Search results for: optimal transport

Authors: Majdah Alnefaie

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The ‘netnographic study’ and data mining have been used to monitor the public interaction on Social Media Sites (SMSs) to understand what the motivational factors influence the Saudi intentions regarding allowing women driving in Saudi Arabia in 2018. The netnographic study monitored the publics’ textual and visual communications in Twitter, Snapchat, and YouTube. SMSs users’ communications method is also known as electronic word of mouth (eWOM). Netnography methodology is still in its initial stages as it depends on manual extraction, reading and classification of SMSs users text. On the other hand, data mining is come from the computer and physical sciences background, therefore it is much harder to extract meaning from unstructured qualitative data. In addition, the new development in data mining software does not support the Arabic text, especially local slang in Saudi Arabia. Therefore, collaborations between social and computer scientists such as ‘netnographic study’ and data mining will enhance the efficiency of this study methodology leading to comprehensive research outcome. The eWOM communications between individuals on SMSs can promote a sense that sharing their preferences and experiences regarding politics and social government regulations is a part of their daily life, highlighting the importance of using SMSs as assistance in promoting participation in political and social. Therefore, public interactions on SMSs are important tools to comprehend people’s intentions regarding the new government regulations in the country. This study aims to answer this question, "What factors influence the Saudi Arabians' intentions of Saudi female's car-driving in 2018". The study utilized qualitative method known as netnographic study. The study used R studio to collect and analyses 27000 Saudi users’ comments from 25th May until 25th June 2018. The study has developed data collection model that support importing and analysing the Arabic text in the local slang. The data collection model in this study has been clustered based on different type of social networks, gender and the study main factors. The social network analysis was employed to collect comments from SMSs owned by governments’ originations, celebrities, vloggers, social activist and news SMSs accounts. The comments were collected from both males and females SMSs users. The sentiment analysis shows that the total number of positive comments Saudi females car driving was higher than negative comments. The data have provided the most important factors influenced the Saudi Arabians’ intention of Saudi females car driving including, culture and environment, freedom of choice, equal opportunities, security and safety. The most interesting finding indicted that women driving would play a role in increasing the individual freedom of choice. Saudi female will be able to drive cars to fulfill her daily life and family needs without being stressed due to the lack of transportation. The study outcome will help Saudi government to improve woman quality of life by increasing the ability to find more jobs and studies, increasing income through decreasing the spending on transport means such as taxi and having more freedom of choice in woman daily life needs. The study enhances the importance of using use marketing research to measure the public opinions on the new government regulations in the country. The study has explained the limitations and suggestions for future research.

Keywords: netnographic study, data mining, social media, Saudi Arabia, female driving

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Authors: Wiam El Kheir, Anais Dumais, Maude Beaudoin, Bernard Marcos, Nick Virgilio, Benoit Paquette, Nathalie Faucheux, Marc-Antoine Lauzon

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The high infiltrative pattern of glioblastoma multiforme cells (GBM) is the main cause responsible for the actual standard treatments failure. The tumor high heterogeneity, the interstitial fluid flow (IFF) and chemokines guides GBM cells migration in the brain parenchyma resulting in tumor recurrence. Drug delivery systems emerged as an alternative approach to develop effective treatments for the disease. Some recent studies have proposed to harness the effect CXC-lchemokine-ligand-12 to direct and control the cancer cell migration through delivery system. However, the dynamics of the brain environment on the delivery system remains poorly understood. Nanoparticles (NPs) and hydrogels are known as good carriers for the encapsulation of different agents and control their release. We studied the release of CXCL12 (free or loaded into NPs) from an alginate-based hydrogel under static and indirect perfusion (IP) conditions. Under static conditions, the main phenomena driving CXCL12 release from the hydrogel was diffusion with the presence of strong interactions between the positively charged CXCL12 and the negatively charge alginate. CXCL12 release profiles were independent from the initial mass loadings. Afterwards, we demonstrated that the release could tuned by loading CXCL12 into Alginate/Chitosan-Nanoparticles (Alg/Chit-NPs) and embedded them into alginate-hydrogel. The initial burst release was substantially attenuated and the overall cumulative release percentages of 21%, 16% and 7% were observed for initial mass loadings of 0.07, 0.13 and 0.26 µg, respectively, suggesting stronger electrostatic interactions. Results were mathematically modeled based on Fick’s second law of diffusion framework developed previously to estimate the effective diffusion coefficient (Deff) and the mass transfer coefficient. Embedding the CXCL12 into NPs decreased the Deff an order of magnitude, which was coherent with experimental data. Thereafter, we developed an in-vitro 3D model that takes into consideration the convective contribution of the brain IFF to study CXCL12 release in an in-vitro microenvironment that mimics as faithfully as possible the human brain. From is unique design, the model also allowed us to understand the effect of IP on CXCL12 release in respect to time and space. Four flow rates (0.5, 3, 6.5 and 10 µL/min) which may increase CXCL12 release in-vivo depending on the tumor location were assessed. Under IP, cumulative percentages varying between 4.5-7.3%, 23-58.5%, 77.8-92.5% and 89.2-95.9% were released for the three initial mass loadings of 0.08, 0.16 and 0.33 µg, respectively. As the flow rate increase, IP culture conditions resulted in a higher release of CXCL12 compared to static conditions as the convection contribution became the main driving mass transport phenomena. Further, depending on the flow rate, IP had a direct impact on CXCL12 distribution within the simulated brain tissue, which illustrates the importance of developing such 3D in-vitro models to assess the efficiency of a delivery system targeting the brain. In future work, using this very model, we aim to understand the impact of the different phenomenon occurring on GBM cell behaviors in response to the resulting chemokine gradient subjected to various flow while allowing them to express their invasive characteristics in an in-vitro microenvironment that mimics the in-vivo brain parenchyma.

Keywords: 3D culture system, chemokines gradient, glioblastoma multiforme, kinetic release, mathematical modeling

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Authors: Rachael Andrews, Viktoria McMillan, Shuaib Nasser, Christopher M. Roberts

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Background and objective: The National Review of Asthma Deaths (NRAD) found that asthma management and care was inadequate in 26% of cases reviewed. Major shortfalls identified were adherence to national guidelines and standards and, particularly, the organisation of care, including supervision and monitoring in primary care, with 70% of cases reviewed having at least one avoidable factor in this area. 5.4 million people in the UK are diagnosed with and actively treated for asthma, and approximately 60,000 are admitted to hospital with acute exacerbations each year. The majority of people with asthma receive management and treatment solely in primary care. This has therefore created concern that many people within the UK are receiving sub-optimal asthma care resulting in unnecessary morbidity and risk of adverse outcome. NRAD concluded that a national asthma audit programme should be established to measure and improve processes, organisation, and outcomes of asthma care. Objective: To develop a primary care dataset enabling extraction of information from GP practices in Wales and providing robust data by which results and lessons could be drawn and drive service development and improvement. Methods: A multidisciplinary group of experts, including general practitioners, primary care organisation representatives, and asthma patients was formed and used as a source of governance and guidance. A review of asthma literature, guidance, and standards took place and was used to identify areas of asthma care which, if improved, would lead to better patient outcomes. Modified Delphi methodology was used to gain consensus from the expert group on which of the areas identified were to be prioritised, and an asthma patient and carer focus group held to seek views and feedback on areas of asthma care that were important to them. Areas of asthma care identified by both groups were mapped to asthma guidelines and standards to inform and develop primary and secondary care datasets covering both adult and pediatric care. Dataset development consisted of expert review and a targeted consultation process in order to seek broad stakeholder views and feedback. Results: Areas of asthma care identified as requiring prioritisation by the National Asthma Audit were: (i) Prescribing, (ii) Asthma diagnosis (iii) Asthma Reviews (iv) Personalised Asthma Action Plans (PAAPs) (v) Primary care follow-up after discharge from hospital (vi) Methodologies and primary care queries were developed to cover each of the areas of poor and variable asthma care identified and the queries designed to extract information directly from electronic patients’ records. Conclusion: This paper describes the methodological approach followed to develop primary care datasets for a National Asthma Audit. It sets out the principles behind the establishment of a National Asthma Audit programme in response to a national asthma mortality review and describes the development activities undertaken. Key process elements included: (i) mapping identified areas of poor and variable asthma care to national guidelines and standards, (ii) early engagement of experts, including clinicians and patients in the process, and (iii) targeted consultation of the queries to provide further insight into measures that were collectable, reproducible and relevant.

Keywords: asthma, primary care, general practice, dataset development

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Authors: Natia Jalagonia, Tinatin Kuchukhidze

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Polymer electrolytes (PE) play an important part in electrochemical devices such as batteries and fuel cells. To achieve optimal performance, the PE must maintain a high ionic conductivity and mechanical stability at both high and low relative humidity. The polymer electrolyte also needs to have excellent chemical stability for long and robustness. According to the prevailing theory, ionic conduction in polymer electrolytes is facilitated by the large-scale segmental motion of the polymer backbone, and primarily occurs in the amorphous regions of the polymer electrolyte. Crystallinity restricts polymer backbone segmental motion and significantly reduces conductivity. Consequently, polymer electrolytes with high conductivity at room temperature have been sought through polymers which have highly flexible backbones and have largely amorphous morphology. The interest in polymer electrolytes was increased also by potential applications of solid polymer electrolytes in high energy density solid state batteries, gas sensors and electrochromic windows. Conductivity of 10-3 S/cm is commonly regarded as a necessary minimum value for practical applications in batteries. At present, polyethylene oxide (PEO)-based systems are most thoroughly investigated, reaching room temperature conductivities of 10-7 S/cm in some cross-linked salt in polymer systems based on amorphous PEO-polypropylene oxide copolymers.. It is widely accepted that amorphous polymers with low glass transition temperatures Tg and a high segmental mobility are important prerequisites for high ionic conductivities. Another necessary condition for high ionic conductivity is a high salt solubility in the polymer, which is most often achieved by donors such as ether oxygen or imide groups on the main chain or on the side groups of the PE. It is well established also that lithium ion coordination takes place predominantly in the amorphous domain, and that the segmental mobility of the polymer is an important factor in determining the ionic mobility. Great attention was pointed to PEO-based amorphous electrolyte obtained by synthesis of comb-like polymers, by attaching short ethylene oxide unit sequences to an existing amorphous polymer backbone. The aim of presented work is to obtain of solid polymer electrolyte membranes using PMHS as a matrix. For this purpose the hydrosilylation reactions of α,ω-bis(trimethylsiloxy)methyl¬hydrosiloxane with allyl triethylene-glycol mo¬nomethyl ether and vinyltriethoxysilane at 1:28:7 ratio of initial com¬pounds in the presence of Karstedt’s catalyst, platinum hydrochloric acid (0.1 M solution in THF) and platinum on the carbon catalyst in 50% solution of anhydrous toluene have been studied. The synthesized olygomers are vitreous liquid products, which are well soluble in organic solvents with specific viscosity ηsp ≈ 0.05 - 0.06. The synthesized olygomers were analysed with FTIR, 1H, 13C, 29Si NMR spectroscopy. Synthesized polysiloxanes were investigated with wide-angle X-ray, gel-permeation chromatography, and DSC analyses. Via sol-gel processes of doped with lithium trifluoromethylsulfonate (triflate) or lithium bis¬(trifluoromethylsulfonyl)¬imide polymer systems solid polymer electrolyte membranes have been obtained. The dependence of ionic conductivity as a function of temperature and salt concentration was investigated and the activation energies of conductivity for all obtained compounds are calculated

Keywords: synthesis, PMHS, membrane, electrolyte

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Authors: Mohamad Saab, Florent Real, Francois Virot, Laurent Cantrel, Valerie Vallet

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All spent nuclear fuel reprocessing plants use the PUREX process (Plutonium Uranium Refining by Extraction), which is a liquid-liquid extraction method. The organic extracting solvent is a mixture of tri-n-butyl phosphate (TBP) and hydrocarbon solvent such as hydrogenated tetra-propylene (TPH). By chemical complexation, uranium and plutonium (from spent fuel dissolved in nitric acid solution), are separated from fission products and minor actinides. During a normal extraction operation, uranium is extracted in the organic phase as the UO₂(NO₃)₂(TBP)₂ complex. The TBP solvent can form an explosive mixture called red oil when it comes in contact with nitric acid. The formation of this unstable organic phase originates from the reaction between TBP and its degradation products on the one hand, and nitric acid, its derivatives and heavy metal nitrate complexes on the other hand. The decomposition of the red oil can lead to violent explosive thermal runaway. These hazards are at the origin of several accidents such as the two in the United States in 1953 and 1975 (Savannah River) and, more recently, the one in Russia in 1993 (Tomsk). This raises the question of the exothermicity of reactions that involve TBP and all other degradation products, and calls for a better knowledge of the underlying chemical phenomena. A simulation tool (Alambic) is currently being developed at IRSN that integrates thermal and kinetic functions related to the deterioration of uranyl nitrates in organic and aqueous phases, but not of the n-butyl phosphate. To include them in the modeling scheme, there is an urgent need to obtain the thermodynamic and kinetic functions governing the deterioration processes in liquid phase. However, little is known about the thermodynamic properties, like standard enthalpies of formation, of the n-butyl phosphate molecules and of the UO₂(NO₃)₂(TBP)₂ UO₂(NO₃)₂(HDBP)(TBP) and UO₂(NO₃)₂(HDBP)₂ complexes. In this work, we propose to estimate the thermodynamic properties with Quantum Methods (QM). Thus, in the first part of our project, we focused on the mono, di, and tri-butyl complexes. Quantum chemical calculations have been performed to study several reactions leading to the formation of mono-(H₂MBP), di-(HDBP), and TBP in gas and liquid phases. In the gas phase, the optimal structures of all species were optimized using the B3LYP density functional. Triple-ζ def2-TZVP basis sets were used for all atoms. All geometries were optimized in the gas-phase, and the corresponding harmonic frequencies were used without scaling to compute the vibrational partition functions at 298.15 K and 0.1 Mpa. Accurate single point energies were calculated using the efficient localized LCCSD(T) method to the complete basis set limit. Whenever species in the liquid phase are considered, solvent effects are included with the COSMO-RS continuum model. The standard enthalpies of formation of TBP, HDBP, and H2MBP are finally predicted with an uncertainty of about 15 kJ mol⁻¹. In the second part of this project, we have investigated the fundamental properties of three organic species that mostly contribute to the thermal runaway: UO₂(NO₃)₂(TBP)₂, UO₂(NO₃)₂(HDBP)(TBP), and UO₂(NO₃)₂(HDBP)₂ using the same quantum chemical methods that were used for TBP and its derivatives in both the gas and the liquid phase. We will discuss the structures and thermodynamic properties of all these species.

Keywords: PUREX process, red oils, quantum chemical methods, hydrolysis

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: Deepak Loura

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The most contemporary method of producing horticulture crops both qualitatively and quantitatively is protected cultivation, or greenhouse cultivation, which has gained widespread acceptance in recent decades. Protected farming, commonly referred to as controlled environment agriculture (CEA), is extremely productive, land- and water-wise, as well as environmentally friendly. The technology entails growing horticulture crops in a controlled environment where variables such as temperature, humidity, light, soil, water, fertilizer, etc. are adjusted to achieve optimal output and enable a consistent supply of them even during the off-season. Over the past ten years, protected cultivation of high-value crops and cut flowers has demonstrated remarkable potential. More and more agricultural and horticultural crop production systems are moving to protected environments as a result of the growing demand for high-quality products by global markets. By covering the crop, it is possible to control the macro- and microenvironments, enhancing plant performance and allowing for longer production times, earlier harvests, and higher yields of higher quality. These shielding features alter the environment of the plant while also offering protection from wind, rain, and insects. Protected farming opens up hitherto unexplored opportunities in agriculture as the liberalised economy and improved agricultural technologies advance. Typically, the revenues from fruit, vegetable, and flower crops are 4 to 8 times higher than those from other crops. If any of these high-value crops are cultivated in protected environments like greenhouses, net houses, tunnels, etc., this profit can be multiplied. Vegetable and cut flower post-harvest losses are extremely high (20–0%), however sheltered growing techniques and year-round cropping can greatly minimize post-harvest losses and enhance yield by 5–10 times. Seasonality and weather have a big impact on the production of vegetables and flowers. The variety of their products results in significant price and quality changes for vegetables. For the application of current technology in crop production, achieving a balance between year-round availability of vegetables and flowers with minimal environmental impact and remaining competitive is a significant problem. The future of agriculture will be protected since population growth is reducing the amount of land that may be held. Protected agriculture is a particularly profitable endeavor for tiny landholdings. Small greenhouses, net houses, nurseries, and low tunnel greenhouses can all be built by farmers to increase their income. Protected agriculture is also aided by the rise in biotic and abiotic stress factors. As a result of the greater productivity levels, these technologies are not only opening up opportunities for producers with larger landholdings, but also for those with smaller holdings. Protected cultivation can be thought of as a kind of precise, forward-thinking, parallel agriculture that covers almost all aspects of farming and is rather subject to additional inspection for technical applicability to circumstances, farmer economics, and market economics.

Keywords: protected cultivation, horticulture, greenhouse, vegetable, controlled environment agriculture

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Authors: Rohit T. P., Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards Augmented/Virtual Reality applications the computational demands on these devices are increasing exponentially and we are already reaching the limits of hardware optimizations. This paper proposes a software solution to this problem. By leveraging the capabilities of cloud computing we can offload the work from mobile devices to dedicated rendering servers that are way more powerful. But this introduces the problem of latency. This paper introduces a protocol that can achieve high-performance low latency Augmented/Virtual Reality experience. There are two parts to the protocol, 1) In-flight compression The main cause of latency in the system is the time required to transmit the camera frame from client to server. The round trip time is directly proportional to the amount of data transmitted. This can therefore be reduced by compressing the frames before sending. Using some standard compression algorithms like JPEG can result in minor size reduction only. Since the images to be compressed are consecutive camera frames there won't be a lot of changes between two consecutive images. So inter-frame compression is preferred. Inter-frame compression can be implemented efficiently using WebGL but the implementation of WebGL limits the precision of floating point numbers to 16bit in most devices. This can introduce noise to the image due to rounding errors, which will add up eventually. This can be solved using an improved interframe compression algorithm. The algorithm detects changes between frames and reuses unchanged pixels from the previous frame. This eliminates the need for floating point subtraction thereby cutting down on noise. The change detection is also improved drastically by taking the weighted average difference of pixels instead of the absolute difference. The kernel weights for this comparison can be fine-tuned to match the type of image to be compressed. 2) Dynamic Load distribution Conventional cloud computing architectures work by offloading as much work as possible to the servers, but this approach can cause a hit on bandwidth and server costs. The most optimal solution is obtained when the device utilizes 100% of its resources and the rest is done by the server. The protocol balances the load between the server and the client by doing a fraction of the computing on the device depending on the power of the device and network conditions. The protocol will be responsible for dynamically partitioning the tasks. Special flags will be used to communicate the workload fraction between the client and the server and will be updated in a constant interval of time ( or frames ). The whole of the protocol is designed so that it can be client agnostic. Flags are available to the client for resetting the frame, indicating latency, switching mode, etc. The server can react to client-side changes on the fly and adapt accordingly by switching to different pipelines. The server is designed to effectively spread the load and thereby scale horizontally. This is achieved by isolating client connections into different processes.

Keywords: 2D kernelling, augmented reality, cloud computing, dynamic load distribution, immersive experience, mobile computing, motion tracking, protocols, real-time systems, web-based augmented reality application

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Authors: Daniele Lerede, Gianvito Colucci, Matteo Nicoli, Laura Savoldi

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The European Green Deal is the first internationally agreed set of measures to contrast climate change and environmental degradation. Besides the main target of reducing emissions by at least 55% by 2030, it sets the target of accompanying European countries through an energy transition to make the European Union into a modern, resource-efficient, and competitive net-zero emissions economy by 2050, decoupling growth from the use of resources and ensuring a fair adaptation of all social categories to the transformation process. While the general purpose to allow the realization of the purposes of the Green Deal already dates back to 2019, strategies and policies keep being developed coping with recent circumstances and achievements. However, general long-term measures like the Circular Economy Action Plan, the proposals to shift from fossil natural gas to renewable and low-carbon gases, in particular biomethane and hydrogen, and to end the sale of gasoline and diesel cars by 2035, will all have significant effects on energy supply and demand evolution across the next decades. The interactions between energy supply and demand over long-term time frames are usually assessed via energy system models to derive useful insights for policymaking and to address technological choices and research and development. TEMOA-Europe is a newly developed energy system optimization model instance based on the minimization of the total cost of the system under analysis, adopting a technologically integrated, detailed, and explicit formulation and considering the evolution of the system in partial equilibrium in competitive markets with perfect foresight. TEMOA-Europe is developed on the TEMOA platform, an open-source modeling framework totally implemented in Python, therefore ensuring third-party verification even on large and complex models. TEMOA-Europe is based on a single-region representation of the European Union and EFTA countries on a time scale between 2005 and 2100, relying on a set of assumptions for socio-economic developments based on projections by the International Energy Outlook and a large technological dataset including 7 sectors: the upstream and power sectors for the production of all energy commodities and the end-use sectors, including industry, transport, residential, commercial and agriculture. TEMOA-Europe also includes an updated hydrogen module considering its production, storage, transportation, and utilization. Besides, it can rely on a wide set of innovative technologies, ranging from nuclear fusion and electricity plants equipped with CCS in the power sector to electrolysis-based steel production processes and steel in the industrial sector – with a techno-economic characterization based on public literature – to produce insightful energy scenarios and especially to cope with the very long analyzed time scale. The aim of this work is to examine in detail the scheme of measures and policies for the realization of the purposes of the Green Deal and to transform them into a set of constraints and new socio-economic development pathways. Based on them, TEMOA-Europe will be used to produce and comparatively analyze scenarios to assess the consequences of Green Deal-related measures on the future evolution of the energy mix over the whole energy system in an economic optimization environment.

Keywords: European Green Deal, energy system optimization modeling, scenario analysis, TEMOA-Europe

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Authors: Mahla Azimi

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Introduction: Post-bladder catheter infection is a common and significant healthcare-associated infection that affects individuals with indwelling urinary catheters. These infections can lead to various complications, including urinary tract infections (UTIs), bacteremia, sepsis, and increased morbidity and mortality rates. This article aims to provide a comprehensive review of post-bladder catheter infections, including their causes, risk factors, clinical presentation, diagnosis, treatment options, and preventive measures. Causes and Risk Factors: Post-bladder catheter infections primarily occur due to the colonization of microorganisms on the surface of the urinary catheter. The most common pathogens involved are Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterococcus species. Several risk factors contribute to the development of these infections, such as prolonged catheterization duration, improper insertion technique, poor hygiene practices during catheter care, compromised immune system function in patients with underlying conditions or immunosuppressive therapy. Clinical Presentation: Patients with post-bladder catheter infections may present with symptoms such as fever, chills, malaise, suprapubic pain or tenderness, and cloudy or foul-smelling urine. In severe cases or when left untreated for an extended period of time, patients may develop more severe symptoms like hematuria or signs of systemic infection. Diagnosis: The diagnosis of post-bladder catheter infection involves a combination of clinical evaluation and laboratory investigations. Urinalysis is crucial in identifying pyuria (presence of white blood cells) and bacteriuria (presence of bacteria). A urine culture is performed to identify the causative organism(s) and determine its antibiotic susceptibility profile. Treatment Options: Prompt initiation of appropriate antibiotic therapy is essential in managing post-bladder catheter infections. Empirical treatment should cover common pathogens until culture results are available. The choice of antibiotics should be guided by local antibiogram data to ensure optimal therapy. In some cases, catheter removal may be necessary, especially if the infection is recurrent or associated with severe complications. Preventive Measures: Prevention plays a vital role in reducing the incidence of post-bladder catheter infections. Strategies include proper hand hygiene, aseptic technique during catheter insertion and care, regular catheter maintenance, and timely removal of unnecessary catheters. Healthcare professionals should also promote patient education regarding self-care practices and signs of infection. Conclusion: Post-bladder catheter infections are a significant healthcare concern that can lead to severe complications and increased healthcare costs. Early recognition, appropriate diagnosis, and prompt treatment are crucial in managing these infections effectively. Implementing preventive measures can significantly reduce the incidence of post-bladder catheter infections and improve patient outcomes. Further research is needed to explore novel strategies for prevention and management in this field.

Keywords: post-bladder catheter infection, urinary tract infection, bacteriuria, indwelling urinary catheters, prevention

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Authors: David A. Olasehinde, Peter I. Olasehinde, Segun M. A. Adelana, Dapo O. Olasehinde

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An investigation was carried out on underground water system dynamics within Ilorin metropolis to monitor the subsurface flow and its corresponding pollution. Africa population growth rate is the highest among the regions of the world, especially in urban areas. A corresponding increase in waste generation and a change in waste composition from predominantly organic to non-organic waste has also been observed. Percolation of leachate from non-engineered landfills, the chief means of waste disposal in many of its cities, constitutes a threat to the underground water bodies. Ilorin city, a transboundary town in southwestern Nigeria, is a ready microcosm of Africa’s unique challenge. In spite of the fact that groundwater is naturally protected from common contaminants such as bacteria as the subsurface provides natural attenuation process, groundwater samples have been noted to however possesses relatively higher dissolved chemical contaminants such as bicarbonate, sodium, and chloride which poses a great threat to environmental receptors and human consumption. The Geographic Information System (GIS) was used as a tool to illustrate, subsurface dynamics and the corresponding pollutant indicators. Forty-four sampling points were selected around known groundwater pollutant, major old dumpsites without landfill liners. The results of the groundwater flow directions and the corresponding contaminant transport were presented using expert geospatial software. The experimental results were subjected to four descriptive statistical analyses, namely: principal component analysis, Pearson correlation analysis, scree plot analysis, and Ward cluster analysis. Regression model was also developed aimed at finding functional relationships that can adequately relate or describe the behaviour of water qualities and the hypothetical factors landfill characteristics that may influence them namely; distance of source of water body from dumpsites, static water level of groundwater, subsurface permeability (inferred from hydraulic gradient), and soil infiltration. The regression equations developed were validated using the graphical approach. Underground water seems to flow from the northern portion of Ilorin metropolis down southwards transporting contaminants. Pollution pattern in the study area generally assumed a bimodal pattern with the major concentration of the chemical pollutants in the underground watershed and the recharge. The correlation between contaminant concentrations and the spread of pollution indicates that areas of lower subsurface permeability display a higher concentration of dissolved chemical content. The principal component analysis showed that conductivity, suspended solids, calcium hardness, total dissolved solids, total coliforms, and coliforms were the chief contaminant indicators in the underground water system in the study area. Pearson correlation revealed a high correlation of electrical conductivity for many parameters analyzed. In the same vein, the regression models suggest that the heavier the molecular weight of a chemical contaminant of a pollutant from a point source, the greater the pollution of the underground water system at a short distance. The study concludes that the associative properties of landfill have a significant effect on groundwater quality in the study area.

Keywords: dumpsite, leachate, groundwater pollution, linear regression, principal component

Procedia PDF Downloads 86

Authors: Milica B. Veljković, Milica B. Simović, Marija M. Ćorović, Ana D. Milivojević, Anja I. Petrov, Katarina M. Banjanac, Dejan I. Bezbradica

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In recent decades, the scientific community has recognized the growing importance of prebiotics, and therefore, numerous studies are focused on their economic production due to their low presence in natural resources. It has been confirmed that prebiotics is a source of energy for probiotics in the gastrointestinal tract (GIT) and enable their proliferation, consequently leading to the normal functioning of the intestinal microbiota. Also, products of their fermentation are short-chain fatty acids (SCFA), which play a key role in maintaining and improving the health not only of the GIT but also of the whole organism. Among several confirmed prebiotics, fructooligosaccharides (FOS) are considered interesting candidates for use in a wide range of products in the food industry. They are characterized as low-calorie and non-cariogenic substances that represent an adequate sugar substitute and can be considered suitable for use in products intended for diabetics. The subject of this research will be the production of FOS by transforming sucrose using a fructosyltransferase (FTase) present in commercial preparation Pectinex® Ultra SP-L, with special emphasis on the development of adequate FTase immobilization method that would enable selective isolation of the enzyme responsible for the synthesis of FOS from the complex enzymatic mixture. This would lead to considerable enzyme purification and allow its direct incorporation into different sucrose-based products without the fear that the action of the other hydrolytic enzymes may adversely affect the products' functional characteristics. Accordingly, the possibility of selective immobilization of the enzyme using support with primary amino groups, Purolite® A109, which was previously activated and modified using glutaraldehyde (GA), was investigated. In the initial phase of the research, the effects of individual immobilization parameters such as pH, enzyme concentration, and immobilization time were investigated to optimize the process using support chemically activated with 15% and 0.5% GA to form dimers and monomers, respectively. It was determined that highly active immobilized preparations (371.8 IU/g of support - dimer and 213.8 IU/g of support – monomer) were achieved under acidic conditions (pH 4) provided that an enzyme concentration was 50 mg/g of support after 7 h and 3 h, respectively. Bearing in mind the obtained results of the expressed activity, it is noticeable that the formation of dimers showed higher reactivity compared to the form of monomers. Also, in the case of support modification using 15% GA, the value of the ratio of FTase and pectinase (as dominant enzyme mixture component) activity immobilization yields was 16.45, indicating the high feasibility of selective immobilization of FTase on modified polystyrene resin. After obtaining immobilized preparations of satisfactory features, they were tested in a reaction of FOS synthesis under determined optimal conditions. The maximum FOS yields of approximately 50% of total carbohydrates in the reaction mixture were recorded after 21 h. Finally, it can be concluded that the examined immobilization method yielded highly active, stable and, more importantly, refined enzyme preparation that can be further utilized on a larger scale for the development of continual processes for FOS synthesis, as well as for modification of different sucrose-based mediums.

Keywords: chemical modification, fructooligosaccharides, glutaraldehyde, immobilization of fructosyltransferase

Procedia PDF Downloads 151

Authors: Sesethu G. Njokweni, Marelize Botes, Emile W. H. Van Zyl

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An alternative strategy to the production of bioethanol is by examining the degradability of biomass in a natural system such as the rumen of mammals. This anaerobic microbial community has higher cellulolytic activities than microbial communities from other habitats and degrades cellulose to produce volatile fatty acids (VFA), methane and CO₂. VFAs have the potential to serve as intermediate products for electrochemical conversion to hydrocarbon fuels. In vitro mimicking of this process would be more cost-effective than bioethanol production as it does not require chemical pre-treatment of biomass, a sterile environment or added enzymes. The strategies of the carboxylate platform and the co-cultures of a bovine ruminal microbiota from cannulated cows were combined in order to investigate and optimize the bioconversion of agricultural biomass (apple and grape pomace, citrus pulp, sugarcane bagasse and triticale straw) to high value VFAs as intermediates for biofuel production in a consolidated bioprocess. Optimisation of reactor conditions was investigated using five different ruminal inoculum concentrations; 5,10,15,20 and 25% with fixed pH at 6.8 and temperature at 39 ˚C. The ANKOM 200/220 fiber analyser was used to analyse in vitro neutral detergent fiber (NDF) disappearance of the feedstuffs. Fresh and cryo-frozen (5% DMSO and 50% glycerol for 3 months) rumen cultures were tested for the retainment of fermentation capacity and durability in 72 h fermentations in 125 ml serum vials using a FURO medical solutions 6-valve gas manifold to induce anaerobic conditions. Fermentation of apple pomace, triticale straw, and grape pomace showed no significant difference (P > 0.05) in the effect of 15 and 20 % inoculum concentrations for the total VFA yield. However, high performance liquid chromatographic separation within the two inoculum concentrations showed a significant difference (P < 0.05) in acetic acid yield, with 20% inoculum concentration being the optimum at 4.67 g/l. NDF disappearance of 85% in 96 h and total VFA yield of 11.5 g/l in 72 h (A/P ratio = 2.04) for apple pomace entailed that it was the optimal feedstuff for this process. The NDF disappearance and VFA yield of DMSO (82% NDF disappearance and 10.6 g/l VFA) and glycerol (90% NDF disappearance and 11.6 g/l VFA) stored rumen also showed significantly similar degradability of apple pomace with lack of treatment effect differences compared to a fresh rumen control (P > 0.05). The lack of treatment effects was a positive sign in indicating that there was no difference between the stored samples and the fresh rumen control. Retaining of the fermentation capacity within the preserved cultures suggests that its metabolic characteristics were preserved due to resilience and redundancy of the rumen culture. The amount of degradability and VFA yield within a short span was similar to other carboxylate platforms that have longer run times. This study shows that by virtue of faster rates and high extent of degradability, small scale alternatives to bioethanol such as rumen microbiomes and other natural fermenting microbiomes can be employed to enhance the feasibility of biofuels large-scale implementation.

Keywords: agricultural wastes, carboxylate platform, rumen microbiome, volatile fatty acids

Procedia PDF Downloads 105

Authors: Yuguang Gao, Mingtao Deng

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The integration of independently operating hospitals into a unified healthcare service system has become a strategic imperative in the pursuit of hospitals’ high-quality development. Central to the concept of group governance over such transformation, exemplified by a collaborative medical alliance, is the delineation of shared value, vision, and goals. Given the inherent disparity in capabilities among hospitals within the alliance, particularly in the treatment of different diseases characterized by Disease Related Groups (DRG) in terms of effectiveness, efficiency and resource utilization, this study aims to address the centralized decision-making of intra-hospital patient referral within the medical alliance to enhance the overall production and quality of service provided. We first introduce the notion of production utility, where a higher production utility for a hospital implies better performance in treating patients diagnosed with that specific DRG group of diseases. Then, a Discrete-Event Simulation (DES) framework is established for patient referral among hospitals, where patient flow modeling incorporates a queueing system with fixed capacities for each hospital. The simulation study begins with a two-member alliance. The pivotal strategy examined is a "whether-to-refer" decision triggered when the bed usage rate surpasses a predefined threshold for either hospital. Then, the decision encompasses referring patients to the other hospital based on DRG groups’ production utility differentials as well as bed availability. The objective is to maximize the total production utility of the alliance while minimizing patients’ average length of stay and turnover rate. Thus the parameter under scrutiny is the bed usage rate threshold, influencing the efficacy of the referral strategy. Extending the study to a three-member alliance, which could readily be generalized to multi-member alliances, we maintain the core setup while introducing an additional “which-to-refer" decision that involves referring patients with specific DRG groups to the member hospital according to their respective production utility rankings. The overarching goal remains consistent, for which the bed usage rate threshold is once again a focal point for analysis. For the two-member alliance scenario, our simulation results indicate that the optimal bed usage rate threshold hinges on the discrepancy in the number of beds between member hospitals, the distribution of DRG groups among incoming patients, and variations in production utilities across hospitals. Transitioning to the three-member alliance, we observe similar dependencies on these parameters. Additionally, it becomes evident that an imbalanced distribution of DRG diagnoses and further disparity in production utilities among member hospitals may lead to an increase in the turnover rate. In general, it was found that the intra-hospital referral mechanism enhances the overall production utility of the medical alliance compared to individual hospitals without partnership. Patients’ average length of stay is also reduced, showcasing the positive impact of the collaborative approach. However, the turnover rate exhibits variability based on parameter setups, particularly when patients are redirected within the alliance. In conclusion, the re-structuring of diagnostic disease groups within the medical alliance proves instrumental in improving overall healthcare service outcomes, providing a compelling rationale for the government's promotion of patient referrals within collaborative medical alliances.

Keywords: collaborative medical alliance, disease related group, patient referral, simulation

Procedia PDF Downloads 25

Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

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The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

Procedia PDF Downloads 68

Authors: D. Heinemann, S. Schramm, S. Knabner, D. Baumgarten

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Purpose: Applying solder paste to printed circuit boards (PCB) with stencils has been the method of choice over the past years. A new method uses a jet printer to deposit tiny droplets of solder paste through an ejector mechanism onto the board. This allows for more flexible PCB layouts with smaller components. Due to the viscosity of the solder paste, air blisters can be trapped in the cartridge. This can lead to missing solder joints or deviations in the applied solder volume. Therefore, a built-in and real-time inspection of the printing process is needed to minimize uncertainties and increase the efficiency of the process by immediate correction. The objective of the current study is the design of an optimal imaging system and the development of an automatic algorithm for the detection of applied solder joints from optical from the captured images. Methods: In a first approach, a camera module connected to a microcomputer and LED strips are employed to capture images of the printed circuit board under four different illuminations (white, red, green and blue). Subsequently, an improved system including a ring light, an objective lens, and a monochromatic camera was set up to acquire higher quality images. The obtained images can be divided into three main components: the PCB itself (i.e., the background), the reflections induced by unsoldered positions or screw holes and the solder joints. Non-uniform illumination is corrected by estimating the background using a morphological opening and subtraction from the input image. Image sharpening is applied in order to prevent error pixels in the subsequent segmentation. The intensity thresholds which divide the main components are obtained from the multimodal histogram using three probability density functions. Determining the intersections delivers proper thresholds for the segmentation. Remaining edge gradients produces small error areas which are removed by another morphological opening. For quantitative analysis of the segmentation results, the dice coefficient is used. Results: The obtained PCB images show a significant gradient in all RGB channels, resulting from ambient light. Using different lightings and color channels 12 images of a single PCB are available. A visual inspection and the investigation of 27 specific points show the best differentiation between those points using a red lighting and a green color channel. Estimating two thresholds from analyzing the multimodal histogram of the corrected images and using them for segmentation precisely extracts the solder joints. The comparison of the results to manually segmented images yield high sensitivity and specificity values. Analyzing the overall result delivers a Dice coefficient of 0.89 which varies for single object segmentations between 0.96 for a good segmented solder joints and 0.25 for single negative outliers. Conclusion: Our results demonstrate that the presented optical imaging system and the developed algorithm can robustly detect solder joints on printed circuit boards. Future work will comprise a modified lighting system which allows for more precise segmentation results using structure analysis.

Keywords: printed circuit board jet-printing, inspection, segmentation, solder paste detection

Procedia PDF Downloads 310

Authors: Nada Al-Qallaf, Aisha Al-Barood, Djamel Lekmine, Srinivasan Vedhapuri

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Kuwait Oil Company (KOC) under the supervision of Kuwait National Focal Point (KNFP) is planning to remediate 26 million (M) m3 of oil-contaminated soil in oil fields of Kuwait as a direct and indirect fallout of the Gulf War during 1990-1991. This project is funded by the United Nations Compensation Commission (UNCC) under the Kuwait Environmental Remediation Program (KERP). Oil-contamination of the soil occurred due to the destruction of the oil wells and spilled crude oil across the land surface and created ‘oil lakes’ in low lying land. Aerial fall-out from oil spray and combustion products from oil fires combined with the sand and gravel on the ground surface to form a layer of hardened ‘Tarcrete’. The unique fresh groundwater lenses present in the Raudhatain and Sabriya subsurface areas had been impacted by the discharge and/or spills of dissolved petroleum constituents. These fresh groundwater aquifers were used for drinking water purposes until 1990, prior to invasion. This has significantly damages altered the landscape, ecology and habitat of the flora and fauna and in Kuwait Desert. Under KERP, KOC is fully responsible for the planning and execution of the remediation and restoration projects in KOC oil fields. After the initial recommendation of UNCC to construct engineered landfills for containment and disposal of heavily contaminated soils, two landfills were constructed, one in North Kuwait and another in South East Kuwait of capacity 1.7 million m3 and 0.5 million m3 respectively. KOC further developed the Total Remediation Strategy in conjunction with KNFP and has obtained UNCC approval. The TRS comprises of elements such as Risk Based Approach (RBA), Bioremediation of low Contaminated Soil levels, Remediation Treatment Technologies, Sludge Disposal via Beneficial Recycling or Re-use and Engineered landfills for Containment of untreatable materials. Risk Based Assessment as a key component to avoid any unnecessary remedial works, where it can be demonstrated that human health and the environment are sufficiently protected in the absence of active remediation. This study demonstrates on the risks of tarcrete materials spread over areas 20 Km2 on the fresh Ground water lenses/catchment located beneath the Sabriyah and Raudhatain oil fields in North Kuwait. KOC’s primary objective is to provide justification of using RBA, to support a case with the Kuwait regulators to leave the tarcrete material in place, rather than seek to undertake large-scale removal and remediation. The large-scale coverage of the tarcrete in the oil fields and perception that the residual contamination associated with this source is present in an environmentally sensitive area essentially in ground water resource. As part of this assessment, conceptual site model (CSM) and complete risk-based and fate and transport modelling was carried out which includes derivation of site-specific assessment criteria (SSAC) and quantification of risk to identified waters resource receptors posed by tarcrete impacted areas. The outcome of this assessment was determined that the residual tarcrete deposits across the site area shall not create risks to fresh groundwater resources and the remedial action to remove and remediate the surficial tarcrete deposits is not warranted.

Keywords: conceptual site model, fresh groundwater, oil-contaminated soil, tarcrete, risk based assessment

Procedia PDF Downloads 152

Authors: Maria Areias, Diogo Simões, Ana Figueiredo, Anishur Rahman, Filipa Figueiredo, João Nunes

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It is expected that close to 7 billion people will live in urban areas by 2050. In order to improve the sustainability of the territories and its transition towards circular economy, it’s necessary to understand its metabolism and promote and guide the entrepreneurship answer. The study of a macroeconomic metabolism involves the quantification of the inputs, outputs and storage of energy, water, materials and wastes for an urban region. This quantification and analysis representing one opportunity for the promotion of green entrepreneurship. There are several methods to assess the environmental impacts of an urban territory, such as human and environmental risk assessment (HERA), life cycle assessment (LCA), ecological footprint assessment (EF), material flow analysis (MFA), physical input-output table (PIOT), ecological network analysis (ENA), multicriteria decision analysis (MCDA) among others. However, no consensus exists about which of those assessment methods are best to analyze the sustainability of these complex systems. Taking into account the weaknesses and needs identified, the CiiM - Circular Innovation Inter-Municipality project aims to define an uniform and globally accepted methodology through the integration of various methodologies and dynamic approaches to increase the efficiency of macroeconomic metabolisms and promoting entrepreneurship in a circular economy. The pilot territory considered in CiiM project has a total area of 969,428 ha, comprising a total of 897,256 inhabitants (about 41% of the population of the Center Region). The main economic activities in the pilot territory, which contribute to a gross domestic product of 14.4 billion euros, are: social support activities for the elderly; construction of buildings; road transport of goods, retailing in supermarkets and hypermarkets; mass production of other garments; inpatient health facilities; and the manufacture of other components and accessories for motor vehicles. The region's business network is mostly constituted of micro and small companies (similar to the Central Region of Portugal), with a total of 53,708 companies identified in the CIM Region of Coimbra (39 large companies), 28,146 in the CIM Viseu Dão Lafões (22 large companies) and 24,953 in CIM Beiras and Serra da Estrela (13 large companies). For the construction of the database was taking into account data available at the National Institute of Statistics (INE), General Directorate of Energy and Geology (DGEG), Eurostat, Pordata, Strategy and Planning Office (GEP), Portuguese Environment Agency (APA), Commission for Coordination and Regional Development (CCDR) and Inter-municipal Community (CIM), as well as dedicated databases. In addition to the collection of statistical data, it was necessary to identify and characterize the different stakeholder groups in the pilot territory that are relevant to the different metabolism components under analysis. The CIIM project also adds the potential of a Geographic Information System (GIS) so that it is be possible to obtain geospatial results of the territorial metabolisms (rural and urban) of the pilot region. This platform will be a powerful visualization tool of flows of products/services that occur within the region and will support the stakeholders, improving their circular performance and identifying new business ideas and symbiotic partnerships.

Keywords: circular economy tools, life cycle assessment macroeconomic metabolism, multicriteria decision analysis, decision support tools, circular entrepreneurship, industrial and regional symbiosis

Procedia PDF Downloads 67

Authors: Jessica Ho

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AI has helped government, businesses and industries transform the way they do things. AI is used in automating tasks to improve decision-making and efficiency. AI is embedded in sensors and used in automation to help save time and eliminate human errors in repetitive tasks. Today, we saw the growth in AI using the collection of vast amounts of data to forecast with greater accuracy, inform decision-making, adapt to changing market conditions and offer more personalised service based on consumer habits and preferences. Government around the world share the opportunity to leverage these disruptive technologies to improve productivity while reducing costs. In addition, these intelligent solutions can also help streamline government processes to deliver more seamless and intuitive user experiences for employees and citizens. This is a critical challenge for NSW Government as we are unable to determine the risk that is brought by the unprecedented pace of adoption of AI solutions in government. Government agencies must ensure that their use of AI complies with relevant laws and regulatory requirements, including those related to data privacy and security. Furthermore, there will always be ethical concerns surrounding the use of AI, such as the potential for bias, intellectual property rights and its impact on job security. Within NSW’s public sector, agencies are already testing AI for crowd control, infrastructure management, fraud compliance, public safety, transport, and police surveillance. Citizens are also attracted to the ease of use and accessibility of AI solutions without requiring specialised technical skills. This increased accessibility also comes with balancing a higher risk and exposure to the health and safety of citizens. On the other side, public agencies struggle with keeping up with this pace while minimising risks, but the low entry cost and open-source nature of generative AI led to a rapid increase in the development of AI powered apps organically – “There is an AI for That” in Government. Other challenges include the fact that there appeared to be no legislative provisions that expressly authorise the NSW Government to use an AI to make decision. On the global stage, there were too many actors in the regulatory space, and a sovereign response is needed to minimise multiplicity and regulatory burden. Therefore, traditional corporate risk and governance framework and regulation and legislation frameworks will need to be evaluated for AI unique challenges due to their rapidly evolving nature, ethical considerations, and heightened regulatory scrutiny impacting the safety of consumers and increased risks for Government. Creating an effective, efficient NSW Government’s governance regime, adapted to the range of different approaches to the applications of AI, is not a mere matter of overcoming technical challenges. Technologies have a wide range of social effects on our surroundings and behaviours. There is compelling evidence to show that Australia's sustained social and economic advancement depends on AI's ability to spur economic growth, boost productivity, and address a wide range of societal and political issues. AI may also inflict significant damage. If such harm is not addressed, the public's confidence in this kind of innovation will be weakened. This paper suggests several AI regulatory approaches for consideration that is forward-looking and agile while simultaneously fostering innovation and human rights. The anticipated outcome is to ensure that NSW Government matches the rising levels of innovation in AI technologies with the appropriate and balanced innovation in AI governance.

Keywords: artificial inteligence, machine learning, rules, governance, government

Procedia PDF Downloads 43

Authors: Ayla Hoeta

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In a post-colonial Aotearoa New Zealand, solutions by rangatahi (youth) for rangatahi are essential as is civic participation and building economic agency in an increasingly tough economic climate. Upsouth was an online community crowdsourcing platform developed by The Southern Initiative, in collaboration with Itsnoon that provides rangatahi and whānau (family) a safe space to share lived experience, thoughts and ideas about local kaupapa (issues/topics) of importance to them. The target participants were Māori indigenous peoples and Pacifica groups, aged 14 - 21 years. In the Aotearoa New Zealand context, this participant group is not likely to engage in traditional consultation processes despite being an essential constituent in helping shape better local communities, whānau and futures. The Upsouth platform was active for two years from 2018-2019 where it completed 42 callups with 4300+ participants. The web platform collates the ideas, voices, feedback, and content of users around a callup that has been commissioned by a sponsor, such as Auckland Council, Z Energy or Auckland Transport. A callup may be about a pressing challenge in a community such as climate change, a new housing development, homelessness etc. Each callup was funded by the sponsor with Upsouths main point of difference being that participants are given koha (money donation) through digital wallets for their ideas. Depending on the quality of what participants upload, the koha varies between small micropayments and larger payments. This encouraged participants to develop creative and critical thinking - upskilling for future focussed jobs, enterprise and democratic skills while earning pocket money at the same time. Upsouth enables youth-led action and voice, and empowers them to be a part of a reciprocal and creative economy. Rangatahi are encouraged to express themselves culturally, creatively, freely and in a way they are free to choose - for example, spoken word, song, dance, video, drawings, and/or poems. This challenges and changes what is considered acceptable as community engagement feedback by the local government. Many traditional engagement platforms are not as consultative, do not accept diverse types of feedback, nor incentivise this valuable expression of feedback. Upsouth is also empowering for rangatahi, since it allows them the opportunity to express their opinions directly to the government. Upsouth gained national and international recognition for the way it engages with youth: winning the Supreme Award and the Accessibility and Transparency Award at Auckland Council’s 2018 Engagement Awards, becoming a finalist in the 2018 Digital Equity and Accessibility category of International Data Corporation’s Smart City Asia and Pacific Awards. This paper will fully contextualize the challenges of rangatahi and whānau civic engagement in Aotearoa New Zealand and then present a reflective case study of the Upsouth project, with examples from some of the callups. This is intended to form part of the Divided Cities 22 conference New Ground sub-theme as a critical reflection on a design intervention, which was conceived and implemented by the lead author to overcome the post-colonial divisions of Māori, Pacifica and minority ethnic rangatahi in Aotearoa New Zealand.

Keywords: rangatahi, youth empowerment, civic engagement, enabling, relating, digital platform, participation

Procedia PDF Downloads 41

Authors: Jaco Oosthuizen, Nerina C. Van Der Merwe

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The National Health Laboratory Services in Bloemfontien has been the diagnostic testing facility for 1830 patients for familial breast cancer since 1997. From the cohort, 540 were comprehensively screened using High-Resolution Melting Analysis or Next Generation Sequencing for the presence of point mutations and/or indels. Approximately 90% of these patients stil remain undiagnosed as they are BRCA1/2 negative. Multiplex ligation-dependent probe amplification was initially added to screen for copy number variation detection, but with the introduction of next generation sequencing in 2017, was substituted and is currently used as a confirmation assay. The aim was to investigate the viability of utilizing internationally designed copy number variation detection assays based on mostly European/Caucasian genomic data for use within a South African context. The multiplex ligation-dependent probe amplification technique is based on the hybridization and subsequent ligation of multiple probes to a targeted exon. The ligated probes are amplified using conventional polymerase chain reaction, followed by fragment analysis by means of capillary electrophoresis. The experimental design of the assay was performed according to the guidelines of MRC-Holland. For BRCA1 (P002-D1) and BRCA2 (P045-B3), both multiplex assays were validated, and results were confirmed using a secondary probe set for each gene. The next generation sequencing technique is based on target amplification via multiplex polymerase chain reaction, where after the amplicons are sequenced parallel on a semiconductor chip. Amplified read counts are visualized as relative copy numbers to determine the median of the absolute values of all pairwise differences. Various experimental parameters such as DNA quality, quantity, and signal intensity or read depth were verified using positive and negative patients previously tested internationally. DNA quality and quantity proved to be the critical factors during the verification of both assays. The quantity influenced the relative copy number frequency directly whereas the quality of the DNA and its salt concentration influenced denaturation consistency in both assays. Multiplex ligation-dependent probe amplification produced false positives due to ligation failure when ligation was inhibited due to a variant present within the ligation site. Next generation sequencing produced false positives due to read dropout when primer sequences did not meet optimal multiplex binding kinetics due to population variants in the primer binding site. The analytical sensitivity and specificity for the South African population have been proven. Verification resulted in repeatable reactions with regards to the detection of relative copy number differences. Both multiplex ligation-dependent probe amplification and next generation sequencing multiplex panels need to be optimized to accommodate South African polymorphisms present within the genetically diverse ethnic groups to reduce the false copy number variation positive rate and increase performance efficiency.

Keywords: familial breast cancer, multiplex ligation-dependent probe amplification, next generation sequencing, South Africa

Procedia PDF Downloads 197

Authors: V. Cesaroni, C. Girometta, A. Bernicchia, M. Brusoni, F. Corana, R. M. Baiguera, C. M. Cusaro, M. L. Guglielminetti, B. Mannucci, H. Kawagishi, C. Perini, A. M. Picco, P. Rossi, E. Salerni, E. Savino

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Fungi of the genus Hericium contain various compounds with antibacterial activity, cytotoxic effect on cancer cells and bioactive molecules. Some of the active metabolites stimulate the synthesis of the Nerve Growth Factor (NGF). Recently, the effect of dietary supplement based on Hericium erinaceus on recognition memory and on hippocampal mossy fiber-CA3 neurotransmission was published. The aim of this study was to investigate the presence of Hericium species on Italian territory in order to isolate the strains for further studies and applications. The first step was to collect Hericium sporophores in Tuscany: H. alpestre Pers., H. coralloides (Scop.) Pers. and H. erinaceus (Bull.) Pers. were the species present. The strains of H. alpestre (H.a.1), H. coralloides (H.c.1) and H. erinaceus (H.e.1 & H.e.2) have been isolated in pure culture and preserved in the collection of the University of Pavia (MicUNIPV). The DNA sequences obtained from the strains were compared to other sequences found in international databases. Therefore, it was possible to construct a phylogenetic tree that highlights the clear separation in clades of the sequences and the molecular identification of our strains with the species of Hericium considered. The second step was to cultivate indoor and outdoor H. erinaceus in order to obtain as many sporophores as possible for further chemical analysis. All the procedures for H. erinaceus cultivation have been followed. Among the available recipes for indoor H. erinaceus cultivation, it was used a substrate formulation contained 70% oak sawdust, 20% rice bran, 10% wheat straw, 1% CaCO3 and 1% sucrose. The bioactive compounds present in the mycelia and in the sporophores of H. erinaceus were chemically analyzed in collaboration with the Centro Grandi Strumenti of the University of Pavia using high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ESI-MS/MS). The materials to be analyzed were previously freeze-dried and then extracted with an alcoholic procedure. Preliminary chromatographic analysis revealed the presence of potentially bioactive and structurally different secondary metabolites such as polysaccharides, erinacins, ericenones, steroids and other terpenoids. Ericenones C and D (in sporophores) and erinacin A (in mycelium) have been identified by comparison with the respective standards. These molecules are known to have effects on the Central Nervous System (CNS) cells, which is the main objective of our studies. Thanks to the high sensitivity in the detection of bioactive compounds of H. erinaceus, it will be possible to use the To obtain lyophilized mycelium and the respective culture broth, 4 small pieces (about 5 mm2) of the respective H.e.1 or H.c.1 strains, taken from the margin of growing cultures (MEA), were inoculated into 1 liter of 2% ME (malt extract, Biokar Diagnostics). The static liquid cultures were kept at 24 °C in the dark chamber and fungi grew for one month. 10 replicates for each strain have been done. The method proposed as an analytical screening protocol to determine the optimal growth conditions of the fungus and to improve the production chain of H. erinaceus. These results encourage to carry out chemical analyzes also on H. alpestre and H. coralloides in order to evaluate the presence of bioactive compounds in these two species.

Keywords: Hericium species, Hercium erinaceus bioactive compounds, medicinal mushrooms, mushroom cultivation

Procedia PDF Downloads 116

Authors: Vladislav Grigorovitch, Marina Grigorovitch, David Pearlmutter, Erez Gal

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The concept of “electric island” is involved with achieving the balance between the self-power generation ability of each educational institution and energy consumption demand. Photo-Voltaic (PV) solar system installed on the roofs of educational buildings is a common way to absorb the available solar energy and generate electricity for self-consumption and even for returning to the grid. The main objective of this research is to develop and implement an “electric island” monitoring infrastructure for promoting energy efficiency in educational buildings. A microscale monitoring methodology will be developed to provide a platform to estimate energy consumption performance classified by rooms and subspaces rather than the more common macroscale monitoring of the whole building. The monitoring platform will be established on the experimental sites, enabling an estimation and further analysis of the variety of environmental and physical conditions. For each building, separate measurement configurations will be applied taking into account the specific requirements, restrictions, location and infrastructure issues. The direct results of the measurements will be analyzed to provide deeper understanding of the impact of environmental conditions and sustainability construction standards, not only on the energy demand of public building, but also on the energy consumption habits of the children that study in those schools and the educational and administrative staff that is responsible for providing the thermal comfort conditions and healthy studying atmosphere for the children. A monitoring methodology being developed in this research is providing online access to real-time data of Interferential Therapy (IFTs) from any mobile phone or computer by simply browsing the dedicated website, providing powerful tools for policy makers for better decision making while developing PV production infrastructure to achieve “electric islands” in educational buildings. A detailed measurement configuration was technically designed based on the specific conditions and restriction of each of the pilot buildings. A monitoring and analysis methodology includes a large variety of environmental parameters inside and outside the schools to investigate the impact of environmental conditions both on the energy performance of the school and educational abilities of the children. Indoor measurements are mandatory to acquire the energy consumption data, temperature, humidity, carbon dioxide and other air quality conditions in different parts of the building. In addition to that, we aim to study the awareness of the users to the energy consideration and thus the impact on their energy consumption habits. The monitoring of outdoor conditions is vital for proper design of the off-grid energy supply system and validation of its sufficient capacity. The suggested outcomes of this research include: 1. both experimental sites are designed to have PV production and storage capabilities; 2. Developing an online information feedback platform. The platform will provide consumer dedicated information to academic researchers, municipality officials and educational staff and students; 3. Designing an environmental work path for educational staff regarding optimal conditions and efficient hours for operating air conditioning, natural ventilation, closing of blinds, etc.

Keywords: sustainability, electric island, IOT, smart building

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Authors: Joel A. Gordon, Nazmiye Balta-Ozkan, Seyed Ali Nabavi

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Hydrogen-fueled technologies are rapidly advancing as a critical component of the low-carbon energy transition. In countries historically reliant on natural gas for home heating, such as the UK, hydrogen may prove fundamental for decarbonizing the residential sector, alongside other technologies such as heat pumps and district heat networks. While the UK government is set to take a long-term policy decision on the role of domestic hydrogen by 2026, there are considerable uncertainties regarding consumer preferences for ‘hydrogen homes’ (i.e., hydrogen-fueled appliances for space heating, hot water, and cooking. In comparison to other hydrogen energy technologies, such as road transport applications, to date, few studies have engaged with the social acceptance aspects of the domestic hydrogen transition, resulting in a stark knowledge deficit and pronounced risk to policymaking efforts. In response, this study aims to safeguard against undesirable policy measures by revealing the underlying relationships between the factors of domestic hydrogen acceptance and their respective dimensions: attitudinal, socio-political, community, market, and behavioral acceptance. The study employs an online survey (n=~2100) to gauge how different UK householders perceive the proposition of switching from natural gas to hydrogen-fueled appliances. In addition to accounting for housing characteristics (i.e., housing tenure, property type and number of occupants per dwelling) and several other socio-structural variables (e.g. age, gender, and location), the study explores the impacts of consumer heterogeneity on hydrogen acceptance by recruiting respondents from across five distinct groups: (1) fuel poor householders, (2) technology engaged householders, (3) environmentally engaged householders, (4) technology and environmentally engaged householders, and (5) a baseline group (n=~700) which filters out each of the smaller targeted groups (n=~350). This research design reflects the notion that supporting a socially fair and efficient transition to hydrogen will require parallel engagement with potential early adopters and demographic groups impacted by fuel poverty while also accounting strongly for public attitudes towards net zero. Employing a second-order multigroup confirmatory factor analysis (CFA) in Mplus, the proposed hydrogen acceptance model is tested to fit the data through a partial least squares (PLS) approach. In addition to testing differences between and within groups, the findings provide policymakers with critical insights regarding the significance of knowledge and awareness, safety perceptions, perceived community impacts, cost factors, and trust in key actors and stakeholders as potential explanatory factors of hydrogen acceptance. Preliminary results suggest that knowledge and awareness of hydrogen are positively associated with support for domestic hydrogen at the household, community, and national levels. However, with the exception of technology and/or environmentally engaged citizens, much of the population remains unfamiliar with hydrogen and somewhat skeptical of its application in homes. Knowledge and awareness present as critical to facilitating positive safety perceptions, alongside higher levels of trust and more favorable expectations for community benefits, appliance performance, and potential cost savings. Based on these preliminary findings, policymakers should be put on red alert about diffusing hydrogen into the public consciousness in alignment with energy security, fuel poverty, and net-zero agendas.

Keywords: hydrogen homes, social acceptance, consumer heterogeneity, heat decarbonization

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Authors: Malla Vijayeta, Y. Vijaya Kumar, N. Ramakrishna Raju, K. Satyanarayana

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Global AEC (architecture, engineering, and construction) industry has been coined as one of the most resistive domains in embracing technology. Although this digital era has been inundated with software tools like CAD, STADD, CANDY, Microsoft Project, Primavera etc. the key stakeholders have been working in siloes and processes remain fragmented. Unlike the yesteryears’ simpler project delivery methods, the current projects are of fast-track, complex, risky, multidisciplinary, stakeholder’s influential, statutorily regulative etc. pose extensive bottlenecks in preventing timely completion of projects. At this juncture, a paradigm shift surfaced in construction industry, and Building Information Modeling, aka BIM, has been a panacea to bolster the multidisciplinary teams’ cooperative and collaborative work leading to productive, sustainable and leaner project outcome. Building information modeling has been integrative, stakeholder engaging and centralized approach in providing a common platform of communication. A common misconception that BIM can be used for building/high rise projects in Indian Construction Industry, while this paper discusses of the implementation of BIM processes/methodologies in water and waste water industry. It elucidates about BIM 4D planning and constructability reviews of a Sewage Treatment Plant in India. Conventional construction planning and logistics management involves a blend of experience coupled with imagination. Even though the excerpts or judgments or lessons learnt gained from veterans might be predictive and helpful, but the uncertainty factor persists. This paper shall delve about the case study of real time implementation of BIM 4D planning protocols for one of the Sewage Treatment Plant of Dravyavati River Rejuvenation Project in India and develops a Time Liner to identify logistics planning and clash detection. With this BIM processes, we shall find that there will be significant reduction of duplication of tasks and reworks. Also another benefit achieved will be better visualization and workarounds during conception stage and enables for early involvement of the stakeholders in the Project Life cycle of Sewage Treatment Plant construction. Moreover, we have also taken an opinion poll of the benefits accrued utilizing BIM processes versus traditional paper based communication like 2D and 3D CAD tools. Thus this paper concludes with BIM framework for Sewage Treatment Plant construction which will achieve optimal construction co-ordination advantages like 4D construction sequencing, interference checking, clash detection checking and resolutions by primary engagement of all key stakeholders thereby identifying potential risks and subsequent creation of risk response strategies. However, certain hiccups like hesitancy in adoption of BIM technology by naïve users and availability of proficient BIM trainers in India poses a phenomenal impediment. Hence the nurture of BIM processes from conception, construction and till commissioning, operation and maintenance along with deconstruction of a project’s life cycle is highly essential for Indian Construction Industry in this digital era.

Keywords: integrated BIM workflow, 4D planning with BIM, building information modeling, clash detection and visualization, constructability reviews, project life cycle

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Authors: Ariunaa.S., Javzandulam E., Chimegsaikhan S., Altantsetseg B., Oyungerel S., Bat-Erdene T., Naranbaatar S., Otgonbayar B., Suvdaa N., Tumenbayar B.

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Introduction: Prenatal stress has been linked to heightened allergy sensitivity in offspring. However, there is a notable absence of research on the mesovarium structure of offspring born from mothers subjected to cold stress during pregnancy. Understanding the impact of maternal cold stress on the mesovarium structure could provide valuable insights into reproductive health outcomes in offspring. Objective: This study aims to investigate structural changes in the mesovarium of offspring born from cold-stress affected rats. Material and Methods: 20 female Westar rats weighing around 200g were chosen and evenly divided into four containers; then, 2-3 male rats were introduced to each container. The Papanicolaou method was used to estimate the spermatozoa and estrus period from vaginal swabs taken from female rats at 8:00 a.m. Female rats examined with the presence of spermatozoa during the estrous phase of the estrous cycle are defined as pregnant. Pregnant rats are divided into experimental and control groups. The experimental group was stressed using the model of severe and chronic cold stress for 30 days. They were exposed to cold stress for 3 hours each morning between 8:00 and 11:00 o’clock at a temperature of minus 15 degrees Celsius. The control group was kept under normal laboratory conditions. Newborn female rats from both experimental and control groups were selected. At 2 months of age, rats were euthanized by decapitation, and their mesovaria were collected. Tissues were fixed in 4% formalin, embedded in paraffin, and sectioned into 5μm thick slices. The sections were stained with H&E and digitized by digital microscope. The area of brown fat and inflammatory infiltrations were quantified using Image J software. The blood cortisol levels were measured using ELISA. Data are expressed as the mean ± standard error of the mean (SEM). The Mann-Whitney test was used to compare the two groups. All analyses were performed using Prism (GraphPad Software). A p-value of < 0.05 was considered statistically significant. Result: Offspring born from stressed mothers exhibited significant physiological differences compared to the control group. Specifically, the body weight of offspring from stressed mothers was significantly lower than the control group (p=0.0002). Conversely, the cortisol level in offspring from stressed mothers was significantly higher (p=0.0446). Offspring born from stressed mothers showed a statistically significant increase in brown fat area compared to the control group (p=0.01). Additionally, offspring from stressed mothers had a significantly higher number of inflammatory infiltrates in their mesovarium compared to the control group (p<0.047). These results indicate the profound impact of maternal stress on offspring physiology, affecting body weight, stress hormone levels, metabolic characteristics, and inflammatory responses. Conclusion: Exposure to cold stress during pregnancy has significant repercussions on offspring physiology. Our findings demonstrate that cold stress exposure leads to increased blood cortisol levels, brown fat accumulation, and inflammatory cell infiltration in offspring. These results underscore the profound impact of maternal stress on offspring health and highlight the importance of mitigating environmental stressors during pregnancy to promote optimal offspring outcomes.

Keywords: brown fat, cold stress during pregnancy, inflammation, mesovarium

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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: Mariam Jaber, Abdullah Yahya, Mohammad Alkhedher

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The shift toward sustainable energy solutions increasingly focuses on hydrogen, recognized for its potential as a clean energy carrier. Despite its benefits, hydrogen storage poses significant challenges, primarily due to its low energy density and high volatility. Among the various solutions, pressure vessels designed for hydrogen storage range from Type I to Type V, each tailored for specific needs and benefits. Notably, Type V vessels, with their all-composite, liner-less design, significantly reduce weight and costs while optimizing space and decreasing maintenance demands. This study focuses on optimizing Type V hydrogen storage tanks by examining how different shapes affect performance in drop tests—a crucial aspect of achieving ISO 15869 certification. This certification ensures that if a tank is dropped, it will fail in a controlled manner, ideally by leaking before bursting. While cylindrical vessels are predominant in mobile applications due to their manufacturability and efficient use of space, spherical vessels offer superior stress distribution and require significantly less material thickness for the same pressure tolerance, making them advantageous for high-pressure scenarios. However, spherical tanks are less efficient in terms of packing and more complex to manufacture. Additionally, this study introduces toroidal vessels to assess their performance relative to the more traditional shapes, noting that the toroidal shape offers a more space-efficient option. The research evaluates how different shapes—spherical, cylindrical, and toroidal—affect drop test outcomes when combined with various composite materials and layup configurations. The ultimate goal is to identify optimal vessel geometries that enhance the safety and efficiency of hydrogen storage systems. For our materials, we selected high-performance composites such as Carbon T-700/Epoxy, Kevlar/Epoxy, E-Glass Fiber/Epoxy, and Basalt/Epoxy, configured in various orientations like [0,90]s, [45,-45]s, and [54,-54]. Our tests involved dropping tanks from different angles—horizontal, vertical, and 45 degrees—with an internal pressure of 35 MPa to replicate real-world scenarios as closely as possible. We used finite element analysis and first-order shear deformation theory, conducting tests with the Abaqus Explicit Dynamics software, which is ideal for handling the quick, intense stresses of an impact. The results from these simulations will provide valuable insights into how different designs and materials can enhance the durability and safety of hydrogen storage tanks. Our findings aim to guide future designs, making them more effective at withstanding impacts and safer overall. Ultimately, this research will contribute to the broader field of lightweight composite materials and polymers, advancing more innovative and practical approaches to hydrogen storage. By refining how we design these tanks, we are moving toward more reliable and economically feasible hydrogen storage solutions, further emphasizing hydrogen's role in the landscape of sustainable energy carriers.

Keywords: hydrogen storage, drop test, composite materials, type V tanks, finite element analysis

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Authors: Parth Gupta, Ujjawal Singh, Shashank Kumar, Mansi Chandra, Arnab Sarkar

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Microfluidic devices have emerged as indispensable tools across various scientific disciplines, offering precise control and manipulation of fluids at the microscale. Their efficacy in flow-based research, spanning engineering, chemistry, and biology, relies heavily on the geometric design of microfluidic channels. This work introduces a novel approach to optimise these channels through Response Surface Methodology (RSM), departing from the conventional practice of addressing one parameter at a time. Traditionally, optimising microfluidic channels involved isolated adjustments to individual parameters, limiting the comprehensive understanding of their combined effects. In contrast, our approach considers the simultaneous impact of multiple parameters, employing RSM to efficiently explore the complex design space. The outcome is an innovative microfluidic channel that consumes an optimal sample volume and minimises flow time, enhancing overall efficiency. The relevance of geometric parameter optimization in microfluidic channels extends significantly in biomedical engineering. The flow characteristics of porous materials within these channels depend on many factors, including fluid viscosity, environmental conditions (such as temperature and humidity), and specific design parameters like sample volume, channel width, channel length, and substrate porosity. This intricate interplay directly influences the performance and efficacy of microfluidic devices, which, if not optimized, can lead to increased costs and errors in disease testing and analysis. In the context of biomedical applications, the proposed approach addresses the critical need for precision in fluid flow. it mitigate manufacturing costs associated with trial-and-error methodologies by optimising multiple geometric parameters concurrently. The resulting microfluidic channels offer enhanced performance and contribute to a streamlined, cost-effective process for testing and analyzing diseases. A key highlight of our methodology is its consideration of the interconnected nature of geometric parameters. For instance, the volume of the sample, when optimized alongside channel width, length, and substrate porosity, creates a synergistic effect that minimizes errors and maximizes efficiency. This holistic optimization approach ensures that microfluidic devices operate at their peak performance, delivering reliable results in disease testing. A key highlight of our methodology is its consideration of the interconnected nature of geometric parameters. For instance, the volume of the sample, when optimized alongside channel width, length, and substrate porosity, creates a synergistic effect that minimizes errors and maximizes efficiency. This holistic optimization approach ensures that microfluidic devices operate at their peak performance, delivering reliable results in disease testing. A key highlight of our methodology is its consideration of the interconnected nature of geometric parameters. For instance, the volume of the sample, when optimized alongside channel width, length, and substrate porosity, creates a synergistic effect that minimizes errors and maximizes efficiency. This holistic optimization approach ensures that microfluidic devices operate at their peak performance, delivering reliable results in disease testing.

Keywords: microfluidic device, minitab, statistical optimization, response surface methodology

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