Search results for: credit network

Authors: Danlei Yang, Luofeng Huang

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The integration of digital twin technology with renewable energy systems offers an innovative approach to predicting and optimising performance throughout the entire lifecycle. A digital twin is a continuously updated virtual replica of a real-world entity, synchronised with data from its physical counterpart and environment. Many digital twin companies today claim to have mature digital twin products, but their focus is primarily on equipment visualisation. However, the core of a digital twin should be its model, which can mirror, shadow, and thread with the real-world entity, which is still underdeveloped. For a floating solar energy system, a digital twin model can be defined in three aspects: (a) the physical floating solar energy system along with environmental factors such as solar irradiance and wave dynamics, (b) a digital model powered by artificial intelligence (AI) algorithms, and (c) the integration of real system data with the AI-driven model and a user interface. The experimental setup for the floating solar energy system, is designed to replicate real-ocean conditions of floating solar installations within a controlled laboratory environment. The system consists of a water tank that simulates an aquatic surface, where a floating catamaran structure supports a solar panel. The solar simulator is set up in three positions: one directly above and two inclined at a 45° angle in front and behind the solar panel. This arrangement allows the simulation of different sun angles, such as sunrise, midday, and sunset. The solar simulator is positioned 400 mm away from the solar panel to maintain consistent solar irradiance on its surface. Stability for the floating structure is achieved through ropes attached to anchors at the bottom of the tank, which simulates the mooring systems used in real-world floating solar applications. The floating solar energy system's sensor setup includes various devices to monitor environmental and operational parameters. An irradiance sensor measures solar irradiance on the photovoltaic (PV) panel. Temperature sensors monitor ambient air and water temperatures, as well as the PV panel temperature. Wave gauges measure wave height, while load cells capture mooring force. Inclinometers and ultrasonic sensors record heave and pitch amplitudes of the floating system’s motions. An electric load measures the voltage and current output from the solar panel. All sensors collect data simultaneously. Artificial neural network (ANN) algorithms are central to developing the digital model, which processes historical and real-time data, identifies patterns, and predicts the system’s performance in real time. The data collected from various sensors are partly used to train the digital model, with the remaining data reserved for validation and testing. The digital twin model combines the experimental setup with the ANN model, enabling monitoring, analysis, and prediction of the floating solar energy system's operation. The digital model mirrors the functionality of the physical setup, running in sync with the experiment to provide real-time insights and predictions. It provides useful industrial benefits, such as informing maintenance plans as well as design and control strategies for optimal energy efficiency. In long term, this digital twin will help improve overall solar energy yield whilst minimising the operational costs and risks.

Keywords: digital twin, floating solar energy system, experiment setup, artificial intelligence

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Authors: Alessandra Swiny, Michalis Georgiou, Yiorgos Hadjichristou

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This paper focuses on the questions raised through the work of Unit 5: ‘In/Out of crisis, emergent and adaptive’; an architectural research-based studio at the University of Nicosia. It focusses on sustainable architectural and urban explorations tackling with the ever growing crises in its various types, phases and locations. ‘Great crisis situations’ are seen as ‘great chances’ that trigger investigations for further development and evolution of the built environment in an ultimate sustainable approach. The crisis is taken as an opportunity to rethink the urban and architectural directions as new forces for inventions leading to emergent and adaptive built environments. The Unit 5’s identity and environment facilitates the students to respond optimistically, alternatively and creatively towards the global current crisis. Mark Wigley’s notion that “crises are ultimately productive” and “They force invention” intrigued and defined the premises of the Unit. ‘Weather and nature are coauthors of the built environment’ Jonathan Hill states in his ‘weather architecture’ discourse. The weather is constantly changing and new environments, the subnatures are created which derived from the human activities David Gissen explains. The above set of premises triggered innovative responses by the Unit’s students. They thoroughly investigated the various kinds of crisis and their causes in relation to their various types of Terrains. The tools used for the research and investigation were chosen in contradictive pairs to generate further crisis situations: The re-used/salvaged competed with the new, the handmade rivalling with the fabrication, the analogue juxtaposed with digital. Students were asked to delve into state of art technologies in order to propose sustainable emergent and adaptive architectures and Urbanities, having though always in mind that the human and the social aspects of the community should be the core of the investigation. The resulting unprecedented spatial conditions and atmospheres of the emergent new ways of living are deemed to be the ultimate aim of the investigation. Students explored a variety of sites and crisis conditions such as: The vague terrain of the Green Line in Nicosia, the lost footprints of the sinking Venice, the endangered Australian coral reefs, the earthquake torn town of Crevalcore, and the decaying concrete urbanscape of Athens. Among other projects, ‘the plume project’ proposes a cloud-like, floating and almost dream-like living environment with unprecedented spatial conditions to the inhabitants of the coal mine of Centralia, USA, not just to enable them to survive but even to prosper in this unbearable environment due to the process of the captured plumes of smoke and heat. Existing water wells inspire inversed vertical structures creating a new living underground network, protecting the nomads from catastrophic sand storms in the Araoune of Mali. “Inverted utopia: Lost things in the sand”, weaves a series of tea-houses and a library holding lost artifacts and transcripts into a complex underground labyrinth by the utilization of the sand solidification technology. Within this methodology, crisis is seen as a mechanism for allowing an emergence of new and fascinating ultimate sustainable future cultures and cities.

Keywords: adaptive built environments, crisis as opportunity, emergent urbanities, forces for inventions

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Authors: Nicola Colaninno, Eugenio Morello

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The urban environment affects local-to-global climate and, in turn, suffers global warming phenomena, with worrying impacts on human well-being, health, social and economic activities. Physic-morphological features of the built-up space affect urban air temperature, locally, causing the urban environment to be warmer compared to surrounding rural. This occurrence, typically known as the Urban Heat Island (UHI), is normally assessed by means of air temperature from fixed weather stations and/or traverse observations or based on remotely sensed Land Surface Temperatures (LST). The information provided by ground weather stations is key for assessing local air temperature. However, the spatial coverage is normally limited due to low density and uneven distribution of the stations. Although different interpolation techniques such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), or Multiple Linear Regression (MLR) are used to estimate air temperature from observed points, such an approach may not effectively reflect the real climatic conditions of an interpolated point. Quantifying local UHI for extensive areas based on weather stations’ observations only is not practicable. Alternatively, the use of thermal remote sensing has been widely investigated based on LST. Data from Landsat, ASTER, or MODIS have been extensively used. Indeed, LST has an indirect but significant influence on air temperatures. However, high-resolution near-surface air temperature (NSAT) is currently difficult to retrieve. Here we have experimented Geographically Weighted Regression (GWR) as an effective approach to enable NSAT estimation by accounting for spatial non-stationarity of the phenomenon. The model combines on-site measurements of air temperature, from fixed weather stations and satellite-derived LST. The approach is structured upon two main steps. First, a GWR model has been set to estimate NSAT at low resolution, by combining air temperature from discrete observations retrieved by weather stations (dependent variable) and the LST from satellite observations (predictor). At this step, MODIS data, from Terra satellite, at 1 kilometer of spatial resolution have been employed. Two time periods are considered according to satellite revisit period, i.e. 10:30 am and 9:30 pm. Afterward, the results have been downscaled at 30 meters of spatial resolution by setting a GWR model between the previously retrieved near-surface air temperature (dependent variable), the multispectral information as provided by the Landsat mission, in particular the albedo, and Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM), both at 30 meters. Albedo and DEM are now the predictors. The area under investigation is the Metropolitan City of Milan, which covers an area of approximately 1,575 km2 and encompasses a population of over 3 million inhabitants. Both models, low- (1 km) and high-resolution (30 meters), have been validated according to a cross-validation that relies on indicators such as R2, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). All the employed indicators give evidence of highly efficient models. In addition, an alternative network of weather stations, available for the City of Milano only, has been employed for testing the accuracy of the predicted temperatures, giving and RMSE of 0.6 and 0.7 for daytime and night-time, respectively.

Keywords: urban climate, urban heat island, geographically weighted regression, remote sensing

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Authors: Georges F. Hatoum, Thomas H. Temple, Silvio Garcia, Xiaodong Wu

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Soft Tissue Sarcomas (STS) represent a diverse group of malignancies with heterogeneous clinical and pathological features. The treatment of extremity STS aims to achieve optimal local tumor control, improved survival, and preservation of limb function. The National Comprehensive Cancer Network guidelines, based on the cumulated clinical data, recommend radiation therapy (RT) in conjunction with limb-sparing surgery for large, high-grade STS measuring greater than 5 cm in size. Such treatment strategy can offer a cure for patients. However, when recurrence occurs (in nearly half of patients), the prognosis is poor, with a median survival of 12 to 15 months and with only palliative treatment options available. The spatially-fractionated-radiotherapy (SFRT), with a long history of treating bulky tumors as a non-mainstream technique, has gained new attention in recent years due to its unconventional therapeutic effects, such as bystander/abscopal effects. Combining single fraction of GRID, the original form of SFRT, with conventional RT was shown to have marginally increased the rate of pathological necrosis, which has been recognized to have a positive correlation to overall survival. In an effort to consistently increase the pathological necrosis rate over 90%, multiple fractions of Lattice RT (LRT), a newer form of 3D SFRT, interdigitated with the standard RT as neoadjuvant therapy was conducted in a preliminary clinical setting. With favorable results of over 95% of necrosis rate in a small cohort of patients, a Phase I/II clinical study was proposed to exam the safety and feasibility of this new strategy. Herein the design of the clinical study is presented. In this single-arm, two-stage phase I/II clinical trial, the primary objectives are >80% of the patients achieving >90% tumor necrosis and to evaluation the toxicity; the secondary objectives are to evaluate the local control, disease free survival and overall survival (OS), as well as the correlation between clinical response and the relevant biomarkers. The study plans to accrue patients over a span of two years. All patient will be treated with the new neoadjuvant RT regimen, in which one of every five fractions of conventional RT is replaced by a LRT fraction with vertices receiving dose ≥10Gy while keeping the tumor periphery at or close to 2 Gy per fraction. Surgical removal of the tumor is planned to occur 6 to 8 weeks following the completion of radiation therapy. The study will employ a Pocock-style early stopping boundary to ensure patient safety. The patients will be followed and monitored for a period of five years. Despite much effort, the rarity of the disease has resulted in limited novel therapeutic breakthroughs. Although a higher rate of treatment-induced tumor necrosis has been associated with improved OS, with the current techniques, only 20% of patients with large, high-grade tumors achieve a tumor necrosis rate exceeding 50%. If this new neoadjuvant strategy is proven effective, an appreciable improvement in clinical outcome without added toxicity can be anticipated. Due to the rarity of the disease, it is hoped that such study could be orchestrated in a multi-institutional setting.

Keywords: lattice RT, necrosis, SFRT, soft tissue sarcoma

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Karisma Karisma, Pardis Moslemzadeh Tehrani

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This study aims to conduct a high-level strategic dialogue on the lack of consensus, consistency, and legal certainty regarding blockchain-based energy prosumerism so that appropriate institutional and governance structures can be put in place to address the inadequacies and gaps in the legal and regulatory framework. The drive to achieve national and global decarbonization targets is a driving force behind climate goals and policies under the Paris Agreement. In recent years, efforts to ‘demonopolize’ and ‘decentralize’ energy generation and distribution have driven the energy transition toward decentralized systems, invoking concepts such as ownership, sovereignty, and autonomy of RE sources. The emergence of individual and collective forms of prosumerism and the rapid diffusion of blockchain is expected to play a critical role in the decarbonization and democratization of energy systems. However, there is a ‘regulatory void’ relating to individual and collective forms of prosumerism that could prevent the rapid deployment of blockchain systems and potentially stagnate the operationalization of blockchain-enabled energy sharing and trading activities. The application of broad and facile regulatory fixes may be insufficient to address the major regulatory gaps. First, to the authors’ best knowledge, the concepts and elements circumjacent to individual and collective forms of prosumerism have not been adequately described in the legal frameworks of many countries. Second, there is a lack of legal certainty regarding the creation and adaptation of business models in a highly regulated and centralized energy system, which inhibits the emergence of prosumer-driven niche markets. There are also current and prospective challenges relating to the legal status of blockchain-based platforms for facilitating energy transactions, anticipated with the diffusion of blockchain technology. With the rise of prosumerism in the energy sector, the areas of (a) network charges, (b) energy market access, (c) incentive schemes, (d) taxes and levies, and (e) licensing requirements are still uncharted territories in many countries. The uncertainties emanating from this area pose a significant hurdle to the widespread adoption of blockchain technology, a complementary technology that offers added value and competitive advantages for energy systems. The authors undertake a conceptual and theoretical investigation to elucidate the lack of consensus, consistency, and legal certainty in the study of blockchain-based prosumerism. In addition, the authors set an exploratory tone to the discussion by taking an analytically eclectic approach that builds on multiple sources and theories to delve deeper into this topic. As an interdisciplinary study, this research accounts for the convergence of regulation, technology, and the energy sector. The study primarily adopts desk research, which examines regulatory frameworks and conceptual models for crucial policies at the international level to foster an all-inclusive discussion. With their reflections and insights into the interaction of blockchain and prosumerism in the energy sector, the authors do not aim to develop definitive regulatory models or instrument designs, but to contribute to the theoretical dialogue to navigate seminal issues and explore different nuances and pathways. Given the emergence of blockchain-based energy prosumerism, identifying the challenges, gaps and fragmentation of governance regimes is key to facilitating global regulatory transitions.

Keywords: blockchain technology, energy sector, prosumer, legal and regulatory.

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Authors: Marco Frieslaar, Bing Chu, Eric Rogers

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Stroke is a devastating illness that is the second biggest cause of death in the world (after heart disease). Where it does not kill, it leaves survivors with debilitating sensory and physical impairments that not only seriously harm their quality of life, but also cause a high incidence of severe depression. It is widely accepted that early intervention is essential for recovery, but current rehabilitation techniques largely favor hospital-based therapies which have restricted access, expensive and specialist equipment and tend to side-step the emotional challenges. In addition, there is insufficient funding available to provide the long-term assistance that is required. As a consequence, recovery rates are poor. The relatively unexplored solution is to develop therapies that can be harnessed in the home and are formulated from technologies that already exist in everyday life. This would empower individuals to take control of their own improvement and provide choice in terms of when and where they feel best able to undertake their own healing. This research seeks to identify how effective post-stroke, rehabilitation therapy can be applied to upper limb mobility, within the physical context of a home rather than a hospital. This is being achieved through the design and construction of an automation scheme, based on iterative learning control and the Riener muscle model, that has the ability to adapt to the user and react to their level of fatigue and provide tangible physical recovery. It utilizes a SMART Phone and laptop to construct an iterative learning control (ILC) system, that monitors upper arm movement in three dimensions, as a series of exercises are undertaken. The equipment generates functional electrical stimulation to assist in muscle activation and thus improve directional accuracy. In addition, it monitors speed, accuracy, areas of motion weakness and similar parameters to create a performance index that can be compared over time and extrapolated to establish an independent and objective assessment scheme, plus an approximate estimation of predicted final outcome. To further extend its assessment capabilities, nerve conduction velocity readings are taken by the software, between the shoulder and hand muscles. This is utilized to measure the speed of response of neuron signal transfer along the arm and over time, an online indication of regeneration levels can be obtained. This will prove whether or not sufficient training intensity is being achieved even before perceivable movement dexterity is observed. The device also provides the option to connect to other users, via the internet, so that the patient can avoid feelings of isolation and can undertake movement exercises together with others in a similar position. This should create benefits not only for the encouragement of rehabilitation participation, but also an emotional support network potential. It is intended that this approach will extend the availability of stroke recovery options, enable ease of access at a low cost, reduce susceptibility to depression and through these endeavors, enhance the overall recovery success rate.

Keywords: home-based therapy, iterative learning control, Riener muscle model, SMART phone, stroke rehabilitation

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Authors: Despoina Chochtoula, Aristidis Ilias, Yannis Stamatiou

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Modbus is a protocol that enables the communication among devices which are connected to the same network. This protocol is, often, deployed in connecting sensor and monitoring units to central supervisory servers in Supervisory Control and Data Acquisition, or SCADA, systems. These systems monitor critical infrastructures, such as factories, power generation stations, nuclear power reactors etc. in order to detect malfunctions and ignite alerts and corrective actions. However, due to their criticality, SCADA systems are vulnerable to attacks that range from simple eavesdropping on operation parameters, exchanged messages, and valuable infrastructure information to malicious modification of vital infrastructure data towards infliction of damage. Thus, the SCADA research community has been active over strengthening SCADA systems with suitable data protection mechanisms based, to a large extend, on cryptographic methods for data encryption, device authentication, and message integrity protection. However, due to the limited computation power of many SCADA sensor and embedded devices, the usual public key cryptographic methods are not appropriate due to their high computational requirements. As an alternative, Elliptic Curve Cryptography has been proposed, which requires smaller key sizes and, thus, less demanding cryptographic operations. Until now, however, no such implementation has been proposed in the SCADA literature, to the best of our knowledge. In order to fill this gap, our methodology was focused on integrating Modbus, a frequently used SCADA communication protocol, with Elliptic Curve based cryptography and develop a server/client application to demonstrate the proof of concept. For the implementation we deployed two C language libraries, which were suitably modify in order to be successfully integrated: libmodbus (https://github.com/stephane/libmodbus) and ecc-lib https://www.ceid.upatras.gr/webpages/faculty/zaro/software/ecc-lib/). The first library provides a C implementation of the Modbus/TCP protocol while the second one offers the functionality to develop cryptographic protocols based on Elliptic Curve Cryptography. These two libraries were combined, after suitable modifications and enhancements, in order to give a modified version of the Modbus/TCP protocol focusing on the security of the data exchanged among the devices and the supervisory servers. The mechanisms we implemented include key generation, key exchange/sharing, message authentication, data integrity check, and encryption/decryption of data. The key generation and key exchange protocols were implemented with the use of Elliptic Curve Cryptography primitives. The keys established by each device are saved in their local memory and are retained during the whole communication session and are used in encrypting and decrypting exchanged messages as well as certifying entities and the integrity of the messages. Finally, the modified library was compiled for the Android environment in order to run the server application as an Android app. The client program runs on a regular computer. The communication between these two entities is an example of the successful establishment of an Elliptic Curve Cryptography based, secure Modbus wireless communication session between a portable device acting as a supervisor station and a monitoring computer. Our first performance measurements are, also, very promising and demonstrate the feasibility of embedding Elliptic Curve Cryptography into SCADA systems, filling in a gap in the relevant scientific literature.

Keywords: elliptic curve cryptography, ICT security, modbus protocol, SCADA, TCP/IP protocol

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Authors: Jose Neri, Fabrice Canto, Alastair Magnaldo, Laurent Guillerme, Vincent Dugas

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A miniaturized and automated approach for the volumetric titration of free nitric acid in U(VI) solutions is presented. Free acidity measurement refers to the acidity quantification in solutions containing hydrolysable heavy metal ions such as U(VI), U(IV) or Pu(IV) without taking into account the acidity contribution from the hydrolysis of such metal ions. It is, in fact, an operation having an essential role for the control of the nuclear fuel recycling process. The main objective behind the technical optimization of the actual ‘beaker’ method was to reduce the amount of radioactive substance to be handled by the laboratory personnel, to ease the instrumentation adjustability within a glove-box environment and to allow a high-throughput analysis for conducting more cost-effective operations. The measurement technique is based on the concept of the Taylor-Aris dispersion in order to create inside of a 200 μm x 5cm circular cylindrical micro-channel a linear concentration gradient in less than a second. The proposed analytical methodology relies on the actinide complexation using pH 5.6 sodium oxalate solution and subsequent alkalimetric titration of nitric acid with sodium hydroxide. The titration process is followed with a CCD camera for fluorescence detection; the neutralization boundary can be visualized in a detection range of 500nm- 600nm thanks to the addition of a pH sensitive fluorophore. The operating principle of the developed device allows the active generation of linear concentration gradients using a single cylindrical micro channel. This feature simplifies the fabrication and ease of use of the micro device, as it does not need a complex micro channel network or passive mixers to generate the chemical gradient. Moreover, since the linear gradient is determined by the liquid reagents input pressure, its generation can be fully achieved in faster intervals than one second, being a more timely-efficient gradient generation process compared to other source-sink passive diffusion devices. The resulting linear gradient generator device was therefore adapted to perform for the first time, a volumetric titration on a chip where the amount of reagents used is fixed to the total volume of the micro channel, avoiding an important waste generation like in other flow-based titration techniques. The associated analytical method is automated and its linearity has been proven for the free acidity determination of U(VI) samples containing up to 0.5M of actinide ion and nitric acid in a concentration range of 0.5M to 3M. In addition to automation, the developed analytical methodology and technique greatly improves the standard off-line oxalate complexation and alkalimetric titration method by reducing a thousand fold the required sample volume, forty times the nuclear waste per analysis as well as the analysis time by eight-fold. The developed device represents, therefore, a great step towards an easy-to-handle nuclear-related application, which in the short term could be used to improve laboratory safety as much as to reduce the environmental impact of the radioanalytical chain.

Keywords: free acidity, lab-on-a-chip, linear concentration gradient, Taylor-Aris dispersion, volumetric titration

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Authors: Farzaneh Sasanpour, Saeed Amini Varaki

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Resilience is an important goal for cities as urban areas face an increasing range of challenges in the 21st century; therefore, according to the characteristics of risks, adopting an approach that responds to sensitive conditions in the risk management process is the resilience of cities. In the meantime, most of the resilience assessments have dealt with natural hazards and less attention has been paid to pandemics.In the covid-19 pandemic, the country of Iran and especially the metropolis of Tehran, was not immune from the crisis caused by its effects and consequences and faced many challenges. One of the methods that can increase the resilience of Tehran's metropolis against possible crises in the future is future studies. This research is practical in terms of type. The general pattern of the research will be descriptive-analytical and from the point of view that it is trying to communicate between the components and provide urban resilience indicators with pandemic crises and explain the scenarios, its future studies method is exploratory. In order to extract and determine the key factors and driving forces effective on the resilience of Tehran's urban areas against pandemic crises (Covid-19), the method of structural analysis of mutual effects and Micmac software was used. Therefore, the primary factors and variables affecting the resilience of Tehran's urban areas were set in 5 main factors, including physical-infrastructural (transportation, spatial and physical organization, streets and roads, multi-purpose development) with 39 variables based on mutual effects analysis. Finally, key factors and variables in five main areas, including managerial-institutional with five variables; Technology (intelligence) with 3 variables; economic with 2 variables; socio-cultural with 3 variables; and physical infrastructure, were categorized with 7 variables. These factors and variables have been used as key factors and effective driving forces on the resilience of Tehran's urban areas against pandemic crises (Covid-19), in explaining and developing scenarios. In order to develop the scenarios for the resilience of Tehran's urban areas against pandemic crises (Covid-19), intuitive logic, scenario planning as one of the future research methods and the Global Business Network (GBN) model were used. Finally, four scenarios have been drawn and selected with a creative method using the metaphor of weather conditions, which is indicative of the general outline of the conditions of the metropolis of Tehran in that situation. Therefore, the scenarios of Tehran metropolis were obtained in the form of four scenarios: 1- solar scenario (optimal governance and management leading in smart technology) 2- cloud scenario (optimal governance and management following in intelligent technology) 3- dark scenario (optimal governance and management Unfavorable leader in intelligence technology) 4- Storm scenario (unfavorable governance and management of follower in intelligence technology). The solar scenario shows the best situation and the stormy scenario shows the worst situation for the Tehran metropolis. According to the findings obtained in this research, city managers can, in order to achieve a better tomorrow for the metropolis of Tehran, in all the factors and components of urban resilience against pandemic crises by using future research methods, a coherent picture with the long-term horizon of 2050, from the path Provide urban resilience movement and platforms for upgrading and increasing the capacity to deal with the crisis. To create the necessary platforms for the realization, development and evolution of the urban areas of Tehran in a way that guarantees long-term balance and stability in all dimensions and levels.

Keywords: future research, resilience, crisis, pandemic, covid-19, Tehran

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Authors: Zeyu Zhou, Ziyu Zhao, Xiaochen Yang, Ling AI, Heng Zhai, Stuart Holmes

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As a promising sustainable alternative to traditional fossil fuels, fuel cell technology is highly favoured due to its enhanced working efficiency and reduced emissions. In the context of high-temperature fuel cells (operating above 100 °C), the most commonly used proton exchange membrane (PEM) is the Polybenzimidazole (PBI) doped phosphoric acid (PA) membrane. Grafting is a promising strategy to advance PA-doped PBI PEM technology. The existing grafting modification on PBI PEMs mainly focuses on grafting phosphate-containing or alkaline groups onto the PBI molecular chains. However, quaternary ammonium-based grafting approaches face a common challenge. To initiate the N-alkylation reaction, deacidifying agents such as NaH, NaOH, KOH, K2CO3, etc., can lead to ionic crosslinking between the quaternary ammonium group and PBI. Polyvinylpyrrolidone (PVP) is another widely used polymer, the N-heterocycle groups within PVP endow it with a significant ability to absorb PA. Recently, PVP has attracted substantial attention in the field of fuel cells due to its reduced environmental impact and impressive fuel cell performance. However, due to the the poor compatibility of PVP in PBI, few research apply PVP in PA-doped PBI PEMs. This work introduces an innovative strategy to graft PVP onto PBI to form a network-like polymer. Due to the absence of quaternary ammonium groups, PVP does not pose issues related to crosslinking with PBI. Moreover, the nitrogen-containing functional groups on PVP provide PBI with a robust phosphoric acid retention ability. The nuclear magnetic resonance (NMR) hydrogen spectrum analysis results indicate the successful completion of the grafting reaction where N-alkylation reactions happen on both sides of the grafting agent 1,4-bis(chloromethyl)benzene. On one side, the reaction takes place with the hydrogen atoms on the imidazole groups of PBI, while on the other side, it reacts with the terminal amino group of PVP. The XPS results provide additional evidence from the perspective of the element. On synthesized PBI-g-PVP surfaces, there is an absence of chlorine (chlorine in grafting agent 1,4-bis(chloromethyl)benzene is substituted) element but a presence of sulfur element (sulfur element in terminal amino PVP appears in PBI), which demonstrates the occurrence of the grafting reaction and PVP is successfully grafted onto PBI. Prepare these modified membranes into MEA. It was found that during the fuel cell operation, all the grafted membranes showed substantial improvement in maximum current density and peak power density compared to unmodified one. For PBI-g-PVP 30, with a grafting degree of 22.4%, the peak power density reaches 1312 mW cm⁻², marking a 59.6% enhancement compared to the pristine PBI membrane. The improvement is caused by the improved PA binding ability of the membrane after grafting. The AST test result shows that the grafting membranes have better long-term durability and performance than unmodified membranes attributed to the presence of added PA binding sites, which can effectively prevent the PA leaching caused by proton migration. In conclusion, the test results indicate that grafting PVP onto PBI is a promising strategy which can effectively improve the fuel cell performance.

Keywords: fuel cell, grafting modification, PA doping ability, PVP

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Authors: Karin Schakib-Ekbatan, Annette Roser

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According to the Federal Government’s plans, the German building stock should be virtually climate neutral by 2050. Thus, the “EnEff.Gebäude.2050” funding initiative was launched, complementing the projects of the Energy Transition Construction research initiative. Beyond the construction and renovation of individual buildings, solutions must be found at the neighborhood level. The subject of the presented pilot project is a building ensemble from the Wilhelminian period in Munich, which is planned to be refurbished based on a socially compatible, energy-saving, innovative-technical modernization concept. The building ensemble, with about 200 apartments, is part of the building cooperative. To create an optimized network and possible synergies between researchers and projects of the funding initiative, a Scientific Accompanying Research was established for cross-project analyses of findings and results in order to identify further research needs and trends. Thus, the project is characterized by an interdisciplinary approach that combines constructional, technical, and socio-scientific expertise based on a participatory understanding of research by involving the tenants at an early stage. The research focus is on getting insights into the tenants’ comfort requirements, attitudes, and energy-related behaviour. Both qualitative and quantitative methods are applied based on the Technology-Acceptance-Model (TAM). The core of the refurbishment strategy is a wall heating system intended to replace conventional radiators. A wall heating provides comfortable and consistent radiant heat instead of convection heat, which often causes drafts and dust turbulence. Besides comfort and health, the advantage of wall heating systems is an energy-saving operation. All apartments would be supplied by a uniform basic temperature control system (around perceived room temperature of 18 °C resp. 64,4 °F), which could be adapted to individual preferences via individual heating options (e. g. infrared heating). The new heating system would affect the furnishing of the walls, in terms of not allowing the wall surface to be covered too much with cupboards or pictures. Measurements and simulations of the energy consumption of an installed wall heating system are currently being carried out in a show apartment in this neighborhood to investigate energy-related, economical aspects as well as thermal comfort. In March, interviews were conducted with a total of 12 people in 10 households. The interviews were analyzed by MAXQDA. The main issue of the interview was the fear of reduced self-efficacy within their own walls (not having sufficient individual control over the room temperature or being very limited in furnishing). Other issues concerned the impact that the construction works might have on their daily life, such as noise or dirt. Despite their basically positive attitude towards a climate-friendly refurbishment concept, tenants were very concerned about the further development of the project and they expressed a great need for information events. The results of the interviews will be used for project-internal discussions on technical and psychological aspects of the refurbishment strategy in order to design accompanying workshops with the tenants as well as to prepare a written survey involving all households of the neighbourhood.

Keywords: energy efficiency, interviews, participation, refurbishment, residential buildings

Procedia PDF Downloads 125

Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

Procedia PDF Downloads 188

Authors: Enoch Ocran

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Film Pertinence maintains consistency in storytelling by sustaining the natural flow of action while evoking a particular feeling or emotion from the viewers with selected motion pictures. This study presents a thorough investigation of "Film Pertinence" in videography that examines its influence in Africa and around the world. This research delves into the dynamic realm of visual storytelling through film, with a specific focus on the concept of Film Pertinence (FP). The study’s primary objectives are to conduct a comparative analysis of videography tools and techniques employed in both African and international contexts, examining how they contribute to the achievement of organizational goals and the enhancement of cultural awareness. The research methodology includes a comprehensive literature review, interviews with videographers from diverse backgrounds in Africa and the international arena, and the examination of pertinent case studies. The investigation aims to elucidate the multifaceted nature of videographic practices, with particular attention to equipment choices, visual storytelling techniques, cultural sensitivity, and adaptability. This study explores the impact of cultural differences on videography choices, aiming to promote understanding between African and foreign filmmakers and create more culturally sensitive films. It also explores the role of technology in advancing videography practices, resource allocation, and the influence of globalization on local filmmaking practices. The research also contributes to film studies by analyzing videography's impact on storytelling, guiding filmmakers to create more compelling narratives. The findings can inform film education, tailoring curricula to regional needs and opportunities. The study also encourages cross-cultural collaboration in the film industry by highlighting convergence and divergence in videography practices. At its core, this study seeks to explore the implications of film pertinence as a framework for videographic practice. It scrutinizes how cultural expression, education, and storytelling transcend geographical boundaries on a global scale. By analyzing the interplay between tools, techniques, and context, the research illuminates the ways in which videographers in Africa and worldwide apply film Pertinence principles to achieve cross-cultural communication and effectively capture the objectives of their clients. One notable focus of this paper is on the techniques employed by videographers in West Africa to emphasize storytelling and participant engagement, showcasing the relevance of FP in highlighting cultural awareness in visual storytelling. Additionally, the study highlights the prevalence of film pertinence in African agricultural documentaries produced for esteemed organizations such as the Roundtable on Sustainable Palm Oil (RSPO), Proforest, World Food Program, Fidelity Bank Ghana, Instituto BVRio, Aflatoun International, and the Solidaridad Network. These documentaries serve to promote prosperity, resilience, human rights, sustainable farming practices, community respect, and environmental preservation, underlining the vital role of film in conveying these critical messages. In summary, this research offers valuable insights into the evolving landscape of videography in different contexts, emphasizing the significance of film pertinence as a unifying principle in the pursuit of effective visual storytelling and cross-cultural communication.

Keywords: film pertinence, Africa, cultural awareness, videography tools

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Authors: M. Hadjisoteriou, A. Kyriacou Petrou

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This paper will discuss ideas of social sustainability in urban design and human behavior in multicultural contested sites. It will focus on the potential of the re-reading of the “site” through mapping that acts as a research methodology and will discuss the chosen site of Mouttalos, Cyprus as a place of multiple identities. Through a methodology of mapping using a bottom up approach, a process of disassembling derives that acts as a mechanism to re-examine space and place by searching for the invisible and the non-measurable, understanding the site through its detailed inhabitation patterns. The significance of this study lies in the use of mapping as an active form of thinking rather than a passive process of representation that allows for a new site to be discovered, giving multiple opportunities for adaptive urban strategies and socially engaged design approaches. We will discuss the above thematic based on the chosen contested site of Mouttalos, a small Turkish Cypriot neighbourhood, in the old centre of Paphos (Ktima), SW of Cyprus. During the political unrest, between Greek and Turkish Cypriot communities, in 1963, the area became an enclave to the Turkish Cypriots, excluding any contact with the rest of the area. Following the Turkish invasion of 1974, the residents left their homes, plots and workplaces, resettling in the North of Cyprus. Greek Cypriot refugees moved into the area. The presence of the Greek Cypriot refugees is still considered to be a temporary resettlement. The buildings and the residents themselves exist in a state of uncertainty. The site is documented through a series of parallel investigations into the physical conditions and history of the site. Research methodologies use the process of mapping to expose the complex and often invisible layers of information that coexist. By registering the site through the subjective experiences, and everyday stories of inhabitants, a series of cartographic recordings reveals the space between: happening and narrative and especially space between different cultures and religions. Research put specific emphasis on engaging the public, promoting social interaction, identifying spatial patterns of occupation by previous inhabitants through social media. Findings exposed three main areas of interest. Firstly we identified inter-dependent relationships between permanence and temporality, characterised by elements such us, signage through layers of time, past events and periodical street festivals, unfolding memory and belonging. Secondly issues of co-ownership and occupation, found through particular narratives of exchange between the two communities and through appropriation of space. Finally formal and informal inhabitation of space, revealed through the presence of informal shared back yards, alternative paths, porous street edges and formal and informal landmarks. The importance of the above findings, was achieving a shift of focus from the built infrastructure to the soft network of multiple and complex relations of dependence and autonomy. Proposed interventions for this contested site were informed and led by a new multicultural identity where invisible qualities were revealed though the process of mapping, taking on issues of layers of time, formal and informal inhabitation and the “permanence of the temporary”.

Keywords: contested sites, mapping, social sustainability, temporary urban strategies

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Authors: Abhisek Chakrabarty, Subhraprakash Mandal

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Flash flood is one of the catastrophic natural hazards in the mountainous region of India. The recent flood in the Mandakini River in Kedarnath (14-17th June, 2013) is a classic example of flash floods that devastated Uttarakhand by killing thousands of people.The disaster was an integrated effect of high intensityrainfall, sudden breach of Chorabari Lake and very steep topography. Every year in Himalayan Region flash flood occur due to intense rainfall over a short period of time, cloud burst, glacial lake outburst and collapse of artificial check dam that cause high flow of river water. In Sikkim-Derjeeling Himalaya one of the probable flash flood occurrence zone is Teesta Watershed. The Teesta River is a right tributary of the Brahmaputra with draining mountain area of approximately 8600 Sq. km. It originates in the Pauhunri massif (7127 m). The total length of the mountain section of the river amounts to 182 km. The Teesta is characterized by a complex hydrological regime. The river is fed not only by precipitation, but also by melting glaciers and snow as well as groundwater. The present study describes an attempt to model surface runoff in upper Teesta basin, which is directly related to catastrophic flood events, by creating a system based on GIS technology. The main object was to construct a direct unit hydrograph for an excess rainfall by estimating the stream flow response at the outlet of a watershed. Specifically, the methodology was based on the creation of a spatial database in GIS environment and on data editing. Moreover, rainfall time-series data collected from Indian Meteorological Department and they were processed in order to calculate flow time and the runoff volume. Apart from the meteorological data, background data such as topography, drainage network, land cover and geological data were also collected. Clipping the watershed from the entire area and the streamline generation for Teesta watershed were done and cross-sectional profiles plotted across the river at various locations from Aster DEM data using the ERDAS IMAGINE 9.0 and Arc GIS 10.0 software. The analysis of different hydraulic model to detect flash flood probability ware done using HEC-RAS, Flow-2D, HEC-HMS Software, which were of great importance in order to achieve the final result. With an input rainfall intensity above 400 mm per day for three days the flood runoff simulation models shows outbursts of lakes and check dam individually or in combination with run-off causing severe damage to the downstream settlements. Model output shows that 313 Sq. km area were found to be most vulnerable to flash flood includes Melli, Jourthang, Chungthang, and Lachung and 655sq. km. as moderately vulnerable includes Rangpo,Yathang, Dambung,Bardang, Singtam, Teesta Bazarand Thangu Valley. The model was validated by inserting the rain fall data of a flood event took place in August 1968, and 78% of the actual area flooded reflected in the output of the model. Lastly preventive and curative measures were suggested to reduce the losses by probable flash flood event.

Keywords: flash flood, GIS, runoff, simulation model, Teesta river basin

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Authors: Andrea Scapolo, Zaya Rustamova, Arturo Matute Castro

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The “Voices in Dissent” initiative aims at collecting and making available in a digital format, testimonies, letters, and other narratives produced by victims of political oppression from different geographical spaces across the Atlantic. By recovering silenced voices behind the official narratives, this open-access online database will provide indispensable tools for rewriting the history of authoritarian regimes from the margins as memory debates continue to provoke controversy among academic and popular transnational circles. In providing an extensive database of non-hegemonic discourses in a variety of political and social contexts, the project will complement the existing European and Latin-American studies, and invite further interdisciplinary and trans-national research. This digital resource will be available to academic communities and the general audience and will be organized geographically and chronologically. “Voices in Dissent” will offer a first comprehensive study of these personal accounts of persecution and repression against determined historical backgrounds and their impact on collective memory formation in contemporary societies. The digitalization of these texts will allow to run metadata analyses and adopt comparatist approaches for a broad range of research endeavors. Most of the testimonies included in our archive are testimonies of trauma: the trauma of exile, imprisonment, torture, humiliation, censorship. The research on trauma has now reached critical mass and offers a broad spectrum of critical perspectives. By putting together testimonies from different geographical and historical contexts, our project will provide readers and scholars with an extraordinary opportunity to investigate how culture shapes individual and collective memories and provides or denies resources to make sense and cope with the trauma. For scholars dealing with the epistemological and rhetorical analysis of testimonies, an online open-access archive will prove particularly beneficial to test theories on truth status and the formation of belief as well as to study the articulation of discourse. An important aspect of this project is also its pedagogical applications since it will contribute to the creation of Open Educational Resources (OER) to support students and educators worldwide. Through collaborations with our Library System, the archive will form part of the Digital Commons database. The texts collected in this online archive will be made available in the original languages as well as in English translation. They will be accompanied by a critical apparatus that will contextualize them historically by providing relevant background information and bibliographical references. All these materials can serve as a springboard for a broad variety of educational projects and classroom activities. They can also be used to design specific content courses or modules. In conclusion, the desirable outcomes of the “Voices in Dissent” project are: 1. the collections and digitalization of political dissent testimonies; 2. the building of a network of scholars, educators, and learners involved in the design, development, and sustainability of the digital archive; 3. the integration of the content of the archive in both research and teaching endeavors, such as publication of scholarly articles, design of new upper-level courses, and integration of the materials in existing courses.

Keywords: digital archive, dissent, open educational resources, testimonies, transatlantic studies

Procedia PDF Downloads 104

Authors: Sancho-Esper Franco, Rodríguez Sánchez Carla, Sánchez Carolina, Orús-Sanclemente Carlos

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Water scarcity is a reality that affects many regions of the world and is aggravated by climate change and population growth. Saving water has become an urgent need to ensure the sustainability of the planet and the survival of many communities, where youth and social networks play a key role in promoting responsible practices and adopting habits that contribute to environmental preservation. This study analyzes the persuasion capacity of messages designed to promote pro-environmental behaviors among youth. Specifically, it studies how the efficacy (effectiveness) of the response (personal response efficacy/effectiveness) and the perception of distance from the source of the message influence the water-saving behavior of the audience. To do so, two communication frameworks are combined. First, the Construal Level Theory, which is based on the concept of "psychological distance", that is, people, objects or events can be perceived as psychologically near or far, and this subjective distance (i.e., social, temporal, or spatial) determines their attitudes, emotions, and actions. This perceived distance can be social, temporal, or spatial. This research focuses on studying the spatial distance and social distance generated by cultural differences between influencers and their audience to understand how cultural distance can influence the persuasiveness of a message. Research on the effects of psychological distance between influencers-followers in the pro-environmental field is very limited, being relevant because people could learn specific behaviors suggested by opinion leaders such as influencers in social networks. Second, different approaches to behavioral change suggest that the perceived efficacy of a behavior can explain individual pro-environmental actions. People will be more likely to adopt a new behavior if they perceive that they are capable of performing it (efficacy belief) and that their behavior will effectively contribute to solving that problem (personal response efficacy). It is also important to study the different actors (social and individual) that are perceived as responsible for addressing environmental problems. Specifically, we analyze to what extent the belief individual’s water-saving actions are effective in solving the problem can influence water-saving behavior since this individual effectiveness increases people's sense of obligation and responsibility with the problem. However, in this regard, empirical evidence presents mixed results. Our study addresses the call for experimental studies manipulating different subtypes of response effectiveness to generate robust causal evidence. Based on all the above, this research analyzes whether cultural distance (local vs. international influencer) and the perception of effectiveness of behavior (personal response efficacy) (personal/individual vs. collective) affect the actual behavior and the intention to conserve water of social network users. An experiment of 2 (local influencer vs. international influencer) x 2 (effectiveness of individual vs. collective response) is designed and estimated. The results show that a message from a local influencer appealing to individual responsibility exerts greater influence on intention and actual water-saving behavior, given the cultural closeness between influencer-follower, and the appeal to individual responsibility increases the feeling of obligation to participate in pro-environmental actions. These results offer important implications for social marketing campaigns that seek to promote water conservation.

Keywords: social marketing, influencer, message framing, experiment, personal response efficacy, water saving

Procedia PDF Downloads 62

Authors: Reyard Mutamiswa, Frank Chidawanyika, Casper Nyamukondiwa

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Under stressful thermal environments, insects adjust their behaviour and physiology to maintain key life-history activities and improve survival. For interacting species, mutual or antagonistic, thermal stress may affect the participants in differing ways, which may then affect the outcome of the ecological relationship. In agroecosystems, this may be the fate of relationships between insect pests and their antagonistic parasitoids under acute and chronic thermal variability. Against this background, we therefore investigated the thermal tolerance of different developmental stages of Chilo partellus Swinhoe (Lepidoptera: Crambidae) and its larval parasitoid Cotesia sesamiae Cameron (Hymenoptera: Braconidae) using both dynamic and static protocols. In laboratory experiments, we determined lethal temperature assays (upper and lower lethal temperatures) using direct plunge protocols in programmable water baths (Systronix, Scientific, South Africa), effects of ramping rate on critical thermal limits following standardized protocols using insulated double-jacketed chambers (‘organ pipes’) connected to a programmable water bath (Lauda Eco Gold, Lauda DR.R. Wobser GMBH and Co. KG, Germany), supercooling points (SCPs) following dynamic protocols using a Pico logger connected to a programmable water bath, heat knock-down time (HKDT) and chill-coma recovery (CCRT) time following static protocols in climate chambers (HPP 260, Memmert GmbH + Co.KG, Germany) connected to a camera (HD Covert Network Camera, DS-2CD6412FWD-20, Hikvision Digital Technology Co., Ltd, China). When exposed for two hours to a static temperature, lower lethal temperatures ranged -9 to 6; -14 to -2 and -1 to 4ºC while upper lethal temperatures ranged from 37 to 48; 41 to 49 and 36 to 39ºC for C. partellus eggs, larvae and C. sesamiae adults respectively. Faster heating rates improved critical thermal maxima (CTmax) in C. partellus larvae and adult C. partellus and C. sesamiae. Lower cooling rates improved critical thermal minima (CTmin) in C. partellus and C. sesamiae adults while compromising CTmin in C. partellus larvae. The mean SCPs for C. partellus larvae, pupae and adults were -11.82±1.78, -10.43±1.73 and -15.75±2.47 respectively with adults having the lowest SCPs. Heat knock-down time and chill-coma recovery time varied significantly between C. partellus larvae and adults. Larvae had higher HKDT than adults, while the later recovered significantly faster following chill-coma. Current results suggest developmental stage differences in C. partellus thermal tolerance (with respect to lethal temperatures and critical thermal limits) and a compromised temperature tolerance of parasitoid C. sesamiae relative to its host, suggesting potential asynchrony between host-parasitoid population phenology and consequently biocontrol efficacy under global change. These results have broad implications to biological pest management insect-natural enemy interactions under rapidly changing thermal environments.

Keywords: chill-coma recovery time, climate change, heat knock-down time, lethal temperatures, supercooling point

Procedia PDF Downloads 235

Authors: Jose A. Alvarez Aldegunde, Adrian Fernandez Sanchez, Matthew D. Menden, Bernardo Vila Rodriguez

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Up to five statistical pre-processings have been carried out considering the pollutant records of the stations present in Cartagena de Indias, Colombia, also taking into account the childhood asthma incidence surveys conducted in hospitals in the city by the Health Ministry of Colombia for this study. These pre-processings have consisted of different techniques such as the determination of the quality of data collection, determination of the quality of the registration network, identification and debugging of errors in data collection, completion of missing data and purified data, as well as the improvement of the time scale of records. The characterization of the quality of the data has been conducted by means of density analysis of the pollutant registration stations using ArcGis Software and through mass balance techniques, making it possible to determine inconsistencies in the records relating the registration data between stations following the linear regression. The results obtained in this process have highlighted the positive quality in the pollutant registration process. Consequently, debugging of errors has allowed us to identify certain data as statistically non-significant in the incidence and series of contamination. This data, together with certain missing records in the series recorded by the measuring stations, have been completed by statistical imputation equations. Following the application of these prior processes, the basic series of incidence data for respiratory disease and pollutant records have allowed the characterization of the influence of pollutants on respiratory diseases such as, for example, childhood asthma. This characterization has been carried out using statistical correlation methods, including visual correlation, simple linear regression correlation and spectral analysis with PAST Software which identifies maximum periodicity cycles and minimums under the formula of the Lomb periodgram. In relation to part of the results obtained, up to eleven maximums and minimums considered contemporary between the incidence records and the particles have been identified taking into account the visual comparison. The spectral analyses that have been performed on the incidence and the PM2.5 have returned a series of similar maximum periods in both registers, which are at a maximum during a period of one year and another every 25 days (0.9 and 0.07 years). The bivariate analysis has managed to characterize the variable "Daily Vehicular Flow" in the ninth position of importance of a total of 55 variables. However, the statistical correlation has not obtained a favorable result, having obtained a low value of the R2 coefficient. The series of analyses conducted has demonstrated the importance of the influence of pollutants such as PM2.5 in the development of childhood asthma in Cartagena. The quantification of the influence of the variables has been able to determine that there is a 56% probability of dependence between PM2.5 and childhood respiratory asthma in Cartagena. Considering this justification, the study could be completed through the application of the BenMap Software, throwing a series of spatial results of interpolated values of the pollutant contamination records that exceeded the established legal limits (represented by homogeneous units up to the neighborhood level) and results of the impact on the exacerbation of pediatric asthma. As a final result, an economic estimate (in Colombian Pesos) of the monthly and individual savings derived from the percentage reduction of the influence of pollutants in relation to visits to the Hospital Emergency Room due to asthma exacerbation in pediatric patients has been granted.

Keywords: Asthma Incidence, BenMap, PM2.5, Statistical Analysis

Procedia PDF Downloads 115

Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Francisco J. Ortega, Claudia Roman, Moisés García-Morales, Francisco J. Navarro

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Asphaltic bitumen has been largely used in both industrial and civil engineering, mostly in pavement construction and roofing membrane manufacture. However, bitumen as such is greatly susceptible to temperature variations, and dramatically changes its in-service behavior from a viscoelastic liquid, at medium-high temperatures, to a brittle solid at low temperatures. Bitumen modification prevents these problems and imparts improved performance. Isocyanates like polymeric MDI (mixture of 4,4′-diphenylmethane di-isocyanate, 2,4’ and 2,2’ isomers, and higher homologues) have shown to remarkably enhance bitumen properties at the highest in-service temperatures expected. This comes from the reaction between the –NCO pendant groups of the oligomer and the most polar groups of asphaltenes and resins in bitumen. In addition, oxygen diffusion and/or UV radiation may provoke bitumen hardening and ageing. With the purpose of minimizing these effects, nano-layered-silicates (nanoclays) are increasingly being added to bitumen formulations. Montmorillonites, a type of naturally occurring mineral, may produce a nanometer scale dispersion which improves bitumen thermal, mechanical and barrier properties. In order to increase their lipophilicity, these nanoclays are normally treated so that organic cations substitute the inorganic cations located in their intergallery spacing. In the present work, the combined effect of polymeric MDI and the commercial montmorillonite Cloisite® 20A was evaluated. A selected bitumen with penetration within the range 160/220 was modified with 10 wt.% Cloisite® 20A and 2 wt.% polymeric MDI, and the resulting ternary composites were characterized by linear rheology, X-ray diffraction (XRD) and Atomic Force Microscopy (AFM). The rheological tests evidenced a notable solid-like behavior at the highest temperatures studied when bitumen was just loaded with 10 wt.% Cloisite® 20A and high-shear blended for 20 minutes. However, if polymeric MDI was involved, the sequence of addition exerted a decisive control on the linear rheology of the final ternary composites. Hence, in bitumen/Cloisite® 20A/polymeric MDI formulations, the previous solid-like behavior disappeared. By contrast, an inversion in the order of addition (bitumen/polymeric MDI/ Cloisite® 20A) enhanced further the solid-like behavior imparted by the nanoclay. In order to gain a better understanding of the factors that govern the linear rheology of these ternary composites, a morphological and microstructural characterization based on XRD and AFM was conducted. XRD demonstrated the existence of clay stacks intercalated by bitumen molecules to some degree. However, the XRD technique cannot provide detailed information on the extent of nanoclay delamination, unless the entire fraction has effectively been fully delaminated (situation in which no peak is observed). Furthermore, XRD was unable to provide precise knowledge neither about the spatial distribution of the intercalated/exfoliated platelets nor about the presence of other structures at larger length scales. In contrast, AFM proved its power at providing conclusive information on the morphology of the composites at the nanometer scale and at revealing the structural modification that yielded the rheological properties observed. It was concluded that high-shear blending brought about a nanoclay-reinforced network. As for the bitumen/Cloisite® 20A/polymeric MDI formulations, the solid-like behavior was destroyed as a result of the agglomeration of the nanoclay platelets promoted by chemical reactions.

Keywords: Atomic Force Microscopy, bitumen, composite, isocyanate, montmorillonite.

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Authors: Andrew D. Henshaw, James M. Austin

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Introduction: Human trafficking is an increasingly serious transnational criminal enterprise and social security issue. Despite ongoing efforts to mitigate the phenomenon and a significant expansion of security scrutiny over past decades, it is not receding. This is true for many nations in Southeast Asia, widely recognized as the global hub for trafficked persons, including men, women, and children. Clearly, human trafficking is difficult to address because there are numerous drivers, causes, and motivators for it to persist, such as non-military and non-traditional security challenges, i.e., climate change, global warming displacement, and natural disasters. These make displaced persons and refugees particularly vulnerable. The issue is so large conservative estimates put a dollar value at around $150 billion-plus per year (Niethammer, 2020) spanning sexual slavery and exploitation, forced labor, construction, mining and in conflict roles, and forced marriages of girls and women. Coupled with corruption throughout military, police, and civil authorities around the world, and the active hands of powerful transnational criminal organizations, it is likely that such figures are grossly underestimated as human trafficking is misreported, under-detected, and deliberately obfuscated to protect those profiting from it. For example, the 2022 UN report on human trafficking shows a 56% reduction in convictions in that year alone (UNODC, 2022). Our Approach: To better understand this, our research utilizes a bespoke methodology. Applying a JAM (Juxtaposition Assessment Matrix), which we previously developed to detect flows of dark money around the globe (Henshaw, A & Austin, J, 2021), we now focus on the human trafficking paradigm. Indeed, utilizing a JAM methodology has identified key indicators of human trafficking not previously explored in depth. Being a set of structured analytical techniques that provide panoramic interpretations of the subject matter, this iteration of the JAM further incorporates behavioral and driver indicators, including the employment of Open-Source Artificial Intelligence (OS-AI) across multiple collection points. The extracted behavioral data was then applied to identify non-traditional indicators as they contribute to human trafficking. Furthermore, as the JAM OS-AI analyses data from the inverted position, i.e., the viewpoint of the traffickers, it examines the behavioral and physical traits required to succeed. This transposed examination of the requirements of success delivers potential leverage points for exploitation in the fight against human trafficking in a new and novel way. Findings: Our approach identified new innovative datasets that have previously been overlooked or, at best, undervalued. For example, the JAM OS-AI approach identified critical 'dark agent' lynchpins within human trafficking that are difficult to detect and harder to connect to actors and agents within a network. Our preliminary data suggests this is in part due to the fact that ‘dark agents’ in extant research have been difficult to detect and potentially much harder to directly connect to the actors and organizations in human trafficking networks. Our research demonstrates that using new investigative techniques such as OS-AI-aided JAM introduces a powerful toolset to increase understanding of human trafficking and transnational crime and illuminate networks that, to date, avoid global law enforcement scrutiny.

Keywords: human trafficking, open-source intelligence, transnational crime, human security, international human rights, intelligence analysis, JAM OS-AI, Dark Money

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Authors: Jing Jin

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The incorporation of artificial intelligence (AI) and machine learning (ML) in aircraft manufacturing and aerospace supply chains leads to the generation of a substantial amount of data among various tiers of suppliers and OEMs. Identifying the high-quality information challenges decision-makers. The application of AI/ML models necessitates access to 'high-quality' information to yield desired outputs. However, the process of information sharing introduces complexities, including distortion through various communication channels and biases introduced by both human and AI entities. This phenomenon significantly influences the quality of information, impacting decision-makers engaged in configuring supply chain systems. Traditionally, distorted information is categorized as 'low-quality'; however, this study challenges this perception, positing that distorted information, contributing to stakeholder goals, can be deemed high-quality within supply chains. The main aim of this study is to identify and evaluate the dimensions of information quality crucial to the UK aerospace supply chain. Guided by a central research question, "What information quality dimensions are considered when defining information quality in the UK aerospace supply chain?" the study delves into the intricate dynamics of information quality in the aerospace industry. Additionally, the research explores the nuanced impact of information distortion on stakeholders' decision-making processes, addressing the question, "How does the information distortion phenomenon influence stakeholders’ decisions regarding information quality in the UK aerospace supply chain system?" This study employs deductive methodologies rooted in positivism, utilizing a cross-sectional approach and a mono-quantitative method -a questionnaire survey. Data is systematically collected from diverse tiers of supply chain stakeholders, encompassing end-customers, OEMs, Tier 0.5, Tier 1, and Tier 2 suppliers. Employing robust statistical data analysis methods, including mean values, mode values, standard deviation, one-way analysis of variance (ANOVA), and Pearson’s correlation analysis, the study interprets and extracts meaningful insights from the gathered data. Initial analyses challenge conventional notions, revealing that information distortion positively influences the definition of information quality, disrupting the established perception of distorted information as inherently low-quality. Further exploration through correlation analysis unveils the varied perspectives of different stakeholder tiers on the impact of information distortion on specific information quality dimensions. For instance, Tier 2 suppliers demonstrate strong positive correlations between information distortion and dimensions like access security, accuracy, interpretability, and timeliness. Conversely, Tier 1 suppliers emphasise strong negative influences on the security of accessing information and negligible impact on information timeliness. Tier 0.5 suppliers showcase very strong positive correlations with dimensions like conciseness and completeness, while OEMs exhibit limited interest in considering information distortion within the supply chain. Introducing social network analysis (SNA) provides a structural understanding of the relationships between information distortion and quality dimensions. The moderately high density of ‘information distortion-by-information quality’ underscores the interconnected nature of these factors. In conclusion, this study offers a nuanced exploration of information quality dimensions in the UK aerospace supply chain, highlighting the significance of individual perspectives across different tiers. The positive influence of information distortion challenges prevailing assumptions, fostering a more nuanced understanding of information's role in the Industry 4.0 landscape.

Keywords: information distortion, information quality, supply chain configuration, UK aerospace industry

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Authors: Deepika Joshi

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Operations in automotive industry rely greatly on information shared between Supply Chain (SC) partners. This leads to efficient and effective management of SC activity. Automotive sector in India is growing at 14.2 percent per annum and has huge economic importance. We find that no study has been carried out on the role of information sharing in SC management of Indian automotive manufacturers. Considering this research gap, the present study is planned to establish the significance of information sharing in Indian auto-component supply chain activity. An empirical research was conducted for large scale auto component manufacturers from India. Twenty four Supply Chain Performance Indicators (SCPIs) were collected from existing literature. These elements belong to eight diverse but internally related areas of SC management viz., demand management, cost, technology, delivery, quality, flexibility, buyer-supplier relationship, and operational factors. A pair-wise comparison and an open ended questionnaire were designed using these twenty four SCPIs. The questionnaire was then administered among managerial level employees of twenty-five auto-component manufacturing firms. Analytic Network Process (ANP) technique was used to analyze the response of pair-wise questionnaire. Finally, twenty-five priority indexes are developed, one for each respondent. These were averaged to generate an industry specific priority index. The open-ended questions depicted strategies related to information sharing between buyers and suppliers and their influence on supply chain performance. Results show that the impact of information sharing on certain performance indicators is relatively greater than their corresponding variables. For example, flexibility, delivery, demand and cost related elements have massive impact on information sharing. Technology is relatively less influenced by information sharing but it immensely influence the quality of information shared. Responses obtained from managers reveal that timely and accurate information sharing lowers the cost, increases flexibility and on-time delivery of auto parts, therefore, enhancing the competitiveness of Indian automotive industry. Any flaw in dissemination of information can disturb the cycle time of both the parties and thus increases the opportunity cost. Due to supplier’s involvement in decisions related to design of auto parts, quality conformance is found to improve, leading to reduction in rejection rate. Similarly, mutual commitment to share right information at right time between all levels of SC enhances trust level. SC partners share information to perform comprehensive quality planning to ingrain total quality management. This study contributes to operations management literature which faces scarcity of empirical examination on this subject. It views information sharing as a building block which firms can promote and evolve to leverage the operational capability of all SC members. It will provide insights for Indian managers and researchers as every market is unique and suppliers and buyers are driven by local laws, industry status and future vision. While major emphasis in this paper is given to SC operations happening between domestic partners, placing more focus on international SC can bring in distinguished results.

Keywords: Indian auto component industry, information sharing, operations management, supply chain performance indicators

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Authors: Tolulope Aremu

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The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process.

Keywords: liquid detergent manufacturing, defect detection, machine learning, support vector machines, convolutional neural networks, defect characterization, predictive maintenance, quality control, fast-moving consumer goods

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Authors: Glykeria A. Visvini, George Ν. Mathioudakis, Amaia Soto Beobide, George A. Voyiatzis

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Polymer nanocomposites are materials in which a polymer matrix is reinforced with nanoscale inclusions, such as nanoparticles, nanoplates, or nanofibers. These nanoscale inclusions can significantly enhance the mechanical, thermal, electrical, and other properties of the polymer matrix, making them attractive for a wide range of industrial applications. These properties can be tailored by adjusting the type and the concentration of the nanoinclusions, which provides a high degree of flexibility in their design and development. An important property that polymeric membranes can exhibit is water vapor permeability (WVP). This can be accomplished by various methods, including the incorporation of micro/nano-fillers into the polymer matrix. In this way, a micro/nano-pore network can be formed, allowing water vapor to permeate through the membrane. At the same time, the membrane can be stretched uni- or bi-axially, creating aligned or cross-linked micropores in the composite, respectively, which can also increase the WVP. Nowadays, in industry, stretched films reinforced with CaCO3 develop micro-porosity sufficient to give them breathability characteristics. Carbon-based nanomaterials, such as graphene oxide (GO), are tentatively expected to be able to effectively improve the WVP of corresponding composite polymer films. The presence in the GO structure of various functional oxidizing groups enhances its ability to attract and channel water molecules, exploiting the unique large surface area of graphene that allows the rapid transport of water molecules. Polypropylene (PP) is widely used in various industrial applications due to its desirable properties, including good chemical resistance, excellent thermal stability, low cost, and easy processability. The specific properties of PP are highly influenced by its crystalline behavior, which is determined by its processing conditions. The development of the β-crystalline phase in PP, in combination with stretching, is anticipating improving the microporosity of the polymer matrix, thereby enhancing its WVP. The aim of present study is to create breathable PP composite membranes using carbon-based nanomaterials, such as graphene oxide (GO), reduced graphene oxide (rGO), and graphene nanoplatelets (GNPs). Unlike traditional methods that rely on the drawing process to enhance the WVP of PP, this study intents to develop a low-cost approach using melt mixing with β-nucleating agents and carbon fillers to create highly breathable PP composite membranes. The study aims to investigate how the concentration of these additives affects the water vapor transport properties of the resulting PP films/membranes. The presence of β-nucleating agents and carbon fillers is expected to enhance β-phase growth in PP, while an alternation between β- and α-phase is expected to lead to improved microporosity and WVP. Our ambition is to develop highly breathable PP composite films with superior performance and at a lower cost compared to the benchmark. Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call «Special Actions "AQUACULTURE"-"INDUSTRIAL MATERIALS"-"OPEN INNOVATION IN CULTURE"» (project code: Τ6YBP-00337)

Keywords: carbon based nanomaterials, nanocomposites, nucleating agent, polypropylene, water vapor permeability

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

Procedia PDF Downloads 72

Authors: Prem K. Mony, Bharadwaj Amrutur, Prashanth Thankachan, Swarnarekha Bhat, Suman Rao, Maryann Washington, Annamma Thomas, N. Sheela, Hiteshwar Rao, Sumi Antony

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The disease burden among mothers and newborns is caused mostly by a handful of avoidable conditions occurring around the time of childbirth and within the first month following delivery. Real-time monitoring of vital parameters of mothers and neonates offers a potential opportunity to impact access as well as the quality of care in vulnerable populations. We describe the design, development and testing of an innovative wearable device for remote biomonitoring (RBM) of body temperatures in mothers and neonates in a hospital in southern India. The architecture consists of: [1] a low-cost, wearable sensor tag; [2] a gateway device for ‘real-time’ communication link; [3] piggy-backing on a commercial GSM communication network; and [4] an algorithm-based data analytics system. Requirements for the device were: long battery-life upto 28 days (with sampling frequency 5/hr); robustness; IP 68 hermetic sealing; and human-centric design. We undertook pre-clinical laboratory testing followed by clinical trial phases I & IIa for evaluation of safety and efficacy in the following sequence: seven healthy adult volunteers; 18 healthy mothers; and three sets of babies – 3 healthy babies; 10 stable babies in the Neonatal Intensive Care Unit (NICU) and 1 baby with hypoxic ischaemic encephalopathy (HIE). The 3-coin thickness, pebble-design sensor weighing about 8 gms was secured onto the abdomen for the baby and over the upper arm for adults. In the laboratory setting, the response-time of the sensor device to attain thermal equilibrium with the surroundings was 4 minutes vis-a-vis 3 minutes observed with a precision-grade digital thermometer used as a reference standard. The accuracy was ±0.1°C of the reference standard within the temperature range of 25-40°C. The adult volunteers, aged 20 to 45 years, contributed a total of 345 hours of readings over a 7-day period and the postnatal mothers provided a total of 403 paired readings. The mean skin temperatures measured in the adults by the sensor were about 2°C lower than the axillary temperature readings (sensor =34.1 vs digital = 36.1); this difference was statistically significant (t-test=13.8; p<0.001). The healthy neonates provided a total of 39 paired readings; the mean difference in temperature was 0.13°C (sensor =36.9 vs digital = 36.7; p=0.2). The neonates in the NICU provided a total of 130 paired readings. Their mean skin temperature measured by the sensor was 0.6°C lower than that measured by the radiant warmer probe (sensor =35.9 vs warmer probe = 36.5; p < 0.001). The neonate with HIE provided a total of 25 paired readings with the mean sensor reading being not different from the radian warmer probe reading (sensor =33.5 vs warmer probe = 33.5; p=0.8). No major adverse events were noted in both the adults and neonates; four adult volunteers reported mild sweating under the device/arm band and one volunteer developed mild skin allergy. This proof-of-concept study shows that real-time monitoring of temperatures is technically feasible and that this innovation appears to be promising in terms of both safety and accuracy (with appropriate calibration) for improved maternal and neonatal health.

Keywords: public health, remote biomonitoring, temperature surveillance, wearable sensors, mothers and newborns

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Authors: Utkarsh A. Mishra, Ankit Bansal

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

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

Procedia PDF Downloads 223