Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

The reliable prediction of dissolved oxygen concentration (DO) is significantly crucial for protecting the health of the aquatic ecosystem. The current research employed four different single AI-based models, namely long short-term memory neural network (LSTM), extreme learning machine (ELM), Hammerstein-Weiner (HW) and general regression neural network (GRNN) for modeling the DO concentration of Kinta River, Malaysia using available water quality (WQ) parameters. Afterwards, the first scenario used four different ensemble techniques (ET). Two linear, i.e. simple averaging ensemble (SAE) and weighted averaging ensemble (WAE) and two nonlinear namely; backpropagation neural network ensemble (BPNN-E) and HW ensemble (HW-E). The second scenario employed a hybrid random forest (RF) ensemble in order to enhance the prediction accuracy of the single models. The WQ parameters were subjected to a different pre-analysis test to ascertain their stability. The four-model combinations are generated using the nonlinear sensitivity input selection approach. The modeling performance was assessed using the statistical measures of Nash-Sutcliffe coefficient efficiency (NSE), Willmott’s index of agreement (WI), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE) and correlation coefficient (CC). The results of the single AI-based models demonstrated that HW (M3) served as the best model for predicting DO concentration. For ensemble results, BPNN-E (WI = 0.9764) was superior to the other three ET with average decreased of more than 2% with regards to MAE. Investigation on the hybrid RF ensemble demonstrated the reliable accuracy for all the hybrid models with better predictive skill shown by the HW-RF (CC = 0.981) ensemble. The overall results verified the promising impact of HW-M3, ET and hybrid RF ensemble for the prediction of the DO concentration in the Kinta River, Malaysia.
A Complex Community in a Simple Habitat: An Experimental Study with Bacteria and Phage

Continuous culture populations of the bacterium especially coli and its virulent virus T7 have been studied as a model of a predator—prey in a simple habitat. These organisms maintain apparently stable states of coexistence in: (1) a phage—limited situation where all of the bacteria are sensitive to the coexisting virus and the sole, and potentially limiting carbon source, glucose, is present in excess; and (2) a resource—limited situation where the majority of the bacteria are resistant to these phage and in which there is little free glucose. The composition of these interacting populations is examined in detail and evidence indicating that this simple experimental culture system can support relatively complex communities is presented. In the predator—limited situation, two populations at each of two trophic levels can be maintained; the wild—type bacterial and phage strains, denoted B0 and T70, a mutant bacterial clone which is resistant to T70, denote B1 and a host range mutant phage, T71 which is capable of growth on both B0 and B1. In the resource—limited situation, three populations of bacteria and two populations of phage can coexist. The include the above described clones and a third bacterial strain, B2, which is resistant to both T70 and T71. In phage—free competition, the wild—type B0 bacterial clone has a marked advantage relative to both B1 and B2 while no difference is detected between B1 and B2. When competing for a B0 host, the wild—type T70 phage clone has a marked advantage over T71. The fit of these observations to some previously developed theory of resource—limited growth, competition and predation is discussed and a mechanism to account for the persistence of these communities is presented. The latter assumes that their stability can be attributed solely to intrinsic factors, i.e., the population growth and interaction properties of the organisms in this continuous culture habitat.
Optimizing LSTM Based Network For Forecasting Stock Market

In this modern era, the financial market, more specifically, the stock markets all over the world, deal with an enormous amount of real-time data that facilitates the data analytics and prediction in the field of finance. The main objective of this paper is to propose a novel model of neural network based on Long-Short Term Memory (LSTM) and utilizing one of the most powerful evolutionary algorithms, namely the Differential Evolution (DE), to forecast the next day’s stock price of a company. This study focuses on optimizing the ten network hyperparameters related to the detection of temporal patterns of a given dataset, namely, the size of the time window, batch size, the number of LSTM units in hidden layers, the number of hidden layers (LSTM and dense), dropout coefficient for each layer, and the network training optimization algorithm. To the best of our knowledge, this is the first time that all this set of parameters have been optimized simultaneously. Then, the LSTM has been optimized by DE to gain the lower root mean squared error (RMSE) for prediction. The proposed model achieved 8.092 RMSE as its objective value, which is better in comparison with the best statistical forecasting models such as NAIVE, ETS, and SARIMA, which are the-state-of-the-art methods in this filed. Moreover, for shortening the training time as the main source of computational expensiveness, the proposed method works with a lower number of epochs. By this way, DE tries to find a shallower and faster network even with higher accuracy, which is a remarkable approach.
Weather-based Fault Prediction in Electricity Networks with Artificial Neural Networks

Predicting weather-related outages in electricity networks is an important issue for distribution system operators. In this study, we apply a data-driven approach and train artificial neural networks to predict faults in the electricity network. In our experiments, we utilize the meteorological data and fault records collected for the period of1.1. 2011-31.12. 2013 in central Finland. Assuming that there might be long-term dependencies between weather conditions and faults in the network, we investigate simple recurrent neural networks, long short-term memory networks, and traditional multilayer perceptrons. Taking into account the meteorological observations preceding faults and varying this period from several hours to several days, we found that 6 hours prior to faults included the sufficient information to make accurate predictions. Also, there was no need in more complicated recurrent neural networks as multilayer perceptron was able to predict events with the large number of faults more accurately. Besides, while forecasting all types of faults and wind-related faults only, oversampling allowed the model to predict rare high peaks.
Genetic Diversity Within and Between Hatchery Strains of Japanese Flounder Paralichthys olivaceus Assessed by Means of Microsatellite and Mitochondrial DNA Sequencing Analysis δ

We assessed genetic divergence within and between hatchery and wild populations of Japanese flounder Paralichthys olivaceus by means of microsatellite and mitochondrial DNA (mtDNA) sequencing analysis. Three hundred individuals derived from three hatchery strains and 190 individuals from three wild populations were examined. All 11 microsatellites screened were polymorphic in all samples. Sequences of the mtDNA control region of Japanese flounder were highly variable; of approximately 443 base pairs sequenced, 132 sites were variable among 490 individuals. The number of microsatellite alleles and mtDNA haplotypes, and mtDNA haplotype diversity showed marked reductions in the hatchery strains compared with the wild populations. Both molecular markers yielded high values of F-statistics between the hatchery strains, and between the hatchery strains and wild populations. According to a phylogenetic tree topology on the basis of inter-individual genetic relatedness as estimated from microsatellite data, the three hatchery strains were genetically separated, possibly caused by random genetic drift. The DNA markers employed in this study should provide an ideal means for genetic monitoring of Japanese flounder hatchery stocks.
An Improved MOEA/D Algorithm for the Carbon Black Production Line Static and Dynamic Multiobjective Scheduling Problem

The make-to-order (MTO) manufacturers generally make production plans based on orders, which can help enterprises effectively avoid market risks, reduce market pressure and improve competitiveness. However, due to the characteristics of MTO production mode, the order static scheduling problem and rush order dynamic rescheduling problem have become more and more important for these MTO manufacturers. Therefore, in this paper, we take the packaging production line of a typical carbon black production enterprise as the research background to study the carbon black production line static and dynamic multiobjective scheduling problem. Firstly, multiobjective optimization models of both order static scheduling and rush order dynamic rescheduling are established. Then the improved MOEA/D algorithm combined the heuristic algorithm based on heuristic rules and discrete dynamic local search is developed to solve these two models. Based on the actual production data, eight instances of order static scheduling problems of different scales and four instances of rush order dynamic rescheduling problems of different scales are constructed respectively. Experimental results illustrate that the improved MOEA/D is effective and superior in solving these two problems.
Artículo original Integrones clase 1 asociados a plásmidos en cepas de Klebsiella pneumoniae

Integrons are genetic elements able to capture resistance genes, usually located in plasmids and responsible for dissemination of resistance in Gram negative bacilli. The presence of class 1 integrons and its association with plasmids in 23 multiresistant Klebsiella pneumoniae strains, isolated from different services of hospital "Antonio Patricio de Alcala" Sucre-Venezuela was studied. By bacterial conjugation and polymerase chain reaction the transference of resistance determinants and the presence of integrons class 1 were evalu- ated. All isolates were resistant to ceftazidime and aztreonam. Conjugation assays demonstrated the presence of conjugative plasmids in all K. pneumoniae strains analyzed. Integrons were detected in 10 (43.47%) isolates. One integron element was present in nine strains and two elements in one single strain. Integrons identified were associated a plasmids. The presence of integrons class 1 carried by plamids must play an important role in the epidemiology of antibiotic resistance in clinical strains.
Dual-Polarized Ultrawideband Eleven Antenna Fed by Modified Passive Balun

A dual-polarized Eleven antenna fed by two passive baluns is proposed in this letter. As the dual-polarized Eleven antenna generally has four differential ports, the feed structure is carefully designed to reduce the number of input ports, and consequently, simplify the overall structure. The simulated results show that this antenna can operate from 5 to 16 GHz with the voltage standing-wave ratio of the two feeding ports less than three. The 10 dB beamwidth is around 100° in the whole operating frequency band, which is suitable as a feed of wideband transmitarrays or reflectors. The performance of the presented antenna is validated by experiments.
Discovering Numerous Strassen’s Equivalent Equations Using a Simple Micro Multimodal GA: Evolution in Action

Solving real-world complex optimization problems using simple metaheuristic algorithms is a challenging but attractive task. Making matrix multiplication efficient is one of the interesting problems. This time-consuming algebric operation is required in many applications in science and engineering, thus reducing its complexity targets more efficient computation. In fact, many of practical and theatrical complicated calculations can be modeled efficiently as matrix-based operations, therefore matrix multiplication is computationally expansive operator among all others. In this paper, a simple metaheuristic method based on Micro Genetic Algorithm is proposed to find Strassen’s equivalent solutions which is an algebraic method to compute the product of two matrices with minimal number of multiplications. Since there are numerous optimal solutions, the modeled problem is a large-scale and highly multi-modal optimization problem. The proposed method could find more than 160, 000 valid solutions with same complexity as Strassen’s in a large-scale search space. Among all discovered solutions found using the proposed method, there are 701 distinct solutions which is the maximum number of discovered Strassen’s equivalent solutions to the best of our knowledge. The proposed algorithm is simple but very efficient to find more and more solutions, in fact, that is a great demonstration of “evolution in action” to tackle real-world complex problems like the current one, which just one set of its equations has been discovered by the Germen mathematicians and has remained mysterious for more than 50 years.
Consumption of Herbaceous Vegetation and Water During Reproduction and Development of Merriam's Kangaroo Rat, Dipodomys merriami

Field data collected from a population of kangaroo rats, Dipodomys merriami, revealed that consumption of green vegetation is correlated with lactation but not the earlier stages of reproductive activity. This suggested that herbaceous vegetation plays a minor role in initiating reproduction but plays a major role in providing support for lactation. To evaluate the requirements for water during preand postnatal development, food and water intakes of female Merriam's kangaroo rat were measured in the laboratory during the nonreproductive state, during gestation, and during lactation. Nonreproductive and gestating females both consumed an average of 4.8 g water per day. During gestation growth rates are low, and water losses are probably of the same magnitude as in nonreproductive adults. Thus, water requirements during gestation would be similar to those during the nonreproductive state. Water intake increased markedly during lactation, averaging 10.7 g per day. Food intake increased to support the energy demands of growth. Thus, water intake derived from the catabolism of food increased as well. However, the majority of the increase in water intake resulted from increased consumption of carrots, which contained abundant free water. During lactation, nursling growth was supported at an average rate of 2.1 g water per day per nursling; the minimum support required is probably somewhat less. Two females were able to successfully wean a single nursling apiece while consuming water at about the same rate as nonreproductive females and with no demands on the body stores of the nursing mothers. Except in some cases of small litter size, sources of free water (e.g., herbaceous vegetation) are necessary for successful weaning of kangaroo rat nurslings.
Chaotic Quantum-inspired Evolutionary Algorithm: enhancing feature selection in BCI

Quantum-inspired Evolutionary Algorithms (QiEAs) have demonstrated to be very effective in several applications. In particular, employing this algorithm for feature selection as a wrapper technique in Brain-Computer Interfaces applications was recently proposed with great results. Moreover, the training time of the model was decreased while maintaining a high classification accuracy, both essential conditions for a successful BCI. The drawback of this model was the sensitiveness to changes in the direction and magnitude of the rotation angle, which can produce adverse effects in both performance and convergence time. Chaotic systems and Evolutionary algorithms, when combined, can enhance the convergence rate and speed of the evolutionary process, incrementing the capacity of reaching the global optima. In this paper we explore the effects of adding ergodicity to a QiEA by the employment of chaotic maps in two operators: chaotic uniform crossover and chaotic quantum update gate. To validate the proposed approach, six commonly used chaotic maps are tested with data of Motor Imagery (MI) Electroencephalography (EEG) of right and left hand movement. The results of these experiments are compared with the ones of a QiEA and a classical Genetic Algorithm (GA). In the proposed model, Wavelet Packet Decomposition is employed as the time-frequency analysis to characterize the signal, whereas a Multilayer Perceptron Neural Network is used as a classifier. The results demonstrated that Chaotic QiEAs can significantly improve the convergence time of the model with only a small loss in the final accuracy.
Predicting The Resource Needs And Outcomes of Computationally Intensive Biological Simulations

Accurately simulating viral dynamics within a human body is often computationally intensive, thus requiring dedicated computing infrastructures. This limits the use of simulations in personalized medicine since patients may not all have access to such infrastructures or the possibility of waiting weeks for predictions. Lowering the computational burden by simplifying the models is a challenge since simulation results must remain accurate in order to safely support the decision-making activities of patients. In this work, we use machine learning to predict the results of a simulation (i.e. surrogate model) and its computational resources (i.e. performance model). This allows patients to use machine learning as a computationally light proxy to simulations and to know how long an accurate simulation would take. We demonstrate that machine learning can build such surrogate and performance models with low errors for five previously developed models of HIV, while outperforming interpolation methods commonly used for surrogate modeling.
A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the vision research and visual processing scientific community can agree on. This makes it critical to have a clear view on the notion of contrast and its use in various applications. One issue to consider is how to define and particularly use this important measure in developing image processing and analysis methods. In this article, we present a critical review of contrast measures and associated models developed by the scientific community in vision, optics, and image processing. We also provide learned lessons and guidelines on the use of appropriate contrast measures in selected visual information processing and analysis applications. We discuss challenges and propose research avenues for models enriched by recent findings in the field of human vision research and machine learning. We believe that this work serves as a guideline and can potentially open new research perspectives to the scientific community working on visual information processing and analysis.
Design and Analysis of a MEMS Based Transdermal Drug Delivery System

The design and analysis of a Micro Electromechanical System-based (MEMS-based) Transdermal Drug Delivery System are presented in the research article. A conceptual design for the delivery of Levodopa was proposed by the authors. Major components of the system were identified as a microfluidic pump, microneedle array and the microfluidic channels connecting the system. The working principles of these components were selected according to the persisting requirements and design considerations. Simulations were conducted to evaluate the performance and to optimize the design of the system. A fabrication method for the system was proposed by the authors as a stacked layer. The results of the simulations conducted were presented. The simulations show positive results with the performance of the system and do not suggest a mode of mechanical failure of the system at the given boundary conditions. The paper concludes with recommendations for future work.
Investigating Optimal Regimes for Prediction in the Stock Market

Forecasting stock prices in the market its known to be an extremely difficult task, where even the predictability of the series itself is a controversial matter. The present study investigates the existence of periods within the series more suitable for prediction, and whether the identification and exploitation of those periods could be learned from data. In order to do that, the Predictability Crawler (P-Craw) framework is proposed. The technique uses optimizations routines such as the Particle Swarm optimization (PSO) or Genetic Algorithms (GA) to select subsets of historical data where statistical learning algorithms can be more efficiently trained. When tested against simulated data, The P-Craw is able to reliably identify the optimal subsets in scenarios ranging from 40% to 100% of predictable samples in the data. To access if the framework brings any improvement when used in a real world scenario, it is tested in a dataset containing intraday data from the Brazilian stocks exchange (BOVESPA). When benchmarked against training with all the samples for the series in the BOVESPA dataset the use of the framework is able to significantly raise the Correct Directional Changes (CDC) of the trained models while reducing the Mean Absolute Error (MAE) in up to 19%.
How Much Does Roxarsone Contribute to Coccidiosis Control in Broilers when Used in Combination with Ionophores

Abstract Roxarsone was tested for anticoccidial efficacy against several individual and mixed field isolates of Eimeria tenella, E. acervulina, E. maxima, and E. brunetti in laboratory tests, and for its contribution to the anticoccidial efficacy of commonly-used anticoccidial products. Roxarsone displayed moderate to good activity against E. tenella and E. brunetti at 45.4 g/ton, but only marginal activity against other species. When used in combination with ionophores, there was considerable improvement in control of field isolates that were known to be resistant to the ionophores alone. Overall, there was good evidence that improvement in performance under field conditions resulted from the anticoccidial activity of roxarsone rather than its growth promotion characteristics.
Video Background/Foreground Separation Model Based on Non-Convex Rank Approximation RPCA and Superpixel Motion Detection

Traditional robust principal component analysis (RPCA) is very prone to voids in the process of background/foreground separation of complex scene videos and easy to misjudge the dynamic background as a moving target, which makes the separation effect unideal. In order to address this problem, this article introduces the super-pixel segmentation technique into the RPCA model. First, the Linear Spectral Clustering algorithm (LSC) is used to mark the super-pixel segmentation of the video sequence and a super-pixel grouping matrix is obtained. Then a new video background/foreground separation model is proposed based on the non-convex rank approximation RPCA and super-pixel motion detection (SPMD) technique. The Otsu algorithm is used to obtain the motion mask matrix and the augmented lagrange alternating direction method is used to solve the improved RPCA model. The results of numerical experiment show that the method proposed in this article has a higher accuracy in the detection of moving objects in dynamic background.