Search results for: artificial stock markets

Authors: N. Tumenbayeva

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At the present time in Kazakhstan, the area for saxaul growing is strongly depopulateddue to anthropogenic and other factors. To prevent further reduction of natural haloxylon forest area their artificial crops are offered. Seed germination and survival of young plants in such haloxylon crops are very low. Insects, as one of the most important nutrient factors have appreciable effect on seed germination and saxaul productivity at the all stages of its formation. Insects, feeding on leaves, flowers, seeds and developing inside the trunk, branches, twigs, roots have a change in its formation and influence on the lifespan of saxaul. Representatives of Lepidoptera troop (Lepidopteraare the most harmful pests forsaxaul. As a result of our research we have identified 15 species of Lepidoptera living on haloxylon which display very different cycles and different types of food relations. It allows them to inhabit a variety of habitats, and feeding on various parts of saxaul. Some of them cause significant and sometimes very heavy damage for saxaul. There are 17identified species of Lepidoptera from the Coleophoridaefamily - 1, Gelechidae - 5, Pyralidae - 4, Noctuidae - 4, Lymantridae- 1, Cossidae - 2 species. At the same time we found 8 species for the first time, which have not been mentioned in the literature before. According to food specialization they are divided into monophages (2 types), oligophages (6 species) and polyphages (3 species). By affinity to plant parts, leaves and seeds are fed by 8 species, shoots by 1 specie, scions by 5 species, flowers, scions, seeds by 1, and 2species damage the roots and trunks. In whole installed seasonal groups of Lepidoptera - saxaul pests in the desert area, confined to the certain parts of the year, as well as certain parts of the plant for feeding. Harmfulness, depending on their activity appear during the growing season is also different.

Keywords: saxaul, Lepidoptera, insecta, haloxylon

Procedia PDF Downloads 321

Authors: Modi M. Ahmed, Noor Al-Dousari, Ali M. Al-Dousari

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Cornulaca aucheri is an annual herb consider as disturbance indicator currently visible and widely distributed in disturbed lands in Liyah area. Such area is suffered from severe land degradation due to multiple interacting factors such as, overgrazing, gravel and sand quarrying, military activities and natural process. The restoration program is applied after refilled quarries sites and levelled the surface irregularities in order to rehabilitate the natural vegetation and wildlife to its original shape. During the past 10 years of rehabilitation, noticeable greenery healthy cover of Cornulaca sp. are shown specially around artificial lake and playas. The existence of such species in high density it means that restoration program has succeeded and transit from bare ground state to Cornulaca and annual forb state. This state is lower state of Range State Transition Succession model, but it is better than bare soil. Cornulaca spp is native desert plant grows in arid conditions on sandy, stony ground, near oasis, on sand dunes and in sandy depressions. The sheep and goats are repulsive of it. Despite its spiny leaves, it provides good grazing for camels and is said to increase the milk supply produced by lactating females. It is about 80 cm tall and has stems that branched from the base with new faster greenery growth in the summer. It shows good environmental potential to be managed as natural types used for the restoration of degraded lands in desert areas.

Keywords: land degradation, range state transition succession model, rehabilitation, restoration program

Procedia PDF Downloads 362

Authors: Usharani Rathinam

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Although policies directing inclusion of marginalized were in effect, majority of chronically impoverished in India belonged to schedule caste and schedule tribes. Also, taking into account that poverty is gendered; destitute women belonged to lower social order whose need is not largely highlighted at policy level. This paper discusses on social relations poverty which highlights on how social order that existed structurally in the society can perpetuate chronic poverty, followed by a critical review on social inclusion policies of India, its merits and demerits in addressing chronic poverty. Multiple case study design is utilized to address this concern in four districts of India; Jhansi, Tikamgarh, Cuddalore and Anantapur. These four districts were selected by purposive sampling based on the criteria; the district should either be categorized as a backward district or should have a history of high poverty rate. Qualitative methods including eighty in-depth interviews, six focus group discussions, six social mapping procedures and three key informant interviews were conducted in 2011, at each of the locations. Analysis of the data revealed that irrespective of gender, schedule castes and schedule tribe participants were found to be chronically poor in all districts. Caste based discrimination is exhibited at both micro and macro levels; village and institutional levels. At village level, lower caste respondents had lesser access to public resources. Also, within institutional settings, due to confiscation, unequal access to resources is noticed, especially in fund distribution. This study found that half of the budget intended for schedule caste and schedule tribes were confiscated by upper caste administrative staffs. This implies that power based on social hierarchy marginalize lower caste participants from accessing better economic, social, and political benefits, that had led them to suffer long term poverty. This study also explored the traditional ties between caste, social structure and bonded labour as a cause of long-term poverty. Though equal access is being emphasized in constitutional rights, issues at micro level have not been reflected in formulation of these rights. Therefore, it is significant for a policy to consider the structural complexity and then focus on issues such as equal distribution of assets and infrastructural facilities that will reduce exclusion and foster long-term security in areas such as employment, markets and public distribution.

Keywords: caste, inclusion policies, India, social order

Procedia PDF Downloads 204

Authors: Yassine Bouzouita, Oumaima Belghith, Cyrine Zitoun, Charles Bonneau

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Performance measurement framework (PMF) is an essential tool in any organization to assess the performance of its processes. It guides businesses to stay on track with their objectives and benchmark themselves from the market. With the growing trend of the digital transformation of business processes, led by innovations in artificial intelligence (AI) & Big Data applications, developing a mature system capable of capturing the impact of digital solutions across different industries became a necessity. Based on the conducted research, no such system has been developed in academia nor the industry. In this context, this paper covers a variety of methodologies on performance measurement, overviews the major AI and big data applications in the banking sector, and covers an exhaustive list of relevant metrics. Consequently, this paper is of interest to both researchers and practitioners. From an academic perspective, it offers a comparative analysis of the reviewed performance measurement frameworks. From an industry perspective, it offers exhaustive research, from market leaders, of the major applications of AI and Big Data technologies, across the different departments of an organization. Moreover, it suggests a standardized classification model with a well-defined structure of intelligent digital solutions. The aforementioned classification is mapped to a centralized library that contains an indexed collection of potential metrics for each application. This library is arranged in a manner that facilitates the rapid search and retrieval of relevant metrics. This proposed framework is meant to guide professionals in identifying the most appropriate AI and big data applications that should be adopted. Furthermore, it will help them meet their business objectives through understanding the potential impact of such solutions on the entire organization.

Keywords: AI and Big Data applications, impact assessment, metrics, performance measurement

Procedia PDF Downloads 194

Authors: Gloria Pinilla, Jose Manuel Baena, Patricia Gálvez-Martín, Juan Antonio Marchad

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Cartilage is a dense connective tissue with limited self-repair properties. Currently, the therapeutic use of autologous or allogenic chondrocytes makes up an alternative therapy to the pharmacological treatment. The design of a bioprinted 3D cartilage with chondrocytes and biodegradable biomaterials offers a new therapeutic alternative able of bridging the limitations of current therapies in the field. We have developed an enhanced printing processes-Injection Volume Filling (IVF) to increase the viability and survival of the cells when working with high-temperature thermoplastics without the limitation of the scaffold geometry in contact with cells. We have demonstrated the viability of the printing process using chondrocytes for cartilage regeneration. This development will accelerate the clinical uptake of the technology and overcomes the current limitation when using thermoplastics as scaffolds. An alginate-based hydrogel combined with human chondrocytes (isolated from osteoarthritis patients) was formulated as bioink-A and the polylactic acid as bioink-B. The bioprinting process was carried out with the REGEMAT V1 bioprinter (Regemat 3D, Granada-Spain) through a IVF. The printing capacity of the bioprinting plus the viability and cell proliferation of bioprinted chondrociytes was evaluated after five weeks by confocal microscopy and Alamar Blue Assay (Biorad). Results showed that the IVF process does not decrease the cell viability of the chondrocytes during the printing process as the cells do not have contact with the thermoplastic at elevated temperatures. The viability and cellular proliferation of the bioprinted artificial 3D cartilage increased after 5 weeks. In conclusion, this study demonstrates the potential use of Regemat V1 for 3D bioprinting of cartilage and the viability of bioprinted chondrocytes in the scaffolds for application in regenerative medicine.

Keywords: cartilage regeneration, bioprinting, bioink, scaffold, chondrocyte

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Authors: Luay Jum'a

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Many challenges are facing third-party logistics (3PL) providers in the domestic and global markets which create a volatile decision making environment. All these challenges such as managing changes in consumer behaviour, demanding expectations from customers and time compressions have turned into complex problems for 3PL providers. Since the movement towards increased outsourcing outpaces movement towards insourcing, the need to achieve a competitive advantage over competitors in 3PL market increases. This trend continues to grow over the years and as a result, areas of strengths and improvements are highlighted through the analysis of the LSQ factors that lead to B2B customers’ satisfaction which become a priority for 3PL companies. Consequently, 3PL companies are increasingly focusing on the most important issues from the perspective of their customers and relying more on this value of information in making their managerial decisions. Therefore, this study is concerned with providing guidance for improving logistics service quality (LSQ) levels in the context of 3PL industry in Jordan. The study focused on the most important factors in LSQ and used a managerial tool that guides 3PL companies in making LSQ improvements based on a quadrant analysis of two main dimensions: LSQ declared importance and LSQ inferred importance. Although, a considerable amount of research has been conducted to investigate the relationship between logistics service quality (LSQ) and customer satisfaction, there remains a lack of developing managerial tools to aid in the process of LSQ improvement decision-making. Moreover, the main advantage for the companies to use 3PL service providers as a trend is due to the realised percentage of cost reduction on the total cost of logistics operations and the incremental improvement in customer service. In this regard, having a managerial tool that help 3PL service providers in managing the LSQ factors portfolio effectively and efficiently would be a great investment for service providers. One way of suggesting LSQ improvement actions for 3PL service providers is via the adoption of analysis tools that perform attribute categorisation such as Importance–Performance matrix. In mind of the above, it can be stated that the use of quadrant analysis will provide a valuable opportunity for 3PL service providers to identify improvement opportunities as customer service attributes or factors importance are identified in two different techniques that complete each other. Moreover, the data were collected through conducting a survey and 293 questionnaires were returned from business-to-business (B2B) customers of 3PL companies in Jordan. The results showed that the LSQ factors vary in their importance and 3PL companies should focus on some LSQ factors more than other factors. Moreover, ordering procedures, timeliness/responsiveness LSQ factors considered being crucial in 3PL businesses and therefore they need to have more focus and development by 3PL service providers in the Jordanian market.

Keywords: logistics service quality, managerial decisions, quadrant analysis, third party logistics service provider

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

Procedia PDF Downloads 153

Authors: Vasile Caunii, Andreea D. Serban, Mihaela Ivancia

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The paper presents a new approach in terms of the circular economy of technologies for feeding and aeration of accumulations and water basins for fish farming and aquaculture. Because fish is and will be one of the main foods on the planet, the use of bio-eco-technologies is a priority for all producers. The technologies proposed in the paper want to reduce by a substantial percentage the costs of operation of ponds and water accumulation, using non-polluting technologies with minimal impact on the environment. The paper proposes two innovative, intelligent systems, fully automated that use a common platform, completely eco-friendly. One system is intended to aerate the water of the fish pond, and the second is intended to feed the fish by dispersing an optimal amount of fodder, depending on population size, age and habits. Both systems use a floating platform, regenerative energy sources, are equipped with intelligent and innovative systems, and in addition to fully automated operation, significantly reduce the costs of aerating water accumulations (natural or artificial) and feeding fish. The intelligent system used for feeding, in addition, to reduce operating costs, optimizes the amount of food, thus preventing water pollution and the development of bacteria, microorganisms. The advantages of the systems are: increasing the yield of fish production, these are green installations, with zero pollutant emissions, can be arranged anywhere on the water surface, depending on the user's needs, can operate autonomously or remotely controlled, if there is a component failure, the system provides the operator with accurate data on the issue, significantly reducing maintenance costs, transmit data about the water physical and chemical parameters.

Keywords: bio-eco-technologies, economy, environment, fish

Procedia PDF Downloads 145

Authors: Taek Hwan Lee, Moon Ho Do, Lalita Subedi, Young Un Park, Sun Yeou Kim

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Natural and artificial toxic substances namely neurotoxins induce the bitter effect in the nervous system termed as neurotoxicity. It can modulate the normal functioning of the nervous system either hyperactivate it or damage homeostasis of neuronal system. Neurotoxins induced toxicity ultimately kills the neuron. The present study investigated the neuroprotective effects of germinated Dolichos lablab on 6-hydroxydopamine (6-OHDA)-induced neurotoxicity using SH-SY5Y neuroblastoma cells. Germination is a process of plant growth from a seed. Sprouting of a seedling from a seed induced many molecular changes in the seed in order to prepare it for further growth. Because of these molecular and chemical changes, the neuroprotective effect of Dolichos lablab is higher in the germinated form than in the normal condition. SH-SY5Y cells were treated with Dolichos lablab extract (50, 100 g/ml) followed by 6-OHDA (25M) induced toxicity. Cell Viability was measured to check the cell survival against 6-OHDA induced toxicity using MTT assay. Dolichos lablab showed a neuroprotective effect against 6-OHDA induced neuronal cell death in neuroblastoma cell at a higher concentration of 100g/ml however the effect is much better even at the lower concentration after germination 50g/ml. Cell survival was increased dramatically after 15 h of germination and increased with time of germination in concentration dependent manner. Trigonelline as a representative compound was validated in germinated Dolichos lablab by HPLC analysis that might enhance the neuroprotective effect of Dolichos lablab. This result suggests that Dolichos lablab possess neuroprotective effect in neuroblastoma cells against 6-OHDA however its activity was more potent in the germinated form.

Keywords: dolichos lablab, germination, neuroprotection, trigonelline

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Authors: J.P. Henriques, E. R. Neto, G. Almeida, G. Ribeiro, J.V. Coutinho, A.B. Lugli

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Industry 4.0, or the Fourth Industrial Revolution, is transforming the relationship between people and machines. In this scenario, some technologies such as Cloud Computing, Internet of Things, Augmented Reality, Artificial Intelligence, Additive Manufacturing, among others, are making industries and devices increasingly intelligent. One of the most powerful technologies of this new revolution is the Digital Twin, which allows the virtualization of a real system or process. In this context, the present paper addresses the linear and nonlinear dynamic study of a didactic level plant using Digital Twin. In the first part of the work, the level plant is identified at a fixed point of operation, BY using the existing method of least squares means. The linearized model is embedded in a Digital Twin using Automation Studio® from Famous Technologies. Finally, in order to validate the usage of the Digital Twin in the linearized study of the plant, the dynamic response of the real system is compared to the Digital Twin. Furthermore, in order to develop the nonlinear model on a Digital Twin, the didactic level plant is identified by using the method proposed by Hammerstein. Different steps are applied to the plant, and from the Hammerstein algorithm, the nonlinear model is obtained for all operating ranges of the plant. As for the linear approach, the nonlinear model is embedded in the Digital Twin, and the dynamic response is compared to the real system in different points of operation. Finally, yet importantly, from the practical results obtained, one can conclude that the usage of Digital Twin to study the dynamic systems is extremely useful in the industrial environment, taking into account that it is possible to develop and tune controllers BY using the virtual model of the real systems.

Keywords: industry 4.0, digital twin, system identification, linear and nonlinear models

Procedia PDF Downloads 141

Authors: Albana Hasimi, Edlira Tako, Elvin Çomo, Partizan Malkaj, Blerina Papajani, Ledjan Malaj, Mirela Ndrita

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Many endeavors have been exerted during the last years for developing new artificial polymeric membranes which fulfill the demanded conditions for biomedical uses. One of the most tested polymers is Poly(vinyl alcohol) [PVA]. Ours groups, is based on the possibility of using PVA for personal protective equipment against covid. In them, we explore the possibility of modifying the properties of the polymer by adding Montmorillonite [MMT]. Heat-treatment above the glass transition temperature are used to improve mechanical properties mainly by increasing the crystallinity of the polymer, which acts as a physical network. Temperature-Modulated Differential Scanning Calorimetry (TMDSC) measurements indicated that the presence of 0.5% MMT in PVA causes a higher Tg value and shaped peak of crystallinity. Decomposition is observed at two of the melting points of the crystals during heating 25-240oC and overlap of the recrystallization ridges during cooling 240-25oC. This is indicative of the presence of two types (quality or structure ) of polymer crystals. On the other hand, some indication of improvement of the quality of the crystals by heat-treatment is given by the distinct non-reversing contribution to melting. Data on sorption and transport of water in polyvinyl alcohol films: PVA pure and PVA/MMT matrix, modified by thermal treatment, are presented. The thermal treatment has aftereffect the films become more rigid, and because of this, the water uptake is significantly lower in membranes. That is indicates by analysis of the resulting water uptake kinetics. The presence 0.5% w/w of MMT has no significant impact on the properties of PVA membranes. Water uptake kinetics deviates from Fick’s law due to slow relaxation of glassy polymer matrix for all membranes category.

Keywords: crystallinity, montmorillonite, nanocomposite, poly (vinyl alcohol)

Procedia PDF Downloads 117

Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

Procedia PDF Downloads 104

Authors: Gwendy Darras, Mattias Desmet

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Previous research showed that psychological distress has a negative impact on the disease course of viral infections. For COVID-19, the same association was observed in small samples of specific segments of the population (e.g. health care workers). The present study presents a more refined analysis of this association, measuring a broader spectrum of psychological distress in a large sample (n=3084) of the general Flemish population. Several types of psychological distress (state, trait and health anxiety, depression, intra-, and interpersonal stress) are registered throughout three periods: one year before the contamination, one week before the contamination, and during the contamination. In doing so, validated scales such as DASS-21, IIP-32, and FCV-19S are used. Furthermore, the course of COVID-19 is registered in several ways: number of symptoms, number of days sick leave due to COVID-19, and number of days the symptoms have lasted. Also, different control variables such as vaccination status, medical and psychological history are taken into account. Statistical analysis shows that all types of psychological distress are positively correlated with the severity of the COVID-19 disease course. Anxiety during the contamination shows the strongest correlation, but psychological distress one year before the onset of COVID-19 was still significantly associated with the worsening of the disease course. As the assessment of the latter type of distress happened before the onset of the COVID-19 disease course, retrospective bias resulting in artificial associations between self-reported stress and COVID-19 severity is unlikely to have impacted the observations. In view of possible future pandemics, it is important to focus on general stress and anxiety reduction in the general population as soon as possible. It is also advisable to minimize the use of stress-inducing messages to encourage the population to adhere to the measures issued during a pandemic.

Keywords: anxiety, COVID-19, depression, psychoneuroimmunology, psychological distress, stress

Procedia PDF Downloads 79

Authors: Lu Song Ma Li Zhi

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With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

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Authors: Juliane Spaak

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A life-cycle assessment was performed, looking at seven retrofit projects in New Zealand using LCAQuickV3.5. The study found that retrofits save up to 80% of embodied carbon emissions for the structural elements compared to a new building. In other words, it is only a 20% carbon investment to transform and extend a building’s life. In addition, the systems were evaluated by looking at environmental impacts over the design life of these buildings and resilience using FEMA P58 and PACT software. With the increasing interest in Zero Carbon targets, significant changes in the building and construction sector are required. Emissions for buildings arise from both embodied carbon and operations. Based on the significant advancements in building energy technology, the focus is moving more toward embodied carbon, a large portion of which is associated with the structure. Since older buildings make up most of the real estate stock of our cities around the world, their reuse through structural retrofit and wider refurbishment plays an important role in extending the life of a building’s embodied carbon. New Zealand’s building owners and engineers have learned a lot about seismic issues following a decade of significant earthquakes. Recent earthquakes have brought to light the necessity to move away from constructing code-minimum structures that are designed for life safety but are frequently ‘disposable’ after a moderate earthquake event, especially in relation to a structure’s ability to minimize damage. This means weaker buildings sit as ‘carbon liabilities’, with considerably more carbon likely to be expended remediating damage after a shake. Renovating and retrofitting older assets plays a big part in reducing the carbon profile of the buildings sector, as breathing new life into a building’s structure is vastly more sustainable than the highest quality ‘green’ new builds, which are inherently more carbon-intensive. The demolition of viable older buildings (often including heritage buildings) is increasingly at odds with society’s desire for a lower carbon economy. Bringing seismic resilience and carbon best practice together in decision-making can open the door to commercially attractive outcomes, with retrofits that include structural and sustainability upgrades transforming the asset’s revenue generation. Across the global real estate market, tenants are increasingly demanding the buildings they occupy be resilient and aligned with their own climate targets. The relationship between seismic performance and ‘sustainable design’ has yet to fully mature, yet in a wider context is of profound consequence. A whole-of-life carbon perspective on a building means designing for the likely natural hazards within the asset’s expected lifespan, be that earthquake, storms, damage, bushfires, fires, and so on, ¬with financial mitigation (e.g., insurance) part, but not all, of the picture.

Keywords: retrofit, sustainability, earthquake, reuse, carbon, resilient

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Authors: Juan Zhou, Lusan Liu, Yanzhong Zhu, Kuixuan Lin, Wenqian Cai, Yu Wang, Xing Wang

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During the past few decades, there has been an increase in the number of coastal land reclamation projects for residential, commercial and industrial purposes in more and more coastal cities of China, which led to the destruction of the wetlands and loss of the sensitive marine habitats. Meanwhile, the influences and nature of these projects attract widespread public and academic concern. For identifying the trend of landscape (esp. Coastal reclamation) and ecological environment changes, understanding of which interacted, and offering a general science for the development of regional plans. In the paper, a case study was carried out in Bohai Bay area, based on the analysis of remote sensing data. Land use maps were created for 1954, 1970, 1981, 1990, 2000 and 2010. Landscape metrics were calculated and illustrated that the degree of reclamation changes was linked to the hydrodynamic environment and macrobenthos community. The results indicated that the worst of the loss of initial areas occurred during 1954-1970, with 65.6% lost mostly to salt field; to 2010, Coastal reclamation area increased more than 200km² as artificial landscape. The numerical simulation of tidal current field in 2003 and 2010 respectively showed that the flow velocity in offshore became faster (from 2-5 cm/s to 10-20 cm/s), and the flow direction seem to go astray. These significant changes of coastline were not conducive to the spread of pollutants and degradation. Additionally, the dominant macrobenthos analysis from 1958 to 2012 showed that Musculus senhousei (Benson, 1842) spread very fast and had been the predominant species in the recent years, which was a disturbance tolerant species.

Keywords: Bohai Bay, coastal reclamation, landscape change, spatial patterns

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Authors: J. N. Nackler, K. Saleh Pascha, W. Winter

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WHISCERS™ (Whole House In-Situ Carbon and Energy Reduction Solution) is an innovative process for Internal Wall Insulation (IWI) for energy-efficient retrofitting of heritage building, which uses laser measuring to determine the dimensions of a room, off-site insulation board cutting and rapid installation to complete the process. As part of a multinational investigation consortium the Austrian part adapted the WHISCERS system to local conditions of Vienna where most historical buildings have valuable stucco facades, precluding the application of an external insulation. The Austrian project contribution addresses the replacement of commonly used extruded polystyrene foam (XPS) with renewable materials such as wood and wood products to develop a more sustainable IWI system. As the timber industry is a major industry in Austria, a new innovative and more sustainable IWI solution could also open up new markets. The first approach of investigation was the Life Cycle Assessment (LCA) to define the performance of wood fibre board as insulation material in comparison to normally used XPS-boards. As one of the results the global-warming potential (GWP) of wood-fibre-board is 15 times less the equivalent to carbon dioxide while in the case of XPS it´s 72 times more. The hygrothermal simulation program WUFI was used to evaluate and simulate heat and moisture transport in multi-layer building components of the developed IWI solution. The results of the simulations prove in examined boundary conditions of selected representative brickwork constructions to be functional and usable without risk regarding vapour diffusion and liquid transport in proposed IWI. In a further stage three different solutions were developed and tested (1 - glued/mortared, 2 - with soft board, connected to wall with gypsum board as top layer, 3 - with soft board and clay board as top layer). All three solutions presents a flexible insulation layer out of wood fibre towards the existing wall, thus compensating irregularities of the wall surface. From first considerations at the beginning of the development phase, three different systems had been developed and optimized according to assembly technology and tested as small specimen in real object conditions. The built prototypes are monitored to detect performance and building physics problems and to validate the results of the computer simulation model. This paper illustrates the development and application of the Internal Wall Insulation system.

Keywords: internal insulation, wood fibre, hygrothermal simulations, monitoring, clay, condensate

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Authors: Mamdouh Milad Adly Morkos

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Despite having the greatest rates of mortality and morbidity in the world, low- and middle-income (LMIC) nations trail high-income nations in terms of the number of clinical trials, the number of qualified researchers, and the amount of research information specific to their people. Health inequities and the use of precision medicine may be hampered by a lack of local genomic data, clinical pharmacology and pharmacometrics competence, and training opportunities. These issues can be solved by carrying out health care infrastructure development, which includes data gathering and well-designed clinical pharmacology training in LMICs. It will be advantageous if there is international cooperation focused at enhancing education and infrastructure and promoting locally motivated clinical trials and research. This paper outlines various instances where clinical pharmacology knowledge could be put to use, including pharmacogenomic opportunities that could lead to better clinical guideline recommendations. Examples of how clinical pharmacology training can be successfully implemented in LMICs are also provided, including clinical pharmacology and pharmacometrics training programmes in Africa and a Tanzanian researcher's personal experience while on a training sabbatical in the United States. These training initiatives will profit from advocacy for clinical pharmacologists' employment prospects and career development pathways, which are gradually becoming acknowledged and established in LMICs. The advancement of training and research infrastructure to increase clinical pharmacologists' knowledge in LMICs would be extremely beneficial because they have a significant role to play in global health

Keywords: electromagnetic solar system, nano-material, nano pharmacology, pharmacovigilance, quantum theoryclinical simulation, education, pharmacology, simulation, virtual learning low- and middle-income, clinical pharmacology, pharmacometrics, career development pathways

Procedia PDF Downloads 76

Authors: Chen Zhi-Wei, Hsieh Wei-Fang

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Taiwan annual rainfall is global average of 2.5 times, plus city excessive development, green constantly to reduced, instead of is big area of artificial base disc, makes Taiwan rainy season during occurred of storm cannot timely of emissions, led to flood constantly, and rain also cannot was retained again using, led to city hydrological balance suffered damage, and to Regulation city of by brings of negative effect, increased green covered rate became most effective of method, and city land limited, so roof green gradually became a alternative program. Green roofs have become one of the Central and local government policy initiatives for urban development, in foreign countries, such as the United States, and Japan, and Singapore etc. Development of roof greening as an important policy, has become a trend of the times. In recent years, many experts and scholars are also on the roof greening all aspects of research, mostly for green roof for the environmental impact of benefits, such as: carbon reduction, cooling, thermostat, but research on the benefits of green roofs under water cut but it is rare. Therefore, this research literature from green roof in to view and analyze what kind of medium suitable for roof greening and use of green base plate combination simulated green roof structure, via different proportions of the medium with water retention plate and drainage board, experiment with different planting base plate combination of water conservation performance. Research will want to test the effect of roof planting base mix, promotion of relevant departments and agencies in future implementation of green roofs, prompted the development of green roofs, which in the end Taiwan achieve sustainable development of the urban environment help.

Keywords: thin-layer roof greening and planting medium, water efficiency

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Authors: Nazli Darkhal

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The use of gems and ornaments has been common in Iran since the beginning of history. The prosperity of the country, the wealth, and the interest of the people of this land in luxurious and glorious life, combined with beauty, have always attracted the attention of the gems and ornaments of the Iranian people. Iranians are famous in the world for having a long history of collecting and recognizing precious stones. In this case, we can use the unique treasure of national jewelry. Raman spectroscopy method is one of the oscillating spectroscopy methods that is classified in the group of nondestructive study methods, and like other methods, in addition to several advantages, it also has disadvantages and problems. Micro Raman spectroscopy is one of the different types of Raman spectroscopy in which an optical microscope is combined with a Raman device to provide more capabilities and advantages than its original method. In this way, with the help of Raman spectroscopy and a light microscope, while observing more details from different parts of the historical sample, natural or artificial pigments can be identified in a small part of it. The EDX electron microscope also functions as the basis for the interaction of the electron beam with the matter. The beams emitted from this interaction can be used to examine samples. In this article, in addition to introducing the micro Raman spectroscopy method, studies have been conducted on the structure of three samples of existing stones in the historic citadel of Bam. Using this method of study on precious and semi-precious stones, in addition to requiring a short time, can provide us with complete information about the structure and theme of these samples. The results of experiments and gemology of the stones showed that the selected beads are agate and jasper, and they can be placed in the chalcedony group.

Keywords: bam citadel, precious and semi-precious stones, Raman spectroscopy, scanning electron microscope

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Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

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Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

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Authors: Jacqui Robertson

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The sustainable governance of groundwater is of the utmost importance in an arid country like Australia. Groundwater has been relied on by our agricultural and pastoral communities since the State was settled by European colonialists. Nevertheless, the rapid establishment of a coal seam gas (CSG) industry in Queensland, Australia, has had extensive impacts on the pre-existing groundwater users. Managed aquifer recharge of important aquifers in Queensland, Australia, using treated coal seam gas produced water has been used to reduce the impacts of CSG development in Queensland Australia. However, the process has not been widely adopted. Negative environmental outcomes are now acknowledged as not only engineering, scientific or technical problems to be solved but also the result of governance failures. An analysis of the regulatory context for aquifer injection using treated CSG water in Queensland, Australia, using Ostrom’s Common Pool Resource (CPR) theory and a ‘heat map’ designed by the author, highlights the importance of governance arrangements. The analysis reveals the costs and benefits for relevant stakeholders of artificial recharge of groundwater resources in this context. The research also reveals missed opportunities to further active management of the aquifer and resolve existing conflicts between users. The research illustrates the importance of strategically and holistically evaluating innovations in technology that impact water resources to reveal incentives that impact resource user behaviors. The paper presents a proactive step that can be adapted to support integrated water resource management and sustainable groundwater development.

Keywords: managed aquifer recharge, groundwater regulation, common-pool resources, integrated water resource management, Australia

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Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

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Authors: Luis Enrique Escalante Ochoa

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This paper attempts to assess the impacts of Climate change (CC) on inequalities in the labour market. CC will have the most serious effects on some vulnerable economic sectors, such as agriculture, livestock or tourism, but also on the most vulnerable population groups. The objective of this research is to evaluate the impact of CC on the labour market and particularly on Mexican women. Influential documents such as the synthesis reports produced by the Intergovernmental Panel on Climate Change (IPCC) in 2007 and 2014 revived a global effort to counteract the effects of CC, called for an analysis of the impacts on vulnerable socio-economic groups and on economic activities, and for the development of decision-making tools to enable policy and other decisions based on the complexity of the world in relation to climate change, taking into account socio-economic attributes. We follow up this suggestion and determine the impact of CC on vulnerable populations in the Mexican labour market, taking into account two attributes (gender and level of qualification of workers). Most studies have focused on the effects of CC on the agricultural sector, as it is considered a highly vulnerable economic sector to the effects of climate variability. This research seeks to contribute to the existing literature taking into account, in addition to the agricultural sector, other sectors such as tourism, water availability, and energy that are of vital importance to the Mexican economy. Likewise, the effects of climate change will be extended to the labour market and specifically to women who in some cases have been left out. The studies are sceptical about the impact of CC on the female labour market because of the perverse effects on women's domestic work, which are too often omitted from analyses. This work will contribute to the literature by integrating domestic work, which in the case of Mexico is much higher among women than among men (80.9% vs. 19.1%), according to the 2009 time use survey. This study is relevant since it will allow us to analyse impacts of climate change not only in the labour market of the formal economy, but also in the non-market sphere. Likewise, we consider that including the gender dimension is valid for the Mexican economy as it is a country with high degrees of gender inequality in the labour market. In the OECD economic study for Mexico (2017), the low labour participation of Mexican women is highlighted. Although participation has increased substantially in recent years (from 36% in 1990 to 47% in 2017), it remains low compared to the OECD average where women participate around 70% of the labour market. According to Mexico's 2009 time use survey, domestic work represents about 13% of the total time available. Understanding the interdependence between the market and non-market spheres, and the gender division of labour within them is the necessary premise for any economic analysis aimed at promoting gender equality and inclusive growth.

Keywords: climate change, labour market, domestic work, rural sector

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Authors: Vânia Costa, Nuno De Sá Teixeira, Ana C. Santos, Eduardo Santos

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Financial literacy and numeracy have been regarded as paramount for rational household decision making in the increasing complexity of financial markets. However, financial decisions are often made under sub-optimal circumstances, including cognitive overload. The present study aims to clarify how financial literacy and numeracy, taken as relevant expert knowledge for financial decision-making, modulate possible effects of cognitive load. Participants were required to perform a choice between a sure loss or a gambling pertaining a financial investment, either with or without a competing memory task. Two experiments were conducted varying only the content of the competing task. In the first, the financial choice task was made while maintaining on working memory a list of five random letters. In the second, cognitive load was based upon the retention of six random digits. In both experiments, one of the items in the list had to be recalled given its serial position. Outcomes of the first experiment revealed no significant main effect or interactions involving cognitive load manipulation and numeracy and financial literacy skills, strongly suggesting that retaining a list of random letters did not interfere with the cognitive abilities required for financial decision making. Conversely, and in the second experiment, a significant interaction between the competing mnesic task and level of financial literacy (but not numeracy) was found for the frequency of choice of a gambling option. Overall, and in the control condition, both participants with high financial literacy and high numeracy were more prone to choose the gambling option. However, and when under cognitive load, participants with high financial literacy were as likely as their illiterate counterparts to choose the gambling option. This outcome is interpreted as evidence that financial literacy prevents intuitive risk-aversion reasoning only under highly favourable conditions, as is the case when no other task is competing for cognitive resources. In contrast, participants with higher levels of numeracy were consistently more prone to choose the gambling option in both experimental conditions. These results are discussed in the light of the opposition between classical dual-process theories and fuzzy-trace theories for intuitive decision making, suggesting that while some instances of expertise (as numeracy) are prone to support easily accessible gist representations, other expert skills (as financial literacy) depend upon deliberative processes. It is furthermore suggested that this dissociation between types of expert knowledge might depend on the degree to which they are generalizable across disparate settings. Finally, applied implications of the present study are discussed with a focus on how it informs financial regulators and the importance and limits of promoting financial literacy and general numeracy.

Keywords: decision making, cognitive load, financial literacy, numeracy

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Dimitrios Kafetzopoulos

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Nowadays, companies are more concerned with adopting their own strategies for increased efficiency and sustainability. Dynamic environments are fertile fields for developing operational changes. For this purpose, organizations need to implement an advanced management philosophy that boosts changes to companies’ operation. Changes refer to new applications of knowledge, ideas, methods, and skills that can generate unique capabilities and leverage an organization’s competitiveness. So, in order to survive and compete in the global and niche markets, companies should incorporate the adoption of operational changes into their strategy with regard to their products and their processes. Creating the appropriate culture for changes in terms of products and processes helps companies to gain a sustainable competitive advantage in the market. Thus, the purpose of this study is to investigate the role of both incremental and radical changes into operations of a company, taking into consideration not only product changes but also process changes, and continues by measuring the impact of these two types of changes on business efficiency and sustainability of Greek manufacturing companies. The above discussion leads to the following hypotheses: H1: Radical operational changes have a positive impact on firm efficiency. H2: Incremental operational changes have a positive impact on firm efficiency. H3: Radical operational changes have a positive impact on firm sustainability. H4: Incremental operational changes have a positive impact on firm sustainability. In order to achieve the objectives of the present study, a research study was carried out in Greek manufacturing firms. A total of 380 valid questionnaires were received while a seven-point Likert scale was used to measure all the questionnaire items of the constructs (radical changes, incremental changes, efficiency and sustainability). The constructs of radical and incremental operational changes, each one as one variable, has been subdivided into product and process changes. Non-response bias, common method variance, multicollinearity, multivariate normal distribution and outliers have been checked. Moreover, the unidimensionality, reliability and validity of the latent factors were assessed. Exploratory Factor Analysis and Confirmatory Factor Analysis were applied to check the factorial structure of the constructs and the factor loadings of the items. In order to test the research hypotheses, the SEM technique was applied (maximum likelihood method). The goodness of fit of the basic structural model indicates an acceptable fit of the proposed model. According to the present study findings, radical operational changes and incremental operational changes significantly influence both efficiency and sustainability of Greek manufacturing firms. However, it is in the dimension of radical operational changes, meaning those in process and product, that the most significant contributors to firm efficiency are to be found, while its influence on sustainability is low albeit statistically significant. On the contrary, incremental operational changes influence sustainability more than firms’ efficiency. From the above, it is apparent that the embodiment of the concept of the changes into the products and processes operational practices of a firm has direct and positive consequences for what it achieves from efficiency and sustainability perspective.

Keywords: incremental operational changes, radical operational changes, efficiency, sustainability

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Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon

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This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence

Procedia PDF Downloads 323