanically excited with vibrations, it introduces spurious amplitude and phase modulation to the input signal that can potentially affect the link quality.

Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) and innovative trend analysis (ITA) were examined. Additionally, we utilized hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) network (i.e. CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. The results demonstrate the following essential points: (1) The trend analysis results of the total monthly rainfall values indicate a significant decreasing trend with the negative z-score (MK= -3.7541 and MMK= - 4.0773). ITA also indicated a significant downwards trend of rainfall in the study area. (2) As the forecasting horizon increases, the prediction accuracy of the models and CEEMDAN-combined models gradually improves, reaching their optimum performance at the 12-month horizon. (3) CEEMDAN efficiently stabilizes time-series data, resulting in greater prediction accuracy in the hybrid model compared to individual models at all timescales. (4) The RMSE and R2 values for the hybrid CEEMDAN-ARIMA-LSTM model at a 12-month forecasting horizon are 0.042 and 0.995, demonstrating the applicability of this hybrid approach for drought forecasting.

Objective: To analyse the association between maternal risk factors such as age, body mass index (BMI) and cigarette smoking, with perinatal outcomes. Materials and Methods: This a retrospective analysis from prospectively collected data performed at Hospital Universitario de Torrejón (Madrid, Spain), between September 2017 and December 2019. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination at 11+0 to 13+6 weeks' gestation were invited to participate. Association between preeclampsia, preterm birth, gestational diabetes mellitus (GDM), small for gestational age (SGA) or fetal growth restricted (FGR) neonates and type of delivery according to maternal age, BMI, and cigarrete smoking was studied. Logistic mixed models were used to analyze the data. Results: 1921 patients were included in the analysis. Women of 40 years old had a significantly higher risk of having GDM (Odds Ratio (OR) 1.61, 95% Confidence Interval (CI) 1.08 to 2.36), and SGA neonates (OR 1.54, 95% CI 1.00 to 2.37). Women with a BMI &lt;18 had an increased rate of birth SGA and FGR neonates (OR 3.28, 95% CI 1.51 to 7.05, and OR 3.73, 95% CI 1.54 to 8.37, respectively), whereas women with a BMI ≥35 had a higher risk of GDM (OR 3.10, 95% CI 1.95 to 4.89). Smoking increased the risk of having SGA and FGR neonates (OR 1.83, 95% CI 1.36 to 2.46, and OR 1.91, 95% CI 1.29 to 2.78). Conclusions: Advanced maternal age, low or high BMI, and smoking status are significant risk factors for pregnancy complications. Both clinicians and society should concentrate their efforts on addressing these factors to enhance reproductive health.

The fetal splenic artery pulsatility index (PI) is a parameter that reflects fetal well-being and has been used as a predictor of adverse pregnancy outcomes. The aim of this study was to investigate the predictive value of splenic artery PI in GDM class A1 cases for NICU admission. In this prospective case controlled study, only sixty single pregnancy cases diagnosed with gestational diabetes mellitus class A1 were evaluated. Fetal splenic artery Doppler parameters Peak systolic velocity (PSV), Pulsatility index (PI), Resistivity index (RI) and End diastolic velocity (EDV) were measured in all cases. The rate of re-quirement of need of neanoatal intensive care unit was noted. In cases with need fetal intensive care, fetal splenic PI index was found to be statistically significantly lower than in healthy cases without it (0.94 ± 0.29 vs. 1.70 ± 0.53, respectively, p

Droughts are a common occurrence in various climates and are primarily caused by a prolonged decrease in rainfall. Several factors contribute to droughts, including temperature, wind speed, relative humidity, rainfall timing, amount, and intensity during the growing season. The objective of this study is to establish a new index, named soil moisture and evapotranspiration revealed drought index (SERDI), for displaying dry and wet conditions based on combining both soil moisture and evapotranspiration (Penman-Monteith) to improve drought early warning and its severity globally. For validation of the SERDI with other indices such as LST, VHI, NDVI, and NDWI, different ways used such as R-square, RMSE, MAPE, and P-value to estimate the accuracy, variability of data, the forecast conditions, and how the significance of data. The results showed that the low RMSE and high r² were found between SERDI with LST and VHI. In contrast, the low R- square and high RMSE were between SERDI with NDVI and NDWI in most of the semi-arid areas. Furthermore, most of the semi-arid areas from Iran, Iraq, Syria, Jordan, and Israel experienced moderate and severe dry conditions, except some parts of these regions had normal conditions in Iran and Syria. The SERDI analysis revealed a strong correlation between (LST) and a moderate correlation with (VHI) across all study areas. However, the relationship between other indices, like (NDWI) and (NDVI), varied depending on the regions. To conclude, SERDI can be used globally for detecting drought based on soil moisture and evapotranspiration.

Imperfection sensitivity of large amplitude vibration of curved single-walled carbon nanotubes (SWCNTs) is considered in this study. The SWCNT is modeled as a Timoshenko nano-beam and its curved shape is included as an initial geometric imperfection term in the displacement field. Geometric nonlinearities of von Kármán type and nonlocal elasticity theory of Eringen are employed to derive governing equations of motion of nano-beam. Spatial discretization of governing equations and associated boundary conditions is performed using differential quadrature (DQ) method and the corresponding nonlinear eigenvalue problem is iteratively solved. Effects of value and location of the geometric imperfection, and the nonlocal small-scale parameter on the nonlinear frequency ratio and imperfection sensitivity of a curved SWCNT for various boundary conditions are investigated. The results show that the geometric imperfection plays a significant role in the nonlinear vibration characteristics of curved SWCNTs.

(1) Background: Parents of children with autism spectrum disorders often experience psychological distress, which can affect the quality of childcare they provide. It is crucial to screen for psychiatric symptoms among these parents. The Core Symptom Index (CSI) is a widely used tool for assessing general symptoms such as depression, anxiety, and somatic symptoms, with demonstrated validity and reliability across various populations. However, there is limited research on the validity and reliability of the CSI in the general Chinese population. This study aimed to investigate the psychometric properties of the Chinese version of the CSI among parents of children with autism spectrum disorders using Confirmatory Factor Analysis (CFA). (2) Methods: A total of 794 Chinese parents raising children with autism participated in this study. All completed the CSI, along with the social inhibition subscale of the Interpersonal Problems Inventory and the Couple Satisfaction Index. Factorial validity was assessed using CFA to determine how well the bifactor three-factor model fits the data. Various structural models were compared using model fit indices. Convergent and discriminant validity were examined by exploring correlations with the social inhibition subscale and the Couple Satisfaction Index. Invariance testing of the CSI was conducted across multiple groups based on gender, age, and education using CFA. The reliability of the CSI was evaluated using McDonald’s omega coefficients. (3) Results: The bifactor model emerged as the best-fitting model for the data, suggesting that the total score of the CSI adequately represents overall psychiatric symptoms. The CSI exhibited significant correlations with the social inhibition subscale (r = 0.41, p &lt; 0.01) and smaller correlation coefficients with the Couple Satisfaction Index (r = -0.16, p &lt; 0.05), indicating both convergent and discriminant validity. Invariant tests supported scalar invariance levels based on gender and age but only partial invariance for education. The Chinese version of the CSI demonstrated high consistency, with McDonald's omega coefficients ranging between 0.86 and 0.95. (4) Conclusions: The bifactor model of the Chinese version of the CSI is validated, making it a suitable tool for measuring depression, anxiety, and somatization symptoms among parents of children with autism spectrum disorders. Further research on other Chinese populations is encouraged.

Equilibrium climate sensitivity (ECS) is the change in global mean temperature expected to result from doubling atmospheric CO2 concentration from pre-industrial levels. Extensive research during the past 40 years has not reduced the uncertainty associated with ECS. Sherwood et al. [1] applied Bayesian statistics to evidence from climate-process physics, historical observations and earlier proxies to reduce the range of ECS from 1.5 – 4.5 K to 2.6 – 4.1 K. This paper examines their methods and many of the assumptions they made. It also evaluates two additional periods in the Holocene to show that factors other than CO2 drove recent climate change. It identifies potential systematic errors resulting from adding non-equilibrium short-term adjustments to the radiative forcing of greenhouse gases and from underestimating the effects of solar irradiance, ocean currents and aerosols. These factors have resulted in estimates of the forcing by CO2 that far exceed the apparent effects in paleoclimate data.

The Warburg effect refers to the phenomenon that cancer cells produce energy via glycolysis instead of cellular respiration. Glycolysis generated no net protons. The Warburg effect may be malignant cells’ built-in mechanism to antagonize the buildup of protons via Krebs cycle and other pathways with compromised cellular respiration. Data described in this study indicated that cancer cells were less sensitive to the presence of oxalate than non-cancer model cell lines 16HBE14o- and HaCaT. Malignant cells may resort on organic acids such as oxalate and their calcium salts to antagonize strong acids. This experiment sheds light on the role of Warburg effect in cancer cell metabolism and homeostasis.

Evidence shows that weightloss improves insulin sensitivity but few studies have examined the effect of macronutrient composition independently of weight loss on direct measures of insulin sensitivity. We randomised 89 overweight or obese women to either a standard diet (StdD) that was intended to be low in fat and relatively high in carbohydrate (n=42) or to a relatively high protein (up to 30% of energy), relatively high fibre (>30g/day) diet (HPHFib) (n=47) for 10 weeks. Advice regarding strict adherence to energy intake goals was not given. Insulin sensitivity and secretion was assessed by a novel method - the Dynamic Insulin Sensitivity and Secretion Test (DISST). Although there were significant improvements in body composition and most cardiometabolic risk factors on HPHFib, insulin sensitivity was reduced by 19.3% (95% CI: 31.8, 4.5%; p=0.013) in comparison with StdD. We conclude that the reduction in insulin sensitivity after a diet relatively high in both protein and fibre, despite cardiometabolic improvements, suggests insulin sensitivity may reflect metabolic adaptations to dietary composition for maintenance of glucose homeostasis, rather than impaired metabolism.

Multifunctional landscapes are complex systems that integrate biophysical, cultural-historical and socio-economic components into unique territorial structures and regional ensembles. Facing increasing pressures generated by urbanization, climate change and overexploitation of natural resources, assessing the sustainability of these landscapes has become an urgent priority. The present research is based on the development and parametrization of a new tool for evaluating the sustainability of multifunctional landscapes that aims to measure the level of biocultural resilience of a landscape, integrating biological, cultural and socio-economic indicators in a single analytical framework. This index, called the biocultural resilience index (BRI), provides a holistic perspective on the dynamics of landscapes, allowing the identification of vulnerability areas and the development of conservation and sustainable management strategies. The evaluation of the of multifunctional landscapes sustainability requires a comprehensive approach that has to integrate both biological aspects, such as biodiversity and ecosystem health, as well as identity and cultural aspects, such as cultural value and the relationship of the local communities with the environment. In this context, this study highlights the fact that the development of appropriate tools and methods for assessing and monitoring the sustainability of multifunctional landscapes becomes essential for their sustainable management and for protecting the benefits they offer to society. Our findings show that the biocultural resilience index (BRI) can be used in a variety of contexts and fields, including spatial planning, biodiversity conservation, sustainable rural development and ecotourism. In the meanwhile, this index offers a robust and flexible analytical framework, adaptable to the needs and particularities of different regions and communities.

The acoustic startle response (ASR) is an explosive motor response leaded by a sudden and intense acoustic stimulus. ASR has several clinically relevant forms of plasticity, including habituation and sensorimotor gating. Although ASR and its plasticity have been intensively studied in different animal models, including zebrafish (Danio rerio) larvae, information in adult zebrafish is still very scarce. In this manuscript we present Zebra_K, a new automated high-content kinematic analysis platform for assessing ASR, its habituation and prepulse inhibition (PPI), a quantitative measure of sensorimotor gating, in adult zebrafish. The analysis of the kinematic parameters in 1,875 ASR in adult zebrafish has shown a single response wave consistent with the short-latency C-bend described in zebrafish larvae. Moreover, protocols have been designed and validated in Zebra_K for the analysis of sensitivity, habituation and PPI of this response. Then, this platform has been used to determine the effect of pharmacological and physiological modulators of ASR plasticity. As described in other animal models, the NMDA-receptor antagonist ketamine increased the responsiveness to the acoustic stimuli, decreasing habituation and leading to complete abolition of PPI. Moreover, the effect of the gender on zebrafish ASR plasticity has been analyzed for the first time. Females exhibited higher responsiveness and a lower habituation and PPI than males, a result consistent with the gender effect described in other animal models and in humans. These results enhance the interest of using adult zebrafish to assess the potential effect of chemicals or genetic manipulations on ASR plasticity.

Motor imagery requires mental representation of the body, which is obtained by the integration of exteroceptive and interoceptive information. The aim of the study was to investigate the role of interoceptive sensitivity in motor imagery performed through visual and kinesthetic modalities in participants with low (lows, N = 26), medium (mediums, N = 11) and high hypnotizability scores (highs, N = 16), measured by the Stanford Hypnotic Susceptibility Scale: Form A. These groups display different motor imagery abilities and interoceptive sensitivity. The efficacy of motor imagery was measured by chronometric index and self-reported experience, and interoceptive sensitivity by the Multisensory Assessment of Interoceptive Awareness (MAIA) questionnaire. EEG alpha and beta power spectral densities were extracted from recordings acquired during baseline, actual movement, visually and kinesthetically imagined movements. The chronometric indices did not reveal significant differences among groups and between imagery modalities. In contrast, the self-reported imagery efficacy indicated better kinesthetic imagery in highs and mediums than in lows, and no modality difference among lows. MAIA dimensions sustained the differences in sub-jective experience and almost all the EEG differences, slightly different in highs, mediums and lows. This is the first report of a major role played by interoceptive sensitivity in motor imagery.

Surrogate model based optimization method is widely-used to accelerate the design and optimization process~\cite{marler2004survey}. The input of regression model used in the surrogate model are numbers, which requires users to parametrize the geometries. In this paper, a new parameterization-free surrogate model is introduced and its corresponding uncertainty quantification and sensitivity analysis method are discussed. The input of new surrogate model methods is surface mesh of simulation domain. \Gls{gnns} is used to extract geometric information, and \Gls{cnns} is used to predict contours. This framework bypasses parameterization,as a consequence, uncertainties introduced by manual parameterization is reduced. However, such changes compared with conventional surrogate model methods impose great challenge on uncertainties quantification and sensitivity analysis. Uncertainties quantification in this paper means the error bar of prediction results, which is calculated by Gaussian Process Regression method in current surrogate method. In this paper, a new quantification method achieved by Kullback-Leibler divergence (KLD) is introduced. And the sensitivity analysis is conducted by Automatic Differentiation, which gives a Jacobian matrix of inputs. The method and analysis mentioned above are demonstrated by a low-pressure steam turbine rotator and its exhaust system.

This paper initiates the study of the mathematical aspects of the ad-hoc Lanzhou index. If G is a graph with the vertex set {x1,⋯,xn}, then the ad-hoc Lanzhou index of G is defined by Lz¯(G)=∑i=1ndi(n−1−di)2, where di represents the degree of the vertex xi. Several identities for the ad-hoc Lanzhou index, involving some existing topological indices are established. The problems of finding graphs with the extremum values of the ad-hoc Lanzhou index from the following sets of graphs are also attacked: (i) set of all connected ξ-cyclic graphs of a fixed order, (ii) set of all connected molecular ξ-cyclic graphs of a fixed order, (iii) set of all graphs of a fixed order, (iv) set of all connected molecular graphs of a fixed order.

In geotechnical engineering applications, it is very important to obtain the undrained shear strength of remolded soils accurately and reliably. This study aims to obtain a trustworthy solution to determine the undrained shear strength of remolded clay mixtures using Atterberg limit test results in various states of consistency. An experimental study was carried out involving a wide range of clay mixtures of varying plasticity and geological origin. In the analyses, the variation of the remolded undrained shear strength depending on the cone penetration depth, water content, flow index, liquidity index and log liquidity index were investigated. In the study, the highest undrained shear strength of 100% Na-montmorillonite (NaM) was obtained at 171.89 kPa at 56.60% water content, while the lowest undrained shear strength was obtained for 100% Sepiolite (S), 9.28 kPa at 31.65% water content. The results of this study revealed that the shear strength is significantly affected by soil conditions, rather than dominant clay mineral. Besides, it was observed that the undrained shear strength at the plastic limit was approximately 30-35 times greater than that at liquid limit. The equations of liquid limit-flow index and plasticity index- flow index were proposed. It was concluded that the interdependence between undrained shear strength, liquidity index, log liquidity index, and flow index is not unique due to the different physical and chemical properties of clays.

Monte Carlo techniques have been extensively used for planning laser-based clinical procedures such as photobiomodulation. However, the effect of several biological tissue characteristics, including morphological structure and physiological parameters, has not been carefully addressed in many applications. Specifically, many questions remain concerning the effect of skin phototype and body mass index on the efficacy of photobiomodulation for extraoral therapies. To address these questions, a Monte Carlo simulation model was developed to analyze the effects of body mass index-dependent skin structure and Fitzpatrick skin type, specifically tailored for the morphological characteristics of cheek tissue. The model describes the settings of a typical oral photobiomodulation treatment protocol for pain relief, namely the use of 660 nm and 808 nm laser wavelengths and a therapeutic dose of 2.0 J/cm² on the masseter muscle. The simulations were used to train a machine learning predictive model aimed at accelerating the treatment planning stage and assessing the importance of patient-specific parameters. A multiple regression approach was adopted to predict muscle dose and treatment time for the effective delivered dose.

Unquestionably the rapidly changing climate and, therefore, alterations in the associated bioclimate, constitute an alarming reality with implications in daily practice and natural capital management. In this research the present and projected bioclimate evolution over the phytogeographical regions of Greece is displayed. For this purpose, ultrahigh-resolution computation results on the spatial distribution of the Emberger index’s Q2 classes of bioclimatic characterisation are analysed and illustrated for the first time. The assessments are performed over the reference period (1970–2000) and two future time frames (2021–2040; 2041–2060) under the RCP4.5 and RCP8.5 emission scenarios. By 2060 and under the extreme RCP8.5, intense xerothermic trends are demonstrated owing to the resulting significant spatial evolution mainly of the Arid-Hot, Semi-Arid-Very Hot, Semi-Arid-Hot and Semi-Arid-Temperate Q2 classes, respectively, over the phytogeographical regions of Kiklades (up to 29% occupation), Kriti and Karpathos (up to 30%), West Aegean Islands (up to 26%), North East (up to 56%)–North Central (up to 31%). The RCP8.5 long-term period exhibits the strongest impacts over approximately the right half of the Greek territory the bioclimate of which appears as more dry-thermal in the future.

Lunar seismology is a critical area of research, providing insights into the Moon's internal structure, composition, and thermal history, as well as informing the design of safe and resilient habitats for future human settlements. This paper presents the development of a state-of-the-art, three-axis broadband seismometer with a low frequency range of 0.001-1 Hz and a target sensitivity over an order of magnitude greater than previous Apollo-era instruments. The paper details the design, assembly, methodology and test results. We compare the acceleration noise of our prototype and commercial seismometers across all three axes. Increasing the test mass and reducing its natural frequency may further improve performance. These advancements in seismometer technology hold promise for enhancing our understanding of the Moon's and other celestial bodies' internal structures, and for informing the design of future landed missions to ocean worlds.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

The aim of this report is to evaluate the segmental rib index (RI) from T1-T12 spinal level in mild and moderate idiopathic scoliosis (IS) curves of thoracic, thoracolumbar and lumbar type by gender. The relationship of segmental RI to the frontal plane radiological deformity presented as the Cobb angle and to the posterior truncal surface deformity presented as the scoliometric readings of Angle of Trunk Rotation (ATR) in these patients is also assessed. Any statistical significant rela-tionship of these parameters would be very important for biomechanical relations of the rib cage (RC) deformity presented as the rib hump deformity (RHD) and the deformity in the spine and it would thus provide valuable information about scoliogeny. The segmental rib index (RI) is presented in 83 boys and girls with mild and moderate IS. The measurements include the scoliometric readings for truncal asymmetry (TA), the Cobb angle assessment and the segmental RI from T1-T12. The statistical package SPSS 23 was used for statistical analysis. The TA is documented and the Cobb angle is presented by gender and curve type. The segmental RI of thoracic, thoracolumbar and lumbar curves are presented for the first time. The correlations of the segmental RI to surface deformity presented as the rib hump deformity (RHD) in all IS patients and particularly in thoracic curves, to Cobb angle by gender and age and the comparison of the segmental RI index of asymmetric but not scoliotic children to the scoliotic peers by curve (in thoracic, thoracolumbar, lumbar curves) in boys and girls are presented. The findings emphasize the significant protagonistic role of the thoracic asymmetry in relation to the spinal deformity mainly in girls for the thoracic and in boys for the thoracolumbar curves. The cut-off point of age of the examined scoliotics is 14 years, that is when the RI shows stronger correlation with the spinal deformity, namely when the thoracic deformity is decisively effective to the developing thoracic spinal deformity, in terms of Cobb angle. In summary, the results of this study provide aetiological implications for IS, as far as the role of thorax is con-cerned.

The Metropolitan Economic Freedom Index (MEFI) ranks cities based on their support of free market enterprise. In its current state, MEFI purports to measure three constructs (government spending, taxation, and labor market freedom) with three equally weighted variables for each one, assuming perfect substitutability of variables. This study investigates the statistical consistency of MEFI through Confirmatory Factor Analysis. Multiple models investigate current variable selection by providing a potentially better indicator of labor market freedom, aggregation assumptions by removing the requirements for fixed and equal weights, and statistical consistency by evaluating the fit between the data and models. Results indicate that the current MEFI model is not statistically consistent with the data, that weighting of variables should not be equal, that variable selection should be investigated, and that constructs should be re-imagined. The models investigated provide an initial starting point for redefining MEFI.

Oral cancer represents a significant global public health challenge, contributing substantially to cancer incidence and mortality. Despite established risk factors such as tobacco use and alcohol consumption, early detection remains crucial for effective treatment. This study introduces a novel approach using a transistor-based biosensor system for detecting the P90 (CIP2A) protein. We have tested the presence of CIP2A in human leukoplakia samples that can rise into oral squamous cell carcinoma. The method employs commercially available glucose test strips functionalized with P90 antibodies, providing high sensitivity and a low limit of detection, which is five orders lower than commercial ELISA kits. A specially designed printed circuit board (PCB) facilitates accurate measurements, and the device's performance is optimized through characteristic tests. Human sample testing validates the biosensor's effectiveness in distinguishing samples after cell lysis. This study contributes to advancing accurate and cost-effective diagnostic approaches for oral pre-cancer and cancer tissues.

Astringency is an important sensory characteristic of food and beverages containing polyphenols. However, astringency perception in elderly people has not been documented. The aim of the present work was to evaluate sensitivity to astringency as a function of age and salivary flow. Fifty-four panellists, including 30 elderly people (age=75±4.2 years) and 24 young people (age=29.4±3.8 years), participated in this study. Astringency sensitivity was evaluated by the 2-alternative forced choice (2-AFC) procedure using tannic acid solutions. Whole saliva was collected for 5 min before and after sensory tests. The results showed that the astringency threshold was significantly higher in the elderly group than in the young group. Moreover, a negative correlation between salivary flow and threshold was observed in the young group only. These results showed a difference in oral astringency perception as a function of age for the first time. This difference can be linked to salivary properties that differ as a function of age.

The inaccurate estimates of linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrades speech enhancement performance. The existing methods proposed a tuning of the biased Kalman gain particularly in stationary noise condition. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then construct a whitening filter (with its coefficients computed from the estimated noise) and employed to the noisy speech, yielding a pre-whitened speech, from where the speech LPC parameters are computed. Then construct KF with the estimated parameters, where the robustness metric offsets the bias in Kalman gain during speech absence to that of the sensitivity metric during speech presence to achieve better noise reduction. Where the noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on NOIZEUS corpus demonstrates that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

The interpretation of the local microenvironment of extracellular matrix for malignant tumor cells is in intimate relation with metastatic spread of cancer cells involving the associated issues of cellular proliferation and drug responsiveness. This study was aimed to assess the combination of both surface topographies (fiber alignments) and different stiffness of the polymeric substrates (PLLA and PCL) and collagen substrates (coat and gel) to elucidate the effect of the cellular morphology on cellular proliferation and drug sensitivities of two different types of breast cancer cells (MDA-MB-231 and MCF-7). The morphological spreading parameter of (Nuclear/Cytoplasm) induced by the anthropogenic substrates has correlated intimately with the cellular proliferation and the drug sensitivity (IC₅₀) of cancer cells. This study demonstrated the promising results of the parameter for the evaluation of cancer cell malignancy.

Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, par-ticularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought pre-diction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to con-ventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

This article analyzes the determinants of the "Broadband Price Index" in Europe. The data used refer to 28 European countries between 2016 and 2021. The database used is the Digital, Economy and Society Index-DESI of the European Commission. The data were analyzed using the following econometric techniques, namely Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS and Dynamic Panel. The value of the "Broadband Price Index" is positively associated with the DESI Index, and "Connectivity" while it is negatively associated with "Fixed Broadband Take Up", "Fixed Broadband Coverage", "Mobile Broadband", "e-Government", "Advanced Skills and Development", "Integration of Digital Technology", "At Least Basic Digital Skills ", "Above Basic Digital Skills "," At Least Basic Software Skills ". A cluster analysis was carried out below using the k-Means algorithm optimized with the Silhouette coefficient. The analysis revealed the existence of three clusters. Finally, an analysis of the machine learning algorithms was carried out to predict the future value of the "Broadband Price Index". The result shows that the most useful algorithm for prediction is the Artificial Neural Network-ANN with an estimated value equal to an amount of 9.21%.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

Antigen-based rapid diagnostic tests for Lymphatic filariasis do not come with quality control (QC) materials, and research and disease control programmes rely on stored positive plasma samples. This study was undertaken to evaluate the use of freeze-dried Wuchereria bancrofti antigen positive plasma samples to serve as QC materials for LF RDTs. 10 well characterized W. bancrofti positive samples were lyophilized and stored at 4°C, 28°C and 40°C. The samples were evaluated using the Filaria Test Strips before lyophilization and after one and three (3) months of storage. The sensitivity and stability of the lyophilized samples were evaluated. The results revealed a loss of sensitivity and stability with increasing temperature and duration of storage. The results are further discussed in terms of the use of Dried Blood Spot (DBS) in diagnostics studies on LF requiring quantitative assessments, and the need for thoughtful DBS preparation and storage.

Many metrics exist for the evaluation of binary classifiers, all with their particular advantages and shortcomings. Recently an “Efficiency Index” for the evaluation of classifiers has been proposed, based on the consistency, or matching, and contradiction, or mismatching, of outcomes. This metric and its confidence intervals are easy to calculate from base data in a 2x2 contingency table, and values can be qualitatively and semi-quantitatively categorised. For medical tests, in which context the Efficiency Index was originally proposed, it facilitates communication of risk (of correct diagnosis versus misdiagnosis) to both clinicians and patients. Variants of the efficiency index (balanced, unbiased) which take into account disease prevalence and test cut-offs have also been described. The objectives of the current paper were firstly to extend the EI construct to other formulations (balanced level, quality) and secondly to explore the utility of EI and all four of its variants when applied to the dataset of a large prospective test accuracy study of a cognitive screening instrument. This showed that the balanced level, quality, and unbiased formulations of EI are more stringent measures.

Let G[F,Vk, Huv] be the graph with k pockets, where F is a simple graph of order n ≥ 1,Vk= {v1,v2,··· ,vk} is a subset of the vertex set of F and Hvis a simple graph of order m ≥ 2,v is a specified vertex of Hv. Also let G[F,Ek, Huv] be the graph with k edge pockets, where F is a simple graph of order n ≥ 2, Ek= {e1,e2,···ek} is a subset of the edge set of F and Huvis a simple graph of order m ≥ 3, uv is a specified edge of Huvsuch that Huv− u is isomorphic to Huv− v. In this paper, we derive closed-form formulas for resistance distance and Kirchhoff index of G[F,Vk, Hv] and G[F,Ek, Huv] in terms of the resistance distance and Kirchhoff index F, Hv and F, Huv, respectively.

Canine distemper virus (CDV) poses a substantial threat to diverse carnivores, leading to sys-temic and often fatal diseases. Accurate and prompt diagnosis is paramount for effective man-agement and curbing further transmission. This study evaluates the diagnostic efficacy of drop-let digital PCR (ddPCR) in comparison to conventional reverse transcription (RT-PCR) and quantitative reverse transcription real-time PCR (RT-qPCR). Seventy-six clinical samples were collected from dogs with CDV symptoms diagnosed by specialized veterinarians, and sixteen samples from asymptomatic individuals. Conventional PCR, quantitative real-time PCR, and ddPCR were deployed, and their diagnostic capabilities were meticulously assessed. DdPCR ex-hibited heightened analytical sensitivity, reaching a detection limit of 3 copies/μL, whereas RT-qPCR had a detection limit of 86 copies/μL. The comparative analysis between clinical diagnosis and molecular techniques, including RT-PCR and RT-qPCR, demonstrated low concordance, with Kappa coefficients of 0.268 and 0.324, respectively. In contrast, ddPCR, showed a moderate concordance, with a Kappa coefficient of 0.477. Sensitivity was 42.39% for RT-PCR, 57.89% for RT-qPCR and 72.37% for ddPCR, with 100% of specificity for all methods. This study under-scores ddPCR's superior sensitivity and concordance with clinical CDV diagnosis, even at low viral concentrations, suggesting it as a promising alternative for CDV diagnosis.

Topology optimization has become a very popular design tool when an optimum design with a concrete objective and subjected to several restrictions is searched. However, the resultant shape of the optimization process isn’t usually easy to manufacture with a typical manufacturing method as machining, being necessary an interpretation and validation of the result; besides, some parameters can be chosen, and the final shape is directly dependent on them. In this paper, a sensitivity analysis of the main parameters involved in 3D topology optimization, the penalization and the filter radius, is carried out for the concrete case of the density-based method, analyzing how manufacturable the results are for 3 axis machining. Also, a new filter and method is proposed for obtaining highly manufacturable and low interpretable parts. The main aim of the study is to help designers to choose the more appropriate parameters and showing what they must have into account for obtaining the desired optimized shapes. Note that the density-based topology optimization method has been chosen due to its popularity in commercial packages, so the conclusions may have direct applications in designers’ work. Finally, the results of the study are verified through different cases for checking the validity of the conclusions.

This study delves into the Schrödinger equation's new invariants, shedding light on their crucial implications for theoretical physics and beyond. Specifically, it explores the behavior of scattering amplitudes and bound states concerning the choice of coordinate systems. The research unveils that while the energy of bound states remains invariant under coordinate transformations, the phase of scattering amplitudes undergoes variations, underscoring its pivotal role in theories reliant on phase normalization. Moreover, these findings have played a pivotal role in constructing estimates for three-dimensional Navier-Stokes equations, enhancing our ability to model complex fluid dynamics with greater precision and reliability. Additionally, in seismic exploration, tomography, and ultrasound imaging, these properties allow researchers to optimize phase selection, facilitating the most effective interpretation of measurement results. This strategic phase manipulation ensures clearer insights into subsurface structures, tissue composition, and fluid dynamics, with the flexibility to seamlessly transition back to the original coordinate system post-interpretation. This interdisciplinary synergy underscores the profound impact of fundamental theoretical research on advancing practical applications across diverse scientific domains.

This paper evaluates the global sustainable competitiveness of three blocks of power (US, China and EU) based on OGT (Organic Growth Theory) structure. In addition, a sensitivity analysis of COVID-19 on three measures of global sustainable competitiveness, economy, environment and social is also presented. OGT is a well-thought-through and standard framework for evaluation of any type of organiza-tion in their pathway to experience truly sustainable growth. GDP (Gross Domestic Product) is used as an economic measure; total carbon emissions equivalent is used as an environmental measure and HMI (Human Development Index) as a social measure. It is concluded that the pandemic has paused the evolution of countries to be truly world centric countries. However, the recovery plans seem to be highly aligned with the OGT scores and it is expected that the scores to improve dramatically.

The current study explores the problem of illiquidity in the corporate bond market and examines a broad set of liquidity proxies through empirical investigation. Purpose: The purpose of this paper is to give solution to the global issue of illiquidity in the corporate bond market. The problem is identified by many researchers and this paper attempts to find out the viable solution of the "fungibility route" as an alternative to the "liquidity route". Design/ Methodology: An Analysis of a sample size of 2,34,772 trade data of corporate bonds and a sample size of 2,00,607 trade data of G-securities is done to identify the problem. Findings / Solution proposed: A mathematical model based on credit risk differential and sensitivity differential is proposed to find out the fair value at which an illiquid bond can be swapped by a liquid bond. To arrive at the fair value of the illiquid bond, we have calculated risk adjusted discount rate using modified duration and credit risk differential. With our solution, it is possible to enhance the liquidity of the bond not directly but through fungibility route. Originality: the research is first of its kind in which solution is given to the global problem of corporate bond illiquidity. To execute the trade in the fungibility route, proposed parties involved are the Fungibility platform, the initiator, and the bidder. The derived fair value would facilitate trade and serve as an exit strategy for illiquid bond holders worldwide.

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and dorsal pathway of HVS, a dual-pathway convolutional neural network is proposed for BIQA task. The proposed method consists of two pathways: “what” pathway, which mimics the ventral pathway of HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, the gradient images weighted by the contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. The experiments conducted on six databases show that the proposed method achieves the state-of-the-art performance.

This research investigates a model for the COVID-19 outbreak in Indonesia by analyzing comorbid diseases, self-quarantine, government-provided quarantine, and immunization regimens. Some model parameters are derived from academic journals, others are extrapolated from data collected by the Indonesian Ministry of Health in 2020, and others are assumed. Evaluation of the model reveals non-endemic equilibrium points, endemic equilibrium points, and the basic reproduction number (BRN). The contact rate and infected probability factors impact the increase in the number of COVID-19-infected individuals with comorbid disease within a group. According to the sensitivity analysis of the BRN, the key parameters impacting the spread of COVID-19 are the rate of susceptible recruitment, the rate of contact, the COVID-19 infection death rate, and the likelihood of infection. In addition, sensitivity analyses are provided to examine the effect of parameter changes on subpopulations. We discovered that the natural death rate is the most sensitive parameter based on the sensitivity index after reaching equilibrium. Symmetry aspects appear in some of the visualization of the model’s solution and the BRN and parameters’ sensitivity.

A 3-month (June-August) regime of the year 2002 summer rainfall (JJA2002) was simulated with 30 physics combinations using the Weather Research and Forecasting (WRF) model at 12-km horizontal grid resolution. The objective is to examine summer rainfall sensitivity to parameterization of microphysical, convective, and boundary layer processes and identify the best possible combination of parameterization options that perform relatively better in simulating the spatial and temporal distribution of summer rains over Ethiopia. The WRF simulated rainfall was evaluated against station data and satellite rainfall products (CHIRPS and ENACTS) using mean absolute error, Pearson and Pattern correlation coefficients (PCC), pattern correlation, and error in a number of rainy days as evaluation metrics. Summer rainfall is found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All simulations captured the spatial distribution of mean seasonal precipitation with PCC ranging from 0.89-0.94. However, all simulations overestimated precipitation amount and number of rainy days. Out of the 30, the simulations that use a combination of Grell-3D cumulus scheme, ACM2 boundary layer, Lin Microphysics, Dudhai shortwave radiation, and RRTM longwave radiation scheme ranked the top and provided the most realistic simulation in terms of amount and spatio-temporal distribution of summer rainfall.

Introduction: The viral respiratory disorder known as severe acute respiratory syndrome (SARS) is caused by the SARS-associated coronavirus. End of May 2021, there have been over 170 million reported cases and more than 3.53 million deaths worldwide. In the field of cardiovascular performance, the shock index (SI) is a simple and widely used formula that measures the physiological response prior to systemic hypotension. It is obtained by dividing the heart rate by the systolic blood pressure. Another parameter, the modified shock index (MSI), is defined as the ratio of heart rate to mean arterial pressure (MAP). Methods: In this retrospective study, we examined the data of COVID-19 patients who were admitted to the ICU between June 1st, 2020, and January 31st, 2021. The patient details were collected from the Medical Records Department (MRD) in a tertiary care center. A case-recorded proforma was used to record the patient information, which was then analyzed using SPSS software. Results: The mean age of the patients was 67 years, consisting of 64 males (68.9%) and 29 females (31.1%). The standard deviation was calculated as 13.557. Among the patients, the minimum age recorded was 28, while the maximum age was 97. Patients with known CAD (Coronary Artery Disease) had a higher mortality rate among comorbidities. The p-value for Shock Index and Mortality Status was 0.205 (with an alpha value greater than 0.05), indicating no significant association. However, the modified shock index (MSI) showed a significant association with mortality status. The Pearson chi-square value for MSI and Mortality Status was 4.19, with a p-value of 0.041 (with an alpha value less than 0.05). These results suggest that there is a higher risk of mortality for patients with a Modified Shock Index of less than 0.7 or greater than 1.3. The MSI Odds ratio was found to be 4.500, indicating that such patients have a 4.5 times higher risk of mortality compared to others. Conclusion: In conclusion, our study found that there is no significant association between the Shock Index (SI) and mortality, as the p-value was greater than 0.05. However, the Modified Shock Index (MSI) showed a significant association with mortality status, with a p-value less than 0.05. This suggests that patients with a Modified Shock Index less than 0.7 or greater than 1.3 are at a higher risk of mortality. It is important to consider these findings when assessing the prognosis and potential interventions for COVID-19 patients.

Accurate and reliable mid-long runoff prediction (MLTRP) is of great importance in water re-sources management. However the MLTRP is not suitable in each basin and how to evaluate the applicability of MLTRP is still a question. Therefore, the total mutual information (TMI) index is developed in this study based on the predictor selection method using mutual information (MI) and partial MI (PMI). The relationship between the TMI and the predictive performance of five models is analyzed by applying five models in 222 forecasting scenarios in Australia. The results over 222 forecasting scenarios demonstrate that, compared with the MI, the developed TMI index can better represent the available information in the predictors, and has more significant negative correlation with the RRMSE with the correlation coefficient between -0.62 and -0.85. This means the model's predictive performance will become better along with the increase of TMI, and there-fore the developed TMI index can be used to evaluate the applicability of MLTRP. When TMI is more than 0.1, the available information in the predictors can support the construction of MLTRP models. In addition, the TMI is used to partly explain the difference of predictive performance among five models. In general, the complex models, which can better utilize the contained infor-mation, are more sensitive to the TMI, and have more significant improvement in terms of predic-tive performance along with the increase of TMI. This research can provide support for the study of MLTRP.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Background and Objectives: To investigate the relationship between the SII index and the micro-macro complications and death that develop in the first year and the following 3 years, following the calculation of the index in type 2 diabetic retinopathy patients. Materials and Methods: This study was performed retrospectively with 523 type 2 diabetic retinopathy patients admitted to the endocrinology outpatient clinic of our hospital between January 2019 and December 2019. The demographic, and clinical characteristics of the patients were evaluated using descriptive statistics. Shapiro-Wilk test was used for the normal distribution of quantitative data; Mann-Whitney U, Mc Nemar Chi-square, and Cochran's Q tests were used to analyze SII values between groups and complication rates by years; sensitivity and specificity were determined by ROC analysis and considered statistically significant with a significance level of P

Heat index (HI) is a biometeorological indicator that combines temperature and relative humidity. This study aimed to investigate the relationship between heat index and daily counts of diarrhoea hospitalisation in Dhaka, Bangladesh. Data on daily diarrhoea hospitalisations and meteorological variables from 1981–2010 were collected. We categorised heat index of >94.3°F, >100.7°F and >105°F as high, very high and extremely high heat index, respectively. We applied time-series adjusted generalised linear model (GLM) with negative binomial distribution to investigate the effects of heat index and extreme heat index on hospitalisations for diarrhoea. Effects were assessed for all ages, <5 children and by gender. A unit higher HI, and high, very high and extremely high HI were associated with 0.8%, 8%, 7% and 9% increase in diarrhoea hospitalisation in all ages, respectively. The effects varied slightly by gender and were most pronounced in <5 children with a rise of 1°F in high, very high and extremely high HI associated with a 14.1% (95% CI: 11.3% – 17.0%), 18.3% (95% CI: 13.4% – 23.5%) and 18.1% (95% CI: 8.4% – 28.6%) increase of diarrhoea, respectively. This suggest that heat index may serve as an alternative indicator for measuring the combined effects temperature and humidity on diarrhoea.

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

The Work Ability Index (WAI) is the most widely used questionnaire for the self-assessment of working ability. Because of its different applications, shorter versions, and widespread use in healthcare activities, it is worthwhile assessing its characteristics. The WAI was distributed online among the workers of a healthcare company; the results were compared with data contained in the workers' personal health records and with absence registers. 340 out of 575 workers (59.1%) participated; 6.5% of them reported poor work ability. Exploratory factor analysis indicated that the one-factor version best described the characteristics of the WAI. The scores of the complete WAI, the shorter form without the list of diseases, and the minimal one-item version (WAS) had equal distribution and were significantly correlated. The WAI score was inversely related to age and significantly lower in women than in men, but it was higher in night workers than in colleagues due to the probable effect of selective factors. The WAI score was also correlated with absenteeism, but no differences were found between males and females in the average number of absences, suggesting that cultural or emotional factors influence the self-rating of the WAI. Workers tended to overreport illnesses in the online survey compared to data collected during occupational health checks. Musculoskeletal disorders were the most frequently reported illnesses (53%). Psychiatric illnesses affected 21% of workers and had the greatest impact on work ability. Multilevel ergonomic and human factor intervention seems to be needed to recover the working capacity of healthcare workers.

Chronic obstructive pulmonary disease (COPD) patients are particularly susceptible to respiratory infections like influenza, which exacerbate symptoms and increase healthcare utilization. While smoking cessation and influenza vaccination are recommended preventive measures, their combined impact on healthcare resource utilization is underexplored. The Charlson Comorbidity Index (CCI) assesses comorbidity burden in COPD patients and may influence healthcare outcomes. We conducted a retrospective analysis of 357 COPD patients, evaluating smoking cessation success over one year and influenza vaccination receipt, stratifying patients by CCI scores. Healthcare utilization outcomes included emergency room visits, hospitalizations, and medical expenses. Results showed that 51.82% of patients quit smoking and 59.66% received influenza vaccination, with higher comorbidity prevalence in advanced COPD stages (p=0.002). Both smoking cessation and influenza vaccination independently correlated with decreased emergency room visits, hospital admissions, days, and costs. Patients who both quit smoking and received influenza vaccination exhibited the lowest healthcare utilization rates. In conclusion, smoking cessation and influenza vaccination significantly reduce healthcare resource utilization in COPD patients, with the combination yielding synergistic benefits, particularly in those with lower CCI scores. Integrating these interventions and comorbidity management in COPD strategies is essential for optimizing patient outcomes and healthcare efficiency.

Background: The transversus abdominis plane (TAP) block has gained increasing importance as a very effective pain relief in for caesarean section. This study aimed to compare bupivacaine (Group B), bupivacaine with dexmedetomidine (Group BD) and bupivacaine with dexamethasone (Group BDx) applied in ultrasound-guided TAP block in the treatment of CS postoperative pain. Material and Method: American Society of Anesthesiologists I and II pregnant women who underwent cesarean section were included in the study. Forty patients were admitted in each group. All patients were applied TAP block with ultrasound guidance by administering the medications of the group in which they were included. Results: Body mass index (BMI) and operation duration were higher in Group B (p*=0.01 and 0.004, respectively). Instant tramadol requirement and amount were higher in Group B at some of follow up times. Patient satisfaction scale were better in Group BD and Group BDx. Conclusion: TAP block significantly reduces pain after cesarean section, prolongs the time to first analgesic request, and reduces the need for additional opioid analgesics. The addition of adjuvant to the TAP block usually improves the postoperative clinical condition. However, some special conditions such as body mass index (BMI) may negatively affect the clinical situation. Comprehensive, standardized, randomized controlled studies are needed to compare results under different conditions.

A Clinical Prognostic Index (CPI) for improved patient selection for stereotactic liver-directed radiotherapy (SBRT) was developed. Using a large single-center database, 195 patients treated with SBRT for local control, including 66 with hepatocellular carcinoma (HCC) and 129 with metastatic liver disease, were analyzed. Only patients ineligible for alternative treatments were included. Overall survival was 11.9 months and 9.4 months in the HCC group and metastatic groups, respectively. In the combined dataset, Child-Pugh Score (CPS) (p=0.002), serum albumin (p=0.039) and presence of extrahepatic disease (p=0.012) were significant predictors of early death on multivariable analysis and were included in the CPI (total score 0 to 5). Median survival was 23.8, 9.1, 4.5 and 2.6 months for patients with 0, 1-2, 3 and 4-5 points, respectively. In the HCC dataset, CPS (p&lt;0.001) and gross tumor volume (p=0.013) were predictive of early death. In the metastatic dataset, serum albumin (p&lt;0.001) and primary disease site (p=0.003) were predictive of early death. The AUC for the combined, HCC, and metastatic datasets are 0.78, 0.84, 0.80 respectively. Poor liver function (defined by CPS and serum albumin) and extrahepatic disease were most predictive of early death, providing clinically important expected survival information for patients and caregivers.

Using the h-index to assess individual researchers is not only unethical and unfair but also inaccurate and misleading. This index fails to offer a reliable measure of a researcher's impact based on their citation scores. Beyond statistical and conceptual considerations, authorship practices, especially in the case of multi-authored publications, give rise to significant problems that often go unnoticed. While some modifications of the h-index have been proposed to mitigate these weaknesses, the fundamental deficiencies persist. Most of these flaws have been effectively addressed by the c-score, a composite citation index. The c-score excludes self-citations, normalizes the number of citations by considering the number of authors in each paper, and takes into account first, single, and last authorship. This approach provides a more realistic measure of the impact of each individual researcher based on raw citations.

The Randić index of a graph G is the sum of (dG(u)dG(v))−1/2 over all edges uv of G, where dG(u) denotes the degree of vertex u in G. In this paper, we investigate a few graph transformations that decrease Randić index of graph. By applying those transformations, we determine the minimum Randić index on tricyclic graphs, and characterize the corresponding extremal graphs.

This purpose was to compare the ability of body shape index (ABSI) and body roundness index (BRI) with waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and body adiposity index (BAI) to predict metabolic risk. The cross-sectional study was conducted in 17,360 Chinese subjects (were aged 18-95 years old) who escaped cardiovascular disease (CVD) or diabetes. Biochemical and anthropometric variables were measured by trained staff. Receiver operating characteristic curve (ROC) and optimal cutoff values of obesity indices were recruited to compare the predictive ability for metabolic risk factors. The mean age of subjects was 53.7(13.1) years, 41.6 % were males. Within young group (<60 years), the areas under the curve (AUC) demonstrated that WC, BMI, WHR, WHtR and BRI were able to similarly predict high metabolic risk in males (0.74 vs. 0.74 vs. 0.73 vs. 0.73 vs. 0.73) and in females (0.73 vs. 0.73 vs. 0.71 vs. 0.73 vs. 0.73), while the approximate predictive ability were only acquired in males (0.73 vs. 0.73 vs. 0.70 vs. 0.73 vs. 0.73) within elder group (≥60 years). The optimal cut-off values of BRI for high metabolic risk were calculated in males (<60 y: 3.49 vs. ≥60 y: 3.46) and females (<60 y: 3.47 vs. ≥60 y: 3.60). Meanwhile, BRI displayed a strong prediction to elevated BP and elevated TG in males (AUC = 0.64; AUC = 0.70) and to elevated BP, elevated TG and elevated SUA in females (AUC = 0.67; AUC = 0.69; AUC =0.70). BRI was able to similarly predict high metabolic risk compared to WC, BMI, WHR, WHtR and BAI, while ABSI was not. Moreover, BRI revealed specific predictive ability for elevated BP, elevated TG and elevated SUA.

Since the beginning of the year 2020, the global healthcare system has been challenged by the threat of the SARS-COV 2 virus. Molecular, antigen, and antibody testing are the mainstay to identify infected patients and fight the virus. Molecular and antigen tests that detect the presence of the virus are relevant in the acute phase only. Serological assays detect antibodies to the Sars-CoV-2 virus in the recovering and recovered phase. Each testing methodology has its advantages and disadvantages. To evaluate the test methods, sensitivity (percent positive agreement - PPA) and specificity (percent negative agreement – PNA) are the most common metrics utilized, followed by the positive and negative predictive value (PPV and NPV), the probability that a positive or negative test result represents a true positive or negative patient. In this paper, we illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. We demonstrate the value of reporting PFP (probability of false positive results), PFN (probability of false negative results), and costs to patients and healthcare. These risk metrics can be calculated from the risk drivers of PPA and PNA combined with estimates of prevalence, cost, and Reff number (people infected by one positive SARS COV-2).

This paper introduces innovative sensitivity indices based on Cliff's Delta for global sensitivity analysis of structural reliability. These indices build on Sobol's method, using binary outcomes (success or failure), but avoid the need to calculate failure probability Pf and the associated distributional assumptions of resistance R and load F. Cliff's Delta, originally for ordinal data, evaluates the dominance of resistance over load without specific assumptions. The mathematical formulations for computing Cliff's Delta between R and F quantify structural reliability by assessing the random realizations of R > F using a double-nested-loop approach. The derived sensitivity indices, based on the squared value of Cliff's Delta​, exhibit properties analogous to those in Sobol's sensitivity analysis, including first-order, second-order, and higher-order indices. This provides a comprehensive framework for evaluating the contributions of input variables and their interactions on structural reliability. This method is particularly significant for FEM applications, where repeated simulations of R or F are computationally intensive. The double-nested-loop algorithm of Cliff's Delta maximizes the extraction of information about structural reliability from these simulations. However, the high computational demand of Cliff's Delta is a disadvantage. Future research should optimize computational demands, especially for small Pf​, where the inner loop may often be unnecessary.

In mobile applications such as geomagnetic surveying, two major effects hamper the use of optically pumped magnetometers: dead zones, sensor orientations where the sensors signal amplitude drops; and heading errors, a dependence of the measured magnetic field value on the sensor orientation. We present a concept for an omnidirectional magnetometer to overcome both these effects. The sensor uses two cesium vapor cells, interrogated by circularly-polarized amplitude-modulated laser light split into two beams running perpendicular to each other. This configuration is experimentally investigated using a setup wherein the laser beam and magnetic field direction can be freely adjusted relative to each other within a magnetically shielded environment. We demonstrate that a dead-zone free magnetometer can be realized with nearly isotropic magnetic-field sensitivity. While in the current configuration we observe heading errors emerging from light shifts and due to the nonlinear Zeeman effect, we introduce a straightforward approach to suppress these systematic effects in an advanced sensor realisation.

This study aimed to improve sensitivity of ferrous particle sensors used in various mechanical systems such as wind power gearboxes to detect abnormalities by measuring the amount of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, its ability to detect abnormalities is limited because it only measures amounts of ferrous particles collected on the top of the sensor. To overcome this limitation, this study evaluated sensibility of an existing sensor using a multi-physics analysis method and improved it by changing the core shape inside the sensor. The detection ability of the improved sensor was analytically shown to be better than the existing sensor. This study highlights the use of analytical methods and multi-physics analysis for developing a ferrous particle sensor and presents a method to compare sensibility analytically. Results of the improved sensor are expected to enhance the ability to detect abnormalities in the lubrication system where ferrous wear particles are generated. This study will contribute to the development of more effective ferrous particle sensors, which can potentially improve the performance and reliability of mechanical systems.

The optimization of the pile driver frame is very important to the overall performance of the pile driver. This paper takes the large body structure of a model of hydraulic static pile driver as the research object. ANSYS APDL finite element software is used to perform static analysis and sensitivity analysis on the large body structure of the pile driver. On the premise of meeting the design requirements, according to the most dangerous working conditions of the pile driver, the size optimization of the frame structure of the pile driver is carried out based on sensitivity analysis. The mathematical model is established with the plate thickness as the design variable, the strength and stiffness of the body structure as the constraints, and the minimum mass of the body structure as the objective function. The optimization results show that the optimization design model based on sensitivity analysis not only meets the strength and stiffness conditions of the large structure, but also reduces the structural quality by 22.1% and greatly saves the production cost.