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This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You'll find a new balance of classical ideas and modern insights into machine learning. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries. From back cover.

It is essential to know the proportion of health care workers (HCW) who are COVID 19 positive, as well as the severity and mortality among them. This systematic review was performed according to the Preferred Reporting Items for Systematic review and meta-analysis. Databases including PubMed, EMBASE and Web of Science were searched from December-31, 2019 to April-23, 2020. The search was limited to the studies that reported the data on the number of COVID-19 positive healthcare workers, among the COVID-19 positive patients. Case reports, duplicate publications, reviews, and family-based studies were excluded. The methodological quality of studies was assessed by the Appraisal tool for Cross-Sectional Studies (AXIS) tool. In this systematic review and meta-analysis, we pooled eleven studies to investigate the above factors. The overall proportion of HCW who were SARS-CoV-2 positive among all COVID-19 patients was 10.1% (95%CI: 5.3–14.9). This proportion varied according to the country of study i.e. China (7 studies) - 4.2%, 95%CI:2.4–6.0; United States (3 studies) – 17.8%, 95%CI:7.5–28.0; and Italy (1 study) – 9.0%, 95%CI:8.6–9.4. The incidence of severe or critical disease in HCW (9.9%, 95%CI:0.8–18.9) was significantly lower (p < 0.001) than the incidence of severe or critical disease in all COVID-19 positive patients (29.4%, 95%CI:18.6–40.2). Similarly, the mortality among HCW (0.3%, 95%CI:0.2–0.4) was also significantly lower (p < 0.001) as compared to that of all patients (2.3%, 95%CI:2.2–2.4). Health care workers who are COVID-19 positive constituted a significant proportion of all COVID-19 patients; but the severity and mortality were lower among them. •Meta-analysis of eleven studies showed that nearly 10% of COVID-19 positive patients are health care workers•The incidence of severe disease in health care workers (9.9%) was significantly lower than its incidence among all COVID-19 positive patients (29.4%)•The mortality among health care workers (0.3%) was also significantly lower as compared to that of all patients (2.3%)

Jugaad is an Indian name for versatility and improvisation, a sensibility for improvisation, an ability for improvisation and an enabling of improvisation. This paper proposes the idea of Jugaad Infrastructure for versatile socio‐material infrastructure arrangements that inhabit and thrive in the messy aesthetics of everyday life. It does so by extending the focus of infrastructure geographies from ‘big stuff’ to little devices such as solar lamps that gain significance when deployed in big numbers. The paper advances two ideas. First, it argues that jugaad circumvents the formal–informal boundary set by designers. By piercing this boundary, jugaad affords more fluid socio‐material relationships involving infrastructures and their users. In so doing, jugaad affords versatility. Second, it develops the idea of Jugaad Infrastructure. Jugaad Infrastructure folds two things into it. First, infrastructures that are designed in ways that facilitate jugaad, albeit within firmly maintained boundaries and attempt to capitalise on people's aptitude for jugaad to take different forms, inhabit different spaces, enable different purposes and all this while somewhat retaining their shape. They are easy to maintain. This helps them travel to, function and stay in different places. In this way, small devices spread around in large numbers to become big infrastructure. Second, it represents the ensembles of fluid socio‐material relationships and resources involving infrastructures and their users through which infrastructures are tailored to ‘better’ fit everyday lives and needs. Jugaad Infrastructure inhabits the liminal spaces of struggle between designers claiming jugaad as a limited practice that leads to stable innovations and users deploying unlimited jugaadas an everyday practice of socio‐material flux. The paper is based on qualitative research conducted in India during 2012–2013, 2016 and 2017 using participant observations, discussions and interviews with users, entrepreneurs, market players and designers, in addition to documentary evidence from reports and websites. Short Jugaad is an Indian name for versatility and improvisation, a sensibility for improvisation, an ability for improvisation and an enabling of improvisation. This paper proposes the idea of Jugaad Infrastructure for versatile socio‐material infrastructure arrangements that inhabit and thrive in the messy aesthetics of everyday life. When development is deployed through the market, Jugaad Infrastructure reveals jugaad's neoliberal co‐option, where private companies in the development marketplace adopt the logics of jugaad within carefully managed parameters to sell ‘better’ products and make money from marginalised people and places, but also jugaad's capacity to exceed the neoliberal co‐option by empowering people to reconfigure these ‘better’ products to ‘better’ fit their lives and needs, rather than buying more and different products.

We systematically investigate the fundamental oscillation frequencies of dark matter admixed neutron stars, focusing on models with self-interacting fermionic dark matter that couples to normal matter solely through gravity. The analysis is carried out within a two-fluid formalism under the relativistic Cowling approximation, where the perturbation equations follow from the linearized energy–momentum conservation laws of both components. We find that the mass-scaled fundamental frequencies of the nuclear (dark) fluid in dark core (halo) configurations exhibit a remarkably tight correlation with the total stellar compactness. This universality persists across the dark matter parameter space explored in this study and is largely insensitive to the choice of nuclear equation of state. In contrast, we also find the breakdown of such universality with the tidal deformability, i.e., the same frequencies show substantial deviations from universality when expressed in terms of the tidal deformability. These contrasting behaviors highlight possible observational imprints of dark matter in neutron star interiors.

Images are an important source of information and copy-move forgery (CMF) is one of the vicious forgery attacks. Its objective is to conceal sensitive information from the image. Hence, authentication of an image from human eyes become arduous. Reported techniques in literature for detection of CMF are suffering from the limitations of geometric transformations of forged region and computation cost. In this paper, a deep learning CNN model is developed using multi-scale input with multiple stages of convolutional layers. These layers are divided into two blocks i.e. encode and decoder. In encoder block, extracted feature maps from convolutional layers of multiple stages are combined and down sampled. Similarly, in decoder block extracted feature maps are combined and up sampled. A sigmoid activation function is used to classify pixels into forged or non-forged using the final feature map. To validate the model two different publicly available datasets are used. The performance of the proposed model is compared with state-of-the-art methods which show that the presented data-driven approach is better. Graphic Abstract

The bacterial wilt disease of solanaceous crops incited by Ralstonia solanacearum is a menace to the production of solanaceous vegetables all over the world. Among the agro climatic zones of West Bengal, India growing solanaceous vegetables, the maximum and minimum incidence of bacterial wilt was observed in Red and Lateritic zone (42.4%) and Coastal and Saline zone (26.9%), respectively. The present investigation reports the occurrence of bacterial wilt of Bottle gourd by R . solanacearum Sequevar 1–48 for the first time in India. Two new biovars (6 and 3b) along with biovar 3 have been found to be prevalent in West Bengal. Under West Bengal condition, the most predominant Sequevar was I-48 (75%) followed by I-47 (25%). Low genetic variation (18.9%) among agro climatic zones (ACZs) compared to high genetic variation (81.1%) within revealed occurrence of gene flow among these ACZs. Standard genetic diversity indices based on the concatenated sequences of the seven genes revealed ACZ-6 as highly diverse among five agro climatic zones. The multi locus sequence analysis illustrated occurrence of synonymous or purifying selection in the selected genes in West Bengal and across world. Under West Bengal conditions maximum nucleotide diversity was observed for the gene gyr B. Occurrence of significant recombination was confirmed by pairwise homoplasy test (θ = 0.47*) among the RSSC isolates of West Bengal, belonging to Phylotype I. Phylotype I isolates of West Bengal are involved in exchange of genetic material with Phylotype II isolates. In case of worldwide RSSC collection, eleven significant recombination events were observed among the five phylotypes. Phylotype IV was genetically most diverse among all the Phylotypes. The most recombinogenic phylotype was Phylotype III. Further, the most diverse gene contributing to the evolution of RSSC worldwide was observed to be endoglucanase ( egl ).

In the last few decades, the demand for cement production increased and caused a massive ecological issue by emitting 8% of the global CO2, as the making of 1 ton of ordinary Portland cement (OPC) emits almost a single ton of CO2. Significant air pollution and damage to human health are associated with the construction and cement industries. Consequently, environmentalists and governments have ordered to strongly control emission rates by using other ecofriendly supplemental cementing materials. Rice husk is a cultivated by-product material, obtained from the rice plant in enormous quantities. With no beneficial use, it is an organic waste material that causes dumping issues. Rice husk has a high silica content that makes it appropriate for use in OPC; burning it generates a high pozzolanic reactive rice husk ash (RHA) for renewable cement-based recyclable material. Using cost-effective and commonly obtainable RHA as mineral fillers in concrete brings plentiful advantages to the technical characteristics of concrete and to ensure a clean environment. With RHA, concrete composites that are robust, highly resistant to aggressive environments, sustainable and economically feasible can be produced. However, the production of sustainable and greener concrete composites also has become a key concern in the construction industries internationally. This article reviews the source, clean production, pozzolanic activity and chemical composition of RHA. This literature review also provides critical reviews on the properties, hardening conditions and behaviors of RHA-based concrete composites, in addition to summarizing the research recent findings, to ultimately produce complete insights into the possible applications of RHA as raw building materials for producing greener concrete composites—all towards industrializing ecofriendly buildings.

Cancer is one of the major healthcare challenges across the globe. Several anticancer drugs are available on the market but they either lack specificity or have poor safety, severe side effects, and suffer from resistance. So, there is a dire need to develop safer and target-specific anticancer drugs. More than 85% of all physiologically active pharmaceuticals are heterocycles or contain at least one heteroatom. Nitrogen heterocycles constituting the most common heterocyclic framework. In this study, we have compiled the FDA approved heterocyclic drugs with nitrogen atoms and their pharmacological properties. Moreover, we have reported nitrogen containing heterocycles, including pyrimidine, quinolone, carbazole, pyridine, imidazole, benzimidazole, triazole, β-lactam, indole, pyrazole, quinazoline, quinoxaline, isatin, pyrrolo-benzodiazepines, and pyrido[2,3-d]pyrimidines, which are used in the treatment of different types of cancer, concurrently covering the biochemical mechanisms of action and cellular targets.

In this study, we investigate the impact of dark matter (DM) on neutron stars (NSs) using a two-fluid formalism that treats nuclear matter (NM) and DM as gravitationally coupled components. Employing NM equations of state spanning a wide range of stiffness and a self-interacting asymmetric fermionic DM framework, we explore the emergence of DM core- and halo-dominated structures and their observational implications. Constraints from gravitational waves (GW170817), NICER X-ray measurements (PSR J0030+0451), and pulsar mass limits (PSR J0740+6620) delineate a consistent parameter space for DM properties derived from these multi-messenger observations. DM halo-dominated configurations, while consistent with PSR J0740+6620’s mass limits and NICER’s radius measurements for PSR J0030+0451, are ruled out by the tidal deformability bounds inferred from the GW170817 event. Consequently, the combined limits inferred from the observational data of GW170817, PSR J0030+0451, and PSR J0740+6620 support the plausibility of DM core-dominated configurations. Constraints on the DM self-interaction strength from galaxy cluster dynamics further refine the DM parameter space permitted by NS observations. This work bridges multi-messenger astrophysics and cosmology, providing insights into DM interactions and their implications for NS structure, evolution, and observational signatures.

Towards practical realization of brain-inspired computing in a scalable physical system, we investigate a network of coupled micromechanical oscillators. We numerically simulate this array of all-to-all coupled nonlinear oscillators in the presence of stochasticity and demonstrate its ability to synchronize and store information in the relative phase differences at synchronization. Sensitivity of behavior to coupling strength, frequency distribution, nonlinearity strength, and noise amplitude is investigated. Our results demonstrate that neurocomputing in a physically realistic network of micromechanical oscillators with silicon-based fabrication process can be robust against noise sources and fabrication process variations. This opens up tantalizing prospects for hardware realization of a low-power brain-inspired computing architecture that captures complexity on a scalable manufacturing platform.