We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea\'s regulations. Results show that the optimized assignments, compared to current ones, reduced work time on average and led to a significant reduction in total skimmer capacity. Additionally, we present a deep neural network-based surrogate model, greatly enhancing efficiency compared to simulation-based optimization. Addressing inefficiencies in mobilizing locations that store oil skimmers, further optimization aimed to minimize mobilized locations and was validated through scenario-based simulations resembling actual situations. Based on major oil spills in South Korea, this strategy significantly reduced work time and required locations. These findings demonstrate the effectiveness of the proposed genetic algorithm and mobilized location minimization strategy in enhancing oil spill response operations.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

UNASSIGNED: Researchers have recently focused on the biological and synthetic effects of 1, 2, and 4-triazole fused heterocyclic molecules because they have tremendous medicinal value. The objective of the present study was to carry out the 3D QSAR evaluation on the substituted 1,2, and 4 triazole derivatives for anticancer potential using k-Nearest Neighbor-Molecular Field Analysis (kNN-MFA) method.
UNASSIGNED: Using the molecular design suite, a three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was undertaken on a series of 4-amino-5-(pyridin3yl)-4H-1, 2, and 4-triazole-3-thiol anticancer drugs (Vlife MDS). This study used a genetic algorithm and a manual selection approach on 20 substituted 1, 2, and 4-triazole derivatives. Based on the genetic algorithm (GA), the 3D-QSAR model was generated. Statistical significance and predictive capacity were evaluated using internal and external validation.
UNASSIGNED: The most significant model has a correlation coefficient of 0.9334 (squared correlation coefficient r2 = 0.8713), showing that biological activity and descriptors have a strong relationship. The model exhibited internal predictivity of 74.45 percent (q2 = 0.2129), external predictivity of 81.09 percent (pred r2 = 0.8417), and the smallest error term for the predictive correlation coefficient (pred r2se = 0.1255). The model revealed steric (S 1047--0.0780--0.0451S 927) and electrostatic (E 1002) data points that contribute remarkably to anticancer activity. A molecular field study demonstrates a link between the structural features of substituted triazole derivatives and their activities.
UNASSIGNED: The good-to-moderate anticancer potential of compounds confirms the significant pharmacological role of 1,2,4-triazole derivatives. These results could lead to the identification of potential chemical compounds with optimal anticancer activity and minimal side effects.

Background: Topical lidocaine microemulsion preparations with low toxicity, low irritation, strong transdermal capability and convenient administration are urgently needed. Methods: Box-Behnken design was performed for three preparation conditions of 5% lidocaine microemulsions: mass ratio of the mass ratio of surfactant/(oil phase + surfactant) (X1), the mass ratio of olive oil/(α-linolenic acid + linoleic acid) (X2) and the water content W% (X3). Then, five multi-objective genetic algorithms were used to optimize the three evaluation indices to optimize the effects of lidocaine microemulsion preparations. Finally, the ideal optimization scheme was experimentally verified. Results: Non-dominated Sorting Genetic Algorithm-II was used for 30 random searches. Among these, Scheme 2: X1 = 0.75, X2 = 0.35, X3 = 75%, which resulted in Y1 = 0.17 μg/(cm2·s) and Y2 = 0.74 mg/cm2; and the Scheme 19: X1 = 0.68, X2 = 1.42, X3 = 75% which resulted in Y1 = 0.14 μg/(cm2·s) and Y2 = 0.80 mg/cm2, provided the best matches for the objective function requirements. The maximum and average fitness of the method have reached stability after 3 generations of evolution. Experimental verification of the above two schemes showed that there were no statistically significant differences between the measured values of Y1 and Y2 and the predicted values obtained by optimization (p > 0.05) and are close to the target value. Conclusion: Two lidocaine microemulsion preparation protocols were proposed in this study. These preparations resulted in good transdermal performance or long anesthesia duration, respectively.

The wastewater treatment efficiency of Diplosphaera mucosa VSPA was enhanced by optimising five input parameters and increasing the biomass yield. pH, temperature, light intensity, wastewater percentage (pollutant concentration), and N/P ratio were optimised, and their effects were studied. Two competitive techniques, response surface methodology (RSM) and artificial neural network (ANN), were applied for constructing predictive models using experimental data generated according to central composite design. Both MATLAB and Python were used for constructing ANN models. ANN models predicted the experimental data with high accuracy and less error than RSM models. Generated models were hybridised with a genetic algorithm (GA) to determine the optimised values of input parameters leading to high biomass productivity. ANN-GA hybridisation approach performed in Python presented optimisation results with less error (0.45%), which were 7.8 pH, 28.8 °C temperature, 105.20 μmol m-2 s-1 light intensity, 93.10 wastewater % (COD) and 23.5 N/P ratio.

This study investigates nonlinear kernels for multitrait (MT) genomic prediction using support vector regression (SVR) models. We assessed the predictive ability delivered by single-trait (ST) and MT models for 2 carcass traits (CT1 and CT2) measured in purebred broiler chickens. The MT models also included information on indicator traits measured in vivo [Growth and feed efficiency trait (FE)]. We proposed an approach termed (quasi) multitask SVR (QMTSVR), with hyperparameter optimization performed via genetic algorithm. ST and MT Bayesian shrinkage and variable selection models [genomic best linear unbiased predictor (GBLUP), BayesC (BC), and reproducing kernel Hilbert space (RKHS) regression] were employed as benchmarks. MT models were trained using 2 validation designs (CV1 and CV2), which differ if the information on secondary traits is available in the testing set. Models\' predictive ability was assessed with prediction accuracy (ACC; i.e. the correlation between predicted and observed values, divided by the square root of phenotype accuracy), standardized root-mean-squared error (RMSE*), and inflation factor (b). To account for potential bias in CV2-style predictions, we also computed a parametric estimate of accuracy (ACCpar). Predictive ability metrics varied according to trait, model, and validation design (CV1 or CV2), ranging from 0.71 to 0.84 for ACC, 0.78 to 0.92 for RMSE*, and between 0.82 and 1.34 for b. The highest ACC and smallest RMSE* were achieved with QMTSVR-CV2 in both traits. We observed that for CT1, model/validation design selection was sensitive to the choice of accuracy metric (ACC or ACCpar). Nonetheless, the higher predictive accuracy of QMTSVR over MTGBLUP and MTBC was replicated across accuracy metrics, besides the similar performance between the proposed method and the MTRKHS model. Results showed that the proposed approach is competitive with conventional MT Bayesian regression models using either Gaussian or spike-slab multivariate priors.

A priori estimation of analyte response is crucial for the efficient development of liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure-property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC-ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC-ESI/MS that provided a holistic overview of the analyte\'s response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA-GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.

Childhood obesity constitutes a major risk factor for future adverse health conditions. Multicomponent parent-child interventions are considered effective in controlling weight. Τhe ENDORSE platform utilizes m-health technologies, Artificial Intelligence (AI), and serious games (SG) toward the creation of an innovative software ecosystem connecting healthcare professionals, children, and their parents in order to deliver coordinated services to combat childhood obesity. It consists of activity trackers, a mobile SG for children, and mobile apps for parents and healthcare professionals. The heterogeneous dataset gathered through the interaction of the end-users with the platform composes the unique user profile. Part of it feeds an AI-based model that enables personalized messages. A feasibility pilot trial was conducted involving 50 overweight and obese children (mean age 10.5 years, 52% girls, 58% pubertal, median baseline BMI z-score 2.85) in a 3-month intervention. Adherence was measured by means of frequency of usage based on the data records. Overall, a clinically and statistically significant BMI z-score reduction was achieved (mean BMI z-score reduction -0.21 ± 0.26, p-value < 0.001). A statistically significant correlation was revealed between the level of activity tracker usage and the improvement of BMI z-score (-0.355, p = 0.017), highlighting the potential of the ENDORSE platform.

Explainable Artificial Intelligence (XAI) makes AI understandable to the human user particularly when the model is complex and opaque. Local Interpretable Model-agnostic Explanations (LIME) has an image explainer package that is used to explain deep learning models. The image explainer of LIME needs some parameters to be manually tuned by the expert in advance, including the number of top features to be seen and the number of superpixels in the segmented input image. This parameter tuning is a time-consuming task. Hence, with the aim of developing an image explainer that automizes image segmentation, this paper proposes Ensemble-based Genetic Algorithm Explainer (EGAE) for melanoma cancer detection that automatically detects and presents the informative sections of the image to the user. EGAE has three phases. First, the sparsity of chromosomes in GAs is determined heuristically. Then, multiple GAs are executed consecutively. However, the difference between these GAs are in different number of superpixels in the input image that result in different chromosome lengths. Finally, the results of GAs are ensembled using consensus and majority votings. This paper also introduces how Euclidean distance can be used to calculate the distance between the actual explanation (delineated by experts) and the calculated explanation (computed by the explainer) for accuracy measurement. Experimental results on a melanoma dataset show that EGAE automatically detects informative lesions, and it also improves the accuracy of explanation in comparison with LIME efficiently. The python codes for EGAE, the ground truths delineated by clinicians, and the melanoma detection dataset are available at https://github.com/KhaosResearch/EGAE.

Background: The Braden Scale is commonly used to determine Hospital-Acquired Pressure Injuries (HAPI). However, the volume of patients who are identified as being at risk stretches already limited resources, and caregivers are limited by the number of factors that can reasonably assess during patient care. In the last decade, machine learning techniques have been used to predict HAPI by utilizing related risk factors. Nevertheless, none of these studies consider the change in patient status from admission until discharge. Objectives: To develop an integrated system of Braden and machine learning to predict HAPI and assist with resource allocation for early interventions. The proposed approach captures the change in patients\' risk by assessing factors three times across hospitalization. Design: Retrospective observational cohort study. Setting(s): This research was conducted at ChristianaCare hospital in Delaware, United States. Participants: Patients discharged between May 2020 and February 2022. Patients with HAPI were identified from Nursing documents (N = 15,889). Methods: Support Vector Machine (SVM) was adopted to predict patients\' risk for developing HAPI using multiple risk factors in addition to Braden. Multiple performance metrics were used to compare the results of the integrated system versus Braden alone. Results: The HAPI rate is 3%. The integrated system achieved better sensitivity (74.29 ± 1.23) and detection prevalence (24.27 ± 0.16) than the Braden scale alone (sensitivity (66.90 ± 4.66) and detection prevalence (41.96 ± 1.35)). The most important risk factors to predict HAPI were Braden sub-factors, overall Braden, visiting ICU during hospitalization, and Glasgow coma score. Conclusions: The integrated system which combines SVM with Braden offers better performance than Braden and reduces the number of patients identified as at-risk. Furthermore, it allows for better allocation of resources to high-risk patients. It will result in cost savings and better utilization of resources. Relevance to clinical practice: The developed model provides an automated system to predict HAPI patients in real time and allows for ongoing intervention for patients identified as at-risk. Moreover, the integrated system is used to determine the number of nurses needed for early interventions. Reporting Method: EQUATOR guidelines (TRIPOD) were adopted in this research to develop the prediction model. Patient or Public Contribution: This research was based on a secondary analysis of patients\' Electronic Health Records. The dataset was de-identified and patient identifiers were removed before processing and modeling.