Calculation Of Variance Research Articles

Occupational Therapy Interventions and Their Impact on Work Performance and Activities of Daily Living in Persons with Multiple Sclerosis

Background. Multiple sclerosis (MS) is a chronic neurological disease that affects the central nervous system, leading to various physical, cognitive, and emotional challenges that significantly impact daily activities and work performance. Occupational therapy (OT) plays an important role in managing MS by providing interventions designed to improve functional performance, promote independence, and enhance overall well-being. Aim. To evaluate the impact of OT interventions on work performance and activities of daily living in individuals with MS. Methods. A total of 776 participants diagnosed with MS were included in this study. Data were collected through structured questions extracted from the MS Quality of Life-54 questionnaire. The study assessed the relationships between the type of MS, work performance, daily activities, and the effectiveness of OT interventions. Statistical analyses included Chi-Square tests, correlation analysis, calculations of standard deviation and variance, Box Plots, and ANOVA tests. Results. The study found that 65% of participants reported reduced working hours, 78% performed fewer activities than desired, and 66% experienced limitations in work tasks or daily activities due to MS. Emotional problems also impacted work performance and daily activities. The ANOVA test revealed significant differences in reduced working hours, fewer activities, and the impact of emotional problems among different types of MS. Specifically, the relapsing-remitting type of MS showed the highest impact on work performance and daily activities. Conclusions. OT plays a crucial role in addressing the functional and emotional challenges associated with MS. The interventions are particularly beneficial in improving work performance and daily activities, especially for individuals with the relapsing-remitting type of MS. Individualized OT strategies should be tailored to address the specific needs of individuals with different types of MS. Keywords: occupational therapy; multiple sclerosis; daily activities; work performance.

Analisis Pusat Pertanggungjawaban Pusat Kos dan Evaluasi Kinerja pada Pabrik Tahu UD. Sumber Makmur Bangkalan

This study aims to design an accountability report that can be the basis for measuring performance for UD. Sumber Makmur, which previously did not have such a system. This research uses descriptive quantitative research methods. The data collection techniques used in this study include in-depth interviews and documentation to collect data related to organizational structure, performance evaluation, and company financial reports for the period 2022-2023. Analysis was conducted using variance calculations between budget and actual costs, and using Return on Investment (ROI) ratios to evaluate performance. The findings show that the implementation of accountability reports and performance evaluation at UD. Sumber Makmur is still not going well. The company has not prepared a detailed cost budget for each department, which has led to inefficiencies in spending and using resources including a significant decrease in net profit in some periods, unfavorable cost variances, and low Return on Investment (ROI). This study has limitations in that it only focuses on one company, so the results may not be generalizable to all SMEs. SMEs need to implement a more detailed accounting system, budgeting by department, and transparent performance evaluation. These steps can improve the company's cost efficiency, profitability, and competitiveness in the market.

Investigating the impact of different loading modalities on bone quality among athletes in various sports

The impact of different loading modalities on bone quality is a crucial area of study for understanding athletic performance and injury prevention. This research investigates how high-impact, low-impact, and resistance training activities influence Bone Mineral Density (BMD), cortical thickness, trabecular number, and stiffness index among athletes from various sports disciplines. A total of 152 athletes from different regions in China were assessed using advanced diagnostic techniques, including Dual-energy X-ray Absorptiometry (DXA), Quantitative Ultrasound (QUS), and Peripheral Quantitative Computed Tomography (pQCT). The study also examines the interaction between demographic factors, such as age and gender, and their effects on bone adaptation. Statistical analyses, including Analysis of Variance (ANOVA) and effect size calculations, were employed to quantify the impact of each loading modality. Results reveal that high-impact sports significantly enhance BMD and bone microarchitecture, showing the highest effect sizes among all groups. Resistance training also demonstrates positive, though less pronounced, outcomes, while low-impact activities contribute minimally to bone development. The findings emphasize the importance of loading intensity and modality for optimizing bone health, providing evidence-based recommendations for athletes, coaches, and healthcare professionals to design effective training programs that enhance skeletal strength and prevent injury.

Start-Up and Steady-State Regimes Automatic Separation in Induction Motors by Means of Short-Time Statistics

Induction motors are widely used machines in a variety of applications as primary components for generating rotary motion. This is mainly due to their high efficiency, robustness, and ease of control. Despite their high robustness, these machines can experience failures throughout their lifespan due to various mechanical, electrical, and environmental factors. To prevent irreversible failures and all the implications and costs associated with breakdowns, various methodologies have been developed over the years. Many of these methodologies have focused on analyzing various physical quantities, either during start-up transients or during steady-state operations. This involves the use of specific techniques depending on the focus of the methodology (start-up transients or steady-state) to obtain optimal results. In this regard, it is of great importance to develop methods capable of separating and detecting the start-up transient of the motor from the steady state. This will enable the development of automatic diagnostic methodologies focused on the specific operating state of the motor. This paper proposes a methodology for the automatic detection of start-up transients in induction motors by using magnetic stray flux signals and processing by means of statistical indicators in time-sliding windows, the calculation of variances with a proposed method, and obtaining optimal values for the design parameters by using a Particle Swarm Optimization (PSO). The results obtained demonstrate the effectiveness of the proposed method for the start-up and steady-state regimes automatic separation, which is validated on a 0.746 kW induction motor supplied by a variable frequency drive (VFD).

Lung cancer classification model using convolutional neural network with feature ranking process

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting the importance of early detection to improve patient outcomes. The goal of this study is to create a computer-aided diagnosis (CAD) system that detects and classifies lung cancer based on medical images using a Convolutional Neural Network (CNN) and feature extraction techniques. The study employs the LIDC-IDRI dataset, a comprehensive collection of lung cancer-related medical images, to achieve this goal. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to improve contrast and detail while reducing noise. The Sobel operator is used to refine the segmentation process by enhancing edges and contours in binary images. Morphological operations such as dilation and filling are used to refine the segmented regions even further. Feature extraction is used to extract statistical data and texture characteristics from segmented regions. Mean and variance calculations reveal information about brightness and variability within regions, whereas co-occurrence matrices and Gray-Level Co-occurrence Matrix (GLCM) properties quantify texture features. The correlation between different regions is also evaluated to assess their relationships. Using the pre-processed and ranked features as inputs, a CNN model with five hidden layers is trained. To classify the segmented regions into cancerous and non-cancerous classes, the model learns patterns and relationships in the data. A confusion matrix is used to assess the accuracy, specificity, and sensitivity of the model's predictions, with an emphasis on correctly identifying lung cancer-affected regions. The results show promising results, with the proposed CAD system identifying lung cancer-affected regions with an accuracy of 99.4375%. The system also outperforms other existing methods with a specificity of 99.12% and a sensitivity of 99.26%. These findings highlight the developed system's potential as a valuable tool for early lung cancer detection, assisting doctors in making accurate diagnoses and improving patient outcomes.

Prediction of additive genetic variances of descendants for complex families based on Mendelian sampling variances.

The ability to predict the outcome of selection and mating decisions enables breeders to make strategically better selection decisions. To improve genetic progress, those individuals need to be selected whose offspring can be expected to show high genetic variance next to high breeding values. Previously published approaches enable to predict the variance of descendants of 2 future generations for up to 4 founding haplotypes, or 2 outbred individuals, based on phased genotypes, allele effects, and recombination frequencies. The purpose of this study was to develop a general approach for the analytical calculation of the genetic variance in any future generation. The core development is an equation for the prediction of the variance of double haploid lines, under the assumption of no selection and negligible drift, stemming from an arbitrary number of founder haplotypes. This double haploid variance can be decomposed into gametic Mendelian sampling variances (MSVs) of ancestors of the double haploid lines allowing usage for non-double haploid genotypes that enables application in animal breeding programs as well as in plant breeding programs. Together with the breeding values of the founders, the gametic MSV may be used in new selection criteria. We present our idea of such a criterion that describes the genetic level of selected individuals in 4 generations. Since breeding programs do select, the assumption made for predicting variances is clearly violated, which decreases the accuracy of predicted gametic MSV caused by changes in allele frequency and linkage disequilibrium. Despite violating the assumption, we found high predictive correlations of our criterion to the true genetic level that was obtained by means of simulation for the "corn" and "cattle" genome models tested in this study (0.90 and 0.97). In practice, the genotype phases, genetic map, and allele effects all need to be estimated meaning inaccuracies in their estimation will lead to inaccurate variance prediction. Investigation of variance prediction accuracy when input parameters are estimated was not part of this study.

Psychometric properties of International Trauma Questionnaire (ITQ) and symptom structure of complex post-traumatic stress disorder among Chinese patients with MDD

AimsTo assess the validity and internal reliability of the International Trauma Questionnaire (ITQ) among patients diagnosed with major depressive disorder (MDD) and to explore the network structure of Complex post-traumatic stress disorder (CPTSD) among MDD patients in China. MethodsEligible individuals were recruited from a large tertiary hospital in Guangdong Province. Trained researchers conducted in-person interviews and administered self-report questionnaires, including demographics, medical information, and psychological assessments. Confirmatory factor analyses (CFA) and network analysis were performed, with calculations of Average Variance Extracted (AVE), Cronbach's α, and composite reliability. ResultsA total of 113 patients with MDD participated in this study. The correlated six-factor one-order model was a good representation of the latent structure of ITQ (χ2= 60.114, df = 39, P = 0.017, SRMR = 0.070, RMSEA = 0.050, TLI = 0.952, CFI = 0.972, BIC = 175.508). All ITQ subscales possessed acceptable convergent validity and internal reliability, except for affective dysregulation and re-experiencing. The square root of AVE for affective dysregulation was lower than its correlations with other clusters. Network analysis revealed that node C4 (‘I feel worthless’), as a core symptom, was significantly associated with the development of CPTSD. ConclusionsThe clinical applicability of the ITQ was demonstrated by its overall validity and reliability among patients with MDD. However, the affective dysregulation and re-experiencing clusters still need to be revised and enhanced. Timely screening, recognition, and diagnosis are critical due to the worse clinical outcomes seen in comorbid patients.

Exploring the Tribological Behavior and Surface Characteristics of Polymethyl-Methacrylate/Hydroxyapatite (PMMA/HA) Composites: Investigating the Effect of Hydroxyapatite Weight Ratios on Wear Performance, Surface Roughness, and Hardness

This study conducts an analysis of the tribological characteristics of polymethyl-methacrylate (PMMA) composites reinforced with varying weight ratios of hydroxyapatite (HA) nanoparticles. It encompasses a systematic assessment of both hardness and surface roughness using the Shore-D hardness testing method and profilometer measurements, respectively. The results reveal a correlation between the HA nanoparticles weight ratio and hardness, indicating an increase in HA nanoparticles weight ratio corresponded to enhanced hardness. Moreover, higher HA nanoparticles weight ratios result in rougher surfaces, impacting composite performance. Specifically, the composite containing 5%-HA exhibited a Rq of 6 µm, which incrementally rose to 6.3 µm for the 10%-HA composite and significantly increased to 11.1 µm for the 15%-HA composite. Increasing HA weight ratios led to higher Rz values: 28 µm for 5%-HA, 35.1 µm for 10%-HA, and 72.5 µm for 15%-HA, indicating greater surface irregularities. The wear rates and friction coefficients are examined under applied forces ranging from 5 to 15 newtons across three distinct HA weight ratios (5%, 10%, and 15%). The results show a decreasing trend in wear rates with increasing weight ratios of HA, evidenced by weight loss rates of 0.0052 gr, 0.0011 gr, and 0.006 gr for samples S.1, S.2, and S.3, respectively, under a 15 N applied force. The friction coefficient analyses highlight complex dependencies on sliding distance, applied force, and HA weight ratio. Statistical tests, such as One-way analysis of variance (ANOVA) and effect size calculations, confirm significant applied force effects on friction coefficients, offering insights for optimizing PMMA/HA composite performance in biomedical applications.

Dynamics in Realized Volatility Forecasting: Evaluating GARCH Models and Deep Learning Algorithms Across Parameter Variations

AbstractThe modeling and forecasting of return volatility for the top three cryptocurrencies, which are identified by the highest trading volumes, is the main focus of the study. Eleven different GARCH-type models were analyzed using a comprehensive methodology in six different distributions, and deep learning algorithms were used to rigorously assess each model’s forecasting performance. Additionally, the study investigates the impact of selecting dynamic parameters for the forecasting performance of these models. This study investigates if there are any appreciable differences in forecast outcomes between the two different realized variance calculations and variations in training size. Further investigation focuses on how the use of expanding and rolling windows affects the optimal window type for forecasting. Finally, the importance of choosing different error measurements is emphasized in the framework of comparing forecasting performances. Our results indicate that in GARCH-type models, 5-minute realized variance shows the best forecasting performance, while in deep learning models, median realized variance (MedRV) has the best performance. Moreover, it has been determined that an increase in the training/test ratio and the selection of the rolling window approach both play important roles in achieving better forecast accuracy. Finally, our results show that deep learning models outperform GARCH-type models in volatility forecasts.

Epigenetics: Unraveling Hidden Phenotypic Diversity in Livestock

The missing link between nature and nurture i.e. epigenetics is the process of turning genes on and off or can be explained as a phenomenon that includes all the process that affects phenotypic expressions without causing any alteration in the genetic sequence. Epigenetic regulation can be done by post-translational modification of histones but is also linked to the regulation of gene expression by non-coding RNAs, genome instabilities or any other force that could modify a phenotype. Epigenomic patterns in various tissues reveal the regulatory functions of epigenetic mechanisms in livestock growth and health. DNA methylation modifications can explain poor offspring performance due to maternal stressors like metabolic disorder, heat stress and negative energy balance. It is an attractive field for livestock breeding, as it can help identify missing causality and heritability of traits and diseases. Certain methylation patterns are more likely to increase in specific environments, explaining phenotypic diversity between individuals. Accurate recording of the environment and epigenomes can provide more accurate and precise calculations of phenotypic variance. Methods to estimate the contribution of epigenetics to covariance between relatives have been developed which can be beneficial for obtaining favorable methylation patterns that impact disease resistance and economic traits.

Efficient approximation of stochastic turning process based on power spectral density

Turning is one of the most important material removal processes in manufacturing, where the proper understanding of the process is crucial for the quality of the final product. In this study, the stochastic cutting force is utilized to enhance the existing 1-degree-of-freedom turning model. A stochastic model is adopted to address the stochastic resonance phenomenon occurring near stability boundaries. Additionally, a novel simplified stochastic model is introduced with additive noise only. The comparison of the two models reveals that, with the recommended noise intensity of 0.1 to 1%, there is no significant difference in the stability charts and mean square characteristics between the two models. As a result, the time-consuming numerical methods can be bypassed, as the simplified model offers a computationally more efficient analytical approach to compute variance based on power spectral density (PSD). By combining techniques such as D-separation to determine stability boundaries and the PSD-based variance calculation, it only takes a minute instead of hours to construct a heatmap using the introduced simplified stochastic turning model that clearly outlines dangerous stochastic resonance regions inside the stable domain.

Optimization of bromocresol green degradation using ozone micro bubbles: response surface analysis and techno-commercial aspects of a 75 kL/day scale-up plant

Degradation of bromocresol green in alcohol-water solution using ozone micron sized bubbles is reported. A high speed impeller is used to generate micro bubbles of ozone in liquid while ozone is generated using a commercial ozone generator. A 3-level-3-factor Box–Behnken experimental design is used to statistically identify relative importance of the operating parameters studied namely impeller speed, initial concentration of dye and treatment time. Ozone dosage is kept fixed at 4 g/h. Impeller speed is found to be the most dominant factor according to Analysis of Variance (ANOVA) calculations. Kinetics for degradation of bromocresol green in solution is also reported. A second order kinetics is seen to fit the experimental (concentration–time) data, corresponding value of rate constant being 0.0153 L/mg-s. Based on the laboratory scale data a detailed techno-commercial analysis of a scaled up (75 kL/day) plant for ozone based degradation of bromocresol is presented. All relevant economic indicators pertaining to the scaled up plant are reported. A specific treatment cost of Rs. 100/m3 of treated water yields a return on investment (ROI) of 68.77 and discounted payback period of 5 years.

Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used “robust” variance estimate of Barlow (Biometrics 50:1064–1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.

Effect of emerging digital technologies and methodologies combined with incentives on HbA1c in patients with type 2 diabetes mellitus: study protocol for a parallel, open randomized controlled trial

BackgroundType 2 diabetes mellitus (T2DM) is a common metabolic disease that requires long-term management and treatment. Digital intervention, as an emerging medical model, has been widely used in the treatment of T2DM patients. Behavioral economics theory provides a favorable perspective for studying the effect of digital intervention because it can reveal the decision-making mechanisms behind human behavior and provide more effective interventions for digital intervention. The purpose of this trial is to evaluate the impact of behaviorally based digital intervention on T2DM patients’ HbA1c, self-monitoring of blood glucose (SMBG) testing rate, diabetes self-efficacy, and other indicators compared to conventional treatment.MethodsThis trial is a prospective randomized controlled trial conducted at the First People’s Hospital of Kunshan City from April 1, 2023, to December 31, 2024, with a follow-up period of 3 months. The specific randomization method was established and implemented through the EDC clinical trial center’s randomization system. We will measure and collect baseline data from three groups, including Group A: digital intervention + virtual incentives + conventional treatment, Group B: digital intervention + physical incentives + conventional treatment, and Group C: conventional treatment. HbA1c, weight, SMBG testing rate, diabetes self-efficacy, and diabetes-related medical expenses will be recorded at baseline, 1 month, 2 months, and 3 months for all three groups. The Shapiro-Wilk test will be used to test for normality, and Pearson correlation analysis will be used for correlation analysis. Dropouts will be analyzed separately. Analysis of variance or exact probability calculation will be used to compare demographic data and other baseline indicators.DiscussionThis study is a novel clinical trial that integrates multiple disciplines (economics and medicine) and uses digital technology to deliver the intervention. Most published studies were offline interventions based on behavioral economics theory, but very few were on online interventions for T2DM patients. This study has both novelty and social value.Trial registrationChinese Clinical Trial Registry ChiCTR2300070753. Registered on 2023/04/22.

Randomly Pruning the Sachdev‐Ye‐Kitaev Model

AbstractThe Sachdev‐Ye‐Kitaev model (SYK) is renowned for its short‐time chaotic behavior, which plays a fundamental role in its application to various fields such as quantum gravity and holography. The Thouless energy, representing the energy scale at which the universal chaotic behavior in the energy spectrum ceases, can be determined from the spectrum itself. When simulating the SYK model on classical or quantum computers, it is advantageous to minimize the number of terms in the Hamiltonian by randomly pruning the couplings. In this paper, it is demonstrated that even with a significant pruning, eliminating a large number of couplings, the chaotic behavior persists up to short time scales. This is true even when only a fraction of the original couplings in the fully connected SYK model, specifically , is retained. Here, represents the number of sites, and . The properties of the long‐range energy scales, corresponding to short time scales, are verified through numerical singular value decomposition (SVD) and level number variance calculations.

Sample Size and Geometric Morphometrics Methodology Impact the Evaluation of Morphological Variation.

Geometric morphometrics has had a profound impact on our understanding of morphological evolution. However, factors such as sample size and the views and elements selected for two-dimensional geometric morphometric (2DGM) analyses, which are often dictated by specimen availability and time rather than study design, may affect the outcomes of those analyses. Leveraging large intraspecific sample sizes (n>70) for two bat species, Lasiurus borealis and Nycticeius humeralis, we evaluate the impact of sample size on calculations of mean shape, shape variance, and centroid size. Additionally, we assessed the concordance of multiple skull 2D views with one another and characterized morphological variation in skull shape in L. borealis and N. humeralis, as well as a closely related species, Lasiurus seminolus. Given that L. seminolus is a morphologically cryptic species with L. borealis, we assessed whether differences in skull shape and in 2DGM approach would allow species discrimination. We found that reducing sample size impacted mean shape and increased shape variance, that shape differences were not consistent across views or skull elements, and that trends shown by the views and elements were not all strongly associated with one another. Further, we found that L. borealis and L. seminolus were statistically different in shape using 2DGM in all views and elements. These results underscore the importance of selecting appropriate sample sizes, 2D views, and elements based on the hypothesis being tested. While there is likely not a generalizable sample size or 2D view that can be employed given the wide variety of research questions and systems evaluated using 2DGM, a generalizable solution to issues with 2DGM presented here is to run preliminary analyses using multiple views, elements, and sample sizes, thus ensuring robust conclusions.

Prediction and Diagnosis of Electric Vehicle Battery Fault Based on Abnormal Voltage: Using Decision Tree Algorithm Theories and Isolated Forest

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.

Abstract 12811: Incidence and Phenotypic Predictors of Arrhythmias in Idiopathic Pulmonary Arterial Hypertension

Introduction: Arrhythmias are common in patients with idiopathic pulmonary arterial hypertension (IPAH). However prospective analysis of arrhythmias in a large cohort of IPAH patients using implantable cardiac monitors (ICMs) is lacking. Aims: Establish the rate of arrhythmias in a prospective cohort of IPAH patients using ICMs. Determine factors that predict arrhythmia occurrence in IPAH. Elucidate whether arrhythmia incidence is related to adverse outcomes. Methods: ICMs were implanted into 80 IPAH patients across two UK centres. Arrhythmia and clinical worsening events (defined as death, transplant or hospitalisation due to PAH worsening) were prospectively recorded and characteristics associated with arrhythmia incidence identified. The arrhythmia rate in IPAH patients was compared to 72 age and sex-matched patients without IPAH who had ICMs implanted for clinical indications. Comparisons of data were performed using analysis of variance, χ2 or Kruskal-Wallis calculations as appropriate. The Cox proportional hazards model was used to resolve independent risk factors for significant arrhythmia occurrence. A two-tailed probability level of <0.05 was considered significant. Results: Over 186 patient-years follow-up 79 arrhythmia events were noted in 40% IPAH patients vs 78 events in 41% comparators. Arrhythmia incidence was related to right atrial (RA) enlargement at time of implant, baseline conduction disease and symptom reporting. Arrhythmia incidence predicted clinical worsening (figure).20% of patients had targeted arrhythmia intervention as a direct consequence of ILR studies. Only RA size was independently associated with clinically significant arrhythmia on multivariate analysis Conclusion: Arrhythmias are frequent in IPAH patients and predict worse outcomes. RA enlargement is independently associated with clinically significant arrhythmia incidence. IPAH should be considered a high-risk group for continuous monitoring studies.

Validating DayCent-CR for cropland soil carbon offset reporting at a national scale

Regenerative soil management practices have been shown to increase soil organic carbon in cropland previously under conventional management, and farmers that adopt regenerative practices could be eligible to participate in carbon offset programs. Due to the high cost of soil sampling at large scales, project developers of agricultural carbon offset programs may employ a hybrid measurement and modeling approach to SOC quantification. While biogeochemical models allow for carbon crediting to occur on larger scales than soil sampling alone would allow, any model used must be unbiased and shown to adequately predict SOC changes, with known uncertainty, across the crops, practice changes, and geographies of interest. The “credit-ready” version of the DayCent ecosystem model, DayCent-CR, was evaluated for performance across 14 combinations of crops and practice categories. Model calibration and validation was performed with a Bayesian Markov chain Monte Carlo approach using k-fold cross validation and 668 SOC stock change measurements from 41 agricultural research sites. Overall model performance met the guidelines established by Climate Action Reserve’s Soil Enrichment Protocol: ≥90% of model prediction intervals covered the measured value, and mean bias in all categories was less than pooled measurement uncertainty. Importantly, posterior distributions of DayCent-CR parameters and variance components enable the calculation of variance, which can then be used to calculate an uncertainty deduction that is applied to overall project credits to ensure conservatism. The calibrated model parameters are therefore valid for use in crediting programs within the domain of the validation dataset.

Impact of patent quality on enterprises’ export competitiveness under the background of big data and Internet of Things

After entering the new century, the role of science and technology and innovation in promoting economic development has become increasingly obvious, and the Chinese government has also attached great importance to patent management. With the joint efforts of the people of the whole country, the number of patent applications in China has been among the highest in the world for many consecutive years. However, from the perspective of patent quality, there is still a certain gap with developed countries, resulting in generally low efficiency for Chinese export enterprises. Therefore, it is of great significance to explore the relationship between the quality of patents and the competitiveness of exporting enterprises. Through variance calculation, we constructed a countermeasure system for using intellectual property rights to enhance competitiveness. The experimental results show that the patent quality is proportional to the competitiveness of export enterprises, and the higher the patent quality, the stronger the competitiveness of export enterprises. The development of this study further clarifies the important value of patent quality, which helps export enterprises adjust their development strategies and effectively enhance their competitiveness.