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Nowadays, the increase in criminal activities has resulted in a massive generation of crime reports describing the details of the crime incidents. Analyzing these reports for crime type prediction helps the law enforcement agencies deal with crime prevention strategies. But it is quite a demanding and difficult task to consider these reports individually and determine their crime types. In the proposed work, an efficient classifier has been designed to analyze the crime reports which not only predict the crime types of the reports but at the same time upgrades itself with the help of new crime reports. Therefore, this task demands an incremental supervised learning technique that continuously learns the existing classifier based on the new set of reports and information already extracted from the old set of reports. Developing an incremental classifier infuses the knowledge that keep coming from the newly generated reports and help in increasing the report-discriminating power of the classifier. In this work, we have applied a Bi-objective Particle Swarm Optimization technique for generating an efficient incremental classifier for classifying and predicting the crime reports dynamically. Crime reports of different countries, such as India, the United States of America, and the United Arab Emirates, have been collected from online classified newspapers to measure the performance of the proposed as well as some state-of-the-art classifiers. Also, the method has been evaluated based on an unbiased police witness narrative crime reports and finally, a statistical test has been performed using all four considered datasets to measure the statistical significance of the proposed methodology.

To provide a comprehensive assessment of women cancer in India utilizing the systematically collected data on all cancers by the National Cancer Registry Programme (NCRP).The study examined 10,2287 cancer cases among women cancers providing cancer burden for major anatomical sites. Aggregated data of 28 PBCRs and 58 HBCRs under NCRP for 2012-16 was analysed for incidence rates, trends, cumulative risk of developing cancer, stage at detection and treatments offered.Study results have found region -wide variation of women cancers by indicating highest proportions in western followed by southern region of India. North-Eastern region had lowest proportion. It was observed that breast is highest ranking cancer in most registry areas of urban agglomerations of country while cancer cervix was leading site in registries of rural areas like Barshi (15.3) and OsmanabadBeed (13.1). States of Mizoram (23.2) and Tripura (9.5) along with Pasighat, Cachar and Nagaland. Median age of occurrence for women for these anatomical sites ranged from 45 to 60 years of age. For cancer breast, cervix and ovary -most cases were detected with regional spread. These findings were different for cancer corpus uteri where registries have reported higher proportions (49.3 %) of localized stage at detection. Loco regional cancers had higher proportions of multimodality treatments.Study provides a foundation for assessing the status of women cancers in the country. Variations between geographies would guide appropriate support for action to strengthen efforts to improve cancer prevention and control in underserved areas of the country. This would facilitate advocacy for better investments and research on women cancers.

Paraphrases are well-known as synonyms that express the same context in different articulations. Extracting paraphrases from a large text corpus is a challenging task in Natural Language Processing applications. The present work proposes a graph based clustering technique for discovering labels of crime reports based on extracted paraphrases from large untagged crime corpora. Initially, the entity pairs are represented as shallow parse trees where the headword in each tree reflects the actual meaning of the phrase between the entities. Though the phrases having similar headwords have been collected together, there exist many phrases between the entities that express similar context without sharing the same headword. Therefore, clustering is done to create a group of similar meaning phrases termed as paraphrases. A complete weighted graph is constructed with the phrases as nodes and cosine similarity between pair of phrases as the weight of an edge with the phrases as terminal nodes. The graph is made sparse by removing edges with weights less than a threshold value and clustering coefficient has been calculated for each node. The subgraph(s) comprising node(s) with the highest clustering coefficient has been extracted with their adjacent edges. The remaining nodes with their adjacent edges in the graph are added one at a time to an extracted subgraph, if and only if the average clustering coefficient of the resultant subgraph increases and an agglomerative merging technique is applied to merge the extracted subgraphs until no merging takes place. Finally, each subgraph represents a cluster of phrases, yields one aspect of crime. Based on the extracted paraphrases, the reports can be easily labelled. The proposed work deals with crime reports for United States of America (USA), United Arab Emirates (UAE) and India and the evaluation is performed in terms of various supervised and unsupervised techniques.

We report here a low-cost, field-deployable smartphone-based sensing system for accurate estimation of phosphorus (in phosphate form) in water and agricultural soil. The sensing principle is based on the standard ascorbic acid protocol where heteropoly acid-phosphomolybdic acid reagent changes color upon treatment with ascorbic acid, which can be detected by the designed sensing system. A 3D printed cradle housing the optical source and other optical components of the sensor is coupled to the built-in rear camera of the phone and subsequently, convert it into a compact handheld system. Two freely available android applications have been used for the detection and analysis of phosphate concentration. The HSV (Hue-Saturation-Value) color model's V-channel values of the captured images are used to correlate with the phosphate concentrations of the samples. The results obtained with the proposed sensor are found similar to laboratory-grade spectrophotometer data. The designed sensing system has an ability to measure phosphate concentration variations as low as 0.01 mg/L and 0.03 mg/L in the range 0–1 mg/L and 1–5 mg/L, respectively, with good accuracy (%bias

Background: Ewing sarcoma is a malignant tumour found mainly in childhood and adolescence. The present study aims at analyzing the data on Ewing sarcoma cases of bone from the National Cancer Registry Programme, India to provide incidence, patterns, and trends in the Indian population. Materials and Methods: The data of five Population Based Cancer Registries (PBCR) of Bangalore, Mumbai, Chennai, Bhopal and Delhi over 30 years period (1982– 2011) were used to calculate the Age Specific and Age Standardized Incidence Rates (ASpR and ASIR), and trends in incidence was analyzed by linear and Joinpoint Regression. Results: Ewing sarcoma comprised around 15 % of all bone malignancies. Sixty-eight percent were 0–19 years, with 1.6 times risk of tumour in bones of limbs as compared to other bones. The highest incidence rate (per million) was in the 10–14 years age group (male −4.4, female −2.9) with significantly increasing trend in ASpR observed in both sexes. Pooled ASIR per million for all ages was higher in male (1.6) than female (1.0) with an increasing rate ratio of ASIR with increase in age. Trend of pooled ASIR for all ages was significantly increased in both sexes. Twelve percent cases were reported in ≥30 years of age. Conclusion: This paper has described population based measurements on burden and trends in incidence of skeletal Ewing in India. These may steer further research questions on the clinical and molecular epidemiology to explain factors associated with the increasing incidence of Ewing sarcoma bone observed in India. Keywords: Ewing sarcoma, Bone, Incidence, Cancer Registry, India

Introduction Cancer is the second leading cause of death globally. Adverse effects of chemotherapy affect physiological and psychological aspects of human life. Self-care practice to manage side effects can improve the quality of life. Aims The study investigates the relationship between knowledge on the adverse effects of chemotherapy and self-care ability to manage chemotherapy survivors' side effects. Subjects and methods This was a single centre, Cross-sectional, descriptive study. A face-to-face interview guided by a structured questionnaire was conducted among 100 cancer patients admitted at the daycare centre to receive repeated chemotherapy cycles. Information gathered included knowledge on chemotherapy and its adverse effects and self-care ability to manage them. Results Results indicated 58% had an above-average level of experience on chemotherapy and side effects,16.42 ± 3.56 Vs 8.52 ± 3.37.To overcome hair loss, 77% of patients preferred to use a wig/scarf to cover the head. To reduce nausea and vomiting symptoms, 48% chose to consume less food. Knowledge on side effects of chemotherapy moderate positively related to self-care practices with (r = 0.55,p = 0.001 level), which shows a significant raise on knowledge level will improve their self-care practices. Knowledge of the adverse effects of chemotherapy was not associated with the demographic variables of study participants. Conclusion Study concluded that it was essential to enhance awareness and overcome adverse chemotherapy effects via counselling services using current modalities for better self-care practices.

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 μg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard., Graphical abstract Image 1

Although the impact of lockdown on Air Quality Index(AQI) was given enough attention but investigation on AQI during partial lockdown, change of the worst AQI hot spot pattern and its consistency, spatio-temporal dynamics of core-periphery divide of pollution over megacities were lacking. The present study explored the above mentioned issues along with monitoring of AQI of India during lockdown and partial lockdown based on the daily data of Central Pollution Control Board(CBCB). Gi-index, instability index, least squares regression and frequency approaches were used for analyzing AQI hot spot, spatial instability of AQI, trend of AQI and consistency of Pollution State Presence Frequency (PSPF). In result, clear improvement of AQI was observed since average AQI reduced from 110 in pre-lockdown to 73 in lockdown I and 93 in partial lockdown. The average AQI of the mega cities was improved up to 55%–75% in lockdown. However during partial lockdown, with restoration of economic activities the air quality was observed to degrade again. AQI hotspot and PSPF were identified high in and around Delhi and industrial hubs. Positive trend of AQI change, instability of AQI were found gradually high in partial lockdown period and these effects was observed greater in urban and industrial belts. Though all these facts signify anthropogenic activities as a major source of air pollution but shutting down economic activities lockdown couldn’t be a permanent solution to combat it. Hence, prioritizing green energy sources, improve technologies, utilize energy sustainably that could reduce the pollution level.

River island (locally known as charland) in river Ganges from Rajmahal hill to Farakka barrage of India is now under human habitat dominated by the environmentally evicted people triggered by bank erosion, but these are under different physical vulnerability like bank erosion, flooding etc. Considering the physical inconvenience, and inaccessibility, the present work intended to model the livelihood vulnerability state (LVS) by using advance machine learning algorithms, like Artificial neural network (ANN), Random forest (RF), Random subspace (RS) and Support vector machine (SVM). For LVS modelling, field and remote sensing based 26 parameters were selected. We classified the parameters as exposure (11 parameters), sensitivity (4 parameters) and adaptive capacity (11 parameters). We modelled LVS for overall condition, exposure, adaptive capacity, and sensitivity. Application of these algorithms in this field is unique and its robustness and precision in result is highly satisfactory. LVS models clearly identified 39% to 53% of areas having high to very high vulnerability and these are located at the edge of the charlands. Among the models, SVM outperformed as per the result of accuracy assessment. Therefore, it can be treated as a representative algorithm for LVS modelling. Among the 26 parameters, bank erosion, unhygienic condition, and Below Poverty Level (BPL) household parameters were found as highly dominating based on the findings of information and gain ratio. The correlation with LVS and three individual models (exposure, adaptive capacity, and sensitivity) exhibited that the Exposure model was highly correlated (r = 0.87) with high statistical significance (0.001 level).

Background Vagus nerve stimulation (VNS) paired with forelimb training drives robust, specific reorganization of movement representations in the motor cortex. The mechanisms that underlie VNS-dependent enhancement of map plasticity are largely unknown. The cholinergic nucleus basalis (NB) is a critical substrate in cortical plasticity, and several studies suggest that VNS activates cholinergic circuitry. Objective We examined whether the NB is required for VNS-dependent enhancement of map plasticity in the motor cortex. Methods Rats were trained to perform a lever pressing task and then received injections of the immunotoxin 192-IgG-saporin to selectively lesion cholinergic neurons of the NB. After lesion, rats underwent five days of motor training during which VNS was paired with successful trials. At the conclusion of behavioral training, intracortical microstimulation was used to document movement representations in motor cortex. Results VNS paired with forelimb training resulted in a substantial increase in the representation of proximal forelimb in rats with an intact NB compared to untrained controls. NB lesions prevent this VNS-dependent increase in proximal forelimb area and result in representations similar to untrained controls. Motor performance was similar between groups, suggesting that differences in forelimb function cannot account for the difference in proximal forelimb representation. Conclusions Together, these findings indicate that the NB is required for VNS-dependent enhancement of plasticity in the motor cortex and may provide insight into the mechanisms that underlie the benefits of VNS therapy.

The proposed work describes a rough set based incremental crime report labelling technique. The named entities are recognized from the available crime reports to identify the phrases existing between entity pairs. The phrases are vectorized considering intervening context words and a quick reduct generation algorithm is applied to minimize their dimensions. A graph based clustering algorithm has been applied to cluster the phrases and later they have been labelled based on some centrality measure techniques. Each current report, represented by a set of phrases, is labelled by the labels of clusters in which its phrases reside. Thus, a current report may be labelled as a multi-class report. Each new report is labelled incrementally using existing labelled clusters with the help of rough set theory. The phrases of the new report are partitioned into equivalence classes using indiscernibility relation and the lower approximations of the clusters of phrases are obtained considering these equivalence classes as target sets. If the lower approximation of a cluster contains all the phrases of the new report, then the report is labelled by the label of that cluster; otherwise, probability based membership values have been assigned to the report to represent the degree of its belongingness into different classes. The existing clusters and equivalence classes of phrases generate a new set of labelled clusters, which are further used for labelling upcoming reports. Thus, the proposed method labels crime reports in an incremental way. The work is validated by various indices and compared with many state-of-the-art clustering and classification algorithms. Experimental results show the effectiveness of the method in the crime report labelling.

Plants have evolved a number of molecular strategies and regulatory mechanisms to cope with abiotic stresses. Among the various key factors/regulators, transcription factors (TFs) play critical role(s) towards regulating the gene expression patterns in response to stress conditions. Altering the expression of the key TFs can greatly influence plant stress tolerance. OsHBP1b (accession no. KM096571) is one such TF belonging to bZIP family, localized within the Saltol QTL, whose expression is induced upon salinity treatment in the rice seedlings. qRT-PCR based expression studies for OsHBP1b in seedlings of contrasting genotypes of rice showed its differential regulation in response to salinity stress. A GFP based in vivo study showed that the OsHBP1b protein is nuclear localized and possesses the trans-activation activity. As compared to the WT tobacco plants, the transgenic plants ectopically expressing OsHBP1b showed better survival and favourable osmotic parameters (such as germination and survival rate, membrane stability, K(+)/Na(+) ratio, lipid peroxidation, electrolyte leakage and proline contents) under salinity and drought stress. Under salinity conditions, the transgenic plants accumulated lower levels of reactive oxygen species as compared to the WT. It was also accompanied by higher activities of antioxidant enzymes (such as ascorbate peroxidase and superoxide dismutase), thereby demonstrating that transgenic plants are physiologically better adapted towards the oxidative damage. Taken together, our findings suggest that OsHBP1b contributes to abiotic stress tolerance through multiple physiological pathways and thus, may serve as a useful 'candidate gene' for improving multiple stress tolerance in crop plants.

The Delay Tolerant Network (DTN) came as a saviour for heterogeneous networks marked by intermittent connectivity and low energy levels. By the introduction of the BUNDLE layer in addition to the TCP/IP layers, the DTN tries to achieve what the TCP/IP could not i.e. allow communication in challenged networks. The Postbox DTN is a type of DTN which has a persistent node known as Postbox which is always ON. All nodes have a direct connection to it and no other connection between nodes exist. Hence all communication is made via and by the Postbox just like the real world Postbox scenario. Apart from the novel concept of a 1 hop routing strategy, it is very easy to deploy. However its ease of use comes at a huge cost of energy usage by its always ON Postboxes. This work proposes a modified Postbox model which conserves energy without depreciating its performance. Simulation results confirm our hypothesis.

Soil salinization has become a severe constraint for crop production world-wide which necessitated development or induced enhancement of salt stress tolerance in plant life to sustain production in saline lands. Recognition and prospecting of valuable stress tolerant genes from natural microbial resources of saline habitat is obscure to date. Therefore, the investigation was towards isolation and characterization of marine salt stress tolerant microbes along the East coast of India for revelation of effective salt stress tolerant genes. Salt stress tolerance was assessed from 98 bacterial isolates obtained from 28 water and soil samples. Among them, 35 isolates which failed to grow beyond 4% salt were discarded and remainder 63 isolates were selected for further functional analysis and only seven isolates recorded ≥8% NaCl stress tolerance. Phylogeny revealed that four isolates belong to Firmicutes and three isolates were members of Proteobacteria. Ribosomal Database Project Release-11 and SILVA SSU database based genotyping and taxonomic identity analysis confirmed that the higher (20%) salt stress tolerant bacteria were Staphylococcus sp., Enterococcus sp., Enterobacter sp. and Proteus sp. To investigate candidate, as well as, novel salt stress tolerant genes, the seven bacterial isolates would provide new horizon to focus on the recent developments of salinity stress tolerance. In addition, the findings evidently point out the diversity of salt stress tolerant marine bacteria in coastal Odisha and West Bengal, India.

Noise nuisance has been emerged as a critical challenge against the psychological and physical peace all over the world. Researches proved that big cities are highly affected by noise nuisance but noise state in a meso level town like English Bazar Municipality (EBM) is the present focus of work. The present work intends to explore the principal sources of noise, spatial noise mapping and diurnal noise cycle in the residential and heavy traffic area. The level of noise annoyance is tried to compute among different exposed or unexposed folks. Finally, it is also tried to recognize the most vulnerable area of the town exposed to noise using multi-criteria decision approach. From the result it is found that traffic (58.65%) and construction works (21.15%) are the major sources of noise. Office time (around 10am) is the noisiest time due to maximum traffic and floating population. In this time, 39.21% to total study area experiences noise level >70dBA. Even in the mid night, noise level exceeds 70dBA along National Highway-34. Here, noise annoyance is sensitive to age, sex, economic folks and facing of window in relation to road. 9.94% area is emerged as the most vulnerable area to noise exposition.

ion of a fingerprint in the form of a hash can be used for secure authentication. The main challenge is in finding the right choice of features which remain relatively invariant to distortions such as rotation, translation and minutiae insertions and deletions, while at the same time capturing the diversity across users. In this paper, an alignment-free novel fingerprint hashing algorithm is proposed which uses a graph comprising of the inter-minutia minimum distance vectors originating from the core point as a feature set called the minimum distance graph. Matching of hashes has been implemented using a corresponding search algorithm. Based on the experiments conducted on the FVC2002-DB1a and FVC2002-DB2a databases, we obtained an equal error rate of 2.27%. The computational cost associated with our fingerprint hash generation and matching processes is relatively low, despite its success in capturing the minutia positional variations across users.

Exposure to poor air quality is associated with a multitude of hematological and immunological alterations. Cardio vascular diseases, rather than respiratory ailments, are the most important cause of death from air pollution exposure. Thus, hematological, immunological and cardiovascular alterations in healthy individuals exposed to vehicular pollution (one of the leading source of air pollution in growing metropolitan cities) are investigated in this study. A total number of 2218 (21–65 years old) adults residing in Delhi participated in this study. As control, 642 age and sex matched healthy subjects from the rural areas of Uttaranchal were enrolled. Arterial blood pressure (BP) was measured by a sphygmomanometer. Blood samples were collected and routine hematology was done. Lymphocyte subset analysis and platelet P-selectin expression was measured by flow cytometry. Air quality data was collected from Central and State Pollution Control Boards and was also measured onsite by portable, battery-operated laser photometer. The prevalence of hypertension was nearly 4-times higher in Delhi when compared to the control. Platelet P-selectin was remarkably upregulated in residents of Delhi. They had depleted number of CD4+ T-helper cells and CD19+ B cells but elevated level of CD56+ natural killer cells. Altered lymphocyte subtypes and increased number of P-selectin-positive platelets suggest altered immunity (that may compromise body's defense against infections) and hypercoagulable state, a risk factor for cardiovascular diseases. The current study has identified poor air quality of Delhi as a key contributor to several adverse health conditions experienced by the general population of the city, which not only makes the quality of life compromised but also put them at a greater risk of developing cardiovascular ailments later in life.

In the present work, we have studied the hydrogen absorption–desorption properties of the Ti2CrV alloy, and effect of cycling on the hydrogen storage capacity. The material has been characterized for the structure, morphology, pressure composition isotherms, hydrogen storage capacity, hydrogen absorption kinetics and the desorption profile at different temperatures in detail. The Ti2CrV crystallizes in body centered cubic (bcc) structure like TiCrV. The pressure composition isotherm of the alloy has been measured at room temperature and at 373K. The Ti2CrV alloy shows maximum hydrogen storage capacity of 4.37 wt.% at room temperature. The cyclic hydrogen absorption capacity of Ti2CrV alloy has been investigated at room temperature upto 10th cycle. The hydrogen storage capacity decreased progressively with cycling initially, but the alloy can maintain steady cyclic hydrogen absorption capacity 3.5 wt.% after 5th cycle. To get insight about the desorption behavior of the hydride in-situ desorption has been done at different temperatures and the amount of hydrogen desorbed has been calculated. The TG (Thermo gravimetric) and DTA analysis has been done on uncycled hydride shows that the surface poisoned sample gives a desorption onset temperature of 675K. The DSC measurement of uncycle and multi-cycled saturated hydrides shows that the hydrogen desorption temperature decreasing with cycling.

The causal delay of finite impulse response filters can be minimized by the use of recursive Minimum-phase filters, i.e., IIR filters. However, in IIR filters delay distortion arises which is undesirable. Many algorithms have been proposed, which try to attain both these goals, of minimizing causal delay and delay distortion, simultaneously. Usually for a long input sequence to be filtered, block convolution techniques are used such as overlap-save method (OSM) and overlap-add method (OAM). However, in these methods the output sequence has a finite group delay with respect to input. To reduce that group delay, in this paper, we have proposed the technique of enhanced modified overlap and save method. In our method, we first make the impulse response (IR) causal (i.e., h(n)=0 for n

Arginine is a common substrate for both inducible nitric oxide synthase (iNOS) and arginase. The competition between iNOS and arginase for arginine contributes to the outcome of several parasitic and bacterial infections. Salmonella infection in macrophage cell line RAW264.7 induces iNOS. Because the availability of l-arginine is a major determinant for nitric oxide (NO) synthesis, we hypothesize that in the Salmonella infected macrophages NO production may be regulated by arginase. Here we report for the first time that Salmonella up-regulates arginase II but not arginase I isoform in RAW264.7 macrophages. Blocking arginase increases the substrate l-arginine availability to iNOS for production of more nitric oxide and perhaps peroxynitrite molecules in the infected cells allowing better killing of virulent Salmonella in a NO dependent manner. RAW264.7 macrophages treated with iNOS inhibitor Aminoguanidine reverts the attenuation in arginase-blocked condition. Further, the NO block created by Salmonella was removed by increasing concentration of l-arginine. The whole-mice system arginase I, although constitutive, is much more abundant than the inducible arginase II isoform. Inhibition of arginase activity in mice during the course of Salmonella infection reduces the bacterial burden and delays the disease outcome in a NO dependent manner.