Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating the Effects of External Perturbations on a Gene Regulatory Network using Feedback Controllers

Gene regulatory networks are generally robust in nature. However, unwanted perturbations arising out of extreme environmental conditions or external pathogen attacks may lead them to malfunction. Potentially, this can have an adverse effect on the biochemical functions of a living system. In this work, we have proposed a computational model based on negative feedback control to eliminate the effects of such unwanted perturbations. We have implemented the recurrent neural network formalism for modelling the underlying network dynamics from a given time-series gene expression dataset. The artificial bee colony optimisation technique has been employed for model parameter estimation. The controller used in this work is of the proportional-integral-derivative type. To the best of our knowledge, this is one of the first research works in this domain to consider a completely non-linear scenario. A 10-gene DREAM4 benchmark network has been considered in this work. The results obtained herein show that the proposed formalism can mitigate the unwanted effects of external disturbances effectively.
Genetic Aspects of the Profit Equation in a Broiler Enterprise

Abstract THE HIGH LEVEL of efficiency of broiler producers today is the result breeding practices. In the main, this improvement has been effected in the broiler progeny, not in the performance of the parent stock. Yet costs in a broiler enterprise are determined, not only by the efficiency of broiler progeny, but also by the performance of the parent flock, particularly its rate of egg production, feed efficiency and hatchability. Specific knowledge concerning the relative importance of parent and progeny performance determining profitability of a strain or cross seems to be lacking. Strain and Nordskog (1962) concluded that broiler weight and broiler feed conversion were the most important factors determining net income in the Maine Random Sample Broiler tests. Egg production in the parent flock was of little importance. Nordskog (1960), using egg laying test data, found that egg production was the most important single factor affecting income. However, both of …
Adversarial Learning for Multi-Task Sequence Labeling With Attention Mechanism

With the requirements of natural language applications, multi-task sequence labeling methods have some immediate benefits over the single-task sequence labeling methods. Recently, many state-of-the-art multi-task sequence labeling methods were proposed, while still many issues to be resolved including (C1) exploring a more general relationship between tasks, (C2) extracting the task-shared knowledge purely and (C3) merging the task-shared knowledge for each task appropriately. To address the above challenges, we propose MTAA, a symmetric multi-task sequence labeling model, which performs an arbitrary number of tasks simultaneously. Furthermore, MTAA extracts the shared knowledge among tasks by adversarial learning and integrates the proposed multi-representation fusion attention mechanism for merging feature representations. We evaluate MTAA on two widely used data sets: CoNLL2003 and OntoNotes5.0. Experimental results show that our proposed model outperforms the latest methods on the named entity recognition and the syntactic chunking task by a large margin, and achieves state-of-the-art results on the part-of-speech tagging task.
Channel Estimation Based on Adaptive Denoising for Underwater Acoustic OFDM Systems

Underwater acoustic (UWA) communications systems suffer from a low signal-to-noise ratio (SNR) and a doubly selective channel, which are caused by many limitations, such as high propagation loss, slow propagation speed, and time-varying environmental factors. To overcome a low SNR, various diversity techniques are often adopted in UWA communications. However, such diversity can only be exploited with knowledge of the channel. Accordingly, channel estimation needs to be performed without obtaining the benefit of diversity. Moreover, accurate side information that can support a channel estimator (CE) is difficult to acquire at a low SNR under doubly selective channel. In this article, a novel CE based on an adaptive denoising is proposed for UWA orthogonal frequency-division multiplexing (OFDM) systems. The proposed method exploits two different types of pilot symbols. Channel impulse response (CIR) is estimated based on primary pilot symbols. By minimizing the squared error of the received secondary pilot symbols, a near-optimal denoising window is adaptively determined based on the channel length for the given CIR estimate. The proposed method does not require a priori information about channel statistics and SNR values. Analysis on the effect of denoising and the performance of the proposed denoising window estimator are also presented. Simulation and at-sea experiments verify that the proposed method has superior performance, compared with conventional CEs over diverse channel conditions. Complexity analysis shows that the proposed method is computationally efficient. Therefore, the proposed method is effective for real-time UWA OFDM systems under a harsh UWA channel with strong noise.
Group Composition for Collaborative Learning With Distributed Leadership in MOOCs Using Particle Swarm optimization

The Massive Open Online Courses (MOOCs) are online courses with open enrollment that involves a huge amount of students from different locations, with different backgrounds and interests. The large number of students implies an enormous and not manageable amount of interactions. This fact, along with the different interests of the students, results in low quality interactions. Due to the large amount of students, it also becomes impossible the composition of learning groups manually. Due to these characteristics present in MOOCs, a method for the formation of groups was developed in this work, as an attempt to meet the dichotomy that exists between the collective. For the formation of groups, an adaptation of the Particle Swarm optimization algorithm was proposed on the basis of three criteria, level of knowledge, interests and leadership profiles, forming groups with different levels of knowledge, interests similar and distributed leadership, providing a better interaction and construction of knowledge. On a test basis, the algorithm demonstrated that can meet the criteria for grouping in a computation time and is more efficient than the model of random groups. The tests also showed that the algorithm is robust considering the various data sets and variations of iterations.
A CMA-ES algorithm for solving the steelmaking scheduling problem involving time buffers

The production system of a steelmaking plant always is thought of as a special hybrid flow shop (HFS). However, most scheduling models are not performed very well in practical environments due to practical problems are more complex than classic versions, and the algorithms are weak in solving problems with various scales and structures. This paper studies a practical scheduling problem involving time buffers, and propose an improved covariance matrix adaptation evolution strategy (CMA-ES) algorithm without painstaking parameter selections. First, the studied problem is formulated as a mixed-integer linear programming (MILP) model. Second, we develop an improved CMA-ES algorithm with a problem-oriented encoding and decoding scheme, and a differential evolution (DE) based restart policy. Finally, the proposed algorithm is compared with the standard DE algorithm and the state-of-the-art algorithm named CCABC. Experimental results carried out on a variety of synthetic scheduling benchmarks demonstrate that the proposed technique shows the advantage in seeking optimums.
ELEVATIONAL DISTRIBUTION OF TREE DIVERSITY IN LOWER HIMALAYA: A CASE STUDY OF PHULCHOKI HILL, NEPAL

Changes in tree species composition with elevation have been studied at three distinct altitudinal ranges on the basis of dominance, in sub-tropical and temperate forests on south slopes of Phulchoki hill, central part of lower Himalaya, Nepal. The greatest number of tree species was found in the low altitude (1600 m), followed by intermediate altitude (1950 m). The highest altitude (2650 m) contained only one distinct tree species (Oak). Site I has higher species diversity (Shannon Index: 2.1863, Evenness Index: 0.852) and density (5575 individual per hectare) dominated by Castonopsis indica, Quercus glauca, Myrica esculenta and myrsine capitellate. Site II has average species diversity (Shannon Index: 1.759, Evenness Index: 0.9) and an average density (2150 individual per hectare) dominated by Rhododendron arboretum, Castanopsis tribuloides and Quercus incana. At site III the forest is entirely dominated by Quercus semicarpifolia , a high altitude Oak. A linear trend of decreasing tree species diversity and density along with elevations was seen which might be because of intense afforestation by communities at lower elevation as site I and II under community managed forest and lower altitudinal ecozone with clear zonation of elevational vegetation types. International Journal of Environment Volume-4, Issue-3, June-August 2015 Page: 130-139
METHYL JASMONATE BRINGS ABOUT RESISTANCE AGAINST SALINITY STRESSED TOMATO PLANTS BY ALTERING BIOCHEMICAL AND PHYSIOLOGICAL PROCESSES

This manuscript mainly defines about the effect of foliar application of methyl jasmonate (C13H20O3) (MeJA) on physiological and biochemical processes in tomato under both saline and non-saline conditions. Two tomato genotypes Rio Grande (tolerant) and Savera (sensitive) were grown in pots having sand as growth medium. The salinity substantially decreased the physiological and biochemical parameters. Different doses of MeJA (0.0, 10, 20, 30, 40, 50, 60 µM) were applied on both control and salt stressed tomato plants. Methyl Jasmonate MeJA significantly ameliorated the deleterious effects of salinity on tomato plants by inducing the physiological and biochemical resistance. Different parameters responded to MeJA at various extents. Our findings illustrate that all the parameters responded to foliar application of MeJA and it is quite helpful creating physiological and biochemical resistance in salinity stressed tomato plants.
Efficient Estimation of the Helicopter Blade Parameter by Independent Component Analysis

We propose an efficient method to estimate various parameters of a helicopter blade including the number <inline-formula> <tex-math notation="LaTeX">$B_{num}$ </tex-math></inline-formula> and the length by separating the blade signal using independent component analysis (ICA) and an analytic solution obtained by monostatic/bistatic geometry. The proposed method is composed of two parts, one for even <inline-formula> <tex-math notation="LaTeX">$B_{num}$ </tex-math></inline-formula> and one for odd <inline-formula> <tex-math notation="LaTeX">$B_{num}$ </tex-math></inline-formula>. For even <inline-formula> <tex-math notation="LaTeX">$B_{num}$ </tex-math></inline-formula> the blade parameters are estimated using ICA plus analytic estimation. For odd <inline-formula> <tex-math notation="LaTeX">$B_{num}$ </tex-math></inline-formula> the parameters are estimated using two ICAs plus analytic estimation. Experimental results using the radar signal of a blade model calculated by physical optics demonstrate that the proposed method successfully separates the blade signals for both even and odd <inline-formula> <tex-math notation="LaTeX">$B_{num}\text{s}$ </tex-math></inline-formula> and estimates parameters with high accuracy.
A Symmetric Points Search and Variable Grouping Method for Large-scale Multi-objective Optimization

In this paper, we propose a new method for large scale multi-objective optimization based on symmetric points search and variable grouping, named SSVG. The main idea is to use variable grouping scheme first to divide the original decision space into several subspaces. In each subspace, the symmetric points of the points in population form some potential search directions. Using the search directions, the possibility of finding the optimal solutions will increase greatly. Moreover, in order to decrease the dimension of problem, a new transformation function which transforms the decision space into a lower dimension search space (weight vector space) is designed. Furthermore, experiments are conducted on some benchmarks with 200, 500 and 1000 decision variables and the proposed algorithm SSVG is compared with three state-of-the-art algorithms: MOEA/DVA, WOF and LSMOF. The results show that the proposed algorithm outperforms the compared algorithms in term of convergence and diversity.
Non-dominated Solution Sampling Using Environmental Selection in EMO algorithms

This work focuses on the environmental selection methods incorporated in several evolutionary multi-objective optimization (EMO) algorithms for sampling representative nondominated solutions from a large non-dominated solution set. Evolutionary multi- and many-objective optimization generally provides a large set of non-dominated solutions. They are useful for precisely approximating the Pareto front but harm decision making when selecting one solution among them. Sampling and presenting a representative set of solutions is a promising method for addressing this issue. The selection of a subset of solutions from a large set of solutions is a type of combinatorial optimization problem. Its difficulty is increased by increasing the size of the non-dominated solution set, because the number of selection combinations of the solutions increases exponentially. This work focuses on environmental selection as a reasonable method to sample a solution subset from a large set. We compare 17 environmental selection methods incorporated in EMO algorithms and show that the one-by-one selection or deletion approach is suitable for sampling a representative nondominated set.
Ka-Band Coplanar Magic-T Based on Gap Waveguide Technology

A coplanar Magic-T is proposed based on a combination of ridge and <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane groove gap waveguides for <italic>Ka</italic>-band applications. All four ports of the proposed Magic-T are coplanar. The impedance bandwidth of the proposed Magic-T is about 43% covering the whole <italic>Ka</italic>-band from 26 to 40 GHz. The Magic-T isolation is better than 40 dB in the whole bandwidth while its insertion loss is about 0.25 dB.
Identifying Genes Associated With Autism Spectrum Disorders by Random Walk Method With Significance Tests

Autism spectrum disorders (ASD) are generally defined as a development disorder typically characterized by social interaction and communication ailments and stereotyped actions due to combined genetic and environmental factors. Different critical aspects contribute to ASD, and consensus has been reached among autism researchers about its predominant genetic factors. However, the pathogenesis of ASD has not been fully revealed, and a systematic method must be developed to identify the genes related to this disease. Here, we predicted new ASD-associated genes by random walk method on the basis of prior-known ASD genes from AutDB. New genes such as RAC3, AC1 (ADCY1), PKC (PRKC) gamma, EPH receptor A5, WNT3A, calretinin, RAS-R, KLF4, and calpain 3 were found to play an irreplaceable role in ASD pathogenesis.
A 22.5–31.2-GHz Continuously Tuning Frequency Synthesizer With 8.7-GHz Chirp for FMCW Applications

This letter presents a fractional-N frequency synthesizer with an ultrawideband continuously tuning range of 32.4% centered at 26.85- and 8.7-GHz chirp for frequency-modulated continuous-wave (FMCW) radars. The synthesizer uses a fractional-N 9-GHz charge-pump phase-locked loop (CPPLL) cascaded by an injection-locked frequency tripler (ILFT). A voltage-controlled oscillator (VCO) with transformer (TFM) coupling for an enhanced linear control voltage range, a better quality factor and good isolation from the load seen by the resonator, and a wide output voltage range charge-pump (CP) are used to achieve the ultrawideband CPPLL. The ILFT is implemented using a weakly coupled TFM for broadband locking range and 3-dB output power. The synthesizer is fabricated in the 40-nm CMOS technology and consumes 28.5 mW. The phase noise (PN) of the synthesizer at 27 GHz is −97.87 dBc/Hz at 1 MHz and the prototype is capable of generating a triangle or sawtooth chirp of 8.7-GHz modulation bandwidth with an rms FM error of 0.04% of BWchirp.
Efficient Coherent Direction-of-Arrival Estimation and Realization Using Digital Signal Processor

A novel efficient coherent direction-of-arrival (DOA) estimation method is devised in this article. First, a new cost function without the knowledge of source number is developed exploiting the Toeplitz matrices’ joint diagonalization structure. Then, the revised steering vectors are used in the place of projection weights of the steering vectors to reconstruct the power spectrum in both noise and signal subspaces. The coherent DOAs are estimated using the 1-D search. Furthermore, the computational complexity of the proposed method is significantly reduced using the Nystrom approximation. Finally, the developed theoretical model is implemented on the TMS320C6678 digital signal processor (DSP) to exemplify the efficacy of the novel method.
Efficient Calculation of Stabilization Parameters in RF Power Amplifiers

This article proposes an efficient method for the calculation of the stabilization parameters in RF power amplifiers operating in periodic large-signal regimes. Stabilization is achieved by applying the principles of linear control theory for periodic linear time-varying (PLTV) systems. A numerical method is proposed to obtain the harmonic transfer function that represents the system linearized around the large-signal steady state. Then, a feedback analysis is performed to calculate the closed-loop poles of the PLTV system. The proposed approach is demonstrated with two examples. First, a three-stage amplifier that exhibits a low-frequency oscillation for increasing values of input power is correctly stabilized. Next, the stabilization of an unstable design that exhibits an odd-mode parametric oscillation is presented. The results of the proposed technique are compared to those obtained with the conventional parametric stability simulation. These examples serve to illustrate the capability and efficiency of the proposed approach.
A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems

This paper presents a simplified adaptive neuro-fuzzy inference system (ANFIS) controller to control nonlinear multi-input multi-output (MIMO) systems. This controller uses only few rules to provide the control actions, instead of the full combination of all possible rules. Consequently, the proposed controller possesses several advantages over the conventional ANFIS controller especially the reduction in execution time, and hence, it is more appropriate for real time control. A real-coded genetic algorithm (GA) was utilized to optimize the premise and the consequent parameters of the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Accordingly, the necessity for the teaching signal required by other optimization techniques has been eliminated. Furthermore, the GA was employed to determine the input and output scaling factors for this controller, instead of the widely used trial and error method. Two nonlinear MIMO systems were chosen to be controlled by this controller. In addition, the controller robustness to output disturbances was also investigated and the results clearly showed the notable accuracy and the generalization ability of this controller. Moreover, the result of a comparative study with a conventional MIMO ANFIS controller has indicated the superiority of the simplified MIMO ANFIS controller.
Trend Mining 2.0: Automating the Discovery of Variable Trends in the Objective Space

Practical multi-criterion decision making not only involves the articulation of preferences in the objective space, but also a consideration of how the variables impact these preferences. Trend mining is a recently proposed visualization technique that offers the decision maker a quick overview of the variables’ effect on the structure of the objective space and easily discover interesting variable trends. The original trend mining approach relies on a set of predefined reference directions along which an interestingness score is measured for each variable. In this paper, we relax this requirement by automating the approach to find optimal reference directions that maximize the interestingness for each variable. Additional extensions include the use of an Achievement Scalarizing Function (ASF) for ranking solutions along a given reference direction, and an updated interestingness score formulation for more appropriately handling discrete variables. We demonstrate the working of the extended approach on DTLZ2 and WFG2 benchmarks for up to five objectives and on a biobjective engineering design problem. The results show that the ability of the proposed approach to detect variable trends in high dimensional objective spaces is heavily dependent on the quality of the solutions used.
A heuristic algorithm for districting problems with similarity constraints

Redistricting problems arise when due to evolving attributes, current districts get increasingly inefficient or even infeasible. A typical example are electoral districts of roughly the same population, which after changes in its distribution, become imbalanced. Since the cost of creating entirely new districts, in general, is too high, redistricting problems have two conflicting objectives: to find an improved or optimal solution while keeping the changes to existing districts small. In this paper we propose an efficient heuristic for redistricting problems, which can consider similarity to existing districts together with several other constraints, such as balance, connectedness, and compactness. In an experimental study, we evaluate the contribution of heuristic strategies, the effect of different attribute modification models, and the effect of different similarity metrics on the quality of the redefined districts.
A 27–42-GHz Low Phase Error 5-Bit Passive Phase Shifter in 65-nm CMOS Technology

A 27–42-GHz 5-bit passive switching phase shifter is presented using 65-nm CMOS technology for the fifth generation (5G) applications. In the 180° phase shift bit, a reflection-type-based biphase modulator topology is developed to achieve good phase shifting performance over a wide bandwidth. The measured insertion loss (IL) of all 32 states is 12.4 ± 1.2 dB at 39 GHz and 11.4 ± 2.3 dB at 28 GHz with zero dc power. The phase shifter demonstrates an ultralow root mean square (rms) phase error of 0.7° and a low rms amplitude error of 0.8 dB at 37 GHz with an inherent digital phase-control mechanism. The rms phase error is less than 3.8° and the rms amplitude error is less than 2.1 dB from 27 to 42 GHz.