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Surveys are commonly used to facilitate research in epidemiology, health, and the social and behavioral sciences. Often, these surveys are not simple random samples, and respondents are given weights reflecting their probability of selection into the survey. We show that using survey weights can be beneficial for evaluating the quality of predictive models when splitting data into training and test sets. In particular, we characterize model assessment statistics, such as sensitivity and specificity, as finite population quantities and compute survey-weighted estimates of these quantities with test data comprising a random subset of the original data. Using simulations with data from the National Survey on Drug Use and Health and the National Comorbidity Survey, we show that unweighted metrics estimated with sample test data can misrepresent population performance, but weighted metrics appropriately adjust for the complex sampling design. We also show that this conclusion holds for models trained using upsampling for mitigating class imbalance. The results suggest that weighted metrics should be used when evaluating performance on test data derived from complex surveys. [ABSTRACT FROM AUTHOR]

Featured Application: The following application aims to reduce non-value-added work, with a net reduction in time and an increase in reliability through the introduction of artificial intelligence in logistics. Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector, specifically the implementation of text recognition algorithms that use advanced artificial intelligence technology to perform tasks and activities typically carried out by human operators. The implementation under analysis was carried out at an Italian company in Tolentino, in the province of Macerata. The implementation of this tool focuses on the ability to autonomously read shipping notes, with the aim of simplifying the flow of information in the goods receipt phase and reducing the workload of warehouse staff. As illustrated in the following chapters, when artificial intelligence follows several trainings, technical improvements and structural changes in shipping notes, the flow is greatly simplified, significantly facilitating this logistics phase. [ABSTRACT FROM AUTHOR]

This paper deals with a class of parabolic reaction-diffusion equations driven by the fractional Laplacian as the diffusion operator over a bounded domain with zero Dirichlet external condition. Using a comparison principle and monotone iteration method, we establish existence and uniqueness results. We apply the existence result to the logistic growth problems with constant yield harvesting by constructing an ordered pair of positive sub- and supersolution of the corresponding elliptic problem. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodsResultsConclusionsThis study aims to evaluate the diagnostic significance of serum uric acid (UA), glutathione (GSH), and amyloid-β1-42 (Aβ1-42) levels in relation to disease progression and cognitive impairment in patients with Parkinson’s disease (PD).A total of 209 PD patients with disease duration ranging from 4.0 to 6.8 years were enrolled. Based on the Hoehn-Yahr staging system, patients were classified into Early (n = 67), Medium-term (n = 70), and Advanced (n = 72) stages. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), dividing the cohort into CD (cognitive dysfunction, n = 94) and NO-CD (no cognitive dysfunction, n = 115) groups. Serum UA, GSH, and Aβ1-42 levels were analyzed for correlations with clinical data. Independent risk factors and diagnostic value were determined through multivariable logistic regression models and receiver operating characteristic curve analysis.Serum UA and GSH levels progressively declined with advancing disease stage, while Aβ1-42 increased. Compared to the NO-CD group, the CD group showed lower serum UA and GSH levels, and higher Aβ1-42 levels. Serum UA and GSH were inversely correlated with disease duration, levodopa equivalent daily dose, and Unified Parkinson's Disease Rating Scale scores, while Aβ1-42 showed positive correlations. UA (p = 0.006), GSH (p < 0.001), and Aβ1-42 (p = 0.040) were independent predictors of disease stage. Similarly, UA (p = 0.003), GSH (p < 0.001), and Aβ1-42 (p < 0.001) were independent predictors of cognitive dysfunction. The combined assessment of these markers demonstrated a higher area under the curve (AUC) than individual markers for disease and cognitive decline identification.Serum UA, GSH, and Aβ1-42 are independent predictors of disease progression and cognitive decline in PD patients. Their combined use offers enhanced diagnostic accuracy for disease staging and cognitive impairment in PD. [ABSTRACT FROM AUTHOR]

This study is aimed at investigating the crystallization kinetics of two structurally related polymers, Nylon 6,6 (PA66) and Nylon 11 (PA11), by differential scanning calorimetry (DSC) in the scope of a logistic-based model using a model fitting approach. By this method, the values of the rate parameters for each specific temperature are obtained from fitting all points of the crystallization exotherm that were accurately recorded at that temperature. This method differs from Arrhenius-based model fitting approaches, in which the initial and final parts of the exotherm do not usually match the shape of Arrhenius-based models and are therefore discarded for fitting. Furthermore, in other kinetic approaches that fall outside the scope of this article, kinetic parameters are typically obtained from specific points in the crystallization exotherm, and good fits cannot generally be obtained nor is that the goal of those approaches. The DSC curves of both polymers obtained at different temperatures are analysed to determine the crystallization kinetics. One of the most insightful parameters of the model is the crystallization rate. Its dependence on temperature is analysed for both polymers and compared to others. The other parameters can also help to better understand some of the crystallization features of these polymers. In addition, the information retrieved from this study can be useful to adjust processing conditions. [ABSTRACT FROM AUTHOR]

Purpose: The main aim of the article is to present the author's model of the maturity of logistic centres, in its improved version. Design/methodology/approach: To carry out the research and prepare the article, an expert (Delphic) method was used, using the knowledge of experts professionally connected with logistic centres, and a literature review. Findings: The conclusion of the research and analysis of the results is an improved model of logistic centre maturity compared to previous work. The developed model of logistic centre maturity allows for a comprehensive analysis of the logistic centre's maturity, locating points in need of improvement, and allows continuity and reliability of the centre's operation to be ensured. Practical implications: The model developed has practical use for management of logistics centres and is a universal one, meaning it can be utilised in any logistic centre in order to improve its inner workings. Originality/value: The main value of the paper is improvement upon author's original model of the maturity of logistics centres, with inclusion of additional arenas. The developed model is the authors’ own input into logistics science. [ABSTRACT FROM AUTHOR]

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Abstract Background The wild boar (Sus scrofa) was extinct in Sweden when a few animals established in the 1970s. Over the past 35 years, the species has made a substantial comeback. In this paper, we analyse wild boar population growth using three indices of population size. We also map the legislative decisions and research prompted by the expanding population. We discuss to what extent, in the eyes of the state, the view of wild boar and the management focus has shifted over time, from a perceived pest (eradication) to scarce (conservation), overabundant (reduction/control) or somewhere in between (sustainable management). Results Wild boar harvest started in the early 1990s with a few hundred animals annually and peaked at 161,000 in 2020/2021. The distribution now comprises most of southern Sweden. Analyses of harvest and traffic accidents involving wild boar showed that the population grew exponentially until 2010/2011, after which the increase levelled off. Thus, logistic growth models showed the best fit for the full study period. We recorded 38 legislative decisions or commissions to government agencies regarding wild boar. The first decision in 1981 was to eradicate the free-ranging population. In 1987 however, the parliament decided that wild boar is native to Sweden and should be allowed in restricted extent. Later decisions mainly concerned hunting regulations and hunting methods as direct means to increase harvest and regulate the population. Another topic, increasing in importance over time, was to facilitate the use of wild boar meat to indirectly stimulate harvest. A local outbreak of African swine fever in 2023 necessitated a stamping out strategy in the affected area. We found 44 scientific papers regarding the present free-ranging population. Topics include movements and feeding patterns, hunting, reproduction, and population development. Conclusions The state historically regarded wild boar as a pest to be eradicated. This changed with the decision that wild boar should be allowed in restricted extent, suggesting a conservation approach. In response to population growth, the focus shifted to means facilitating sustainable management and, lately, reducing growth. The story of wild boar in Sweden illustrates attempts to mitigate conflicts and balance interests in wildlife management.

Copyright of Acta Agronomica Sinica is the property of Crop Science Society of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Logistics depends on a set of features that are related to the nature of its work, whether it is at the level of a country, an establishment, or a small business environment, and the most important of these features: a study of the proximity of important places, which contribute to providing resources to the company, or the state. Identify potential means of communication and transportation that can be used to communicate with other parties, or to obtain appropriate resources. Materials and Methods: When studying the design and the mechanism for forming the architectural environment of the logistic centers associated with the process of developing types of food supplies taking into account modern considerations of the market economy, then the scientific and theoretical approaches, methodologies and practical recommendations necessary for this matter must be studied such as : analysis of scientific, normative, methodological and design studies; Conducting a full study of the mechanism of work of the agricultural logistics centers Modeling agricultural projects, taking into consideration innovative techniques. Results: This research was provided an architectural design based on the use of a logistic center. This is to represent the logistics center itself in global practice, which proposes to provide a local business-oriented logistics center with its own guiding method. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Ablai Khan KazUIRandWL: Series 'International Relations & Regional Studies' is the property of Kazakh Ablai Khan University of International Relations & World Languages and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is concerned with the existence and non-existence of positive periodic solutions for ∂ u ∂ t - div (| ∇ u | p - 2 ∇ u) = u q (a - b u β) with p > 1 , where a = a (x , t) and b = b (x , t) are positive periodic functions. Some numerical simulation also be implemented to verify our results. [ABSTRACT FROM AUTHOR]

Background: The wild boar (Sus scrofa) was extinct in Sweden when a few animals established in the 1970s. Over the past 35 years, the species has made a substantial comeback. In this paper, we analyse wild boar population growth using three indices of population size. We also map the legislative decisions and research prompted by the expanding population. We discuss to what extent, in the eyes of the state, the view of wild boar and the management focus has shifted over time, from a perceived pest (eradication) to scarce (conservation), overabundant (reduction/control) or somewhere in between (sustainable management). Results: Wild boar harvest started in the early 1990s with a few hundred animals annually and peaked at 161,000 in 2020/2021. The distribution now comprises most of southern Sweden. Analyses of harvest and traffic accidents involving wild boar showed that the population grew exponentially until 2010/2011, after which the increase levelled off. Thus, logistic growth models showed the best fit for the full study period. We recorded 38 legislative decisions or commissions to government agencies regarding wild boar. The first decision in 1981 was to eradicate the free-ranging population. In 1987 however, the parliament decided that wild boar is native to Sweden and should be allowed in restricted extent. Later decisions mainly concerned hunting regulations and hunting methods as direct means to increase harvest and regulate the population. Another topic, increasing in importance over time, was to facilitate the use of wild boar meat to indirectly stimulate harvest. A local outbreak of African swine fever in 2023 necessitated a stamping out strategy in the affected area. We found 44 scientific papers regarding the present free-ranging population. Topics include movements and feeding patterns, hunting, reproduction, and population development. Conclusions: The state historically regarded wild boar as a pest to be eradicated. This changed with the decision that wild boar should be allowed in restricted extent, suggesting a conservation approach. In response to population growth, the focus shifted to means facilitating sustainable management and, lately, reducing growth. The story of wild boar in Sweden illustrates attempts to mitigate conflicts and balance interests in wildlife management. [ABSTRACT FROM AUTHOR]

Mathematical models may aid researchers in describing biological processes, like growth, in animals. This study aimed to collect the body weight data of 18 Boer goat castrates and 20 Boer goat does, from birth until maturity, to model growth and determine growth trends. This is a novel investigation as sufficient information on an age-weight database for these two Boer goat sexes from birth to maturity, is lacking. Using age-weight data, four nonlinear models, namely the Brody, Gompertz, Logistic and Von Bertalanffy growth models, were plotted and evaluated. The model parameters of each growth model were compared for differences between the two sexes. The statistical effectiveness of fit was determined for each model using AIC and RMSE, with R2 also being considered. All models except the Brody model, predicted significantly heavier mature weights for castrates. The Brody model was deemed unfit to describe Boer goat growth as the function severely over-predict weights from birth until maturity for both sexes. The Von Bertalanffy (R2 = 91.3) and Gompertz functions (R2 = 91.3) showed the best fit for Boer goat castrates, while the Gompertz model (R2 = 95.1) showed the best fit for Boer goat does. The Gompertz function is the preferred model to depict Boer goat growth overall, as it accurately characterized growth of both sexes. According to the Gompertz model the age at which the inflection point of the growth curve was reached, did not differ significantly between castrates and does (141.80 days versus 136.31 days). There was also no significant difference in maturation rate between the two sexes. [ABSTRACT FROM AUTHOR]

Abstract Background To examine the risk factors associated with mortality in individuals suffering from acute diquat poisoning and to develop an effective prediction model using clinical data. Methods A retrospective review was conducted on the clinical records of 107 individuals who were hospitalized for acute diquat poisoning at a tertiary hospital in Sichuan Province between January 2017 and September 30, 2023, and further categorized into survivor and nonsurvivor groups based on their mortality status within 30 days of poisoning. The patient’s demographic information, symptoms within 24 h of admission, and details of the initial clinical ancillary examination, as well as the APACHE II score, were documented. The model was developed using backward stepwise logistic regression, and its performance was assessed using receiver operating characteristic curves, calibration curves, Brier scores, decision curve analysis curves, and bootstrap replicates for internal validation. Results Multifactorial logistic regression analysis revealed that blood pressure (hypertension, OR 19.73, 95% CI 5.71–68.16; hypotension, OR 61.38, 95% CI 7.40–509.51), white blood count (OR 1.35, 95% CI 1.20–1.52), red cell distribution width-standard deviation (OR 1.22, 95% CI 1.08–1.38), and glomerular filtration rate (OR 0.96, 95% CI 0.94–0.97) were identified as independent risk factors for mortality in patients with diquat. Subsequently, a nomogram with an area under the curve of 0.97 (95% CI: 0.93–1) was developed. Internal bootstrap resampling (1000 repetitions) confirmed the model’s adequate discriminatory power, with an area under the curve of 0.97. Decision curve analysis demonstrated greater net gains for the nomogram, while the clinical impact curves indicated greater predictive validity. Conclusion The nomogram model developed in this study using available clinical data enhances the prediction of risk for DQ patients and has the potential to provide valuable clinical insights to guide patient treatment decisions.

AbstractThe sustainable business model is critical in designing a performance model for logistics service companies by taking into account the dominant factors in managing all the resources needed to carry out operational processes, including the implementation of the logistics system as an effort to increase organizational competitiveness and resilience in the logistics services industry in a sustainable manner. The problems faced today are in line with the increasing frequency of using logistics services with the growth of E-commerce so the competition for logistics services companies, especially in urban areas, is getting tighter. This study used Mix Methods Research (MMR) with an explanatory design strategy and Structural Equation Model-Partial Least Square (PLS) analysis. In this study, a measurement model simulation used a system dynamic approach. Previous studies used the variant-based structural equation method but were limited to 1st-order models. This study aimed to formulate a more comprehensive sustainable logistics management model using the 2nd-order model and continued with a measurement model simulation up to 2040. The population in the study were employees of urban logistics services companies at the supervisor level with more than 5 years of service. Data were collected through questionnaires, observation, Focus Group Discussion (FGD), and literature study from January to August 2023. Based on the results, economic factors, environmental factors, social factors, and customer factors had a positive influence of 13.8 % (0.138 × 100%), 12.9 % (0.129 × 100%), 14.2 % (0.142 × 100%) and 12.9% (0.129 × 100%), respectively, meaning mutual support to the sustainable logistics model. Based on dynamic system analysis, the sustainable logistics model will have an increasing valuation every year. Thus, the relationship between variables in the sustainable logistics model was good in maintaining the company sustainability. This study is expected to give an exact recommendation for logistics companies to maintain their sustainability by always improving the four main factors with the positive influence namely environmental, customer, social, and economic factors.

Several qualitative models have been proposed to explain significant historical shifts in both societal and technological domains. Despite advancements in modeling, certain transitions remain enigmatic, such as the early shift from hunter-gatherer to agriculture-dependent societies, marked by a substantial increase in effort. Another perplexity involves the coordination of agricultural activities into cities and civilizations, despite the larger overhead effort and loss of independence. The exploration of simplified models featuring aggregate, dynamic, and nonlinear processes holds the potential to uncover distinctive facets of each transition. The transitions under consideration span from hunter-gatherer societies to agricultural societies and then to early civilizations.  Other analogies are suggested for further exploration for transitions to market systems, capitalism, industrialization, and sustainable societies, incorporating factors like land pressures, economies of scale, suppressed growth, and chain reactions. Diverse modeling approaches can be employed for these transitions. Initially, fundamental characteristics, such as the width and midpoint of transitions, are deduced by analyzing historical events influencing the transition. However, this approach offers limited insights into the dynamics or parameters of the transition. For a more comprehensive understanding, two historical transitions are examined using a simple phenomenological model. These simplified models do not aim to quantitatively address the intricate details of actual historical mechanisms; instead, they leverage analogies to natural systems to gain insights.

Background: To examine the risk factors associated with mortality in individuals suffering from acute diquat poisoning and to develop an effective prediction model using clinical data. Methods: A retrospective review was conducted on the clinical records of 107 individuals who were hospitalized for acute diquat poisoning at a tertiary hospital in Sichuan Province between January 2017 and September 30, 2023, and further categorized into survivor and nonsurvivor groups based on their mortality status within 30 days of poisoning. The patient's demographic information, symptoms within 24 h of admission, and details of the initial clinical ancillary examination, as well as the APACHE II score, were documented. The model was developed using backward stepwise logistic regression, and its performance was assessed using receiver operating characteristic curves, calibration curves, Brier scores, decision curve analysis curves, and bootstrap replicates for internal validation. Results: Multifactorial logistic regression analysis revealed that blood pressure (hypertension, OR 19.73, 95% CI 5.71–68.16; hypotension, OR 61.38, 95% CI 7.40–509.51), white blood count (OR 1.35, 95% CI 1.20–1.52), red cell distribution width-standard deviation (OR 1.22, 95% CI 1.08–1.38), and glomerular filtration rate (OR 0.96, 95% CI 0.94–0.97) were identified as independent risk factors for mortality in patients with diquat. Subsequently, a nomogram with an area under the curve of 0.97 (95% CI: 0.93–1) was developed. Internal bootstrap resampling (1000 repetitions) confirmed the model's adequate discriminatory power, with an area under the curve of 0.97. Decision curve analysis demonstrated greater net gains for the nomogram, while the clinical impact curves indicated greater predictive validity. Conclusion: The nomogram model developed in this study using available clinical data enhances the prediction of risk for DQ patients and has the potential to provide valuable clinical insights to guide patient treatment decisions. [ABSTRACT FROM AUTHOR]

The changes of medical solid waste (MSW) output in recent years have had a significant impact on the spread of the virus. There is a high-risk transmission of MSW in various stages such as storage, transportation, and treatment during the COVID-19. To cope with the risks brought by the epidemic, normalized prevention consumes a large amount of protective clothing, medical masks, goggles, packaging bags, and other related medical supplies. There is a significant uncertainty in the amount of MSW output that poses a risk of COVID-19 infection in the event of an emergency, which increases the difficulty of collecting and handling epidemic prevention MSW. The analysis of MSW data from 2000 to 2022 found a stable growth trend before 2019. However, the MSW data was a sudden increase trend from 2020 to 2022, and the COVID-19 in China was characterized by an initial stage, an outbreak stage, and a stable growth stage. The range of MSW output during the epidemic was (1.19–1.75) × 106 t a−1. The amount of MSW was approximately 1.19 × 106 t a−1 during the normalized epidemic period, and its treatment cost was as high as 3.57 × 109 yuan (RMB)·a−1. The distribution of MSW output was uneven due to factors such as climate conditions, population data, and local economy. This study has important reference value for epidemic medical material reserves and MSW treatment. [ABSTRACT FROM AUTHOR]

According to the latest Higher Education Census - 2021, the accumulated dropout rate of students in the entry course between the years 2012 - 2021 jumped from 13% to 59%, with a progressive increase. The issue of dropout in higher education is complex, as it affects the student's life, the institution and the economy of a country, which is why it is a source of discussion both nationally and internationally. Therefore, the general objective of this study is to verify whether there is a correlation between some previously selected variables on evasion, based on previous studies, with the withdrawal of students from undergraduate courses at the Federal University of Alfenas - UNIFAL-MG. For this purpose, quantitative research methodology was used with the application of binary logistic regression carried out in the GRETL Software (Gnu Regression, Econometrics and Time-series Library). The results indicate that a certain variable cannot be adopted as a standard to justify evasion, as the same variable can be associated, influence and explain student evasion, as well as having a completely opposite, insignificant correlation, depending on the institution under analysis. Furthermore, there is no general solution to the problem, although some variables may be more evident than others. [ABSTRACT FROM AUTHOR]

Each stage of the construction of the rail transit network has unique dynamic characteristics. It can provide recommendations for rail transit network planning and phased construction by evaluating the degree of evolution of the urban rail transit network and dividing the evolution stages precisely. Starting from the allometric growth relationship between transfer nodes and ordinary nodes in the urban rail transit network, this paper defines the evolution level of the rail transit network and the growth rate difference between transfer nodes and ordinary nodes and deduces the dynamic logistic equation of URT network evolution level using the mathematical derivative and logarithmic relationship. This paper reveals the dynamic law of the evolution process of the urban rail transit network at a theoretical level, analyzes the dynamic evolution process on this basis, determines the threshold for phasing the evolution stage, and divides it into four evolution stages. Finally, the results of the theoretical derivation are validated by the evolution data of the Beijing Rail Transit Network (1984–2020) over 45 years. The verification results are substantially congruent with the conclusions of the theoretical derivation, demonstrating the accuracy of the theoretical model and its practical usefulness in directing the building of the rail transit network. [ABSTRACT FROM AUTHOR]

This paper examines the impact of strategic sub-components of the Global Competitiveness Index (GCI) on the Logistics Performance Index (LPI), positing a correlation between the LPI and selected GCI factors, specifically within the infrastructure domain--namely, the indicators of road quality, rail service efficiency, port efficiency, air transport efficiency, and electrification. The objective is to identify which indicators within the infrastructure sector most significantly affect the Logistics Performance Index. In this study, the LPI is the dependent variable, while the five previously mentioned competitiveness indicators act as independent variables. A panel data regression analysis was employed to evaluate how these independent variables influence the dependent variable. The data for this study were derived from the World Bank, specifically the LPI of the World Bank and the World Economic Forum, covering countries in the ASEAN region from 2012 to 2020. The findings indicate that, among the five indicators, port efficiency significantly impacts the Logistics Performance Index in the ASEAN region. Furthermore, air transportation efficiency and electrification significantly influence the Logistics Performance Index. Conversely, road quality and rail service efficiency do not significantly affect the Logistics Performance Index in the ASEAN region. [ABSTRACT FROM AUTHOR]

The logistics and supply chain management (SCM) sector plays a paramount role in the economic development and growth of countries. In the USA, the effectiveness and efficiency of logistics and SCM functions directly influence regional organizational performance and long-term economic sustainability. The prime objective of this research is to explore the phenomenon of optimizing regional business performance through the application of data and business analytics in logistics and supply chain management for the sustainable growth of the US economy. In this study, the researcher employed machine learning methodologies, specifically ANN, RNN, and SVM, to forecast lead times for purchasing aluminum products. In the research, historical data was collected from the database of one of the aluminum-producing companies in the USA for the last 10 years. In particular, a sample of 38,500 orders of aluminum profiles was adopted for the current study. Retrospectively, the Recurrent Neural Network and the Support Vector Machine displayed the most favorable outcomes in predicting lead time in the supply chain. Particularly, RNN had the least Mean Average Error (MAE) on the testing set (447.72), followed by SVM (453.04), MLR (453.22), and NN (455.41). By deploying these algorithms, the government can optimize inventory degrees, minimize stockouts, and reduce excess inventory. This results in enhanced efficiency, diminished carrying costs, and elevated consumer satisfaction, leading to cost savings and heightened profitability for government companies within the supply chain. [ABSTRACT FROM AUTHOR]

Sex estimation models are specific to populations and cannot be generalized due to genetic and environmental variabilities. This retrospective cross-sectional study, conducted between January and September 2023 at the Tamale Teaching Hospital, included 119 (50.9 %) females and 115 (49.1 %) males aged 23–82 years. Measurements, including manubrium length (M), manubrium width (MW), sternal body length (B), combined manubrium and sternal body lengths (CL), corpus sterni width at first sternebrae (CSWS1), and corpus sterni width at third sternebrae (CSWS3), were obtained from Computerized Tomographic (CT) images of the sternum using DICOM Viewer, accurate to 0.1 cm. Subsequently, sternal area (SA) and sternal index (SI) were calculated. Univariable and stepwise multivariable discriminant function analysis (DFA) and logistic regression (LR) models were developed using a training sample (70 %), and cross-validation was performed on a holdout sample (30 %). Results showed that the linear measurements, excluding M, and sternal area were higher in males than females (P50 % in females (P

Abstract Background Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis. Results Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters. Conclusions Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes.

Stress is a mental health problem that is hazardous if not recognized early. A promising approach for noninvasive stress detection involves leveraging camera technology; however, there are notable challenges involved in this method, particularly regarding signal accuracy and quality, which are primarily caused by motion artifacts. This study aims to improve stress classification accuracy by using web camera-based imaging photoplethysmography signals. We introduce a frame alignment method that can significantly correct noise to mitigate motion artifacts. We use heart rate (HR) and HR variability metrics to monitor stress and classify the measurements into stress and no-stress conditions. The sample in this study comprises students who were stressed using the arithmetic task method. Our findings showed that the mean HR and low-frequency component increased under stressful conditions while the high-frequency component decreased. The primary contribution of this study involves refining the accuracy and reducing the time required for stress classification time. Notably, the proposed approach markedly improves the accuracy, with substantial noise correction in the resultant signal. We evaluate various classification methods, including logistic regression, support vector machine, naïve Bayes, and random forest. Our results demonstrate that logistic regression achieved the highest accuracy of 98.5%, with a receiver operating curve value of 98%, an F1 score of 98.4%, and a computation time of 4.7 s. Overall, the proposed methodology holds considerable promise for camera-based noninvasive human stress assessment.

Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector, specifically the implementation of text recognition algorithms that use advanced artificial intelligence technology to perform tasks and activities typically carried out by human operators. The implementation under analysis was carried out at an Italian company in Tolentino, in the province of Macerata. The implementation of this tool focuses on the ability to autonomously read shipping notes, with the aim of simplifying the flow of information in the goods receipt phase and reducing the workload of warehouse staff. As illustrated in the following chapters, when artificial intelligence follows several trainings, technical improvements and structural changes in shipping notes, the flow is greatly simplified, significantly facilitating this logistics phase.

Background: Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis. Results: Here, we present Dashing Growth Curves, an interactive web application (http://dashing-growth-curves.ethz.ch/) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters. Conclusions: Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes. [ABSTRACT FROM AUTHOR]

Objective To explore the key influencing factors of diabetic retinopathy (DR), analyze the current situation of DR, and construct a risk prediction modeL Methods Based on the diabetic complication early warning data set published by the national population and health science data sharing platform, the key influencing factors of DR were obtained by univariate and multivariate Logistic regression analyses. The entropy weighting method, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the rank-sum ratio (RSR) were used to quantify the risk of DR development in patients and stratified into three levels: high, medium, and low. Logistic regression, random forest, and support vector machine models were constructed, respectively, and model fusion was performed using voting, averaging, and weighted averaging to evaluate the model predictive effect and obtain the best predictive model. Results Finally, 14 indexes including age and hyperlipidemia were extracted as key influencing factors. The stratification results showed that there were 50 diabetic patients without DR in this data set with a risk of about 82.99%, which was a high-risk group for DR and needed more attention. The best prediction effect was obtained from the voting machine fusion model (Acc: 80.18%, Fl: 0.7868). Conclusion The key influencing factors of DR are analyzed, providing the direction of treatment and prevention. The low, medium, and high -risk groups of DR are classified for risk early warning. By comparing the effect between the models, the prediction model of morbidity risk of DR is constructed, providing insights for clinical early warning and data analysis. [ABSTRACT FROM AUTHOR]

In this paper, we study the asymptotic profiles of positive solutions for diffusive logistic equations. The aim is to study the sharp effect of linear growth and nonlinear function. Both the classical reaction-diffusion equation and nonlocal dispersal equation are investigated. Our main results reveal that the linear and nonlinear parts of reaction term play quite different roles in the study of positive solutions. [ABSTRACT FROM AUTHOR]

As deep learning technology matures, it's being widely deployed in fields like image classification and speech recognition. However, training a functional deep learning model requires vast computing power and a large training dataset, leading to the emergence of a new business model of selling pre-trained models. However, these models are highly susceptible to theft, which poses a threat to the interests of their creators. Moreover, the network topology and weight parameters are considered intellectual property. To address these challenges, a method that can tag trained models to claim ownership without affecting their performance is necessary. Therefore, we propose a novel neural network watermarking protocol. In this method, the trigger set is constructed differently from previous methods by using a key obtained from the authority to generate a scrambling sequence, followed by using the sequence to scramble the pixels and assign their original labels. Finally, the trigger set is put into the network training together with the original training set to complete the watermark embedding. Since Logistic chaos mapping is nonlinear, unpredictable, and sensitive to initial values, we use Logistic chaos mapping as the generation method of dislocation sequence. We involve a third-party copyright center in the embedding process to prevent forgery attacks. The third-party only needs to store the disruption key and timestamp for each owner, reducing their storage burden. Our experimental results demonstrate that the ResNet model exhibits a mere 0.05 percentage point decrease in accuracy when using fine-tuning for watermark embedding, and a mere 0.03 percentage point decrease when using the training-from-scratch method. On the other hand, when using the SENet model, embedding watermarks via fine-tuning resulted in a 1.35 percentage point decrease in classification accuracy, while embedding watermarks from training-from-scratch resulted in a 0.94 percentage point increase in classification accuracy. Furthermore, our model exhibited robustness against various attacks in the robustness experiments, including model fine-tuning, model compression, and watermark overlay. [ABSTRACT FROM AUTHOR]

Copyright of Investigación e Innovación en Ingenierías is the property of Universidad Simon Bolivar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Engineering & Management Sciences (2498-700X) / Műszaki és Menedzsment Tudományi Közlemények is the property of University of Debrecen and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The most common type of central nervous system tumor in adults is glioma, accounting for about 70% of all brain tumors. Prediction of glioma growth became interesting to be analyzed to measure how severe the glioma is, plan the proper treatment, and estimate how long the patient can survive. This paper uses the Reaction-diffusion equation to interpret the glioma concentration in the grey and white matter of the brain based on three different growth functions. These are exponential, logistic, and Gompertzian growth functions. The Crank-Nicolson scheme is applied here to solve this model as an initial step to carry out the simulation. It is used since this scheme is unconditionally stable and has a higher order of accuracy. This scheme produces a linear system equation for exponential growth function while a nonlinear system for logistic and Gompertzian growth function. The simulation results include concentration, maximum concentration, cell number, and average speed of glioma cells for each growth function given here. As a result, the maximum glioma concentration grows infinitely for exponential growth but converges to specific values for logistic and Gompertzian growth functions. Furthermore, this paper gives the sensitivity analysis for the model to see the behavior of glioma concentration based on parameter changes. These are initial glioma position, net proliferation rate, and diffusivity coefficient in grey and white matter. It shows that net proliferation affects glioma concentration growth more than others. [ABSTRACT FROM AUTHOR]

Recent developments in research have heightened the need for different kinds of analysis methods. The mixed-effects logistic model is popular in analyzing correlated binary data. However, this model sometimes suffers from collinearity problems. In this article, we introduced a new type of estimator based on Liu-estimators to face this issue. Simulation shows the precision of the new estimator in terms of the mean square errors are better than the restricted maximum likelihood estimator. Finally, the proposed method was applied in a dataset related to the female sexual function. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify the high-risk factors for caesarean scar pregnancy (CSP) and establish a nomogram to predict the risk of caesarean scar pregnancy in pregnant women with a history of caesarean section. Among 1273 pregnant women with a history of caesarean section, 70% of the patients (892 patients, training sample) were randomly selected for analysis, and a prediction model was generated. The remaining patients (381 patients, validation sample) were validated for the model. Four high-risk factors for CSP were established, including: parity, number of previous abortions, uterus position, and early vaginal bleeding. The area under the curve of the nomogram for the training set was 0.867 and that for the validation set was 0.881, indicating good performance. Calibration curves for predicting CSP showed good calibrations. Decision curve analyses showed good application prospects for the model. Our results show that our nomogram for predicting CSP risks can be a practical tool to help in the early identification of CSP. What is already known on this subject? The high-risk factors for "caesarean scar pregnancy", An simple nomogram could be constructed to predict the risk of the disease through these high-risk factors. What do the results of this study add? This study can quickly predict whether the patient is a high-risk group for uterine scar pregnancy based on the patient's previous pregnancy, early vaginal bleeding and uterine position. What are the implications of these findings for clinical practice and/or further research? Caesarean scar pregnancy was secondary Long-term complications after caesarean section that with a high risk of pregnancy. In this study, we established a nomogram based on the number of cases of CSP and a control group with a history of caesarean section delivery at term, The high-risk factors were assigned a certain risk value in the early stage, if the woman contains more high-risk factors, the higher the risk of developing CSP, it should be highly valued in the early stage, and the rate of visiting a doctor should be increased. [ABSTRACT FROM AUTHOR]