Effective Decision Support Tool Research Articles

Integrated image and location analysis for wound classification: a deep learning approach

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79–100% for Region of Interest (ROI) without location classifications, 73.98–100% for ROI with location classifications, and 78.10–100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

Key Performance Indicators for Decision Support in Building Retrofit Planning: An Italian Case Study

To achieve climate and energy goals in the building sector, the current pace of renovating existing structures must double, overcoming prevailing barriers. Key Performance Indicators play a pivotal role in science-based decision making, emphasizing both direct and indirect benefits of building retrofits. The authors aim to contribute to proper metric identification for multi-perspective building performance assessment and formulate a methodology supporting energy planning decisions. They introduce the Global Cost per Emission Savings (GCES), an aggregated indicator encompassing both public (CO2 emissions) and private (global cost) perspectives of diverse retrofit technologies for building HVAC systems. Applied to the Italian residential building stock via the Reference Building approach, the methodology is tested using condensing gas boilers, biomass boilers, and electric heat pumps, revealing diverse environmental and economic performances. Addressing the establishment of effective decision-support tools for policymakers, the paper explores the potential impact of various policies on the favorability of technologies. Different policy scenarios are delineated to analyze how distinct approaches may influence the attractiveness of technologies. Notably, in the baseline scenario, biomass boilers hold an advantage over heat pumps according to the GCES index. However, scenarios involving technology-specific incentives or a greenhouse gases emission tax failed to alter the technological ranking, leaving heat pumps financially uncompetitive. In contrast, the TXPM scenario positions heat pumps as the most financially appealing option, penalizing biomass boilers for high particulate matter emissions.

A Comparative Analysis of Chatgpt Vs Expert in Managing Anticancer Drug in Patients Renal Insufficiency

Introduction The appropriate and safe use of anticancer drugs in patients with renal insufficiency (RI) remains a significant challenge in oncology. Recent advancements in natural language processing and machine learning have introduced novel possibilities for clinical decision support. ChatGPT, a state-of-the-art language model, presents a potential solution to support healthcare professionals (HCPs) in making informed treatment management for patients with RI. But a question remains, how ChatGPT would perform is such situations? The aim of this analysis was to compare the recommendations from (human) experts vs ChatGPT regarding the management of anticancer drug in RI patients and more specifically about the potential dose adjustment. Methods In this work, we aimed to compare the performance of ChatGPT 3.5 with experts that are making recommendations on SiteGPR.com. SiteGPR is providing recommendations on how to manage drugs in RI patients and is led by “Service ICAR” from the Nephrology department of the French Pitié-Salpêtrière Hospital (Paris, France). The prompt, “Is there a need to adapt the dose of [name of the drug] in patients with renal impairment and if yes for which level of renal function? was run in a separate ”new chat“ for 175 anticancer drugs in March 2023. Answers were collected and compared with the recommendations from SiteGPR. Both answers from ChatGPT and recommendations from SiteGPR were categorised as ”Yes“ when dose adjustment was required, ”No“ when adjustment was not necessary, and ”ND“ when no data were available. For both ”Yes“ and ”ND“, the level of renal function for which there is a need to change the dose (”Yes“) and for which there are no data below (”ND“) was also collected Results Among 175 anticancer drugs, ChatGPT and SiteGPR had the same general conclusion for 37.1% (65/175) of these drugs (Table, Bold) when the level of RI was not analysed. When ChatGPT had a recommendation to adapt (Yes) or not to adapt (No) the dose of an anticancer drug (n=77 Yes or No drugs), SiteGPR did not agree for 57.1% (44/77) of the drugs. Reversely, when SiteGPR had a strong recommendation to adapt (Yes) or not to adapt (No) the dose of an anticancer drug (n=118 Yes or No drugs), ChatGPT was not aligned in 72.0% (85/118) of the cases. From a clinical perspective, if a HCP had followed ChatGPT instead SiteGPR, patients would have been exposed to a misused for 48.6% (85/175) of the drugs. Both ChatGPR and SiteGPR were not aware of few drugs, however, there is always a way to contact “Service ICAR” if a drug was not investigated on the website. Regarding the level of renal function for which there was a need to adapt the dose (Yes), ChatGPT was able to give a cut-off for 9.6% (7/73) of the anticancer drugs, vs 100.0% (42/42) for SiteGPR. Finally, from a quality perspective, it is important to mention that ChatGPT answers were not always clear. Regarding the level of renal function for which there was no data (ND), ChatGPT was able to give a cut-off for 12.0% (11/92) of the anticancer drugs, vs 100.0% (44/44) for SiteGPR. Finally, from a quality perspective, it is important to mention that ChatGPT answers were not always clear. At the end, when comparing the general recommendations and when considering the level of renal function and after excluding all N/A drugs, ChatGPT and SiteGPR had the same conclusion for only 5.6% (9/161) of anticancer drugs. Conclusion Our findings suggest that ChatGPT does not have (yet) the potential to be an effective clinical decision support tool for optimizing anticancer drug management in RI patients. Further investigations will compare the recommended dose and monitoring between ChatGPT 4.0 and SiteGPR and will also include other AI models. Combining the power of artificial intelligence with human expertise can lead to more personalized and evidence-based treatment decisions in cancer patients. However, it is crucial to acknowledge that while ChatGPT shows some promise, human clinicians remain indispensable for addressing intricate and unique patient situations.

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

PurposeDistribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.Design/methodology/approachThe problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.FindingsFor the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.Originality/valueThe proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations

PurposeAn accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.Design/methodology/approachDuring the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.FindingsThe proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.Research limitations/implicationsThe model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.Practical implicationsThe model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.Originality/valueThe model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

Utilizing a combined Delphi-FAHP-TOPSIS technique to assess the effectiveness of the water supply service in Algeria

The sustainable management of drinking water supply services (WSS) is essential to ensure continuous supply to all citizens. In this paper, a combined approach between the Delphi method and two Multi-Criteria Decision Making (MCDM) methods has been developed to assess the performance of WSS. The suggested methodology is composed of three phases divided into twenty-one steps. The first phase is the application of the Delphi method to select the evaluation criteria and the alternatives based on the responses of a group of experts. A total of 12 criteria and 6 alternatives are identified from past studies and expert opinions. The second phase is the use of the Fuzzy Analytic Hierarchy Process (FAHP) method to calculate the relative weights of the criteria. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank the alternatives according to their performance. Finally, an application of the developed framework was established for a case study of Souk-Ahras city (Algeria). The results from FAHP indicate that the financial autonomy and the technical performance criteria are of paramount importance WSS performance evaluation with weights (19.5%) and (19%) respectively. The two alternatives (reduced water loss and water price reform) are the best solutions to have a sustainable WSS. This study presents an effective decision support tool allowing decision makers and WSS managers to better assess performance criteria and the best alternatives.

Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting

Prediction of electricity price is crucial for national electricity markets supporting sale prices, bidding strategies, electricity dispatch, control and market volatility management. High volatility, non-stationarity and multi-seasonality of electricity prices make it significantly challenging to estimate its future trend, especially over near real-time forecast horizons. An error compensation strategy that integrates Long Short-Term Memory (LSTM) network, Convolution Neural Network (CNN) and the Variational Mode Decomposition (VMD) algorithm is proposed to predict the half-hourly step electricity prices. A prediction model incorporating VMD and CLSTM is first used to obtain an initial prediction. To improve its predictive accuracy, a novel error compensation framework, which is built using the VMD and a Random Forest Regression (RF) algorithm, is also used. The proposed VMD-CLSTM-VMD-ERCRF model is evaluated using electricity prices from Queensland, Australia. The results reveal highly accurate predictive performance for all datasets considered, including the winter, autumn, spring, summer, and yearly predictions. As compared with a predictive model without error compensation (i.e., the VMD-CLSTM model), the proposed VMD-CLSTM-VMD-ERCRF model outperforms the benchmark models. For winter, autumn, spring, summer, and yearly predictions, the average Legates and McCabe Index is seen to increase by 15.97%, 16.31%, 20.23%, 10.24%, and 14.03%, respectively, relative to the benchmark models. According to the tests performed on independent datasets, the proposed VMD-CLSTM-VMD-ERCRF model can be a practical stratagem useful for short-term, half-hourly electricity price forecasting. Therefore the research outcomes demonstrate that the proposed error compensation framework is an effective decision-support tool for improving the predictive accuracy of electricity price. It could be of practical value to energy companies, energy policymakers and national electricity market operators to develop their insight analysis, electricity distribution and market optimization strategies.

Ensemble extended belief rule-based systems with different similarity measures for classification problems

The extended belief rule-based (EBRB) system is shown to have the potential to handle both quantitative and qualitative information under uncertainty, and it has been used as an effective tool for decision support and classification problems. However, despite these advances, several drawbacks have emerged recently, and the most significant one is caused by its similarity measure using Euclidean distance, which could lead to counterintuitive individual matching degrees, while other widely used similarity measures have not been studied for their application in the EBRB system. To this end, seventeen similarity measures are investigated and applied in the EBRB system in this paper, and based on the analysis, an ensemble method for EBRB system with different similarity measures is proposed. Firstly, the problem of the similarity measure of the conventional EBRB system is investigated. Then, a variety of similarity measures are analyzed and their application in the EBRB system is studied. Next, the ensemble method for EBRB systems with different similarity measures is proposed, which consists of two parts, the adaptive weight learning method for determining the weight of each EBRB system with different similarity measures, and the evidential reasoning (ER)-based combination method for combining the inferential results of different methods. Finally, 25 classification datasets are studied to test the performance of EBRB systems with different similarity measures as well as the proposed method, and the results are compared with existing works. The comparison results show that the proposed method could not only achieve better results than any other EBRB systems with different similarity measures, but also outperform other conventional classifiers on some classification datasets, especially small-scale datasets.

Development and validation of an artificial intelligence-powered acne grading system incorporating lesion identification.

The management of acne requires the consideration of its severity; however, a universally adopted evaluation system for clinical practice is lacking. Artificial intelligence (AI) evaluation systems hold the promise of enhancing the efficiency and reproducibility of assessments. Artificial intelligence (AI) evaluation systems offer the potential to enhance the efficiency and reproducibility of assessments in this domain. While the identification of skin lesions represents a crucial component of acne evaluation, existing AI systems often overlook lesion identification or fail to integrate it with severity assessment. This study aimed to develop an AI-powered acne grading system and compare its performance with physician image-based scoring. A total of 1,501 acne patients were included in the study, and standardized pictures were obtained using the VISIA system. The initial evaluation involved 40 stratified sampled frontal photos assessed by seven dermatologists. Subsequently, the three doctors with the highest inter-rater agreement annotated the remaining 1,461 images, which served as the dataset for the development of the AI system. The dataset was randomly divided into two groups: 276 images were allocated for training the acne lesion identification platform, and 1,185 images were used to assess the severity of acne. The average precision of our model for skin lesion identification was 0.507 and the average recall was 0.775. The AI severity grading system achieved good agreement with the true label (linear weighted kappa = 0.652). After integrating the lesion identification results into the severity assessment with fixed weights and learnable weights, the kappa rose to 0.737 and 0.696, respectively, and the entire evaluation on a Linux workstation with a Tesla K40m GPU took less than 0.1s per picture. This study developed a system that detects various types of acne lesions and correlates them well with acne severity grading, and the good accuracy and efficiency make this approach potentially an effective clinical decision support tool.

A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers

Accurate prediction of crop production is essential in effectively managing the food security and economic resilience of agricultural countries. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop production forecasting. We predict the quarterly production of 325 crops (including fruits, vegetables, cereals, non-food, and industrial crops) across 83 provinces in the Philippines. Using a comprehensive dataset of 10,949 time series over 13 years, we demonstrate that a global forecasting approach using a state-of-the-art deep learning architecture, the transformer, significantly outperforms popular tree-based machine learning techniques and traditional local forecasting approaches built on statistical and baseline methods. Our results show a significant 84.93%, 80.69%, and 79.54% improvement in normalized root mean squared error (NRMSE), normalized deviation (ND), and modified symmetric mean absolute percentage error (msMAPE), respectively, over the next-best methods. By leveraging cross-series information, our proposed method is scalable and works well even with time series that are short, sparse, intermittent, or exhibit structural breaks/regime shifts. The results of this study further advance the field of applied forecasting in agricultural production and provide a practical and effective decision-support tool for policymakers that oversee crop production and the agriculture sector on a national scale.

Efficient Classification of Brain Tumors Images Using Neural Network Technique

Using biopsy, brain tumor classification is performed, which is not normally conducted before definitive brain surgery. Technology improvement and machine learning help radiologists with the diagnostics of tumors without invasive measures. Convolutional neural network (CNN) is the machine-learning algorithm that achieved substantial results in image classification and segmentation. Some of the most notable primary brain tumors are meningiomas, gliomas, and pituitary tumors. Gliomas is a general term for a tumor that arises from brain tissues other than nerve cells and blood vessels. But, meningiomas arise from membranes that cover the brain and surround the central nervous system, whereas pituitary tumors are the lumps that sit inside the skull. The most notable important difference between these three types is that meningiomas are generally benign, and gliomas are commonly malignant. This project develops a new CNN architecture to classify brain tumor types. With i) good generalization capability and ii) good execution speed, the newly developed CNN architecture is being used as an effective decision-support tool for radiologists in diagnostics. Python is used for the development of the project. Keywords: Deep Learning, Neural Network, Brain Tumor, MRI Images.

An automated heart disease prediction approach using linearly support vector regression and stacked linear swarm optimization

In today’s world, mining and learning applications play an essential role in healthcare sectors and intend to transform all the data into an understandable form. However, the healthcare sectors require an automated disease prediction system for better medical analysis and emphasize better prediction accuracy for evaluation purposes. In this paper, a new automated prediction model based on Linearly Support Vector Regression and Stacked Linear Swarm Optimization (LSVR-SLSO) has been proposed to predict heart disease accurately. Primarily, the features are analyzed in a linear and non-linear manner using LSVR feature learning approaches. The extracted features are then fed into the SLSO model in order to extract the global optimal solutions. These global solutions will reduce the data dimensionality and computational complexity during the evaluation phase. Moreover, the optimal solution facilitates the proposed model to predict heart disease appropriately. The simulation can be carried out through the MATLAB environment by utilizing a publicly available benchmark heart disease dataset. The performance results evident that the proposed LSVR-SLSO model can efficiently predict heart disease with superior accuracy of 98%, precision of 98.76%, and recall of 99.7% when compared with conventional approaches. The better performance of the proposed model will pave the way to act as an effective clinical decision support tool for physicians during an emergency.

ГЕОІНФОРМАЦІЙНА СИСТЕМА – ДІЄВИЙ ІНСТРУМЕНТ ДОСЛІДЖЕННЯ ВПЛИВУ ГІБРИДНОЇ ВІЙНИ НА СОЦІАЛЬНУ БЕЗПЕКУ ЛЮДИНИ

Social security of the state is the ability to guarantee and provide its citizens with safe and decent living standards. Study of the impact of the war on the state of national security, in particular human social security, will help to identify weaknesses in the state policy implementation in the political, economic and social areas. The impact of Russia's full-scale war against our country on the social security of Ukrainians requires substantiation of new, more effective mechanisms and tools for strengthening human social security in times of war. Under these circumstances, there is a need to apply new technologies to manage the socio-economic processes of the state for analysis and decision-making, and to reflect the current situation. These technologies will make it possible to predict scenarios and offer managers at all levels the best ways to achieve a balanced result. We need technologies that can effectively combine space and time with large amounts of special data in the form of attribute information about objects in the operational environment, background information about regions, climatic conditions, socio-economic conditions and processes in the region, etc. The need for prompt decision-making based on real indicators of anthropogenic, social, and environmental nature, taking into account the factors of space and time in a particular region or area of study, necessitates the involvement of specialized software platforms – geographic information systems that combine analytical capabilities with geospatial analysis. Today, a large number of general-purpose geographic information systems have been developed, but despite the variety of such software products, there is still a need to create specialized geographic information documents and databases that allow obtaining up-to-date geographic information data, the use of which leads to a real reduction in the time required to solve specialized tasks of information management systems. The analysis and decision support system in the environment of existing geographic information platforms will allow to take into account the influence of external factors, analyze the possibility of development, expansion, and monitor the state of the general environment when assessing the current state of input data, using information on the spatial distribution of the necessary resources, information on the competitive environment, etc. With the help of spatial and surface analysis modules, a GIS user can analyze spatially compatible objects and their interrelationships, taking into account the dynamics of their development. Currently, the world leader among geographic information systems is the ArcGIS products. It is a complete system that allows you to collect, organize, manage, analyze, exchange and distribute geographic data. Additionally, ArcGIS, depending on the type and specification of the platform, has other specialized modules that provide a wide range of opportunities for working with geodata and their visual display, namely: creation, management, visualization, analysis and exchange of geographic information. In addition, there are many compatible applications and extensions that integrate into the GIS platform and are focused on solving highly specialized tasks, such as assessing the state of the environment (water, soil, vegetation) or forecasting the development of natural phenomena and man-made emergencies. The article analyzes and substantiates the approaches to the use of a geographic information system as an effective tool for studying the impact of a full-scale war on human social security, improving the efficiency, completeness and quality of information and spatial support for management processes. The modern and full-featured ArcGIS system is considered as a platform that allows not only to form a geographic information system of any scale and purpose, but also to form on its basis an effective system of socio-economic monitoring, which is combined with decision support systems. The article presents an algorithm for studying the impact of a full-scale war on human social security using the ArcGIS system as an effective decision support tool with the use of spatially specialized databases.

Importance of interindividual interactions in eco-evolutionary population dynamics: The rise of demo-genetic agent-based models.

The study of eco-evolutionary dynamics, that is of the intertwinning between ecological and evolutionary processes when they occur at comparable time scales, is of growing interest in the current context of global change. However, many eco-evolutionary studies overlook the role of interindividual interactions, which are hard to predict and yet central to selective values. Here, we aimed at putting forward models that simulate interindividual interactions in an eco-evolutionary framework: the demo-genetic agent-based models (DG-ABMs). Being demo-genetic, DG-ABMs consider the feedback loop between ecological and evolutionary processes. Being agent-based, DG-ABMs follow populations of interacting individuals with sets of traits that vary among the individuals. We argue that the ability of DG-ABMs to take into account the genetic heterogeneity-that affects individual decisions/traits related to local and instantaneous conditions-differentiates them from analytical models, another type of model largely used by evolutionary biologists to investigate eco-evolutionary feedback loops. Based on the review of studies employing DG-ABMs and explicitly or implicitly accounting for competitive, cooperative or reproductive interactions, we illustrate that DG-ABMs are particularly relevant for the exploration of fundamental, yet pressing, questions in evolutionary ecology across various levels of organization. By jointly modelling the effects of management practices and other eco-evolutionary processes on interindividual interactions and population dynamics, DG-ABMs are also effective prospective and decision support tools to evaluate the short- and long-term evolutionary costs and benefits of management strategies and to assess potential trade-offs. Finally, we provide a list of the recent practical advances of the ABM community that should facilitate the development of DG-ABMs.

Simulation of Dynamic Urban Growth Boundary Combining Urban Vitality and Ecological Networks: A Case Study in Chengdu Metropolitan Area

The Chengdu Metropolitan Area, located on the eastern edge of the world’s highest plateau, has experienced a period of integrating urban and rural area development for decades. With rapid urbanization and population growth, the vulnerability and security of the ecological environment have become critical aspects to consider in sustainability. Moreover, the presence of different levels of vitality in the study area has a crucial impact on land-use change. Hence, we propose a growth boundary study based on the theory of urban vitality and ecological networks. We focus on identifying the inefficient urban land and urban development potential land, explore their expansion probabilities to conduct spatial simulations for the next 15 years, and combine the ecological network to form a reasonable spatial pattern. Results showed that the proposed model could simulate the urban growth state more accurately within a certain space scale and integrate different limits and influences to simulate different growth strategies under multiple planning periods. Thus, the proposed model can be an effective decision support tool for the government.

Habitat Quality Assessment and Ecological Risks Prediction: An Analysis in the Beijing-Hangzhou Grand Canal (Suzhou Section)

With the fast pace of global urbanization, anthropogenic disturbances not only lead to frequent disasters, but also cause direct and indirect ecological and economic losses. To reduce the adverse effects of anthropogenic disturbances as part of sustainable ecosystem management, assessments of habitat quality and ecological risk are necessary. The objectives of this study are to analyze environmental conditions of the Beijing-Hangzhou Grand Canal (Suzhou section) for evaluating habitat quality and habitat degradation, and to conduct ecological-risk early warning assessment in this section. The Grand Canal is the longest and first canal in the world to be artificially excavated from natural rivers and lakes. By evaluating habitat quality using the InVEST suite of open-source software models for mapping and valuing the ecosystem, it was found that the natural lands with high habitat quality such as wetlands, forests and lakes along the Suzhou section of the Grand Canal have gradually decreased, while construction lands such as roads and buildings have gradually increased; there is a clear trend of decreasing areas with high habitat quality and increasing areas with low habitat quality, which is likely the result of urbanization. It was also found that the region has a high habitat degradation index, meaning that areas located at the junction of different land types are vulnerable to the surrounding environment due to narrow buffer zones that allow areas with high habitat quality to be easily affected by areas with low habitat quality. In terms of ecological risks, it was found that the natural land area with high habitat quality in the downstream locations was declining, thereby increasing the risks of pollution and flooding events while reducing the ecosystem’s resilience. The valuation model used in this study can be used as an effective decision-support tool to prioritize important ecological areas for conservation in the Grand Canal, and can also be adapted for use in the ecosystem management of other regions.

A mixed-method study exploring experiences, perceptions, and acceptability of using a safe delivery mHealth application in two district hospitals in Rwanda

BackgroundInnovative use of mobile health (mHealth) technology in timely management of childbirth complications is a promising strategy, but its evidence base is limited. The Safe Delivery mHealth Application (SDA) is one of the recent mhealth applications (loaded in smartphones) which is a clinical decision support and training tool for basic emergency obstetric and newborn care (BEmONC). This paper describes, the health providers’ experiences, perceptions, and acceptability of using the SDA, as well as the perceptions of key stakeholders.MethodsA mixed-methods approach was utilized. Quantitative methods consisted of a self-reported acceptability survey, administered to 54 nurses and midwives, including questions on their usage and perceptions of the SDA. Descriptive statistics were employed to analyze the survey data. Qualitative methods included two focus group discussions with 24 nurses and midwives, and six key informant interviews with stakeholders (maternity matrons, responsible for maternal and child health, and district hospital managers). Thematic analysis was performed and selected quotations used to illustrate themes. The study took place in two district hospitals in Rwanda.ResultsQuantitative results found that 31 (57.4%) participants used the SDA four to six times per week. Many participants felt more confident (53.7%) and better at their job (40.7%) since having the SDA. Likert scale survey responses (1–5, 1 = Strongly Disagree, 5 = Strongly Agree) indicated general agreement that SDA is easy to use (Mean = 4.46), is an effective decision support tool (4.63), and training tool (4.65). Qualitative results included themes on perceived usefulness; professional growth acquired through the use of the SDA; SDA, an empowering, intuitive, and user-friendly technology; desired SDA features and functions; benefits of SDA as perceived by key informants, and future use of the SDA.ConclusionsThe nurses and midwives perceive the SDA as having improved their ability to manage childbirth complications. Key stakeholders also perceive the SDA as a useful tool with a reasonable cost and recommend its implementation in routine practices. This study deepens the understanding of the potential benefits of mHealth such as the SDA in low-income settings, like Rwanda. It also provides more evidence on the impact of mHealth in assuring quality BEmONC.

Classifying tumor brain images using parallel deep learning algorithms

One of the most important resources used in today's world is image. Medical images can play an essential role in helping diagnose diseases. Doctors and specialists use medical images to diagnose brain diseases. Convolution neural networks are among the most important deep learning methods in which several layers are trained powerfully. The proposed method is a deep parallel convolution neural network model consisting of AlexNet and VGGNet networks. The structure of the layers is different for the two types of networks. The features are combined at one point and then categorized using the softmax function. To test the network, we used different criteria with images in the database. In clinical diagnosis, generalizability means prediction for people from whom we have no observation. On this account, we did not include individuals' observations in training set in the test set. Compared to the existing models, the proposed model results show that our network has achieved better results. The best result for the database was FIGSHARE, which achieved 99.14% accuracy on binary class and 98.78% on multi-class, which was much better than other SVM models. We tested the best available result with the existing database and compared the results with the proposed model, which was the best model answer for all evaluation criteria. These results suggest that the proposed model can be used as an effective decision support tool for radiologists in medical diagnosis.

An integrated life-cycle maintenance modeling and simulation framework for aero engine fleet management decision support

Aero engine fleet management concerns the engine life-cycle maintenance, repair, and overhaul process decision making as well as the associated fleet spare inventory management, which is strongly influenced by the environmental and operating conditions they are subjected to in service. In this paper a comprehensive life-cycle simulation framework is proposed for engine fleet management decision making support, covering the entire process from individual engine shop visit maintenance modeling to fleet level engine spare inventory planning. The result of case study shows that the proposed simulation framework is useful not only for lifetime reliability and maintenance cost prediction and fleet engine spares planning by properly accounting for the varying environmental and operating conditions, but also provides an effective decision support tool for engine fleet maintenance management.