Statistical Model Research Articles

Global variation in ecoregion flammability thresholds

Anthropogenic climate change is altering the state of worldwide fire regimes, including by increasing the number of days per year when vegetation is dry enough to burn. Indices representing the percent moisture content of dead fine fuels as derived from meteorological data have been used to assess geographic patterns and temporal trends in vegetation flammability. To date, this approach has assumed a single flammability threshold, typically between 8 and 12%, controlling fire potential regardless of the vegetation type or climate domain. Here we use remotely sensed burnt area products and a common fire weather index calculated from global meteorological reanalysis data to identify and describe geographic variation in fuel moisture as a flammability threshold. This geospatial analysis identified a wide range of flammability thresholds associated with fire activity across 772 ecoregions, often well above or below the commonly used range of values. Many boreal and temperate forests, for example, can ignite and sustain wildfires with higher estimated fuel moisture than previously identified; Mediterranean forests, in contrast, tend to sustain fires with consistently low estimated fuel moisture. Statistical modelling showed that flammability thresholds derived from burnt area are primarily driven by climatological variables, particularly precipitation and temperature. Our analysis also identified complex associations between vegetation structure, fuel types, and climatic conditions highlighting the complexity in vegetation–climate–fire relationships globally. Our study provides a critical, necessary step in understanding and describing global pyrogeography and tracking changes in spatial and temporal fire activity.

Machine learning for clinical outcome prediction in cerebrovascular and endovascular neurosurgery: systematic review and meta-analysis

BackgroundMachine learning (ML) may be superior to traditional methods for clinical outcome prediction. We sought to systematically review the literature on ML for clinical outcome prediction in cerebrovascular and endovascular...

Soil and climate‐dependent ingrowth inference: broadleaves on their slow way to conquer Swiss forests

Forests provide essential ecosystem services that range from the production of timber to the mitigation of natural hazards. Rapid environmental changes, such as climate warming or the intensification of disturbance regimes, threaten forests and endanger forest ecosystem services. In light of these challenges, it is essential to understand forests' demographic processes of regeneration, growth, and mortality and their relationship with environmental conditions. Specifically, understanding the regeneration process in present‐day forests is crucial since it lays the foundation for the structure of future forests and their tree species composition. We used Swiss National Forest Inventory (NFI) data covering vast bio‐geographic gradients over four decades to achieve this understanding. Trees that reached a diameter at breast height of 12 cm between two consecutive NFI campaigns were used to determine regeneration and were referred to as ingrowth. Employing three independent statistical models, we investigated the number, species, and diameter of these ingrowth trees. The models were subsequently implemented into a forest simulator to project the development of Swiss forests until the mid‐21st century. The simulation results showed an ingrowth decrease and a shift in its species composition, marked by a significant reduction in Norway spruce Picea abies and concurrent increases in broadleaves. Nevertheless, the pace of this change towards climatically better adapted species composition is relatively slow and is likely to slow down even further as ingrowth declines in the future, in contrast to the fast‐changing climatic conditions. Hence, support through adaptive planting strategies should be tested in case ingrowth does not ensure the resilience of forests in the future. We conclude that since the regeneration of forests is becoming increasingly challenging, the current level at which ecosystem services are provided might not be ensured in the coming decades.

Reducing delivery insurance costs through risk score model for food delivery company

In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers’ profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America.

Impact of Innovations on SME Performance in Ho Chi Minh City (Vietnam)

Impact of Innovations on SME Performance in Ho Chi Minh City (Vietnam)

Economic evaluation of mineral deposit: A bottom-up approach

Proper economic evaluation of a mineral deposit is critical to effective investment decision-making in a mineral project. However, this often requires detailed mine production scheduling to produce a schedule that reflects the actual cash flow when the project comes on stream. Because of the dexterity required for this task many mine planners and explorationist attempt to use statistical formulas that approximate the mine scheduling and value of the project. The mathematical model developed for production scheduling often produces a constant production rate schedule over an approximate life span of the project. In this paper, we have attempted to apply a bottom-up approach that begins with geometrical modelling and equipment deployment pattern to define the number of equipment required for each sequence of operation based on available workfront in the development of each bench. Then, based on the number of equipment and the production rate at each sequence of operations, a production schedule is developed. This production schedule therefore will reflect annual cash flow since it is based on the sequence of operation. Application of this method on the west pit of Itakpe mine shows a considerable net present value and internal rate of return of the deposit compared with the evaluation made using statistical models. The NPV of the west pit was found to be USD621 million as against USD122.41 million using Nwosu’s formula and USD123.85 million Taylor’s formula. The above value of NPV using the proposed method shows the maximum expected NPV of the mineral project-based technical restrictions. An understanding of this value can guide the mineral property owner in decision-making.

Study of 113In(α, α) elastic scattering to determine α-optical potential relevant for astrophysical γ-process

Abstract The α-optical potential is one of the key input parameters used to measure the reaction rate of the (γ, α)-process using the Hauser-Feshbach statistical model and the principle of detailed balance. α-elastic scattering experiment on 113In p-nucleus was carried out in the energy range E lab = 26−32 MeV. The vacuum evaporation technique was used to prepare the 113In target (∼86 μg cm−2). An energy-dependent local optical potential parameters set was obtained by analysing the experimental elastic scattering angular distribution data. The local potential parameters are extrapolated for lower energies and are used to calculate the 113In(α, γ) reaction cross-section.

A century of statistical Ecology.

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

Psychometric validation of the Danish version of the Major Depression Inventory using data from the Lolland-Falster health study (LOFUS)

Purpose The Major Depression Inventory (MDI) is a widely used self-rating depression scale commonly in primary care in Denmark. It has not been subject to robust psychometric validation in a general population setting. The aim of this study was to evaluate the psychometric measurement properties of the MDI when applied in the general population. Methods We evaluated statistical psychometric validity using modern test theory (confirmatory factor analysis, item response theory models and Rasch measurement theory) testing local independence and differential item function across groups defined by gender, age, education, and chronic disease status. Separate analyses across different strata and across different statistical models were employed. Results Regarding structural validity we consistently identified local dependence for the item two pairs (MDI2,MDI3) and (MDI4,MDI5) across strata. This result was confirmed by bifactor CFA models and item screening. We further identified substantial differential item functioning with respect to age group and with respect to chronic disease. We identified quantified the magnitude of this lack of measurement invariance. Conclusion The MDI is psychometrically valid in homogenous sub populations, but the disclosed evidence of local dependence means that published estimates of its reliability cannot be trusted. The lack of measurement invariance means that the instrument cannot be used to compare individuals or groups unless they are similar in terms of age group and chronic disease status.

SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions

BackgroundDetecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge.ResultsHere, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs.ConclusionsHere, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP–SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer.Availability and implementation: https://github.com/scutdy/SSO/blob/master/SEEI.zip.

Statistical mechanics model for Clifford random tensor networks and monitored quantum circuits

Statistical mechanics model for Clifford random tensor networks and monitored quantum circuits

Classifying PDO Kalamata Olive Oil from Geographic Origins of the Messenia Region based on Statistical Machine Learning

Kalamata is a smart city located in southeastern Greece in the Mediterranean basin and it is the capital of the Messenia regional unit. It is known for the famous Protected Designation of Origin (PDO) Kalamata olive oil produced mainly from the Koroneiki olive variety. The PDO Kalamata olive oil, established by Council regulation (EC) No 510/2006, owes its quality and special characteristics to the geographical environment, olive tree variety, and human factor. The PDO Kalamata olive oil is produced exclusively in the regional unit of Messenia, being the main profit of local farmers. However, soil chemical composition, microclimates, and agronomic factors are changed within the Messenia spatial area leading to differentiation of PDO Kalamata olive oil characteristic. In this paper, we use statistical machine learning algorithms to determine the geographical origin of Kalamata olive oil at PDO level based on synchronous excitation−emission fluorescence spectroscopy of olive oils. Evaluations of the statistical models are promising for differentiating the origin of PDO Kalamata olive oil with high values of prediction accuracy thus enabling companies that process and bottle kalamata olive oil to choose olive oil from a specific region of Messenia that fulfills certain characteristics. Concretely, the current research effort focuses on a specific olive oil variety within a limited geographic region. Intuitively, future research should also focus on validation of the proposed methodology to other olive oil varieties and production areas.

Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting doselocations.

Admission Prediction Using Time Series Analysis

Efficient resource allocation and strategic planning in educational institutions heavily rely on accurate predictions of student admissions. This paper presents a detailed investigation into the application of time series analysis techniques for admission prediction. We explore the utilization of historical admission data, spanning multiple academic cycles, to train and evaluate several time series models.The study encompasses a comparative analysis of traditional statistical models such as autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) alongside more advanced deep learning techniques like long short-term memory (LSTM) networks. By evaluating the performance metrics, including accuracy, robustness, and computational efficiency, we aim to identify the most suitable model for admission prediction tasks. Moreover, the research delves into the impact of various external factors such as changes in the academic calendar, socio-economic indicators, and demographic shifts on admission patterns. Understanding these factors is crucial for enhancing the predictive capabilities of the models and enabling institutions to adapt their strategies accordingly. The experimental results and comparative analysis provide valuable insights for educational institutions, enabling them to make data-driven decisions regarding enrollment management strategies and resource allocation. Ultimately, this research contributes to the advancement of admission prediction methodologies, facilitating more efficient and informed decision-making processes in the educational domain.

Profiling of seized Cannabis sativa L. flowering tops by means of microwave-assisted hydro distillation and gas chromatography analyses

This research aimed to support police forces in their battle against illicit drug trafficking by means of a multi-technique approach, based on gas chromatography. In detail, this study was focused on the profiling of volatile substances in narcotic Cannabis sativa L. flowering tops. For this purpose, the Scientific Investigation Department, RIS Carabinieri of Messina, provided 25 seized samples of Cannabis sativa L. The content of Δ9-tetrahydrocannabinol (THC), useful to classify cannabis plant as hemp (≤ 0.2 %) or as marijuana (> 0.2 %), was investigated. Essential oils of illicit drug samples were extracted using a microwave-assisted hydro-distillation (MAHD) system; GC–MS and GC-FID analytical techniques were used for the characterization of the terpenes and terpenoids fingerprint. Furthermore, the enantiomeric and carbon isotopic ratios of selected chiral compounds were investigated using a heart-cutting multidimensional GC (MDGC) approach. The latter exploited a combination of an apolar column in the first dimension, and a chiral cyclodextrin-based column in the second one, prior to parallel isotope-ratio mass spectrometry (C-IRMS) and MS detection. Finally, all the data were gathered into a statistical model, to demonstrate the existence of useful parameters to be used for the classification of seized samples.

A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts.

The impact of land use and cover changes on river flows in Wundanyi Catchment of Taita Hills, Kenya (1970–2030)

<p>Taita hills are one of the most important biodiversity hotspots of Kenya but experiencing a high rate of deforestation due conversion of its original forestland to agriculture and settlement during the last century. These landscape dynamics coupled with rainfall fluctuations in these critical ecosystems may significantly affect water resource distribution and food security in Taita Taveta County and its environs. This study aimed to establish the trends of land use/cover change (LUCC) in Wundanyi catchment from 1970 to 2030 and predict their specific and combined effects on surface runoff and stream flow in the same period. The analysis was based on statistical trend analysis and dynamic landscape modeling using both historical and primary data from Wundanyi catchment, and Landsat TM and ETM+ imagery of Taita Hills for 1990, 2000, and 2010. Results show highly variable mean seasonal and annual values of discharge in Wundanyi catchment probably attributed to environmental changes affecting Taita Hills in general and Wundanyi catchment in particular. Compared to 1990, major land use/cover changes in 2010 were featured by the expansion of built-up area (250%), plantation forest (23.7%), broadleaved forest (17.4%), and thicket (15.9%). Was also notable the decrease in woodland (−30.3%), cropland (−21.6%), and shrubland (−0.8%). Dynamic spatial trends by the year 2030 will be evidenced by increased thicket by 0.41% per annum (<em>R</em><sup>2</sup> = 81.6%), and by decreased plantation forests (−0.13%; <em>R</em><sup>2</sup> = 91.3%), woodland (−0.10%; <em>R</em><sup>2</sup> = 77.6%), shrubland (−0.11%; <em>R</em><sup>2</sup> = 85.2%), broadleaved forests (−0.03%; <em>R</em><sup>2</sup> = 56.6%) and cropland (−0.09%; <em>R</em><sup>2</sup> of 84.4). These changes will shape the catchment landscape and influence its hydrology, unless the existing forest and agricultural policy interventions are enforced. Hence, crop diversification, agroforestry, and soil and water conservation structures are recommended to maintain effective control of LUCC on hydrological processes going on in the Wundanyi catchment.<strong></strong></p>

Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP, RMSEP, and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives.

Comparison of Amount of Space Closure During En- Masse Retraction of Maxillary Anterior Teeth Using Three Accelerated Orthodontic Treatment Techniques- Micro Osteo-Perforation, Photo Bio-Modulation and Peizocision : A Retrospective Study

AIM: The study aimed at comparing the amount of space closure using peizocision (PZ) and micro- osteoperforation (MOP) and low-level laser therapy (LLLT). Materials and methods: A retrospective study consisted of 3 groups, sample number of minimum 8 patients per group were estimated using statistical model called an Analysis of Variance, or ANOVA model. Three sets of data were collected. Which included patients who were treated with upper first premolar extraction for en- masse retraction of maxillary anterior teeth using NiTi closed coil spring with regional acceleratory procedures. The data collected was divided into 3 groups, GROUP 1- micro osteo perforations (n=8), GROUP 2- photo bio modulation (n=8), GROUP 3- peizocision (n=8).sing digital Vernier calipers, a direct approach was used to measure all the stone casts obtained before canine retraction (T0) and after the retraction was completed after three months (T1). The statistical analysis was performed on the data acquired about the amount of space closure of en-masse retraction of maxillary anteriors in the PZ, LLLT, and MOP groups. Results: Compared to MOP (2.60mm) and LLLT (2.44mm), the PZ group had a greater mean amount of space closure (5.06mm). In conclusion, the quantity of space closure in piezocision was significantly higher than that in MOP and LLLT, although there was no statistically significant difference between the two. It is imperative that treatment duration be shortened. Finding the most effective and practical method to speed up tooth movement using the biomechanical mechanisms that are currently in place is therefore necessary

Nutrient contents and microbial communities as mediators of the effects of land-use in ecosystem functioning in alpine ecosystems from Central Mexico

Abstract Purpose The interplay between microbial taxonomy, functional diversity, and land-use changes across diverse ecosystems must be better understood. This study explores, through a statistical modeling approach, the intricate relationships between microbial communities, soil properties (nutrient contents and physicochemical parameters), and land-use changes, using stable isotopes ratio (δ13C/δ15N) as ecosystem function indicator. Methods Using Structural Equation Modeling (SEM) this study aims to investigate how soil microbial communities, soil properties and ecosystem function are interconnected in three ecosystems undergoing land-use change in Nevado de Toluca, Mexico. Results Multivariate analyses of microbial communities and soil parameters for the different land-uses reveal significant variation in both microbial composition and soil properties (i.e. nutrient contents) mainly explained by the land-use history of the studied ecosystems. Through the SEM approach, it was possible to disentangle the relative contribution of land-use change, microbial community composition and nutrient contents to the ecosystem function defined as the δ13C/δ15N ratio. Conclusions Microbial communities are the main drivers of soil ecosystem functioning. However, through SEM approach it is possible to formally test direct and indirect relationships impacting microbial communities and the resulting functional consequences, contributing to a mechanistic understanding of the ecological implications of land-use change, aiding in sustainable land-use decision-making.