Search results for: delay tolerant networks

Authors: Tapan Kumar Dhar

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How well are our city forms designed to adapt to climate change and its resulting uncertainty? What urban design tools can be used to measure and improve resilience to climate change, and how would they do so? In addressing these questions, this paper considers indeterminacy, a concept originated in the resilience literature, to measure the resilience of built environments. In the realm of urban design, ‘indeterminacy’ can be referred to as built-in design capabilities of an urban system to serve different purposes which are not necessarily predetermined. An urban system, particularly that with a higher degree of indeterminacy, can enable the system to be reorganized and changed to accommodate new or unknown functions while coping with uncertainty over time. Underlying principles of this concept have long been discussed in the urban design and planning literature, including open architecture, landscape urbanism, and flexible housing. This paper argues that the concept indeterminacy holds the potential to reduce the impacts of climate change incrementally and proactively. With regard to sustainable development, both planning and climate change literature highly recommend proactive adaptation as it involves less cost, efforts, and energy than last-minute emergency or reactive actions. Nevertheless, the concept still remains isolated from resilience and climate change adaptation discourses even though the discourses advocate the incremental transformation of a system to cope with climatic uncertainty. This paper considers indeterminacy, as an urban design tool, to measure and increase resilience (and adaptive capacity) of Long Bay’s coastal settlements in Negril, Jamaica. Negril is one of the popular tourism destinations in the Caribbean highly vulnerable to sea-level rise and its associated impacts. This paper employs empirical information obtained from direct observation and informal interviews with local people. While testing the tool, this paper deploys an urban morphology study, which includes land use patterns and the physical characteristics of urban form, including street networks, block patterns, and building footprints. The results reveal that most resorts in Long Bay are designed for pre-determined purposes and offer a little potential to use differently if needed. Additionally, Negril’s street networks are found to be rigid and have limited accessibility to different points of interest. This rigidity can expose the entire infrastructure further to extreme climatic events and also impedes recovery actions after a disaster. However, Long Bay still has room for future resilient developments in other relatively less vulnerable areas. In adapting to climate change, indeterminacy can be reached through design that achieves a balance between the degree of vulnerability and the degree of indeterminacy: the more vulnerable a place is, the more indeterminacy is useful. This paper concludes with a set of urban design typologies to increase the resilience of coastal settlements.

Keywords: climate change adaptation, resilience, sea-level rise, urban form

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Authors: Gaurav Prabhudesai, Shaurya Kaushal, Pulkit Dubey, B. D. Pant

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The current race of miniaturization of circuits, systems, modules and networks has resulted in portable and mobile wireless systems having tremendous capabilities with small volume and weight. The power drivers or the power pack, electrically driving these modules have also reduced in proportion. Normally, the power packs in these mobile or fixed systems are batteries, rechargeable or non-rechargeable, which need regular replacement or recharging. Another approach to power these modules is to utilize the ambient energy available for electrical driving to make the system self-sustained. The current paper presents an overview of the different MEMS (Micro-Electro-Mechanical Systems) based techniques used for the harvesting of vibration energy to electrically drive a WSN (wireless sensor network) or a mobile module. This kind of system would have enormous applications, the most significant one, may be in cell phones.

Keywords: energy harvesting, WSN, MEMS, piezoelectrics

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Authors: Mounir Belattar

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In this paper the Analysis of voltage waves that propagate along a lossless exponential nonuniform line is presented. For this analysis the parameters of this line are assumed to be varying function of the distance x along the line from the source end. The approach is based on the tow-port networks cascading presentation to derive the ABDC parameters of transmission using Picard-Carson Method which is a powerful method in getting a power series solution for distributed network because it is easy to calculate poles and zeros and solves differential equations such as telegrapher equations by an iterative sequence. So the impedance, admittance voltage and current along the line are expanded as a Taylor series in x/l where l is the total length of the line to obtain at the end, the main transmission line parameters such as voltage response and transmission and reflexion coefficients represented by scattering parameters in frequency domain.

Keywords: ABCD parameters, characteristic impedance exponential nonuniform transmission line, Picard-Carson's method, S parameters, Taylor's series

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Authors: Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, Nitin Agarwal

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Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities.

Keywords: affective computing, deep learning, emotion recognition, multimodal

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Authors: Florin Leon, Silvia Curteanu

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The Bacterial Foraging Optimization (BFO) algorithm is inspired by the behavior of bacteria such as Escherichia coli or Myxococcus xanthus when searching for food, more precisely the chemotaxis behavior. Bacteria perceive chemical gradients in the environment, such as nutrients, and also other individual bacteria, and move toward or in the opposite direction to those signals. The application example considered as a case study consists in establishing the dependency between the reaction yield of hydrogels based on polyacrylamide and the working conditions such as time, temperature, monomer, initiator, crosslinking agent and inclusion polymer concentrations, as well as type of the polymer added. This process is modeled with a neural network which is included in an optimization procedure based on BFO. An experimental study of BFO parameters is performed. The results show that the algorithm is quite robust and can obtain good results for diverse combinations of parameter values.

Keywords: bacterial foraging, hydrogels, modeling and optimization, neural networks

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Authors: Serguei Cheloukhine

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This study examines corrupting and moonlighting enterprise among Russian law enforcement (Police) since the 2009 Police Reform (hereto forward referred to as Reform). This research is based on the survey of about two dozen police officers in Russia’s regions. In addition, we analyzed statistics on crime, policing and socio-economic situation in Russian regions. Congruently, some data on the police officer’s off-duty activities was collected from the Internet sites. These Reforms sought to curb corruption at all levels of the Russian civil service and among uniformed law enforcement (Police) personnel. Many thought that the rebranding of the Militsiya as ‘Politsiya’ (Police) would have a transformational effect, both within the organization as well as how others perceived it. Ultimately, the rebranding effort failed; the only actual changes were the organization’s name and its personnel's uniforms. In fact, the Reforms seems to have contributed to even more corruption and abuse of power, as well an expansion of Law Enforcement’s ties to Corrupt Networks.

Keywords: bribery, corruption, moonlighting, police reform, Russia

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Authors: Fiona Mwaniki

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Climate change is considered a serious threat to sustainable development globally and as one of the greatest ecological, economic and social challenges of our time. The global demand for food is projected to increase by 60% by 2050. Small holder farmers who are vulnerable to the adverse effects of climate change are expected to contribute to this projected demand. Effective climate change education and communication is therefore required for smallholder and subsistence farmers’ in order to build communities that are more climate change aware, prepared and resilient. In Kenya radio is the most important and dominant mass communication tool for agricultural extension. This study investigated the potential role of radio in influencing farmers’ understanding and use of climate change information. The broad aims of this study were three-fold. Firstly, to identify Kenyan farmers’ perceptions and responses to the impacts of climate change. Secondly, to develop radio programs that communicate climate change information to Kenyan farmers and thirdly, to evaluate the impact of information disseminated through radio on farmers’ understanding and responses to climate change mitigation and adaptation. This study was conducted within the farming community of Kilifi County, located along the Kenyan coast. Education and communication about climate change was undertaken using radio to make available information understandable to different social and cultural groups. A mixed methods pre-and post-intervention design that provided the opportunity for triangulating results from both quantitative and qualitative data was used. Quantitative and qualitative data was collected simultaneously, where quantitative data was collected through semi structured surveys with 421 farmers’ and qualitative data was derived from 11 focus group interviews, six interviews with key informants and nine climate change experts. The climate change knowledge gaps identified in the initial quantitative and qualitative data were used in developing radio programs. Final quantitative and qualitative data collection and analysis enabled an assessment of the impact of climate change messages aired through radio on the farming community in Kilifi County. Results of this study indicate that 32% of the farmers’ listened to the radio programs and 26% implemented technologies aired on the programs that would help them adapt to climate change. The most adopted technologies were planting drought tolerant crops including indigenous crop varieties, planting trees, water harvesting and use of manure. The proportion of farmers who indicated they knew “a fair amount” about climate change increased significantly (Z= -5.1977, p < 0.001) from 33% (at the pre intervention phase of this study) to 64% (post intervention). However, 68% of the farmers felt they needed “a lot more” information on agriculture interventions (43%), access to financial resources (21%) and the effects of climate change (15%). The challenges farmers’ faced when adopting the interventions included lack of access to financial resources (18%), high cost of adaptation measures (17%), and poor access to water (10%). This study concludes that radio effectively complements other agricultural extension methods and has the potential to engage farmers’ on climate change issues and motivate them to take action.

Keywords: climate change, climate change intervention, farmers, radio

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Authors: Carlo Schick

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Legitimacy can be regarded the very fabric of political orders. Up to this point, IR scholarship was particularly interested in the legitimacy of nation-states, international regimes and of non-governmental actors. The legitimacy of collaborative governance comprising public, private and civic actors, however, has not received much attention from an IR perspective. This is partly due to the fact that the concept of legitimacy is difficult to operationalise and measure in settings where there is no clear boundary between political authorities and those who are subject to collaborative governance. In this case, legitimacy cannot be empirically approached in its own terms, but can only be analysed in terms of dialectic legitimation processes. The author develops a three-fold analytical framework based on a dialogical understanding of legitimation. Legitimation first has to relate to public legitimacy demands and contestations of collaborative governance and second to legitimacy claims issued by collaborative governance networks themselves. Lastly, collaborative governance is dependent on constant self-legitimisation. The paper closes with suggesting a discourse analytic approach to further empirical research on the legitimacy of collaborative governance.

Keywords: legitimacy, collaborative governance, discourse analysis, dialectic legitimation

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

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Authors: Habib Muhammetoglu, I. Ethem Karadirek, Selami Kara, Ayse Muhammetoglu

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The problem of non-revenue water (NRW) from municipal water distribution networks is common in many countries such as Turkey, where the average yearly water losses are around 50% . Water losses can be divided into two major types namely: 1) Real or physical water losses, and 2) Apparent or commercial water losses. Total water losses in Antalya city, Turkey is around 45%. Methods: A research study was conducted to develop appropriate methodologies to reduce NRW. A pilot study area of about 60 thousands inhabitants was chosen to apply the study. The pilot study area has a supervisory control and data acquisition (SCADA) system for the monitoring and control of many water quantity and quality parameters at the groundwater drinking wells, pumping stations, distribution reservoirs, and along the water mains. The pilot study area was divided into 18 District Metered Areas (DMAs) with different number of service connections that ranged between a few connections to less than 3000 connections. The flow rate and water pressure to each DMA were on-line continuously measured by an accurate flow meter and water pressure meter that were connected to the SCADA system. Customer water meters were installed to all billed and unbilled water users. The monthly water consumption as given by the water meters were recorded regularly. Water balance was carried out for each DMA using the well-know standard IWA approach. There were considerable variations in the water losses percentages and the components of the water losses among the DMAs of the pilot study area. Old Class B customer water meters at one DMA were replaced by more accurate new Class C water meters. Hydraulic modelling using the US-EPA EPANET model was carried out in the pilot study area for the prediction of water pressure variations at each DMA. The data sets required to calibrate and verify the hydraulic model were supplied by the SCADA system. It was noticed that a number of the DMAs exhibited high water pressure values. Therefore, pressure reducing valves (PRV) with constant head were installed to reduce the pressure up to a suitable level that was determined by the hydraulic model. On the other hand, the hydraulic model revealed that the water pressure at the other DMAs cannot be reduced when complying with the minimum pressure requirement (3 bars) as stated by the related standards. Results: Physical water losses were reduced considerably as a result of just reducing water pressure. Further physical water losses reduction was achieved by applying acoustic methods. The results of the water balances helped in identifying the DMAs that have considerable physical losses. Many bursts were detected especially in the DMAs that have high physical water losses. The SCADA system was very useful to assess the efficiency level of this method and to check the quality of repairs. Regarding apparent water losses reduction, changing the customer water meters resulted in increasing water revenue by more than 20%. Conclusions: DMA, SCADA, modelling, pressure management, leakage detection and accurate customer water meters are efficient for NRW.

Keywords: NRW, water losses, pressure management, SCADA, apparent water losses, urban water distribution networks

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Authors: Dilshad Ali Mohammed, Ziyad Nayef Shamsulddin Aldoski, Millet Salim Mohammed

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The increase in levels of traffic congestion along urban signalized arterials needs efficient traffic management. The application of traffic signal coordination can improve the traffic operation and safety for a series of signalized intersection along the arterials. The objective of this study is to evaluate the benefits achievable through actuated traffic signal coordination and make a comparison in control delay against the same signalized intersection in case of being isolated. To accomplish this purpose, a series of eight signalized intersections located on two major arterials in Duhok City was chosen for conducting the study. Traffic data (traffic volumes, link and approach speeds, and passenger car equivalent) were collected at peak hours. Various methods had been used for collecting data such as video recording technique, moving vehicle method and manual methods. Geometric and signalization data were also collected for the purpose of the study. The coupling index had been calculated to check the coordination attainability, and then time space diagrams were constructed representing one-way coordination for the intersections on Barzani and Zakho Streets, and others represented two-way coordination for the intersections on Zakho Street with accepted progression bandwidth efficiency. The results of this study show great progression bandwidth of 54 seconds for east direction coordination and 17 seconds for west direction coordination on Barzani Street under suggested controlled speed of 60 kph agreeable with the present data. For Zakho Street, the progression bandwidth is 19 seconds for east direction coordination and 18 seconds for west direction coordination under suggested controlled speed of 40 kph. The results show that traffic signal coordination had led to high reduction in intersection control delays on both arterials.

Keywords: bandwidth, congestion, coordination, traffic, signals, streets

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Authors: Mehwish Raza

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Mobile learning (m-learning) offers unique affordances to learners, setting them free of limitations posed by time and geographic space; thus becoming an affordable device for convenient distant learning. There is a plethora of research available on mobile learning projects planned, implemented and evaluated across disciplines in the context of developed countries, however, the potential of m-learning at different educational levels remain unexplored with little evidence of research carried out in developing countries. Despite the favorable technical infrastructure offered by cellular networks and boom in mobile subscriptions in the developing world, there is limited focus on utilizing m-learning for education and development purposes. The objective of this review is to unify findings from m-learning projects that have been implemented in developing countries such as Pakistan, Bangladesh, Philippines, India, and Tanzania for teachers’ in-service training. The purpose is to draw upon key characteristics of mobile learning that would be useful for future researchers to inform conceptualizations of mobile learning for developing countries.

Keywords: design model, developing countries, key characteristics, mobile learning

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Authors: Peggy S. C. van Teunenbroek

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Recently, philanthropic crowd-funding -the raising of external funding from a large audience via social networks or social media- emerged as a new funding instrument for the Dutch cultural sector. However, such philanthropic crowdfunding in the US and the Netherlands is less successful than any other form of crowdfunding. We argue that social aspects are an important stimulus in philanthropic crowd-funding since previous research has shown that crowdfunding is stimulated by something beyond financial merits. Put simply, crowd-funding seems to be a socially motivated activity. In this paper we focus on the effect of social information, described as information about the donation behavior of previous donors. Using a classroom experiment we demonstrated a positive effect of social information on the donation behavior in crowdfunding campaigns. Our study extends previous research by showing who is affected by social information and why, and highlights how social information can be used to stimulate individuals to donate more to crowdfunding projects.

Keywords: online donation behavior, philanthropic crowdfunding, social information, social influence, social motivation

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Authors: Mohammadhosein Hasanbeig, Lacra Pavel

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In this paper, we study a distributed control algorithm for the problem of unknown area coverage by a network of robots. The coverage objective is to locate a set of targets in the area and to minimize the robots’ energy consumption. The robots have no prior knowledge about the location and also about the number of the targets in the area. One efficient approach that can be used to relax the robots’ lack of knowledge is to incorporate an auxiliary learning algorithm into the control scheme. A learning algorithm actually allows the robots to explore and study the unknown environment and to eventually overcome their lack of knowledge. The control algorithm itself is modeled based on game theory where the network of the robots use their collective information to play a non-cooperative potential game. The algorithm is tested via simulations to verify its performance and adaptability.

Keywords: distributed control, game theory, multi-agent learning, reinforcement learning

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Authors: Mohamed Abouelresh, Subhajit Kumar, Lamidi Babalola, Septriandi Chan, Ali Al Musabeh A., Thadickal V. Joydas, Bruno Pulido

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Neom has a relatively long coastline on the Red Sea and the Gulf of Aqaba, which is about 300 kilometres long, in addition to many naturally formed bays along the Red Sea coast. Undoubtedly, these coasts provide an excellent opportunity for tourism and other activities; however, these coastal areas host a wide range of salinity-dependent ecosystems that need to be protected. The main objective of the study was to identify the coastal features, including tidal flats and salt flats, along the NEOM coast. A base map of the study area generated from the satellite images contained the main landform features and, in particular, the boundaries of the inland and coastal sabkhas. A field survey was conducted to map and characterize the intertidal and sabkha landforms. The coastal and inner coastal areas of NEOM are mainly covered by the quaternary sediments, which include gravel sheets, terraces, raised reef limestone, evaporite successions, eolian dunes, and undifferentiated sand/gravel deposits (alluvium, alluvial outwash, wind-blown sand beach). There are different landforms that characterizes the NEOM coast, including rocky coast, tidal zone, and sabkha. Sabkha area ranges between a few to tens of square kilometers. Coastal sabkha extended across the shoreline of NEOM, specifically at Gayal and Sharma areas, while the continental sabkha only existed at Gayal Town. The inland Sabkha at Gayal is mainly composed of a thin (15-25 cm) evaporite crust composed of a dark brown, cavernous, rugged, pitted, colloidal salty sand layer with salt-tolerant vegetation. The inland Sabkha is considered a groundwater-driven sedimentary system as indicated by syndepositional intra-sediment capillary evaporites, which precipitate in both marine and continental salt flats. Gayal coastal Sabkha is made up of tidal inlets, tidal creeks, and lagoons followed in a landward direction with well-developed sabkha layers. The surface sediments of the coastal Sabkha are composed of unlithified calcareous, gypsiferous, coarse to medium sands, and silt with bioclastic fragments underlain by several organic-rich layers. The coastal flat is graded landward into widespread, flat vegetated Sabkhas dissected by tributaries of the fluvial system, which debouches to the Red Sea. The coast from Gayal to Magna through Ras El-Sheikh Humaid is continuously subjected to tidal flows, which create an intertidal depositional system. The intertidal flats at NEOM are extensive, nearly horizontal land forming a very dynamic system in which several physical, chemical, geomorphological, and biological processes are acting simultaneously. The current work provides a field-based identification of the coastal sabkha and intertidal sites at NEOM. However, the mutual interaction between tidal flows and sabkha development, particularly at Gayal, needs to be well understood through comprehensive field and lab analysis.

Keywords: coast, intertidal, deposition, sabkha

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Authors: Vidhi Chandra, Arshpreet Singh Grewal

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A five-year-old male child born from non-consanguineous marriage, who presented with complaints of growth retardation and no appreciable increase in the penile size since birth and he was posted for de-gloving of penis with dissection of corpora under anaesthesia. After thorough preoperative evaluation it was revealed that patient had peculiar facial dysmorphism that of Robinow Syndrome, high arched palate, Mallampati grade III, mesomelic limbs, scoliotic spine and short stature. All routine investigation were within normal limit, electrocardiography (ECG) and 2D-Echocardiography (ECHO) were normal. In antero-posterior roentgenogram chest showed butterfly and hemivertebrae at multiple levels. The patient was considered to be ASA II. On the day of surgery after ensuring fasting of 6 hours, patient was taken in operation theatre, all standard ASA monitoring was done with ECG, non-invasive blood pressure, peripheral oxygen saturation (SpO2) and body temperature. The patient was pre-oxygenated with 100% oxygen with anatomical face mask. General anaesthesia was induced with Sevoflurane 1-8%, and airway was secured with an appropriate size supraglottic airway and anaesthesia was maintained with nitrous oxide and oxygen in 1:1 ratio along with sevoflurane 2%. An ultrasound guided caudal block was given owing to the skeletal deformities making it difficult even under USG guidance. Post operatively patient was given supportive care with proper hydration, antibiotics, anti-inflammatory and analgesics. He was discharged the next day and followed up weekly for a month. DISCUSSION Robinow syndrome is genetically inherited as autosomal dominant, autosomal recessive or heterogenous disorder involving tyrosine kinase ROR2 gene located on chromosome 9. It has low incidence with no preponderance for any gender. Though intelligence is normal but developmental delay and mental retardation occurs in 20%cases

Keywords: Robinow Syndrome, dwarfism, paediatric, anaesthesia

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

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Authors: Jose L. Oliver, Taras Agryzkov, Leandro Tortosa, Jose F. Vicent, Javier Santacruz

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This paper aims to demonstrate how a topological study of an urban street network can be used as a tool to be applied to some heritage conservation areas in a city. In the last decades, we find different kinds of approaches in the discipline of Architecture and Urbanism based in the so-called Sciences of Complexity. In this context, this paper uses mathematics from the Network Theory. Hence, it proposes a methodology based in obtaining information from a graph, which is created from a network of urban streets. Then, it is used an algorithm that establishes a ranking of importance of the nodes of that network, from its topological point of view. The results are applied to a heritage area in a particular city, confronting the data obtained from the mathematical model, with the ones from the field work in the case study. As a result of this process, we may conclude the necessity of implementing some actions in the area, and where those actions would be more effective for the whole heritage site.

Keywords: graphs, heritage cities, spatial analysis, urban networks

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Authors: Dulal Bhaumik, Fei Jie, Runa Bhaumik, Bikas Sinha

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Human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease and autism. fMRI has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose mixed-effects models together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities in whole brain studies. Results are illustrated with a large data set known as Autism Brain Imaging Data Exchange or ABIDE which includes 361 subjects from 8 medical centers. We believe that our findings have addressed adequately the small sample inference problem, and thus are more reliable for therapeutic target for intervention. In addition, our result can be used for early detection of subjects who are at high risk of developing neurological disorders.

Keywords: ABIDE, autism spectrum disorder, fMRI, mixed-effects model

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Authors: Heather Wyman-Pain, Yuankai Bian, Furong Li

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The decreasing use of fossil fuel power stations has a negative effect on the stability of the electricity systems in many countries. Nuclear power stations have traditionally provided minimal ancillary services to support the system but this must change in the future as they replace fossil fuel generators. This paper explains the development of the four most popular reactor types still in regular operation across the world which have formed the basis for most reactor development since their commercialisation in the 1950s. The use of nuclear power in four countries with varying levels of capacity provided by nuclear generators is investigated, using the primary frequency response provided by generators as a measure for the electricity networks stability, to assess the need for nuclear generators to provide additional support as their share of the generation capacity increases.

Keywords: frequency control, nuclear power generation, power system stability, system inertia

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Authors: Mohsen Sanayei, Jerzy Szpunar

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The present study investigates the effects of deformation strain and annealing temperature on the formation of twin boundaries, deformation and recrystallization texture evolution and grain boundary networks and connectivity. The resulting microstructures were characterized using Electron Backscatter Diffraction (EBSD) and X-Ray Diffraction (XRD) both immediately following small amount of deformation and after short time annealing at high temperature to correlate the micro and macro texture evolution of these alloys. Furthermore, this study showed that the process of grain boundary engineering, consisting cycles of deformation and annealing, is found to substantially reduce the mass and size of random boundaries and increase the proportion of low Coincidence Site Lattice (CSL) grain boundaries.

Keywords: coincidence site lattice, grain boundary engineering, electron backscatter diffraction, texture, x-ray diffraction

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Authors: Azzouzi Fadoua

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This study focuses on the diagnosis of the dynamics of natural resources in a semi-arid mountainous weakened by natural vulnerability and anthropogenic action. This is evident in the forms of hydraulic erosion and degradation of agricultural land. The rate of this damaged land is 53%, with a strong presence of concentrated erosion; this shows that balanced and semi-balanced environments are less apparent to the Watershed, representing 47%. The results revealed the crucial role of the slopes and the density of the hydraulic networks to facilitate the transport of fine elements, at the level of the slopes with low vegetation intensity, to the lake of the dam. Something that endangers the siltation of the latter. After the study of natural and anthropogenic elements, it turned out that natural vulnerability is an integral part of the current dynamic, especially when it coincides with the overexploitation of natural resources, in this case, the exploitation of steep slopes for the cultivation of cereals and overgrazing. This causes the soil to pile up and increase the rate of runoff.

Keywords: watershed, erosion, natural vulnerability, anthropogenic

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Authors: Molla Tadesse Lakew

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Researchers tried to find out the socio-economic and supply-side constraint factors to adoption. However, they overlooked the role of social networks and relationships among the community. Therefore, the aims of this review were to review the roles and negative effects of social capital. Based on its contents, relevancy, and time duration, only 121 (journals, books, and paper reports) were selected. It concludes that social capital has an indispensable role in facilitating cooperation and connection between members of the farmers' community, informal and experiential knowledge sharing, and access to research-based knowledge and contributes to reducing the transaction cost of adoption. On the contrary, inside the black box of social capital, the negative effects include the exclusion of outsider’s knowledge and experiences, excessive claims on group members, and restrictions on individual freedom.

Keywords: social capital, local institutions, adoption, Ethiopia

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Authors: Younes El Khchine, Mohammed Sriti

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A parametric study has been conducted to analyse the flow around S809 airfoil of a wind turbine in order to better understand the characteristics and effects of laminar separation bubble (LSB) on aerodynamic design for maximizing wind turbine efficiency. Numerical simulations were performed at low Reynolds numbers by solving the Unsteady Reynolds Averaged Navier-Stokes (URANS) equations based on C-type structural mesh and using the γ-Reθt turbulence model. A two-dimensional study was conducted for the chord Reynolds number of 1×10⁵ and angles of attack (AoA) between 0 and 20.15 degrees. The simulation results obtained for the aerodynamic coefficients at various angles of attack (AoA) were compared with XFoil results. A sensitivity study was performed to examine the effects of Reynolds number and free-stream turbulence intensity on the location and length of the laminar separation bubble and the aerodynamic performances of wind turbines. The results show that increasing the Reynolds number leads to a delay in the laminar separation on the upper surface of the airfoil. The increase in Reynolds number leads to an accelerated transition process, and the turbulent reattachment point moves closer to the leading edge owing to an earlier reattachment of the turbulent shear layer. This leads to a considerable reduction in the length of the separation bubble as the Reynolds number is increased. The increase in the level of free-stream turbulence intensity leads to a decrease in separation bubble length and an increase in the lift coefficient while having negligible effects on the stall angle. When the AoA increased, the bubble on the suction airfoil surface was found to move upstream to the leading edge of the airfoil, that causes earlier laminar separation.

Keywords: laminar separation bubble, turbulence intensity, S809 airfoil, transition model, Reynolds number

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Authors: Soumaya Sallem, Marc Olivas

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This contribution presents a method for detecting, locating, and characterizing soft faults in a complex wired network. The proposed method is based on multi-carrier reflectometry MCTDR (Multi-Carrier Time Domain Reflectometry) combined with a multi-objective genetic algorithm. In order to ensure complete network coverage and eliminate diagnosis ambiguities, the MCTDR test signal is injected at several points on the network, and the data is merged between different reflectometers (sensors) distributed on the network. An adapted multi-objective genetic algorithm is used to merge data in order to obtain more accurate faults location and characterization. The proposed method performances are evaluated from numerical and experimental results.

Keywords: wired network, reflectometry, network distributed diagnosis, multi-objective genetic algorithm

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Authors: Ewa Szarek-Gwiazda, Agata Z. Wojtal, Agnieszka Pociecha, Andrzej Kownacki, Dariusz Ciszewski

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Mining of metal ores is one of the largest sources of heavy metals, which deteriorate aquatic systems. The response of organisms to environmental changes can be well recorded in sediments of the affected water bodies and may be reconstructed based on analyses of organisms' remains. The present study aimed at the response of diatoms (Bacillariophyta), Cladocera, and Chironomidae communities to the impact of Zn-Pb mine water discharge recorded in sediment cores of small subsidence ponds on the Chechło River floodplain (Silesia–Krakow Region, southern Poland). We hypothesize various responses of the above groups to high metal concentrations (Cd, Pb, Zn, and Cu). The investigated ponds were formed either during the peak of the ore exploitation (DOWN) or after mining cessation (UP). Currently, the concentrations of dissolved metals (in µg g⁻¹) in water reached up to 0.53 for Cd, 7.3 for Pb, and up to 47.1 for Zn. All the sediment cores from subsidence ponds were heavily polluted with Cd 6.7–612 μg g⁻¹, Pb 0.1–10.2 mg g⁻¹, and Zn 0.5–23.1 mg g⁻¹. Core sediments varied also in respect to pH 5.8-7.1 and concentrations of organic matter (5.7-39.8%). The impact of high metal concentrations was expressed by the occurrence of metal-tolerant taxa like diatoms – Nitzschia amphibia, Sellaphora nigri, and Surirella brebisonii var. kuetzingii; Cladocera – Chydorus sphaericus (dominated in cores from all ponds), and Chironomidae – Chironomus and Cricotopus especially in the DOWN ponds. Statistical analysis exhibited a negative impact of metals on some taxa of diatoms and Cladocera but only on Polypedilum sp. from Chironomidae. The abundance of such diatoms like Gomphonema utae, Staurosirella pinnata, Eunotia bilunaris, and Cladocera like Alona, Chydorus, Graptoleberis, and Pleuroxus decreased with increasing Pb concentration. However, the occurrence or dominance of more sensitive species of diatoms and Cladocera indicates their adaptation to higher metal loads, which was facilitated by neutral pH and slightly alkaline waters. Diatom assemblages were generally resistant to Zn, Pb, Cu, and Cd pollution, as indicated by their large similarity to populations from non-contaminated waters. Comparison with reference objects clearly indicates the dominance of Achnanthidium minutissimum, Staurosira venter, and Fragilaria gracilis in very diverse assemblages of unpolluted waters. The distribution of the Cladocera and Chironomidae taxa depended on the habitat type. The DOWN ponds with stagnant water and overgrown with macrophytes were more suitable for cladocerans (14 taxa, higher diversity) than the UP ponds with river water flowing through their centre and with a small share of macrophytes (8 taxa). The Chironominae, mainly Chironomus and Microspectra, were abundant in cores from the UP ponds with muddy bottoms. Inversely, the density of Orthocladiinae, especially genus Cricotopus, was related to the organic matter content and dominated in cores from the DOWN ponds. The presence of diatoms like Nitzschia amphibia, Sellaphora nigri, and Surirella brebisonii var. kuetzingii, cladocerans: Bosmina longirostris, Chydorus sphaericus, Alona affinis, and A. rectangularis as well as Chironomidae Chironomus sp. (UP ponds) and Psecrotanypus varius (DOWN ponds) indicate the influence of the water trophy on their distribution.

Keywords: Chironomidae, Cladocera, diatoms, metals, Zn-Pb mine, sediment cores, subsidence ponds

Procedia PDF Downloads 80

Authors: C. W. F. Che Wan Putra, M. Alshawi, M. S. Al Ahbabi, M. Jabakhanji

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The construction industry is a multi-disciplinary and multi-national industry, which has an important role to play within the overall economy of any country. There are major challenges to an improved performance within the industry. Particularly lacking is, the ability to capture the large amounts of information generated during the life-cycle of projects and to make these available, in the right format, so that professionals can then evaluate alternative solutions based on life-cycle analysis. The fragmented nature of the industry is the main reason behind the unavailability and ill utilisation of project information. The lack of adequately engaging clients and managing their requirements contributes adversely to construction budget and schedule overruns. This is a difficult task to achieve, particularly if clients are not continuously and formally involved in the design and construction process, which means that the design intent is left to designers that may not always satisfy clients’ requirements. Client lead is strongly recognised in bringing change through better collaboration between project stakeholders. However, one of the major challenges is that collaboration is operated under conventional procurement methods, which hugely limit the stakeholders’ roles and responsibilities to bring about the required level of collaboration. A research has been conducted with a typical project in the UAE. A qualitative research work was conducted including semi-structured interviews with project partners to discover the real reasons behind this delay. The case study also investigated the real causes of the problems and if they can be adequately addressed by BIM and IPD. Special focus was also placed on the Client leadership and the role the Client can play to eliminate/minimize these problems. It was found that part of the ‘key elements’ from which the problems exist can be attributed to the client leadership and the collaborative environment and BIM.

Keywords: client leadership, building information modelling (BIM), integrated project delivery (IPD), case study

Procedia PDF Downloads 324

Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Oluwayemisi Femi-Pius, Kazeem Arogundade, Eberechukwu Eke, Jimmy Eko

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Introduction: Globally, community involvement in improving their own health has been widely adopted as a strategy in Sub-Saharan Africa principally to ensure equitable access to essential health care as well as improve the uptake of maternal and newborn health services especially in poor-resource settings. Method: The Saving Mother Giving Life (SMGL) Initiative implemented by Pathfinder International with funding support from USAID conducted a Health Facility Assessment (HFA) and found out that maternal mortality ratio in Cross River State was 812 per 100,000 live birth and perinatal mortality was 160 per 1000 live birth. To reduce maternal and perinatal mortality, Pathfinder International mobilized, selected and trained community members as community volunteers, traditional birth attendants, and emergency transport service volunteer drivers mainly to address the delay in decision making and reaching the health facility among pregnant women. Results: The results showed that maternal mortality ratio in Cross River State decrease by 25% from 812 per 100,000 live birth at baseline to 206 per 100,000 live birth at June 2018 and perinatal mortality reduced by 35% from 160 per 100,000 at baseline to 58 per 1000 live birth at June 2018. Data also show that ANC visit increased from 7,451 to 11,344; institutional delivery increased from 8,931 at baseline to 10,784 in June 2018. There was also a remarkable uptake of post-partum family planning from 0 at baseline to 233 in June 2018. Conclusion: There is clear evidence that community involvement yields positive maternal outcomes and is pivotal for sustaining most health interventions.

Keywords: maternal mortality, Nigeria, pathfinder international, perinatal mortality, saving mother giving life

Procedia PDF Downloads 192