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The chaos-based cryptography techniques are used widely to protect digital information from intruders. The chaotic systems have some of special features that make them suitable for the purpose of encryption. These systems are highly unpredictable and are highly sensitive or responsive to the initial conditions, also known as butterfly effect. This sensitive dependence on initial conditions make these systems to exhibit an intricate dynamical behaviour. However, this dynamical behaviour is not much complex in simple one-dimensional chaotic maps. Hence, it becomes easy for an intruder to predict the contents of the message being sent. The proposed work in this paper introduces an improved method for encrypting images, which uses cosine transformation of 3-D Intertwining Logistic Map (ILM). The proposed approach has been split into three major parts. In the first part, Secure Hash Function-256 (SHA-256) is used with cosine transformed ILM (CT-ILM) to generate the chaotic sequence. This chaotic sequence is used by high-efficiency scrambling to reduce the correlations between the adjacent pixels of the image. In the second part, the image is rotated to move all the pixels away from their original position. In the third part, random order substitution is applied to change the value of image pixels. The effectiveness of the proposed method has been tested on a number of standard parameters such as correlation coefficient, Entropy and Unified average change in intensity. The proposed approach has also been tested for decryption parameters like mean square error and peak signal to noise ratio. It can easily be observed from the obtained results that the proposed method of image encryption is more secure and time efficient than some earlier proposed techniques. The approach works for both color and grey scale images.

Drought is a stochastic natural hazard that is caused by intense and persistent shortage of precipitation. The initial shortage of rainfall subsequently impacts the agriculture and hydrology sectors. Marathwada region of India comes under highly drought prone area in the country. Recent times have shown the increase in occurrence of agricultural drought in the non-monsoon season. The deviation from normal rainfall in the month of October causes soil moisture deficit which triggers an agricultural drought in the early-Rabi season. The traditional remote sensing based agricultural drought monitoring indices lack in identifying the early-season (ES) drought. An attempt has been made in the present study, to map ES agricultural drought in the Aurangabad district of Marathwada region using remote sensing. The meteorological deficit in the month of October, has been assessed using Standardized Precipitation Index (SPI). Impact of meteorological fluctuations on agricultural system in terms of dryness/wetness was evaluated using the Shortwave Angel Slope Index (SASI) derived using MODIS (Terra) Level-3, 8 daily, surface reflectance data for the October months of 2001–2012. It was observed that the area experiences moderate to severe drought 5 times with 12 years of study period (2001–2012). SASI and its parameters were estimated for each week of October month. SASI maps were further classified in four categories viz. moist vegetation; dry vegetation; moist soil and dry soil. The detailed analyses if these maps indicate that agricultural stress occurs in this area even if there is no meteorological stress. However, whenever, there is meteorological stress the area under agricultural stress exceeds more than 50% of the study region. A frequency distribution map of ES drought was prepared to identify the most drought prone area of the district and to alternately identify the irrigated area of the district.

The performance evaluation of surface irrigation projects has been hitherto done at regional or project level due to the non-availability of a detailed spatial database of irrigation projects. Recent advancements in the capabilities of remote sensing techniques and Geographical Information System (GIS) have opened avenues of field level mapping and database creation for irrigation projects. In view of this, the application of remote sensing and GIS in command area mapping and detailed performance evaluation at the sub-project level (division level) was attempted, in the present study. The command area of Upper Wardha irrigation project in Amravati district of Maharashtra, India was mapped for irrigation infrastructures (canals, hydraulic structures, etc.), crop acreage, and other infrastructures (viz. road, railway, settlement, etc.) using high resolution Cartosat-1, IRS P6 LISS-IV data, and temporal IRS P6 LISS-III data. The performance of the irrigation project was evaluated using eight comparative indicators classified under three groups, namely agricultural performance, water-use performance, and physical performance. Remote sensing data along with field observed data were used to derive inputs for these performance indicators. The results indicate that the project has a positive impact on agriculture and economic growth of the region; however, water has been excessively used in some parts of the command area while supply has been inadequate in the tail region of the command. The water supply in the command area was reliable, however, not equally distributed among all the distributaries. The overall performance of the irrigation project was evaluated, and the divisions, which need immediate attention to improve the efficiency, were identified in this study.

Patch testing is the gold standard diagnostic tool for cell-mediated type IV hypersensitivity reactions like allergic contact dermatitis. Sensitized individuals have primed antigen-specific T lymphocytes that cause a reaction when antigens are applied to the skin owing to prior sensitization. Patch testing can be used in the adult and pediatric populations, but it is contraindicated in patients with a known history of severe allergic reactions to suspected allergens, generalized active dermatitis, or extensive eczema. Patch test systems can be a comprehensive panel (70-80 allergens), but they can also be targeted and limited to a more common allergen series (35 allergens). The decision for allergen selection should be based on an accurate patient history, physical examination, and availability of allergens. Measurement and interpretation of the test results requires training and experience, as well as consideration of relevance and clinical history. Patch testing is generally considered safe with just a few known complications: excited skin syndrome, active sensitization, and rarely anaphylaxis or other cutaneous complications. Appropriately pretesting patient education can help to mitigate some of these complications. Based on patch testing results, patients should be educated regarding proper allergen avoidance measures to resolve symptoms of allergic contact dermatitis.

Quantum Key Distribution (QKD) is a rapidly advancing field, yet there is often a divide between theory, experiment, and practice. One difficulty in evaluating novel QKD protocols is in the post-processing needed. We are reporting here on the current development of a modular QKD software stack researchers can use to evaluate the performance of new QKD protocols, either using real hardware or simulated. We are designing our system to be modular and extendable allowing for rapid prototyping of new systems.

Stage-gate process is a project management technique that has been used to manage major technology development and deployment projects for over 50 years. It emphasizes quality, risk, and value, and provides an orderly framework for decision making with multiple projects carried out concurrently. This paper will examine the potential application of this process to NDE 4.0 developments. This paper will also explore the factors that make a stage-gate process successful, and how biases affect the outcomes of gate reviews. It explores which biases are problematic and how we can de-bias our own interpretation of them through conscious awareness and re-thinking the Stage-Gate Process. It also discusses how to create an environment where biases are minimized or eliminated. During research and reporting of this manuscript, the human authors synthesized their decades of experience in engaging with technology development decisions, with contributions from an artificial intelligence agent GPT-3 on human biases and motivations. This was done to capture the firsthand experience of collaborating with AI in the spirit of increasing the role of machine intelligence in human–machine systems, typical of NDE. Since this appeared to be an innovative approach, the authors decided to use the gated process in creating this paper with AI as a co-author. The details are presented as a case study within the paper. The topic is important because, managers are struggling to embrace the NDE 4.0 initiative, despite its importance and their willingness. Leveraging AI partnership to even address this important topic is a testament to cognitive capability of learning machines to help inspection systems with creating test procedures, making decisions, and creating test reports. This manuscript is the first attempt at qualitative treatment of human factors in gated innovation process with research assisted by an Artificial Intelligence based language model (non-human). The first paragraph of the abstract above is completely AI generated.

Since the electricity demand is increasing globally, load forecasting techniques have become immensely important in forecasting the electricity demands and it also helps the policy makers. The aim of our project is to perform short-term load forecasting, i.e. up to a week ahead. Household owners can estimate the upcoming load and power distribution organization would know the demand and could be prepared henceforth. Our attempt is to generate useful insights and forecast as accurately as possible. We are using different techniques starting from Naive Bayes, Classical Linear Methods (like ARIMA), and some Machine Learning Algorithms (like LinearRegression, Ridge, Lasso, HuberRegressor, ElasticNet, Lars, LassoLars, PassiveAggressiveRegressor, RANSAC Regressor, SGD Regressor) to make predictions. And we are also using Deep Learning algorithms like CNN, LSTM and combining CNN-LSTM to get more accurate predictions. In the end we will compare all the predictions from all the models that we have used and determine which model makes the best prediction.

With the global health crisis—COVID—19, while many organizations claim to be purpose-driven however the purpose needs to be defined in view of the impact on the employees and given the urgency and complexity of the challenges being faced in the organizations, the need of the hour is to create an environment that offers transparency, trust and hope in the system and the leadership. It has become pertinent for organizations to have open and honest discussions with their employees regarding the business realities, share upcoming plans and encourage collaborative partnership. To embrace this “new normal” reality, it is also imperative for organizations and leaders to adapt and change their leadership styles to address the current crisis. This chapter focuses on how design thinking, strategy, and systematic steps can transform any ambitious goals to fit the organizational culture and appeal to people while addressing key current and uncertain challenges thereby integrating change management into engagement in the area of Human Resources. A case study on Project Propel Initiative from Movenpick Resort Kuredhivaru Maldives provides a structured and systematic approach with accountability in harmony with HR processes and people to make the HR function more robust and better organized with improved control and risk management, a tighter focus on priorities and strategic strengths, and closer alignment with what employees and senior leaders want in routine operations and also during a crisis situation of COVID-19.

Two decades of economics research has repeatedly made the assertion that organizations as well as individuals do not have adequate incentive to invest in cybersecurity. Absent security, associated costs are imposed on third parties rather than producers of insecurity. Cybersecurity is thus a private good with externalities, one that will require regulation to prevent market failure. Underlying this body of research is the assumption that all organizations have the same business drivers, a similar attack surface, and a uniformly informed consumer base. This paper questions these assumptions and outlines seven naturally occurring incentives for organizations to invest in cybersecurity. Furthermore, I provide examples of how these incentives have driven investment in cybersecurity across different sectors. While the applicability of these incentives differs both across and within sectors, any cybersecurity public policy interventions must consider the resulting nuances. Cybersecurity covenants established absent the sword of regulation may be both more effective and sustainable, as they evolve with the experience and exposure of the stakeholders.

Background There are limited data regarding the long-term outcomes of spironolactone use for women with acne and its effect on truncal acne. Objective To comprehensively describe outcomes of patients treated with spironolactone in routine clinical practice, including long-term outcomes. Methods We performed a retrospective case series of 403 adult women treated for acne with spironolactone at an academic medical center between 2008 and 2019. Rates of objective, as assessed by Comprehensive Acne Severity Scale scores, and subjective acne clearance were evaluated, as well as rates of treatment discontinuation, dosage changes, and drug survival. Logistic regression was used to assess for association between incidence of menstrual adverse effects and combined oral contraceptive use. Results As evaluated by Comprehensive Acne Severity Scale scores, at the first follow-up, 75.5%, 84.0%, and 80.2% of patients with available data had reduction or complete clearance of acne on the face, chest, and back, respectively. The mean drug survival was 470.7 days. Menstrual adverse effects were less common among those using combined oral contraception (odds ratio, 0.23; 95% confidence interval, 0.11-0.50). Limitations This study was conducted at a single academic medical center. Conclusions Spironolactone improves clinical outcomes and is well tolerated for many adult women with acne using it for an extended duration.

Remote sensing and hydrological models are one of the foremost tools for rapid and comprehensive study of flood hazards and disasters in any parts of the world. Current study is focused on severe 2018 Kerala flood, and is done using various remote sensing data, geospatial tools and combination of hydrological/hydrodynamic/topographical models. Flood mapping is done with pre and post floods remote sensing datasets. For pre-Flood analysis, Normalized Difference Water Index (NDWI) map was prepared on Google Earth Engine (GEE), using Sentinel-2 images for the period of Feb. 2017 to identify permanent water bodies. For post-Flood analysis, GEE was used to download the pre-processed and thermal noise removed Sentinel-1 SAR image for Aug. 9, 2018, Aug. 14 and Aug. 21, 2018 and flood maps were generated using this data. In addition to SAR data, probable flood inundation areas using topography-based flood inundation tool HAND (Height Above Nearest Drainage tool) was also utilized. Hydrological simulation was carried out for all 12 major river sub-basins of Kerala, where floods are reported. Indian Meteorological Department-Global Precipitation Measurement (IMD-GPM) gridded daily data is used as input meteorological data for hydrological simulations. The hydrological simulations results were verified using published Central Water Commission (CWC) reports and reservoirs data for India-WRIS. The hydrodynamic simulation was also performed for simulating the Idukki dam release data and flood condition in downstream areas. Overall, an integrated study and developed approach can be utilized by state and central water and disaster management agencies to develop flood early warning systems.

In India, water resources are managed at different levels, i.e. at central level by Ministry of Water Resources, River Development & Ganga Rejuvenation, Central Water Commission and Central Ground Water Board, at states level by state water resources departments, and at local level by Municipal Corporation and Panchayati Raj Institutions (PRIs). As per India’s national water policy of year 2012 focuses on adaption to climate change, enhancement of water availability, water demand management by efficient water use practices, management of floods and droughts, water supply and sanitation, trans-boundary rivers, conservation of water bodies and infrastructure, and finally research and training needs for each theme. Geospatial technology has unique role in all aforementioned themes. Therefore, research and training in use of Geospatial Technology (GST) in water sector is needed for each theme at different levels of water administration and water utilisation. The current paper discusses the existing framework and content of capacity building in water sector and geospatial technology in use at various government organizations and institutes. The major gap areas and future capacity building requirements are also highlighted, along with duration and timelines of training/capacity building programs. The use of distance learning/educations tools, social media, and e-learning are also highlighted in promoting use of GST in water sector. The emerging technological trends such as, new remote sensing sensors for measuring water cycle components, ground sensors based field instruments, cloud based data integration and computational models, webGIS based water information portals and training needs of new technologies are also emphasised.

Aim To study the histology of resected specimens of jejunoileal atresia (JIA) and to explore its probable therapeutic implications. Methods Biopsies were taken from the resected specimens of 32 patients of JIA. Sections were taken at the atretic ends and successively at every 5 cm in resected proximal bowel. They were stained for light microscopy and immunohistochemistry for CD117 (Interstitial cell of Cajal), α smooth muscle antibody (SMA), neuron filament protein (NFP) and calretinin. Histological findings on light microscopy and immune reactivity intensity for NFP, calretinin and α-SMA were recorded. Time taken to achieve full enteral nutrition (FEN; defined as attainment of oral feeds at 100 ml/kg/day without any intravenous fluid supplementation) was recorded and correlated with CD117 count by non-parametric Spearman rank correlation coefficient. Results Light microscopy failed to detect any abnormality in majority (n = 27, 84%) of JIA specimens. Mucosal ulceration (7.8%), submucosal inflammatory cells (8%), focal muscle thinning (2.5%), decreased ganglion cells and nerve fibers (3.8%) were noted on light microscopy. However all of them had normal α SMA, calretinin and NFP immunoreactivity. Mean ICC counts at proximal and distal atretic segment were 6.56 ± 3.79 and 8.37 ± 3.21 per HPF respectively. Serial sections in proximal dilated segment did not reveal any definite or well demarcated increase in ICC counts. CD117 counts at the proximal cut end of atresia was less than 6 per high power field (hpf) in 15 patients (46.8%) while at the site of anastomosis a count of more than 6/hpf was observed in 73% patients. There was no direct correlation of ICC counts with attainment of FEN. Conclusion Absence of major morphological abnormalities in the proximal dilated bowel contradicts the findings of earlier studies on histology of JIA in smaller cohorts. Our observations reiterate that bowel plication rather than massive bowel resection should be the preferred management in babies with JIA. Level of evidence Prognosis study (Level II).

The increased use of monoclonal antibodies that target the immune checkpoint T cell receptor programmed death-1 (PD1) to treat numerous solid tumors has led to several reports describing associated cutaneous adverse events. Although lichenoid reactions have been well described, we propose that PD1 inhibitor-induced inverse lichenoid eruption (PILE) is a distinct variant. We describe two patients who presented with nearly identical deeply erythematous, malodorous, eroded anogenital plaques with focal crusting. Diagnosis of PILE was established given the biopsy findings and temporal association with PD1 inhibitor therapy. Treatment with clobetasol ointment was successful without necessitating discontinuation of immunotherapy. The findings were consistent with the only other previously published case of inverse lichenoid eruption in the groin secondary to PD1 inhibitors.

Trichosporon is a genus of yeast-like fungi. It is perhaps most widely known as the cause of white piedra, a benign superficial mycosis seen in immunocompetent individuals in tropical and subtropical regions. However, the incidence of invasive trichosporonosis has increased in immunocompromised patients, most notably those with hematologic malignancies. The following case illustrates characteristic features of trichosporonosis fungemia.

Deep neural networks (DNNs) are currently the state of the art for many modern artificial intelligence (AI) applications. You have surely been touched by one of its influences. Automatic tagging of contacts in Facebook, personal digital assistants (Siri/Cortana/Alexa/ Google Assistant), or automatic creation of albums by Google Photos; all use variants of a DNN. Since the breakthrough application of DNNs to image recognition, Natural Language processing, Machine Translation, Spam & Fraud detection and Speech Recognition, the number of applications that use DNNs has exploded.Smart Grid is fundamentally defined as a system that has bi-directional information flow in addition to bi-directional flow of Energy. The Energy flow is bidirectional as there is a mechanism through which distributed generation and distributed storage can be integrated with the grid. The Information flow is bidirectional as the information flows from the devices to the central server and control signals flow from the server to the smart devices.This paper seeks to explore the modern Deep Leaning (DL) and Deep Neural Networks (DNN) landscape, and point to some of the possible directions in the space of Smart metering and Smart Grid.

In recent years, a staggering increase in the development and use of unmanned aerial vehicles has been noticed in a comprehensive range of applications. This paper is based on the utilization of autonomous quadcopter in plantation monitoring (Krishna in Agricultural drones: a peaceful Pursuit, [1]). Agricultural drones are set to revolutionize the global food generation system. Agricultural drones are already flocking and hovering over farms situated in a few agrarian zones. This quadcopter will autonomously navigate, avoid collisions and collect data using computer vision for post-analysis and drop seeds in specified locations. Using aerial quadcopter for surveying vast agricultural land can reduce human efforts. The quadcopter is designed to detect moving objects and identify rodents using an object recognition method. The motive of the paper is to design a low-cost unmanned autonomous aerial vehicle system which will accurately and efficiently locate potential threats and notify the owners about their location and severity.

The bulk of semiconductor technology has been based on silicon till today. But silicon has its own limitations. It is not transparent to visible light and hence it cannot be used in certain applications. ZnO is a material which is transparent to visible light. In this paper, we compare the electrical performance of ZnO Thin film Transistors using different gate insulators. Certain performance indices and material indices were considered as the selection criteria for electrical performance. A methodology known as Ashby’s approach was adopted to find out the best gate insulators and based on this methodology various charts were plotted to compare different properties of competing materials. This work concludes that Y2O3 is the best insulator followed by ZrO2 and HfO2.

Umbilical hernia is a very commonly encountered condition. Conservative management suffices in most of the cases. However, the complications such as incarceration, rupture with evisceration are extremely rare and warrants emergency surgery. We present the rare case of a 40-day-old female infant with ruptured umbilical hernia following ulceration due to the local application of Asafoetida paste. While many medicinal uses of Asafoetida have been reported in the past literature, there has been no mention of any complication due to its local application. Our patient was successfully managed with emergency mini-laparotomy and resection of approximately 40 cm of gangrenous small bowel with primary anastomosis. The patient has been attaining normal growth patterns on follow-up.

Water is a very crucial element to sustain life on earth. The availability of water varies both spatially and temporally. India being a water stress country, the per capita availability of water is reducing with time. It is predicted that by 2050 around 54% of the country will be water scarce. The changing climate along with the ever-increasing population is putting additional stress on water availability. The science of water, its availability and distribution on earth and its atmosphere, is generally regarded as hydrology. The important aspects of hydrology and hydraulic or more broadly water resources are taught as the subject or optional subjects the branch of Civil Engineering at almost all engineering institutes/colleges at the undergraduate level. It is also one of the specializations for post-graduate level studies. It is, by now, well proven that the geospatial technology play crucial role in water resources assessment, planning and management. However, the young minds (graduate and post-graduate students) are just being trained for typical hydrology using traditional means and approaches. The advancement and potential of geospatial technology has drawn the attention of academician, and it has been started as a subject mostly at the post-graduate courses. Recently, some of the institutions have started courses on geo-informatics at the graduate level. However, the hydrology and geospatial technology are generally taught as two separate subjects under different course at different levels mostly under the broad subject of Civil Engineering. The present paper emphasis on needs and ways of updating Civil Engineering course curriculum by focusing on incorporation of applications of geospatial technology in hydrology as regular subject.

Reservoir sedimentation is the major problem, due to it every year the reservoir capacity is lost to considerable amount. Surveying for assessment of the reservoir by conventional approach is time and money consuming. Geospatial technology provides ample opportunity in this field through the availability of high resolution satellite data from sensors such as Sentinel, Indian Remote Sensing Satellite, Landsat, and SPOT have been used to calculate the water spread area of the reservoir. However, due to presence of cloud in most of the optical data during onset of monsoon, the water spread at the lowest reservoir level could not be mapped. In turn the revised capacity or sedimentation is generally assessed between either below full reservoir level (FRL) or above maximum draw down level (MDDL). Nowadays, the microwave synthetic aperture radar (SAR) data at reasonable spatial resolution is available freely in public domain. Moreover, microwave data has capability to penetrate cloud and the information below cloud can easily be retrieved. To overcome the issues related to optical data, in the present study, the reservoir sedimentation for Ghataprabha reservoir was estimated using SAR data. Sentinel-1A data was used to delineate the water spread area for the water year of 2016–17. The original live storage capacity (1974) was estimated to be 1434.14 Mm3 at FRL 662.940 m by the authorities using the hydrographic survey during the commissioning of the reservoir in the year 1974. The live storage capacity was found out to be 1366.14 Mm3 at FRL, however, as per original elevation-area-capacity curve the live capacity is around 1262.404 Mm3 at 660.50 m. Estimated live storage capacity from Remote sensing approach (2016–17) was assesses as 1182.5 Mm3 at 660.51 m. The storage capacity has reduced from 1262.40 Mm3 (1974) to 1182.51 Mm3 i.e. around 171.732 Mm3. As per present analysis the rate of sedimentation is around 4 Mm3/yr. It was realized that using the SAR microwave data, the revised capacity of the reservoir from its near MDDL to FRL could be assessed through remote sensing approach.

The Himalayas are one of the largest reservoirs of freshwater in the form of glaciers and snow outside the Polar region (Mani 1981). There are around 32,392 glaciers, covering an area of about 71,182 km2 in the Indian part of the Himalaya (SAC 2011). Among all, North Western Himalaya (NWH) has the largest area under seasonal and perennial snow cover. This snow/glacier melt contributes significantly to perennial rivers like the Ganga and the Indus during lean time. The Indus Basin is comprised of Chennab, Jhelum, Rawi, Satluj and Beas River subbasins, whereas Upper Ganga Basin is comprised of Bhagirathi, Alaknanda, Mandakini, Dhauliganga and Pindar subbasins. Moreover, these basins have huge hydropower potential, which is a matter of concern during lean period (Kasturirangan et al. 2013).

This paper intends to draw a representation of blockchain transaction over a peer to peer network, representing the change in the newly developed network and solution over obsolete banking technology. This paper will draw clarity over the transaction by blockchain system and methods. The focus of paper contemplates the different association with the transaction, and concludes towards the high relished transaction system and functioning of the complete working and levelled system. The blockchain is another layer to run the existing stack of the Internet protocols, adding a new entire table of the given protocols and generated consensus by other user or participants. The advanced data isn't duplicated however is disseminated in various blocks and when the block consolidates they frame blockchain. In this day and age, the blockchain innovation is considered as the most secure path for exchanges.

This paper intends to draw the versions of the given and the transcript form the Big Data over the given form of the supply chain, this demystifies the given and comply the formed and complete system of the given Supply Chain(SCM) and the Big Data towards the completion to produce maximum productivity of the control management and the support production towards the formed from the control of customer and the market relying on the network. The solution is generated from the layer constructed from the three layers which are certainly formed by the Volume, Velocity and Variety which is blended with the K-Clustering algorithm, summing towards a better nature of market disclosure and trend.

Sign Language is a mode of communication for differently abled persons –Mute (Dumb). Sign language is the hand gestures used by people to communicate. American Sign Language is the most widely used Sign language which has its own syntax and rules and by following these rules and syntax, mute people can communicate. We have used image recognition to recognize the hand gestures. Detection of hand motions and processing of finger movement to identify what the mute person is communicating. Computer vision in python recognizes the hand gestures of a mute person and gives its output to a chatbot. Chatbot is a computer program which conducts a conversation via voice recognition. Such programs are often designed to convincingly simulate to help human beings with different tasks. This paper presents image recognition of Sign Language through hand gestures and integrate it with chatbot and gives us an audio as well as text output.

There is a compelling need to reduce carbon footprints. With the pollution bringing the capital of India to a standstill in November 2017, this is being emphatically reiterated. This paper aims to investigate the use of DC in homes and also the use of solar energy to reduce electricity demand drastically. Renewable energy in homes comes from solar panels in its DC form. Similarly, most of the electronic gadgets used in homes today consume DC. Brush Less DC (BLDC) motors and inverter technology have enabled other appliances to operate on DC. Mainly due to unavailability of power and inefficiency of power distribution, ‘24×7’ uninterrupted electricity remains a dream even for most of the urban population today. Rural scenario is still worse. To remain powered during outages, most houses store energy in DC batteries, which is again inefficiently converted to AC by inverters. Because of these compelling reasons, DC power replacing AC in homes is gaining momentum. Some projects are also in progress. Focus of this paper is to answer the pertinent questions; what are the main drivers of DC power in homes and how this initiative could be supported by working on Indian standards and promoting an eco-system of DC appliances. Based on experiments and quantitative analysis, a safe and power efficient AC-DC hybrid solution is proposed for fulfilling the basic needs of uninterrupted power and integration of renewable energy for essential functions of homes in rural as well as urban scenarios.

How do cybercrime markets emerge, evolve, and persist? How can cybercrime be prevented, decreased and mitigated? Extant anti-cybercrime efforts have concentrated on deterrence through criminal prosecution and technical mitigation. Deterrence-only strategies, however, may be more expensive for the network than for attackers, particularly considering the asymmetric nature of computer security. The implicit assumption of deterrence, i.e. criminals are strictly self-optimizing rational agents cognizant only of a cost-benefit function, is contentious. Criminal actions are constrained/enabled by the institutional structures of their immediate neighborhood. Exposure to crime (or probability of victimization is similarly influenced. Thus, this paper examines the respective economic, structural, and cultural theories in criminology and explores their relevance online. We discuss the implications for technical solutions, security design, as well as public policy. An intuitive position is to lower the entry cost of legal enterprise, and thereby increase the opportunity cost of cybercrime engagement. We also discuss solutions that allow simultaneous investments (to reduce crime online) by both public and private entities, while mitigating for potential moral hazard. Our concluding argument, then, is for complementing deterrence with policy solutions that preemptively engage potential criminals as legitimate market stakeholders. In addition to the explicit examination of deterrence theories of cybercrime, this work offers a broader consideration of cybercrime that is grounded in theories of crime offline.

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October–April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct–Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

The traditional approaches to encourage gamers to exercise have been two-fold: incorporating in-game incentives and designing games that are based on exercise. We discuss the drawbacks of these approaches and introduce two new design constructs: facilitative and rememberative. A facilitative design introduces external interaction mechanisms that reward physical exertion with an increase of control over the utility and entertainment value of a game. The rememberative approach leverages "ambient" design artifacts embedded into the gaming experience reminding the gamer to exercise. We propose to explore the use of these design constructs in existing games and discuss two prototypes based on them.

Introduction Upper tract damage (UTD) is a life-threatening complication of neurogenic bladder (NB). Early identification of risk factors for UTD and institution of remedial measures may probably prevent UTD. The aim was to study the predictors of UTD in children 2 years or older with NB. Method This cross-sectional, observational study over 2 years included 30 children. UTD was defined as serum creatinine of >1 mg/dL or society of fetal urology grade III–IV hydronephrosis or hydroureteronephrosis on ultrasonography or renal scars on 99mtechnetium dimercaptosuccinic scan or subnormal glomerular filtration rate (GFR) for age. The evaluated clinical variables were age at presentation, gender, palpable bladder lump, and recurrent urinary tract infection (UTI). Bladder wall thickness (BWT), grade and laterality of vesicoureteric reflux (VUR), status of the bladder neck, post-void residue (PVR), and level and type of intraspinal lesions were also noted. Urodynamic studies were performed for functional bladder assessment. A p-value 1 mg% (2, 6%), SFU III–IV (11, 36%), renal scars (12, 40%), and subnormal GFR in (2, 6%) patients. Clinical risk factors for UTD were delayed presentation (p = 0.034), palpable bladder lump (p ≤ 0.001; OR 38.5; CI 5.6–262.5), and recurrent UTI (p = 0.033, OR 4.125, CI 0.913–18.630). The presence of significant PVR, trabeculated bladder, spin-top urethra, and bilateral VUR were identified as radiological risk factors for UTD. Mean BWT in patients with and without UTD was 4.69 ± 1.78 mm and 2.91 ± 1.08 mm respectively. BWT predictive of UTD was 3.05 mm ( Figure). The mean detrusor leak point pressure (DLPP) did not vary significantly in those with and without UTD (36.82 ± 14.74 and 29.09 ± 10.44 cmH2O, respectively), yet 75% patients with DLPP > 40 cmH2O had UTD (p = 0.038, OR 5.4, CI 0.84–34.84). DLPP 40 cm H2O were identified as potential risk factors for UTD. This study highlights the significance of BWT as a predictor of UTD in NB. Figure. Ultrasonogram of a child with neurogenic bladder with increased bladder wall thickness (BWT) and SFU grade IV hydroureteronephrosis. Yellow cross denotes BWT. Figure options Download full-size image Download high-quality image (236 K) Download as PowerPoint slide

Harike wetland in the Indian state of Punjab is formed by a barrage built downstream on the confluence of rivers Satluj and Beas, with the idea of storing and providing irrigation and drinking water to parts of Southern Punjab and adjoining Rajasthan. Due to decrease in flow at Harike and deforestation etc. in the catchment area, the wetland is reducing in the last few years. In this study, the analysis of rainfall/runoff data has been carried out to see the effect of decreasing trend of runoff on wetland area. Wetland area has been delineated using remote sensing technique. The analysis of rainfall, discharge and ground water level showed that the flow pattern is decreasing at Harike. The remote sensing data revealed that the wetland area has reduced approximately 30% over the last 13 years.

Wireless ad-hoc networks are unplanned, quickly deployable, infrastructureless, and realistic for networking in emergency or restricted situations. They have actively been studied and proposed for post-disaster recovery. Our approach uses Linux Live USB nodes, which can be any available laptop computer at disaster-affected site, to boot the guest OS using USB flash drive with a variety of customized software packages demanded in post-disaster situations. Using an additional Wi-Fi USB adapter, Linux Live USB nodes help to easily form wireless ad-hoc network and serve user devices through Wi-Fi hotspot. While a lot of studies have been done over simulation, it is crucial to understand how wireless ad-hoc networks behave and their performance in the real field. In this paper, we examine different scenarios of wireless ad-hoc network deployment inside a building: 1) the assignment of frequency (Single Channel or Multi Channel), and 2) choice of mesh and routing protocol (OSLR or batman-adv). According to the results of field trials, our primary option of deployment will be Multi Channel batman-adv to cover multiple rooms inside the building expected to be evacuation shelter.

Participation on Online Social Networks OSNs inherently requires information sharing and thus exposes individuals to privacy risks. Risk mitigation then has been encouraged through adoption of usable privacy controls. Apparently stronger privacy enhancing technologies PETs decrease both risk and perceptions of risk. As a result individuals feel safer and may respond by in fact accepting more risk. Such perverse results have been observed offline. Risk perception offline has been understood to be a function of characteristics of the risks involved rather than as a calculus grounded only in the probability of the risk and the magnitude of harm. In this work we use nine characteristics of risk from a classic and proven offline model of perceived risk to conduct a survey based evaluation of perceptions of privacy risks on Facebook. We find that these dimensions of risk provide a statistically significant explanation of perceived risk of information sharing on Facebook.

This work addresses fundamental questions about the nature of cybercriminal organization. We investigate the organization of three underground forums: BlackhatWorld, Carders and L33tCrew to understand the nature of distinct communities within a forum, the structure of organization and the impact of enforcement, in particular banning members, on the structure of these forums. We find that each forum is divided into separate competing communities. Smaller communities are limited to 100–230 members, have a two-tiered hierarchy akin to a gang, and focus on a subset of cybercrime activities. Larger communities may have thousands of members and a complex organization with a distributed multi-tiered hierarchy more akin to a mob; such communities also have a more diverse cybercrime portfolio compared to smaller cohorts. Finally, despite differences in size and cybercrime portfolios, members on a single forum have similar operational practices, for example, they use the same electronic currency.

We report the design and development of a software system that provides a unified platform for acquisition, storage and retrieval of scientific, situational and infrastructural data in the context of disasters. The system supports (i) data acquisition from heterogeneous sources (including sensor data from various research groups, field data from disaster management agencies and citizens, and relevant information from the internet) and ensures interoperability, (ii) high availability and scalability and (iii) a wide range of data visualization and retrieval modes. We believe that such a system would go a long way in assisting researchers and disaster management agencies in enhancing disaster preparedness, mitigation and recovery operations.

The utility of social networks is contingent on information sharing, which constrains privacy. Engaging in online social networks is seen to indicate that the participants either do not care about privacy, do not understand the risks of information sharing, or find privacy controls to be unusable. Given the contextual nature of privacy all three models may exist in the population and in fact in a single person. We introduce the three distinct academic threads leading to each model, i.e. rational economics, risk behavior, and human computer interaction. Further, we conduct a survey based study to elicit the relative strengths of these explanations for information sharing on Facebook (n=384). Our findings indicate that while all three explanations are relevant, their relative strengths are different. The perceived risk of sharing information is the most important determinant of privacy behaviors; to a lesser extent usability of privacy controls is important. Finally, privacy preferences is the least important factor; thus, the explanation that people don’t care is weakest explanation of the privacy paradox (on Facebook).We argue that Facebook should invest in both more usable privacy controls and awareness of those controls. In general better controls and more information increased information sharing and lowered the perceptions of risk, thereby aligning expressed attitudes with observed behaviors and addressing the paradox. We note that privacy risks behaviors are determined by dread risk and familiarity on Facebook, as for risks offline. Thus, Facebook privacy efforts should leverage prior research in risk communication offline. Specifically, risk averse behaviors can be encouraged if risk communication focuses on severe, catastrophic, and rare consequences of information sharing.

The challenges of an aging population require the adoption of in- home and medical technologies to complement the traditional caregiver model. Adoption of such technologies is, however, impinged by privacy concerns. This study investigates a four dimensional framework that explains the tradeoffs between functionality and privacy as constructed by older adults. The four dimensions constitute perceived utility, data granularity, data recipient, and activity sensitivity. We conducted a survey based study to empirically examine the applicability and robustness of this framework. Our results have implications for the adoption of a wide range of privacy enhancing technologies. By focusing on the intersection of an under-studied group (non-technical older adults) and sensitive data (medical and at home), this work has the potential to enable Privacy Enhancing Technologies (PETs) that might be widely adopted. Author has checked copyright AMS

Underground forums enable technical innovation among criminals as well as allow for specialization, thereby making cybercrime economically efficient. The success of these forums is contingent on collective action twixt a variety of stakeholders. What distinguishes sustainable forums from those that fail? We begin to address these questions by examining underground forums under an economic framework that has been used to prescribe institutional choices in other domains, such as fisheries and forests. This framework examines the sustainability of cybercrime forums given a self governance model for a common-pool resource. We analyze five distinct forums: AntiChat (AC), BadHackerZ (BH), BlackhatWorld (BW), Carders (CC), and L33tCrew (LC). Our analyses indicate that successful/sustainable forums: 1) have easy/cheap community monitoring, 2) show moderate increase in new members, 3) do not witness reduced connectivity as the network size increases, 4) limit privileged access, and 5) enforce bans or fines on offending members. We define success as forums demonstrating small world effect.

The Time Machine (TM) is a spike-based computation architecture that represents synaptic weights in time. This choice of weight representation allows the use of virtual synapses, providing an excellent tradeoff in terms of flexibility, arbitrary weight connections and hardware usage compared to dedicated synapse architectures. The TM supports an arbitrary number of synapses and is limited only by the number of simultaneously active synapses to each neuron. SpikeSim, a behavioral hardware simulator for the architecture, is described along with example algorithms for edge detection and objection recognition. The TM can implement traditional spike-based processing as well as recently developed time mode operations where step functions serve as the input and output of each neuron block. A custom hybrid digital/analog implementation and a fully digital realization of the TM are discussed. An analog chip with 32 neurons, 1024 synapses and an address event representation (AER) block has been fabricated in 0.5 μm technology. A fully digital field-programmable gate array (FPGA)-based implementation of the architecture has 6,144 neurons and 100,352 simultaneously active synapses. Both implementations utilize a digital controller for routing spikes that can process up to 34 million synapses per second.

Privacy risks have been addressed through technical solutions such as Privacy-Enhancing Technologies (PETs) as well as regulatory measures including Do Not Track. These approaches are inherently limited as they are grounded in the paradigm of a rational end user who can determine, articulate, and manage consistent privacy preferences. This assumes that self-serving efforts to enact privacy preferences lead to socially optimal outcomes with regard to information sharing. We argue that this assumption typically does not hold true. Consequently, solutions to specific risks are developed - even mandated - without effective reduction in the overall harm of privacy breaches. We present a systematic framework to examine these limitations of current technical and policy solutions. To address the shortcomings of existing privacy solutions, we argue for considering information sharing to be transactions within a community. Outcomes of privacy management can be improved at a lower overall cost if peers, as a community, are empowered by appropriate technical and policy mechanisms. Designing for a community requires encouraging dialogue, enabling transparency, and supporting enforcement of community norms. We describe how peer production of privacy is possible through PETs that are grounded in the notion of information as a common-pool resource subject to community governance.

Popular platforms including Android and Facebook have adopted a permissions-based model. Under this model applications (apps) are required to declare specific access to user information required for functionality. We conducted two user studies on Amazon's Mechanical Turk to test the efficacy of these permissions requests on the Android platform. We found permissions were ineffective, even with the addition of an additional text warning. Conversely, we found that an app's download count had a strong effect on app installations. In order to determine if it was a failure of our text-based warning, we ran a second experiment with a previously proven visual indicator.

When and how should we sanction network (service) providers, software vendors, or application services provider to mitigate the harm of security and privacy risks? Here we apply an economic framework that compares two sanctioning regimes: ex ante and ex post. Specifically, we introduce, translate, and apply the model by Garoupa et al. for security and privacy risks online. We identify under which conditions the different sanctions are economically efficient. We argue that for well known security risks, such as botnets, the economically efficient solution would be ex-ante sanctions. Simultaneously, privacy risks, which are contextual, poorly understood, new, and whose distribution across demographics would be difficult to estimate, should be managed through ex-post sanctions. To the extent that providers are judgment-proof, the sanctions can be non-monetary, e.g. reputation-based. Finally, resource allocation is suboptimal when privacy risks are treated disjointly from security risks. Thus, the relative merits of either security or privacy investmenta should take into account the opportunity cost of mitigating the other. Finally, we provide an analysis of existing policy measures with the case study of Do Not Track and botnet takedowns. We address two kinds of regulatory regimes for sanctions: 1) ex ante and 2) ex post. Ex Ante, or action-based sanctions, is a regime that prohibits specific actions. For example, for environmental risks it may be illegal to store industrial waste in a container above a certain volume, below a specific tensile strength etc. In automobile safety ex ante. Regulation manifests as speed limits, where it is considered too dangerous for individuals to drive above a certain limit. Thus, ex ante sanctions are action dependent. Online these sanctions are part of policy initiatives like Do Not Track. (Arguably, there is no direct financial sanction to those who do not comply. However, indirect sanctions through reputation loss are equally relevant.)Ex post, or harm-based regulation, sanctions after the fact. For example, instead of mandating a specific kind of container to store industrial waste, the government might decide that the respective industries can make better decisions. However, if specific companies are lax and there is a spill, that company would be required to provide for damages. Thus, ex post sanctions are harm dependent. If a potentially hazardous activity does not have any negative consequences, there are no sanctions. Online these sanctions manifest as FTC enforcement against Google for privacy breaches due to Buzz.Currently both sanctioning regimes are being used to develop public policy responses to security and privacy risks online. These sanctions are being enforced by agencies such as Federal Bureau of Investigation (FBI). Often, however, agencies such as the Federal Trade Commission (FTC), traditionally enforcement agencies, are also being tasked with informing and creating policy. The actions of both these agencies, in enforcement as well as policy, have been controversial. FBI takedowns of botnets such at Nitol have been criticized for their collateral damage. FTC initiatives, such as Do Not Track, similarly have been under attack from both who are privacy advocates and those who prefer the free market approach. It is unclear under what conditions each of these types of sanctions are economically sensible. Given that both ex ante and ex post sanctions are being used, which are more effective for security and privacy risks online? We begin to answer this question by using an economic framework that compares the effectiveness of the two distinct regimes of ex ante and ex post sanctions. This research is based on previous work by Garoupa et al. We being by introducing the general model. We then extend this model by considering an inequitable distribution of risk. We analyze existing policies using the economic framework being considered. We discuss the broader scope of sanctions and the implications of policy. We conclude with specific insights for enforcement agencies.

Purpose Older adults are more susceptible to fraud offline than younger adults. As they increasingly use the internet for activities including managing financial assets, this susceptibility is transferred online. Thus, there is an imminent need to communicate the risks inherent in these new technologies, especially that of data disclosure, to older adults. These risks might best be communicated by using appropriate mental models and grounding analogies in more familiar risks, e.g. physical risks. Using videos rather than text may improve comprehension as well as address other concerns of aging, e.g. attention and memory. While videos can lead to richer comprehension, multi-media communications can challenge cognitive reserves. We present the design of narrative-driven risk communication videos that leverage physical analogies to answer the following questions: (i) What are the determinants of older adults perception of online risk, specifically for responding to phishing and malware e-mails?; and (ii) What is the effect on comprehension when using videos as opposed to text? Method To investigate the determinants of older adults’ perceptions of online risk, Garg and Camp investigated a nine-dimensional model of risk perception that is based on an expressed preferences. They found that not all the nine dimensions are equally relevant online. They proposed a five-dimensional model for online risks consisting of voluntariness, immediacy, control, chronic-catastrophic, and severity. These dimensions were adapted to create a survey to assess elders’ determinants of risk. For example, voluntariness is redefined as, “To what extent does an older adult have a choice in being exposed to this risk? (1=Voluntary, 5= Involuntary)”. Our second question, whether video is more effective than text in communicating risk, was evaluated by participant comprehension: participants’ ability to identify the risk, the attack vector, the impact of risk if exploited, and strategies to avoid or mitigate the risk. We conducted pilot studies with a convenience sample of 12 older adults (8 female and 4 male). Six participants watched the videos, the other six read the textual description of the risks, and each filled out associated surveys. Results & Discussion All 12 participants rated the risk of responding to be higher than that of not responding, but not all items on the five dimensions were rated higher for responding. This indicates that not all dimensions have equal weights in the construction of perceived risk. Participants in the video group were more likely to verbalize the risk of responding or not responding, suggesting videos might be better at explaining online risks to older adults.

In this paper we leverage a canonical nine dimensional model of offline risk perception to better understand online risk perceptions. Understanding risk perception facilitates the development of better risk communication and mitigation technologies. We conducted a classic off-line survey to identify the dimensions of online risk perceptions of end users. These results were different from those observed for offline risks. When investigating the original nine dimensional model we found that severity of a risk was the biggest factor in shaping risk perception. We note that technically similar dimensions were not clustered together by participants. We further reduced the nine dimensional model to a four dimensional framework. The variance explained by the original framework was 13.76%. This increased to 83.6% under the reduced model. Under the new model time was the most significant determinant of perceived risk. We found that risks that had a physical analogue were considered more risky and were much better understood. Finally, we discuss the experimental challenges in using offline risk models for online risks.

There are significant differences between older and younger adults in terms of risk perception and risk behaviors offline. The previously unexplored existence of this dissimilitude online is the motivation for our work. What are the risk perceptions of older adults? How are these correlated with the classic dimensions of risk perception offline? Can we leverage episodic memory, particularly relevant for older adults, to increase the efficacy of risk communication? We conduct a survey based experiment with two groups: video (n=136) and text (113). We find that leveraging episodic memory using video risk communication can improve the ability of elders to avoid phishing attacks and downloading malware. The applicability of the dimensions of risk were different based not only the risk but also the mode of risk communication.