Search results for: green infrastructure network

Authors: Yusuf Hussaini Atulukwu, Daramola Japheth, Tabitit S. Tabiti, Daramola Elizabeth Lara

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In view of the recent limitations faced by the township concerning poorly constructed and in some cases non - existence of drainage facilities that resulted into incessant flooding in some parts of the community poses threat to life,property and the environment. The research seeks to address this issue by showing the spatial distribution of drainage network in Bida Urban using Geographic information System techniques. Relevant features were extracted from existing Bida based Map using un-screen digitization and x, y, z, data of existing drainages were acquired using handheld Global Positioning System (GPS). These data were uploaded into ArcGIS 9.2, software, and stored in the relational database structure that was used to produce the spatial data drainage network of the township. The result revealed that about 40 % of the drainages are blocked with sand and refuse, 35 % water-logged as a result of building across erosion channels and dilapidated bridges as a result of lack of drainage along major roads. The study thus concluded that drainage network systems in Bida community are not in good working condition and urgent measures must be initiated in order to avoid future disasters especially with the raining season setting in. Based on the above findings, the study therefore recommends that people within the locality should avoid dumping municipal waste within the drainage path while sand blocked or weed blocked drains should be clear by the authority concerned. In the same vein the authority should ensured that contract of drainage construction be awarded to professionals and all the natural drainages caused by erosion should be addressed to avoid future disasters.

Keywords: drainage network, spatial, digitization, relational database, waste

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Authors: Aghbalou Nihad, Charki Abderafi, Saida Rahali, Reklaoui Kamal

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Maximize the benefit from the wind energy potential is the most interest of the wind power stakeholders. As a result, the wind tower size is radically increasing. Nevertheless, choosing an appropriate wind turbine for a selected site require an accurate estimate of vertical wind profile. It is also imperative from cost and maintenance strategy point of view. Then, installing tall towers or even more expensive devices such as LIDAR or SODAR raises the costs of a wind power project. Various models were developed coming within this framework. However, they suffer from complexity, generalization and lacks accuracy. In this work, we aim to investigate the ability of neural network trained using the Bayesian Regularization technique to estimate wind speed profile up to height of 100 m based on knowledge of wind speed lower heights. Results show that the proposed approach can achieve satisfactory predictions and proof the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights.

Keywords: bayesian regularization, neural network, wind shear, accuracy

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Authors: Jalalludin Abdul Malek, Zurinah Tahir

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When the Multimedia Super Corridor (MSC) was launched in 1995, it became the catalyst for the implementation of the intelligent city concept, an area that covers about 15 x 50 kilometres from Kuala Lumpur City Centre (KLCC), Putrajaya and Kuala Lumpur International Airport (KLIA). The concept of intelligent city means that the city has an advanced infrastructure and infostructure such as information technology, advanced telecommunication systems, electronic technology and mechanical technology to be utilized for the development of urban elements such as industries, health, services, transportation and communications. For example, the Golden Triangle of Kuala Lumpur has also many intelligent buildings developed by the private sector such as the KLCC Tower to implement the intelligent city concept. Consequently, the intelligent buildings in the Golden Triangle can be linked directly to the Putrajaya Intelligent City and Cyberjaya Intelligent City within the confines of the MSC. However, the reality of the situation is that there are not many intelligent buildings within the Golden Triangle Kuala Lumpur scope which can be considered of high-standard intelligent buildings as referred to by the Intelligence Quotient (IQ) building standard. This increases the need to implement the real ‘intelligent city’ concept. This paper aims to show the strengths and weaknesses of the intelligent buildings in the Golden Triangle by taking into account aspects of 'intelligence' in the areas of technology and infrastructure of buildings.

Keywords: intelligent city concepts, intelligent building, Golden Triangle, Kuala Lumpur

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Authors: Neil Erick Q. Madariaga, Noel B. Linsangan

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Body Mass Index (BMI) is one of the different ways to monitor the health of a person. It is based on the height and weight of the person. This study aims to compute for the BMI using an Android tablet by obtaining the height of the person by using a camera and measuring the weight of the person by using a weighing scale or load cell. The height of the person was estimated by applying background subtraction to the image captured and applying different processes such as getting the vanishing point and applying Artificial Neural Network. The weight was measured by using Wheatstone bridge load cell configuration and sending the value to the computer by using Gizduino microcontroller and Bluetooth technology after the amplification using AD620 instrumentation amplifier. The application will process the images and read the measured values and show the BMI of the person. The study met all the objectives needed and further studies will be needed to improve the design project.

Keywords: body mass index, artificial neural network, vanishing point, bluetooth, wheatstone bridge load cell

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Melake Kuflom

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European power networks involve the use of multiple overhead transmission lines to construct a highly duplicated system that delivers reliable and stable electrical energy to the distribution level. The transmission line protection applied in the existing GB transmission network are normally independent unit differential and time stepped distance protection schemes, referred to as main-1 & main-2 respectively, with overcurrent protection as a backup. The increasing penetration of renewable energy sources, commonly referred as “weak sources,” into the power network resulted in the decline of fault level. Traditionally, the fault level of the GB transmission network has been strong; hence the fault current contribution is more than sufficient to ensure the correct operation of the protection schemes. However, numerous conventional coal and nuclear generators have been or about to shut down due to the societal requirement for CO2 emission reduction, and this has resulted in a reduction in the fault level on some transmission lines, and therefore an adaptive transmission line protection is required. Generally, greater utilization of renewable energy sources generated from wind or direct solar energy results in a reduction of CO2 carbon emission and can increase the system security and reliability but reduces the fault level, which has an adverse effect on protection. Consequently, the effectiveness of conventional protection schemes under low fault levels needs to be reviewed, particularly for future GB transmission network operating scenarios. The proposed paper will evaluate the transmission line challenges under high penetration of renewable energy sources andprovides alternative viable protection solutions based on the problem observed. The paper will consider the assessment ofrenewable energy sources (RES) based on a fully rated converter technology. The DIgSILENT Power Factory software tool will be used to model the network.

Keywords: fault level, protection schemes, relay settings, relay coordination, renewable energy sources

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Authors: Ahmed Kiani

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The United Arab Emirates is clearly facing a multitude of challenges in curbing its greenhouse gas emissions to meet its pre-allotted framework of Kyoto protocol and COP21 targets due to its hunger for modernization, industrialization, infrastructure growth, soaring population and oil and gas activity. In this work, we focus on the bonafide zero emission electric vehicles market penetration in the country’s transport industry for emission reduction. We study the global electric vehicle market trends, the complementary battery technologies and the trends by manufacturers, emission standards across borders and prioritized advancements which will ultimately dictate the terms of future conditions for the United Arab Emirate transport industry. Based on our findings and analysis at every stage of current viability and state-of-transport-affairs, we postulate policy recommendations to local governmental entities from a supply and demand perspective covering aspects of technology, infrastructure requirements, change in power dynamics, end user incentives program, market regulators behavior and communications amongst key stakeholders. 

Keywords: electric vehicles, greenhouse gas emission reductions, market analysis, policy recommendations

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Authors: Ira Joseph

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The LNG paradox addresses the issue of how the most expensive form of gas supply, which is LNG, will grow in an end user market where demand is most competitive, which is power generation. In this case, LNG demand growth is under siege from two entirely different directions. At one end is price; it will be extremely difficult for gas to replace coal in Asia due to the low price of coal and the age of the generation plants. Asia's coal fleet, on average, is less than two decades old and will need significant financial incentives to retire before its state lifespan. While gas would cut emissions in half relative to coal, it would also more than double the price of the fuel source for power generation, which puts it in a precarious position. In most countries in Asia other than China, this cost increase, particularly from imports, is simply not realistic when it is also necessary to focus on economic growth and social welfare. On the other end, renewables are growing at an exponential rate for three reasons. One is that prices are dropping. Two is that policy incentives are driving deployment, and three is that China is forcing renewables infrastructure into the market to take a political seat at the global energy table with Saudi Arabia, the US, and Russia. Plus, more renewables will lower import growth of oil and gas in China, if not end it altogether. Renewables are the predator at the gate of gas demand in power generation and in every year that passes, renewables cut into demand growth projections for gas; in particular, the type of gas that is most expensive, which is LNG. Gas does have a role in the future, particularly within a domestic market. Once it crosses borders in the form of LNG or even pipeline gas, it quickly becomes a premium fuel and must be marketed and used this way. Our research shows that gas will be able to compete with batteries as an intermittency and storage tool and does offer a method to harmonize with renewables as part of the energy transition. As a baseload fuel, however, the role of gas, particularly, will be limited by cost once it needs to cross a border. Gas converted into blue or green hydrogen or ammonia is also an option for storage depending on the location. While this role is much reduced from the primary baseload role that gas once aspired to land, it still offers a credible option for decades to come.

Keywords: natural gas, LNG, demand, price, intermittency, storage, renewables

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Authors: Manju Bala Jassi

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The Covid 19 pandemic has badly mauled Dubai’s GDP heavily dependent on hospitality, tourism, entertainment, logistics, property and the retail sectors. In the context of the World Health protocols on social distancing for better maintenance of health and hygiene, the revival of the battered tourism and hospitality sectors has serious lessons for designers- interiors and public places. The tangible and intangible aesthetic elements and design –ambiance, materials, furnishings, colors, lighting and interior with architectural design issues of tourism and hospitality need a rethink to ensure a memorable tourist experience. Designers ought to experiment with sustainable places of tourism and design, develop, build and projects are aesthetic and leave as little negative impacts on the environment and public as possible. In short, they ought to conceive public spaces that makes use of little untouched materials and energy, and creates pollution and waste that are minimal. The spaces can employ healthier and more resource-efficient prototypes of construction, renovation, operation, maintenance, and demolition and thereby mitigate the environment impacts of the construction activities and it is sustainable These measures encompass the hospitality sector that includes hotels and restaurants which has taken the hardest fall from the pandemic. The paper sought to examine building energy efficiency and materials and design employed in public places, green buildings to achieve constructive sustainability and to establish the benefits of utilizing energy efficiency, green materials and sustainable design; to document diverse policy interventions, design and Spatial dimensions of tourism and hospitality sectors; to examine changes in the hospitality, aviation sector especially from a design perspective regarding infrastructure or operational constraints and additional risk-mitigation measures; to dilate on the nature of implications for interior designers and architects to design public places to facilitate sustainable tourism and hospitality while balancing convenient space and their operations' natural surroundings. The qualitative research approach was adopted for the study. The researcher collected and analyzed data in continuous iteration. Secondary data was collected from articles in journals, trade publications, government reports, newspaper/ magazine articles, policy documents etc. In depth interviews were conducted with diverse stakeholders. Preliminary data indicates that designers have started imagining public places of tourism and hospitality against the backdrop of the government push and WHO guidelines. For instance, with regard to health, safety, hygiene and sanitation, Emirates, the Dubai-based airline has augmented health measures at the Dubai International Airport and on board its aircraft. It has leveraged high tech/ Nano-tech, social distancing to encourage least human contact, flexible design layouts to limit the occupancy. The researcher organized the data into thematic categories and found that the Government of Dubai has initiated comprehensive measures in the hospitality, tourism and aviation sectors in compliance with the WHO guidelines.

Keywords: Covid 19, design, Dubai, hospitality, public spaces, tourism

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Authors: Muhammad Abdullah Arafat, Nahrin Nowrose

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Wireless charging of electric vehicles is becoming more and more attractive as large amount of power can now be transferred to a reasonable distance using magnetic resonance coupling method. However, proper tuning of the compensation network is required to achieve maximum power transmission. Due to the variation of coil distance from the nominal value as a result of change in tire condition, change in weight or uneven road condition, the tuning of the compensation network has become challenging. In this paper, a tuning method has been described to determine the optimum values of the compensation network in order to maximize the average output power. The simulation results show that 5.2 percent increase in average output power is obtained for 10 percent variation in coupling coefficient using the optimum values without the need of additional space and electro-mechanical components. The proposed method is applicable to both static and dynamic charging of electric vehicles.

Keywords: coupling coefficient, electric vehicles, magnetic resonance coupling, tuning capacitor, wireless power transfer

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

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Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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Authors: Kaddi Mohammed, Benahmed Khelifa D. Benatiallah

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A sensor network consists of a large number of sensors deployed in areas to monitor and communicate with each other through a wireless medium. The collected routing data in the network consumes most of the energy of the sensor nodes. For this purpose, multiple routing approaches have been proposed to conserve energy resource at the sensors and to overcome the challenges of its limitation. In this work, we propose a new low energy consumption routing protocol for wireless sensor networks based on a meta-heuristic methods. Our protocol is to operate more fairly energy when routing captured data to the base station.

Keywords: WSN, routing, energy, heuristic

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Authors: Sladjana Popovic

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The concept of vertical space urbanization, defined in English as "air rights development," can be considered a mechanism for the development of public spaces in urban areas of high density. A chronological overview of the transformation of space within the vertical projection of the existing traffic infrastructure that penetrates through the central areas of a city is given in this paper through the analysis of two illustrative case studies: more advanced and recent - "Plot 13" in Boston, and less well-known European example of structures erected above highways throughout Italy - the "Pavesi auto grill" chain. The backbone of this analysis is the examination of the possibility of yielding air rights within the vertical projection of public structures in the two examples by considering the factors that would enable its potential application in capitals in Southeastern Europe. The cession of air rights in the Southeastern Europe region, as a phenomenon, has not been a recognized practice in urban planning. In a formal sense, legal and physical feasibility can be seen to some extent in local models of structures built above protected historical heritage (i.e., archaeological sites); however, the mechanisms of the legal process of assigning the right to use and develop air rights above public structures is not a recognized concept. The goal of the analysis is to shed light on the influence of institutional participants in the implementation of innovative solutions for vertical urbanization, as well as strategic planning mechanisms in public-private partnership models that would enable the implementation of the concept in the region. The main question is whether the manipulation of the vertical projection of space could provide for innovative urban solutions that overcome the deficit and excessive use of the available construction land, particularly above the dominant public spaces and traffic infrastructure that penetrate central parts of a city. Conclusions reflect upon vertical urbanization that can bridge the spatial separation of the city, reduce noise pollution and contribute to more efficient urban planning along main transportation corridors.

Keywords: air rights development, innovative urbanism, public-private partnership, transport infrastructure, vertical urbanization

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Sogand Moshfeghi, Alireza Biglari

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Diabetes is an endocrine disorder that is commonly treated with insulin. However, long-term usage of insulin and hypoglycemic chemical drugs can result in various side effects. Therefore, it is crucial to explore effective compounds with minimal side effects for diabetes treatment. This study aimed to compare the hypoglycemic effects of hydroalcoholic extracts derived from Silybum marianum, Camellia sinensis (green tea), and Urtica dioica plants. Male Wistar rats were allocated to 5 groups. Group 1 received normal Salin. Other groups were diabetic (induced by Streptozotocin 65 mg/kg Ip), group 2 received normal Salin (Ip, qod. 21 days). Group 3 received Silybum Marianum L, hydroalcoholic extract (100 mg/kg, ip.qod, 21 days). Group 4 received Camellia sinesis L, hydroalcoholic extract (100mg/kg,ip,qod,21 days), and group 5 received Urtica dioica L. hydroalcoholic extract (100mg/kg, ip,qod,21 days). Blood samples were collected at 14 and 21 days after the initial injection to evaluate the blood glucose levels. On the fourteenth day, the blood glucose levels for the diabetic groups were as follows: Group 2: 424.7±34.5, Group 3: 390.7±10.5, Group 4: 350.4±16.9, and Group 5: 340±20.5. On the 21st day, the respective blood glucose levels were: Group 2: 432±5.0, Group 3: 410.16±5.0, Group 4: 264.3±17.5, and Group 5: 270.7±24.5. Statistical analysis using the Tukey Anova test indicated that on the fourteenth day, both the green tea and Urtica groups exhibited significant hypoglycemic effects. Furthermore, on the 21st day, Urtica dioica extract demonstrated comparable effects to Camellia Sinensis extract, while Silybum Marianum extract did not significantly lower blood glucose levels compared to the diabetic group. In conclusion, the hydroalcoholic extracts from Camellia sinensis and Urtica dioica plants exhibited promising hypoglycemic effects in diabetic rats. These findings provide valuable insights into the potential use of natural plant extracts as alternative or complementary treatments for diabetes, warranting further investigation to harness their therapeutic benefit effectively.

Keywords: Camellia sinesis, glucose, Silybum marianum, Urtica dioica

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: Yudhajit Datta

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Will is a phenomenon that has puzzled humanity for a long time. It is a belief that Will Power of an individual affects the success achieved by an individual in life. It is thought that a person endowed with great will power can overcome even the most crippling setbacks of life while a person with a weak will cannot make the most of life even the greatest assets. Behavioral aspects of the human experience such as will are rarely subjected to quantitative study owing to the numerous uncontrollable parameters involved. This work is an attempt to subject the phenomena of will to the test of an artificial neural network. The claim being tested is that will power of an individual largely determines success achieved in life. In the study, an attempt is made to incorporate the behavioral phenomenon of will into a computational model using data pertaining to the success of individuals obtained from an experiment. A neural network is to be trained using data based upon part of the model, and subsequently used to make predictions regarding will corresponding to data points of success. If the prediction is in agreement with the model values, the model is to be retained as a candidate. Ultimately, the best-fit model from among the many different candidates is to be selected, and used for studying the correlation between success and will.

Keywords: will power, will, success, apathy factor, random factor, characteristic function, life story

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Authors: Ashok V. Sutagundar, Sunilkumar S. Manvi

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Energy, delay and bandwidth are the prime issues of wireless sensor network (WSN). Energy usage optimization and efficient bandwidth utilization are important issues in WSN. Event triggered data aggregation facilitates such optimal tasks for event affected area in WSN. Reliable delivery of the critical information to sink node is also a major challenge of WSN. To tackle these issues, we propose an event driven dynamic clustering and data aggregation scheme for WSN that enhances the life time of the network by minimizing redundant data transmission. The proposed scheme operates as follows: (1) Whenever the event is triggered, event triggered node selects the cluster head. (2) Cluster head gathers data from sensor nodes within the cluster. (3) Cluster head node identifies and classifies the events out of the collected data using Bayesian classifier. (4) Aggregation of data is done using statistical method. (5) Cluster head discovers the paths to the sink node using residual energy, path distance and bandwidth. (6) If the aggregated data is critical, cluster head sends the aggregated data over the multipath for reliable data communication. (7) Otherwise aggregated data is transmitted towards sink node over the single path which is having the more bandwidth and residual energy. The performance of the scheme is validated for various WSN scenarios to evaluate the effectiveness of the proposed approach in terms of aggregation time, cluster formation time and energy consumed for aggregation.

Keywords: wireless sensor network, dynamic clustering, data aggregation, wireless communication

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Authors: L. Rakesh, T. Y. Heblekar

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This study entails the development and optimization of a mathematical model for a floating drum biogas reactor from first principles using thermal and empirical considerations. The model was derived on the basis of mass conservation, lumped mass heat transfer formulations and empirical biogas formation laws. The treatment leads to a system of coupled nonlinear ordinary differential equations whose solution mapped four-time independent controllable parameters to five output variables which adequately serve to describe the reactor performance. These equations were solved numerically using fourth order Runge-Kutta method for a range of input parameter values. Using the data so obtained an Artificial Neural Network with a single hidden layer was trained using Levenberg-Marquardt Damped Least Squares (DLS) algorithm. This network was then fine-tuned for optimal mapping by varying hidden layer size. This fast forward model was then employed as a health score generator in the Bacterial Foraging Optimization code. The optimal operating state of the simplified Biogas reactor was thus obtained.

Keywords: biogas, floating drum reactor, neural network model, optimization

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Authors: Muhammad Rehan Usman, Muhammad Arslan Usman, Soo Young Shin

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The new era of digital communication has brought up many challenges that network operators need to overcome. The high demand of mobile data rates require improved networks, which is a challenge for the operators in terms of maintaining the quality of experience (QoE) for their consumers. In live video transmission, there is a sheer need for live surveillance of the videos in order to maintain the quality of the network. For this purpose objective algorithms are employed to monitor the quality of the videos that are transmitted over a network. In order to test these objective algorithms, subjective quality assessment of the streamed videos is required, as the human eye is the best source of perceptual assessment. In this paper we have conducted subjective evaluation of videos with varying spatial and temporal impairments. These videos were impaired with frame freezing distortions so that the impact of frame freezing on the quality of experience could be studied. We present subjective Mean Opinion Score (MOS) for these videos that can be used for fine tuning the objective algorithms for video quality assessment.

Keywords: frame freezing, mean opinion score, objective assessment, subjective evaluation

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Authors: Yousef Alqaryouti, Mariam Al Suwaidi, Raed Mohmood AlKhuwaildi, Hind Kolthoum, Issa Youssef, Mohammed Al Imam

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The UAE has experienced rapid economic growth due to its mature oil production industry, leading to a surge in urbanization and infrastructure development in the construction sector. Sustainable development practices are becoming increasingly important, and the UAE government has taken proactive measures to promote them, including the introduction of sustainable building codes, energy-efficient technologies, and renewable energy sources. Initiatives such as the Masdar City project and the Emirates Green Building Council further demonstrate the government's commitment to a cleaner and healthier environment. By adopting sustainable practices, the UAE can reduce its carbon footprint, lessen its reliance on fossil fuels, and achieve cost savings in the long run. The purpose of this paper is to conduct a thorough review of the current state of sustainability in the construction industry of the UAE. Our research methodology includes a local market survey and qualitative observational analysis of executed housing construction projects by the Mohammed Bin Rashid Housing Establishment. The market survey assesses eleven different challenging factors that affect sustainable construction project delivery. The qualitative observational research is based on data collected from three projects, including construction progress, bill of quantity, and construction program. The study concludes that addressing these challenges requires a collaborative team approach, incentivized contracts, traditional project management practices, an integrated project team, and an increase in sustainability awareness among stakeholders. The recommendations proposed in this study aim to promote and improve the application of sustainability in the UAE's construction industry for the future.

Keywords: sustainability, construction, challenges, opportunities, case study, market survey

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Authors: Ayman M. Mansour, Bilal Hawashin, Hesham Alsalem

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Faster delivery of laboratory test results is one of the most noticeable signs of good laboratory service and is often used as a key performance indicator of laboratory performance. Despite the availability of technology, the delivery time of clinical laboratory test results continues to be a cause of customer dissatisfaction which makes patients feel frustrated and they became careless to get their laboratory test results. The Medical Clinical Laboratory test results are highly sensitive and could harm patients especially with the severe case if they deliver in wrong time. Such results affect the treatment done by physicians if arrived at correct time efforts should, therefore, be made to ensure faster delivery of lab test results by utilizing new trusted, Robust and fast system. In this paper, we proposed a distributed Multi-Agent System to enhance and faster the process of laboratory test results delivery using SMS. The developed system relies on SMS messages because of the wide availability of GSM network comparing to the other network. The software provides the capability of knowledge sharing between different units and different laboratory medical centers. The system was built using java programming. To implement the proposed system we had many possible techniques. One of these is to use the peer-to-peer (P2P) model, where all the peers are treated equally and the service is distributed among all the peers of the network. However, for the pure P2P model, it is difficult to maintain the coherence of the network, discover new peers and ensure security. Also, security is a quite important issue since each node is allowed to join the network without any control mechanism. We thus take the hybrid P2P model, a model between the Client/Server model and the pure P2P model using GSM technology through SMS messages. This model satisfies our need. A GUI has been developed to provide the laboratory staff with the simple and easy way to interact with the system. This system provides quick response rate and the decision is faster than the manual methods. This will save patients life.

Keywords: multi-agent system, delivery process, GSM technology, clinical laboratory results

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Authors: Lateef Damilare Olusesi

Abstract:

This study will attempt to add to the limited literature on Nigerian Hospitality by exploring the role of sustainable leadership and green human resource management practices. The study will identify leadership perception of sustainable leadership practices, key perceptions based on behaviours, attributes, and skills if they differ across the hospitality sector in Nigeria, and how the identified perception compares to those found in previous research. Also, it explores the leadership competencies most valued in the Nigerian hospitality industry. Furthermore, assesses the benefits and risks of sustainability practices in the Nigerian hospitality industry; this will raise awareness for long-term sustainable practices through integrating renewable energy systems, reducing water and energy consumption, minimizing waste, and promoting an eco-conscious environment for both guests and employees. This will address challenges faced by all levels of management on sustainability practices, and how the influence of these practices will help the organisation to achieve its stated goals and remain competitive. Lastly, the study will propose a multi-level perception competence model (PCM) that contributes to the understanding of sustainable leadership. This competencies model will integrate knowledge, skills, and attitudes of sustainable leadership paradigms to establish a proposed multi-level model that will cut across top, middle, and lower management within the hospitality industry in Nigeria. Competencies in sustainability comprise knowledge, skills, and attitudes that facilitate effective task execution concerning real-world sustainability issues and opportunities. This model would facilitate sustainable leadership policies and strategies that are adaptable, collaborative, and centered on sustainability practices, prioritizing skills, and long-term sustainability objectives. Therefore, the specific objectives are; To examine the perception of sustainable leadership within the Hospitality industry. To identify the sustainable leadership competencies that are effective in the Hospitality industry. To investigate top, middle, and lower management's key challenges in adopting sustainable leadership.To develop a multi-level competencies-based model that contributes to the understanding of sustainable leadership Hospitality industry.

Keywords: sustainable leadership, green human resource mnagement, leadership, hospitality

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Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

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Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Marthin Dody Josias Sumajouw, Bryan Wijaya, Servie O. Dapas, Ronny E. Pandaleke, Banu Handono, Fabian J. Manoppo

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Ordinary Portland Cement (OPC) takes a big role as a concrete binder in infrastructure construction purposes, nevertheless, it produces CO2 emissions abundantly. To reduce the CO2 emissions produced by OPC concrete, nowadays, geopolymer material become one of the solutions due to it being a binder made from waste with pozzolan material. In concrete industries, geopolymer concrete has evolved as a more environmentally friendly material than OPC concrete. The geopolymer concrete was created without the usage of OPC known as cementless concrete materials. Geopolymer concrete obtains silicon and aluminum from industrial by-products such as fly ash, ground granulated blast furnace slag, and kaolinite. A highly alkaline solution chemically activates Si and Al, forming a matrix that holds together the loose aggregates as well as additional unreacted components in the mixture. They are then dissolved in alkaline activating solutions, where they polymerize into molecular chains, resulting in rigid binders. This research aims to get an eco-friendly material that can reduce the use of OPC as a binder and be used for infrastructure development end-products such as Curbs, Pavement Bricks, and Wall Bricks. This research was conducted as applied research to develop new products of environmentally friendly materials by utilizing fly ash and employed for infrastructure development, particularly for the production of end products such as Curbs, Pavement Bricks, and Wall Bricks. Three types of end products with various dimensions and mix designs have been made and tested in the laboratory, resulting in quantitative datasets to be used for identifying patterns and relationships among density, compressive strength, flexural strength, and water absorption. The result found that geopolymer binders can be used for the production of curbs, pavement bricks, and wall bricks. Geopolymer curbs have an average compressive strength of 19,36 MPa, which can be determined as K-233 concrete. Geopolymer pavement bricks have an average compressive strength of 20,79 MPa. It can be used in parking areas and determined as the grade B of pavement bricks according to SNI 03-0691-1996. Geopolymer wall bricks have an average compressive strength of 11,24 MPa, which can be determined as the grade I of Wall Bricks according to SNI 03-0349-1989.

Keywords: absorption, compressive strength, curbs, end products, geopolymer, pavement bricks, wall bricks

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Authors: Adamu Abdullahi, On Francisca, Saidu Isah Rambo, G. N. Obunadike, D. T. Chinyio

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The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings: The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed.

Keywords: algorithm, CoAP, DoS, IoT, machine learning

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

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Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

Procedia PDF Downloads 151