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Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

The Coronavirus pandemic has taken the world by surprise in its ability to change the way everything functions today. It has severely impacted the health, both mental as well as physical, of citize...

Nowadays it is imperative to maintain a high level of security to ensure secure communication of information between various institutions and organizations. With the growing use of internet over the years, the number of attacks over the internet have escalated. A powerful Intrusion Detection System (IDS) is required to ensure the security of a network. The aim of an IDS is to monitor the active processes in a network and to detect any deviation from the normal behavior of the system. When it comes to machine learning, optimization is the process of obtaining the maximum accuracy from a model. Optimization is vital for IDSs in order to predict a wide variety of attacks with utmost accuracy. The effectiveness of an IDS is dependent on its ability to correctly predict and classify any anomaly faced by a computer system. During the last two decades, KDD_CUP_99 has been the most widely used data set to evaluate the performance of such systems. In this study, we will apply different Machine Learning techniques on this data set and see which technique yields the best results.

Paclobutrazol (PBZ) role in drought management of maize is least understood. In maize, root traits are linked with surface water management. Over three years, early and terminal deficit irrigation (EDI and TDI) with or without PBZ were imposed on DKC-9144 and PG-2475 maize varieties. Several allometric parameters viz. stem height, stem diameter, leaf area and root traits along with physiological processes were measured. Implication of these parameters in the management of soil surface irrigation in terms of water use efficiency (WUE) was demonstrated in maize. Increased number of lateral roots and root number density in DKC-9144 provided more surface area for water absorption for better management of EDI. Root growth rates showed a similar pattern with root length, root surface areas, and root numbers in EDI. Elevated expressions of ZmRTCL, ZmRTCS and ZmARF34 in EDI and EDI plus PBZ were associated with seminal roots and root laterals initiation. Under TDI alone or in combination with PBZ, root lengths (BRL, CRL, SRL) and root surface areas varied in DKC-9144 and PG-2475 over control. Furthermore, correlation analysis showed that decrease in WUE under TDI was significantly associated with a reduction in stem thickness and leaf surface area. For WUE_N in TDI and PBZ plus TDI, structural equation modelling proposed, brace root surface area (BRSA_N) as a positive contributor, while a negative contributor was seminal root surface area (SRSA_N). Present study explained the importance of specific root traits and their association with other allometric parameters for improving WUE in DKC-9144 variety of maize and the crop in general.

Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random Forest, Extra trees, AdaBoost, and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for comparative analysis among all the classifiers used. This analysis gives knowledge, investigates difficulties, and future opportunities to propel machine learning in networking. This paper can give a basic understanding of data analytics in terms of security using Machine Learning techniques.

Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. Over the last decade, data has increased exponentially in networking domains, so naturally, there is a need to understand this data to get useful insights. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute to the outdated KDD'99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random forest, Extra trees, AdaBoost and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for a comparative analysis among all the classifiers used. This analysis gives knowledge, investigate difficulties, and future opportunities to propel machine learning in networking. Along these lines, it is a beneficial contribution to enhance the understanding of machine learning for networking, pushing the limits of automation using machine learning for better network management. This paper can give a basic understanding of the data analytics in terms of security using Machine Learning techniques.

Plants are sedentary organisms that constantly sense changes in their environment and react to various environmental cues. On a short-time scale, plants respond through alterations in their physiology, and on a long-time scale, plants alter their development and pass on the memory of stress to the progeny. The latter is controlled genetically and epigenetically and allows the progeny to be primed for future stress encounters, thus increasing the likelihood of survival. The current study intended to explore the effects of multigenerational heat stress in

Sleep is an important factor for sports performance. Physiological delay in sleep–wake phase and sports training pose adolescent athletes at increased risk of sleep problems. The aim of the study was to observe the sleep pattern and effect of non-pharmacological sleep strategies in adolescent archers. Fifteen male adolescent archers participated. Sleep measures were recorded using ‘School Sleep Habits Survey’ questionnaire. Awareness of benefits of good sleep on recovery in sports was done for coaches, staff and players. Institution of a sleep consultation and practice of non-pharmacological strategies to improve sleep for players was carried out for a year. Sleep measures and archery scores were analysed. Sleep pattern showed delayed sleep onset and decreased sleep duration. Post-intervention, total sleep duration increased during weekdays and weekends and sleep onset latency significantly reduced during weekdays (p = 0.008). Archery practice shooting score over the year significantly improved (p = 0.018). Sleep in adolescent archers showed altered sleep parameters. Self-awareness and sleep hygiene education of coaches and players along with non-pharmacological interventions showed a significant improvement in sleep and archery performance. CTRI/2017/05/008676.

Sex-determination in Mercurialis annua is not related to chromatin conformation or DNA methylation of floral homeotic genes but might be regulated upstream of these genes by one or more unknown gender-specific factors that affect hormonal homeostasis., In plants, dioecy characterizes species that carry male and female flowers on separate plants and it occurs in about 6% of angiosperms; however, the molecular mechanisms that underlie dioecy are essentially unknown. The ability for sex-reversal by hormone application raises the hypothesis that the genes required for the expression of both sexes are potentially functional but are regulated by epigenetic means. In this study, proteomic analysis of nuclear proteins isolated from flower buds of females, males, and feminized males of the dioecious plant Mercurialis annua revealed differential expression of nuclear proteins that are implicated in chromatin structure and function, including floral homeotic proteins. Focusing on floral genes, we found that class B genes were mainly expressed in male flowers, while class D genes, as well as SUPERMAN-like genes, were mainly expressed in female flowers. Cytokinin-induced feminization of male plants was associated with down-regulation of male-specific genes concomitantly with up-regulation of female-specific genes. No correlation was found between the expression of class B and D genes and the changes in DNA methylation or chromatin conformation of these genes. Thus, we could not confirm DNA methylation or chromatin conformation of floral genes to be the major determinant regulating sexual dimorphisms. Instead, determination of sex in M. annua might be controlled upstream of floral genes by one or more sex-specific factors that affect hormonal homeostasis. A comprehensive model is proposed for sex-determination in M. annua.

Background Weightlifting is a power and dynamic strength game. Performance of weightlifters during competition depends on absolute and relative power output achieved by them. To enhance power and strength in weightlifters, training programmes have frequent high-intensity exercise sessions. On an informal interaction with the sportspersons, they often complain of problems after intense training. There was a felt need to analyse the problems they perceive after intense training. Method A feedback form was developed after brainstorming with coaches and sports medicine physicians of a sports institute. Thirty-six male weightlifters aged 19.78 ± 4.71 yrs (mean ± standard deviation) of an institute participated after obtaining informed consent. The feedback form was collected and analysed. Result After an intense exercise, participants reported complaints such as increased fatigue (n = 27), sleep problems (n = 26), restlessness (n = 18) and decreased appetite (n = 12). Of the sleep problems, 69.4% (n = 25 out of 36) noticed worsening of sleep, 50% (n = 18) noticed sleep was light after intense training, 58.3% (n = 21) felt difficulty in falling asleep, 47.2% (n = 7) complained of frequent waking up during sleep at night and 77.8% (n = 28) felt sleepy after waking up. Conclusion Intense training encounters certain problems such as fatigue, sleep disturbances, restlessness and decreased appetite.

Cadmium (Cd) inhibits plant growth, perturbs nutrient uptake, and affects chloroplast ultrastructure. The role of Cd stress in affecting growth and physiology and ameliorative effects of gibberellins (GAs) in Cd-induced toxicity in mung bean are lesser-known. This study comprehensively investigated Cd stress (CdCl2, IC50—500 µM L−1) with or without GA3 on mung bean (Vigna radiata L. Var. SML-668). In our methodology, a total of 80 mung bean plants (15 days old of uniform height) were divided into four groups, and each group (n = 20) was subjected to four different treatments (Control, CdCl2, GA3, CdCl2+GA3) twice during the entire life cycle of mung bean plants (until harvest 85–90 days). Results revealed negative impacts of Cd stress on shoot morphometry (plant height, leaf surface area, stem diameter, shoot fresh weight, number of leaves, number of pods, length, and diameter of pods), root morphometry (root length, root surface area, root dry weight, nodule number and nodule diameter), photosynthetic pigments, and agronomic traits. GA3 application ameliorated Cd stress by modulating shoot and root growth, improving overall plant metabolism, photosynthetic pigments, and shoot and root morphometry and transcript abundance of VrPCS1, VrIRT1, VrIRT2 and VrCD29. Thus, we propose GA3 application for the effective management of Cd-induced phytotoxicity in mung bean plants.

This article aims to achieve higher-order numerical approximation to the solutions of a class of singularly perturbed parabolic problems which can consist of the time-dependent convection coefficient and generally possess regular boundary layer. In order to fulfill the aim, at first we develop and analyze an efficient numerical method by discretizing the model problem using a new finite difference scheme on an appropriate layer-adapted mesh in the spatial direction, and the time derivative using the backward-Euler method on an equidistant mesh. We adopt the two-stage discretization process to establish the parameter-uniform estimate in the discrete supremum norm; and provide stability analysis in both the temporal and spatial discretization cases. Afterwards, we apply the Richardson extrapolation technique solely in the temporal direction for enhancing the temporal accuracy. We finally show that the resulting numerical solution is globally second-order convergent with respect to both the spatial and the temporal variables. At the end, numerous numerical results are presented to corroborate the theoretical findings; and also to demonstrate the computational efficiency and the accuracy of the present numerical method in comparison with the existing numerical method. Besides this, we extend the computational experiment by solving the singularly perturbed semi-linear parabolic problem.

Cadmium (Cd) inhibits plant growth, perturb nutrient uptake and affect chloroplast ultra structure. Cd soil pollution is mainly contributed by excessive use of phosphate fertilizers, nickel Cd batteries, plating and sewage sludge. Research investigations deciphering role of Cd in affecting overall performance of mung bean is least understood. Likewise ameliorative effects of gibberellins (GAs) in Cd induced toxicity in mung bean are lesser known. In this context, effects of Cd stress (CdCl2, IC50 −500 µM L−1) with or without GA3 application on mung bean (Vigna radiata L. Var. SML-668) plants were comprehensively investigated under controlled conditions. In brief, a total of 80 mung bean plants (15 days old of uniform height) were divided into four groups, with each group (n=20) subjected to four different treatments (Control, CdCl2, GA3, CdCl2+GA3), twice during the entire life cycle of mung bean plants (until harvest 85-90 days). Results revealed negative impacts of Cd stress on shoot morphometry (plant height, leaf surface area, stem diameter, shoot fresh weight, number of leaves, number of pods, length and diameter of pods), root morphometry (root length, root surface area, root dry weight, nodule number and nodule diameter), photosynthetic pigments and agronomic traits. GA3 ameliorated Cd stress by modulating shoot and root growth rates, improving overall plant metabolism, photosynthetic pigments, and shoot and root morphometry and transcript abundance of VgPCS1, VgPCS2, VgCdR and VgIRT1. Current study proposes GA3 application for the effective management of Cd induced phytotoxicity in mung bean plants.

Bryophytes are potent metal absorbers, thriving well on heavy metal (HM)-polluted soils. Mechanisms controlling uptake, compartmentalization and impacts of HMs on bryophytes life cycle are largely unknown. The current study is an effort to decipher mechanisms of nickel (Ni) excess-induced effects on the phenological events of two bryophytes, Asterella wallichiana and Plagiochasma apendiculatum growing in natural habitats. Observations revealed Ni-excess induced negative impacts on abundance, frequency of occurrence of reproductive organs, population viability and morphological traits, spore viability and physiological attributes of both the liverworts. Results led us conclude that P. appendiculatum survived better with the lowest impact on its life cycle events than A. wallichiana under Ni excess in natural habitats. Our findings collectively provide insights into the previously unknown mechanisms of Ni-induced responses in liverworts with respect to phenological attributes, as well as demonstrate the potential of P. appendiculatum to survive better in Ni excess habitats.

Cannabis seed germination is an important process for growers and researchers alike. Many biotechnological applications require a reliable sterile method for seed germination. This protocol outlines a seed germination procedure for Cannabis sativa using a hydrogen peroxide (H2O2) solution as liquid germination media. In this protocol, all three steps including seed sterilization, germination, and seedlings development were carried out in an H2O2 solution of different concentrations; 1% H2O2 solution showed the fastest and the most efficient germination. This protocol also exhibited high germination efficiency for very old cannabis seeds with lower viability. Overall, this protocol demonstrates superior germination compared to water control and reduces the risk of contamination, making it suitable for tissue culture and other sensitive applications.

This article deals with a class of singularly perturbed convection-diffusion parabolic problems with discontinuous source term exhibiting both boundary and weak interior layers. In order to solve this class of problems, we discretize the time derivative by the backward-Euler method on the uniform mesh; and for the spatial discretization, a new finite difference scheme is proposed utilizing a layer resolving piecewise-uniform Shishkin mesh. We discuss the monotonocity of the proposed method. Further, we demonstrate through the numerical experiment that the proposed method converges uniformly with respect to the perturbation parameter \(\varepsilon \); and is almost second-order accurate in space, regardless of the larger and smaller values of \(\varepsilon \). In addition to this, we compare the present numerical method with the classical implicit upwind finite difference scheme.

Multilevel feedback queue scheduling (MLFQ) algorithm is based on the concept of several queues in which a process moves. In earlier scenarios there are three queues defined for scheduling. The two higher level queues are running on Round Robin scheduling and last level queue is running on FCFS (First Come First Serve). A fix time quantum is defined for RR scheduling and scheduling of process depends upon the arrival time in ready queue. Previously a lot of work has been done in MLFQ. In our propose algorithm Smart Job First Multilevel feedback queue (SJFMLFQ) with smart time quantum (STQ), the processes are arranged in ascending order of their CPU execution time and calculate a Smart Priority Factor SPF on which processes are scheduled in queue. The process which has lowest SPF value will schedule first and the process which has highest SF value will schedule last in queue. Then a smart time quantum (STQ) is calculated for each queue. As a result, we found decreasing in turnaround time, average waiting time and increasing throughput as compared to the previous approaches and hence increase in the overall performance.

Plants are sedentary organisms that constantly sense changes in their environment and react to various environmental cues. On a short-time scale, plants respond through alterations in their physiology, and on a long-time scale, plants alter their development and pass on the memory of stress to the progeny. The latter is controlled genetically and epigenetically and allows the progeny to be primed for future stress encounters, thus increasing the likelihood of survival. The current study intended to explore the effects of multigenerational heat stress in Arabidopsis thaliana. 25 generations of Arabidopsis thaliana were propagated in the presence of heat stress. The multigenerational stressed lineage F25H exhibited a higher tolerance to heat stress and elevated frequency of homologous recombination, as compared to the parallel control progeny F25C. A comparison of genomic sequences revealed that the F25H lineage had a three-fold higher number of mutations (SNPs and INDELs) as compared control lineages, suggesting that heat stress induced genetic variations in the heat-stressed progeny. The F25H stressed progeny showed a 7-fold higher number of non-synonymous mutations than the F25C line. Methylome analysis revealed that the F25H stressed progeny showed a lower global methylation level in the CHH context than the control progeny. The F25H and F25C lineages were different from the parental control lineage F2C by 66,491 and 80,464 differentially methylated positions (DMPs), respectively. F25H stressed progeny displayed higher frequency of methylation changes in the gene body and lower in the body of transposable elements (TEs). Gene Ontology analysis revealed that CG-DMRs were enriched in processes such as response to abiotic and biotic stimulus, cell organizations and biogenesis, and DNA or RNA metabolism. Hierarchical clustering of these epimutations separated the heat stressed and control parental progenies into distinct groups which revealed the non-random nature of epimutations. We observed an overall higher number of epigenetic variations than genetic variations in all comparison groups, indicating that epigenetic variations are more prevalent than genetic variations. The largest difference in epigenetic and genetic variations was observed between control plants comparison (F25C vs F2C), which clearly indicated that the spontaneous nature of epigenetic variations and heat-inducible nature of genetic variations. Overall, our study showed that progenies derived from multigenerational heat stress displayed a notable adaption in context of phenotypic, genotypic and epigenotypic resilience.

As the circumstances are changing, mankind has turned out to be more inclined to snappy and speedier correspondence and access to information. The correspondence happens in numerous structures (e.g., presently, this correspondence is all the more a virtual substance than a physical one). So as to keep up fast correspondence, the coming age will depend on exceptionally tried and true, canny and self-learning/self-modifying correspondence organizers. In this context, this chapter reviews the most important machine learning techniques with the direct applicability in wireless ad-hoc systems. A guide of machine learning methods and their relevance is also provided. Different applications of ad-hoc wireless networks are discussed in terms of energy-aware communications, optimal node deployment and localization, resource allocation, and scheduling.