Search results for: massive multiple input multiple output (MIMO)

Authors: Emmanuel Ogundimu, Esther Akinlabi, Mutiu Erinosho

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Welding is described as the process of joining metals so that bonding can be created as a result of inter-atomic penetration. This study investigated the influence of heat input on the efficiency of the welded joints of 304 stainless steel. Three welds joint were made from two similar 304 stainless steel plates of thickness 6 mm. The tensile results obtained showed that the maximum average tensile strength of 672 MPa is possessed by the sample A1 with low heat input. It was discovered that the tensile strength, % elongation and weld joint efficiency decreased with the increase in heat input into the weld. The average % elongation for the entire samples ranged from 28.4% to 36.5%. Sample A1 had the highest joint efficiency of 94.5%. However, the optimum welding current of 190 for TIG- MIG hybrid welding of type-304 austenite stainless steel can be recommended for advanced technological applications such as aircraft manufacturing, nuclear industry, automobile industry, and processing industry.

Keywords: microhardness, microstructure, tensile, MIG welding, process, tensile, shear stress TIG welding, TIG-MIG welding

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Authors: James Pinfold

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The MoEDAL (Monopole and Exotics Detector at the LHC) experiment deployed at IP8 on the Large Hadron Collider ring was the first dedicated search experiment to take data at the Large Hadron Collider (LHC) in 2010. It was designed to search for Highly Ionizing Particle (HIP) avatars of new physics such as magnetic monopoles, dyons, Q-balls, multiply charged particles, massive, slowly moving charged particles and long-lived massive charge SUSY particles. We shall report on our search at LHC’s Run-2 for Magnetic monopoles and dyons produced in p-p and photon-fusion. In more detail, we will report our most recent result in this arena: the search for magnetic monopoles via the Schwinger Mechanism in Pb-Pb collisions. The MoEDAL detector, originally the first dedicated search detector at the LHC, is being reinstalled for LHC’s Run-3 to continue the search for electrically and magnetically charged HIPs with enhanced instantaneous luminosity, detector efficiency and a factor of ten lower thresholds for HIPs. As part of this effort, we will search for massive l long-lived, singly and multiply charged particles from various scenarios for which MoEDAL has a competitive sensitivity. An upgrade to MoEDAL, the MoEDAL Apparatus for Penetrating Particles (MAPP), is now the LHC’s newest detector. The MAPP detector, positioned in UA83, expands the physics reach of MoEDAL to include sensitivity to feebly-charged particles with charge, or effective charge, as low as 10-3 e (where e is the electron charge). Also, In conjunction with MoEDAL’s trapping detector, the MAPP detector gives us a unique sensitivity to extremely long-lived charged particles. MAPP also has some sensitivity to long-lived neutral particles. The addition of an Outrigger detector for MAPP-1 to increase its acceptance for more massive milli-charged particles is currently in the Technical Proposal stage. Additionally, we will briefly report on the plans for the MAPP-2 upgrade to the MoEDAL-MAPP experiment for the High Luminosity LHC (HL-LHC). This experiment phase is designed to maximize MoEDAL-MAPP’s sensitivity to very long-lived neutral messengers of physics beyond the Standard Model. We envisage this detector being deployed in the UGC1 gallery near IP8.

Keywords: LHC, beyond the standard model, dedicated search experiment, highly ionizing particles, long-lived particles, milli-charged particles

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Authors: Mohammad Amin Hamedirad, Seyed Javad Vaziri Kang Olyaei

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Project planning and control are one of the most critical issues in the management of construction projects. Traditional methods of project planning and control, such as the critical path method or Gantt chart, are not widely used for planning projects with discrete and repetitive activities, and one of the problems of project managers is planning the implementation process and optimal allocation of its resources. Massive concreting projects is also a project with discrete and repetitive activities. This study uses the concept of simulating discrete events to manage resources, which includes finding the optimal number of resources considering various limitations such as limitations of machinery, equipment, human resources and even technical, time and implementation limitations using analysis of resource consumption rate, project completion time and critical points analysis of the implementation process. For this purpose, the concept of discrete-event simulation has been used to model different stages of implementation. After reviewing the various scenarios, the optimal number of allocations for each resource is finally determined to reach the maximum utilization rate and also to reduce the project completion time or reduce its cost according to the existing constraints. The results showed that with the optimal allocation of resources, the project completion time could be reduced by 90%, and the resulting costs can be reduced by up to 49%. Thus, allocating the optimal number of project resources using this method will reduce its time and cost.

Keywords: simulation, massive concreting, discrete event simulation, resource management

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Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

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Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

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Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

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Authors: Ankit Shai

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CRC or Cyclic Redundancy Check is one of the most common, and one of the most powerful error-detecting codes implemented on modern computers. Most of the modern communication protocols use some error detection algorithms in digital networks and storage devices to detect accidental changes to raw data between transmission and reception. Cyclic Redundancy Check, or CRC, is the most popular one among these error detection codes. CRC properties are defined by the generator polynomial length and coefficients. The aim of this project is to implement an efficient FPGA based CRC-8 that accepts a 32 bit input, taking into consideration optimal chip area and high performance, using VHDL. The proposed architecture is implemented on Xilinx ISE Simulator. It is designed while keeping in mind the hardware design, complexity and cost factor.

Keywords: cyclic redundancy checker, CRC-8, 32-bit input, FPGA, VHDL, ModelSim, Xilinx

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Authors: Wenbo Duan, Hossein Habibi, Vassilios Kappatos, Cem Selcuk, Tat-Hean Gan

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Ultrasonic guided waves are often used to remove ice or fouling in different structures, such as ship hulls, wind turbine blades and so on. To achieve maximum sound power output, it is important that multiple transducers are arranged in a particular way so that a desired mode can be excited. The objective of this paper is thus to provide a theoretical basis for generating a particular mode in a finite width rectangular plate which can be used for removing potential ice or fouling on the plate. The number of transducers and their locations with respect to a particular mode will be investigated, and the link between dispersion curves and practical applications will be explored. To achieve this, a semi-analytical finite element (SAFE) method is used to study the dispersion characteristics of all the modes in the ultrasonic frequency range. The detailed modal shapes will be revealed, and from the modal analysis, the particular mode with the strongest yet continuous transverse and axial displacements on the surfaces of the plate will be chosen for the purpose of removing potential ice or fouling on the plate. The modal analysis is followed by providing information on the number, location and amplitude of transducers needed to excite this particular mode. Modal excitation is then implemented in a standard finite element commercial package, namely COMSOL Multiphysics. Wave motion is visualized in COMSOL, and the mode shapes generated in SAFE is found to be consistent with the mode shapes generated in COMSOL.

Keywords: dispersion analysis, finite width plate, guided wave, modal excitation

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Authors: Odey Moses Ogah, Felix Terhemba Ikyereve

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The study was carried out to analyzed risks in financing agriculture by agricultural cooperatives in Benue State, Nigeria. The study made use of research questionnaires for data collection. A multistage sampling technique was used to select a sample of 210 respondents from 21 agricultural cooperatives. Both descriptive and inferential statistics were employed in data analysis. Loan defaulting (66.7%) and reduction in savings by members (51.4%) were the major causes of risks faced by agricultural cooperatives in financing agriculture in the study area. Other causes include adverse changes in commodity prices (48.6%), disaster (45.7%), among others. It was found that risks adversely influence the profitability and competition of agricultural cooperatives (82.9%). Multiple regression analysis results showed that the coefficient of multiple determinations was 0.67, implying that the explanatory variables included in the model accounted for 67% of the variation in the level of profitability of agricultural cooperatives. The number of loans, average amount of loan and the interest rate were significant and important determinants of profitability of the cooperatives. The majority of the respondents (88.6%) made use of loan guarantors as a strategy of managing loan default/no repayment. It was found that the majority (70%) of the respondents were faced with the challenge of lack of insurance cover. The study recommends that agricultural cooperative officials should be encouraged to undergo formal training and education to easily acquire administrative skills in the management of agricultural loans; Farmer's loan size should be increased and released on time to enable them to use it effectively. Policies that enhance insuring farm activities should be put in place to discourage farmers from risk aversion.

Keywords: agriculture, analysis, cooperative, finance, risks

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Authors: Mahnaz Hosseinzadeh, Mohammad Reza Mehregan

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In this paper, we have developed a morphological field to assist multi- methodology (combining methodologies together in whole or part) in Operations Research (OR) for the problem contexts in Iranian organizations. So, we have attempted to identify some dimensions for problem context according to Iranian organizational problems. Then, a general morphological program is designed which helps the OR practitioner to determine the suitable OR methodology as output for any configuration of conditions in a problem context as input and to reveal the fields necessary to be improved in OR. Applying such a program would have interesting results for OR practitioners.

Keywords: hard, soft and emancipatory operations research, General Morphological Analysis (GMA), multi-methodology, problem context

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Authors: Deng Qiaoshan

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With the rise of feminism in the global film industry, feminist expressions in Chinese films have also evolved, reflecting societal focus on gender issues. This article focuses on young Chinese female directors such as Yang Lina, Teng Congcong, and Yang Mingming. Their films now present richer female perspectives and consciously incorporate unique female life experiences. They highlight women's real-life struggles, portraying ’struggling’ female identities—characters facing professional failures and desire identity issues, ultimately returning to family roles. These films commonly explore the ‘mother-daughter relationship’, with some using genre storytelling for commercial appeal and others deconstructing the ‘myth of motherhood’ to reflect reality, rewriting traditional maternal roles. The ‘struggling’ female identity in these directors' films shows an aesthetic of ‘pseudo-reality’, blending realistic situations with poetic, lyrical elements, reflecting their creative traits and internal conflicts. These contradictions are closely related to the unique creative context of Chinese cinema in which they operate. Emerging under China's strict film censorship system, film industrialization, consumerist culture, and internet environment, new-generation directors face multiple demands. How to ‘survive’ amidst complex commercial requirements while creating films with a clear feminist consciousness is the fundamental dilemma faced by young Chinese female directors.

Keywords: female directors, feminism film, female dilemma, film censorship system

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Research on damage of gears and gear pairs using vibration signals remains very attractive, because vibration signals from a gear pair are complex in nature and not easy to interpret. Predicting gear pair defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information generally obscured by the noise from dynamic factors of other gear pairs. This article presents the value of cepstrum analysis in vehicle gearbox fault diagnosis. Cepstrum represents the overall power content of a whole family of harmonics and sidebands when more than one family of sidebands is present at the same time. The concept for the measurement and analysis involved in using the technique are briefly outlined. Cepstrum analysis is used for detection of an artificial pitting defect in a vehicle gearbox loaded with different speeds and torques. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. Also, a method for fault diagnosis of gear faults is presented based on order cepstrum. The procedure is illustrated with the experimental vibration data of the vehicle gearbox. The results show the effectiveness of cepstrum analysis in detection and diagnosis of the gear condition.

Keywords: cepstrum analysis, fault diagnosis, gearbox, vibration signals

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Authors: I. Muslih, A. Alkhalailah, A. Merdji

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Photovoltaic efficiency is highly affected by dust accumulation; the dust particles prevent direct solar radiation from reaching the panel surface; therefore a reduction in output power will occur. A study of dust and soiling accumulation effect on the output power of PV panels was conducted for different periods of time from May to October in three countries of the MENA region, Jordan, Egypt, and Algeria, under local weather conditions. This study leads to build a more realistic equation to estimate the power reduction as a function of time. This logarithmic function shows the high reduction in power in the first days with 10% reduction in output power compared to the reference system, where it reaches a steady state value after 60 days to reach a maximum value of 30%.

Keywords: dust effect, MENA, solar energy, PV system

Procedia PDF Downloads 208

Authors: Khadijah Saeed Khan

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One of the major current societal issues of Swedish society is to integrate newcomer refugees well into the host society. The cultural integration issue is one of the under debated topic in the literature, and this study intends to meet this gap from the Swedish perspective. The purpose of this study is to explore the role and types of cultural landscapes of refugee women in Sweden and how these landscapes help or hinder the settlement process. The cultural landscapes are referred to as a set of multiple cultural activities or practices which refugees perform in a specific context and circumstances (i.e., being in a new country) to seek, share or use relevant information for their settlement. Information plays a vital role in various aspects of newcomers' lives in a new country. This article has an intention to highlight the importance of multiple cultural landscapes as a source of information (regarding employment, language learning, finding accommodation, immigration matters, health concerns, school and education, family matters, and other everyday matters) for refugees to settle down in Sweden. Some relevant theories, such as information landscapes and socio-cultural theories, are considered in this study. A qualitative research design is employed, including semi-structured deep interviews and participatory observation with 20 participants. The initial findings show that the refugee women encounter many information-related and integration-related challenges in Sweden and have built a network of cultural landscapes in which they practice various co-ethnic cultural and religious activities at different times of the year. These landscapes help them to build a sense of belonging with people from their own or similar land and assist them to seek and share relevant information in everyday life in Sweden.

Keywords: cultural integration, cultural landscapes, information, women refugees

Procedia PDF Downloads 131

Authors: Tzu-Hao Li, Shie-Liang Hsieh, Han-Chieh Lin, Ying-Ying Yang

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TNF superfamily-stimulated pathogenic cascades and macrophage (M1)/kupffer cells (KC) polarization are important in the pathogenesis of ischemia-reperfusion (IR) liver injury in animals with hepatic steatosis (HS). Decoy receptor 3 (DcR3) is a common upstream inhibitor of the above-mentioned pathogenic cascades. The study evaluated whether modulation of these DcR3-related cascades was able to protect steatotic liver from IR injury. Serum and hepatic DcR3 levels were lower in patients and animals with HS. Accordingly, the effects of pharmacologic and genetic DcR3 replacement on the IR-related pathogenic changes were measured. Significantly, DcR3 replacement protected IR-Zucker(HS) rats and IR-DcR3-Tg(HS) mice from IR liver injury. The beneficial effects of DcR3 replacement were accompanied by decreased serum/hepatic TNF, soluble TNF-like cytokine 1A (TL1A), Fas ligand (Fas-L) and LIGHT, T-helper-cell-1 cytokine (INF) levels, neutrophil infiltration, M1 polarization, neutrophil-macrophage/KC-T-cell interaction, hepatocyte apoptosis and improved hepatic microcirculatory failure among animals with IR-injured steatotic livers. Additionally, TL1A, Fas-L, LIGHT and TLR4/NFB signals were found to mediate the DcR3-related protective effects of steatotic livers from IR injury. Using multimodal in vivo and in vitro approaches, we found that DcR3 was a potential agent to protect steatotic livers from IR injury by simultaneous blocking the multiple IR injury-related pathogenic changes.

Keywords: Decoy 3 receptor, ischemia-reperfusion injury, M1 polarization, TNF superfamily

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Authors: Mark Linden, Michael Brown, Lynne Marsh, Maria Truesdale, Stuart Todd, Nathan Hughes, Trisha Forbes, Rachel Leonard

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Background: The COVID-19 pandemic exacerbated the already significant strain placed on family carers of people with profound and multiple intellectual disabilities (PMID), given the withdrawal of many services during lockdown. The aim of this study was to explore the experiences of family carers of people with PMID during the COVID-19 pandemic. Methods: Online focus groups were conducted with family carers (n=126) from across the UK and the Republic of Ireland. Participants were asked about their experiences of the COVID-19 pandemic, coping strategies, and challenges faced. Focus groups were audio recorded, transcribed verbatim and analyzed through thematic analysis. Findings: Three themes emerged from our analysis of the data: (i) COVID-19 as a double-edged sword, (ii) The struggle for support (iii) the Constant nature of caring. These included 11 subthemes: (i) ‘COVID-19 as a catalyst for change’, ‘Challenges during COVID-19: dealing with change’, ‘Challenges during COVID-19: fear of COVID-19’, ‘The online environment: the new normal’ (ii) ‘Invisibility of male carers’, ‘Carers supporting carers’, ‘The only service you get is lip service: non-existent services’, ‘Knowing your rights’ (iii) ‘Emotional response to the caring role: Feeling devalued’, ‘Emotional response to the caring role: Desperation of caring’, ‘Multiple demands of the caring role.’ Conclusions: Poor or inconsistent access to services and support has been an ongoing difficulty for many family carers. The COVID-19 pandemic has only further intensified these difficulties, increasing family carers' stress. There is an urgent need to design services, such as online support programs, in partnership with family carers that adequately address their needs.

Keywords: intellectual disabilities, family carer, COVID-19, disability

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: Z. Masoumi, B. Moaveni

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This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied.

Keywords: Kalman filter (KF), least square estimation (LSE), structural health monitoring (SHM), structural system identification

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Authors: Aaron Liggett

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This research focuses on the visionary proposal made by the Olmsted Brothers Landscape Architecture firm in the 1920s for a network of interconnected parkways in Los Angeles. Their envisioned parkways aimed to address environmental and cultural strains by providing green space for recreation, wildlife habitat, and stormwater management while serving as multimodal transportation routes. Although the parkways were never constructed, through an evidence-based approach, this research presents a framework for evaluating the potential functionality and success of the parkways by modeling and visualizing their quantitative and qualitative landscape performance and benefits. Historical documents and innovative digital modeling tools produce detailed analysis, modeling, and visualization of the parkway designs. A set of 1928 construction documents are used to analyze and interpret the design intent of the parkways. Grading plans are digitized in CAD and modeled in Sketchup to produce 3D visualizations of the parkway. Drainage plans are digitized to model stormwater performance. Planting plans are analyzed to model urban forestry and biodiversity. The EPA's Storm Water Management Model (SWMM) predicts runoff quantity and quality. The USDA Forests Service tools evaluate carbon sequestration and air quality. Spatial and overlay analysis techniques are employed to assess urban connectivity and the spatial impacts of the parkway designs. The study reveals how the integration of blue infrastructure, green infrastructure, and transportation infrastructure within the parkway design creates a multifunctional landscape capable of offering alternative spatial and temporal uses. The analysis demonstrates the potential for multiple functional, ecological, aesthetic, and social benefits to be derived from the proposed parkways. The analysis of the Olmsted Brothers' proposed Los Angeles parkways, which predated contemporary ecological design and resiliency practices, demonstrates the potential for providing multiple functional, ecological, aesthetic, and social benefits within urban designs. The findings highlight the importance of integrated blue, green, and transportation infrastructure in creating a multifunctional landscape that simultaneously serves multiple purposes. The research contributes new methods for modeling and visualizing landscape performance benefits, providing insights and techniques for informing future designs and sustainable development strategies.

Keywords: landscape architecture, ecological urban design, greenway, landscape performance

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Authors: MD Mashiur Rahman, Muhammad Arshadul Haque

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Emerging conservation agriculture (CA) is an option for improving soil health and maintaining environmental sustainability for intensive agriculture, especially in the tropical climate. Three years lengthy research experiment was performed in arid climate from 2018 to 2020 at research field of Bangladesh Agricultural Research Station (RARS)F, Jamalpur (soil texture belongs to Agro-Ecological Zone (AEZ)-8/9, 24˚56'11''N latitude and 89˚55'54''E longitude and an altitude of 16.46m) to evaluate the effect of CA approaches on energy use efficiency and a streamlined life cycle greenhouse gas (GHG) emission of wheat production. For this, the conservation tillage practices (strip tillage (ST) and minimum tillage (MT)) were adopted in comparison to the conventional farmers' tillage (CT), with retained a fixed level (30 cm) of residue retention. This study examined the relationship between energy consumption and life cycle greenhouse gas (GHG) emission of wheat cultivation in Jamalpur region of Bangladesh. Standard energy equivalents megajoules (MJ) were used to measure energy from different inputs and output, similarly, the global warming potential values for the 100-year timescale and a standard unit kilogram of carbon dioxide equivalent (kg CO₂eq) was used to estimate direct and indirect GHG emissions from the use of on-farm and off-farm inputs. Farm efficiency analysis tool (FEAT) was used to analyze GHG emission and its intensity. A non-parametric data envelopment (DEA) analysis was used to estimate the optimum energy requirement of wheat production. The results showed that the treatment combination having MT with optimum energy inputs is the best suit for cost-effective, sustainable CA practice in wheat cultivation without compromising with the yield during the dry season. A total of 22045.86 MJ ha⁻¹, 22158.82 MJ ha⁻¹, and 23656.63 MJ ha⁻¹ input energy for the practice of ST, MT, and CT was used in wheat production, and output energy was calculated as 158657.40 MJ ha⁻¹, 162070.55 MJ ha⁻¹, and 149501.58 MJ ha⁻¹, respectively; where energy use efficiency/net energy ratio was found to be 7.20, 7.31 and 6.32. Among these, MT is the most effective practice option taken into account in the wheat production process. The optimum energy requirement was found to be 18236.71 MJ ha⁻¹ demonstrating for the practice of MT that if recommendations are followed, 18.7% of input energy can be saved. The total greenhouse gas (GHG) emission was calculated to be 2288 kgCO₂eq ha⁻¹, 2293 kgCO₂eq ha⁻¹ and 2331 kgCO₂eq ha⁻¹, where GHG intensity is the ratio of kg CO₂eq emission per MJ of output energy produced was estimated to be 0.014 kg CO₂/MJ, 0.014 kg CO₂/MJ and 0.015 kg CO₂/MJ in wheat production. Therefore, CA approaches ST practice with 30 cm residue retention was the most effective GHG mitigation option when the net life cycle GHG emission was considered in wheat production in the silt clay loam soil of Bangladesh. In conclusion, the CA approaches being implemented for wheat production involving MT practice have the potential to mitigate global warming potential in Bangladesh to achieve soil carbon footprint, where the life cycle assessment approach needs to be applied to a more diverse range of wheat-based cropping systems.

Keywords: conservation agriculture and tillage, energy use efficiency, life cycle GHG, Bangladesh

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Authors: Ahmad Mohsin, Syda Fatima, Falak Nawaz, Aman Ullah Khan

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Selection of an appropriate architectural style is vital to the success of target web service under development. The nature of architecture design and selection for service-oriented computing applications is quite different as compared to traditional software. Web Services have complex and rigorous architectural styles to choose. Due to this, selection for accurate architectural style for web services development has become a more complex decision to be made by architects. Architectural style selection is a multi-criteria decision and demands lots of experience in service oriented computing. Decision support systems are good solutions to simplify the selection process of a particular architectural style. Our research suggests a new approach using DSS for selection of architectural styles while developing a web service to cater FRs and NFRs. Our proposed DSS helps architects to select right web service architectural pattern according to the domain and non-functional requirements. In this paper, a rule base DSS has been developed using CLIPS (C Language Integrated Production System) to support decisions using multi-criteria requirements. This DSS takes architectural characteristics, domain requirements and software architect preferences for NFRs as input for different architectural styles in use today in service-oriented computing. Weighted sum model has been applied to prioritize quality attributes and domain requirements. Scores are calculated using multiple criterions to choose the final architecture style.

Keywords: software architecture, web-service, rule-based, DSS, multi-criteria requirements, quality attributes

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Authors: Vishweshwar V. Dixit

Abstract:

Kannudi is a reference editor introducing a method of input for Kannada, called OHOK!, that is, Ottu Hāku Ottu Koḍu!. This is especially suited for pressure-sensitive input devices, though the current online implementation uses the regular mechanical keyboard. OHOK! has three possible modes, namely, sva-ottu (self-conjunct), kandante (as you see), and andante (as you say). It may be noted that kandante mode does not follow the phonetic order. However, this model may work well for those who are inclined to visualize as they type rather than vocalize the sounds. Kannudi also demonstrates how domain knowledge can be effectively used to potentially increase speed, accuracy, and user-friendliness. For example, selection of a default vowel, automatic shunyification, and arkification. Also implemented are four types of Deletes that are necessary for phono-syllabic languages like Kannada.

Keywords: kannada, conjunct, reference editor, pressure input

Procedia PDF Downloads 83

Authors: Shef Amir Arasy

Abstract:

In 2023, the world's population will be 7.8 billion, which has increased significantly in the last 20 years. Every country in the world is experiencing the impacts of climate change directly and indirectly. However, the community still needs to build massive infrastructure and buildings. The massive CO2 emissions which lead to climate change come from cement usage in construction activity. Bamboo is one of the most sustainable materials for reducing carbon emissions and releasing more than 30% oxygen compared to the mass of trees. Besides, bamboo harvest time is faster than other sustainable materials, around 3-4 years. Furthermore, Bamboo has a high tensile strength, which can provide ductility effectively to prevent damage to buildings during an earthquake. By the finite element method, this research analyzes bamboo configuration and connection for tall building structures under different earthquake frequencies and fire. The aim of this research is to provide proper design and connection of bamboo buildings that can be more reliable than concrete structures.

Keywords: bamboo, concrete, ductility, earthquake.

Procedia PDF Downloads 56

Authors: Kelvin Lo

Abstract:

Nowadays, numerical modelling in geotechnical engineering is very common but sophisticated. Many advanced input settings and considerable computational efforts are required to optimize the design to reduce the construction cost. To optimize a design, it usually requires huge numerical models. If the optimization is conducted manually, there is a potentially dangerous consequence from human errors, and the time spent on the input and data extraction from output is significant. This paper presents an automation process introduced to numerical modelling (Plaxis 2D) of a trench excavation supported by a secant-pile retaining structure for a top-down tunnel project. Python code is adopted to control the process, and numerical modelling is conducted automatically in every 20m chainage along the 200m tunnel, with maximum retained height occurring in the middle chainage. Python code continuously changes the geological stratum and excavation depth under groundwater flow conditions in each 20m section. It automatically conducts trial and error to determine the required pile length and the use of props to achieve the required factor of safety and target displacement. Once the bending moment of the pile exceeds its capacity, it will increase in size. When the pile embedment reaches the default maximum length, it will turn on the prop system. Results showed that it saves time, increases efficiency, lowers design costs, and replaces human labor to minimize error.

Keywords: automation, numerical modelling, Python, retaining structures

Procedia PDF Downloads 43

Authors: Ziying Wu, Danfeng Yan

Abstract:

Multi-Access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based Vehicle-Aware Multi-Access Edge Computing Network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

Keywords: multi-access edge computing, computation offloading, 5th generation, vehicle-aware, deep reinforcement learning, deep q-network

Procedia PDF Downloads 96

Authors: Sareh Rajabi, Taha Anjamrooz, Salwa Bheiry

Abstract:

The continuous increase in the world population, resource shortage and the warning of climate change cause various environmental and social issues to the world. Thus, sustainability concept is much needed nowadays. Organizations are progressively falling under strong worldwide pressure to integrate sustainability practices into their project decision-making development. Construction projects in the industry are amongst the most significant, since it is one of the biggest divisions and of main significance for the national economy and hence has a massive effect on the environment and society. So, it is important to discover approaches to incorporate sustainability into the management of those projects. This study presents a combined sustainability index of projects with sustainable transportation which has been formed as per a comprehensive literature review and survey study. Transportation systems enable the movement of goods and services worldwide, and it is leading to economic growth and creating jobs while creating negative impacts on the environment and society. This research is study to quantify the sustainability indicators, through 1) identifying the importance of sustainable transportation indicators that are based on the sustainable practices used for the construction projects and 2) measure the effectiveness of practices through these indicators on the three sustainable pillars. A total 26 sustainability indicators have been selected and grouped under each related sustainability pillars. A survey was used to collect the opinion about the sustainability indicators by a scoring system. A combined sustainability index considering three sustainable pillars can be helpful in evaluating the transportation sustainable practices of a project and making decisions regarding project selection. In addition to focus on the issue of financial resource allocation in a project selection, the decision-maker could take into account the sustainability as an important key in addition to the project’s return and risk. The purpose of this study is to measure the performance of transportation sustainability which allow companies to assess multiple projects selection. This is useful to decision makers to rank and focus more on future sustainable projects.

Keywords: sustainable transportation, transportation performances, sustainable indicators, sustainable construction practice, sustainability

Procedia PDF Downloads 130

Authors: Wang Zhanquan, Yang Zeping, Gu Chunhua, Zhu Fazhi, Guo Weibin

Abstract:

Recently, Massive Open Online Courses (MOOCs) are becoming the new trend of education. There are many problems under the environment of Database Principle curriculum teaching process in MOOCs, such as teaching ideas and theories which are out of touch with the reality, how to carry out the technical teaching and interactive practice in the MOOCs environment, thus the methods of database course under the environment of MOOCs are proposed. There are three processes to deal with problem solving in the research, which are problems proposed, problems solved, and inductive analysis. The present research includes the design of teaching contents, teaching methods in classroom, flipped classroom teaching mode under the environment of MOOCs, learning flow method and large practice homework. The database designing ability is systematically improved based on the researching methods.

Keywords: problem solving-driven, MOOCs, teaching art, learning flow;

Procedia PDF Downloads 358

Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

Abstract:

One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

Procedia PDF Downloads 91

Authors: SeungKu Ahn, Sewon Lee

Abstract:

This study examines the effects of regulatory policies on corporate R&D activities and innovation and suggests regulatory directions for the enhancement of corporate performance. This study employs a regression model with R&D activities as dependent variables and the regulatory index as an independent variable. The results of this study are as follows: The regulation is negatively associated with the input and output of R&D activities. The regulation encourages small and medium-sized firms to invest in R&D. The regulation has a positive effect on patent applications for small and medium-sized firms.

Keywords: governmental regulation, research and development performance, small and medium-sized firms, technological innovation

Procedia PDF Downloads 253