Search results for: vernacular and traditional architecture

Authors: N. F. F. Ebecken, G. C. Pereira

Abstract:

Although networks provide a powerful approach to the study of a wide variety of ecological systems, their formulation usually does not include various types of interactions, interactions that vary in space and time, and interconnected systems such as networks. The emerging field of 'multilayer networks' provides a natural framework for extending ecological systems analysis to include these multiple layers of complexity as it specifically allows for differentiation and modeling of intralayer and interlayer connectivity. The structure provides a set of concepts and tools that can be adapted and applied to the ecology, facilitating research in high dimensionality, heterogeneous systems in nature. Here, ecological multilayer networks are formally defined based on a review of prior and related approaches, illustrates their application and potential with existing data analyzes, and discusses limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers a largely untapped potential to further address ecological complexity, to finally provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.

Keywords: ecological networks, multilayered networks, sea ecology, Brazilian Coastal Area

Procedia PDF Downloads 136

Authors: Samuel Holder

Abstract:

Mass incarceration in the United States is a human rights issue, not merely a civil rights problem. It is a human rights problem not only because the United States has a high rate of incarceration, but more importantly because of who is jailed, for what purpose they are jailed and, ultimately, the manner in which they are jailed. To sustain the scale of the criminal justice system, one of the darker policies involves a multi-tiered strategy of fee- and fine-collection, targeting, usually, the most vulnerable and poor, many of whom run into the law via small offenses that do not rise to the level of felonies. This paper advances the notion that this debt collection-to-incarceration pipeline is tantamount to a modern-day debtors’ prison system. This article seeks to confront the thorny issue of incarceration via criminal justice debt from a human rights and cause-lawyering position. It will argue that a two-pronged cause-lawyering strategy: the first focused on traditional litigation along constitutional grounds, and the second, an advocacy approach rooted in grassroots campaigns, designed to shift the normative operation and understanding of the rights of marginalized and racialized offenders. Ultimately, the argument suggests that this approach will be effective in combatting the (often highly privatized) criminal justice debt system and bring the roles of 'incapacitation, rehabilitation, deterrence, and retribution' back into the criminal justice legal conversation. Part I contextualizes and historicizes the role of fees, penalties, and fines in American criminal justice. Part II examines the emergence of private industry in the criminal justice system, and its role in the acceleration of profit-driven criminal justice debt collection and incarceration. Part III addresses the failures of the federal and state law and legislation in combatting predatory incarceration and debt collection in the criminal justice system, particularly as waged against the indigent and/or ethnically or racially marginalized. Part IV examines the potential for traditional cause-lawyering litigation along constitutional grounds, using case studies across contexts for illustration. Finally, Part V will review the radical cause-lawyer’s role in the normative struggle in redefining prisoners’ rights and the rights of the marginalized (and racialized) as they intersect at the crossroads of criminal justice debt. This paper will conclude with recommendations for litigation and advocacy, drawing on hypotheses advanced, and informed by case studies from a variety of both national and international jurisdictions.

Keywords: cause-lawyering, criminal justice debt, human rights, judicial fees

Procedia PDF Downloads 154

Authors: Raluca-Ioana Antonescu

Abstract:

The paper treats about the Korean public diplomacy and national branding strategies, and how the Korean language textbooks were used in order to construct the Korean national image. The field research of the paper stands at the intersection between Linguistics and Political Science, while the problem of the research is the role of language and culture in national branding process. The research goal is to contribute to the literature situated at the intersection between International Relations and Applied Linguistics, while the objective is to conceptualize the idea of national branding by emphasizing a dimension which is not much discussed, and that would be the education as an instrument of the national branding and public diplomacy strategies. In order to examine the importance of language upon the national branding strategies, the paper will answer one main question, How is the Korean language used in the construction of national branding?, and two secondary questions, How are explored in literature the relations between language and national branding construction? and What kind of image of South Korea the language textbooks for foreigners transmit? In order to answer the research questions, the paper starts from one main hypothesis, that the language is an essential component of the culture, which is used in the construction of the national branding influenced by traditional elements (like Confucianism) but also by modern elements (like Western influence), and from two secondary hypothesis, the first one is that in the International Relations literature there are little explored the connections between language and national branding, while the second hypothesis is that the South Korean image is constructed through the promotion of a traditional society, but also a modern one. In terms of methodology, the paper will analyze the textbooks used in Romania at the universities which provide Korean Language classes during the three years program B.A., following the dialogs, the descriptive texts and the additional text about the Korean culture. The analysis will focus on the rank status difference, the individual in relation to the collectivity, the respect for the harmony, and the image of the foreigner. The results of the research show that the South Korean image projected in the textbooks convey the Confucian values and it does not emphasize the changes suffered by the society due to the modernity and globalization. The Westernized aspect of the Korean society is conveyed more in an informative way about the Korean international companies, Korean internal development (like the transport or other services), but it does not show the cultural changed the society underwent. Even if the paper is using the textbooks which are used in Romania as a teaching material, it could be used and applied at least to other European countries, since the textbooks are the ones issued by the South Korean language schools, which other European countries are using also.

Keywords: confucianism, modernism, national branding, public diplomacy, traditionalism

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Authors: Kira Rysakova, Vitaly Novikov

Abstract:

An improved method of obtaining enzymatic collagen hydrolysate from the tissues of marine hydrobionts is proposed, which allows to obtain hydrolysate without pre-isolation of pure collagen. The method can be used to isolate enzymatic collagen hydrolysate from the waste of industrial processing of Red King crab and non-traditional objects - marine holothurias. Comparative analysis of collagen hydrolysates has shown the possibility of their use in a number of nutrient media, but this requires additional optimization of their composition and biological tests on wide sets of test strains of microorganisms.

Keywords: collagen hydrolysate, marine hydrobionts, red king crab, marine holothurias, enzymes, exclusive HPLC

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Authors: Chuluundorj Begz

Abstract:

This paper presents the dynamic, psycho-cognitive approach to study of human verbal thinking on the basis of typologically different languages /as a Mongolian, English and Russian/. Topological equivalence in verbal communication serves as a basis of Universality of mental structures and therefore deep structures. Mechanism of verbal thinking consisted at the deep level of basic concepts, rules for integration and classification, neural networks of vocabulary. In neuro cognitive study of language, neural architecture and neuro psychological mechanism of verbal cognition are basis of a vector-based modeling. Verbal perception and interpretation of the infinite set of meanings and propositions in mental continuum can be modeled by applying tensor methods. Euclidean and non-Euclidean spaces are applied for a description of human semantic vocabulary and high order structures.

Keywords: Euclidean spaces, isomorphism and homomorphism, mental lexicon, mental mapping, semantic memory, verbal cognition, vector space

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Authors: Nino Naneishvili

Abstract:

The study of religious minorities and their musical culture has attracted scant academic attention in Georgia. Within wider Georgian society, it would seem that the focus of discourse to date has been on the traditional orthodox religion and its musical expression, with other forms of religious expression regarded as intrinsically less valuable. The goal of this article is to study Georgia's different religious and musical picture which, this time, is presented on the example of the Pentecostals. The first signs of the Pentecostal movement originated at the end of the 19th Century in the USA, and first appeared in Georgia as early as 1914. An ethnomusicological perspective allows the use of anthropological and sociological approaches. The basic methodology is an ethnographic method. This involved attending religious services, observation, in-depth interviews and musical material analysis. This analysis, based on a combined use of various theoretical and methodological approaches, reveals that Georgian Pentecostals, apart from polyphonic singing, are characterised by “ bi-musicality.“ This phenomenon together with Georgian three part polyphony combines vocalisation within “social polyphony.“ The concept of back stage and front stage is highlighted. Chanters also try to express national identity. In some cases however it has been observed that they abandon or conceal certain musical forms of expression which are considered central to Georgian identity. The famous hymn “Thou art a Vineyard” is a case in point. The reason given for this omission within the Georgian Pentecostal church is that within Pentecostal doctrine, God alone is the object of worship. Therefore there is no veneration of Saints as representatives of the Divine. In some cases informants denied the existence of this hymn, and others explain that the meaning conveyed to the Vineyard is that of Jesus Christ and not the Virgin Mary. Others stated that they loved Virgin Mary and were therefore free to sing this song outside church circles. The results of this study illustrates that one of the religious minorities in Georgia, the Pentecostals, are characterised by a deviation in musical thinking from Homo Polyphonicus. They actively change their form of musical worship to secondary ethno hearing – bi-musicality. This outcome is determined by both new religious thinking and the process of globalization. A significant principle behind this form of worship is the use of forms during worship which are acceptable and accessible to all. This naturally leads to the development of modern forms. Obtained material does not demonstrate a connection between traditional religious music in general. Rather, it constitutes an independent domain.

Keywords: Georgia, globalization, music, pentecostal

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Authors: Robin Becker, Nane Roetmann, Manfred Helmus

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Construction companies lack junior staff for construction management. According to a nationwide survey of students, however, the profession lacks attractiveness. The conflict between the traditional job profile and the current desires of junior staff for contemporary and flexible working models must be resolved. Increasing flexibility is essential for the future viability of small and medium-sized enterprises. The implementation of modern work modules can help here. The following report will present the validation results of the developed work modules in construction practice.

Keywords: modern construction management, construction industry, work modules, shortage of junior staff, sustainable personnel management, making construction management more attractive, working time model

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Authors: Jun Wang, Chenglei Diao, Emanuele Pagone, Jialuo Ding, Stewart Williams

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As a crucial component in aircraft, landing gear systems are responsible for supporting the plane during parking, taxiing, takeoff, and landing. Given the need for high load-bearing capacity over extended periods, 300M ultra-high strength steel (UHSS) is often the material of choice for crafting these systems due to its exceptional strength, toughness, and fatigue resistance. In the quest for cost-effective and sustainable manufacturing solutions, Wire Arc Additive Manufacturing (WAAM) emerges as a promising alternative for fabricating 300M UHSS landing gears. This is due to its advantages in near-net-shape forming of large components, cost-efficiency, and reduced lead times. Cranfield University has conducted an extensive preliminary study on WAAM 300M UHSS, covering feature deposition, interface analysis, and post-heat treatment. Both Gas Metal Arc (GMA) and Plasma Transferred Arc (PTA)-based WAAM methods were explored, revealing their feasibility for defect-free manufacturing. However, as-deposited 300M features showed lower strength but higher ductility compared to their forged counterparts. Subsequent post-heat treatments were effective in normalising the microstructure and mechanical properties, meeting qualification standards. A 300M UHSS landing gear demonstrator was successfully created using PTA-based WAAM, showcasing the method's precision and cost-effectiveness. The demonstrator, measuring Ф200mm x 700mm, was completed in 16 hours, using 7 kg of material at a deposition rate of 1.3kg/hr. This resulted in a significant reduction in the Buy-to-Fly (BTF) ratio compared to traditional manufacturing methods, further validating WAAM's potential for this application. A "cradle-to-gate" environmental impact assessment, which considers the cumulative effects from raw material extraction to customer shipment, has revealed promising outcomes. Utilising Wire Arc Additive Manufacturing (WAAM) for landing gear components significantly reduces the need for raw material extraction and refinement compared to traditional subtractive methods. This, in turn, lessens the burden on subsequent manufacturing processes, including heat treatment, machining, and transportation. Our estimates indicate that the carbon footprint of the component could be halved when switching from traditional machining to WAAM. Similar reductions are observed in embodied energy consumption and other environmental impact indicators, such as emissions to air, water, and land. Additionally, WAAM offers the unique advantage of part repair by redepositing only the necessary material, a capability not available through conventional methods. Our research shows that WAAM-based repairs can drastically reduce environmental impact, even when accounting for additional transportation for repairs. Consequently, WAAM emerges as a pivotal technology for reducing environmental impact in manufacturing, aiding the industry in its crucial and ambitious journey towards Net Zero. This study paves the way for transformative benefits across the aerospace industry, as we integrate manufacturing into a hybrid solution that offers substantial savings and access to more sustainable technologies for critical component production.

Keywords: WAAM, aircraft landing gear, microstructure, mechanical performance, life cycle assessment

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Authors: Yo-Sheng Lin, Jen-How Lee, Chien-Chin Wang

Abstract:

A 60-GHz receiver front-end using standard 90-nm CMOS technology is reported. The receiver front-end comprises a wideband low-noise amplifier (LNA), and a double-balanced Gilbert cell mixer with a current-reused RF single-to-differential (STD) converter, an LO Marchand balun and a baseband amplifier. The receiver front-end consumes 34.4 mW and achieves LO-RF isolation of 60.7 dB, LO-IF isolation of 45.3 dB and RF-IF isolation of 41.9 dB at RF of 60 GHz and LO of 59.9 GHz. At IF of 0.1 GHz, the receiver front-end achieves maximum conversion gain (CG) of 26.1 dB at RF of 64 GHz and CG of 25.2 dB at RF of 60 GHz. The corresponding 3-dB bandwidth of RF is 7.3 GHz (58.4 GHz to 65.7 GHz). The measured minimum noise figure was 5.6 dB at 64 GHz, one of the best results ever reported for a 60 GHz CMOS receiver front-end. In addition, the measured input 1-dB compression point and input third-order inter-modulation point are -33.1 dBm and -23.3 dBm, respectively, at 60 GHz. These results demonstrate the proposed receiver front-end architecture is very promising for 60 GHz direct-conversion transceiver applications.

Keywords: CMOS, 60 GHz, direct-conversion transceiver, LNA, down-conversion mixer, marchand balun, current-reused

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Authors: Calister Wingang Makebe, Wilson Ambindei Agwanande, Emmanuel Jong Nso, P. Nisha

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Traditional liquid-state fermentation processes of Annona muricata L. juice can result in fluctuating product quality and quantity due to difficulties in control and scale up. This work describes a laboratory-scale batch fermentation process to produce a probiotic Annona muricata L. enzymatically extracted juice, which was modeled using the Doehlert design with independent extraction factors being incubation time, temperature, and enzyme concentration. It aimed at a better understanding of the traditional process as an initial step for future optimization. Annona muricata L. juice was fermented with L. acidophilus (NCDC 291) (LA), L. casei (NCDC 17) (LC), and a blend of LA and LC (LCA) for 72 h at 37 °C. Experimental data were fitted into mathematical models (Monod, Logistic and Luedeking and Piret models) using MATLAB software, to describe biomass growth, sugar utilization, and organic acid production. The optimal fermentation time was obtained based on cell viability, which was 24 h for LC and 36 h for LA and LCA. The model was particularly effective in estimating biomass growth, reducing sugar consumption, and lactic acid production. The values of the determination coefficient, R2, were 0.9946, 0.9913 and 0.9946, while the residual sum of square error, SSE, was 0.2876, 0.1738 and 0.1589 for LC, LA and LCA, respectively. The growth kinetic parameters included the maximum specific growth rate, µm, which was 0.2876 h-1, 0.1738 h-1 and 0.1589 h-1 as well as the substrate saturation, Ks, with 9.0680 g/L, 9.9337 g/L and 9.0709 g/L respectively for LC, LA and LCA. For the stoichiometric parameters, the yield of biomass based on utilized substrate (YXS) was 50.7932, 3.3940 and 61.0202, and the yield of product based on utilized substrate (YPS) was 2.4524, 0.2307 and 0.7415 for LC, LA, and LCA, respectively. In addition, the maintenance energy parameter (ms) was 0.0128, 0.0001 and 0.0004 with respect to LC, LA and LCA. With the kinetic model proposed by Luedeking and Piret for lactic acid production rate, the growth associated, and non-growth associated coefficients were determined as 1.0028 and 0.0109, respectively. The model was demonstrated for batch growth of LA, LC, and LCA in Annona muricata L. juice. The present investigation validates the potential of Annona muricata L. based medium for heightened economical production of a probiotic medium.

Keywords: L. acidophilus, L. casei, fermentation, modelling, kinetics

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Authors: Ahmed Abdulfatah Yusuf, Collins Oduor Ondiek

Abstract:

The automation of farming activities can have a transformational impact on the agricultural sector, especially from the emerging new technologies such as the Internet of Things (IoT). The system uses water level sensors and soil moisture sensors that measure the content of water in the soil as the values generated from the sensors enable the system to use an appropriate quantity of water, which avoids over or under irrigation. Due to the increase in the world’s population, there is a need to increase food production. With this demand in place, it is difficult to increase crop yield using the traditional manual approaches that lead to the wastage of water, thus affecting crop production. Food insecurity has become a scourge greatly affecting the developing countries and agriculture is an essential part of human life and tends to be the mainstay of the economy in most developing nations. Thus, without the provision of adequate food supplies, the population of those living in poverty is likely to multiply. The project’s main objective is to design and develop an IoT (Internet of Things) microcontroller-based Smart Irrigation System. In addition, the specific research objectives are to find out the challenges with traditional irrigation approaches and to determine the benefits of IoT-based smart irrigation systems. Furthermore, the system includes Arduino, a website and a database that works simultaneously in collecting and storing the data. The system is designed to pave the way in attaining the Sustainable Development Goal (SDG 1), which aims to end extreme poverty in all forms by 2030. The research design aimed at this project is a descriptive research design. Data was gathered through online questionnaires that used both quantitative and qualitative in order to triangulate the data. Out of the 32 questionnaires sent, there were 32 responses leading to a 100% response rate. In terms of sampling, the target group of this project is urban farmers, which account for about 25% of the population of Nairobi. From the findings of the research carried out, it is evident that there is a need to move away from manual irrigation approaches due to the high wastage of water to the use of smart irrigation systems that propose a better way of conserving water while maintaining the quality and moisture of the soil. The research also found out that urban farmers are willing to adopt this system to better their farming practices. However, this system can be improved in the future by incorporating it with other features and deploying it to a larger geographical area.

Keywords: crop production, food security, smart irrigation system, sustainable development goal

Procedia PDF Downloads 139

Authors: Yali Zheng, Peipei Luo, Shinan Chen, Jiasheng Hao, Hong Cheng

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Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments.

Keywords: indoor navigation, low light, RGB-D camera, vision based

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Authors: Ryan C. Zlomek

Abstract:

The value of using comics in the literacy classroom has been explored since the 1930s. At that point in time researchers had begun to implement comics into daily lesson plans and, in some instances, had started the development process for comics-supported curriculum. In the mid-1950s, this type of research was cut short due to the work of psychiatrist Frederic Wertham whose research seemingly discovered a correlation between comic readership and juvenile delinquency. Since Wertham’s allegations the comics medium has had a hard time finding its way back to education. Now, over fifty years later, the definition of literacy is in mid-transition as the world has become more visually-oriented and students require the ability to interpret images as often as words. Through this transition, comics has found a place in the field of literacy education research as the shift focuses from traditional print to multimodal and media literacies. Comics are now believed to be an effective resource in bridging the gap between these different types of literacies. This paper seeks to better understand what students learn from the process of reading comics and how those skills line up with the core principles of media literacy education in the United States. In the first section, comics are defined to determine the exact medium that is being examined. The different conventions that the medium utilizes are also discussed. In the second section, the comics reading process is explored through a dissection of the ways a reader interacts with the page, panel, gutter, and different comic conventions found within a traditional graphic narrative. The concepts of intersubjective acts and visualization are attributed to the comics reading process as readers draw in real world knowledge to decode meaning. In the next section, the learning processes that comics encourage are explored parallel to the core principles of media literacy education. Each principle is explained and the extent to which comics can act as an intermediary for this type of education is theorized. In the final section, the author examines comics use in his computer science and technology classroom. He lays out different theories he utilizes from Scott McCloud’s text Understanding Comics and how he uses them to break down media literacy strategies with his students. The article concludes with examples of how comics has positively impacted classrooms around the United States. It is stated that integrating comics into the classroom will not solve all issues related to literacy education but, rather, that comics can be a powerful multimodal resource for educators looking for new mediums to explore with their students.

Keywords: comics, graphics novels, mass communication, media literacy, metacognition

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Authors: Ahmet Kaya

Abstract:

Quantum mechanism is one of the most important approaches to calculating non-ruin probability. We apply standard Dirac notation to model given Hamiltonians. By using the traditional method and eigenvector basis, non-ruin probability is found for several examples. Also, non-ruin probability is calculated for two different Hamiltonian by using the tensor product. Finally, the path integral method is applied to the examples and comparison is made for stochastic simulations and path integral calculation.

Keywords: quantum physics, Hamiltonian system, path integral, tensor product, ruin probability

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Authors: Tariku Nefo Duke

Abstract:

Embelia is a genus of climbing shrubs in the family Myrsinaceae. Embelia schimperi is as important in traditional medicine as the other species in the genus. The plant has been much known as a local medicine for the treatment of tapeworms. In this project, extraction, phytochemical screening tests, isolation, and characterization of berries of the Embelia schimperi plant have been conducted. The chemical investigations of methanol and ethyl acetate (1:1) ratio extracts of the berries lead to the isolation of three new compounds. The compounds were identified to be alkaloids coded as AD, AN, and AG. Structural elucidations of the isolated compounds were accomplished using spectroscopic methods (IR, UV, ¹H NMR, ¹³C NMR, DEPT and 2D NMR, HPLC, and LC-MS). The alkaloid coded as (AN) has a wide MIC range of 6.31-25.46 mg/mL against all tested bacteria strains.

Keywords: Embelia schimper, HPLC, alkaloids, 2D NMR, MIC

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Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: B. C. Biermann

Abstract:

This in-depth exploration investigates the transformative integration of urban vibrancy into contemporary interior design through the strategic incorporation of vivid wall pieces. Bridging the gap between public dynamism and private tranquility, this study delves into the nuanced methodologies, creative processes, and profound impacts of this innovative approach. Drawing inspiration from street art's dynamic language and the timeless allure of natural beauty, these artworks serve as conduits, orchestrating a dialogue that challenges traditional boundaries and redefines the relationship between external chaos and internal sanctuaries. The fusion of urban vibrancy with contemporary interior design represents a paradigm shift, where the inherent dynamism of public spaces harmoniously converges with the curated tranquility of private environments. This paper aims to explore the underlying principles, creative processes, and transformative impacts of integrating vivid wall pieces as instruments for bringing the "outside in." Employing an innovative and meticulous methodology, street art elements are synthesized with the refined aesthetics of contemporary design. This delicate balance necessitates a nuanced understanding of both artistic realms, ensuring a synthesis that captures the essence of urban energy while seamlessly blending with the sophistication of modern interior design. The creative process involves a strategic selection of street art motifs, colors, and textures that resonate with the organic beauty found in natural landscapes, creating a symbiotic relationship between the grittiness of the streets and the elegance of interior spaces. This groundbreaking approach defies traditional boundaries by integrating dynamic street art into interior spaces, blurring the demarcation between external chaos and internal tranquility. Vivid wall pieces serve as dynamic focal points, transforming physical spaces and challenging conventional perceptions of where art belongs. This redefinition asserts that boundaries are fluid and meant to be transcended. Case studies illustrate the profound impact of integrating vivid wall pieces on the aesthetic appeal of interior spaces. Urban vibrancy revitalizes the atmosphere, infusing it with palpable energy that resonates with the vivacity of public spaces. The curated tranquility of private interiors coexists harmoniously with the dynamic visual language of street art, fostering a unique and evolving relationship between inhabitants and their living spaces. Emphasizing harmonious coexistence, the paper underscores the potential for a seamless dialogue between public urban spaces and private interiors. The integration of vivid wall pieces acts as a bridge rather than a dichotomy, merging the dynamism of street art with the curated elegance of contemporary design. This unique visual tapestry transcends traditional categorizations, fostering a symbiotic relationship between contrasting worlds. In conclusion, this paper posits that the integration of vivid wall pieces represents a transformative tool for contemporary interior design, challenging and redefining conventional boundaries. By strategically bringing the "outside in," this approach transforms interior spaces and heralds a paradigm shift in the relationship between urban aesthetics and contemporary living. The ongoing narrative between urban vibrancy and interior design creates spaces that reflect the dynamic and ever-evolving nature of the surrounding environment.

Keywords: Art Integration, Contemporary Interior Design, Interior Space Transformation, Vivid Wall Pieces

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Authors: Praveena Sridhar, Nihar Devathi, Parikshit Chakraborty

Abstract:

As the software product changes versions across releases, there are changes to the API’s and features and the upgrades become necessary. Hence, it becomes imperative to get the impact of upgrading the dependent components. This tool finds out API changes across two versions and their impact on other API’s followed by execution of the automated regression suites relevant to updates and their impacted areas. This tool has 4 layer architecture, each layer with its own unique pre-assigned capability which it does and sends the required information to next layer. This are the 4 layers. 1) Comparator: Compares the two versions of API. 2) Analyzer: Analyses the API doc and gives the modified class and its dependencies along with implemented interface details. 3) Impact Filter: Find the impact of the modified class on the other API methods. 4) Auto Executer: Based on the output given by Impact Filter, Executor will run the API regression Suite. Tool reads the java doc and extracts the required information of classes, interfaces and enumerations. The extracted information is saved into a data structure which shows the class details and its dependencies along with interfaces and enumerations that are listed in the java doc.

Keywords: automation impact regression, java doc, executor, analyzer, layers

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Authors: Khader S. Al-Aidmat, Venus W. Samawi

Abstract:

Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

Procedia PDF Downloads 372

Authors: R. Berrached, H. Ait Mahamed, A. Iddou

Abstract:

Water pollution is nowadays a serious problem, due to the increasing scarcity of water and thus to the impact induced by such pollution on the human health. Various techniques are made use of to deal with water pollution. Among the most used ones, some can be enumerated: the bacterian bed, the activated sludge, lagoons as biological processes and coagulation-flocculation as a physic-chemical process. These processes are very expensive and a decreasing in efficiency treatment with the increase of the initial pollutants concentration. This is the reason why research has been reoriented towards the use of adsorption process as an alternative solution instead of the other traditional processes. In our study, we have tempted to explore the characteristics of hydroxides of Al and Fe to purify contaminated water by two industrial dyes SBL blue and SRL-150 orange. Results have shown the efficiency of the two materials on the blue SBL dye.

Keywords: metallic hydroxydes, dyes, purification, adsorption

Procedia PDF Downloads 322

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Kim Young-Jin, Kim Byung-Kon

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Although more efforts to expand the application of BIM (Building Information Modeling) technologies have be pursued in recent years than ever, it’s true that there have been various challenges in doing so, including a lack or absence of relevant institutions, lots of costs required to build BIM-related infrastructure, incompatible processes, etc. This, in turn, has led to a more prolonged delay in the expansion of their application than expected at an early stage. Especially, attempts to save costs for building BIM-related infrastructure and provide various BIM services compatible with domestic processes include studies to link between BIM and cloud computing technologies. Also in this study, the author attempted to develop a cloud BIM service operation model through analyzing the level of BIM applications for the construction sector and deriving relevant service areas, and find how to link BIM services to the cloud operation model, as through archiving BIM data and creating a revenue structure so that the BIM services may grow spontaneously, considering a demand for cloud resources.

Keywords: construction IT, BIM (building information modeling), cloud computing, BIM service based cloud computing

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Authors: Jaspreet Kaur, Parminder Nain, Vipin Saini, Sumitra Dahiya

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Elaeocarpus sphaericus has been traditionally used in the Indian traditional medicine system for the treatment of stress, anxiety, depression, palpitation, epilepsy, migraine and lack of concentration. The study was investigated to evaluate the neurological potential such as anxiolytic, muscle relaxant and sedative activity of methanolic extract of Elaeocarpus sphaericus stem bark (MEESSB) in mice. Preliminary phytochemical screening and acute oral toxicity of MEESSB was carried out by using standard methods. The anxiety was induced by employing Elevated Plus-Maze (EPM), Light and Dark Test (LDT), Open Field Test (OFT) and Social Interaction test (SIT). The motor coordination and sedative effect was also observed by using actophotometer, rota-rod apparatus and ketamine-induced sleeping time, respectively. Animals were treated with different doses of MEESSB (i.e.100, 200, 400 and 800 mg/kg orally) and diazepam (2 mg/kg i.p) for 21 days. Brain neurotransmitters like dopamine, serotonin and nor-epinephrine level were estimated by validated methods. Preliminary phytochemical analysis of the extract revealed the presence of tannins, phytosterols, steroids and alkaloids. In the acute toxicity studies, MEESSB was found to be non-toxic and with no mortality. In anxiolytic studies, the different doses of MEESSB showed a significant (p<0.05) effect on EPM and LDT. In OFT and SIT, a significant (p<0.05) increase in ambulation, rearing and social interaction time was observed. In the case of motor coordination activity, the MEESSB does not cause any significant effect on the latency to fall off from the rotarod bar as compared to the control group. Moreover, no significant effects on ketamine-induced sleep latency and total sleeping time induced by ketamine were observed. Results of neurotransmitter estimation revealed the increased concentration of dopamine, whereas the level of serotonin and nor-epinephrine was found to be decreased in the mice brain, with MEESSB at dose 800 mg/kg only. The study has validated the folkloric use of the plant as an anxiolytic in Indian traditional medicine while also suggesting potential usefulness in the treatment of stress and anxiety without causing sedation.

Keywords: anxiolytic, behavior experiments, brain neurotransmitters, elaeocarpus sphaericus

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Authors: Zaki Ahmad Khan, Jamshed Siddiqui, Abdus Samad

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The Scheduling and mapping of tasks on a set of processors is considered as a critical problem in parallel and distributed computing system. This paper deals with the problem of dynamic scheduling on a special type of multiprocessor architecture known as Linear Crossed Cube (LCQ) network. This proposed multiprocessor is a hybrid network which combines the features of both linear type of architectures as well as cube based architectures. Two standard dynamic scheduling schemes namely Minimum Distance Scheduling (MDS) and Two Round Scheduling (TRS) schemes are implemented on the LCQ network. Parallel tasks are mapped and the imbalance of load is evaluated on different set of processors in LCQ network. The simulations results are evaluated and effort is made by means of through analysis of the results to obtain the best solution for the given network in term of load imbalance left and execution time. The other performance matrices like speedup and efficiency are also evaluated with the given dynamic algorithms.

Keywords: dynamic algorithm, load imbalance, mapping, task scheduling

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Eduardo Remirez, Luis Moreno

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The rapid growth of the private satellite launchers sector is leading the space race. Hence, with the privatization of the sector, all the companies are racing for a more efficient and reliant way to set satellites in orbit. Having detected the current needs for power management in the launcher vehicle industry, the Modular Power Bus is proposed as a technology to revolutionize power management in current and future Launcher Vehicles. The MPBus Project is committed to develop a new power bus architecture combining ejectable batteries with the main bus through intelligent nodes. These nodes are able to communicate between them and a battery controller using an improved, data over DC line technology, expected to reduce the total weight in two main areas: improving the use of the batteries and reducing the total weight due to harness. This would result in less weight for each launch stage increasing the operational satellite payload and reducing cost. These features make the system suitable for a number of launchers.

Keywords: modular power bus, Launcher vehicles, ejectable batteries, intelligent nodes

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Mary M. Park

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Hollywood has had a long and enduring history of showcasing the United States military to civilian audiences, and the portrayals of soldiers in films have had a definite impact on the civilian perception of the US military. The growing gap between the civilian population and the military in the US has led to certain stereotypes of military personnel to proliferate, especially in the area of militarized masculinity, which has often been harmful to the psychological and spiritual wellbeing of military personnel. Examining Hollywood's portrayal of soldiers can serve to enhance our understanding of how civilians may be influenced in their perception of military personnel. Moreover, it can provide clues as to how male military personnel may also be influenced by Hollywood films as they form their own military identity. The post 9/11 era has seen numerous high budget films lionizing a particular type of soldier, the 'warrior-hero', who adheres to a traditional form of hegemonic masculinity and exhibits traits such as physical strength, bravery, stoicism, and an eagerness to fight. This paper examines how the portrayal of the 'warrior-hero' perpetuates a negative stereotype that soldiers are a blend of superheroes and emotionless robots and, therefore, inherently different from civilians. This paper examines the portrayal of militarized masculinity in three of the most successful war films of the post-9/11 era; Black Hawk Down (2001), The Hurt Locker (2008), and American Sniper (2014). The characters and experiences of the soldiers depicted in these films are contrasted with the lived experiences of soldiers during the Iraq and Afghanistan wars. Further, there is an analysis of popular films depicting ancient warriors, such as Troy (2004) and 300 (2007), which were released during the early years of the War on Terror. This paper draws on the concept of hegemonic militarised masculinity by leading scholars and feminist international relations theories on militarized masculinity. This paper uses veteran testimonies collected from a range of public sources, as well as previous studies on the link between traditional masculinity and war-related mental illness. This paper concludes that the seemingly exclusive portrayal of soldiers as 'warrior-heroes' in films in the post-9/11 era is misleading and damaging to civil-military relations and that the reality of the majority of soldiers' experiences is neglected in Hollywood films. As civilians often believe they are being shown true depictions of the US military in Hollywood films, especially in films that portray real events, it is important to find the differences between the idealized fictional 'warrior-heroes' and the reality of the soldiers on the ground in the War on Terror.

Keywords: civil-military relations, gender studies, militarized masculinity, social pyschology

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Ananta Kumar Das, Sujit Kumar Chakrabarti

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Architectural tactics such as heartbeat, ping-echo, encapsulate, encrypt data are techniques that are used to achieve quality attributes of a system. Detecting architectural tactics has several benefits: it can aid system comprehension (e.g., legacy systems) and in the estimation of quality attributes such as safety, security, maintainability, etc. Architectural tactics are typically spread over the source code and are implicit. For large codebases, manual detection is often not feasible. Therefore, there is a need for automated methods of detection of architectural tactics. This paper presents a formalization of the heartbeat architectural tactic and a program analytic approach to detect this tactic in source code. The experiment of the proposed method is done on a set of Java applications. The outcome of the experiment strongly suggests that the method compares well with a manual approach in terms of its sensitivity and specificity, and far supersedes a manual exercise in terms of its scalability.

Keywords: software architecture, architectural tactics, detecting architectural tactics, program analysis, AST, alias analysis

Procedia PDF Downloads 142