Additive Model Research Articles

Eco-climate drivers shape virome diversity in a globally invasive tick species.

Spillovers of viruses into human occur more frequently under warmer conditions, particularly arboviruses. The invasive tick species Haemaphysalis longicornis poses a significant public health threat due to its global expansion and its potential to carry a wide range of pathogens. We analyzed meta-transcriptomic data from 3595 adult H. longicornis ticks collected between 2016 and 2019 in 22 provinces across China, encompassing diverse ecological conditions. Generalized additive modelling revealed that climate factors exerted a stronger influence on the virome of H. longicornis compared to other ecological factors, such as ecotypes, distance to coastline, animal host, tick gender, and anti-viral immunity. We investigated the mechanistic understanding of how climate changes drive the tick virome using causality inference and emphasized its significance for public health. Our findings demonstrated that higher temperatures and lower relative humidity/precipitation contribute to variations in animal host diversity, leading to an increased diversity of tick virome, particularly the evenness of vertebrate associated viruses. This finding may explain the evolution of tick-borne viruses into generalists across multiple hosts, thereby increasing the probability of spillover events involving tick-borne pathogens. Deep learning projections indicate that the diversity of H. longicornis virome is expected to increase in 81.9% of regions under the SSP8.5 scenario from 2019-2030. Extension of surveillance should be implemented to avert the spread of tick-borne diseases.

Dose-Dependent Association Between Body Mass Index and Mental Health and Changes Over Time.

Overweight and obesity affect 340 million adolescents worldwide and constitute a risk factor for poor mental health. Understanding the association between body mass index (BMI) and mental health in adolescents may help to address rising mental health issues; however, existing studies lack comprehensive evaluations spanning diverse countries and periods. To estimate the association between BMI and mental health and examine changes over time from 2002 to 2018. This was a repeated multicountry cross-sectional study conducted between 2002 and 2018 and utilizing data from the Health Behaviour in School-aged Children (HBSC) survey in Europe and North America. The study population consisted of more than 1 million adolescents aged 11 to 15 years, with all surveyed children included in the analysis. Data were analyzed from October 2022 to March 2023. Mental health difficulties were measured by an 8-item scale for psychological concerns, scoring from 0 to 32, where a higher score reflects greater psychosomatic issues. BMI was calculated using weight divided by height squared and adjusted for age and sex. Data were fitted by multilevel generalized additive model. Confounders included sex, living with parents, sibling presence, academic pressure, the experience of being bullied, family affluence, screen time, and physical activity. Our analysis of 1 036 869 adolescents surveyed from 2002 to 2018, with a mean (SD) age of 13.55 (1.64) years and comprising 527 585 girls (50.9%), revealed a consistent U-shaped association between BMI and mental health. After accounting for confounders, adolescents with low body mass and overweight or obesity had increased psychosomatic symptoms compared to those with healthy weight (unstandardized β, 0.14; 95% CI, 0.08 to 0.19; unstandardized β, 0.27; 95% CI, 0.24 to 0.30; and unstandardized β, 0.62; 95% CI, 0.56 to 0.67, respectively), while adolescents with underweight had fewer symptoms (unstandardized β, -0.18; 95% CI, -0.22 to -0.15). This association was observed across different years, sex, and grade, indicating a broad relevance to adolescent mental health. Compared to 2002, psychosomatic concerns increased significantly in 2006 (unstandardized β, 0.19; 95% CI, 0.11 to 0.26), 2010 (unstandardized β, 0.14; 95% CI, 0.07 to 0.22), 2014 (unstandardized β, 0.48; 95% CI, 0.40 to 0.56), and 2018 (unstandardized β, 0.82; 95% CI, 0.74 to 0.89). Girls reported significantly higher psychosomatic concerns than boys (unstandardized β, 2.27; 95% CI, 2.25 to 2.30). Compared to primary school, psychosomatic concerns rose significantly in middle school (unstandardized β, 1.15; 95% CI, 1.12 to 1.18) and in high school (unstandardized β, 2.12; 95% CI, 2.09 to 2.15). Our study revealed a U-shaped association between adolescent BMI and mental health, which was consistent across sex and grades and became stronger over time. These insights emphasize the need for targeted interventions addressing body image and mental health, and call for further research into underlying mechanisms.

Behavior, site use and demographics of shortfin devil rays, Mobula kuhlii, at a newly-discovered cleaning area in South Africa

Many shark and ray species show affinity to specific sites, making these areas critical for their survival. These include cleaning stations: locations on reefs where cleaner fish remove parasites and clean wounds, which is important for maintaining health. Cleaning stations also function as social gathering sites, or resting points, where courtship and mating can occur. In this study, we identify an aggregation site for the shortfin devil ray, Mobula kuhlii (Family Mobulidae) within the Aliwal Shoal Marine Protected Area in KwaZulu-Natal (KZN), South Africa, and document their behavior. Remote underwater video was used to collect footage of M. kuhlii being cleaned by blue streak cleaner wrasse, Labroides dimidiatus. Generalized additive models (GAMs) were used to assess environmental predictors of M. kuhlii presence on Aliwal Shoal. Mixed models were used to assess the same environmental predictors and their correlation with mean M. kuhlii cleaning duration and number of L. dimidiatus bites per second at the identified cleaning station site. M. kuhlii were present in 56% of observation days, with group numbers up to > 150 individuals. Sea surface temperature was a significant predictor for M. kuhlii presence, while a north to south current was significantly associated with longer mean cleaning durations. These results support findings of mobulid studies in KZN that show increased habitat use during summer temperatures (24–25 °C) and suggest these sites to be important for individual health and social interaction. We hope these findings can be used for development of location-specific management plans to safeguard this Endangered species.

The relationship between weight-adjusted-waist index and suicidal ideation: evidence from NHANES

BackgroundAmidst growing evidence of the intricate link between physical and mental health, this study aims to dissect the relationship between the waist-to-weight index (WWI) and suicidal ideation within a representative sample of the US population, proposing WWI as a novel metric for suicide risk assessment.MethodsThe study engaged a sample of 9500 participants in a cross-sectional design. It employed multivariate logistic and linear regression analyses to probe the association between WWI and suicidal ideation. It further examined potential nonlinear dynamics using a weighted generalized additive model alongside stratified analyses to test the relationship's consistency across diverse demographic and health variables.ResultsOur analysis revealed a significant positive correlation between increased WWI and heightened suicidal ideation, characterized by a nonlinear relationship that persisted in the adjusted model. Subgroup analysis sustained the association's uniformity across varied population segments.ConclusionsThe study elucidates WWI's effectiveness as a predictive tool for suicidal ideation, underscoring its relevance in mental health evaluations. By highlighting the predictive value of WWI, our findings advocate for the integration of body composition considerations into mental health risk assessments, thereby broadening the scope of suicide prevention strategies.

The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study.

The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets. This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED. We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS. The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

Association of BDNF polymorphism with gestational diabetes mellitus risk: a novel insight into genetic predisposition.

Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder during pregnancy with potential long-term health implications for the mother and child. The interplay between genetics and GDM susceptibility remains an area of active research. Recently, brain-derived neurotrophic factor (BDNF) was investigated in relation to obesity and impaired glucose metabolism and pathogenesis. We aimed to investigate the association of common BDNF polymorphisms, with GDM risk in Israeli females. A cohort of 4,025 Israeli women data for BDNF common SNPs was analyzed for potential association with GDM using binary logistic regressions analysis (SPSS 29.0 and R) adjusted for confounding variables (age, T1DM, T2DM, PCOS) under different genetic models. The GDM and Non-GDM genetic frequencies for the BDNF rs925946 Tag-SNP were significantly different. The genetic frequencies were 54.16 %, and 66.91 % for the wild type (GG), 38.88 and 29.64 % for the heterozygotes (TC), and 6.94 and 3.48 % for the risk allele homozygotes (TT) for the GDM non-GDM populations, respectively. Carriers of BDNF rs925946 were significantly associated with higher risk for GDM, following the dominant genetic model (OR=1.7, 95 % CI 1.21-2.39, p=0.002), the recessive genetic model (OR=2.05, 95 % CI 1.04-4.03, p=0.03), and the additive genetic model (OR=1.62, 95 % CI 1.13-2.3, p=0.008). This association persisted after adjusting for age, T1DM, T2DM, and polycystic ovary syndrome (PCOS). Carrying BDNF rs925946 polymorphism predisposes to a higher risk of GDM pathogenesis. Its role and implications warrant further investigation, especially when considering preventive measures for GDM development.

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh.

A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change. Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change. We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters. Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null. Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

Associations between blood manganese levels and sarcopenia in adults: insights from the National Health and Nutrition Examination Survey

BackgroundWhile increasing concerns arise about the health effects of environmental pollutants, the relationship between blood manganese (Mn) and sarcopenia has yet to be fully explored in the general population.ObjectiveThis study aims to investigate the association between blood manganese (Mn) levels and sarcopenia in adults.MethodsIn our study, we evaluated 8,135 individuals aged 18–59 years, utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011 to 2018. We employed generalized additive model (GAM) to discern potential non-linear relationships and utilized the two-piecewise linear regression model to probe the association between blood Mn levels and sarcopenia.ResultsAfter adjusting for potential confounders, we identified non-linear association between blood Mn levels and sarcopenia, with an inflection point at 13.45 μg/L. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 1.006 (0.996 to 1.048) and 1.082 (1.043 to 1.122), respectively. Subgroup analysis showed that the effect sizes of blood Mn on sarcopenia have significant differences in gender and different BMI groups.ConclusionOur results showed that a reverse U-shaped curve between blood Mn levels and sarcopenia, with an identified the inflection point at blood Mn level of 13.45 μg/L.

The combined effects of physical and virtual models in learning cellular biology

Physical models have long been used in science education for visualization of complex cellular structure and dynamics during face-to-face lecture (F2F). Recent advancement of technology has enabled us to create virtual models and to share knowledge remotely. This study aims to find out whether physical and virtual models work synergistically to enhance student engagement in learning an undergraduate Life Sciences module. Three independent experiments were conducted to assess learning effectiveness on three biology concepts through four learning approaches: video with virtual model only, video with integration of virtual and physical model demonstration, F2F lecture using virtual model only, F2F lecture using virtual and physical models. Participants were randomly assigned to different groups each with one learning approach. Data collected through pre- and post-tests revealed that significant improvement in learning scientific concepts occurred in one of three controlled experiments when the video contains both virtual and physical models, while no obvious difference found in the other two experiments. This data suggests that well-prepared digital media alone may convey scientific information well and additional physical models do not aid in information acquisition. However, feedback survey on student learning experience showed that all participants preferred to learn from physical models. In all three experiments, students consistently voted that the physical models attracted their attention and enhanced their interests. They made better mind maps and raised more questions. These hint that a combination of digital media with physical models might improve engagement and promote higher order thinking.

The stomatal response to vapor pressure deficit drives the apparent temperature response of photosynthesis in tropical forests.

As temperature rises, net carbon uptake in tropical forests decreases, but the underlying mechanisms are not well understood. High temperatures can limit photosynthesis directly, for example by reducing biochemical capacity, or indirectly through rising vapor pressure deficit (VPD) causing stomatal closure. To explore the independent effects of temperature and VPD on photosynthesis we analyzed photosynthesis data from the upper canopies of two tropical forests in Panama with Generalized Additive Models. Stomatal conductance and photosynthesis consistently decreased with increasing VPD, and statistically accounting for VPD increased the optimum temperature of photosynthesis (Topt) of trees from a VPD-confounded apparent Topt of c. 30-31°C to a VPD-independent Topt of c. 33-36°C, while for lianas no VPD-independent Topt was reached within the measured temperature range. Trees and lianas exhibited similar temperature and VPD responses in both forests, despite 1500 mm difference in mean annual rainfall. Over ecologically relevant temperature ranges, photosynthesis in tropical forests is largely limited by indirect effects of warming, through changes in VPD, not by direct warming effects of photosynthetic biochemistry. Failing to account for VPD when determining Topt misattributes the underlying causal mechanism and thereby hinders the advancement of mechanistic understanding of global warming effects on tropical forest carbon dynamics.

Predictors of valproic acid steady-state serum levels in adult and pediatric psychiatric inpatients: a comparative analysis.

Valproic acid (VPA) is commonly used as a second-line mood stabilizer or augmentative agent in severe mental illnesses. However, population pharmacokinetic studies specific to psychiatric populations are limited, and clinical predictors for the precision application of VPA remain undefined. To identify steady-state serum VPA level predictors in pediatric/adolescent and adult psychiatric inpatients. We analyzed data from 634 patients and 1,068 steady-state therapeutic drug monitoring (TDM) data points recorded from 2015 to 2021. Steady-state VPA levels were obtained after tapering during each hospitalization episode. Electronic patient records were screened for routine clinical parameters and co-medication. Generalized additive mixed models were employed to identify independent predictors. Most TDM episodes involved patients with psychotic disorders, including schizophrenia (29.2%) and schizoaffective disorder (17.3%). Polypharmacy was common, with the most frequent combinations being VPA + quetiapine and VPA + promethazine. Age was significantly associated with VPA levels, with pediatric/adolescent patients (< 18 years) demonstrating higher dose-adjusted serum levels of VPA (β = 7.6±2.34, p < 0.001) after accounting for BMI. Women tended to have higher adjusted VPA serum levels than men (β = 5.08±1.62, p < 0.001). The formulation of VPA (Immediate-release vs. extended-release) showed no association with VPA levels. Co-administration of diazepam exhibited a dose-dependent decrease in VPA levels (F = 15.7, p < 0.001), suggesting a potential pharmacokinetic interaction. This study highlights the utility of population-specific pharmacokinetic data for VPA in psychiatric populations. Age, gender, and co-administration of diazepam were identified as predictors of VPA levels. Further research is warranted to establish additional predictors and optimize the precision application of VPA in psychiatric patients.

US County-Level Variation in Availability and Prevalence of Black Physicians in 1906

Black physicians are substantially underrepresented in the US health care workforce, with detrimental effects on the health and health care experiences of Black individuals. These contemporary gaps can be traced to the early days of the medical profession using the first edition of the American Medical Directory (AMD). To identify state- and county-level patterns related to the training and availability of Black physicians relative to their White counterparts in the 1906 AMD. For this cross-sectional study, data for 41 828 physician entries in 18 US states in or adjacent to the South as well as the District of Columbia were extracted from the 1906 AMD and aggregated to 1570 counties. Data analysis was performed between September 2023 and January 2024. County-level exposure variables included population density, racial composition, and illiteracy rate among US-born White residents as well as an index of terrain ruggedness and the number of lynchings in the previous decade. Median values of physicians' distance from place of practice to place of medical training (by race of physician) were also used as an exposure variable. There were 4 county-level outcomes: (1) presence of any Black physician, (2) proportion of Black physicians per Black population, (3) proportion of White physicians per White population, and (4) community representativeness (reported as the community representativeness ratio). The cross-sectional analysis used generalized additive mixed models with state-level random effects. Across 1570 counties, Black physicians comprised 746 (1.8%) of the 41 828 physicians in the dataset. Black physicians tended to train further from their place of practice than their White counterparts. The proportion of Black physicians per 1000 Black residents was 0.08 compared with 1.62 for White physicians; these proportions varied substantially by state. At the county level, the presence of any Black physician was associated with percentage Black population (odds ratio [OR], 28.94 [95% CI, 9.77 to 85.76]; P ≤ .001), population density (OR, 2.63 [95% CI, 2.03 to 3.40]; P ≤ .001), and distance to the nearest Black medical school (OR, 0.62 [95% CI, 0.42 to 0.92]; P = .02). A variety of structural disadvantages are illustrated in this cross-sectional study of county-level sociodemographic and geographic characteristics associated with the prevalence of Black physicians in the earliest days of the profession. To demonstrate its broader utility for health disparities research, the dataset has been made publicly available with a visualization platform.

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

The relationship between oxidative balance score and erectile dysfunction in the U.S. male adult population

Oxidative stress strongly influences the pathophysiology of erectile dysfunction (ED). In this study, we used the oxidative balance score (OBS), a composite index, to measure the effects of oxidative stress triggered by diet and lifestyle factors. Here, we conducted a cross-sectional study to determine the statistical relationship between OBS and ED among adult males in the U.S. The data from 3318 participants in the National Health and Nutrition Examination Survey (NHANES) 2001–2004 were analyzed. Weighted logistic regression was used to correct for confounding factors and acquire nationwide representative estimates. Generalized additive modeling was used to explore the nonlinear relationship. We also supplemented subgroup and sensitivity analysis to examine the robustness of the main results. Multivariate logistic regression indicated a consistent negative linear association between OBS and ED across all participants [OR (95% CI) = 0.96 (0.94, 0.98)]. After categorizing OBS into tertiles, participants in the highest tertile had 43% lower odds of having ED than those in the lowest tertile [OR (95% CI) = 0.57 (0.37, 0.87)]. The generalized additive model also visualized the linear trend of this association. Furthermore, this linear relationship remained relatively consistent, regardless of whether subgroup or sensitivity analyses were performed. Our findings suggest that adopting a lifestyle and diet pattern that promotes favorable OBS may effectively protect against the development of ED, regardless of the underlying causes.

The power of Para sport: the effect of performance-focused swimming training on motor function in adolescents with cerebral palsy and high support needs (GMFCS IV) – a single-case experimental design with 30-month follow-up

ObjectiveThis study aims to evaluate the effect of a performance-focused swimming programme on motor function in previously untrained adolescents with cerebral palsy and high support needs (CPHSN) and to determine...

Efficient reliability assessment of slope stability with insightful sensitivity analysis concerning the safety factor

The limit state functions (LSFs) of slope stability are nonlinear, implicit, or derived from numerical simulations, and additional surrogate models are usually used for its reliability problems. Alternatively, an efficient numerical procedure using a variant of the HLRF-BFGS algorithm is proposed to address this issue without additional surrogate models. This proposed procedure simultaneously conducts reliability assessment and sensitivity analysis concerning the safety factor by suggested calculations of partial derivatives using the finite difference method (FDM). Two highly nonlinear numerical examples are presented to validate its convergence and to investigate the step length of the FDM. Results show that the proposed procedure can get considerable accuracy while the HLRF algorithm cannot converge, and its required iterations are about 30% and 81% less than those of the limited step-size iteration algorithm. In addition, a step length not greater than 0.1 is suggested after investigations. Application to a classical two-layered slope is illustrated. Reliability results show that the proposed procedure can get the same accuracy as those of surrogate models after only 5 iterative steps. Sensitivity results show that the cohesion of the upper soil is the only sensitivity factor, and the most possible potential slide surface lies in the upper soil.

Biofusion design and parameter optimization for a novel passive assisted knee exoskeleton robot based on eight-bar mechanism

Abstract In an effort to alleviate the issue of knee joint fatigue and injury during lower limb ambulation, a novel passive assisted exoskeleton robot with human–machine interaction is investigated to assist the movement of the human knee joint. The design of the exoskeleton configuration takes into consideration the physiological structure and gait function of the knee joint, ensuring that it satisfies the requirements for motion, force, and gait function of the knee joint. To explore the interaction between the wearer and the exoskeleton, a human–machine kinematic model after wearing exoskeleton is established, which is instrumental in analyzing the integration motion of the wearer and exoskeleton. In addition, the dynamic and static models of the knee joint after wearing the exoskeleton are established, utilizing the Newton–Euler method and force polygon method, respectively, to evaluate the effectiveness of the exoskeleton. Moreover, the size parameters and spring stiffness of the exoskeleton are optimized, using both human body kinematic model and mechanic model. Furthermore, the effectiveness of the exoskeleton in proving assistance is evaluated through human body simulation, using OpenSim. The results indicate that the exoskeleton significantly reduces the knee joint torque by 48.42%.

Sign Language Detection Using CNN and LSTM Based Model

In the context of our current work, we have taken a major step in examining and contrasting the effectiveness of several machine learning models for sign language recognition. Convolutional Neural Network (CNN) model building was the focus of our minor project, which resulted in the successful publication of a research paper [6]. Our method was validated by this model, which showed significant accuracy in recognizing sign language. We wanted to build a Long Short-Term Memory (LSTM) model for the same reason as we moved on to our larger project. The aim was not only to construct an additional efficient model but also to conduct a comparative analysis between the LSTM and the CNN model that was previously constructed. This comparison included a thorough assessment of both models based on a number of variables, including accuracy, loss, speed, and others. We can now confidently state that we have created an LSTM model for sign language recognition that is fully operational. In addition, we have compared our CNN model with our LSTM model, assessing both models according to training accuracy, training loss, validation accuracy, validation loss, and training duration. This development opens the door for more research in this area and represents an important turning point in our study. Our research will be strengthened by the solid foundation our findings provide. Keywords: Convolutional Neural Network, Long Short-Term Memory, Sign Language Detection, Training, Validation

Decoupling the multi-drivers of urban extreme heat environment in urban agglomerations using ensemble learning

Urban extreme heat (UEH) has been imparting a formidable menace to various facets of sustainable urban and regional development. While increasing studies have examined the urban thermal environment, a gap exists in decoupling the nonlinear multi-drivers of UEH environment in intra-urban regions, especially across the urban agglomerations. In this study, we estimated UEH through the land surface temperature (LST) in the extended summer season exceeding a specific threshold computed through cross-regional statistics. Furthermore, an additive interpretable ensemble learning model enhanced via the Bayesian Optimization algorithm (BO) and Monte Carlo Simulation framework (MCS) was employed to dissect the intricate nonlinear interplay between UEH and intra-urban morphology, green-blue composition, and atmospheric determinants. Our results highlight the indispensably suppressive effect of green and blue factors on UEH, whereas urban morphological variables generally exhibit an opposite trend. Interestingly, we reveal heterogeneity responses of building heights, elevated urban structures mitigate UEH in temperate monsoon and subtropical monsoon zones and exhibit diminished marginal utility in temperate continental zones. Furthermore, the arid climatic characteristics are expected to exert an unexpectedly enhancing effect on water bodies' cooling capacity. These findings provide heterogeneous local guidance for landscape and urban planning towards the severe challenges of climate change.

High-accuracy emulators for observables in ΛCDM, Neff, Σmν, and w cosmologies

Abstract We use the emulation framework CosmoPower to construct and publicly release neural network emulators of cosmological observables, including the Cosmic Microwave Background (CMB) temperature and polarization power spectra, matter power spectrum, distance-redshift relation, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) observables, and derived parameters. We train our emulators on Einstein-Boltzmann calculations obtained with high-precision numerical convergence settings, for a wide range of cosmological models including ΛCDM, wCDM, ΛCDM+Neff, and ΛCDM+Σmν. Our CMB emulators are accurate to better than 0.5% out to ℓ = 104 which is sufficient for Stage-IV data analysis, and our P(k) emulators reach the same accuracy level out to k = 50 Mpc−1, which is sufficient for Stage-III data analysis. We release the emulators via an online repository (CosmoPower Organisation), which will be continually updated with additional extended cosmological models. Our emulators accelerate cosmological data analysis by orders of magnitude, enabling cosmological parameter extraction analyses, using current survey data, to be performed on a laptop. We validate our emulators by comparing them to class and camb and by reproducing cosmological parameter constraints derived from Planck TT, TE, EE, and CMB lensing data, as well as from the Atacama Cosmology Telescope Data Release 4 CMB data, Dark Energy Survey Year-1 galaxy lensing and clustering data, and Baryon Oscillation Spectroscopic Survey Data Release 12 BAO and RSD data.