Depth detection limit of a fluorescent object in tissue-like medium with background emission in continuous-wave measurements: a phantom study.

Significance: Although the depth detection limit of fluorescence objects in tissue has been studied, reports with a model including noise statistics for designing the optimum measurement configuration are missing. We demonstrate a variance analysis of the depth detection limit toward clinical applications such as noninvasively assessing the risk of aspiration. Aim: It is essential to analyze how the depth detection limit of the fluorescence object in a strong scattering medium depends on the measurement configuration to optimize the configuration. We aim to evaluate the depth detection limit from theoretical analysis and phantom experiments and discuss the source-detector distance that maximizes this limit. Approach: Experiments for detecting a fluorescent object in a biological tissue-mimicking phantom of ground beef with background emission were conducted using continuous wave fluorescence measurements with a point source-detector scheme. The results were analyzed using a model based on the photon diffusion equations. Then, variance analysis of the signal fluctuation was introduced. Results: The model explained the measured fluorescence intensities and their fluctuations well. The variance analysis showed that the depth detection limit in the presence of ambient light increased with the decrease in the source-detector distance, and the optimum distance was in the range of 10 to 15 mm. The depth detection limit was found to be ∼ 30 mm with this optimum distance for the phantom. Conclusions: The presented analysis provides a guide for the optimum design of the measurement configuration for detecting fluorescence objects in clinical applications.

Camera-in-the-loop based test scenario generation method for pedestrian collision avoidance system.

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test.

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales. METHODS: This approach is mainly based on movement quantization through analysis of variance in patients' skeletons detected in outpatient videos. The patients' skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations. RESULTS: The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor "thigh angle", with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%). CONCLUSIONS: The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

Metabolomics Analysis of Different Quinoa Cultivars Based on UPLC-ZenoTOF-MS/MS and Investigation into Their Antioxidant Characteristics.

In recent years, quinoa, as a nutritious and sustainable food material, has gained increasing popularity worldwide. To investigate the diversity of nutritional characteristics among different quinoa cultivars and explore their potential health benefits, metabolites of five quinoa cultivars (QL-1, SJ-1, SJ-2, KL-1 and KL-2) were compared by non-targeted metabolomics analysis based on UPLC-ZenoTOF-MS/MS in this study. A total of 248 metabolites across 13 categories were identified. Although the metabolite compositions were generally similar among the different quinoa cultivars, significant variations existed in their respective metabolite contents. Among the identified metabolites, amino acids/peptides, nucleosides, saponins and phenolic acids were the most abundant. Notably, SJ-1 exhibited the most distinct metabolite profile when compared to the other cultivars. Amino acids/peptides and nucleosides were found to be crucial factors contributing to the unique metabolite profile of SJ-1. Collectively, these aforementioned metabolites accounted for a substantial 60% of the total metabolites observed in each quinoa variety. Additionally, a correlation between the DPPH radical scavenging activity and the free phenolic content of quinoa was observed. Variations in phenolic content resulted in different antioxidant capacities among the quinoa cultivars, and SJ-1 exhibited lower phenolic levels and weaker antioxidant activity than the others. These results can provide important information for the development of quinoa resources.

Insight into the effect of different nanocellulose types on protein-based bionanocomposite film properties.

This paper investigates the effect of five different types of nanocellulose on the properties of protein-based bionanocomposite films (PBBFs) and the mechanism of action. The results show that TEMPO-oxidized nanocellulose (TNC) PBBFs have the smoothest surface structure. This is because some hydroxyl groups in TNC are converted to carboxyl groups, increasing hydrogen bonding and cross-linking with proteins. Bacterial nanocellulose (BNC) PBBFs have the highest crystallinity. Filamentous BNC can form an interlocking network with protein, promoting effective stress transfer in the PBBFs with maximum tensile strength. The PBBFs of lignin nanocellulose (LNC) have superior elasticity due to the presence of lignin, which gives them the greatest creep properties. The PBBFs of cellulose nanocrystals (CNCs) have the largest water contact angle. This is because the small particle size of CNC can be uniformly distributed in the protein matrix. The different types of nanocellulose differ in their microscopic morphology and the number of hydroxyl groups and hydrogen bonding sites on their surfaces. Therefore, there are differences in the spatial distribution and the degree of intermolecular cross-linking of different types of nanocellulose in the protein matrix. This is the main reason for the differences in the material properties of PBBFs.

Modeling of nonlinear and nonstationary stochasticity for atomic ensembles.

This paper addresses the problem of stochastic modeling of atomic ensembles under multi-source noise and makes the model interpretable. First, based on Itô's lemma and Allan variance analysis (ITÔ-AVAR), an approach is proposed to model nonstationary stochastic submodels of atomic ensembles. On this basis, the variance decomposition and nonlinear optimization algorithms are utilized to hybridize modeling atomic ensembles with nonlinear and nonstationary properties. Second, an Itô's lemma dynamic allan variance analysis (ITÔ-DAVAR) approach is developed for online modeling of atomic ensembles. Further, an atomic ensembles sensitivity enhancement scheme based on the proposed approach is given, which effectively promotes the progress of quantum instrument engineering. Finally, the proposed scheme are deployed in the optical pumping magnetometer and spin-exchange relaxation-free comagnetometer, respectively, while experimentally verifying the sensitivity of the spin-exchange relaxation-free comagnetometer reaches 5.36×10-6degs-1Hz-1/2.

Factors contributing to learning satisfaction with blended learning teaching mode among higher education students in China.

Blended learning has increasingly grown in importance as a method of classroom instruction in Chinese higher education classrooms in the context of fast-evolving network information technology, higher standards of educational informatization, and growing attention to the reform of teaching modes in higher education. The efficiency of blended learning can be increased by better understanding the students' learning satisfaction and its key influencing factors. Based on the theories of constructivism and phenomenology, the study constructs an index system of student satisfaction with blended learning in higher education, and conducts a questionnaire survey on 650 students with blended learning experience in 6 universities in Sichuan Province, China, obtaining 598 valid questionnaires after reviewing the collected questionnaires for missing values. This study uses descriptive statistical analysis (DSA), one-way ANOVA, Pearson correlation analysis, and multiple linear regression (MLR) to analyze the effects of each factor in the index system on satisfaction. Results indicate that the overall level of student satisfaction with blended learning in universities is moderately high, with students' self-satisfaction being the lowest, and that substantial disparities exist in the evaluation of satisfaction with blended learning on various online resources, online teaching forms, and offline teaching methods. This study applies multiple linear regression (MLR) to conclude that students' learning attitudes, curriculum design, and teachers' teaching methods are the most important factors influencing satisfaction with blended learning in universities. Results indicate that a blended learning system be built from the three dimensions of students, teachers, and curriculum, offering a theoretical foundation and point of reference for the ongoing reform of blended learning in higher education. The study is of great significance in optimizing teaching quality and deepening the reform of blended learning.

A New Analysis of Real-Time Fatality Rate in the Initial Stage of COVID-19.

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources.

Natural Zeolites for the Sorption of Ammonium: Breakthrough Curve Evaluation and Modeling.

The excessive use of ammonium fertilizer and its associated leakage threatens aquatic environments around the world. With a focus on the treatment of drinking water, the scope of this study was to evaluate and model the breakthrough curves for NH4+ in zeolite-filled, fixed-bed columns. Breakthrough experiments were performed in single- and multi-sorbate systems with the initial K+ and NH4+ concentrations set to 0.7 mmol/L. Breakthrough curves were successfully modeled by applying the linear driving force (LDF) and Thomas models. Batch experiments revealed that a good description of NH4+ sorption was provided by the Freundlich sorption model (R2 = 0.99), while unfavorable sorption was determined for K+ (nF = 2.19). Intraparticle diffusion was identified as the rate limiting step for NH4+ and K+ during breakthrough. Compared to ultrapure water, the use of tap, river, and groundwater matrices decreased the treated bed volumes by between 25% and 69%-as measured at a NH4+ breakthrough level of 50%. The concentrations of K+ and of dissolved organic carbon (DOC) were identified as the main parameters that determine NH4+ sorption in zeolite-filled, fixed-bed columns. Based on our results, the LDF and Thomas models are promising tools to predict the breakthrough curves of NH4+ in zeolite-filled, fixed-bed columns.

Seasonal Variation Analysis for Weekly Cases, Deaths, and Hospitalizations of COVID-19 in the United States.

Knowing the seasonality of COVID-19 helps decision-makers to take suitable interventions against the pandemic. In this study, we performed the Brown-Forsythe variance analysis on seasonal variations on different indicators based on the data on COVID-19 for the United States provided publicly by WHO. Our study finds that the seasonality of weekly cases and deaths of COVID-19 are strongly statistically supported by the data. The weekly total cases(/deaths) in winter are three to seven times(/two to three times) more than the other three single seasons. The ICU patients in winter and autumn are four to five times more than spring. The weekly hospital admissions in winter are four times more than spring. The mean of the positive rate in winter is five times more than spring. The findings of this research can be a reference in decision-making when taking interventions against the pandemic, such as taking stricter interventions in winter while considering less strict interventions in summer, etc.

Mechanical mechanism of rib spalling and sensitivity analysis of gangue parameters to rib spalling in gangue-bearing coal seams.

Rib spalling is one of the main factors restricting the safe and efficient production of the fully mechanized mining face in gangue-bearing coal seams, and the gangue has significant influence on the occurrence of rib spalling. In this study, the instability process and mechanical mechanism of rib spalling in gangue-bearing coal seams were studied, and the sensitivity of gangue parameters to rib spalling was analyzed. The simulation test of rib spalling under different gangue parameters was carried out by orthogonal tests. The width and depth of rib spalling were taken as evaluation indexes, and the influence of gangue parameters on the rib spalling was analyzed by variance analysis and significance tests. The results show that the failure process of rib spalling is characterized by the fracturing failure of the lower coal body, shear failure of the gangue layer, and the falling off of the upper coal body caused by the gravity; the gangue parameters (thickness, density, joint inclination, and internal friction angle) have an important influence on the sliding instability of the coal wall. In the sensitivity analysis, the influence of gangue parameters on the width of rib spalling is ordered as gangue density > joint inclination > gangue thickness > internal friction angle; the influence of gangue parameters on the depth of rib spalling is ordered as gangue density > joint inclination > internal friction angle > gangue thickness. Besides, the greater the gangue density, the less damage caused by stress concentration, and the lower the risk of rib spalling.

Variance Analysis in China's Coal Mine Accident Studies Based on Data Mining.

The risk of coal mine accidents rises significantly with mining depth, making it urgent for accident prevention to be supported by both scientific analysis and advanced technologies. Hence, a comprehensive grasp of the research progress and differences in hotspots of coal mine accidents in China serves as a guide to find the shortcomings of studies in the field, promote the effectiveness of coal mine disaster management, and enhance the prevention and control ability of coal mine accidents. This paper analyzes Chinese and foreign literature based on data mining algorithms (LSI + Apriori), and the findings indicate that: (1) 99% of the available achievements are published in Chinese or English-language journals, with the research history conforming to the stage of Chinese coal industry development, which is characterized by "statistical description, risk evaluation, mechanism research, and intelligent reasoning". (2) Chinese authors are the primary contributors that lead and contribute to the continued development of coal mine accident research in China globally. Over 81% of the authors and over 60% of the new authors annually are from China. (3) The emphasis of the Chinese and English studies is different. Specifically, the Chinese studies focus on the analysis of accident patterns and causes at the macroscale, while the English studies concentrate on the occupational injuries of miners at the small-scale and the mechanism of typical coal mine disasters (gas and coal spontaneous combustion). (4) The research process in Chinese is generally later than that in English due to the joint influence of the target audience, industrial policy, and scientific research evaluation system. After 2018, the Chinese studies focus significantly on AI technology in deep mining regarding accident rules, regional variation analysis, risk monitoring and early warning, as well as knowledge intelligence services, while the hotspots of English studies remain unchanged. Furthermore, both Chinese and English studies around 2019 focus on "public opinion", with Chinese ones focusing on serving the government to guide the correct direction of public opinion while English studies focus on critical research of news authenticity and China's safety strategy.

Values of blockchain for risk-averse high-tech manufacturers under government's carbon target environmental taxation policies.

Today, high-tech industries such as consumer electronics commonly face government rules on carbon emissions. Among the rules, carbon emission tax as well as extended producer responsibility (EPR) tax are two important measures. Using blockchain, the policy makers can better determine the carbon target environmental taxation (CTET) policy with accurate information. In this paper, based on the mean-variance framework, we study the values of blockchain for risk-averse high-tech manufacturers who are under the government's CTET policy. To be specific, the government first determines the optimal CTET policy. The high-tech manufacturer then reacts and determines its optimal production quantity. We analytically prove that the CTET policy simply relies on the setting of the optimal EPR tax. Then, in the absence of blockchain, we consider the case in which the government does not know the manufacturer's degree of risk aversion for sure and then derive the expected value of using blockchain for the high-tech manufacturers. We study when it is wise for the high-tech manufacturer and the government to implement blockchain. To check for robustness, we consider in two extended models respectively the situations in which blockchain incurs non-trivial costs as well as having an alternative risk measure. We analytically show that most of the qualitative findings remain valid.

Critical comparison of statistical methods for quantifying variability and uncertainty of microbial responses from experimental data.

Variability and uncertainty are important factors for quantitative microbiological risk assessment (QMRA). In this context, variability refers to inherent sources of variation, whereas uncertainty refers to imprecise knowledge or lack of it. In this work we compare three statistical methods to estimate variability in the kinetic parameters of microbial populations: mixed-effect models, multilevel Bayesian models, and a simplified algebraic method previously suggested. We use two case studies that analyse the influence of three levels of variability: (1) between-strain variability (different strains of the same species), (2) within-strain variability (biologically independent reproductions of the same strain) and, at the most nested level, (3) experimental variability (species independent technical lab variability resulting in uncertainty about the population characteristic of interest) on the growth and inactivation of Listeria monocytogenes. We demonstrate that the algebraic method, although relatively easy to use, overestimates the contribution of between-strain and within-strain variability due to the propagation of experimental variability in the nested experimental design. The magnitude of the bias is proportional to the variance of the lower levels and inversely proportional to the number of repetitions. This bias was very relevant in the case study related to growth, whereas for the case study on inactivation the resulting insights in variability were practically independent of the method used. The mixed-effects model and the multilevel Bayesian models calculate unbiased estimates for all levels of variability in all the cases tested. Consequently, we recommend using the algebraic method for initial screenings due to its simplicity. However, to obtain parameter estimates for QMRA, the more complex methods should generally be used to obtain unbiased estimates.

Evaluation of the Rheological Properties of Virgin and Aged Asphalt Blends.

To evaluate the effects of the source and admixture of aged asphalt on the rheological properties of reclaimed asphalt binders, the relative viscosity (Δη), relative rutting factor (ΔG*/sinδ), and relative fatigue factor (ΔG*sinδ) were selected as evaluation indicators based on the Strategic Highway Research Program (SHRP) tests to characterize the rheological properties of a reclaimed asphalt binder under medium- and high-temperature conditions. The results of the study showed that the viscosity, rutting factor, and fatigue factor of the reclaimed asphalt binder increased with the addition of aged asphalt; however, the effect of the source and admixture of aged asphalt could not be assessed. The relative viscosity, relative rutting factor, and relative fatigue factor are sensitive to the source, admixture, temperature, and aging conditions, which shows the superiority of these indicators. Moreover, the relative viscosity and relative rutting factor decreased linearly with increasing temperature under high-temperature conditions, while the relative fatigue factor increased linearly with increasing temperature under medium-temperature conditions. In addition, the linear trends of the three indicators were independent of the source and admixture of aged asphalt. These results indicate that the evaluation method used in this study can be used to assess the effects of virgin asphalt and aged asphalt on the rheological properties of reclaimed asphalt binders, and has the potential for application. The viscosity of recycled asphalt increases, and the rutting factor and fatigue factor both increase. The high-temperature stability of reclaimed asphalt is improved, and the fatigue crack resistance is weakened.

Study on Group Differences in Migrant Workers' Urban Integration in China.

Objective: This manuscript evaluates and tests the group differences in migrant workers' urban integration from the perspectives of individual characteristics and migration characteristics, so as to provide theoretical support and practical guidance for the government to issue more effective assistance policies. Methods: Multilevel comprehensive evaluation method and Entropy method are used to calculate the urban integration level of migrant workers, and one-way ANOVA and optimal scaling regression are used to test the group differences in migrant workers' urban integration. Results: Based on the questionnaire data of 854 migrant workers in China, the scale of migrant workers' urban integration has good reliability and validity. The overall level of migrant workers' urban integration is 49.61% and there exist group differences in migrant workers' urban integration. The impact of education level, income level, and migration time on migrant workers' urban integration is significantly positive, whereas the impact of migration distance on migrant workers' urban integration is significantly negative. The urban integration level of migrant workers who have family members accompanying them is higher than that of migrant workers who have no family members accompanying them. Gender, age, and marriage have no significant impact on migrant workers' urban integration. Strengths and Limitations of This Study: This study aims to measure and test the group differences in migrant workers' urban integration using ANOVA and optimal scaling regression. However, the shortcomings of this study are the selection of the "migrant workers' urban integration" scale and the representativeness of the sample used in this study. Conclusion: There are group differences in migrant workers' urban integration with different education levels, income levels, migration distances, migration times, and statuses of family members accompanying. In the policy of promoting migrant workers' urban integration, we should accurately count the characteristics of migrant workers and give more attention to migrant workers with low education levels, low-income levels, long migration distances, short migration times, and no family accompany.

Gait variability between younger and older adults: An equality of variance analysis.

BACKGROUND: To estimate gait variability, several methods have been routinely used which provide a measure of global variability. A recent study introduced a group waveform variability method which provides a point-by-point measurement of data variance equality. This can identify where in the gait cycle the significant differences in variability exist. RESEARCH QUESTION: Do waveform differences exist in equality of variance and group means in lower limb biomechanical variables between healthy younger and older adults during a gait task? METHODS: Twenty healthy younger (19-44 years old, age=29.9(7.0) years, body mass index= 24.6(3.2)kg/m2, females= 10) and 20 healthy older (55-79 years old, age=63.6(5.5) years, body mass index= 25.9(2.7)kg/m2, females= 10) adults who were free from lower limb injuries and had no musculoskeletal or neurological disorders. Temporospatial outcomes, sagittal and frontal lower limb joint angles and moments, along with joint powers were examined as participants walked at a self-selected pace. Waveform patterns were normalized to the gait cycle and compared using equality of variance and statistical parametric mapping techniques. RESULTS: No difference in walking speed existed between the younger or older groups (P > .05). The older group had greater variability (P < .05) in sagittal hip angles, as well as greater frontal ankle angle and moment variability. The younger group had significantly greater mean (P < .05) ankle power generation prior to toe-off. SIGNIFICANCE: This study provided a baseline of temporal differences in variance between healthy younger and older individuals. Its findings warrant the use of the equality of variance test to compare temporal differences for a variety of populations and tasks. Older adults generally had more variability than the younger adults, with many differences occurring near the transition from double- to single-limb support. The statistical parametric mapping analysis showed that the older adults could not generate as much ankle power as the younger adults prior to toe-off.

Comparison of feed evaluation models on predictions of milk protein yield on Québec commercial dairy farms.

Feed evaluation models (FEM) are a core part in dairy cow feeding. As these models are developed using different biological and mathematical approaches mainly tested in a research context, their abilities to predict production in commercial farms need to be validated, even more so when they are used outside the context of their development. Four FEM-National Research Council, 2001 (NRC_2001); Cornell Net Carbohydrate and Protein System, 2015 (CNCPS); NorFor, 2011; and INRA, 2018 (INRA_2018)-were evaluated on their abilities to predict daily milk protein yield (MPY) of 541 cows from 23 dairy herds in the province of Québec, Canada. The effects of cow and diet characteristics were tested on the residuals of MPY. Sensitivity and uncertainty analyses were then performed to evaluate the influence of the uncertainty of the main characteristics of cows and feed ingredients measured on the farm and used in the 4 FEM on the predictions of metabolizable protein (MP) supply and MPY. The 4 models had acceptable predictions of MPY, with concordance correlation coefficients (CCC) ranging from 0.75 to 0.82 and total bias ranging from 12.8% to 19.3% of the observed mean. The Scandinavian model NorFor had the best predictions with a CCC of 0.82, whereas the 3 other models had similar CCC at 0.75 to 0.76. The INRA_2018 and NRC_2001 models presented strong central tendency biases. Removing herd effect put the 4 FEM at the same level of performance, with 11.9 to 12.4% error. Analyzing model behavior within a herd seems to partly negate the effect of using predicted dry matter intake (DMI) in the comparison of models. Diet energy density, days in milk, and MPY estimated breeding value were related to the residual in the 4 models, and Lys and Met (as percent of MP) only in NRC_2001 and NorFor. This suggests that inclusion of these factors in these models would improve MPY predictions. From the sensitivity analysis, for the 4 FEM, DMI and factors affecting its prediction had the greatest influence on the predictions of MP supply and MPY. Of the feed ingredients, forage composition had the greatest effect on these predictions, including a strong effect of legume proportion with NorFor. Diet acid detergent fiber concentration had a very strong effect on MP supply and MPY predictions only in INRA_2018, because of its effect on organic matter digestibility estimation. The range of predictions of MP supply and MPY when combining all these potential uncertainties varied depending on the models. The INRA_2018 model presented the lowest standard deviation (SD) and NorFor the highest SD for the predictions of both MP supply and MPY. Overall, despite the fact that FEM were developed in a research context, their use in a commercial context yields acceptable predictions, with NorFor yielding the best predictions overall, although within-herd responses varied similarly for the 4 tested models.

Portfolio optimization of financial commodities with energy futures.

The recent growth in economic and financial markets has brought the focus on energy derivatives as an alternative investment class for investors, financial analysts, and portfolio managers. The financial modeling and risk management of portfolios using the energy derivatives instrument is a requirement and challenge for researchers in the field. The energy and other commodity futures force the expert investors to investigate the broader investment spectrum and consequently diversify their portfolios using the futures instruments. Going beyond the conventional portfolios and developing out-of-the-box strategies that comply with the changing financial and economic advancements are the keys to long-term sustainability in the financial world. This study investigates the impact of diversification with five energy futures from January 2011 to July 2020 on three traditional commodity futures portfolios. The results show that diversification increased the returns while simultaneously reducing the portfolio volatility in all portfolios. The diversified portfolios provided higher returns than the traditional portfolios for the same level of risk. This study also revealed that the results might improve when a short position in the futures contracts is allowed. Moreover, we conclude that adding multiple energy futures in a portfolio provides enhanced diversification results, whereas the WTI crude oil futures fail to diversify any portfolio considered in the study.

[Spatial variations and its influencing factors of soil carbon and nitrogen on the southern foot of Taihang Mountains, China]. / å¤ªè¡Œå±±å—éº“å¡é¢åœŸå£¤ç¢³æ°®ç©ºé—´å˜å¼‚æ€§åŠå ¶å½±å“å› ç´ .

As an important ecological barrier for the North China Plain, research on the spatial variations of soil nutrients in the southern foot of the Taihang Mountains is of great significance for the forestry ecological construction in this rocky mountainous area. With the typical slopes (the artificial forestland and the natural wild slope) of the southern foot of the Taihang Mountains as the research objects, we used the grid method to arrange sampling points, and combined classical statistics, geostatistics and constrained sorting methods to analyze the spatial variations of soil nutrients. The results showed that: 1) soil total carbon (TC) contents were 6.80-57.05 g·kg-1, and the total nitrogen (TN) contents were 0.74-3.93 g·kg-1. The coefficients of variation of both soil TC and TN were 25.0%-52.8%, belonging to the moderate degree of variation, which were caused by the combination of random and structural factors. The spatial aggregation of soil nutrients decreased with increasing lag distances. 2) The contents of soil nutrients had increasing trends from the top to the bottom of the slopes, with high nutrient values appearing at the bottom of the slopes. 3) The soil bulk density, gravel content, vegetation coverage, and soil water content were the main factors affecting the spatial variability of soil TC and TN on the southern foot of the Taihang Mountains. 4) Soil water content was the main factor affecting soil nutrients at the natural wild slope, but not at the artificial forestland.