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In order to determine the critical elements influencing the frequency and severity of two-wheeler-related traffic crashes, this study explores these incidents along eleven years in Vienna. Applying sophisticated multivariate statistical approaches, a comprehensive dataset is used and includes variables about rider demographics, weather conditions, vehicle features, and crash circumstances to reveal intricate correlations and interactions among these elements. Is there significant and distinctive difference based on gender and age with specific conditions under which crashes are occurring influencing different injury degree. Multiple regression undoubtedly points fields for action in statistically based findings providing the most important answer to this research: why there are so many crashes and what is leading cause of injured two-wheelers. The research yields insightful information that politicians and practitioners of road safety may use to improve two-wheeler safety regulations and lower the number of serious injuries and fatalities. Highlights: The stunning fact that just one rider in every fourteen may be uninjured Every second two-wheel rider sustain light injury and every eight severe Lane type, crash circumstance and consequence proves to be significant predictors for crash [ABSTRACT FROM AUTHOR]

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8–1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework. [ABSTRACT FROM AUTHOR]

Copyright of Nonferrous Metals (Mineral Processing Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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.)

Nucleophilic aromatic substitution reactions play a crucial role in the synthesis of organic compounds and are considered to be one of the most important synthetic transformations. Traditionally, these reactions have been described as stepwise processes in textbooks. However, with further research, the existence of concerted nucleophilic aromatic substitution reactions has been widely confirmed. This paper provides a brief overview of the stepwise pathway of nucleophilic aromatic substitution reactions and focuses on several examples of concerted reactions, highlighting the importance of reaction design in mechanistic studies. Integrating cutting-edge research findings into teaching can enhance students’ understanding of nucleophilic aromatic substitution reactions. [ABSTRACT FROM AUTHOR]

Seagrass meadow is one of the blue-carbon ecosystems capable of absorbing and storing carbon more effectively in the bodies and sediments than terrestrial ecosystems. However, nationwide data on its carbon stock remains elusive due to limitations and challenges in data collection and mapping. Seagrass percent cover and biomass, which were closely related with above-ground carbon stock, can be effectively mapped and monitored using remote sensing techniques. Therefore, this study aimed to compare the accuracy of 4 scenarios as well as assess the performance of random forest and stepwise regression methods, for mapping seagrass percent cover and biomass in Nusa Lembongan, Bali, Indonesia. The scenarios were experimented using only atmospherically corrected images, sunglint, water, as well as sunglint and water column corrected images. Furthermore, WorldView-3 images and in-situ seagrass data were used, with the image corrected by applying the scenarios. Random forest and stepwise regression methods were adopted for mapping and modelling. The optimum mapping scenario and method were chosen based on R2, RMSE, and seagrass spatial distribution. The results show that the atmospherically corrected image produced the best seagrass percent cover and biomass map. Range of R2 using random forest and stepwise regression model was 0.49–0.64 and 0.50–0.58, with RMSE ranging from 18.50% to 21.41% and 19.36% to 20.72%, respectively. Based on R2, RMSE, and seagrass spatial distribution, it was concluded that the random forest model produced better mapping results, specifically for areas with high seagrass percent cover.

Prediction of the antibacterial activity of new chemical compounds is an important task, due to the growing problem of bacterial drug resistance. Generalized linear models (GLMs) were created using 85 amidrazone derivatives based on the results of antimicrobial activity tests, determined as the minimum inhibitory concentration (MIC) against Gram-positive bacteria: Staphylococcus aureus, Enterococcus faecalis, Micrococcus luteus, Nocardia corallina, and Mycobacterium smegmatis. For the analysis of compounds characterized by experimentally measured MIC values, we included physicochemical properties (e.g., molecular weight, number of hydrogen donors and acceptors, topological polar surface area, compound percentages of carbon, nitrogen, and oxygen, melting points, and lipophilicity) as potential predictors. The presence of R1 and R2 substituents, as well as interactions between melting temperature and R1 or R2 substituents, were also considered. The set of potential predictors also included possible biological effects (e.g., antibacterial, antituberculotic) of tested compounds calculated with the PASS (Prediction of Activity Spectra for Substances) program. Using GLMs with least absolute shrinkage and selection (LASSO), least-angle regression, and stepwise selection, statistically significant models with the optimal value of the adjusted determination coefficient and of seven fit criteria were chosen, e.g., Akaike's information criterion. The most often selected variables were as follows: molecular weight, PASS_antieczematic, PASS_anti-inflam, squared melting temperature, PASS_antitumor, and experimental lipophilicity. Additionally, relevant to the bacterial strain, the interactions between melting temperature and R1 or R2 substituents were selected, indicating that the relationship between MIC and melting temperature depends on the type of R1 or R2 substituent. [ABSTRACT FROM AUTHOR]

Purpose: External snapping hip syndrome (ESHS) was historically attributed to isolated iliotibial band (ITB) contracture. However, the gluteus maximus complex (GMC) may also be involved. This study aimed to intraoperatively identify the ESHS origin and assess the outcomes of endoscopic treatment based on the identified aetiological type. Methods: From 2008-2014, 30 consecutive patients (34 hips) with symptomatic ESHS cases refractory to conservative treatment underwent endoscopic stepwise "fan-like" release, gradually addressing all known reasons of ESHS: from the isolated ITB, through the fascial part of the GMC until a partial release of gluteus maximus femoral attachment occurred. Snapping was assessed intra-operatively after each surgical step and prospectively recorded. Functional outcomes were assessed via the MAHORN Hip Outcome Tool (MHOT-14). Results: Twenty seven patients (31 hips) were available to follow-up at 24-56 months. In all cases, complete snapping resolution was achieved intra-operatively: in seven cases (22.6%) after isolated ITB release, in 22 cases (70.9%), after release of ITB + fascial part of the GMC, and in two cases (6.5%) after ITB + fascial GMC release + partial release of GM femoral insertion. At follow-up, there were no snapping recurrences and MHOT-14 score significantly increased from a pre-operative average of 46 to 93(p<0.001). Conclusion: Intraoperative identification and gradual addressing of all known causes of ESHS allows for maximum preservation of surrounding tissue during surgery while precisely targeting the directly involved structures. Endoscopic stepwise "fan-like" release of the ITB and GMC is an effective, tailor-made treatment option for ESHS regardless of the snapping origin in the patients with possibility to manually reproduce the snapping. [ABSTRACT FROM AUTHOR]

Cattle weight development is highly correlated with some body measurements. Based on the relationship between morphometric measurements and body mass, our aim was to develop regression equations to estimate the body weight of Curraleiro Pé-Duro (CPD) cattle to be used in farms that lack access to weighting scales. Data from 1023 animals from four farms on withers height (WH), body length (BL), body score (BS), heart girth (HG), permanent teeth (PT), scrotal perimeter (SP), and live weight were used. The animals were classified into five categories depending on age and/or sex: newborns (NB), calves, weaned animals, cows, and bulls. The best models are GLM with Gamma, Gamma, inverse Gaussian, Gaussian, and Gamma distributions for NB, calves, weaned animals, cows, and bulls, respectively. Predictive modeling for bulls was the best performing overall, with a correlation of 0.97 between the estimated by the model and the obtained with a weighting scale. For NB, calves, weaned animals, and cows, the correlation (r) was 0.85, 0.90, 0.95, and 0.87, respectively. The evaluated models are adequate to be used as a technical solution to estimate weight in a cattle production system. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Teknologi dan Rekayasa Sumber Daya Air is the property of Brawijaya University 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 Jurnal Teknologi dan Rekayasa Sumber Daya Air is the property of Brawijaya University 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.)

Stepwise phase change material (PCM) aggregate concrete has advantages in controlling temperature and resisting frost heave, but its freeze–thaw resistance performance is still unclear. This paper explored the impact of replacing ordinary coarse aggregate with stepwise aggregate on the freeze–thaw resistance characteristics of concrete. Firstly, the compressive strength, splitting tensile strength, and their relationship were evaluated. Then, the freeze–thaw resistance properties of PCM aggregate concrete were investigated, including macroscopic changes, mass loss, relative dynamic elasticity modulus loss, and compressive strength loss. Subsequently, the pore changes before and after freeze–thaw cycles were tested through non-destructive testing and nuclear magnetic resonance (NMR) testing, and the evolution of pores under freeze–thaw cycles was explored. The results show that adding 100% PCM aggregate reduces the strength of concrete by 32%. However, due to the high porosity in the 100% PCM aggregate concrete, it would have an adverse impact on corrosion resistance. The corrosion resistance of concrete increases firstly and then decreases with the addition of PCM aggregate, which can be attributed to PCM aggregate having a limiting effect on pore development. Overall, a substitution rate of 60% is acceptable for compressive strength and corrosion resistance. [ABSTRACT FROM AUTHOR]

Abstract Meteorological conditions affect the dynamics of nitrogen (N) by oat crops. Fuzzy logic allows the development of simulation models involving N management and the non-linearity of meteorological conditions. The objective of this study was to identify the most sustainable N management for oat crops considering N rates applied at sowing and as top-dressing with different timing. Potential variables were selected for the development of a rule base for fuzzy modeling and simulate grain yield for N managements considering the non-linearity of meteorological conditions. The experiment was carried out in Augusto Pestana, RS, Brazil, from 2015 to 2017. A randomized block design with four replications was used, in a 4×3 factorial arrangement, consisted of four N rates applied at sowing (0, 10, 30 and 60 kg/ha), using total N top-dressing rates of 70 kg/ha for the soybean-oat and 100 kg/ha for the maize-oat, applied at three timings (10, 30, and 60 days after emergence). The most sustainable N managements for oat crops were under absence of N and application 10 kg/ha of N at sowing, with the remainder applied as top-dressing at 10 and 30 days after emergence. The N application timing, mean air temperature, and rainfall depth are potential variables for the development of a rule base for fuzzy modeling, and efficient in simulating oat grain yield.

Establishing and maintaining hydrometeorological networks in sub-watersheds is challenging. An analysis of an established network of Rain Gauge Station (RGS) is provided in this study in the form of a comprehensive statistical framework. Hydrometeorological observations are monitored by this network, which aims to gather the most pertinent information while minimizing expenditures. The study location is at Ngasinan Hulu watershed (East Java, Indonesia), with ten rain gauge stations and one automatic water level recorder (AWLR). Errors in reviewing hydrological data in a watershed can cause the resulting data to be inaccurate. Therefore, to solve the problem of incorrect data, it is necessary to rationalize rain gauge stations. The rationalization for this study uses the Stepwise method. The results of the Stepwise-Enter method produce a combination of six rain gauge stations with measured rainfall data and a combination of four rain gauge stations with CHIRPS satellite rainfall data. Therefore, this combination of rain gauge stations is rational because it meets the classical assumption test, and the area is evenly distributed according to WMO standards.

There are many studies related to cost estimation applied in a project, but only a few employ using “stepwise” method. This approach is expected to assist in solving complex problems by establishing a criteria hierarchy, subjectively assessed by interested parties, and incorporating various considerations to develop weights or priorities. The foundation of the stepwise approach relies on well-documented findings, which assert that 80% of the total project cost corresponds to the 20% most expensive components of the work. The research systematically collected data for project development estimates based on the bill of quantities from each year, as well as the HSPK (activity main unit price) in Gresik Regency from 2016 to 2018. This research aims to develop a conceptualized estimation model that provides clear, rapid, and relatively accurate initial project cost information. The results of this research include analysis of stepwise approach on cost realization, analysis of accuracy of the cost estimation model for buildings using the stepwise method, and an analysis of the cost of road and bridge infrastructure projects to facilitate completion in Gresik Regency. The research concludes that the analysis of the Cost Significant Model's impact on cost realization in the Public Works and Spatial Office's Highways Division in Gresik Regency has resulted in a cost estimation model for road development with an accuracy range from -12.7% to 16.0%, with an average accuracy rate of 0.59%. Additionally, there is a very strong correlation (R) of the independent variable (work in Division 2) and the total development cost, indicating that fluctuations in the cost of significant components are closely linked to changes in the overall development cost. The analysis of infrastructure project costs related to roads and bridges for project completion revealed that the highest value among independent variables is associated with Division 7, at Rp. 4,403,054 per m2. This suggests that the cost of Division 7 work is the most influential, while Division 1 (X1) has the lowest cost, at Rp. 35,589 per m2. Furthermore, there is a work component with zero cost, namely, Division 4 works.

Reference evapotranspiration (ET0) is critical in agriculture and irrigation water management, particularly in arid and semi-arid regions. Our study aimed to develop an accurate and efficient model for estimating ET0 using various climatic variables as predictors. This research evaluated two model techniques, i.e., stepwise regression and artificial neural networks (ANNs), to identify the most effective model for calculating ET0. The two models were developed and tested based on climate data obtained from the whole climatic station of Egypt. The CLIMWAT 2.0 program was used to acquire the climate data for Egypt from a total of 32 stations. This software is a dedicated meteorological database created specifically to work with the CROPWAT computer program. The models were developed using average climate data spanning 29 years, from 1991 to 2020. The obtained data were utilized to compute reference evapotranspiration using CROPWAT 8, based on the Penman–Monteith equation. The results showed that the ANN model demonstrated superior performance in ET0 calculations compared to other methods, achieving a coefficient of determination (R2) of 0.99 and a mean absolute percentage error (MAPE) of 2.7%. In contrast, the stepwise model regression yielded an R2 of 0.95 and an MAPE of 8.06. On the other hand, the most influential climatic variables were maximum temperature, humidity, solar radiation, and wind speed. The findings of this study could be applied in various fields, such as agriculture, irrigation, and crop water requirements, to optimize crop growth under limited water resources and global environmental changes. Furthermore, our study identifies the limitations and challenges of applying these models in arid regions, such as data availability constraints and model complexity. We discuss the need for more extensive and reliable datasets and suggest future research directions, including ensemble modeling, remote sensing data integration, and evaluating climate change's impact on ET0 estimation. Overall, this study contributes to the understanding of ET0 estimation in arid regions and provides valuable insights into the applicability of regression models and ANNs. The superior performance of ANNs offers potential advancements in water resource management and agricultural planning, enabling more accurate and informed decision-making processes. [ABSTRACT FROM AUTHOR]

A mild and efficient method for the synthesis of 2,5‐disubstituted chalcogenophenes is reported. This method involves two steps of reactions via one‐pot approach by employing commercially available and cost‐effective alkynes and element chalcogen. Further, by using LiNH2 as the promoter and DMSO/EtOH as the solvent, Cu‐catalyzed Glaser coupling‐cycloaddition processes are dramatically enhanced that a diverse range of chalcogenophenes can be obtained in good to excellent yields under mild conditions. Controlled experiments show that trisulfur radical anion (S3⋅−) is the key intermediate and DMSO/EtOH serves as the hydrogen source, while alcohol may facilitate the transition state. [ABSTRACT FROM AUTHOR]

ABSTRAK Peningkatkan akurasi pada prediksi curah hujan kumulatif bulanan sangat penting dilakukan terutama pada wilayah NON ZOM (Non Zona Musim), mengingat curah hujan mempengaruhi terhadap berbagai sektor kehidupan. Salah satu cara untuk meningkatkan akurasi prediksi curah hujan kumulatif bulanan adalah dengan menggunakan metode regresi linier. Terdapat banyak pilihan metode regresi linier untuk prediksi curah hujan, metode yang sering digunakan antara lain adalah regresi linier berganda dan regresi stepwise. Data curah hujan dasarian yang digunakan adalah 3 titik pos hujan dari tahun 1996 – 2016 dengan prediktor suhu muka laut dan air mampu curah (precipitable water). Penelitian ini menggunakan variasi waktu jeda (time lag) untuk mendapatkan nilai prediksi curah hujan kumulatif terbaik berdasarkan nilai RMSE pada waktu jeda tertentu. Masing – masing metode regresi kemudian digunakan untuk melakukan simulasi prediksi curah hujan kumulatif bulanan tahun 2017 – Februari 2019. Hasil prediksi menggunakan metode regresi linier berganda secara umum menujukkan nilai RMSE terendah pada waktu jeda 1 bulan. Untuk hasil prediksi menggunakan metode regresi stepwise secara umum nilai RMSE terendah terjadi pada waktu jeda simultan. Kata kunci: curah hujan; stepwise; time lag; RMSE ABSTRACT Increasing accuracy in predicting monthly cumulative rainfall is very important especially in the NON ZOM area, considering that rainfall affects various sectors of life. One way to improve the accuracy of predictions of monthly cumulative rainfall is to use the linear regression method. There are many choices of linear regression methods for rainfall prediction, methods that are often used include multiple linear regression and stepwise regression. The dasarian rainfall data used are 3 rain post points from 1996-2016 with predictors of sea surface temperature and precipitable water. This study uses a time lag to get the best cumulative rainfall prediction value based on the RMSE value at a certain time interval. Each regression method is then used to simulate monthly cumulative rainfall prediction for 2017 - February 2019. Prediction results using multiple linear regression methods generally show the lowest RMSE value at 1month time lag. For prediction results using the stepwise regression method in general the lowest RMSE value occurs at the time of simultaneous time lag. Keywords: rainfall; stepwise; time lag; RMSE

Introduction: The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19. Methods: In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning. Results: The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R2 = 0.168 and adjusted R2 = 0.165) performed better than BIC (R2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R2 = 0.174. The GBDT method with the value of R2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19. Conclusion: The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models. [ABSTRACT FROM AUTHOR]

Routine evaluations of cooking time trait in common bean (Phaseolus vulgaris L.) can be performed in different ways resulting in different variables. At the same time, the univariate statistical analysis does not consider the interdependencies between the variables, and may omit important information regarding the genotypes. With this, the objective of this work was to present an alternative proposal for analysis of the cooking time in common bean, allowing the discrimination between genotypes. The experiment used for this approach was conducted under field conditions in the 2017/18 agricultural season in Lages, Santa Catarina, Brazil. The treatments consisted of twelve genotypes, (four parents, structured in two crossings BAF50 x BAF07 and BAF09 x IPR 88 Uirapuru, with their generations F2, F3, F8 and F9). The design used was randomized blocks, with two blocks and two observations in each experimental unit. After the harvest, the response variable cooking time of the grains was measured with a Mattson cooker, considering the cooking time of the 13 initial stems. In the multivariate analysis, the variables cooking time of the second (TCH2), twelfth (TCH12) and thirteenth stem (TCH13) were used based on their significance by the stepwise variable selection method. Multivariate analysis of variance showed differences between genotypes (P<0.05). From the dissimilarity matrix with the Mahalanobis distances and the clustering dendrogram, it was possible to verify the distances of the genotypes derived from crosses BAF50 x BAF07 and BAF09 x IPR 88 Uirapuru. With that, the multivariate analysis enabled the genotypes, additionally the crossing BAF50 x BAF07 showed higher estimates of dissimilarity in the progenies. [ABSTRACT FROM AUTHOR]

Copyright of Agriscientia is the property of Revista AgriScientia 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.)

Inference for structural and treatment parameters while having high-dimensional covariates in the model is increasingly common. The Neyman-orthogonal (NO) estimators in Belloni, Chernozhukov, and Wei (2016, Journal of Business and Economic Statistics 34: 606–619) produce valid inferences for the parameters of interest while using generalized linear model lasso methods to select the covariates. Drukker and Liu (2022, Econometric Reviews 41: 1047–1076) extended the estimators in Belloni, Chernozhukov, and Wei (2016) by using a Bayesian information criterion stepwise method and a testing-stepwise method as the covariate selector. Drukker and Liu (2022) found a family of data-generating processes for which the NO estimator based on Bayesian information criterion stepwise produces much more reliable inferences than the lasso-based NO estimator. In this article, we describe the implementation of posw, a command for the stepwise-based NO estimator for the high-dimensional linear, logit, and Poisson models. [ABSTRACT FROM AUTHOR]

Identifying ecological factors that influence brain size evolution is a major challenge in organismal biology. The extractive foraging hypothesis proposes that large brains are more likely to evolve in taxa that extract prey items from hard-to-access substrates, but this idea has received relatively little attention. Here, we provide a comprehensive test of the extractive foraging hypothesis in woodpeckers, a family of relatively large-brained birds that contains many species that feed on wood-boring larvae extracted from trees. Our results show strong support for the extractive foraging hypothesis. First, woodpeckers that use extractive foraging have relatively larger brains compared to species that forage using other tactics. Second, our comparative analyses suggest not only that big brains are the likely ancestral phenotype among all woodpecker taxa, but also that this trait is associated with innovation in foraging behaviour. We suspect that retaining an ancestral large brain likely increases the probability that a given species evolves to become an extractive forager who eats largely wood-boring larvae, given that species that transitioned to a small brain evolved different diets. Thus, the extractive foraging hypothesis likely applies to woodpeckers because of a historical contingency (large brains) that sets the stage for behavioural innovations to better exploit ecological opportunities. • We tested the extractive foraging hypothesis in woodpeckers. • Species using extractive foraging had larger brains, which is the ancestral state. • Brain size was not correlated with nesting strategy or life history. • Species that transitioned to a small brain evolved different diets. • Large brains appear to be a prerequisite for innovations in extractive foraging. [ABSTRACT FROM AUTHOR]

As avaliações rotineiras do caráter tempo de cozimento em feijão (Phaseolus vulgaris L.) podem ser efetuadas de distintas maneiras resultando em diferentes variáveis. Por vez, a análise estatística univariada não considera as interdependêcias entre as variáveis, podendo omitir importantes informações a respeito dos genótipos. Com isso, o objetivo do trabalho foi dispor uma proposta alternativa para análise do tempo de cozimento em feijão, permitindo a discriminação entre genótipos. O experimento utilizado para esta abordagem foi conduzido em condições de campo na safra agrícola do ano 2017/18 em Lages, Santa Catarina, Brasil. Os tratamentos foram compostos por doze genótipos, sendo quatro genitores, estruturados em dois cruzamentos BAF50 x BAF07 e BAF09 x IPR 88 Uirapuru, com suas gerações F2, F3, F8 e F9. O delineamento utilizado foi blocos casualizados, com dois blocos e duas observações em cada unidade experimental. Posteriormente a colheita, a variável resposta tempo de cocção dos grãos de feijão foi mensurada com o cozedor Mattson, sendo considerado o tempo de cocção das 13 hastes iniciais. Na análise multivariada, as variáveis tempo de cocção da segunda (TCH2), décima segunda (TCH12) e décima terceira haste (TCH13) foram utilizadas com base em sua significância pelo método de seleção de variáveis passo a passo (stepwise). A análise de variância multivariada demonstrou diferença entre os genótipos (P

Stepwise phase change material (PCM) aggregate concrete has advantages in controlling temperature and resisting frost heave, but its freeze–thaw resistance performance is still unclear. This paper explored the impact of replacing ordinary coarse aggregate with stepwise aggregate on the freeze–thaw resistance characteristics of concrete. Firstly, the compressive strength, splitting tensile strength, and their relationship were evaluated. Then, the freeze–thaw resistance properties of PCM aggregate concrete were investigated, including macroscopic changes, mass loss, relative dynamic elasticity modulus loss, and compressive strength loss. Subsequently, the pore changes before and after freeze–thaw cycles were tested through non-destructive testing and nuclear magnetic resonance (NMR) testing, and the evolution of pores under freeze–thaw cycles was explored. The results show that adding 100% PCM aggregate reduces the strength of concrete by 32%. However, due to the high porosity in the 100% PCM aggregate concrete, it would have an adverse impact on corrosion resistance. The corrosion resistance of concrete increases firstly and then decreases with the addition of PCM aggregate, which can be attributed to PCM aggregate having a limiting effect on pore development. Overall, a substitution rate of 60% is acceptable for compressive strength and corrosion resistance.

High-dimensional models that include many covariates which might potentially affect an outcome are increasingly common. This paper begins by introducing a lasso-based approach and a stepwise-based approach to valid inference for a high-dimensional model. It then discusses several essential extensions to the literature that make the estimators more usable in practice. Finally, it presents Monte Carlo evidence to help applied researchers choose which of several available estimators should be used in practice. The Monte Carlo evidence shows that our extensions to the literature perform well. It also shows that a BIC-stepwise approach performs well for a data-generating process for which the lasso-based approaches and a testing-stepwise approach fail. The Monte Carlo evidence also indicates the BIC-based lasso and plugin-based lasso can produce better inferential results than the ubiquitous CV-based lasso. Easy-to-use Stata commands are available for all the methods that we discuss. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office 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.)

Since convergence and diversity are taken into consideration cooperatively in the whole iteration process, the traditional multi-objective optimization algorithms will generate a large number of dominated solutions in the early stage of search, which will inevitably result in the waste of computing resources, even the non-convergence of solving procedure. Aiming at the above shortcoming, this paper proposes a stepwise multi-objective optimization algorithm based on statistical information feedback (SFI-SMOEA), which is composed of three steps, i.e., the optimal value exploration for each objective, rough search for Pareto front, local optimization stage with group division. According to the feature of each step, the corresponding task and strategy is assigned to promote convergence and diversity. In the 2nd and 3rd steps, the solutions are divided into different groups according to the objective function, the information of different regional solutions is statistically analyzed, and then, the statistical feedback information (SFI) of different regional solutions is utilized to guide the parent-selection process. Thus, the distribution and convergence of the solutions can be controlled more precisely. Finally, the proposed algorithm is tested on DTLZ and WFG series issues, and compared with other multi-objective optimization algorithms, from which it is illustrated that the proposed algorithm has the advantages of solving complex and difficult convergence problems. [ABSTRACT FROM AUTHOR]

In 2015, China and India's population represented approximately 35.74% of the total number of people living in the world. Due to the historical context and behavior of the most relevant indicators, this study proposes to utilize a wide variety of demographic, economic, and production indicators from 1952 to 2015 to assess their impact on the GNI in China and India. A comprehensive and new fangled modeling process with stepwise, regularization and distributed lag regression approaches is presented. Accordingly, theoretical results were corroborated through extensive diagnostic tests and an empirical check of the models' predictive capacity. The findings show that GNI in China is most influenced by variables such as reserves in foreign currency and the dependency ratio; whereas, variables of energy production and birth rate were generated for India. Therefore, it's the timing for China to relax the universal two-child policy. Due to the current value below the substitution rate, a gloomy outlook for China's future population and economy is predicted. Conversely, a positive outlook is forecasted for India, given the low price in the future of oil- India's primary raw material.

Postpartum pain is a common problem for pregnant women. Improper treatment may lead to adverse consequences such as greater opioid use, postpartum depression, and development of persistent pain. Therefore, ACOG put forward clinical consensus about the recommendations on pharmacologic stepwise multimodal approach for postpartum pain management in 2021, especially for acute perineal, uterine and incisional pain. In addition, it also provides advices for the treatment of postpartum pain when breastfeeding or discharge, and emphasizes the principle of individualized medication. The recommended clinical medication can follow the step by-step administration of "non-opioid analgesics (such as acetaminophen and non-steroidal anti-inflammatory drugs) - weak opioids - strong opioids (if necessary)". Those can be combined reasonably. We tried to interpreted the main viewpoints of this clinical consensus in this review. [ABSTRACT FROM AUTHOR]

This work aims to study the default risk of family SMEs (small and medium enterprises) in Portugal, more specifically in the Leiria region. For this purpose, a panel data of 2,658 firms over the period 2012-2017 is analyzed. Using an ex-ante classification of defaulting, results suggest that there are more compliant firms and the number of defaulting firms have decreased over the period analyzed. Then, using a logit regression technique and six variables to predict default obtained through the stepwise methodology, results show that defaulting firms compared to compliant firms are usually younger, with higher difficulties in generating return and in being efficient, and more indebted. The Z’-score model was used as a robustness test, and results suggest that this model is inaccurate to the present reality and this specific sample. Therefore, new coefficients were estimated to increase the model’s efficiency. The proposed and the modified Z’-score models have an accuracy of 88.74% and 85.49%, respectively.

On the surface of water ice, a quasi-liquid layer (QLL) has been extensively reported at temperatures below its bulk melting point at 273 K. Approaching the bulk melting temperature from below, the thickness of the QLL is known to increase. To elucidate the precise temperature variation of the QLL, and its nature, we investigate the surface melting of hexagonal ice by combining noncontact, surface-specific vibrational sum frequency generation (SFG) spectroscopy and spectra calculated from molecular dynamics simulations. Using SFG, we probe the outermost water layers of distinct single crystalline ice faces at different temperatures. For the basal face, a stepwise, sudden weakening of the hydrogen-bonded structure of the outermost water layers occurs at 257 K. The spectral calculations from the molecular dynamics simulations reproduce the experimental findings; this allows us to interpret our experimental findings in terms of a stepwise change from one to two molten bilayers at the transition temperature.

The growth in demand and demand for quality in the sheep chain has generated the need for automation techniques in the meat industry and the need to obtain responses with greater speed and standardization. The research aimed to predict tissue characteristics of the carcass and commercial cuts based on measurements obtained by VIA – see oimage analysis, carried out on cold carcasses of hairless lambs, using machine learning employing regressive techniques for variable selection. Information from 72 carcasses of castrated male lambs, aged between 8 and 11 months, with an average cold carcass weight of 16.13 ± 3.98 kg, was used. Images of the right side of the carcasses were captured from the dorsal and lateral views using a digital camera. From the ImageJ2 software, VIA data, measurements and shape descriptors (areas, perimeters, widths, lengths, convexities, solidities) were obtained, combined with cold carcass weight and used to generate four sets of data, called descriptor sets (DSs). Obtaining DS1, DS1', DS2, DS2', DS3, DS3', DS4 AND DS4'. To generate these sets, a database was formed and divided into a training bank (with 70% of the observations) and a test bank (30% of the observations). Multiple linear regression models were developed using Stepwise, LASSO, and Elastic Net regression methods, combined with k-fold cross-validation, to evaluate the performance of the models. The accuracy of the estimates was based on RMSE, R2, Pearson correlation and bias metrics. For the variables tested in this study, the proposed shape descriptors were mostly efficient in predicting tissue and weight variables. DS1' with the LASSO technique presented the best adjustments for variables total muscle and fat followed by shoulder, loin and rib cuts. The descriptors tested by this study were able to predict with quality the vast majority of the characteristics tested, the variable cold carcass weight (CCW), introduced as additional predictor, promoted a consistent improvement in the fits of all models. DS1 presented greater constancy for the twenty-three predicted characteristics and Stepwise presented the worst predictive performance, in relation to LASSO and Elastic Net. Despite close adjustments between the generated models, in general, Elastic Net presented lower performance than LASSO. • The proposed descriptors predicted cuts' commercial weights and tissue composition • Predicted characteristics in a non-invasive way depend on associated descriptors • Descriptors that best predicted the muscular components contained the whole carcass • Cold carcass weight variable improved the adjustments of all models [ABSTRACT FROM AUTHOR]

As required for the reconstruction or maintenance of long-distance gas pipeline during its operation, it is an important link to vent the main pipeline for reconstruction, maintenance and repair during its service life. Through the comparison of the 3 depressurization methods of gas pipeline home and abroad by venting, as well as the verification by the practice of centralized hot processing of Lines 2 and 3 of West-to-East Gas Pipeline Project, a venting method through continuous depressurization of multiple pipeline sections with a compressor specialized for gas pipeline was put forward. As shown in the practice, the stepwise depressurization method, compared with the traditional direct venting method and the venting method by depressurization with an external compressor, can greatly reduce the gas loss, the venting time and the effect of venting noise to surrounding production and economic activities. Additionally, the method, characterized by high-efficiency, economy, safety and environment protection, is particularly applicable to the long-distance gas pipeline with continuous and central maintenance required for multiple sections.

Abstract Background One in four Kenyans aged 18–69 years have raised blood pressure. Despite this high prevalence of hypertension and known association between socioeconomic status and hypertension, there is limited understanding of factors explaining inequalities in raised blood pressure in Kenya. Hence, we quantified the socioeconomic inequality in hypertension in Kenya and decomposed the determinants contributing to such inequality. Methods We used data from the 2015 Kenya STEPwise survey for non-communicable diseases risk factors. We included 4422 respondents aged 18–69 years. We estimated the socioeconomic inequality using the concentration index (C) and decomposed the C using Wagstaff decomposition analysis. Results The overall concentration index of hypertension in Kenya was − 0.08 (95% CI: − 0.14, − 0.02; p = 0.005), showing socioeconomic inequalities in hypertension disfavouring the poor population. About half (47.1%) of the pro-rich inequalities in hypertension was explained by body mass index while 26.7% by socioeconomic factors (wealth index (10.4%), education (9.3%) and paid employment (7.0%)) and 17.6% by sociodemographic factors (female gender (10.5%), age (4.3%) and marital status (0.6%)). Regional differences explained 7.1% of the estimated inequality with the Central region alone explaining 6.0% of the observed inequality. Our model explained 99.7% of the estimated socioeconomic inequality in hypertension in Kenya with a small non-explained part of the inequality (− 0.0002). Conclusion The present study shows substantial socioeconomic inequalities in hypertension in Kenya, mainly explained by metabolic risk factors (body mass index), individual health behaviours, and socioeconomic factors. Kenya needs gender- and equity-focused interventions to curb the rising burden of hypertension and inequalities in hypertension.

Abstract Dearomative heterocyclic [4+2] cycloaddition between the N‐(2,4‐dinitrophenyl)pyridinium ion of nicotinamide and an enol ether was analyzed by Density Functional Theory (DFT) calculations. The calculation revealed that the reaction undergoes stepwise bond formation rather than occurring in a concerted manner. The experimental products were found to be both kinetically and thermodynamically favored. The calculated transition states and intermediate suggested that the high diastereoselectivity is derived from the electrostatic interaction between the 2‐nitro group of the pyridinium ion and the hydrogen of the enol ether.

Abstract Background Due to a lack of standardized guidelines, it is necessary to verify the effectiveness of educational programmes for nursing students' systematic handover training. Aims This study aimed to develop a stepwise nursing handover programme and to examine its effects on awareness of handover Situation, Background, Assessment, Recommendation, communication self‐efficacy and satisfaction with handover education. Design This was a single‐group repeated measures ANOVA study. Methods This study was conducted in a nursing department, using a convenience sample of 25 senior nursing students from April 2 to June 22, 2018. The programme was divided into lectures, scenario role‐playing and nursing case role‐playing. The research questions were analysed using repeated measures ANOVA. Results Awareness of handover Situation, Background, Assessment, Recommendation, communication self‐efficacy and satisfaction with handover education gradually increased after each step of the programme. The stepwise nursing handover programme, progressing from low to high levels of learning, can be used to improve the nursing handover system and handover communication ability.

Understanding bacterial adhesion at the nanoscale is crucial for elucidating biofilm formation, enhancing biosensor performance, and designing advanced biomaterials. However, the dynamics of the critical transition from reversible to irreversible adhesion has remained elusive due to analytical constraints. Here, we probed this adhesion transition, unveiling nanoscale, step-like bacterial approaches to substrates using a plasmonic imaging technique. This method reveals the discontinuous nature of adhesion, emphasizing the complex interplay between bacterial extracellular polymeric substances (EPS) and substrates. Our findings not only deepen our understanding of bacterial adhesion but also have significant implications for the development of theoretical models for biofilm management. By elucidating these nanoscale step-like adhesion processes, our work provides avenues for the application of nanotechnology in biosensing, biofilm control, and the creation of biomimetic materials.

Metal–insulator–metal (MIM) nanocavity, as a lossy Fabry–Pérot (FP) scheme, has been widely studied for color filter and perfect absorber functionality, which can be integrated into functional photonic devices. However, such triple‐layered MIM still lacks the ability to induce multiple intensity levels for meticulous grayscale imaging because of strong spectral sensitivity to cavity thickness variation. Moreover, the MIM stack remains unexplored to multiplex a near‐field nanoprinting and holographic imaging because it is challenging to break the mutual dependence relation between the intensity and phase shift. Herein, a new film stack of dual‐Fabry–Pérot (DFP) nanocavity is proposed to create triple‐fold freedom in light modulations: intensity, phase shift and operation wavelength, which can be respectively modulated by the top/bottom/total cavity length. By spatially arranging the stepwise nanocavities, the designed DFP pattern can show multiplexing dual‐channel imaging, which encrypts/conceals a grayscale nanoprinting image and simultaneously projects a far‐field holographic image. Such DFP strategy allows for new possibility of independent light modulation and creates new dimensional degree of freedom for thin‐film nanocavity. The proposed functional DFP strategy enriches the subwavelength building block design for metasurfaces and thin‐film stacks, which would promisingly empower advanced applications in information multiplexing, imaging encryption/concealing, and many other related fields. [ABSTRACT FROM AUTHOR]

Person re-identification (ReID) is mainly aimed at establishing correct identity correspondence among moving person collected by multiple cameras. Extending labeled data sets with pseudo-labels is one of the common methods of ReID. However, single evaluation standards and fixed screening pseudo-label methods make pseudo-labels gradually weaken their update rate. Based on that, we propose a semi-supervised adaptive stepwise learning (SSAS) method for accelerating the update of pseudo-labels. Using the concept of Kullback–Leibler divergence, a more global pseudo-label update idea (GPLU) is proposed, an evaluation criterion of pseudo-labels is designed to satisfy two conditions: The first is to use simple tracklets as pseudo-label data in the early stage, and the second is to gradually add complex and diverse tracklets as pseudo-label data in the iterative process. Our proposed adaptive pseudo-label screening strategy steadily improves the recognition accuracy of ReID. In addition, we conduct extensive experiments on canonical data sets and the evaluation results suggest the superiority of our method. [ABSTRACT FROM AUTHOR]

A novel quantitative structure–activity relationship study was established on a set of ethyl‐2‐amino‐6‐(3.5‐dimethoxyphenyl)‐4‐(2‐ethoxy‐2‐oxoethyl)‐4H‐chromene‐3 carboxylate (CXL017) derivatives to predict their half‐maximal inhibitory concentration (IC50) values. A principal component analysis pattern was first used to divide the whole data set into training and test sets. Then, stepwise (SW) and genetic algorithm (GA) feature selection approaches were selected to choose the proper molecular descriptors which were then subjected to multiple linear regression (MLR). Accordingly, linear models consisting of seven and six molecular descriptors were constructed and their stability was further checked using leave‐one‐out (LOO) and leave group out (LGO) cross‐validation techniques. In addition, the 15 compounds of the test set were further directed to external statistical validation to assess the forecasting capability of the generated models. A simple comparison of the results obtained by the two methodologies showed that the GA‐MLR model had some superiorities versus the SW‐MLR modeling approach, for example, R2train = 0.832, R2test = 0.816, Q2LOO = 0.788 and Q2LGO = 0.796, Ftrain = 44.01, Ftest = 6.67, MSEtrain = 0.069, MSEtest = 0.053 for the computation of the relevant IC50 values. Based on the information derived from the generated models, some key features are identifiable to appraise the activity of the organic drugs that can be used to design new anticancer agents. [ABSTRACT FROM AUTHOR]

Teachers’ collective efficacy is predictive of students’ success. School-Wide Positive Behavior Interventions and Supports implementation requires the whole team to set itself common goals regarding behavior management. The main purpose of this study was to investigate the medium-term effects of a SWPBIS intervention on teachers’ collective efficacy. Nine schools and 139 teachers and staff members (n intervention = 74, n control = 65) took part in the study. The study shows that SWPBIS implementation has a positive effect on teachers’ collective efficacy both for primary and secondary schools at post-test 1 (ES = +0.80) and 2 (ES = +0.71). Differences are observed at baseline and at posttests according to the educational level. The link between subscales of a school climate instrument and teachers’ collective efficacy is also investigated. The “structure for learning” subscale explains the greatest variance in collective efficacy.

Dalam analisis regresi, salah satu asumsi yang harus dipenuhi adalah tidak adanya hubungan antar variabel independen. Hubungan yang kuat antar variabel independen disebut dengan multikolinieritas. Berbagai metode dapat menanggulangi kasus multikolinieritas, semua itu bergantung pada tujuan dari penelitian. Beberapa metode tersebut adalah ridge regression, principal component regression, regresi robust dan pemilihan model terbaik. Pada penelitian ini, metode pemilihan model terbaik dipilih untuk digunakan karena bertujuan untuk menentukan variabel independen yang signifikan dengan mempertimbangkan korelasi parsial pada data track quality index (TQI) kereta api Indonesia. Untuk mengukur besarnya TQI diperlukan empat indikator yang kemudian menjadi variabel dalam penelitian ini, yaitu lebar jalur, angkatan, listringan dan pertinggian. Hasil analisis menunjukkan variabel pertinggian, angkatan dan listringan berpengaruh besarnya nilai TQI dengan variasi data yang dapat dijelaskan model sebesar 99,7%.

Abstract Background Despite the tremendous therapeutic advances that have stemmed from somatic oncogenetics, survival of some cancers has not improved in 50 years. Osteosarcoma still has a 5-year survival rate of 66%. We propose the natural canine osteosarcoma model can change that: it is extremely similar to the human condition, except for being highly heritable and having a dramatically higher incidence. Here we reanalyze published genome scans of osteosarcoma in three frequently-affected dog breeds and report entirely new understandings with immediate translational indications. Results First, meta-analysis revealed association near FGF9, which has strong biological and therapeutic relevance. Secondly, risk-modeling by multiple logistic regression shows 22 of the 34 associated loci contribute to risk and eight have large effect sizes. We validated the Greyhound stepwise model in our own, independent, case-control cohort. Lastly, we updated the gene annotation from approximately 50 genes to 175, and prioritized those using cross-species genomics data. Mostly positional evidence suggests 13 genes are likely to be associated with mapped risk (including MTMR9, EWSR1 retrogene, TANGO2 and FGF9). Previous annotation included seven of those 13 and prioritized four by pathway enrichment. Ten of our 13 priority genes are in loci that contribute to risk modeling and thus can be studied epidemiologically and translationally in pet dogs. Other new candidates include MYCN, SVIL and MIR100HG. Conclusions Polygenic osteosarcoma-risk commonly rises to Mendelian-levels in some dog breeds. This justifies caninized animal models and targeted clinical trials in pet dogs (e.g., using CDK4/6 and FGFR1/2 inhibitors).