Search results for: utkarsh shrivastava

Authors: Rahul Shrivastava, Manish Tripathi, Mohmmad Yasir, Shailesh Singh

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Life style changes and depletion in atmospheric ozone layer in recent decades lead to increase in skin cancer including both melanoma and nonmelanomas. Natural products which were obtained from different plant species have the potential of anti skin cancer activity. In regard of this, present study focuses the potential effect of Glycosmis pentaphylla against anti skin cancer activity. Different Phytochemical constituents which were present in the roots of Glycosmis pentaphylla were identified and were used as ligands after sketching of their structures with the help of ACD/Chemsketch. These ligands are screened for their anticancer potential with proteins which are involved in skin cancer effects with the help of pyrx software. After performing docking studies, results reveal that Noracronycine secondary metabolite of Glycosmis pentaphylla shows strong affinity of their binding energy with Ribosomal S6 Kinase 2 (2QR8) protein. Ribosomal S6 Kinase 2 (2QR8) has an important role in the cell proliferation and transformation mediated through by N-terminal kinase domain and was induced by the tumour promoters such as epidermal growth factor. It also plays a key role in the neoplastic transformation of human skin cells and in skin cancer growth. Noracronycine interact with THR-493 and MET-496 residue of Ribosomal S6 Kinase 2 protein with binding energy ΔG = -8.68 kcal/mole. Thus on the basis of this study we can say that Noracronycine which present in roots of Glycosmis pentaphylla can be used as lead compound against skin cancer.

Keywords: glycosmis pentaphylla, pyrx, ribosomal s6 kinase, skin cancer

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Authors: Avi Shrivastava

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Title 42, an emergency health decree, has forced the federal authorities to turn away asylum seekers and all other border crossers since last year. When Title 42 was first deployed in immigration detention centers, where many migrants are held when they arrive at the U.S.-Mexico border, the Trump administration embraced it as a strategy. Expulsions Policy and New Border Challenges will be examined in regard to Title 42 concerns. Humanitarian measures for refugees arriving at the US-Mexico border are the focus of this article. To a large extent, this article addresses the implications of the United States' use of Title 42 in expelling refugees and the possible ramifications of doing away with it. A secondary data collecting strategy was used to gather the information for this study, allowing researchers to examine a large number of previously collected data sets. Information about Title 42 may be found in a variety of places, such as scholarly publications, newspapers, books, and the internet. The inquiry employed qualitative and explanatory research approaches. The claim that 1.7 million individuals were forced to leave the country as a result of it was withdrawn. Since CBP and ICE were limited in their ability to process deportees, it employed a very random patchwork technique in selecting the expelled individuals. As a consequence, repeat offenders, particularly those who were single, got a reduced punishment. The government will be compelled to focus on long-overdue but vital border enhancements if expulsions are halted. Title 42 provisions may help expedite the processing of asylum and other types of humanitarian relief. The government is prepared for an increase in arrivals, but ending the program would lead to a return to arrival levels seen during the Title 42 period.

Keywords: migrants, refugees, title 42, medical, trump administration

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Authors: Arpit K. Shrivastava, Nirmal K. Mohakud, Subrat Kumar, Priyadarshi S. Sahu

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Acute diarrhea is one of the major causes of morbidity and mortality among children less than five years of age. Multiple etiologies have been implicated for infectious gastroenteritis causing acute diarrhea. In our study fecal samples (n=165) were collected from children (<5 years) presenting with symptoms of acute diarrhea. Samples were screened for viral, bacterial, and parasitic etiologies such as Rotavirus, Adenovirus, Diarrhoeagenic Escherichia coli (EPEC, EHEC, STEC, O157, O111), Shigella spp., Salmonella spp., Vibrio cholera, Cryptosporidium spp., and Giardia spp. The overall results from our study showed that 57% of children below 5 years of age with acute diarrhea were positive for at least one infectious etiology. Diarrhoeagenic Escherichia coli was detected to be the major etiological agent (29.09%) followed by Rotavirus (24.24%), Shigella (21.21%), Adenovirus (5.45%), Cryptosporidium (2.42%), and Giardia (0.60%). Among the different DEC strains, EPEC was detected significantly higher in <2 years children in comparison to >2 years age group (p =0.001). Concurrent infections with two or more pathogens were observed in 47 of 160 (28.48%) cases with a predominant incidence particularly in <2-year-old children (66.66%) compared to children of 2 to 5 years age group. Co-infection of Rotavirus with Shigella was the most frequent combination, which was detected in 17.94% cases, followed by Rotavirus with EPEC (15.38%) and Shigella with STEC (12.82%). Detection of multiple infectious etiologies and diagnosis of the right causative agent(s) can immensely help in better management of acute childhood diarrhea. In future more studies focusing on the detection of cases with concurrent infections must be carried out, as we believe that the etiological agents might be complementing each other’s strategies of pathogenesis resulting in severe diarrhea.

Keywords: children, co-infection, infectious diarrhea, Odisha

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Authors: Rohit Kumar Shrivastava, Amalia Thomas, Nathalie Vriend, Stefan Luding

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A mechanical wave or vibration propagating through granular media exhibits a specific signature in time. A coherent pulse or wavefront arrives first with multiply scattered waves (coda) arriving later. The coherent pulse is micro-structure independent i.e. it depends only on the bulk properties of the disordered granular sample, the sound wave velocity of the granular sample and hence bulk and shear moduli. The coherent wavefront attenuates (decreases in amplitude) and broadens with distance from its source. The pulse attenuation and broadening effects are affected by disorder (polydispersity; contrast in size of the granules) and have often been attributed to dispersion and scattering. To study the effect of disorder and initial amplitude (non-linearity) of the pulse imparted to the system on the coherent wavefront, numerical simulations have been carried out on one-dimensional sets of particles (granular chains). The interaction force between the particles is given by a Hertzian contact model. The sizes of particles have been selected randomly from a Gaussian distribution, where the standard deviation of this distribution is the relevant parameter that quantifies the effect of disorder on the coherent wavefront. Since, the coherent wavefront is system configuration independent, ensemble averaging has been used for improving the signal quality of the coherent pulse and removing the multiply scattered waves. The results concerning the width of the coherent wavefront have been formulated in terms of scaling laws. An experimental set-up of photoelastic particles constituting a granular chain is proposed to validate the numerical results.

Keywords: discrete elements, Hertzian contact, polydispersity, weakly nonlinear, wave propagation

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Authors: Mahima Shrivastava, Soumya Kar, B. Swetha Malika, Lalu Saheb, M. Muthu Kumar, P. V. Ponambala Moorthi

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Natural hazards and manmade destruction causes both economic and societal losses. Generalized pre-mitigation strategies introduced and adopted for prevention of disaster all over the world are capable of augmenting the resiliency and optimizing the life-cycle cost of facilities. In countries like India where varied topographical feature exists requires location specific mitigation measures and strategies to be followed for better enhancement by event-driven and code-driven approaches. Present state of vindication measures followed and adopted, lags dominance in accomplishing the required development. In addition, serious concern and debate over climate change plays a vital role in enhancing the need and requirement for the development of time bound adaptive mitigation measures. For the development of long-term sustainable policies incorporation of future climatic variation is inevitable. This will further assist in assessing the impact brought about by the climate change on life-cycle cost of facilities. This paper develops more definite region specific and time bound pre-mitigation measures, by reviewing the present state of mitigation measures in India and all over the world for improving life-cycle cost of facilities. For the development of region specific adoptive measures, Indian regions were divided based on multiple-calamity prone regions and geo-referencing tools were used to incorporate the effect of climate changes on life-cycle cost assessment. This study puts forward significant effort in establishing sustainable policies and helps decision makers in planning for pre-mitigation measures for different regions. It will further contribute towards evaluating the life cycle cost of facilities by adopting the developed measures.

Keywords: climate change, geo-referencing tools, life-cycle cost, multiple-calamity prone regions, pre-mitigation strategies, sustainable policies

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Authors: Aneal Khan, Elleine Allapitan, Desmond Koo, Katherine-Ann Piedalue, Shaneel Pathak, Utkarsh Subnis

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Background: Disease management of metabolic, genetic disorders is long-term and can be cumbersome to patients and caregivers. Patient-Reported Outcome Measures (PROMs) have been a useful tool in capturing patient perspectives to help enhance treatment compliance and engagement with health care providers, reduce utilization of emergency services, and increase satisfaction with their treatment choices. Currently, however, PROMs are collected during infrequent and decontextualized clinic visits, which makes translation of patient experiences challenging over time. The GRIT study aims to evaluate a digital health journal application called Zamplo that provides a personalized health diary to record self-reported health outcomes accurately and efficiently in patients with metabolic, genetic disorders. Methods: This is a randomized controlled trial (RCT) (1:1) that assesses the efficacy of Zamplo to increase patient activation (primary outcome), improve healthcare satisfaction and confidence to manage medications (secondary outcomes), and reduce costs to the healthcare system (exploratory). Using standardized online surveys, assessments will be collected at baseline, 1 month, 3 months, 6 months, and 12 months. Outcomes will be compared between patients who were given access to the application versus those with no access. Results: Seventy-seven patients were recruited as of November 30, 2021. Recruitment for the study commenced in November 2020 with a target of n=150 patients. The accrual rate was 50% from those eligible and invited for the study, with the majority of patients having Fabry disease (n=48) and the remaining having Pompe disease and mitochondrial disease. Real-time clinical responses, such as pain, are being measured and correlated to disease-modifying therapies, supportive treatments like pain medications, and lifestyle interventions. Engagement with the application, along with compliance metrics of surveys and journal entries, are being analyzed. An interim analysis of the engagement data along with preliminary findings from this pilot RCT, and qualitative patient feedback will be presented. Conclusions: The digital self-care journal provides a unique approach to disease management, allowing patients direct access to their progress and actively participating in their care. Findings from the study can help serve the virtual care needs of patients with metabolic, genetic disorders in North America and the world over.

Keywords: eHealth, mobile health, rare disease, patient outcomes, quality of life (QoL), pain, Fabry disease, Pompe disease

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Authors: Shikhar Shrivastava, Varun Khare

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Background: We are confronted by a society that is becoming more complex, more mobile, and more dysfunctional. Teen pregnancy, suicide, elopement, and the perusal of dangerous drugs have become commonplace. In addition, children do not settle their disputes as they once did. Guns and knives are quotidian. Therefore, it has been an exigent to have a "Juvenile Code" that would provide specific substantive and procedural rules for juveniles in the justice system. However, until the twentieth century, there was little difference between how the justice system treated adults and children. Age was considered only in terms of appropriate punishment and juveniles were eligible for the same punishment as adults. Findings: The increased prevalence and legislative support for specialized courts, Juvenile Justice Boards, including juvenile drug, mental health and truancy court programs, as well as diversion programs and evidence-based approaches into the fabric of juvenile justice are just a few examples of recent advances. In India, various measures were taken to prosecute young offenders who committed violent crimes as adults. But it was argued that equating juveniles with adult criminals was neither scientifically correct nor normatively defensible. It would defeat the very purpose of the justice system. Methodology and Conclusion: This paper attempts to bring forth the results of analytical and descriptive research that examined changing trends in juvenile justice legislation. It covers the investigative and inspective practices of police, the various administrative agencies who have roles in implementing the legislation, the courts, and the detention centers. In this paper we shall discuss about how the juvenile justice system is the dumping ground for many of a youths’ problem. The changing notions of justice, from retributive to restorative and rehabilitative shall be discussed. A comparative study of the Juvenile act in India and that of the U.S has been discussed. Specific social institutions and forces that explain juvenile delinquency are identified. In addition, various influences on juvenile delinquency are noted, such as families, schools, peer groups and communities. The text concludes by addressing socialization, deterrence, imprisonments, alternatives, restitution and preventions.

Keywords: juvenile, justice system, retributive, rehabilitative, delinquency

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Authors: Ankit Shah, R. K. Shrivastava

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Flood and drought are two main features of hydrology which affect the human life. Floods are natural disasters which cause millions of rupees’ worth of damage each year in India and the whole world. Flood causes destruction in form of life and property. An accurate estimate of the flood damage potential is a key element to an effective, nationwide flood damage abatement program. Also, the increase in demand of water due to increase in population, industrial and agricultural growth, has let us know that though being a renewable resource it cannot be taken for granted. We have to optimize the use of water according to circumstances and conditions and need to harness it which can be done by construction of hydraulic structures. For their safe and proper functioning of hydraulic structures, we need to predict the flood magnitude and its impact. Hydraulic structures play a key role in harnessing and optimization of flood water which in turn results in safe and maximum use of water available. Mainly hydraulic structures are constructed on ungauged sites. There are two methods by which we can estimate flood viz. generation of Unit Hydrographs and Flood Frequency Analysis. In this study, Regional Flood Frequency Analysis has been employed. There are many methods for estimating the ‘Regional Flood Frequency Analysis’ viz. Index Flood Method. National Environmental and Research Council (NERC Methods), Multiple Regression Method, etc. However, none of the methods can be considered universal for every situation and location. The Narmada basin is located in Central India. It is drained by most of the tributaries, most of which are ungauged. Therefore it is very difficult to estimate flood on these tributaries and in the main river. As mentioned above Artificial Neural Network (ANN)s and Multiple Regression Method is used for determination of Regional flood Frequency. The annual peak flood data of 20 sites gauging sites of Narmada Basin is used in the present study to determine the Regional Flood relationships. Homogeneity of the considered sites is determined by using the Index Flood Method. Flood relationships obtained by both the methods are compared with each other, and it is found that ANN is more reliable than Multiple Regression Method for the present study area.

Keywords: artificial neural network, index flood method, multi layer perceptrons, multiple regression, Narmada basin, regional flood frequency

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Authors: Manjusha R. Vagal, Shyam K. Shrivastava, Umesh Mahantshetty, Sudeep Gupta, Supriya Chopra, Reena Engineer, Amita Maheshwari, Atul Buduk

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Vaginal stenosis is a common side effect associated with pelvic radiotherapy in cervical cancer patients which contributes negatively to woman’s health and prevents adequate vaginal/cervical examination. Vaginal dilation with a dilator is routine practice and is internationally advocated as a prophylactic measure to preserve vaginal patency. This retrospective study was carried out with the aim to know the usefulness of vaginal dilation following pelvic radiation therapy in cervical cancer patients in India. Data from medical records of 183 cervical cancer patients, which met the study criteria, were collected related to the stage of the disease, treatment received, commencement period of dilation post radiation therapy, sexual status and side effects associated to dilation practice. Data related to vaginal dimensions as per the length of insertion of a small, medium and large dilator were collected on regular follow-ups until 36 months and/or more. Vaginal dimensions as measured with the length of medium dilator insertion were used for analysis of dilation therapy results using paired t-test. Patients who underwent vaginal dilation with dilator maintained vaginal patency, also the mean vaginal length significantly increased, from 8.02 cm ± 2.69 to 9.96 ± 2.89 cm with a p value <0.001. There was no significant difference found on vaginal patency with different intervals of initiation of dilation therapy. At the third year and more following dilation therapy, significant increase in vaginal length observed with a p value of 0.0001 in both sexually active and inactive patients. Compilation of vaginal dosage during brachytherapy was inadequate, and hence, the secondary objective of the study to determine the effect of radiotherapy on the outcome of rehabilitation intervention was not studied in detail. This retrospective study has found that dilation therapy with vaginal dilators post pelvic radiotherapy is effective in preventing vaginal stenosis and improving vaginal patency and cannot be substituted with vaginal intercourse. Sexual quality of life assessment in the Indian population needs much attention.

Keywords: dilator, sexually active, vaginal dilation, vaginal stenosis

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Authors: Rohit Shrivastava, Stefan Luding

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A mechanical wave is propagation of vibration with transfer of energy and momentum. Studying the energy as well as spectral energy characteristics of a propagating wave through disordered granular media can assist in understanding the overall properties of wave propagation through inhomogeneous materials like soil. The study of these properties is aimed at modeling wave propagation for oil, mineral or gas exploration (seismic prospecting) or non-destructive testing for the study of internal structure of solids. The study of Energy content (Kinetic, Potential and Total Energy) of a pulse propagating through an idealized one-dimensional discrete particle system like a mass disordered granular chain can assist in understanding the energy attenuation due to disorder as a function of propagation distance. The spectral analysis of the energy signal can assist in understanding dispersion as well as attenuation due to scattering in different frequencies (scattering attenuation). The selection of one-dimensional granular chain also helps in studying only the P-wave attributes of the wave and removing the influence of shear or rotational waves. Granular chains with different mass distributions have been studied, by randomly selecting masses from normal, binary and uniform distributions and the standard deviation of the distribution is considered as the disorder parameter, higher standard deviation means higher disorder and lower standard deviation means lower disorder. For obtaining macroscopic/continuum properties, ensemble averaging has been used. Interpreting information from a Total Energy signal turned out to be much easier in comparison to displacement, velocity or acceleration signals of the wave, hence, indicating a better analysis method for wave propagation through granular materials. Increasing disorder leads to faster attenuation of the signal and decreases the Energy of higher frequency signals transmitted, but at the same time the energy of spatially localized high frequencies also increases. An ordered granular chain exhibits ballistic propagation of energy whereas, a disordered granular chain exhibits diffusive like propagation, which eventually becomes localized at long periods of time.

Keywords: discrete elements, energy attenuation, mass disorder, granular chain, spectral energy, wave propagation

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Authors: Arpit Kumar Shrivastava, Subrat Kumar, Rajani Kanta Mohapatra, Priyadarshi Soumyaranjan Sahu

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Computational approaches to predict structure, function and other biological characteristics of proteins are becoming more common in comparison to the traditional methods in drug discovery. Cryptosporidiosis is a major zoonotic diarrheal disease particularly in children, which is caused primarily by Cryptosporidium hominis and Cryptosporidium parvum. Currently, there are no vaccines for cryptosporidiosis and recommended drugs are not effective. With the availability of complete genome sequence of C. hominis, new targets have been recognized for the development of effective and better drugs and/or vaccines. We identified a unique hypothetical epitopic protein in C. hominis genome through BLASTP analysis. A 3D model of the hypothetical protein was generated using I-Tasser server through threading methodology. The quality of the model was validated through Ramachandran plot by PROCHECK server. The functional annotation of the hypothetical protein through DALI server revealed structural similarity with human Transportin 3. Phylogenetic analysis for this hypothetical protein also showed C. hominis hypothetical protein (CUV04613) was the closely related to human transportin 3 protein. The 3D protein model is further subjected to virtual screening study with inhibitors from the Zinc Database by using Dock Blaster software. Docking study reported N-(3-chlorobenzyl) ethane-1,2-diamine as the best inhibitor in terms of docking score. Docking analysis elucidated that Leu 525, Ile 526, Glu 528, Glu 529 are critical residues for ligand–receptor interactions. The molecular dynamic simulation was done to access the reliability of the binding pose of inhibitor and protein complex using GROMACS software at 10ns time point. Trajectories were analyzed at each 2.5 ns time interval, among which, H-bond with LEU-525 and GLY- 530 are significantly present in MD trajectories. Furthermore, antigenic determinants of the protein were determined with the help of DNA Star software. Our study findings showed a great potential in order to provide insights in the development of new drug(s) or vaccine(s) for control as well as prevention of cryptosporidiosis among humans and animals.

Keywords: cryptosporidium hominis, hypothetical protein, molecular docking, molecular dynamics simulation

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Authors: Nishant Shrivastava, D. K. Sehgal

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In Finite Element Technique nodal stresses are calculated through displacement as nodes. In this process, the displacement calculated at nodes is sufficiently good enough but stresses calculated at nodes are not sufficiently accurate. Therefore, the accuracy in the stress computation in FEM models based on the displacement technique is obviously matter of concern for computational time in shape optimization of engineering problems. In the present work same is focused to find out unique points within the element as well as the boundary of the element so, that good accuracy in stress computation can be achieved. Generally, major optimal stress points are located in domain of the element some points have been also located at boundary of the element where stresses are fairly accurate as compared to nodal values. Then, it is subsequently concluded that there is an existence of unique points within the element, where stresses have higher accuracy than other points in the elements. Therefore, it is main aim is to evolve a generalized procedure for the determination of the optimal stress location inside the element as well as at the boundaries of the element and verify the same with results from numerical experimentation. The results of quadratic 9 noded serendipity elements are presented and the location of distinct optimal stress points is determined inside the element, as well as at the boundaries. The theoretical results indicate various optimal stress locations are in local coordinates at origin and at a distance of 0.577 in both directions from origin. Also, at the boundaries optimal stress locations are at the midpoints of the element boundary and the locations are at a distance of 0.577 from the origin in both directions. The above findings were verified through experimentation and findings were authenticated. For numerical experimentation five engineering problems were identified and the numerical results of 9-noded element were compared to those obtained by using the same order of 25-noded quadratic Lagrangian elements, which are considered as standard. Then root mean square errors are plotted with respect to various locations within the elements as well as the boundaries and conclusions were drawn. After numerical verification it is noted that in a 9-noded element, origin and locations at a distance of 0.577 from origin in both directions are the best sampling points for the stresses. It was also noted that stresses calculated within line at boundary enclosed by 0.577 midpoints are also very good and the error found is very less. When sampling points move away from these points, then it causes line zone error to increase rapidly. Thus, it is established that there are unique points at boundary of element where stresses are accurate, which can be utilized in solving various engineering problems and are also useful in shape optimizations.

Keywords: finite elements, Lagrangian, optimal stress location, serendipity

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Authors: Utkarsh Tripathi, Bikalp C. Mandal, Ravi Kumar Umrao, Sirsha Das, M. K. Bhowmic, Joyesh Bagchi, Hemant Kumar

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The Central India Diamond Province (CIDP) is known for the occurrences of primary and secondary sources for diamonds from the Vindhyan platformal sediments, which host several kimberlites, with one operating mine. The known kimberlites are Neo-Proterozoic in age and intrude into the Kaimur Group of rocks. Based on the interpretation of areo-geomagnetic data, three potential zones were demarcated in parts of Chitrakoot and Banda districts, Uttar Pradesh, and Satna district, Madhya Pradesh, India. To validate the aero-geomagnetic interpretation, ground magnetic coupled with a gravity survey was conducted to validate the anomaly and explore the possibility of some pipes concealed beneath the Vindhyan sedimentary cover. Geologically the area exposes the milky white to buff-colored arkosic and arenitic sandstone belonging to the Dhandraul Formation of the Kaimur Group, which are undeformed and unmetamorphosed providing almost transparent media for geophysical exploration. There is neither surface nor any geophysical indication of intersections of linear structures, but the joint patterns depict three principal joints along NNE-SSW, ENE-WSW, and NW-SE directions with vertical to sub-vertical dips. Aeromagnetic data interpretation brings out three promising zones with the bi-polar magnetic anomaly (69-602nT) that represent potential kimberlite intrusive concealed below at an approximate depth of 150-170m. The ground magnetic survey has brought out the above-mentioned anomalies in zone-I, which is congruent with the available aero-geophysical data. The magnetic anomaly map shows a total variation of 741 nT over the area. Two very high magnetic zones (H1 and H2) have been observed with around 500 nT and 400 nT magnitudes, respectively. Anomaly zone H1 is located in the west-central part of the area, south of Madulihai village, while anomaly zone H2 is located 2km apart in the north-eastern direction. The Euler 3D solution map indicates the possible existence of the ultramafic body in both the magnetic highs (H1 and H2). The H2 high shows the shallow depth, and H1 shows a deeper depth solution. In the reduced-to-pole (RTP) method, the bipolar anomaly disappears and indicates the existence of one causative source for both anomalies, which is, in all probabilities, an ultramafic suite of rock. The H1 magnetic high represents the main body, which persists up to depths of ~500m, as depicted through the upward continuation derivative map. Radially Averaged Power Spectrum (RAPS) shows the thickness of loose sediments up to 25m with a cumulative depth of 154m for sandstone overlying the ultramafic body. The average depth range of the shallower body (H2) is 60.5-86 meters, as estimated through the Peters half slope method. Magnetic (TF) anomaly with BA contour also shows high BA value around the high zones of magnetic anomaly (H1 and H2), which suggests that the causative body is with higher density and susceptibility for the surrounding host rock. The ground magnetic survey coupled with the gravity confirms a potential target for further exploration as the findings are co-relatable with the presence of the known diamondiferous kimberlites in this region, which post-date the rocks of the Kaimur Group.

Keywords: Kaimur, kimberlite, Euler 3D solution, magnetic

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Authors: Utkarsh A. Mishra, Ankit Bansal

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At high temperature, radiative heat transfer is the dominant mode of heat transfer. It is governed by various phenomena such as photon emission, absorption, and scattering. The solution of the governing integrodifferential equation of radiative transfer is a complex process, more when the effect of participating medium and wavelength properties are taken into consideration. Although a generic formulation of such radiative transport problem can be modeled for a wide variety of problems with non-gray, non-diffusive surfaces, there is always a trade-off between simplicity and accuracy of the problem. Recently, solutions of complicated mathematical problems with statistical methods based on randomization of naturally occurring phenomena have gained significant importance. Photon bundles with discrete energy can be replicated with random numbers describing the emission, absorption, and scattering processes. Photon Monte Carlo (PMC) is a simple, yet powerful technique, to solve radiative transfer problems in complicated geometries with arbitrary participating medium. The method, on the one hand, increases the accuracy of estimation, and on the other hand, increases the computational cost. The participating media -generally a gas, such as CO₂, CO, and H₂O- present complex emission and absorption spectra. To model the emission/absorption accurately with random numbers requires a weighted sampling as different sections of the spectrum carries different importance. Importance sampling (IS) was implemented to sample random photon of arbitrary wavelength, and the sampled data provided unbiased training of MC estimators for better results. A better replacement to uniform random numbers is using deterministic, quasi-random sequences. Halton, Sobol, and Faure Low-Discrepancy Sequences are used in this study. They possess better space-filling performance than the uniform random number generator and gives rise to a low variance, stable Quasi-Monte Carlo (QMC) estimators with faster convergence. An optimal supervised learning scheme was further considered to reduce the computation costs of the PMC simulation. A one-dimensional plane-parallel slab problem with participating media was formulated. The history of some randomly sampled photon bundles is recorded to train an Artificial Neural Network (ANN), back-propagation model. The flux was calculated using the standard quasi PMC and was considered to be the training target. Results obtained with the proposed model for the one-dimensional problem are compared with the exact analytical and PMC model with the Line by Line (LBL) spectral model. The approximate variance obtained was around 3.14%. Results were analyzed with respect to time and the total flux in both cases. A significant reduction in variance as well a faster rate of convergence was observed in the case of the QMC method over the standard PMC method. However, the results obtained with the ANN method resulted in greater variance (around 25-28%) as compared to the other cases. There is a great scope of machine learning models to help in further reduction of computation cost once trained successfully. Multiple ways of selecting the input data as well as various architectures will be tried such that the concerned environment can be fully addressed to the ANN model. Better results can be achieved in this unexplored domain.

Keywords: radiative heat transfer, Monte Carlo Method, pseudo-random numbers, low discrepancy sequences, artificial neural networks

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Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

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Authors: Avi Shrivastava

Abstract:

Our world continues to grapple with a lot of social issues. Economic growth and scientific advancements have not completely eradicated poverty, homelessness, discrimination and bias, gender inequality, health issues, mental illness, addiction, and other social issues. So, how do we improve the human condition in a world driven by advanced technology? The answer is simple: we will have to leverage technology to address some of the most important social challenges of the day. AI, or artificial intelligence, has emerged as a critical tool in the battle against issues that deprive marginalized and disadvantaged groups of the right to enjoy benefits that a society offers. Social work professionals can transform their lives by harnessing it. The lack of reliable data is one of the reasons why a lot of social work projects fail. Social work professionals continue to rely on expensive and time-consuming primary data collection methods, such as observation, surveys, questionnaires, and interviews, instead of tapping into AI-based technology to generate useful, real-time data and necessary insights. By leveraging AI’s data-mining ability, we can gain a deeper understanding of how to solve complex social problems and change lives of people. We can do the right work for the right people and at the right time. For example, AI can enable social work professionals to focus their humanitarian efforts on some of the world’s poorest regions, where there is extreme poverty. An interdisciplinary team of Stanford scientists, Marshall Burke, Stefano Ermon, David Lobell, Michael Xie, and Neal Jean, used AI to spot global poverty zones – identifying such zones is a key step in the fight against poverty. The scientists combined daytime and nighttime satellite imagery with machine learning algorithms to predict poverty in Nigeria, Uganda, Tanzania, Rwanda, and Malawi. In an article published by Stanford News, Stanford researchers use dark of night and machine learning, Ermon explained that they provided the machine-learning system, an application of AI, with the high-resolution satellite images and asked it to predict poverty in the African region. “The system essentially learned how to solve the problem by comparing those two sets of images [daytime and nighttime].” This is one example of how AI can be used by social work professionals to reach regions that need their aid the most. It can also help identify sources of inequality and conflict, which could reduce inequalities, according to Nature’s study, titled The role of artificial intelligence in achieving the Sustainable Development Goals, published in 2020. The report also notes that AI can help achieve 79 percent of the United Nation’s (UN) Sustainable Development Goals (SDG). AI is impacting our everyday lives in multiple amazing ways, yet some people do not know much about it. If someone is not familiar with this technology, they may be reluctant to use it to solve social issues. So, before we talk more about the use of AI to accomplish social work objectives, let’s put the spotlight on how AI and social work can complement each other.

Keywords: social work, artificial intelligence, AI based social work, machine learning, technology

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