A novel application of deep learning with image cropping: a smart city use case for flood monitoring

© 2020, The Author(s). Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms

Numerical Simulation of Plane Crack Problems Using Extended Isogeometric Analysis

AbstractThis paper presents the simulation of plane crack problems using extended isogeometric analysis (XIGA). In XIGA, both geometry and solution are approximated using NURBS basis functions. Discontinuous Heaviside function is used to model the crack face, while crack tip singularity is modeled using asymptotic crack tip enrichment functions. Few plane crack problems are solved in the presence of multiple holes and inclusions using XIGA. These simulations show that the SIFs obtained using XIGA gives more accurate results as compared to those obtained by XFEM

Explainable artificial intelligence for developing smart cities solutions

Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results

Substrate effect on the structural and electrochemical properties of electrolytic manganese dioxide deposited from sulphate solutions

We studied the effect of anode substrates such as pure lead (Pb), lead antimony (Pb-Sb), and lead-silver (Pb-Ag) on the structural and electrochemical properties of electrolytic manganese dioxide (EMD). X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and chemical analyses were used to determine the structural and chemical characteristics of the EMD samples. The charge–discharge profile was studied in 9 M KOH using a galvanostatic charge-discharge unit. In all the substrates the current efficiencies were more than 99% except with Pb-Sb where it was 90%. Results revealed the nature of the substrate strongly affected the morphology of the deposited material which in turn affected the electrochemical properties of the EMD samples. XRD analyses revealed that the nature of the anode did not affect the crystal structure of the deposited EMD and all the samples were predominantly γ-MnO 2 , which is electrochemically active for energy storage applications. The EMD deposited on lead substrate showed superior discharge capacity of 245 mAhg -1 when compared with other substrates

Recovering Relativistic Nuclear Phenomenology from the Quark-Meson Coupling Model

The quark-meson coupling (QMC) model for nuclear matter, which describes nuclear matter as non-overlapping MIT bags bound by the self-consistent exchange of scalar and vector mesons is modified by the introduction of a density dependent bag constant. The density dependence of the bag constant is related to that of the in-medium effective nucleon mass through a scaling ansatz suggested by partial chiral symmetry restoration in nuclear matter. This modification overcomes drawbacks of the QMC model and leads to the recovery of the essential features of relativistic nuclear phenomenology. This suggests that the modification of the bag constant in the nuclear medium may play an important role in low- and medium-energy nuclear physics.Comment: Revised version to appear in Phys. Lett.

A Census of Lakes in Gandaki Province based on Remote Sensing

Lakes in Nepal play a crucial role in supporting biodiversity conservation, regulating ecosystems, and providing livelihood opportunities for local communities. Many lakes in Nepal hold immense religious and cultural significance for the local community, serving as sacred pilgrimage sites and embodying spiritual entities that are integral to the traditions, beliefs, and practices of the people, making them important cultural landmarks of the country. Despite their significance and importance, lakes in Nepal have faced degradation and challenges. Due to inappropriate infrastructure development, encroachment, and anthropogenic activities, lakes are degraded in Nepal. In recent years, Gandaki Province also witnessed the degradation of lakes. Therefore, this study aimed to assess the status of lake degradation as well as find out the total number of lakes in the Gandaki Province. The study was conducted utilizing both Remote Sensing (RS) techniques and conducting field visits. First of all, the Normalized Difference Water Index (NDWI) was calculated with the help of Google Earth Engine with Sentinal 2A/B satellite. That gives the water area, and then polygons of water bodies were created throughout the province in an identified area. These polygons were uploaded in ArcGIS and a base map was added. In the ArcGIS platform, polygons were further edited for the precise area using very high-resolution imagery. These edited polygons were further verified in Google Earth. Field visits, personal phone inquiries, and group discussions were conducted for further verification. Data were also collected from municipality/rural municipality, elected representatives, and key informants. Altogether 290 lakes (including ponds, lakes, and glacier lakes) were mapped and identified in the Gandaki Province. These lakes cover about 0.1045% of the total surface area of the Gandaki Province. Approximately 60% of the lakes were identified above 3000 m above sea level (asl). Lakes identified below 3000 m asl were mostly mapped from the Kaski, Parbat, and Nawalparasi Districts. The highest number of lakes discovered in Mustang (a total of 73 lakes), encompasses both lakes situated below 3000 m asl and those above 4500 m asl. Many of the wetland areas, most of which are located below 3000 m are currently facing the threat of extinction. Numerous lakes have already been transformed into playgrounds and residential areas, leading to the loss of valuable wetland ecosystems in the Gandaki Province

Biodegradable microparticulate drug delivery system of diltiazem HCl

The efficacy of a drug in a specific application requires the maintenance of appropriate drug blood level concentration during a prolonged period of time. Controlled release delivery is available for many routes of administration and offers many advantages (as microparticles and nanoparticles) over immediate release delivery. These advantages include reduced dosing frequency, better therapeutic control, fewer side effects, and, consequently, these dosage forms are well accepted by patients. Advances in polymer material science, particle engineering design, manufacture, and nanotechnology have led the way to the introduction of several marketed controlled release products and several more are in pre-clinical and clinical development. The objective of this work is to prepare and evaluate diltiazem HCl loaded albumin microparticles using a factorial design. Albumin (natural polymer) microparticles were prepared by emulsion heat-stabilization method. Selected formulations were characterized for their entrapment efficiency, particle size, surface morphology, and release behavior. Analysis of variance for entrapment efficiency indicates that entrapment efficiency is best fitted to a response surface linear model. Surface morphology was studied by scanning electron microscopy. Scanning electron microscopy of the microparticles revealed a spherical, nonporous and uniform appearance, with a smooth surface. The geometric mean diameter of the microparticles was found to be 2-9 µm, which more than 75% were below 3.5 µm and drug incorporation efficiency of 59.74 to 72.48% (w/w). In vitro release profile for formulations containing diltiazem HCl loaded BSA microparticles with heat stabilization technique shows slow controlled the release of the drug up to 24 hours. The release pattern was biphasic, characterized by an initial burst effect followed by a slow release. All selected microparticles exhibited a prolonged release for almost 24 hours. On comparing regression-coefficient (r²) values for Hixson Crowel, Higuchi and Peppas kinetic models, different batches of microparticles showed Fickian, non-Fickian, and diffusion kinetics. The release mechanism was regulated by D:P ratio. From the statistical analysis it was observed that as the drug:polymer (D:P) ratio increased, there was a significant increase in the encapsulation efficiency. Based on the particle size, entrapment efficiency and physical appearance, DTM-3 formulations were selected for in vivo release study and stability study. The in vivo result of drug loaded microparticles showed preferential drug targeting to liver followed by lungs, kidneys and spleen. Stability studies showed that maximum drug content and closest in vitro release to initial data were found in the formulation stored at 4 ºC. In present study, diltiazem HCl loaded BSA microparticles were prepared and targeted to various organs to satisfactory level and were found to be stable at 4 ºC.A eficácia terapêutica de um fármaco depende da manutenção de seu nível plasmático adequado em determinado intervalo de tempo. Nesse sentido, a liberação modificada de fármacos está disponível em muitas vias de administração e oferece muitas vantagens (como micropartículas e nanopartículas) quando comparada às formulações de liberação imediata. Essas vantagens incluem reduzida frequência da dosagem, melhor controle terapêutico e menos efeitos colaterais. Assim sendo, esses produtos apresentam maior aceitação pelos pacientes. Os avanços na ciência dos materiais, na engenharia das partículas, em manufatura e em nanotecnologia permitiram a introdução no mercado de vários produtos de liberação modificada e vários outros se encontram em desenvolvimento pré-clínico e clínico. O objetivo do presente trabalho foi preparar e avaliar o fármaco cloridrato de diltiazem associado a micropartículas de albumina utilizando planejamento fatorial. As micropartículas de albumina, um polímero natural, foram preparadas por método de emulsão empregando estabilização por calor. As formulações selecionadas foram caracterizadas no que se refere à sua eficiência de encapsulamento, tamanho médio de partículas, morfologia de superfície e perfil de liberação do fármaco. A análise de variância relativa à eficiência de encapsulamento indicou superfície de resposta linear. Com referência à morfologia superficial, essa foi avaliada empregando microscopia eletrônica de varredura. Essa análise revelou micropartículas esféricas, não porosas e de aparência uniforme, com superfície lisa. O diâmetro médio das micropartículas foi entre 2 e 9 µm, sendo que mais de 75% das micropartículas se apresentaram abaixo de 3,5 µm. Além disso, a eficiência de encapsulamento foi entre 59,74 e 72,48%. Quanto ao ensaio para avaliação do perfil de liberação in vitro do fármaco associado às micropartículas, as formulações apresentaram liberação lenta até 24 horas. O comportamento foi caracterizado por liberação inicial (efeito burst) seguida por liberação lenta. Todas as fórmulas selecionadas apresentaram liberação prolongada por aproximadamente 24 horas. Na comparação entre os valores de coeficientes de regressão (R²), os modelos propostos por Hixson Crowel, Higuchi e Peppas, para diferentes formulações de micropartículas, demonstraram cinética de liberação de acordo com modelo Fickiano e não-Fickiano. O mecanismo de liberação do fármaco foi regulado pela razão entre o fármaco e o polímero. A análise estatística revelou significativo aumento da eficiência de encapsulamento quando essa razão aumentou. As avaliações relativas à análise dimensional das micropartículas, à eficiência de encapsulamento do fármaco e à morfologia permitiram a seleção da formulação DTM-3 para os ensaios de liberação in vivo e para o estudo da estabilidade. O ensaio de liberação in vivo do fármaco associado às micropartículas demonstrou sítio-alvo preferencial no fígado, seguido pelos pulmões rins e baço. No presente estudo, as micropartículas de albumina contendo cloridrato de diltiazem foram adequadamente preparadas e orientadas satisfatoriamente para vários órgãos. Além disso, a formulação selecionada apresentou estabilidade físico-química a 4 ºC

Particle density fluctuations

Event-by-event fluctuations in the multiplicities of charged particles and photons at SPS energies are discussed. Fluctuations are studied by controlling the centrality of the reaction and rapidity acceptance of the detectors. Results are also presented on the event-by-event study of correlations between the multiplicity of charged particles and photons to search for DCC-like signals.Comment: Talk presented at Quark Matter 2002, Nantes, Franc

Search for DCC in 158A GeV Pb+Pb Collisions

A detailed analysis of the phase space distributions of charged particles and photons have been carried out using two independent methods. The results indicate the presence of nonstatistical fluctuations in localized regions of phase space.Comment: Talk at the PANIC99 Conference, June 9-16, 199

Pion Freeze-Out Time in Pb+Pb Collisions at 158 A GeV/c Studied via pi-/pi+ and K-/K+ Ratios

The effect of the final state Coulomb interaction on particles produced in Pb+Pb collisions at 158 A GeV/c has been investigated in the WA98 experiment through the study of the pi-/pi+ and K-/K+ ratios measured as a function of transverse mass. While the ratio for kaons shows no significant transverse mass dependence, the pi-/pi+ ratio is enhanced at small transverse mass values with an enhancement that increases with centrality. A silicon pad detector located near the target is used to estimate the contribution of hyperon decays to the pi-/pi+ ratio. The comparison of results with predictions of the RQMD model in which the Coulomb interaction has been incorporated allows to place constraints on the time of the pion freeze-out.Comment: 9 pages, 12 figure