User-friendly Model Research Articles

Risk-Based Completion Cost Overrun Ratio Estimation in Construction Projects Using Machine Learning Classification Algorithms: A Case Study

Estimating the completion cost accurately in the early phases of construction projects is critical to their success. However, cost overruns are almost inevitable due to the risks inherent in construction projects. Hence, the completion cost fluctuates throughout the execution phase and requires periodic updates. There is a need for a prompt and user-friendly completion cost estimation model that accounts for fluctuating risk scores and their impacts on the total cost during the execution phase. Machine learning (ML) techniques could address these requirements by providing effective methods for tackling dynamic systems. The proposed approach aims to predict the cost overrun ratio classes of the completion cost according to the changes in the total risk scores at any time of the project. Six classification algorithms were utilized and validated by employing 110 data points from a globally operating construction company. The performances of the algorithms were evaluated with validation and performance indices. The decision tree classifier surpassed other algorithms. Although there are some research limitations, including risk perception, data gathering restrictions, and selecting proper ML algorithms upon data properties, this research improves the planning abilities of construction executives by providing a cost overrun ratio based on changing total risk scores, facilitating swift and simple assessments at any stage of a construction project’s execution.

The impact of maternal serum biomarkers on maternal and neonatal outcomes in twin pregnancies: a retrospective cohort study conducted at a tertiary hospital.

Prior prediction models used for screening preeclampsia (PE) in twin pregnancies were found to be inadequate. In singleton pregnancies, various maternal biomarkers have been shown to be correlated with negative pregnancy outcomes. However, the impact of these biomarkers in twin pregnancies remained uncertain. A retrospective cohort study was carried out on 736 twin pregnancies at a tertiary hospital in Hangzhou, China. Multivariable logistic models were employed to examine the association between levels of serological markers and the likelihood of adverse pregnancy outcomes. The final logistic model was formulated as a user-friendly nomogram. The primary outcome assessed was the occurrence of PE. Results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). The prevalence of PE in the study was 10.3%. When comparing women diagnosed with PE to those without, it was evident that the former group experienced a significantly higher risk of unfavorable maternal and neonatal outcomes. A multivariable logistic regression analysis revealed notable associations between various factors including maternal age, parity, gestational weight gain, a family history of hypertension, as well as levels of cholesterol, albumin, and creatinine and the risk of developing PE, with a significance level of P < 0.05. The concordance index for the constructed nomogram was determined to be 0.792 (95% CI: [0.739-0.844]). Furthermore, an increment of 1 * 1012/L in red blood cell (RBC) count was associated with more than a two-fold increase in the odds of experiencing adverse maternal outcomes (OR 2.247, 95% CI: [1.229-4.107]). However, no significant correlations were identified between any of the examined variables and neonatal outcomes. In this study, we developed a user-friendly predictive model that achieves notable detection rates by incorporating maternal serum biomarker levels alongside maternal characteristics and medical history. Our findings indicate that the probability of adverse maternal outcomes increases with elevated levels of RBCs. Obstetricians should consider intensifying surveillance for these women in clinical practice.

A Highly Efficient and User-Friendly Sensitive Skin Model on the Forearm.

Sensitive skin is a worldwide skin problem, and its assessment of therapeutic efficacy traditionally relies on the facial stinging test. However, this test possesses certain limitations due to its restrictive application site, intense pain sensation, and adverse effects on physical appearance. This study aimed to develop and evaluate a highly efficient and user-friendly sensitive skin simulation model, which combines tape stripping and capsaicin testing on the forearm (FA-TS-CAT), as an alternative to the facial stinging test. A randomized, double-blind, placebo-controlled clinical trial was conducted involving 74 subjects. Skin redness (a* value), transepidermal water loss (TEWL), and self-assessment questionnaires were collected at different time points for analysis. Tape stripping 5 times and 10 min application of capsaicin test were identified as the optimal conditions for the FA-TS-CAT model. Consistent stimulation and natural recovery trends of a* value and TEWL were observed on both the FA-TS-CAT and facial capsaicin test (F-CAT) models within 50 min. After the 4-t-butylcyclohexanol complex emulsion was applied, the a* value in the FA-TS-CAT model exhibited a soothing trend similar to the F-CAT model, with a significantly reduced by 3.99-fold and 3.28-fold at T3 and T4 (p < 0.001), compared to the placebo. Notably, the test efficiency of the FA-TS-CAT model was threefold higher than that of the F-CAT model, and subjects showed more willingness to participate in the FA-TS-CAT test (95.95% vs. 4.05%). These results indicated the FA-TS-CAT is a highly efficient and user-friendly model for sensitive skin, providing a reliable and valid method for clinical research in sensitive skin treatment.

Multiscale molecular modeling of chromatin with MultiMM: From nucleosomes to the whole genome

Motivation: We present a user-friendly 3D chromatin simulation model for the human genome based on OpenMM, addressing the challenges posed by existing models with use-specific implementations. Our approach employs a multi-scale energy minimization strategy, capturing chromatin's hierarchical structure. Initiating with a Hilbert curve-based structure, users can input files specifying nucleosome positioning, loops, compartments, or subcompartments.Results: The model utilizes an energy minimization approach with a large choice of numerical integrators, providing the entire genome's structure within minutes. Output files include the generated structures for each chromosome, offering a versatile and accessible tool for chromatin simulation in bioinformatics studies. Furthermore, MultiMM is capable of producing nucleosome-resolution structures by making simplistic geometric assumptions about the structure and the density of nucleosomes on the DNA.Code availability: Open-source software and the manual are freely available on https://github.com/SFGLab/MultiMM or via pip https://pypi.org/project/MultiMM/.

Development and external validation of a machine-learning based model to predict pre-sarcopenia in MASLD population: Results from NHANES 2017–2018

Introduction and ObjectivesWith rising prevalence of pre-sarcopenia in metabolic dysfunction-associated steatotic liver disease (MASLD), this study aimed to develop and validate machine learning-based model to identify pre-sarcopenia in MASLD population. Materials and MethodsA total of 571 MASLD subjects were screened from the National Health and Nutrition Examination Survey 2017–2018. This cohort was randomly divided into training set and internal testing set with a ratio of 7:3. Sixty-six MASLD subjects were collected from our institution as external validation set. Four binary classifiers, including Random Forest (RF), support vector machine, and extreme gradient boosting and logistic regression, were fitted to identify pre-sarcopenia. The best-performing model was further validated in external validation set. Model performance was assessed in terms of discrimination and calibration. Shapley Additive explanations were used for model interpretability. ResultsThe pre-sarcopenia rate was 17.51 % and 15.16 % in NHANES cohort and external validation set, respectively. RF outperformed other models with area under receiver operating characteristic curve (AUROC) of 0.819(95 %CI: 0.749, 0.889). When six top-ranking features were retained as per variable importance, including weight-adjusted waist, sex, race, creatinine, education and alkaline phosphatase, a final RF model reached an AUROC being 0.824(0.737, 0.910) and 0.732(95 %CI: 0.529, 0.936) in internal and external validation sets, respectively. The model robustness was proved in sensitivity analysis. The calibration curve and decision curve analysis confirmed a good calibration capacity and good clinical usage. ConclusionsThis study proposed a user-friendly model using explainable machine learning algorithm to predict pre-sarcopenia in MASLD population. A web-based tool was provided to screening pre-sarcopenia in community and hospitalization settings.

Brain tumoroids: treatment prediction and drug development for brain tumors with fast, reproducible and easy-to-use personalized models.

generation of patient avatar is critically needed in neuro-oncology for treatment prediction and preclinical therapeutic development. Our objective was to develop a fast, reproducible, low-cost and easy-to-use method of tumoroids generation and analysis, efficient for all types of brain tumors, primary and metastatic. tumoroids were generated from 89 patients: 81 primary tumors including 77 gliomas, and 8 brain metastases. Tumoroids morphology, cellular and molecular characteristics were compared with the ones of the parental tumor by using histology, methylome profiling, pTERT mutations and multiplexed spatial immunofluorescences. Their cellular stability overtime was validated by flow cytometry. Therapeutic sensitivity was evaluated and predictive factors of tumoroid generation were analyzed. All the tumoroids analyzed had similar histological (N=21) and molecular features (N=7) than the parental tumor. Median generation time was 5 days. Success rate was 65 %: it was higher for high grade gliomas and brain metastases versus IDH mutated low grade gliomas. For high-grade gliomas, neither other clinical, neuro-imaging, histological nor molecular factors were predictive of tumoroid generation success. The cellular organization inside tumoroids analyzed by MACSima revealed territories dedicated to specific cell subtypes. Finally, we showed the correlation between tumoroid and patient treatment responses to radio-chemotherapy and their ability to respond to immunotherapy thanks to a dedicated and reproducible 3D analysis workflow. patient-derived tumoroid model that we developed offers a robust, user-friendly, low-cost and reproducible preclinical model valuable for therapeutic development of all type of primary or metastatic brain tumors, allowing their integration into forthcoming early-phase clinical trials.

Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning

The incorporation of rubber fibers (RFs) brings about significant divergence in the characteristics of rubberized concrete when contrasted with traditional varieties. Thus, raising concerns about performance under elevated temperature and prolonged exposure. This study effectively addresses the challenges of incorporating rubber fibers in concrete by using artificial neural network (ANN), gene expression programming (GEP), and bagging to examine the impact of input factors such as water-to-cement ratio (W/C), rubber fiber content (RF), elevated temperature (T), and exposure duration (t) on air-cooled compressive strength (CSA). The comprehensive literature review and advanced modeling techniques reveal that ANN excels in capturing complex relationships. In addition, GEP provides clear and accurate models through its unique approach, and Bagging enhances model stability and accuracy. These methods together offer a robust framework for estimating the CSA of rubberized concrete. Thus, contributing valuable insights for optimizing its use in construction. All three models exhibited strong performance, with the ANN emerging as the most effective choice among the evaluated models. Notably, ANN displayed the highest coefficient of determination (R2) value of 0.984, indicating its superior predictive accuracy compared to both GEP (0.982), and bagging (0.970). Moreover, ANN demonstrated the lowest mean absolute error (MAE) score of 0.621 and root mean square error (RMSE) of 0.867, underscoring its precision in forecasting the CSA of rubberized concrete with minimal deviation from experimental values. In addition, the SHapley Additive exPlaination (SHAP) method is employed to comprehend the model estimations. The ICE and PDP plots demonstrate an initial increase in CSA up to 150°C, followed by a significant decrease as temperature rises. Furthermore, CSA decreases with higher RF contents, and linearly declines with increasing W/C ratio. The SHAP analysis provides clear evidence of the strong negative correlation between T and CSA, along with a negative association with RF. A graphical user interface has been developed to estimate the CSA of rubberized concrete, enabling efficient and user-friendly model interaction without the need for physical experimentation.

Management Effectiveness Evaluation using the Bayesian Belief Network Approach: A case study of Cu Lao Cham Marine Protected Area, Vietnam

Abstract The Kunming-Montreal Global Biodiversity Framework set an ambitious target of safeguarding 30% of the Earth’s land and sea through well-managed Protected Areas (PAs) by 2030. Despite 196 countries committing to expand PA coverage, a lack of evidence on existing PA effectiveness and limited data on human-ecosystem interactions pose formidable challenges to implementation and prioritising conservation funding. Within this context, this paper aims to test a simple and user-friendly model, which is based on Bayesian Belief Networks, in evaluating the management effectiveness of Cu Lao Cham Marine Protected Area (CLC-MPA) in Vietnam. It focuses on assessing whether the MPA is effective in achieving its conservation objectives and if the target resources can endure the current pace of economic development. The study underscores the importance of prioritising eco-tourism and sustainable local economic models amid the intense forces of mass tourism development. It argues that tourism carrying capacity for CLC should be estimated at the outset of sustainable tourism development plan. Besides, there is a need for an integrated approach to MPA management to include legal provisions to address the impacts of tourism and pollution. Also, given the increasing pressure from incoming fishers, it is essential for increasing trans-provincial coordination for better enforcement of MPA regulations.

Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteoporotic fractures and leading to considerable health deterioration and increased mortality. In addressing the critical need for early identification of osteoporosis through routine screening of femoral neck bone mineral density (FNBMD), this study developed a user-friendly prediction model aimed at men aged 50 years and older, a demographic often overlooked in osteoporosis screening. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), the study involved outlier detection and handling, missing value imputation via the K nearest neighbor (KNN) algorithm, and data normalization and encoding. The dataset was split into training and test sets with a 7:3 ratio, followed by feature screening through the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithm. Eight different machine learning algorithms were then employed to construct predictive models, with their performance evaluated through a comprehensive metric suite. The random forest regressor (RFR) emerged as the most effective model, characterized by key predictors such as age, body mass index (BMI), poverty income ratio (PIR), serum calcium, and race, achieving a coefficient of determination (R²) of 0.218 and maintaining robustness in sensitivity analyses. Notably, excluding race from the model resulted in sustained high performance, underscoring the model's adaptability. Interpretations using Shapley additive explanations (SHAP) highlighted the influence of each feature on FNBMD. These findings indicate that our predictive model effectively aids in the early detection of osteoporosis, potentially reducing the incidence of OFNFs in this high-risk population.

P-383 Development and validation of a visualized prediction model for early miscarriage risk in patients undergoing IVF/ICSI procedures: a real-world multi-center study

Abstract Study question What are the key predictors of early miscarriage in patients undergoing IVF/ICSI treatment? Summary answer This study identified risk factors for early miscarriage in IVF/ICSI patients, and developed a validated predictive nomogram, highlighting age-dependent factor variations. What is known already Early miscarriage, occurring in 15-20% of confirmed pregnancies, can lead to complications, psychological distress, and fertility impairment. It is especially prevalent during the first trimester, with up to 80% of all miscarriages happening during this phase. Women who conceive via Assisted Reproductive Technology (ART) face higher stress levels and increased risk of miscarriage. Although many contributing factors to early miscarriage have been identified, there is a scarcity of integrated predictive models for IVF/ICSI patients. This paper aims to develop a validated, user-friendly nomogram to predict first-trimester miscarriage risk in IVF/ICSI patients and help clinicians and patients make informed decisions. Study design, size, duration A retrospective analysis was conducted on 20,322 first cycles out of 31,307 for IVF/ICSI at a reproductive centre in Southern China, After excluding ineligible cycles, 6,724 first fresh cycles were included and randomly divided into a training dataset (n = 4,516) and an internal validation dataset (n = 2,208). An external validation dataset (n = 1,179) was obtained from a reproductive center in Northern China, further ensuring the robustness and generalizability of the findings. Participants/materials, setting, methods This retrospective study analysed 6,724 out of 31,307 IVF/ICSI treatments from 2011 to 2020 at a Southern China reproductive center and 1,179 cycles from a Northern China center (2013-2018) for validation. Early miscarriage was defined as a loss before 12 weeks’ gestation. Patients were divided into two age groups: ≥35 and &amp;lt;35 years. The study followed the TRIPOD statement and received Institutional Review Board approval. Main results and the role of chance The study found statistically significant differences across the training, internal validation, and external validation sets in terms of patient age, BMI, number of good quality embryos, antral follicle count (AFC), basal testosterone (T), luteinizing hormone (LH) levels, among other variables. The early miscarriage rates were 9.43%, 10.55%, and 13.50% across these sets. Elder women (age ≥35 years) had a higher duration of infertility and early miscarriage rate, but lower AFC and numbers of retrieved oocytes, 2PN, and MII oocytes compared to younger women (age &amp;lt;35 years). A nomogram prediction model was developed using variables such as female age, BMI, number of spontaneous abortions, number of induced abortions, and basal FSH, which were found to be independent prognostic factors for early miscarriage. The model’s performance was validated using AUC, calibration plots, and DCA, indicating acceptable performance and good agreement between prediction and observation. The model was more beneficial for predicting miscarriage risk than the treat-all or treat-none schemes. Subgroup analysis for elder and younger women showed different risk factors for miscarriage, notably female age, basal FSH for elder women, and female age, BMI, and the number of spontaneous abortions for younger women. The subgroup models also showed consistent results and good clinical relevance. Limitations, reasons for caution The retrospective design of our study may have introduced certain limitations and potential biases. To overcome these limitations, it is advisable that future research endeavors adopt a prospective clinical study design. Wider implications of the findings This study uses a diverse dataset and rigorous methods to create a user-friendly prediction model for early miscarriage in ART. By considering regional differences, it ensures the model’s broader applicability and accuracy, enabling personalized treatment strategies and improving infertility treatment outcomes. Trial registration number The study was a retrospective study, and it was approved by the Institutional Review Boards of Sun Yat-Sen Memorial Hospital (SYSEC-KY-KS-2021-121) and the First Affiliated Hospital of Xinjiang Medical University (K202106-17) and is reported in accordance with the TRIPOD statement

A self-reported symptom-based decision-making model helps to rule out outpatient cases at low risk for CRC before colonoscopy

ObjectivesEffective exclusion of low-risk symptomatic outpatient cases for colorectal cancer (CRC) remains diagnostic challenges. We aimed to develop a self-reported symptom-based decision-making model for application in outpatient scenarios. MethodsIn total, 8233 symptomatic cases at risk for CRC, as judged by outpatient physicians, were involved in this study at seven medical centers. A decision-making model was constructed using 60 self-reported symptom parameters collected from the questionnaire. Further internal and external validation cohorts were built to evaluate the discriminatory power of the CRC model. The discriminatory power of the CRC model was assessed by the C-index and calibration plot. After that, the clinical utility and user experience of the CRC model were evaluated. ResultsNine symptom parameters were identified as valuable predictors used for modeling. Internal and external validation cohorts verified the adequate discriminatory power of the CRC model. In the clinical application step, all 17 physicians found the model easy to grasp, 99.9 % of the patients were satisfied with the survey form. Application of this model detected all CRC cases. The total consistency ratio of outpatient cases undergoing colonoscopy was 81.4 %. None of the low-risk patients defined by the CRC model had been diagnosed with CRC. ConclusionThis multicenter study developed and validated a simple and user-friendly decision-making model covering self-reported information. The CRC model has been demonstrated to perform well in terms of rapid outpatient decision-making scenarios and clinical utility, particularly because it can better rule out low-risk outpatient cases.

Risk prediction modelling in idiopathic inflammatory myositis-associated interstitial lung disease based on seven factors including serum KL-6 and lung ultrasound B-lines.

To develop a user-friendly nomogram-based predictive model for interstitial lung disease (ILD) in patients with idiopathic inflammatory myositis (IIM). A retrospective study was conducted at Shantou Central Hospital, encompassing 205 IIM patients diagnosed between January 2013 and December 2022. We used the LASSO regression method in the discovery set to select features for model construction, followed by efficacy verification through AUC of ROC. Afterwards, KL-6 values and LUS B-lines number were added into this model to evaluate whether these 2 factors added to the model efficiency. Finally, a web version was constructed to make it more available. Among the 205 IIM patients, 115 (56.1%) patients were diagnosed with ILD, and 90 (43.9%) did not. The predictive model, derived from the training set, comprised four independent risk factors, including age, presence of respiratory symptoms, anti-melanoma differentiation-associated gene 5 (MDA-5) antibody positivity, and anti-aminoacyl transfer RNA synthetase (anti-ARS) antibodies positivity. Notably, anti-TIF1-γ antibody positivity emerged as a protective factor. The AUC of the ROC based on these 5 factors was 0.876 in the training set and 0.861 in the validation set. The AUC of the ROC based on the 5 factors plus KL-6 was 0.922, 5 factors plus B-line number was 0.949 and 5 factors plus both KL-6 and B-line number was 0.951. Accordingly, a nomogram and a web version were developed. This predictive model demonstrates robust capability to assess ILD risk in IIM patients, particularly when augmented with serum KL-6 level or/and LUS B-line number.

A novel score for early prediction of urinary tract infection risk in patients with acute ischemic stroke: a nomogram-based retrospective cohort study

This study aimed to construct and externally validate a user-friendly nomogram-based scoring model for predicting the risk of urinary tract infections (UTIs) in patients with acute ischemic stroke (AIS). A retrospective real-world cohort study was conducted on 1748 consecutive hospitalized patients with AIS. Out of these patients, a total of 1132 participants were ultimately included in the final analysis, with 817 used for model construction and 315 utilized for external validation. Multivariate regression analysis was applied to develop the model. The discriminative capacity, calibration ability, and clinical effectiveness of the model were evaluated. The overall incidence of UTIs was 8.13% (92/1132), with Escherichia coli being the most prevalent causative pathogen in patients with AIS. After multivariable analysis, advanced age, female gender, National Institute of Health Stroke Scale (NIHSS) score ≥ 5, and use of urinary catheters were identified as independent risk factors for UTIs. A nomogram-based SUNA model was constructed using these four factors (Area under the receiver operating characteristic curve (AUC) = 0.810), which showed good discrimination (AUC = 0.788), calibration, and clinical utility in the external validation cohort. Based on four simple and readily available factors, we derived and externally validated a novel and user-friendly nomogram-based scoring model (SUNA score) to predict the risk of UTIs in patients with AIS. The model has a good predictive value and provides valuable information for timely intervention in patients with AIS to reduce the occurrence of UTIs.

A reliable deep-learning-based method for alveolar bone quantification using a murine model of periodontitis and micro-computed tomography imaging

Objectives: This study focuses on artificial intelligence (AI)-assisted analysis of alveolar bone for periodontitis in a mouse model with the aim to create an automatic deep-learning segmentation model that enables researchers to easily examine alveolar bone from micro-computed tomography (µCT) data without needing prior machine learning knowledge.Methods: Ligature-induced experimental periodontitis was produced by placing a small-diameter silk sling ligature around the left maxillary second molar. At 4, 7, 9, or 14 days, the maxillary bone was harvested and processed with a µCT scanner (µCT-45, Scanco). Using Dragonfly (v2021.3), we developed a 3D deep learning model based on the U-Net AI deep learning engine for segmenting materials in complex images to measure alveolar bone volume (BV) and bone mineral density (BMD) while excluding the teeth from the measurements.Results: This model generates 3D segmentation output for a selected region of interest with over 98 % accuracy on different formats of µCT data. BV on the ligature side gradually decreased from 0.87 mm3 to 0.50 mm3 on day 9 and then increased to 0.63 mm3 on day 14. The ligature side lost 4.6 % of BMD on day 4, 9.6 % on day 7, 17.7 % on day 9, and 21.1 % on day 14.Conclusions: This study developed an AI model that can be downloaded and easily applied, allowing researchers to assess metrics including BV, BMD, and trabecular bone thickness, while excluding teeth from the measurements of mouse alveolar bone.Clinical significance: This work offers an innovative, user-friendly automatic segmentation model that is fast, accurate, and reliable, demonstrating new potential uses of artificial intelligence (AI) in dentistry with great potential in diagnosing, treating, and prognosis of oral diseases.

A low-cost, easy-to-use prototype bioreactor model for the investigation of human gut microbiota: validation using a prebiotic treatment.

In vitro gut models allow for the study of the impact of molecules on human gut microbiota composition and function without the implication of the host. However, current models, such as the Simulator of Human Intestinal Microbial Ecosystem (SHIME®), are expensive, time-consuming, and require specialized personnel. Homemade in vitro models that lessen these issues have limited evidence of their humanlike functionality. In this study, we present the development of a low-cost and easy-to-use bioreactor with the proven functionality of human microbiota. In our model, we evaluated the capability of replicating human gut microbiota growth and the response of the human bacterial community to a prebiotic, resistant starch, particularly resistant starch type 2 (RS2). Our bioreactor produced an environment that was stable for pH, temperature, and anaerobic conditions. The bioreactor consistently cultivated bacterial communities over a 48 h time period, replicating the composition of the gut microbiota and the associated metabolite production response to RS2, in line with prior human studies. In response to the RS2 prebiotic, we observed an increase in Bifidobacterium adolescentis and Bifidobacterium faecale and an increase in the production of the short-chain fatty acids such as acetate, propionate, and isobutyrate. Taken together, these data demonstrate that our low-cost and user-friendly prototype bioreactor model provides a favorable environment for the growth of human gut microbiota and can mimic its response to a prebiotic.

Modeling the Economic Value of Green Spaces in Residential Areas of Dar Es Salaam City, Tanzania

The economic value of green spaces in residential areas has been advocated to raise the land and property values. However, the establishment of the economic value of green spaces need many data such that it is costly and needs expertise. The objective of this study was to develop a user-friendly mathematical model that employ few data to determine the economic value of green spaces in residential areas in Dar es Salaam City. The study was cross sectional and employed structured questionnaires as a data collection method. Linear regression model was developed by relating economic values of green spaces to various environmental and socio-economic conditions of households. The study revealed that the net economic value of home greenery was significantly influenced by income of the households, area covered, age of the respondents and green space type. The average net benefit calculated from the model (1,317USD per household per year) was not far from the normal arithmetic mean calculated through traditional methods (1,369USD per household per year). This implies that the model can best predict the overall mean of the net benefit by 96.2%. It is therefore, recommended that the model can be used to estimate the economic value of ecosystem services from home greenery, property value and can guide the establishment of compensation for households due to green space availability.

Role of Ultrasound and Ultrasound-Based Prediction Model in Differentiating Follicular Thyroid Carcinoma From Follicular Thyroid Adenoma.

This study aims to identify distinct ultrasound (US) characteristics for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA), and construct a user-friendly preoperative risk stratification model for thyroid follicular neoplasms. In this retrospective study, patients diagnosed with pathologically confirmed FTA or FTC and undergoing US examinations between July 2017 and June 2021 were designated as the training cohort, and those from July 2021 to June 2023 were enrolled as the external validation set. We systematically assessed and compared the sonographic and clinical characteristics of FTC and FTA. Univariable and multivariable logistic regression analyses were used to assess the association of US features with FTC in the training set. A prediction nomogram model, incorporating US features independently associated with FTC, was developed and validated externally to assess its performance. A total of 645 patients (FTA/FTC = 530/115) were included in the training set, while 197 patients (FTA/FTC = 165/32) constituted the validation set. In the training set, solid composition, hypo-echogenicity, irregular margin, calcification, protrusion sign, trabecular formation, absent or thick halo, and mainly central hypervascularity were identified as independent factors associated with FTC. The prediction nomogram model constructed using these variables showed good performance in differentiating FTC from FTA with an area under the curve of 0.948 in the training set and 0.915 in the validation set. The preoperative nomogram model constructed based on US features serves as an effective tool for the risk stratification of thyroid follicular neoplasms.

Development of a user-friendly hydraulic model for simulating hybrid water distribution networks: a transition toward sector sustainability

ABSTRACT The conventional computational approach for modeling and simulating drinking water distribution networks (WDNs) is time-consuming and prone to human errors. Therefore, this study developed and implemented a hydraulic modeling tool, the ‘Kyema-Net model’, based on the modified Hardy Cross algorithm in Visual Basic, to simulate a hybrid WDN with the inclusion of pipe cost assessment. The model's accuracy was validated by comparing its outputs with those of EPANET 2.2 software. The Kyema-Net model and EPANET 2.2 simulation results were comparable. Considering the WDN case study, the proposed model initialized nodal inflows in proportions of 60 and 40% for any two pipes exiting the node, reducing the computational steps. The model executed three trials to achieve the final solution against four trials for EPANET 2.2. The mean absolute percentage error, coefficient of determination (R2), and root mean square error metrics for flow velocity were ∼0.00%, 1.00, and ∼0.00 m, respectively. The modified resilience index values ranged between 0.30 and 1.09. Implementing the proposed hydraulic modeling tool in designing and/or rehabilitating WDNs demonstrated compliance with numerous social-, economic-, and technological-related Sustainable Development Goals. This would assist various water sector stakeholders in the sustainable planning, operation, and management of WDNS globally.

EasyTS: The Express Lane to Long Time Series Forecasting

Responding to the escalating interest in long-term forecasting within the industry, we introduce EasyTS, a comprehensive toolkit engineered to streamline data collection, analysis, and model creation procedures. EasyTS acts as a unified solution, driving progress in long-term time series forecasting. The platform provides effortless access to various time series datasets, including a newly open-sourced multi-scenario dataset in the electricity domain. Integrated visualization and analysis tools help unveil inherent data features and relationships. EasyTS facilitates a user-friendly model validation approach with versatile evaluation criteria. This toolkit allows researchers to compare their models proficiently against renowned benchmarks. With our ongoing commitment to expanding our dataset collection and enhancing toolkit functionalities, we aspire to contribute significantly to the time series forecasting domain. Code is available at this repository: https://github.com/EdgeBigBang/EasyTS.git.

Micro-Engineered Heart Tissues On-Chip with Heterotypic Cell Composition Display Self-Organization and Improved Cardiac Function.

Advanced in vitro models that recapitulate the structural organization and function of the human heart are highly needed for accurate disease modeling, more predictable drug screening, and safety pharmacology. Conventional3DEngineered Heart Tissues (EHTs) lack heterotypic cell complexity and culture under flow, whereas microfluidic Heart-on-Chip (HoC) models in general lack the 3D configuration and accurate contractile readouts. In this study, an innovative and user-friendly HoC model is developed to overcome these limitations, by culturing human pluripotent stem cell (hPSC)-derived cardiomyocytes (CMs), endothelial (ECs)- and smooth muscle cells (SMCs), together with human cardiac fibroblasts (FBs), underflow, leading to self-organized miniaturized micro-EHTs (µEHTs) with a CM-EC interface reminiscent of the physiological capillary lining. µEHTs cultured under flow display enhanced contractile performance and conduction velocity. In addition, the presence of the EC layer altered drug responses in µEHT contraction. This observation suggests a potential barrier-like function of ECs, which may affect the availability of drugs to the CMs. These cardiac models with increased physiological complexity, will pave the way to screen for therapeutic targets and predict drug efficacy.