To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The aim of the present study was to evaluate the physiological and morphological parameters of pregnant does for early prediction of prenatal litter size. In total, 33 does were screened using ultrasonography and further categorized into three groups based on does bearing twins (n = 12), a single fetus (n = 12), or non-pregnant does (n = 9). The rectal temperature °F (RT) and respiration rate (RR) as physiological parameters, while abdominal girth in cm (AG) and udder circumference in cm (UC) as morphological parameters were recorded at different gestation times, i.e. 118, 125, 132 and 140 days. In addition to this, age (years) and weight at service (kg) were also used. The statistical analyses included analysis of variance (ANOVA) and linear discriminant analysis (LDA). The results indicated that groups had significant (P < 0.05) differences among morphological parameters at each gestation time, with higher AG and UC in does bearing twins followed by a single fetus and non-pregnant does. However, both physiological parameters were non-significantly (P > 0.05) associated with litter size groups. It was also revealed that the studied parameters showed increasing trends over gestation time in single and twin fetus categories, but they were on par among non-pregnant does. The results of the LDA revealed that estimated function based on age, weight at service, RR, RT, AG and UC had greater (ranging from 75.00 to 91.70%) accuracy, sensitivity and specificity at different gestation times. It was concluded that using an estimated function, future pregnant does may be identified in advance for single or twin litter size, with greater accuracy.
Genetic polymorphism research in livestock species aims to assess genetic differences within and among breeds, primarily for conservation and development objectives. The aim of the present study was to determine the point mutation in the IGF-1 gene (g.855G>C and g.857G>A) and its association with performance traits in Munjal sheep. In total, 50 Munjal sheep were selected and the genomic DNA was isolated using the Automated Maxell RSC DNA/RNA purification system and the Maxwell RSC whole blood DNA kit. A reported set of primers was used to amplify the 294-bp fragment encompassing the targeted region, i.e. the 5′ flanking region of the IGF-1 gene. The polymerase chain reaction product of 294-bp size harbouring the g.857G>A mutation in the 5′ flanking region of the IGF-1 gene was digested with HaeII enzyme. Three possible genotypes were defined by distinct banding patterns, i.e. GG (194, 100 bp), GA (294, 194, 100 bp) and AA (294 bp) in the studied population of Munjal sheep. The genotypic and allelic frequencies of g.857G>A single nucleotide polymorphism of the IGF-1 gene indicated that the frequency of the A allele was higher in the studied population, i.e. 0.59 and the GA genotype was found to be the predominant genotype (0.66). Allele A of the IGF-1 gene was found to be associated with higher body weights and can be used in selection criteria for improving the performance of Munjal sheep. The positive effect of the IGF-1 gene on several conformational traits as observed in this study suggests that this area of the ovine IGF-I gene is particularly important and warrants further investigation on a larger population size.
Soil carbon dioxide (CO2) emissions from the field of corn (Zea mays L.) play an important role in global warming. This study investigated temporal variability of soil CO2 fluxes (Rs) with soil temperature (Ts) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes on Rs using the measured high-frequency data. Rs trend was tested by Mann–Kendall and Sen Estimator. Predicted Rss under different climate scenarios were compared using Parallel-line Analysis. The findings indicated that daily Rs exponentially increased with Ts constrained by θ. During the θ of 27–31%, there was a strong exponential relationship between Rs and Ts, but the relationship was weaker for the θ of 38–41% and 22–26%. Soil environmental index (SEI, Ts × θ) significantly impacted Rs with linear regression Rs0.5 = 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends in Rss and relationships among Rs and Ts and θ in different years. Predicted yearly Rss, root Rss and corn yield in 2014–2049 increased with an increase in temperature scenarios, but the Rss significantly increased as temperature rose by 1°C or higher. Predicted yearly Rss, root Rss and yield reduced with precipitation scenario increase, and the root Rss and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearly Rs from cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfield Rs and evaluating sensitivities of different cropland Rss to temperature changes.
The Endangered Kashmir musk deer Moschus cupreus occurs in the western Himalayan region from Nepal to Afghanistan, but there is a lack of comprehensive and reliable information on its range. The region also harbours the Endangered Himalayan musk deer Moschus leucogaster, and this range overlap may have led to misidentification of the two musk deer species and errors in the delimitation of their ranges. Here, using genetic analysis of the mitochondrial DNA control region, we examined the phylogenetic relationship among musk deer samples from three regions in India: Ganderbal District in Jammu and Kashmir, and Kedarnath Wildlife Sanctuary and Nanda Devi Biosphere Reserve, both in Uttarakhand. The Bayesian phylogenetic analysis indicated a close genetic relationship between samples from Jammu and Kashmir, Kedarnath Wildlife Sanctuary and Nanda Devi Biosphere Reserve, validated by previously published sequences of Kashmir musk deer from Nepal. Our analyses confirmed the samples from Uttarakhand to be from the Kashmir musk deer, which was not previously known from this region. Therefore, we recommend further research in this area, to validate species identification and confirm the geographical distribution of the various species of musk deer. In addition, we recommend revision of the range of M. cupreus in the IUCN Red List assessment, to facilitate effective conservation and management of this Endangered species.
We develop some asymptotics for a kernel function introduced by Kohnen and use them to estimate the number of normalised Hecke eigenforms in
whose L-values are simultaneously nonvanishing at a given pair of points each of which lies inside the critical strip.
Introducing cover crops (CC) in annual cropping systems can promote nutrient cycling and improve soil health. However, impacts of CC on soil health indicators vary and depend on the duration of CC, cropping systems, and other environmental conditions. We performed an on-farm assessment of cover cropping impacts on soil health indicators including C and N pools, enzyme activities, and microbial community structure under different no-till maize-based cropping systems (maize (Zea mays L.)–soybean (Glycine max L.) [CS], CS-winter wheat (Triticum aestivum L.) [CSWw], and maize-oats (Avena sativa L.) [CO]). At five farms, fields with different durations of CC were compared to adjacent no CC (NCC) fields. In general, long-term CC enhanced the soil health parameters compared to NCC. Long-term (20-year) winter rye CC had higher water-extractable C and N content, enzyme activities (β-glucosidase (1.2 times greater), urease (5.5 times greater), acid (1.5 times greater) and alkaline (4 times greater) phosphatase, arylsulfatase (0.8 times greater) and fluorescein diacetate (FDA) (0.7 times greater)) and soil bacterial community abundance (1.2 times greater). Short-term (3–6 years) legume and grass CC mixtures increased β-glucosidase (0.9 times), acid (0.7 times) and alkaline (1.5 times) phosphatase, arylsulfatase (3 times), FDA (0.8 times) activities and total phospholipid fatty acid (1.6 times) concentration. However, short-term (3–6 years) winter rye, legume and brassica mixtures did not significantly alter soil microbial community structure. This study showed that implementation of CC for >6 years promoted C, N, S, and P cycling that are beneficial to soil health in maize-based cropping systems.
The nonlinear evolution of electron Weibel instability in a symmetric, counterstream, unmagnetized electron–positron e−/e+ plasmas is studied by a 2D particle-in-cell (PIC) method. The magnetic field is produced and amplified by the Weibel instability, which extracts energy from the plasma anisotropy. A weakly relativistic drift velocity of 0.5c is considered for two counterstreaming e−/e+ plasma flows. Simulations show that in a homogeneous e−/e+ plasma distribution, the magnetic field amplifies exponentially in the linear regime and rapidly decays after saturation. However, in the case of inhomogeneous e−/e+ plasma distribution, the magnetic field re-amplifies at post-saturation. We also find that the amount of magnetic field amplification at post-saturation depends on the strength of the density inhomogeneity of the upstream plasma distribution. The temperature calculation shows that the finite thermal anisotropy exists in the case of an inhomogeneous plasma distribution which leads to the second-stage magnetic field amplification after the first saturation. Such density inhomogeneities are present in a variety of astrophysical sources: for example, in supernova remnants and gamma-ray bursts. Therefore, the present analysis is very useful in understanding these astrophysical sources, where anisotropic density fluctuations are very common in the downstream region of the relativistic shocks and the widely distributed magnetic field.
Bottom-up assembly of nanomaterials using solution-processed methods is ideally suited for use in fabrication of large-area optoelectronic devices. Tailorable visible and near-infrared absorption in shaped nanostructured noble metals is strongly influenced by localized plasmon resonance effects. Obtaining sharp and selective absorption with solution-processed methods is a challenge and requires suitable control on the growth kinetics, which ultimately results in appropriate size and morphology of the final product. In this work, a photo-assisted multigenerational growth process for synthesis of silver nanotriangle ink with narrow linewidth absorbance is developed. This technique combines photochemical and seed-mediated growth approaches. The resulting ink exhibits a sharp absorption at 700 nm with full width at half maximum of ∼170 nm, verified by absorption as well as dynamic light scattering, transmission electron microscopy, and field emission scanning electron microscopy measurements. Numerical modeling using finite-difference time-domain calculations yields a close match with observed absorption and is used to examine electric field distribution and enhancement factor resonating at 720 nm. The synthesis technique is potentially useable for production of highly selective absorbers in solution phase.
One of the major and widely known small scale problem with the Lambda CDM model of cosmology is the “core-cusp” problem. In this study we investigate whether this problem can be resolved using bar instabilities. We see that all the initial bars are thin (b/a < 0.3) in our simulations and the bar becomes thick ( b /a > 0.3) faster in the high resolution simulations. By increasing the resolution, we mean a larger number of disk particles. The thicker bars in the high resolution simulations transfer less angular momentum to the halo. Hence, we find that in the high resolution simulations it takes around 7 Gyr for the bar to remove inner dark matter cusp which is too long to be meaningful in galaxy evolution timescales. Physically, the reason is that as the resolution increases, the bar buckles faster and becomes thicker much earlier on.
We investigate the minor interactions of two disk galaxies with mass ratio of 10:1 in fly-by encounters that do not lead to the merging of the galaxies. In our N-body simulations, we vary only the pericenter distances to see the effect of the fly-by on the bulge of the major galaxy over the course of the trajectory. At different time steps of the evolution, we did two-dimensional fittings of disk, bulge and bar to trace the variation in the sersic index of the bulge. Our results suggest that galaxy bulges can become boxy/disky through flyby interactions of galaxies.
Optimization problems are used to model many real-life problems. Therefore, solving these problems is one of the most important goals of algorithm design. A general optimization problem can be defined by specifying a set of constraints that defines a subset in some underlying space (like the Euclidean space) called the feasible subset and an objective function that we are trying to maximize or minimize, as the case may be, over the feasible set. The difficulty of solving such problems typically depends on how ‘complex’ the feasible set and the objective function are. For example, a very important class of optimization problems is linear programming. Here the feasible subset is specified by a set of linear inequalities (in the Euclidean space); the objective function is also linear. A more general class of optimization problems is convex programming, where the feasible set is a convex subset of a Euclidean space and the objective function is also convex. Convex programs (and hence, linear programs) have a nice property that any local optimum is also a global optimum for the objective function. There are a variety of techniques for solving such problems – all of them try to approach a local optimum (which we know would be a global optimum as well). These notions are discussed in greater detail in a later section in this chapter. The more general problem, the so-called non-convex programs, where the objective function and the feasible subset could be arbitrary can be very challenging to solve. In particular, discrete optimization problems, where the feasible subset could be a (large) discrete subset of points falls under this category.
In this chapter, we first discuss some of the most intuitive approaches for solving such problems. We begin with heuristic search approaches, which try to search for an optimal solution by exploring the feasible subset in some principled manner. Subsequently, we introduce the idea of designing algorithms based on the greedy heuristic.
Heuristic Search Approaches
In heuristic search, we explore the search space in a structured manner. Observe that in general, the size of the feasible set (also called the set of feasible solutions) can be infinite.
The problem of searching is basic in the computer science field and vast amount of literature is devoted to many fascinating aspects of this problem. Starting with searching for a given key in a pre-processed set to the more recent techniques developed for searching documents, the modern civilization forges ahead using Google Search. Discussing the latter techniques is outside the scope of this chapter, so we focus on the more traditional framework. Knuth  is one of the most comprehensive sources of the earlier techniques; all textbooks on data structures address common techniques like binary search and balanced tree-based dictionaries like AVL (Adelson-Velsky and Landis) trees, red–black trees, B-trees, etc. We expect the reader to be familiar with such basic methods. Instead, we focus on some of the simpler and lesser known alternatives to the traditional data structures. Many of these rely on innovative use of randomized techniques, and are easier to generalize for a variety of applications. They are driven by a somewhat different perspective of the problem of searching that enables us to get a better understanding including practical scenarios where the universe is much smaller. The underlying assumption in comparison-based searching is that the universe may be infinite, that is, we can be searching real numbers. While this is a powerful framework, we miss out on many opportunities to develop faster alternatives based on hashing in a bounded universe. We will address both these frameworks so that the reader can make an informed choice for a specific application.
Skip-Lists – A Simple Dictionary
Skip-list is a data structure introduced by Pugh  as an alternative to balanced binary search trees for handling dictionary operations on ordered lists. The reader may recall that linked lists are very amenable to modifications in O(1) time although they do not support fast searches like binary search trees. We substitute complex book-keeping information used for maintaining balance conditions for binary trees by random sampling techniques. It has been shown that given access to random bits, the expected search time in a skip-list of n elements is O(logn), which compares very favorably with balanced binary trees.
This book embodies a distillation of topics that we, as educators, have frequently covered in the past two decades in various postgraduate and undergraduate courses related to Design and Analysis of Algorithms in IIT Delhi. The primary audience were the junior level (3rd year) computer science (CS) students and the first semester computer science post-graduate students. This book can also serve the purpose of material for a more advanced level algorithm course where the reader is exposed to alternate and more contemporary computational frameworks that are becoming common and more suitable.
A quick glance through the contents will reveal that about half of the topics are covered by many standard textbooks on algorithms like those by Aho et al. , Horowitz et al. , Cormen et al. , and more recent ones like those by Kleinberg and Tardos  and Dasgupta et al. . The first classic textbook in this area, viz., that by Aho et al., introduces the subject with the observation ‘The study of algorithms is at the very heart of computer science’ and this observation has been reinforced over the past five decades of rapid development of computer science as well as of the more applied field of information technology. Because of its foundational nature, many of the early algorithms discovered about five decades ago continue to be included in every textbook written including this one – for example, algorithms like FFT, quicksort, Dijkstra's shortest paths, etc.
What motivated us to write another book on algorithms are the several important and subtle changes in the understanding of many computational paradigms and the relative importance of techniques emerging out of some spectacular discoveries and changing technologies. As teachers and mentors, it is our responsibility to inculcate the right focus in the younger generation so that they continue to enjoy this intellectually critical activity and contribute to the enhancement of the field of study. As more and more human activities are becoming computer-assisted, it becomes obligatory to emphasize and reinforce the importance of efficient and faster algorithms, which is the core of any automated process. We are often limited and endangered by the instictive use of ill-designed and brute force algorithms, which are often erroneous, leading to fallacious scientific conclusions or incorrect policy decisions.