Numerical Index Research Articles

A Prognostic Model Incorporating Relevant Peripheral Blood Inflammation Indicator to Predict Postherpetic Neuralgia in Patients with Acute Herpes Zoster.

To determine the risk of postherpetic neuralgia (PHN) in patients with acute herpes zoster (HZ), this study developed and validated a novel clinical prediction model by incorporating a relevant peripheral blood inflammation indicator. Between January 2019 and June 2023, 209 patients with acute HZ were categorized into the PHN group (n = 62) and the non-PHN group (n = 147). Univariate and multivariate logistic regression analyses were conducted to identify risk factors serving as independent predictors of PHN development. Subsequently, a nomogram prediction model was established, and the discriminative ability and calibration were evaluated using the receiver operating characteristic curve, calibration plots, and decision curve analysis (DCA). The nomogram model was internally verified through the bootstrap test method. According to univariate logistic regression analyses, five variables, namely age, hypertension, acute phase Numeric Rating Scale (NRS-11) score, platelet-to-lymphocyte ratio (PLR), and systemic immune inflammation index, were significantly associated with PHN development. Multifactorial analysis further unveiled that age (odds ratio (OR) [95% confidence interval (CI)]: 2.309 [1.163-4.660]), acute phase NRS-11 score (OR [95% CI]: 2.837 [1.294-6.275]), and PLR (OR [95% CI]: 1.015 [1.010-1.022]) were independent risk factors for PHN. These three predictors were integrated to establish the prediction model and construct the nomogram. The area under the receiver operating characteristic curve (AUC) for predicting the PHN risk was 0.787, and the AUC of internal validation determined using the bootstrap method was 0.776. The DCA and calibration curve also indicated that the predictive performance of the nomogram model was commendable. In this study, a risk prediction model was developed and validated to accurately forecast the probability of PHN after HZ, thereby demonstrating favorable discrimination, calibration, and clinical applicability.

Discriminating woody species assemblages from National Forest Inventory data based on phylogeny in Georgia.

Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β-diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species-neutral dissimilarity (Bray-Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large-scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity.

From Antagonism to Fusion: Military and Industrial Society Fused in Militarist Capitalism

ABSTRACT This article reconsiders the classical duality between military and industrial society and the evolutionary scheme from the first to the second. It argues and theorizes the concept of military-capitalist society, or militarist capitalism, including its theocratic-militarist variant. It elaborates a substantive index of militarist capitalism that is composed of certain indicators and proxies of the latter as its components and describes data and data sources concerning the index components. It reports the substantive findings of an empirical analysis, namely numerical militarist capitalism indexes for Western and comparable societies such as Organisation for Economic Co-operation and Development (OECD) countries. The main results are that military capitalism is primarily a phenomenon of regions outside Western Europe; of unregulated, inegalitarian, and coercive capitalism; and of societies dominated by conservatism and religion-overdetermined cultures, such as the United States and other countries. It discusses these findings with reference to these countries. Lastly, it draws conclusions and theoretical implications.

LIASM-NRID: Constructing an atmospheric scattering model for low-light conditions and dehazing nighttime road images

Dehazing of nighttime road images holds significant practical relevance for autonomous driving and intelligent transportation systems operating in nocturnal conditions. To address the limitations of traditional atmospheric scattering models in accurately describing the imaging process in low illumination environments such as nighttime, this study constructs a low illumination environment atmospheric scattering model. Based on this newly constructed model, we propose an algorithm specifically designed for dehazing road images at nighttime. The proposed model incorporates an incident light attenuation factor, an additive noise factor, and a global compensation factor related to the scene's depth. The proposed nighttime road image dehazing algorithm firstly suppresses the interference of additive noise in the original image using wavelet decomposition and soft thresholding operation. Then, a transmittance-solving method containing the primary structural information of the image is designed based on the color attenuation linear model, and the solved transmittance is refined based on the interval gradient image texture structure method. Consequently, the global light map estimation process is formulated as an optimization problem to ensure the accuracy of the atmospheric light value calculation. In the final stages, a novel RGB color equalization method is introduced to address the issue of non-uniform incident light color bias in the dehazed images. Additionally, to eliminate the halo problem in the dehazed images, the adaptive histogram equalization algorithm is modified by incorporating a normalized gamma correction function. The experimental results show that the proposed nighttime road image dehazing algorithm exhibits robust performance and generality across various nighttime scenes. Compared with the representative algorithms for nighttime image dehazing, it can get more excellent dehazing results and has more outstanding performance in all numerical evaluation indexes.

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA<PSNP & PMETA<PBMI) results for YPEL3 in nucleus accumbens and NT5C2, SNAPC3, TMEM245, YPEL3, and ZNF646 in liver. The identified genes help link the genetic variation at obesity risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.

Spatiotemporal Assessment of Water Pollution for Beira Lake, Sri Lanka

Beira Lake, located in Colombo, Sri Lanka, has suffered severe anthropogenic impacts, with previous restoration attempts failing due to a limited understanding of pollutant dynamics. Aiming to fill this gap, a comprehensive study was conducted during dry and wet seasons to assess the spatiotemporal water pollution of Beira Lake, employing key physicochemical parameters, numerical indices, and remote sensing analysis. The water pollution index (WPI) results categorize Beira Lake as highly polluted, with WPI values ranging from 2.38 ± 0.92 in the wet season to 2.53 ± 1.32 in the dry season. Comparatively higher COD levels recorded in the Beira Lake network, especially for Gangarama Lake show significant pollution levels during both the dry and wet seasons, e.g., the highest COD levels, at 306.40 mg/L, were observed during the wet season. The Trophic State Index (TSI) results indicate eutrophic and hypereutrophic conditions in Beira Lake, which are particularly pronounced during the wet season. The heavy metal pollution index (HPI) results suggest elevated heavy metal concentrations in Beira Lake, especially in the wet season. Combined with field investigation results, a remote sensing data analysis between 2016 and 2023 reveals significant improvements in water transparency, suggesting positive effects of recent management interventions. Parameters demanding attention include COD, nitrate, and total phosphate levels due to their consistent exceedance of permissible limits. The PCA results of indices correlations between wet and dry seasons offer valuable insights into the complex dynamics of Beira Lake’s water quality. The study makes recommendations for restoring Beira Lake, including stringent pollution controls, regular dredging, green infrastructure implementation, implementing new rules and regulations, and community engagement.

Acoustic design evaluation in educational buildings using artificial intelligence

Speech intelligibility is a critical aspect of building science, particularly in educational buildings where poor sound quality may have a detrimental impact on students' learning and teachers’ health. However, considering the numerous building regulations proposing varying definitions and ranges of acoustic comfort, calculating the necessary acoustic indicators can be challenging for designers. Speech intelligibility is a crucial component of indoor acoustics and acoustic comfort and can be calculated using formulas, simulation software, and data-based web tools. While formulas are fast, they lack details; acoustic simulation software is highly accurate but time-consuming and expensive. Data-based web tools, including machine learning algorithms, offer both speed and accuracy and are widely accessible. In this study, we present a system utilizing machine learning techniques to predict acoustic indicators, numeric and heatmap, in an educational building. The Pachyderm plugin in the Grasshopper was utilized to conduct extensive simulations in a single educational space, focusing on acoustic indicators in six different frequencies and general modes. Using Catboost and the pix2pix algorithm, the prediction models provide numerical and image indices on the developed dataset. Also, SHAP values were employed to interpret the Catboost model, analyzing the significance of each feature. The results showed remarkable accuracy, (i.e., between 89 % and 99 %) in the numerical portion, and PSNR index ranging from 0.817 to 0.970, and an SSIM index ranging from 15.56 to 31.57 in the image section. By utilizing data-driven methods, the system provides an efficient and accurate approach to calculating acoustic indicators, helping to ensure optimal acoustic environment in educational buildings.

Compression algorithm for colored de Bruijn graphs

A colored de Bruijn graph (also called a set of k-mer sets), is a set of k-mers with every k-mer assigned a set of colors. Colored de Bruijn graphs are used in a variety of applications, including variant calling, genome assembly, and database search. However, their size has posed a scalability challenge to algorithm developers and users. There have been numerous indexing data structures proposed that allow to store the graph compactly while supporting fast query operations. However, disk compression algorithms, which do not need to support queries on the compressed data and can thus be more space-efficient, have received little attention. The dearth of specialized compression tools has been a detriment to tool developers, tool users, and reproducibility efforts. In this paper, we develop a new tool that compresses colored de Bruijn graphs to disk, building on previous ideas for compression of k-mer sets and indexing colored de Bruijn graphs. We test our tool, called ESS-color, on various datasets, including both sequencing data and whole genomes. ESS-color achieves better compression than all evaluated tools and all datasets, with no other tool able to consistently achieve less than 44% space overhead. The software is available at http://github.com/medvedevgroup/ESSColor.

A randomized feasibility study of a positive psychology journaling intervention to support recovery from substance-use disorders

BackgroundIndividuals in early recovery face significant biopsychosocial stressors causing a preponderance of negative affect. Novel interventions are needed to improve mood and well-being to support recovery. Positive Recovery Journaling (PRJ) combines elements of positive psychology, behavioral activation, and journaling to emphasize what is going right and to encourage small, positive steps that align with an individual's values to make life in recovery more rewarding and therefore more reinforcing. Our objective was to determine PRJ's feasibility, acceptability, and impact on a set of strengths-based, multidimensional aspects of recovery, including satisfaction with life, happiness with recovery, and commitment to sobriety. MethodsThe study randomized adults in substance-use disorder treatment (N = 81) to PRJ or control. Those in PRJ were asked to practice PRJ daily and complete online surveys for four weeks; those in the control group completed online surveys for four weeks. We used multi-level modelling to determine intercept and slope for feasibility and acceptability outcomes as well as to compare differences in recovery indicators between treatment and control at baseline and Weeks 2, 4, and 8. We conducted intention-to-treat and per-protocol analyses for each recovery indicator. ResultsParticipants were 53 % female, and 26 % Black, Indigenous, People of Color (BIPOC) and mean age of 39 years. PRJ participants attended 71 % of groups and completed 56 % of the daily PRJ entries. Treatment and control groups rated their study tasks (PRJ for the treatment group, surveys for the control group) as equally easy; however, the PRJ group rated PRJ as significantly more satisfying, helpful, and pleasant. Treatment and control were not significantly different on any recovery indicator. In post hoc analyses, we found that for those with <90 days sobriety at baseline (51 %), PRJ had a statistically significant beneficial effect for satisfaction with life, happiness with recovery, and numerous secondary recovery indicators. DiscussionResults suggest a positive impact of PRJ on numerous recovery indices for those in earliest recovery. Integrating PRJ into support services among those with <90 days sobriety could reinforce what is going well in recovery to encourage its continued maintenance and thereby improve treatment outcomes.

Exploring Students’ Perceptions of Outdoor Biology Learning Activities in Botanical Garden

Outdoor learning has become a relevant learning method to enrich students' learning experience in biology. Botanical gardens, as places that reflect biodiversity, offer unique potential for integrating outdoor learning. This study aims to describe students' perceptions of outdoor learning that has been carried out in the botanical garden as well as the potential and barriers. This research method is a case study. Data collection techniques were questionnaires, structured and semi-structured interviews. The sampling technique used was convenience sampling with a sample of 32 class x students at one of the high schools in Bandung. Responses were expressed in perception numeric index to quantitatively reveal the perceptions of grade 10 students towards the outdoor learning experience that has been carried out. The results revealed that out of 32 respondents, most students showed positive perceptions towards outdoor learning in the botanical garden with a mean index = 55.23 (range 42.5-65.0) with standard deviation (sd) = 6.0. This perspective is influenced by the potential and obstacles in the implementation of outdoor learning conducted in botanical gardens. The implications of this study open up opportunities to design learning strategies that are more effective and can be applied by teachers in organizing biology learning at the high school level.

Linking Computational Fluid Dynamics Modeling to Device-Induced Platelet Defects in Mechanically Assisted Circulation.

Thrombotic and bleeding events are the most common hematologic complications in patients with mechanically assisted circulation and are closely related to device-induced platelet dysfunction. In this study, we sought to link computational fluid dynamics (CFD) modeling of blood pumps with device-induced platelet defects. Fresh human blood was circulated in circulatory loops with four pumps (CentriMag, HVAD, HeartMate II, and CH-VAD) operated under a total of six clinically representative conditions. Blood samples were collected and analyzed for glycoprotein (GP) IIb/IIIa activation and receptor shedding of GPIbα and GPVI. In parallel, CFD modeling was performed to characterize the blood flow in these pumps. Numerical indices of platelet defects were derived from CFD modeling incorporating previously derived power-law models under constant shear conditions. Numerical results were correlated with experimental results by regression analysis. The results suggested that a scalar shear stress of less than 75 Pa may have limited contribution to platelet damage. The platelet defect indices predicted by the CFD power-law models after excluding shear stress <75 Pa correlated excellently with experimentally measured indices. Although numerical prediction based on the power-law model cannot directly reproduce the experimental data. The power-law model has proven its effectiveness, especially for quantitative comparisons.

P-wave alternans rebound following pulmonary vein isolation predicts atrial arrhythmia recurrence.

Numerous P-wave indices have been explored as biomarkers to assess atrial fibrillation (AF) risk and the impact of therapy with variable success. We investigated the utility of P-wave alternans (PWA) to track the effects of pulmonary vein isolation (PVI) and to predict atrial arrhythmia recurrence. This medical records study included patients who underwent PVI for AF ablation at our institution, along with 20 control subjects without AF or overt cardiovascular disease. PWA was assessed using novel artificial intelligence-enabled modified moving average (AI-MMA) algorithms. PWA was monitored from the 12-lead ECG at ~1 h before and ~16 h after PVI (n = 45) and at the 4- to 17-week clinically indicated follow-up visit (n = 30). The arrhythmia follow-up period was 955 ± 112 days. PVI acutely reduced PWA by 48%-63% (p < .05) to control ranges in leads II, III, aVF, the leads with the greatest sensitivity in monitoring PWA. Pre-ablation PWA was ~6 µV and decreased to ~3 µV following ablation. Patients who exhibited a rebound in PWA to pre-ablation levels at 4- to 17-week follow-up (p < .01) experienced recurrent atrial arrhythmias, whereas patients whose PWA remained reduced (p = .85) did not, resulting in a significant difference (p < .001) at follow-up. The AUC for PWA's prediction of first recurrence of atrial arrhythmia was 0.81 (p < .01) with 88% sensitivity and 82% specificity. Kaplan-Meier analysis estimated atrial arrhythmia-free survival (p < .01) with an adjusted hazard ratio of 3.4 (95% CI: 1.47-5.24, p < .02). A rebound in PWA to pre-ablation levels detected by AI-MMA in the 12-lead ECG at standard clinical follow-up predicts atrial arrhythmia recurrence.

Breakage Indices for Constitutive Models to Track the Evolution of Particle Crushing Under Continuous Shearing

Researchers have developed numerous breakage indices, predominantly relying on comparisons between pre- and post-test Grain Size Distribution (GSD) curves determined through conventional sieve analysis. Although these indices enable a precise estimation of particle breakage extent, a significant limitation arises when attempting to integrate them into constitutive models, especially when those models cannot consider GSD as an input. This paper addresses the need for tailored breakage indices designed to suit the specific requirements of different families of constitutive models. The new indices have been developed based on stress and strain tensor invariants or combinations thereof, ensuring the future ease of implementation into various constitutive models. The proposed equations have been validated using five different sets of experiments performed on rockfill material, calcareous, and quartz sands. The comparison between the available experimental data and the estimated degree of particle breakage using the suggested indices demonstrates their adequate performance. The novel indices can be integrated into different constitutive models, including hypoplastic, elastoplastic, and thermodynamically consistent approaches, as well as tested for their practical applicability for boundary value problems.

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models.

Survey of plant species used in the treatment of common ailments in Hawul Local Government Area, Borno State, Nigeria

To record the locals' knowledge of some of the medicinal plant species in the area, a survey of plant species used for the treatment of common ailments was carried out in Hawul Local Government Area Borno State. A semi-structured questionnaire was used to collect the data. Numerous quantitative indices, including Informant Consensus Factor (ICF), Use Value (UV), Fidelity Level (FL), Shannon Weiner Index, Simpson Index, Relative Frequency of Citation (RFC), and Family Importance Value (FIV), were used to quantify the advantages, significance, and coverage of ethnomedicine. The Jaccard Index (JI) was used to assess the degree of homogeneity between the current study and earlier research as well as within indigenous tribes. A total of fifty-five (55) medicinal plants species, which were distributed under twenty-nine (29) families were documented from one hundred (100) informants. The majority of species that have been identified are tree species, and the most common plant portion used to make ethnomedicines is leaf material. The Fabaceae were the predominate families among the species that had been documented (11 species) Accacia nilotica, Piliostigma thonningii, Detarium microcarpum, Faidherbia albida, Sennia siamea, Prosopis Africana, Parkia baglobosa, Tamarindus indica, Pterocarpus erinaceus, Senna occidentalist, Senna obtusifolia, while the least represented was (1 species) Apocynacea. For digestive system problems, the highest ICF score was 0.77. According to the data gathered from the questionnaires, pile, yellow fever, malaria, typhoid, stomach ache, and ulcer were the most prevalent diseases, in that order. Based on UVs, the two most commonly cited ethnomedicinal plant species in the study area were Khaya senegalensis (0.40), Piliostigma thonningii (0.35) while the least was (0.01) Guinea senegalensis. The most common method of preparation was by decoction and the major route of administration was oral while least cited was sit bath. Phytochemical profiles demonstrated that flavonoids, alkaloids phenols, saponins and tannins were the major phytochemical component in Khaya. senegalensis and Piliostigma. thonningii leaves. This further justifies the frequent application of these plants in folklore medicine by the traditional herbal practitioners to treat different forms of illnesses. It is stated that due to their physiologically active ingredients and accessibility, herbal medicine will continue to play a significant role in the community's health care delivery system.

Innovative mapping of groundwater redox status and cation exchange conditions in a GIS environment

Understanding the complexities of regional groundwater quality is crucial for managing groundwater resources. Groundwater quality assessment involves investigating specific dissolved groundwater components, for example, comparing them to established standards. To fully understand all aspects of groundwater quality, one should assess composite properties, one being the redox status and another being the cation exchange condition. The first may, for example, impose a control on the degradation of organic micropollutants. While groundwater numerical indices can be easily interpolated and visualised using various GIS applications, consistently mapping the redox status and cation exchange conditions as non-numerical indices remains challenging. Furthermore, no study has yet conducted a regional-scale mapping of cation exchange classes in a GIS environment using extensive groundwater samples. To deepen our understanding of these groundwater components, we employed ArcGIS in this study to map the redox and cation exchange conditions in two stages. First, we mapped the groundwater components of interest, including Cl, SO4, SO4/Cl, Fe, NO3 and base exchanges of Na and Mg, by the most appropriate interpolation method identified by a geostatistical analysis. Then, variables were combined, and the conditional functions were used in ArcMap's Math toolbox to determine redox status or cation exchange classes. Our innovative GIS method for mapping regional redox status and cation exchange conditions was developed for 3350 groundwater sampling locations in the coastal lowlands of the Western Netherlands. The method was successful, with generally 75%–95% similarity between predicted and observed situations for most classes. The introduced method is more straightforward than others and can map other non-numerical linguistic indices like Wilcox groundwater and irrigation water classifications, as well.

Prevalence of Low Back Pain in Pregnant Females

Background: Low back pain (LBP) during pregnancy is a common condition that affects a significant number of women, impacting their daily activities and overall quality of life. Various studies have documented the prevalence and severity of LBP among pregnant women, highlighting the need for more focused healthcare strategies. Objective: The aim of this study was to assess the prevalence, onset, and severity of LBP in pregnant women, and to evaluate the impact of LBP on their daily activities, with a view to enhancing prenatal care and management approaches. Methods: A cross-sectional study was conducted over six months at two hospitals in Lahore, involving 181 pregnant women selected through non-probability convenience sampling. Data were collected using the Oswestry Low Back Pain Disability Questionnaire and the Numeric Pain Index Scale. Statistical analysis was performed using SPSS version 25, focusing on descriptive statistics to calculate means, standard deviations, and prevalence percentages. Ethical considerations followed the Helsinki Declaration guidelines, with informed consent obtained from all participants. Results: The prevalence of LBP among the participants was 76.8%. The pain onset was most frequently reported in the second trimester (38.1% of cases). Severity assessments revealed 36.5% of participants experienced moderate pain and 40.3% severe pain. LBP significantly impaired daily functions, with many women reporting difficulties in walking, sitting, and engaging in other routine activities. Conclusion: LBP is highly prevalent among pregnant women and begins primarily in the second trimester, with a considerable impact on daily activities. This study emphasizes the necessity for routine prenatal assessment and targeted interventions to manage LBP, which could improve pregnancy outcomes and maternal health.

Equivalence after extension and Schur coupling for Fredholm operators on Banach spaces

Schur coupling (SC) and equivalence after extension (EAE) are important relations for bounded operators on Banach spaces. It has been known for 30 years that the former implies the latter, but only recently Ter Horst, Messerschmidt, Ran and Roelands disproved the converse by constructing a pair of Fredholm operators which are EAE, but not SC.Motivated by this result, we investigate when EAE and SC coincide for Fredholm operators. Fredholm operators which are EAE have the same Fredholm index. Surprisingly, we find that for each integer k and every pair of Banach spaces (X,Y), either no pair of Fredholm operators of index k acting on X and Y, respectively, is SC, or every pair of this kind which is EAE is also SC. Consequently, whether EAE and SC coincide for Fredholm operators of index k depends only on the geometry of the underlying Banach spaces X and Y, not on the properties of the operators themselves.We quantify this finding by introducing two numerical indices which capture the coincidence of EAE and SC and provide a number of examples illustrating the possible values of these indices. Notably, this includes an example showing that the above-mentioned result of Ter Horst et al., which is based on a pair of essentially incomparable Banach spaces, does not extend to projectively incomparable Banach spaces.

Comparative analysis of thermal indices for modeling cold and heat stress in US dairy systems

Quantifying the impact of thermal stress on milk yields is essential to effectively manage present and future risks in dairy systems. Despite the existence of numerous heat indices designed to communicate stress thresholds, little information is available regarding the accuracy of different indices in estimating milk yield losses from both cold and heat stress at large spatio-temporal scales. To address this gap, we comparatively analyzed the performance of existing thermal indices in capturing US milk yield response to both cold and heat stress at the national scale. We selected four commonly used thermal indices: the Temperature and Humidity Index (THI), Black Globe Humidity Index (BGHI), Adjusted Temperature and Humidity Index (THIadj), and Comprehensive Climate Index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981-2020, we systematically compared the patterns of yield sensitivities and statistical performance of the four indices. We found that the US state-level milk yield variability was better explained by the THIadj and CCI, which combine the effects of temperature, humidity, wind, and solar radiation. Our analysis also reveals a continuous and nonlinear responses of milk yields to a range of cold to heat stress across all four indices. This implies that solely relying on fixed thresholds of these indices to model milk yield changes may be insufficient to capture cumulative thermal stress. Cold extremes reduced milk yields comparably to those impacted by heat extremes on the national scale. Additionally, we found large spatial variability in milk yield sensitivities, implying further limitations to the use of fixed thresholds across locations. Moreover, we found decreased yield sensitivity to thermal stress in the most recent two decades, suggesting adaptive changes in management to reduce weather-related risks.

Финансовые индикаторы динамики стоимости ESG-ориентированных компаний Китая

The priority of investing in sustainable development and ESG-oriented companies has become a global trend. Along with the developed markets of the USA and Europe, numerous ESG indices have been formed in countries of other continents. This segment of the stock market shows high growth rates in China. The purpose of the article is to identify the financial indicators that are most closely related to the value dynamics of ESG-oriented companies in China. The empirical base for the comparative analysis of companies adhering to ESG principles and not implementing them in practice was 48 companies included in the SSE 180 ESG Leaders Index of the Shanghai Stock Exchange, and 44 companies from the Shanghai Composite index that are not included in the ESG indices. The analysis of the value dynamics of the two groups of companies showed the higher resilience of ESG-oriented enterprises to the crisis phenomena in the economy. However, the comparative analysis of multipliers did not allow us to conclude that one of the groups of companies is more attractive for investment. Special attention was paid to the study of financial indicators of fundamental analysis. The main conclusion is important for investors: the growth in the value of ESG-neutral companies is not consistent with the positive dynamics of financial indicators, and such investments are riskier than investments in ESG-oriented companies. Further research on investment attractiveness of financial ESG-instruments and individual companies, adhering to the principles of sustainable development and represented in the stock market of China, will be associated with more detailed factor analysis, which is important for Russian investors to make informed decisions when developing a new capital market.