Model Algorithm Research Articles

Principle, structure and application progress of the forest landscape model FireBGCv2.

Forest landscape model can quantitatively simulate the spatiotemporal variations in forest structure and function at the landscape scale based on traditional field survey data and mathematical models, providing a reference for the formulation of scientific forest management strategies. FireBGCv2 is one of the representative models currently used in the research area of forest landscape changes. It can simulate ecological processes at various scales, including trees scale (tree growth, establishment, and mortality), stand scale (carbon and nitrogen pools, fuel treatment, decomposition), site scale (resource competition and species phenology), and landscape scale (seed dispersal and wildfire disturbances), and the effects of those processes on forest landscape structure and function. The advantage of this model lies in its ability to simulate multiple ecological processes while considering the diversity and complexity of ecosystems. However, it also has drawbacks, such as high computational demands and complexity of use. We summarized the basic principles and structure of FireBGCv2 and introduced its application progress in forest landscape research and management. Currently, the application of the FireBGCv2 model, both domestically and internationally, mainly focused on exploring the interactions between fire, climate, and vegetation, quantifying the spatial and temporal dynamics of fires, and describing potential fire dynamics under future climate scenarios and land management strategies. With the in-depth development of forest landscape model theories and applications, the future prospects of FireBGCv2 include improving and updating the model's algorithms, adding new functional modules to explore fire management issues, and meeting the needs of different users.

Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data

BackgroundThe objective was to develop and assess performance of an algorithm predicting suicide-related ICD codes within three months of psychiatric discharge. MethodsThis prognostic study used a retrospective cohort of EHR data from 2789 youth (12 to 20 years old) hospitalized in a safety net institution in the Northeastern United States. The dataset combined structured data with unstructured data obtained through natural language processing of clinical notes. Machine learning approaches compared gradient boosting to random forest analyses. ResultsArea under the ROC and precision-recall curve were 0.88 and 0.17, respectively, for the final Gradient Boosting model. The cutoff point of the model-generated predicted probabilities of suicide that optimally classified the individual as high risk or not was 0.009. When applying the chosen cutoff (0.009) to the hold-out testing set, the model correctly identified 8 positive cases out of 10, and 418 negative cases out 548. The corresponding performance metrics showed 80 % sensitivity, 76 % specificity, 6 % PPV, 99 % NPV, F-1 score of 0.11, and an accuracy of 76 %. LimitationsThe data in this study comes from a single health system, possibly introducing bias in the model's algorithm. Thus, the model may have underestimated the incidence of suicidal behavior in the study population. Further research should include multiple system EHRs. ConclusionsThese performance metrics suggest a benefit to including both unstructured and structured data in design of predictive algorithms for suicidal behavior, which can be integrated into psychiatric services to help assess risk.

A novel machine learning framework for designing high-performance catalysts for production of clean liquid fuels through Fischer-Tropsch synthesis

Fischer-Tropsch synthesis (FTS) attracts great interest as a sustainable route for production of sustainable transportation fuels. Catalyst design and operational conditions tune the selectivity of FTS to liquid fuels, which can be predicted with machine learning models (ML). Herein, a comprehensive dataset including 27 key input features related to the catalyst structure (with the focus on carbon supports), preparation, activation, and FTS operating conditions were investigated to predict the CO conversion and C5+ selectivity using three ML algorithms. Feature engineering and selection were implemented to identify the significant catalyst formulation descriptors. Principal component analysis was used to explore the information space and decrease the dimension of the dataset before ML prediction. In addition to the well-known effects of the operational conditions on FTS, roles of physico-chemical properties of the carbon materials were also extensively analyzed. Random forest (RF) including 4 principal components, indicated the highest accuracy for prediction of the CO conversion and C5+ selectivity with R2 of 0.91 and 0.97, respectively. The proposed framework can provide a reliable strategy in designing efficient carbon supported catalysts for FTS and guide experiments by identifying the key descriptors in the catalyst formulation and operating conditions to enhance the selectivity of liquid fuels.

Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP

Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups. Explainability is provided using Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques. A total of 1094 NPC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. We combined five different ML algorithms to form a uniquely stacked algorithm. The predictive performance of the stacked algorithm was compared with a state-of-the-art algorithm—extreme gradient boosting (XGBoost) to stratify the NPC patients into chance of survival groups. We validated our model with temporal validation (n = 547) and geographic external validation (Helsinki University Hospital NPC cohort, n = 60). The developed stacked predictive ML model showed an accuracy of 85.9% while the XGBoost had 84.5% after the training and testing phases. This demonstrated that both XGBoost and the stacked model showed comparable performance. External geographic validation of XGBoost model showed a c-index of 0.74, accuracy of 76.7%, and area under curve of 0.76. The SHAP technique revealed that age of the patient at diagnosis, T-stage, ethnicity, M-stage, marital status, and grade were among the prominent input variables in decreasing order of significance for the overall survival of NPC patients. LIME showed the degree of reliability of the prediction made by the model. In addition, both techniques showed how each feature contributed to the prediction made by the model. LIME and SHAP techniques provided personalized protective and risk factors for each NPC patient and unraveled some novel non-linear relationships between input features and survival chance. The examined ML approach showed the ability to predict the chance of overall survival of NPC patients. This is important for effective treatment planning care and informed clinical decisions. To enhance outcome results, including survival in NPC, ML may aid in planning individualized therapy for this patient population.

Artificial intelligence (AI) –based machine learning models (ML) for predicting pathological complete response (pCR) in patients with hormone receptor (HoR) –positive/HER2-negative early breast cancer (EBC) undergoing neoadjuvant chemotherapy (NCT): A retrospective cohort study.

597 Background: The rates of pCR after NCT in HoR positive/HER2 negative EBC are low. Thus, clinicians need validated, unbiased, and reliable tools to better predict pCR at individual patients’ level. In this regard, AI can generate ML algorithms with these features, using clinical and pathological characteristics. Methods: Medical records were retrospectively retrieved for patients with EBC receiving NCT followed by surgery. Missing data were imputed using chained random forests. pCR was defined as absence of residual invasive cancer on pathologic evaluation of the breast specimen and lymph nodes (ypT0/isN0). Differences between pCR and non-pCR patients were assessed using t test or chi-squared test. Eight ML models (c5.0, k-nearest neighbour, random forest [RF], neural network, support vector machine [linear/radial], boosted trees and boosted logistic regression) were trained and tuned. A stratified ten-fold cross-validation was used to prevent overfitting. Models performance was evaluated using the area under the receiver operating characteristics curve (AUC). Features importance was assessed. Disease-free survival (DFS) was evaluated using the Kaplan-Maier method and hazard ratios (HRs) using Cox regression. Results: 572 patients were included: 332 (58%) were pre-menopausal and median age was 49.0 (IQR 43-57); 330 (58%) were T2 and 273 (48%) were N1; 437 (76%) had a ductal adenocarcinoma and 308 (54%) were grade 3. Median estrogen and progesterone receptor (ER/PgR) expression was 90 (IQR 80-90) and 57.5 (IQR 5-90); median Ki67 was 35 (IQR 25-55) and 230 (40%) patients were HER2 zero. 565 (99%) patients received a combination of anthracyclines and taxanes; 434 (76%) received sequential and 137 (24%) concomitant CT. pCR was achieved in 87 patients (15%, 95% CI 12-18). Ten variables were included in the model: menopausal status, age, histology, grade, clinical T and N stage, ER/PgR status, Ki67 and HER2 status (zero/low). Among the evaluated models, the RF algorithm had the best performance: the AUC was 0.77 (95% CI 0.71-0.83) and sensitivity and specificity were 0.86 (95% CI 0.82-0.88) and 0.56 (95% CI 0.46-0.66). Variables with the highest importance were Ki67, ER/PgR status, age and nodal status. 511 patients were evaluable for survival: patients with pCR had a significant longer DFS compared to those who did not achieve pCR (HR 0.30; 95% CI 0.14-0.65, p = 0.002). Patients whose pCR was predicted by the model had longer DFS compared to those who pCR was not predicted (HR 0.56, 95% CI 0.21-0.87, p = 0.01). Conclusions: The RF-ML algorithm combining clinical and pathological characteristics has the potential to predict pCR in HoR positive/HER2 negative patients undergoing NCT, thus supporting clinicians to individualize treatment for EBC.

Adapting the two-node model to evaluate sleeping thermal environments

While it is evident that thermal environment plays a significant role in human sleep quality, currently there remains a paucity of literature on the assessment of sleeping thermal environment. In this study, the two-node model has been adapted to evaluate sleeping thermal environments through two phases of work - modifications to the model's inputs and coefficients, and the conversion to a transient model. A calculation method for the model's key input intrinsic bedding insulation, Icl,bed, was proposed. The default heat transfer coefficients in the two-node model were replaced with the values corresponding to a supine human body. The two-node model's algorithm was adapted from its default, steady-state solution to a transient, time-series approach, to accommodate transients in both thermal environmental and physiological parameters through the course of the night. The modified two-node model was used to identify the optimum summer SET*sleep in a Sydney sleep survey database. Nocturnal mean skin temperatures predicted by the modified two-node model agree with the published physiological data in the literature. The upper limit of the Sydney summer bedroom SET*sleep for sleeping persons was 31 °C. In addition, the upper limit of summer operative temperatures was also determined for typical bedding systems. For instance, for the combination of short-sleeved sleepwear and Quilt1 (Warmth level 1), the upper limit of bedroom temperature was recommended to be 26.5 °C. Finally, the bedroom temperature control strategy for optimal sleep thermal comfort was predicted.

Noise content assessment in GNSS coordinate time-series with autoregressive and heteroscedastic random errors

SUMMARYProper representation of the stochastic process of the Global Navigation Satellite System (GNSS) coordinate time-series lays a foundation for realistic velocities estimates and their uncertainties, as well as identifications of functional effects, generally called periodic/offset signals. Various noise models (e.g. autoregressive process) have been used to describe the stochastic process of GNSS data. A realistic stochastic model prefers proper consideration of individual error characteristics of observations. The usual autoregressive (AR) noise model assumes its white-noise components sharing the same constant variances, which might degrade the modelling accuracy of stochastic process. Through using a generalized autoregressive conditional heteroscedasticity (GARCH) process to quantify variance variation of the white-noise components, our current investigation has constituted an AR-GARCH noise model to realize the stochastic model and subsequent noise content assessment. This noise framework and its adjustment algorithm are further considered in the identifications of functional effects. For evaluating the performance of our current algorithm, 500 emulated real time-series were used, they consist of a linear trend, seasonal periodic signals, offsets, gaps (up to 10 per cent) and an AR-GARCH noise process. The algorithm's identification power for functional effects was investigated within a case study in the absence of periodic/offset signals. Furthermore, the algorithm's results were compared with the current state-of-the-art noise models (e.g. white plus flicker noise) using 15 real GNSS coordinate time-series. The results demonstrated that the presented stochastic model has been identified as a preferred noise model. Its model's algorithm can offer reliable noise content although sporadic unidentified periodic/offset signals are still masked in time-series. Moreover, the error volatility of white-noise components was identified via an autoregressive conditional heteroscedasticity Lagrange multiplier (ARCH LM) test and confirmed in the time-series.

A machine-learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction.

Myriophyllum spicatum, more commonly known as Eurasian watermilfoil (EWM), is one of the most invasive aquatic plants in North America, causing negative ecological and economic impacts in ecosystems where it proliferates. Many control strategies have been developed and implemented to mitigate EWM growth and spread, although the results are mixed and there is no consensus on lake‐specific strategies. Here, we describe the development of a predictive model using a support vector technique, that predicts the success of biological pest control using Euhrychiopsis lecontei (the milfoil weevil), a milfoil specialist, to reduce EWM in lakes. Such a model is informed by lake characteristics (limnological and landscape) and augmentation strategies. To develop our predictive model, we performed a metadata analysis from 133 published peer‐reviewed literature and professional reports of milfoil weevil augmentation field experiments that contained information on lake characteristics. The predictive model's algorithm uses a support vector machine (SMV) to learn patterns among lake characteristics, along with the recorded augmentation strategy and the reported success of each study, where success is a measure of EWM change over a season and is recorded in a variety of ways (e.g., EWM biomass change, EWM percent change, EWM visual change, etc.,). Overall, the model results suggests that shallower lakes, more frequent weevil augmentations, and larger weevil overwintering habitat are the most important predictors for EWM reduction success by weevil augmentation. Although watermilfoil weevil augmentation is a promising mitigation strategy, it may not work for all lakes. However, in terms of suggesting weevil augmentation, our model is a valuable tool for lake stakeholders and resource managers, who can use it to determine whether milfoil weevil augmentation, which can be very costly due to the difficulties in finding and raising milfoil weevils, will be a useful and sustainable approach to control EWM in their lake community.

Blood sample profile helps to injury forecasting in elite soccer players

PurposeBy analyzing external workloads with machine learning models (ML), it is now possible to predict injuries, but with a moderate accuracy. The increment of the prediction ability is nowadays mandatory to reduce the high number of false positives. The aim of this study was to investigate if players’ blood sample profiles could increase the predictive ability of the models trained only on external training workloads.MethodEighteen elite soccer players competing in Italian league (Serie B) during the seasons 2017/2018 and 2018/2019 took part in this study. Players’ blood samples parameters (i.e., Hematocrit, Hemoglobin, number of red blood cells, ferritin, and sideremia) were recorded through the two soccer seasons to group them into two main groups using a non-supervised ML algorithm (k-means). Additionally to external workloads data recorded every training or match day using a GPS device (K-GPS 10 Hz, K-Sport International, Italy), this grouping was used as a predictor for injury risk. The goodness of ML models trained were tested to assess the influence of blood sample profile to injury prediction.ResultsHematocrit, Hemoglobin, number of red blood cells, testosterone, and ferritin were the most important features that allowed to profile players and to analyze the response to external workloads for each type of player profile. Players’ blood samples’ characteristics permitted to personalize the decision-making rules of the ML models based on external workloads reaching an accuracy of 63%. This approach increased the injury prediction ability of about 15% compared to models that take into consideration only training workloads’ features. The influence of each external workload varied in accordance with the players’ blood sample characteristics and the physiological demands of a specific period of the season.ConclusionField experts should hence not only monitor the external workloads to assess the status of the players, but additional information derived from individuals’ characteristics permits to have a more complete overview of the players well-being. In this way, coaches could better personalize the training program maximizing the training effect and minimizing the injury risk.

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia

The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients’ need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.

A comparison between constitutive models for the municipal solid waste.

This paper compares the behavioural models of municipal solid waste (MSW) using the corresponding experimental data. To do so, the proposed models are first reviewed and, then, the algorithms and codes of different models are written. After obtaining each model's algorithm, the same experimental data are considered as input, and the strain-stress curve is plotted for each model. In the first method, the total strain in the waste is obtained based on the summation of the elastic, plastic, biological, and creep strains. Afterward, the equivalent stress is obtained. In this method, using biological changes over time, the age of the waste is calculated as an effective parameter in MSW behaviour. Moreover, the effect of creep on the waste is considered independently. In the second algorithm, MSW is considered as fibre and paste material, and the strain-stress curve is obtained. In this method, the waste is considered as a soil model, and the effect of different parameters are calculated. Due to the complexity of the MSW behaviour and considering various parameters, such as the age of the waste, E changes over time, creep, and biological changes, the Krase model has less error than the other models. Using the soil behaviour model for the waste has a significant error, indicating the difference between the results for the behaviours of the two substances.

Target Recognition of Industrial Robots Using Machine Vision in 5G Environment.

The purpose is to solve the problems of large positioning errors, low recognition speed, and low object recognition accuracy in industrial robot detection in a 5G environment. The convolutional neural network (CNN) model in the deep learning (DL) algorithm is adopted for image convolution, pooling, and target classification, optimizing the industrial robot visual recognition system in the improved method. With the bottled objects as the targets, the improved Fast-RCNN target detection model's algorithm is verified; with the small-size bottled objects in a complex environment as the targets, the improved VGG-16 classification network on the Hyper-Column scheme is verified. Finally, the algorithm constructed by the simulation analysis is compared with other advanced CNN algorithms. The results show that both the Fast RCN algorithm and the improved VGG-16 classification network based on the Hyper-Column scheme can position and recognize the targets with a recognition accuracy rate of 82.34%, significantly better than other advanced neural network algorithms. Therefore, the improved VGG-16 classification network based on the Hyper-Column scheme has good accuracy and effectiveness for target recognition and positioning, providing an experimental reference for industrial robots' application and development.

Machine learning to predict venous thrombosis in acutely ill medical patients

BackgroundThe identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer‐based scoring systems. These scores demonstrated modest performance in external data sets. ObjectivesTo evaluate the performance of machine learning models compared to the IMPROVE score. MethodsThe APEX trial randomized 7513 acutely medically ill patients to extended duration betrixaban vs. enoxaparin. Including 68 variables, a super learner model (ML) was built to predict VTE by combining estimates from 5 families of candidate models. A “reduced” model (rML) was also developed using 16 variables that were thought, a priori, to be associated with VTE. The IMPROVE score was calculated for each patient. Model performance was assessed by discrimination and calibration to predict a composite VTE end point. The frequency of predicted risks of VTE were plotted and divided into tertiles. VTE risks were compared across tertiles. ResultsThe ML and rML algorithms outperformed the IMPROVE score in predicting VTE (c‐statistic: 0.69, 0.68 and 0.59, respectively). The Hosmer‐Lemeshow goodness‐of‐fit P‐value was 0.06 for ML, 0.44 for rML, and <0.001 for the IMPROVE score. The observed event rate in the lowest tertile was 2.5%, 4.8% in tertile 2, and 11.4% in the highest tertile. Patients in the highest tertile of VTE risk had a 5‐fold increase in odds of VTE compared to the lowest tertile. ConclusionThe super learner algorithms improved discrimination and calibration compared to the IMPROVE score for predicting VTE in acute medically ill patients.

Retraction Notice: Coupled Model and Algorithm of Spatial Partition Based on Multiple- Factor

In order to calculate the spacing and number of grid cell and get rid of data redundancy caused by subjective experience on the process of spatial partitioning, multi - factor coupled model of spatial partition was established by four constraints—Laves partition identity triangulated regular network, terrain features, data density and adaptive boundary. The model can establish adaptive boundary conditions based on discrete center and the furthest point of samples, and in order to reduce complexity and systematic errors of direct solution of terrain surface equations, the constraints of terrain features and data density were expressed by the terrain surface differential equations. The model's algorithm, based on re- cursive algorithm and automatic generation technology for discrete grids, can generate the 6 resolutions of grids and cal- culate the spacing and number of each resolution cell. Finally, when the sets of data from 110 to 440, by visualization and comparative analysis of 3 kinds of spatial partition methods, the results show that the cost of coupled model's algorithm is 1/10~1/2 of the Delaunay and Square, and also verify that the algorithm is linear convergence and can effectively solve the problem of illegal border and polygons from non - convex.

An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes.

BackgroundA comprehensive technique for earthquake-related casualty estimation remains an unmet challenge. This study aims to integrate risk factors related to characteristics of the exposed population and to the built environment in order to improve communities’ preparedness and response capabilities and to mitigate future consequences.MethodsAn innovative model was formulated based on a widely used loss estimation model (HAZUS) by integrating four human-related risk factors (age, gender, physical disability and socioeconomic status) that were identified through a systematic review and meta-analysis of epidemiological data. The common effect measures of these factors were calculated and entered to the existing model’s algorithm using logistic regression equations. Sensitivity analysis was performed by conducting a casualty estimation simulation in a high-vulnerability risk area in Israel.Resultsthe integrated model outcomes indicated an increase in the total number of casualties compared with the prediction of the traditional model; with regard to specific injury levels an increase was demonstrated in the number of expected fatalities and in the severely and moderately injured, and a decrease was noted in the lightly injured. Urban areas with higher populations at risk rates were found more vulnerable in this regard.ConclusionThe proposed model offers a novel approach that allows quantification of the combined impact of human-related and structural factors on the results of earthquake casualty modelling. Investing efforts in reducing human vulnerability and increasing resilience prior to an occurrence of an earthquake could lead to a possible decrease in the expected number of casualties.

Coupled Model and Algorithm of Spatial Partition Based on Multiple- Factor

In order to calculate the spacing and number of grid cell and get rid of data redundancy caused by subjective experience on the process of spatial partitioning, multi - factor coupled model of spatial partition was established by four constraints—Laves partition identity triangulated regular network, terrain features, data density and adaptive boundary. The model can establish adaptive boundary conditions based on discrete center and the furthest point of samples, and in order to reduce complexity and systematic errors of direct solution of terrain surface equations, the constraints of terrain features and data density were expressed by the terrain surface differential equations. The model's algorithm, based on re- cursive algorithm and automatic generation technology for discrete grids, can generate the 6 resolutions of grids and cal- culate the spacing and number of each resolution cell. Finally, when the sets of data from 110 to 440, by visualization and comparative analysis of 3 kinds of spatial partition methods, the results show that the cost of coupled model's algorithm is 1/10~1/2 of the Delaunay and Square, and also verify that the algorithm is linear convergence and can effectively solve the problem of illegal border and polygons from non - convex.

Modeling complexity in pathologist workload measurement: the Automatable Activity-Based Approach to Complexity Unit Scoring (AABACUS)

Pathologists provide diagnoses relevant to the disease state of the patient and identify specific tissue characteristics relevant to response to therapy and prognosis. As personalized medicine evolves, there is a trend for increased demand of tissue-derived parameters. Pathologists perform increasingly complex analyses on the same ‘cases'. Traditional methods of workload assessment and reimbursement, based on number of cases sometimes with a modifier (eg, the relative value unit (RVU) system used in the United States), often grossly underestimate the amount of work needed for complex cases and may overvalue simple, small biopsy cases. We describe a new approach to pathologist workload measurement that aligns with this new practice paradigm. Our multisite institution with geographically diverse partner institutions has developed the Automatable Activity-Based Approach to Complexity Unit Scoring (AABACUS) model that captures pathologists' clinical activities from parameters documented in departmental laboratory information systems (LISs). The model's algorithm includes: ‘capture', ‘export', ‘identify', ‘count', ‘score', ‘attribute', ‘filter', and ‘assess filtered results'. Captured data include specimen acquisition, handling, analysis, and reporting activities. Activities were counted and complexity units (CUs) generated using a complexity factor for each activity. CUs were compared between institutions, practice groups, and practice types and evaluated over a 5-year period (2008–2012). The annual load of a clinical service pathologist, irrespective of subspecialty, was ∼40 000 CUs using relative benchmarking. The model detected changing practice patterns and was appropriate for monitoring clinical workload for anatomical pathology, neuropathology, and hematopathology in academic and community settings, and encompassing subspecialty and generalist practices. AABACUS is objective, can be integrated with an LIS and automated, is reproducible, backwards compatible, and future adaptable. It can be applied as a robust decision support tool for the assessment of overall and targeted staffing needs as well as utilization analyses for resource allocation.

Order Batching Optimization in Dual Zone Type Warehouse Based on Genetic Algorithms

With the rapid development of e-commerce and the global economy, order picking mode of multiple batches and small quantities becoming more and more, which makes artificial picking system occupy a larger proportion in a variety of ways. The optimization study of the artificial person picking system has a crucial role to enhance the efficiency of batch picking, then increasing customer satisfaction. In this paper, according to the characteristics of the problem, Genetic algorithm was selected as the model's algorithm. The detailed design of coding, crossover method, genetic algorithm in solving process variation method, is used to realize the optimization of the model.

An Extension of Clarke's Model With Stochastic Amplitude Flip Processes

Stochastic modeling is an essential tool for studying statistical properties of wireless channels. In multipath fading channel (MFC) models, the signal reception is modeled by a sum of wave path contributions, and Clarke's model is an important example of such which has been widely accepted in many wireless applications. However, since Clarke's model is temporally deterministic, Feng and Field noted that it does not model real wireless channels with time-varying randomness well. Here, we extend Clarke's model to a novel time-varying stochastic MFC model with scatterers randomly flipping on and off. Statistical properties of the MFC model are analyzed and shown to fit well with real signal measurements, and a limit Gaussian process is derived from the model when the number of active wave paths tends to infinity. A second focus of this work is a comparison study of the error and computational cost of generating signal realizations from the MFC model and from its limit Gaussian process. By rigorous analysis and numerical studies, we show that in many settings, signal realizations are generated more efficiently by Gaussian process algorithms than by the MFC model's algorithm. Numerical examples that strengthen these observations are also presented.

The Effects of Spatial Patterns on the Accuracy of Forest Vegetation Simulator (FVS) Estimates of Forest Canopy Cover

Abstract The Forest Vegetation Simulator (FVS) estimates percent canopy cover without spatially explicit information. Estimates of canopy cover in FVS can be corrected for crown overlap, based on the assumption that trees in a stand are randomly distributed. This research assessed the accuracy of FVS estimates of canopy cover in stands with nonrandom spatial patterns. A method for measuring canopy cover within a geographic information system was developed to compare with FVS estimates of cover for 19, stem-mapped plots across Idaho and Montana. The Ripley's K(d) statistic was used to describe natural and simulated spatial patterns, so that the accuracy of canopy cover estimated by FVS could be considered for groups of plots classified as regular, clustered, or random. Results from the analyses of the effects of spatial patterns indicated that the FVS may underestimate canopy cover by 11% for plots with highly regular spatial patterns and overestimate by 2% for plots with clustered patterns. Although the magnitude of this bias likely is insufficient to justify changing the model's algorithm for canopy cover, users of this model should be aware of the potential bias that can occur as a result of assuming that trees in a stand are randomly distributed. Information on the general spatial pattern of the stand (i.e., clumped, random, and even) could be used by managers to anticipate the expected degree and direction of the bias.