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Due to the important role of concrete in construction sector, a novel metaheuristic method, namely whale optimization algorithm (WOA), is employed for simulating 28-day compressive strength of concrete (CSC). To this end, the WOA is coupled with a neural network (NN) to optimize its computational parameters. Also, dragonfly algorithm (DA) and ant colony optimization (ACO) techniques are considered as the benchmark methods. The CSC influential parameters are cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA). First, a population-based sensitivity analysis is carried out to achieve the most efficient structure of the proposed model. In this sense, the WOA-NN with the population size of 400 and five hidden nodes constructed the best-fitted network. The results revealed that the WOA-NN (Error = 2.0746 and Correlation = 0.8976) presents the most reliable prediction of the CSC, followed by the DA-NN (Error = 2.5138 and Correlation = 0.8209) and ACO-NN (Error = 2.8843 and Correlation = 0.8000) benchmark models. The findings showed that utilizing the WOA optimization technique, along with typical neural network, results in developing a promising tool for modeling the CSC.

By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP) predictive tool for adjusting the connecting weights and biases in predicting the failure probability using seven settlement key factors, namely unit weight, friction angle, elastic modulus, dilation angle, Poisson’s ratio, applied stress, and setback distance. As the first result, incorporating both HHO and DA metaheuristic algorithms resulted in higher efficiency of the MLP. Moreover, referring to the calculated area under the receiving operating characteristic curve (AUC), as well as the calculated mean square error, the DA-MLP (AUC = 0.942 and MSE = 0.1171) outperforms the HHO-MLP (AUC = 0.915 and MSE = 0.1350) and typical MLP (AUC = 0.890 and MSE = 0.1416). Furthermore, the DA surpassed the HHO in terms of time-effectiveness.

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

Evasion of apoptosis is associated with treatment resistance and metastasis in colorectal cancer (CRC). Various cellular processes are associated with evasion of apoptosis. These include overexpression of pro-apoptotic proteins (including p53 and PD-L1), anti-apoptotic proteins (BIRC7/Livin and Bcl-2), chemokine receptors (including DARC), and dysregulation of DNA mismatch repair proteins (including MSH2 and PMS2). The aim of this study was to determine the effect of folinic acid, 5-FU and oxaliplatin (FOLFOX) as a single agent and aspirin plus FOLFOX in various combinations on the aforementioned proteins in human CRC, SW480 cell line and rat models of N-Methyl-N-Nitrosourea (NMU)-induced CRC. In addition, effects of the NMU-induced CRC and chemotherapeutic regimens on haematological and biochemical parameters in the rat models were studied. Immunohistochemistry, immunofluorescence and immunoblot techniques were used to study the expression pattern of the related proteins in the human CRC cells pre- and post-treatment. Double contrast barium enema, post-mortem examination and histological analyses were used to confirm tumour growth and the effect of the treatment in vivo in rat models. Notably, we found in human mucinous CRC, a significant increase in expression of the BIRC7/Livin post-FOLFOX treatment compared with pre-treatment ( p = 0.0001). This increase provides new insights into the prognostic role of BIRC7/Livin in evasion of apoptosis and facilitation of treatment resistance, local recurrence and metastasis particularly among mucinous CRCs post-FOLFOX chemotherapy. These poor prognostic features in the CRC may be further compounded by the significant suppression of DARC, PD-L1, PMS2 and overexpression of MSH2 and anti-apoptotic Bcl-2 and p53 proteins observed in our study (p < 0.05). Importantly, we found a significant reduction in expression of BIRC7/Livin and reactivation of DARC and PD-L1 with a surge in Annexin V expression in rat models of CRC cells post-treatment with a sequential dose of aspirin plus FOLFOX compared with other treatments in vivo ( p <0.05). The mechanistic rational of these effects underscores the importance of expanded concept of possible aspirin combination therapy with FOLFOX sequentially in future CRC management. Validation of our findings through randomized clinical trials of aspirin plus FOLFOX sequentially in patients with CRC is therefore warranted.

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model.

As a prevalent disaster, landslides cause severe loss of property and human life worldwide. The specific objective of this study is to evaluate the capability of artificial neural network (ANN) synthesized with artificial bee colony (ABC) and particle swarm optimization (PSO) evolutionary algorithms, in order to draw the landslide susceptibility map (LSM) at Golestan province, Iran. The required spatial database was created from 12 landslide conditioning factors. The area under curve (AUC) criterion was used to assess the integrity of employed predictive approaches. In this regard, the calculated AUCs of 90.10%, 85.70%, 80.30% and 76.60%, respectively, for SI, PSO-ANN, ABC-ANN and ANN showed that all models have enough accuracy for simulating the LSM, although SI presents the best performance. The landslide vulnerability map obtained by PSO-ANN model is more accurate than other intelligent techniques. In addition, training the ANN with ABC and PSO optimization algorithms conduced to enhancing the reliability of this model. Note that, a total of 76.72%, 23.96%, 30.55% and 5.37% of the study area were labeled as perilous (High and Very high susceptibility classes), respectively by SI, PSO-ANN, ABC-ANN and ANN results.

Somaliland, a self-declared state within Somalia, faces a crisis from internal politics and external influences. After proclaiming independence in 1991, progress in political, economic, and social spheres has been slow. Since 2010, governance has worsened, with government forces involved in conflicts, losing control over parts of the Sool and Sanaag regions. Journalists are detained, and peaceful protests are met with deadly force. The crisis arises from dysfunctional policies and institutions, making the polity deeply divisive and tense. This article evaluates emerging institutions against Somaliland's original goals, an area often overlooked in the literature, which typically analyses state fragility factors separately without linking them to the conditions that led to Somaliland's creation. The study uses data from extensive literature reviews and in-depth interviews with local academics and politicians. It concludes that weak institutions, destructive elite behaviour, and exclusive politics drive state fragility. Immediate and sustained comprehensive interventions are essential to address root causes and guide local state-building efforts.

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.