In the aftermath of a disaster event, it is of utmost important to ensure efficient allocation of emergency resources (e.g. food, water, shelter, medicines) to locations where the resources are needed (need-locations). There are several challenges in this goal, including the identification of resource-needs and resource-availabilities in real time, and deciding a policy for allocating the available resources from where they are available (availability-locations) to the need-locations. In recent years, social media, and especially microblogging sites such as Twitter, have emerged as important sources of real-time information on disasters. There have been some attempts to identify resource-needs and resource-availabilities from microblogging sites. However, there has not been much work on having a policy for optimized and real-time resource allocation based on the information obtained from microblogs. Specifically, the allocation of critical resources must be done in an optimal way by understanding the utility of emergency resources at various need-locations at a given point of time. This paper attempts to develop such a utility-driven model for optimized resource allocation in a post-disaster scenario, based on information extracted from microblogs in real time. Experiments show that the proposed model achieves much better allocation of resources than baseline models—the allocation by the proposed model is not only more efficient in terms of quickly bringing down resource-deficits at various need-locations, but also more fair in distributing the available resources among the various need-locations.
Recent advances in the field of network representation learning are mostly attributed to the application of the skip-gram model in the context of graphs. State-of-the-art analogs of skip-gram model in graphs define a notion of neighborhood and aim to find the vector representation for a node, which maximizes the likelihood of preserving this neighborhood. In this paper, we propose core2vec, a new algorithmic framework for learning low dimensional continuous feature mapping for a node. We utilize the well-established idea that nodes with similar core numbers play equivalent roles in the network, which is a drastic departure from existing network structure agnostic random walk based neighborhood selection approach. We compare our method against competing methods on downstream word similarity task and obtain significant improvement in performance (best 46%).
The local boxicity of a graph G, denoted by lbox(G), is the minimum positive integer l such that G can be obtained using the intersection of k (where k≥l) interval graphs where each vertex of G appears as a non-universal vertex in at most l of these interval graphs. Let G be a graph on n vertices having m edges. Let Δ denote the maximum degree of a vertex in G. We show that, •lbox(G)≤213log⁎ΔΔ. •lbox(G)∈O(nlogn). •lbox(G)≤(213log⁎m+2)m. •the local boxicity of G is at most its product dimension. This connection helps us in showing that the local boxicity of the Kneser graph K(n,k) is at most k2loglogn. The above results can be extended to the local dimension of a partially ordered set due to the known connection between local boxicity and local dimension. Using this connection along with known results it can be shown that there exist graphs of maximum degree Δ having a local boxicity of Ω(ΔlogΔ). There also exist graphs on n vertices and graphs on m edges having local boxicity of Ω(nlogn) and Ω(mlogm), respectively.
The purpose of this study is to determine the impact of adopting a artificial intelligence-embedded customer relationship management (CRM) system for business-to-business relationship management. After reviewing the literature and considering the theory, a conceptual model was developed. The model was validated using the PLS-SEM technique with 312 responses from 14 firms in the B2B context. The study finds that an AI-embedded CRM system has a significant positive impact towards B2B relationship satisfaction and firm performance. Also, the study highlights that there is a negative impact of the moderator ‘technology turbulence’ on the relations of ‘automated decision making’ and ‘operational efficiency’ with ‘B2B relationship satisfaction’, whereas there is a positive impact of moderator ‘leadership support’ on ‘B2B relationship satisfaction’ and ‘firm performance’. There is presently no study on the impact of AI-CRM in the B2B context. Also, the study contributes to the existing literature by incorporating the moderator impact of ‘technology turbulence’ and ‘leadership support’ in the context of AI-embedded CRM systems for B2B relationship management.
This study investigates how B2B relationships can be nurtured in the cultural environment of the Indian management style. We have considered three prominent Indian cultural attributes that influence Indian management style: jugaad (J), visvaas (V), and chalta hai (C). These are perceived to have considerable impacts on relationship management in terms of consumer buying behavior and psychology in the B2B context. This concept has dramatically changed the B2B marketing dynamics, blurring the age-old boundaries between B2B and B2C marketing contexts. With this background, we attempted to understand how the three Indian cultural attributes can impact the B2B relationship. This study highlights that consumers’ buying behavior, in the B2B context, is influenced by their brand identification, purchase engagement, and prestige sensitivity. A conceptual model has been developed and the model has been validated statistically by a survey involving 364 respondents. The study reveals that jugaad, visvaas, and chalta hai considerably affect the business relationship performance of the MNCs doing business with Indian firms, especially on B2B consumer buying behavior and psychology.
Diabetic retinopathy is emerging as a very serious vision disorder in the recent decades, due to escalation of diabetes world-over. This condition can be minimized to a great extend with timely prognosis. Computer-aided detection techniques are very useful for assisting ophthalmologists, for faster diagnosis and intervention. With the advent of digital fundus cameras and the digitization of retinal images, there is a huge availability of digital fundus images with expert-annotated labels. For addressing the challenge of digital image grading, an attempt was made to model the features in digital fundus images, utilizing the non-Euclidean geometry. Here, a Graph Neural Network with supervised learning is suitably adapted for diabetic retinopathy image grading. The images are represented as 3D graphs, to encapsulate discriminate information, as nodes in network. The features extracted from the diabetic retinopathy images, using Scale Invariant Feature Transform technique, is used for graph construction and training. The Diabetic Retinopathy Graph Neural Network namely, DRG-NET model is trained and validated on two publicly available datasets namely Aptos 2019 and Messidor. Ten different types of performance indicators, including accuracy and Cohen’s kappa values, were estimated and used for the comparison of models. For the Aptos and Messidor dataset, the model achieved an accuracy of 0.9954/0.9984, F1-score of 0.9774/0.9968 and kappa score of 0.9930/0.9980, respectively. It is evident from the results that the proposed DRG-NET model shows state-of-the-art performance for retinal image grading.
This study aimed to understand the effect of physiological and dental implant‐related parameter variations on the osseointegration for an implant‐supported fixed prosthesis. Eight design factors were considered (implant shape, diameter, and length; thread pitch, depth, and profile; cantilever [CL] length and implant‐loading protocol). Total 36 implantation scenarios were simulated using finite element method based on Taguchi L36 orthogonal array. Three patient‐specific bone conditions were also simulated by scaling the density and Young's modulus of a mandible sample to mimic weak, normal, and strong bones. Taguchi method was employed to determine the significance of each design factor in controlling the peri‐implant cortical bone microstrain. For normal bone condition, CL length had the maximum contribution (28%) followed by implant diameter (18%), thread pitch (14%), implant length (8%), and thread profile (5%). For strong bone condition, CL and implant diameter had equal contribution (32%) followed by thread pitch (7%) and implant length (5%). For weak bone condition, implant diameter had the highest contribution (31%) followed by CL length (30%), thread pitch (11%) and implant length (8%). The presence of distal CL in dental framework was found to be the most influential design factor, which can cause high strain in the cervical cortical bone. It was seen that implant diameter had more effect compared to implant length toward peri‐implant bone biomechanical response. Implant‐loading time had no significant effect towards peri‐implant bone biomechanical response, signifying immediate loading is possible with sufficient mechanical retention.
During yellow intervals, dilemma zones (DZ) often trigger red light violations (RLV) and unexpected stoppings near stop lines, causing severe collisions at signalized intersections. While several studies in developed countries have tested various countermeasures to eliminate DZ, this aspect is not well explored for developing countries. This study investigated a green signal countdown timer’s (GSCT) efficacy in reducing DZ at signalized intersections. Based on historical crash data, 10 signalized junctions in Delhi, India, 5 with GSCT and 5 without, were chosen. Although India has a permissive yellow law, all the study sites have a flat 5 s yellow change interval with no provision of all-red (AR) intervals, which might have resulted in severe crashes at these locations. Empirical assessments revealed that GSCT minimizes the length of type-I DZ, which refers to a situation when drivers can neither stop nor proceed to the intersection during the yellow signal. Interestingly, GSCT also minimizes the length of type-II DZ, which is an indecision zone based on drivers’ 10% and 90% stopping probabilities. Summation of yellow and all-red intervals (Y + AR) obtained using Institute of Transportation Engineers (ITE) equations was found to be longer than field-allocated 5 s change interval at GSCT-enabled sites. Consequently, GSCT’s effectiveness across various yellow and all-red intervals was investigated in the PTV-VISSIM microsimulation tool, and crossing and rear-end conflicts were extracted using Surrogate Safety Assessment Model (SSAM). Results suggest that GSCT’s presence, along with estimated yellow and all-red intervals, reduces crossing and rear-end conflicts due to RLVs and inconsistent stoppings, respectively.
Vulnerable road users (VRU) such as pedestrians, motorcyclists, and bicyclists, account for more than half of total road traffic fatalities in developing countries. In urban India, VRU consist of more than 80% of the fatalities. Although in Indian cities, the share of VRU is considerably high, suitable VRU-friendly facilities are not efficiently planned. In this context, the present paper aims to develop a systematic approach to enhance VRU safety at the urban intersection level in the context of a developing country. Using 6 years’ crash data (2011–2016) from “Kolkata Police”, India, the applicability of the present research framework is demonstrated. To examine the major risk factors associated with pedestrians, motorcyclists, and non-motorized transport users (NMT: bicycle, cycle-rickshaw, and hand-pull carts), three sets of crash prediction models are developed with the help of Poisson and negative binomial analysis. The study outcome reveals that vehicle volume and speed, inadequate sight distance, and the absence of designated bus stops significantly affect the likelihood of fatal pedestrian crashes. Alternatively, overspending and overtaking behavior by motorcyclists, and restricted sight distance increase the fatality risk of motorcyclists. Speed inconsistency between motorized and non-motorized vehicles, insufficient street lighting, and inadequate sight distance increase the risk of NMT users. The overall study outcomes specify the need for segregation between motorized traffic and VRU at urban intersections by providing dedicated lanes for VRU along with suitable crossing facilities; implementing signalization with a distinct phase for VRU. The study also highlights the importance of speed management measures in urban India.
The present paper examines the role of the built environment on pedestrian-vehicular crashes, pedestrians’ risk perception, and pedestrians’ unsafe activities at the urban signalized junctions in the context of a developing nation. A conceptual framework is established to recognize the possible associations between the built environment, risk exposures, pedestrian activity, risk perception, and pedestrian crashes. The proposed research work is demonstrated with reference to the metropolitan city of Kolkata, India. The Negative binomial models are developed to study the association between the built environment and police-reported crashes. Likewise, to examine the role of the built environment on pedestrian risk perception Ordered logit models are developed. The study outcome shows that an increase in average vehicular speed by 10 km per hour at a junction is expected to increase the chance of pedestrian fatalities by 50%. With an increase in minor road width by one unit, pedestrian fatality risk is likely to rise by 7%. The lack of a pedestrian signal head is expected to increase pedestrians’ perceived risk by 1.2 times; whereas the absence of adequate sight distance is probable to increase pedestrians’ perceived risk by 2.3 times. Subsequently, a set of beta regression models are developed to examine the impact of the built environment on pedestrians’ unsafe activities. The outcomes confirm that ‘pedestrian’s usage of mobile phones while crossing’ (distraction) increases the possibility of pedestrian signal violation behavior by 1.7 times. Inaccessible zebra crossing and on-street parking are likely to increase pedestrians’ risky crossing behavior by 16% and by 14%, respectively.
The soil erosion and sediment yield on the Chota Nagpur Plateau, India have been greatly affected over the years by ever increasing anthropogenic influences and associated land-use/land-cover (LULC) changes. This study coupled a CA-Markov model with the Geospatial Interface for Water Erosion Prediction Project (GeoWEPP) model along with sediment connectivity index (IC) to estimate the decadal-scale soil erosion and sediment yield changes in a representative catchment of Chota Nagpur Plateau for 2001–2040 period. This LULC- Erosion-IC modeling approach in the Konar catchment has revealed (i) concerning patterns of conversions of bare lands and grasslands to agricultural fields and (ii) dominance of the new agricultural lands in the areas with higher connectivity. Results indicated that around 11 % and 13 % of the total catchment area undergoing agriculture will be in the highly erodible and highly connected category respectively by 2040. The corresponding mean erosion rates showed a significant increase for agricultural lands [from 26 to 35 T/Ha/Y], built up areas [from 43 to 48 T/Ha/Y], and bare lands [from 30 to 36 T/Ha/Y] during the time period of 2001 to 2040. The evaluation of mean connectivity values showed future expansions of forests (through agro-forestry, i.e. ∼2400 ha) with low connectivity by 2040. A strong linkage between increased future sediment yield and changing LULC patterns (around +6 % and +8 % of the built up area and agricultural lands and −5% and −9% of bare lands and grasslands respectively) can be observed by 2040. These changes are directly attributed to the sediment yield in the region with approximately 59 % and 97 % increase during 2001–2020 and 2020–2040 periods respectively. This study provided a good understanding of general trends in erosion and sediment yield in the Chota Nagpur Plateau and the influence of ongoing efforts in agro-forestry components and land use change dynamics.
Network connectivity is an important aspect of the built environment. However, it rarely captures the attention of decision-makers while implementing pedestrian projects. In recent years, Space-Syntax (SS) analysis is gaining popularity among researchers due to its ability to clearly demonstrate the topological configuration of a physical network. This research carries out a connectivity analysis of 297 links across three pedestrian networks in Varanasi, India, thereby exploring the relationship between pedestrian volume, SS indices in the Indian context, and other built environment features, such as presence of sidewalk, right-of-way (ROW) and landuse area. Data collection includes developing a GIS database of the pedestrian network, 30-min pedestrian volume counts at various locations, and calculating SS connectivity measures from the configuration of the pedestrian networks. Resulting SS indices were analyzed with existing pedestrian volumes through correlation, stepwise multiple linear regression (MLR), and path models. Results of the analysis showed that the SS index ‘Normalized Angular Integration (NAIN)’ to be highly correlated with pedestrian volume (r = 0.57). Subsequently, the MLR and path models show a statistically significant relationship of pedestrian volume with NAIN, ROW, presence of sidewalk, and commercial area. ROW affects the pedestrian volume both directly and indirectly in a commercial area, thereby indicating a requirement for improving/providing walking facilities in such areas. The study concludes with practical implications for local authorities that highlight the importance of providing walking infrastructures at commercial streets having high connectivity. The study also shows the significance of NAIN as an effective measure of pedestrian street network connectivity.
Rare microbial taxa [bacterial and archaeal operational taxonomic units (OTUs) with mean relative abundance ≤ 0.001%] were critical for ecosystem function, yet, their identity and function remained incompletely understood, particularly in arsenic (As) contaminated rice soils. In the present study we have characterized the rare populations of the As-contaminated rice soil microbiomes from West Bengal (India) in terms of their identity, interaction and potential function. Major proportion of the OTUs (73% of total 38,289 OTUs) was represented by rare microbial taxa (henceforth mentioned as rare taxa), which covered 4.5–15.7% of the different communities. Taxonomic assignment of the rare taxa showed their affiliation to members of Gamma-, Alpha-, Delta- Proteobacteria, Actinobacteria, and Acidobacteria. SO4²⁻, NO3⁻, NH4⁺and pH significantly impacted the distribution of rare taxa. Rare taxa positively correlated with As were found to be more frequent in relatively high As soil while the rare taxa negatively correlated with As were found to be more frequent in relatively low As soil. Co-occurrence-network analysis indicated that rare taxa whose abundance were correlated strongly (R > 0.8) with As also had strong association (R > 0.8) with PO4²⁻, NO3⁻, and NH4⁺. Correlation analysis indicated that the rare taxa were likely to involved in two major guilds one, involved in N-metabolism and the second involved in As/Fe as well as other metabolisms. Role of the rare taxa in denitrification and dissimilatory NO3⁻ reduction (DNRA), As biotransformation, S-, H-, C- and Fe-, metabolism was highlighted from 16S rRNA gene-based predictive analysis. Phylogenetic analysis of rare taxa indicated signatures of inhabitant rice soil microorganisms having significant roles in nitrogen (N) cycle and As-Fe metabolism. This study provided critical insights into the taxonomic identity, metabolic potentials and importance of the rare taxa in As biotransformation and biogeochemical cycling of essential nutrients in As-impacted rice soils. Graphical abstract
Urban water distribution networks (WDNs) in developing economies often refrain from investing in sensor-based leakage management technologies due to financial constraints and other techno-managerial issues. Thus, this study proposes a generalized decision support framework based on network sensitivity analysis (NSA) and multi-criteria decision-making (MCDM) to assess the prospect of effective leakage control through robust sensor placement in existing deficient WDNs. Four sensitivity parameters are formulated for NSA to ascertain the pressure response of the potential sensor positions for diverse hydraulic and leak scenarios. Subsequently, selecting the optimal number of sensors and their relative positions within the WDN is framed as an MCDM problem that entails the simultaneous maximization of Euclidean distances among the potential sensor positions and the leak-induced pressure residuals obtained at these sensors. The proposed methodology is developed on a numerical benchmark network assuming ideal conditions, and its applicability is verified on a sensor-equipped experimental network considering realistic system uncertainties. The outcome of this study aims to provide an insightful understanding of the system behavior that governs its leak localization potential and ascertain the practical challenges of sensor-based leakage monitoring in existing WDNs. Decision-makers of resource-strained utilities can beneficially utilize the proposed framework to assess the environmental and cost trade-offs of employing sensor-based technologies for leakage management and proactive decision-making before its actual implementation.
Using empirical orthogonal function analysis, a stationary atmospheric wavenumber-4 (AW4) pattern is identified in the Southern mid-latitudes during austral summer. The generation mechanism and its linkage to the Southern Hemisphere climate are explored using a linear response model and composite analysis. It is found that AW4 pattern is forced by a Rossby wave source in the upstream region of the upper-tropospheric westerly waveguide. The vortex stretching associated with the anomalous convection over the subtropical western Pacific Ocean (near the New Zealand coast) adjacent to the westerly jet triggers the Rossby wave train around mid-November. This disturbance gets trapped in the Southern Hemisphere westerly jet waveguide and circumnavigates the globe. Around 15–25 days later (in early December), a steady AW4 pattern is established in the Southern mid-latitudes. Further, correlation analysis suggests the AW4 pattern is independent of other natural variabilities such as El Niño/Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole. The AW4 pattern is found to influence the rainfall over different parts of South America and Australia by modulating upper-level divergence.
In this work, we propose a multilingual speech mode transformation (MSMT) model as the front end to improve the robustness of the speech recognition system by transforming the characteristics of conversation and extempore modes of speech into read mode of speech. The proposed front end includes multilingual speech mode classification (MSMC) system and mode-specific MSMT model. The mode-specific MSMT models are developed using a cycle-consistent generative adversarial network (CycleGAN) variant named as weighted CycleGAN (WeCycleGAN). In these models, generator loss is multiplied with relevant weight to learn a strong mapping from conversation and extempore speech to read speech while preserving the linguistic content. The proposed model is developed with non-parallel speech samples of three modes using adversarial networks, which helps in learning among two distributions (extempore vs read or conversation vs read) instead of direct mapping among parallel speech samples. Experiments are conducted on non-parallel speech dataset of conversation, extempore, and read modes from four Indian languages, namely Bengali, Odia, Telugu, and Kannada. The objective evaluation shows that the transformed feature vectors are highly correlated with the target feature vectors. The subjective evaluation shows that the quality of the transformed speech mode is close to the target speech mode. The significance of the proposed MSMT model is demonstrated on speech recognition system. The results report that the performance of speech recognition is significantly improved in the presence of MSMT model.
For many decades, liquid hydrocarbon fuels are being used to propel air-breathing engines. However, the energy content of liquid hydrocarbon fuels has reached close to its limitation. Any further improvement in energy value is quite difficult through chemical methods. Slurry fuels, which are colloidal suspensions of solid energetic particles in liquid fuel, became a potential alternative fuel. Among several solid additives, boron is considered the best choice because of its higher heating values. However, our knowledge about boron-loaded slurry fuel is very limited. Therefore, here in this study, we experimentally investigated the combustion characteristics of boron/Jet A-1 slurry fuel spray in a swirl-stabilized combustor. A particle loading of 10% by weight is considered here for the experiment. The feed boron, as well as the burnt boron samples, were characterized using standard material characterization techniques in order to understand the surface morphology, oxidation behavior, and active boron content. The combustion characteristics of slurry fuel were analyzed through spectroscopy and BO2* chemiluminescence imaging. Positive thermal contribution from boron combustion was quantified via temperature measurements at three different radial locations of the combustor exit. The spectroscopy and chemiluminescence signatures indicate that combustion of boron particles occurs downstream of the dump plane. The exit temperature of boron-laden slurry fuel measured at all three different radial locations is higher compared to neat Jet A-1. An increment of 19% in exit temperature was observed in the case of boron slurry fuel relative to neat Jet A-1. The X-ray diffractograms (XRD) and thermogravimetric analysis (TGA) show the complete oxidation of boron particles.
The study aims at establishing the nonlinear stability theory for a uniform flow along a Brinkman porous layer with two impermeable and isothermal horizontal boundaries. Convection in the flow is initiated by the integrated effect of the buoyant forces in thermal and solutal fields. The cross-diffusion effect is also considered as the contributing factor towards the convective instability in the medium. The equations associated with the nonlinear analysis are derived with the help of the Energy method. The results obtained from the linear stability analysis are compared with those of nonlinear stability analysis and the conditions under which the two stability boundaries are widely separated, are discussed elaborately. It is observed that the stability boundary obtained using the nonlinear analysis is solely affected by the concentration gradient (Sa) and the Soret parameter (Sr). However, the concentration gradient is found to have a destabilizing effect on both linear as well as nonlinear stability boundaries. It is also observed that the oscillatory instability is seen only in the case of oblique rolls, whereas, the longitudinal rolls always occur in the stationary mode. For Sa<0, the basic state flow gets stabilized with the increasing values of the Soret parameter. The flow tends to stabilize with the increasing magnitude of throughflow in both forward as well as backward directions. However, an opposite trend is noticed for La>1 and Sa<0, for the smaller magnitude of throughflow (|Pe|<20).
The localization of energy on chaotic discrete breathers (DBs) arising in a two- dimensional triangular lattice due to the modulation instability of delocalized nonlinear vibrational modes (DNVMs) is analyzed. Three DNVMs with frequencies above the phonon band and demonstrating hard-type anharmonicity (an increase in the vibration frequency with amplitude) are considered. Chaotic DBs have long lifetime, slowly radiate their energy and eventually disappear. The evolution of the macroscopic characteristics of the lattice is observed during the transition from the regime with chaotic DBs to thermal equilibrium. It is established that chaotic DBs with a hard type of anharmonicity reduce the ratio of the total energy to the kinetic energy (and, consequently, reduce the heat capacity). They also reduce lattice pressure at constant area (and therefore reduce thermal expansion). The tensile rigidity of the lattice also decreases due to DBs with a hard type of anharmonicity. The most sensitive to the presence of DBs is the pressure, which in the presence of DBs is approximately 30% less than in thermal equilibrium. The ratio of the total energy to the kinetic energy in the regime of chaotic DBs decreases by about 3%, and the tensile rigidity by only 0.1%.
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