Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies

Methodology framework for prioritisation of renewable energy sources in port areas

Ports play a crucial role in increasing the decarbonisation of urban environments to mitigate the environmental impacts of maritime transport and promote sustainable intermodal mobility. Various efforts have been made to increase energy self-sufficiency using renewable energy sources (RESs) in different ports worldwide. However, the ports played an essential role in the pollution process of the nearest cities due to the short distance and merging with urban areas. In this case, solar and wind were measured using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data of four Lazio province ports. Each RES was evaluated using 10 years of monthly data for mapping and 1 year of hourly data for potential assessment and energy converters installation. Furthermore, the time series method has been considered to design and develop better management of RESs for decision making monitoring the energy needs of ports. This time series method has been applied to the generated energy source based on various parameters of the RESs used in port

On a smoothed penalty-based algorithm for global optimization

This paper presents a coercive smoothed penalty framework for nonsmooth and nonconvex constrained global optimization problems. The properties of the smoothed penalty function are derived. Convergence to an ε -global minimizer is proved. At each iteration k, the framework requires the ε(k) -global minimizer of a subproblem, where ε(k)→ε . We show that the subproblem may be solved by well-known stochastic metaheuristics, as well as by the artificial fish swarm (AFS) algorithm. In the limit, the AFS algorithm convergence to an ε(k) -global minimum of the real-valued smoothed penalty function is guaranteed with probability one, using the limiting behavior of Markov chains. In this context, we show that the transition probability of the Markov chain produced by the AFS algorithm, when generating a population where the best fitness is in the ε(k)-neighborhood of the global minimum, is one when this property holds in the current population, and is strictly bounded from zero when the property does not hold. Preliminary numerical experiments show that the presented penalty algorithm based on the coercive smoothed penalty gives very competitive results when compared with other penalty-based methods.The authors would like to thank two anonymous referees for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundac¸ao para a Ci ˜ encia e Tecnologia within the projects UID/CEC/00319/2013 and ˆ UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on “Early Heart Disease Prediction using Boosting Techniques”, this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost’s superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction

Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches

Landslide is a type of slope process causing a plethora of economic damage and loss of lives worldwide every year. This study aimed to analyze spatial landslide susceptibility mapping in the Khalkhal-Tarom Basin by integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches, i.e., the best-worst method (BWM) and the stepwise weight assessment ratio analysis (SWARA) techniques. For this purpose, the first step was to prepare a landslide inventory map, which was then divided randomly into the ratio of 70/30% for model training and validation. Thirteen conditioning factors were selected based on the previous studies and available data. In the next step, the BWM and the SWARA methods were utilized to determine the relationships between the sub-criteria and landslides. Finally, landslide susceptibility maps were generated by implementing ANFIS-BWM and ANFIS-SWARA ensemble models, and then several quantitative indices such as positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root-mean-square-error, and the ROC curve were employed to appraise the predictive accuracy of each model. The results indicated that the ANFIS-BWM ensemble model (AUC = 75%, RMSE = 0.443) has better performance than ANFIS-SWARA (AUC = 73.6%, RMSE = 0.477). At the same time, the ANFIS-BWM model had the maximum sensitivity, specificity, and accuracy with values of 87.1%, 54.3%, and 40.7%, respectively. As a result, the BWM method was more efficient in training the ANFIS. Evidently, the generated landslide susceptibility maps (LSMs) can be very efficient in managing land use and preventing the damage caused by the landslide phenomenon. Graphical abstract: [Figure not available: see fulltext.

Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy

Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation

Original Article

Objectives: Balance disorder is one of the most common problems after stroke causes falling and fear of falling in some patients. The balance based video games are newly used in people with motor problems. It is very important to use different interventions for balance issues. The aim of this study is to determine the effectiveness of videogame on balance and fear of falling in one participant. Methods: This experimental study was done in a single subject system, A-B design for one patient with chronic stroke. This method including repetitive measures conducted in two phases, baseline and then twelve intervention sessions. Berg Balance Scale, Timed up and go, Functional Reach, the maximum weight bearing in different directions and the deviation from center were conducted for balance assessing. Fear of falling questionnaire was used to assess fear of falling. Analysis of results was done by C-statistic, Bayesian factor, Mann Whitney U, and visual analysis graphs. Results: The results showed significant improvement for balance skills, the maximum force produced by lower extremities and reducing fear of falling parameters. But the deviation from center graphs did not showed distinct pattern. Discussion: All analysis confirmed the efficacy of videogames on balance skills and fear of falling improvement. However, the deviation from center did not show improvement and it seems to need more studies

Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations

Meta-heuristic algorithms have been effectively employed to tackle a wide range of optimisation issues, including structural engineering challenges. The optimisation of the shape and size of large-scale truss structures is complicated due to the nonlinear interplay between the cross-sectional and nodal coordinate pressures of structures. Recently, it was demonstrated that the newly proposed Marine Predator Algorithm (MPA) performs very well on mathematical challenges. The MPA is a meta-heuristic that simulates the essential hunting habits of natural marine predators. However, this algorithm has some disadvantages, such as becoming locked in locally optimal solutions and not exhibiting high exploratory behaviour. This paper proposes two hybrid marine predator algorithms, Nonlinear Marine Predator (HNMPA) and Nonlinear-Chaotic Marine Predator Algorithm (HNCMPA), as improved variations of the marine predator algorithm paired with a hill-climbing (HC) technique for truss optimisation on form and size. The major advantage of these techniques is that they seek to overcome the MPA's disadvantages by using nonlinear values and prolonging the exploration phase with chaotic values; also, the HC algorithm has been used to avoid locally optimum solutions. In terms of truss optimisation performance, the proposed algorithm is compared to fourteen well-known meta-heuristics, including the Dragonfly Algorithm (DA), Henry Gas Solubility optimisation (HGSO), Arithmetic optimisation Algorithm (AOA), Generalized Normal Distribution Optimisation (GNDO), Salp Swarm Algorithm (SSA), Marine Predators Algorithm (MPA), Neural Network Algorithm (NNA), Water Cycle Algorithm (WCA), Artificial Gorilla Troops Optimiser (GTO), Gray Wolf Optimiser (GWO), Moth Flame Optimiser (MFO), Multi-Verse Optimiser (MVO), Equilibrium Optimiser (EO), and Cheetah Optimiser (CO). Furthermore, seven algorithms were chosen to test HNCMPA performance on benchmark optimisation sets, including MPA, MVO, PSO, MFO, SSA, GWO, and WOA. The experiment results demonstrate that the optimisation techniques surpass previously established meta-heuristics in the field of optimisation, encompassing both traditional and CEC problems, by a margin of over 95% in terms of attaining a superior ultimate solution. Additionally, with regards to solving truss optimisation difficulties as a large-scale real-world engineering challenge, the outcomes indicate a performance boost of over 65% in obtaining significantly better solutions for problems involving 260-bar and 314-bar; conversely, in the case of 340-bar issues, the improvement rate is slightly lower at almost 25%

Developing an Extended Virtual Blade Model for Efficient Numerical Modeling of Wind and Tidal Farms

Harnessing renewable and clean energy resources from winds and tides are promising technologies to alter the high level of consumption of traditional energy resources because of their great global potential. In this regard, developing farms with multiple energy converters is of great interest due to the skyrocketing demand for sustainable energy resources. However, the numerical simulation of these farms during the planning phase might pose challenges, the most significant of which is the computational cost. One of the most well-known approaches to resolve this concern is to use the virtual blade model (VBM). VBM is the implementation of the blade element model (BEM). This was done by coupling the blade element momentum theory equations to simulate rotor operation with the Reynolds averaged Navier–Stokes (RANS) equation to simulate rotor wake and the turbulent flow field around it. The exclusion of the actual geometry of blades enables a lower computational cost. Additionally, due to simplifications in the meshing procedure, VBM is easier to set up than the models that consider the actual geometry of blades. One of the main unaddressed limitations of the VBM code is the constraint of modeling up to 10 renewable energy converters within one computational domain. This paper provides a detailed and well-documented general methodology to develop a virtual blade model for the simulation of 10-plus converters within one computational domain to remove the limitation of this widely used and robust code. The extended code is validated for both the single- and multi-converter scenarios. It is strongly believed that the technical contribution of this paper, combined with the current advancement of available computational resources and hardware, can open the gates to simulate farms with any desired number of wind or tidal energy converters, and, accordingly, secure the sustainability and feasibility of clean energies