Search results for: challenging visitors

Authors: Ajish K. Abraham

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Hearing aid is the most fundamental technology employed towards rehabilitation of persons with sensory neural hearing impairment. Hearing in noise is still a matter of major concern for many hearing aid users and thus continues to be a challenging issue for the hearing aid designers. Several techniques are being currently used to enhance the speech at the hearing aid output. Most of these techniques, when implemented, result in reduction of intelligibility of the speech signal. Thus the dissatisfaction of the hearing aid user towards comprehending the desired speech amidst noise is prevailing. Multichannel Wiener Filter is widely implemented in binaural hearing aid technology for noise reduction. In this study, Wiener filter based noise reduction approach is experimented for a single microphone based hearing aid set up. This method checks the status of the input speech signal in each frequency band and then selects the relevant noise reduction procedure. Results showed that the Wiener filter based algorithm is capable of enhancing speech even when the input acoustic signal has a very low Signal to Noise Ratio (SNR). Performance of the algorithm was compared with other similar algorithms on the basis of improvement in intelligibility and SNR of the output, at different SNR levels of the input speech. Wiener filter based algorithm provided significant improvement in SNR and intelligibility compared to other techniques.

Keywords: hearing aid output speech, noise reduction, SNR improvement, Wiener filter, speech enhancement

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Authors: Endale Fantahun Tadesse, Sabika Khalid

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The global call for increased male representation in early childhood education (ECE) has garnered significant attention. Emerging studies have indicated that involving men in ECE can yield positive outcomes for children's physical and psychological development. Challenging the prevailing misconception and stereotype that women dominate the ECE sector is crucial. In light of this, the present study undertakes a systematic review of nine studies on males working in ECE, revealing a dearth of male presence in the field in China as well. To address this issue, substantial structural changes must be implemented to enhance the inadequate pay and working conditions that dissuade both men and women from pursuing a sustainable career in ECE. It is recommended that school leadership raise awareness among female teachers and parents, encouraging them to support and uphold virtuous values for male teachers. Additionally, governing bodies should provide explicit guidelines during training programs to address concerns regarding potential abuse and gender biases. The findings of this review underscore the need for future studies to examine the self-identities of male teachers from various stakeholders' perspectives and explore the consequences of being in the profession through rigorous and robust methodologies that can inform policymakers.

Keywords: male teachers, Early Childhood Education (ECE), self-identity, perception of stakeholders

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Authors: Rahimullah Habibzai

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To achieve a sustainable concrete industry, reduce exploitation of the natural aggregate resources, and mitigate waste concrete environmental burden, one way is to use recycled concrete aggregate. The utilization of low-quality fine aggregate inclusively recycled concrete sand that is produced from crushing waste concrete recently has become a popular and challenging topic among researchers nowadays. This study provides a scientific base for promoting the application of concrete waste as fine aggregate in producing concrete by conducting a comprehensive laboratory program. The mechanical properties of mortar made from recycled concrete fine aggregate (RCFA), that is produced by pulse power crushing concrete waste are satisfactory and capable of being utilized in the construction industry. A better treatment of RCFA particles and enhancing its quality will make it possible to be utilized in producing structural concrete. Pulse power discharge technology is proposed in this research to produce RCFA, which is a more effective and promising technique compared to other recycling methods to generate medium to high-quality recycled concrete fine aggregate with a reduced amount of powder, mitigate the environmental burden, and save more space.

Keywords: construction and demolition waste, concrete waste recycle fine aggregate, pulse power discharge

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Shivam Dwivedi, Sumit Prakash Gupta, Durga Toshniwal

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It is a challenging task in today’s date to keep defence forces in the highest state of combat readiness with budgetary constraints. A huge amount of time and money is squandered in the unnecessary and expensive traditional maintenance activities. To overcome this limitation Defence Intelligent Mission Readiness and Maintenance Scheduling System has been proposed, which ameliorates the maintenance system by diagnosing the condition and predicting the maintenance requirements. Based on new data mining algorithms, this system intelligently optimises mission readiness for imminent operations and maintenance scheduling in repair echelons. With modified data mining algorithms such as Weighted Feature Ranking Genetic Algorithm and SVM-Random Forest Linear ensemble, it improves the reliability, availability and safety, alongside reducing maintenance cost and Equipment Out of Action (EOA) time. The results clearly conclude that the introduced algorithms have an edge over the conventional data mining algorithms. The system utilizing the intelligent condition-based maintenance approach improves the operational and maintenance decision strategy of the defence force.

Keywords: condition based maintenance, data mining, defence maintenance, ensemble, genetic algorithms, maintenance scheduling, mission capability

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Authors: Venkata Ramana Pamu

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This paper is a case study of a 5310 ML capacity Raw Water Reservoir (RWR), situated in Indian state Rajasthan, which is a part of Rajasthan Rural Water Supply & Fluorosis Mitigation Project. This RWR embankment was constructed by locally available material on natural ground profile. Height of the embankment was varying from 2m to 10m.This is due to existing ground level was varying. Reservoir depth 9m including 1.5m free board and 1V:3H slopes were provided both upstream and downstream side. Proper soil investigation, tests were done and it was confirmed that the existing soil is sandy silt. The existing excavated earth was used as filling material for embankment construction, due to this controlling seepage from upstream to downstream be a challenging task. Slope stability and Seismic analysis of the embankment done by Conventional method for both full reservoir condition and rapid drawdown. Horizontal filter at toe level was provided along with upstream side PCC (Plain Cement Concrete) block and HDPE (High Density poly ethylene) lining as a remedy to control seepage. HDPE lining was also provided at storage area of the reservoir bed level. Mulching was done for downstream side slope protection.

Keywords: raw water reservoir, seepage, seismic analysis, slope stability

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

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Authors: S. Kadiyala, M. Berzins, D. Juba, W. Keyrouz

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This paper describes the Material Point Method (MPM) for simulating a single-track laser melting process on an IN718 solid plate. MPM, known for simulating challenging multiphysics problems, is used to model the intricate thermal, mechanical, and fluid interactions during the laser sintering process. This study analyzes the formation of single tracks, exploring the impact of varying laser parameters such as speed, power, and spot diameter on the melt pool and track formation. The focus is on MPM’s ability to accurately simulate and capture the transient thermo-mechanical and phase change phenomena, which are critical in predicting the cooling rates before and after solidification of the laser track and the final melt pool geometry. The simulation results are rigorously compared with experimental data (AMB2022 benchmarks), demonstrating the effectiveness of MPM in replicating the physical processes in laser sintering. This research highlights the potential of MPM in advancing the understanding and simulation of melt pool physics in metal additive manufacturing, paving the way for optimized process parameters and improved material performance.

Keywords: dditive manufacturing simulation, material point method, phase change, melt pool physics

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

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It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Jefferson Hernandez, Juan Padilla

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Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach.

Keywords: price elasticity, volume, correlation structures, Bayesian models

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Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

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An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

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Authors: Sophie Herr, Antoine Leybros, Yves Barre, Sergey Nikitenko, Rachel Pflieger

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The proportion of soil contaminated by a wide range of pollutants (heavy metals, PCBs, pesticides, etc.) of anthropogenic origin is constantly increasing, and it is becoming urgent to address this issue. Among remediation methods, soil washing is an effective, relatively fast, and widely used process. This study assesses its coupling with ultrasound: indeed, sonication induces the formation of cavitation bubbles in solution that enhance local mass transfer through agitation and particle erosion. The removal of target toxic elements Ni(II) and Zn(II) from vermiculite clay has been studied under 20 kHz ultrasound and silent conditions. Several acids were tested, and HCl was chosen as the solvent. The effects of solid/liquid ratio and particle size were investigated. Metal repartition in the clay has been followed by Tessier's sequential extraction procedure. The results showed that more metal elements bound to the challenging residual phase were desorbed with 20 kHz ultrasound than in silent conditions. This supports the promising application of ultrasound for heavy metal desorption in difficult conditions. Further experiments were performed at high-frequency US (362 kHz), and it was shown that fragmentation of the vermiculite particles is then limited, while positive effects of US in the decontamination are kept.

Keywords: desorption, heavy metals, ultrasound, vermiculite

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Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

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Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Raquel Camprubí

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Cultural tourism has been postulated as relevant motivation for tourist over the world during the last decades. In this context, museums are the main attraction for cultural tourists who are seeking to connect with the history and culture of the visited place. From the point of view of an urban destination, museums and other cultural resources are essential to have a strong tourist supply at the destination, in order to be capable of catching attention and interest of cultural tourists. In particular, museums’ challenge is to be prepared to offer the best experience to their visitors without to forget their mission-based mainly on protection of its collection and other social goals. Thus, museums individually want to be competitive and have good positioning to achieve their strategic goals. The life cycle of the destination and the level of maturity of its tourism product influence the need of tourism agents to cooperate and collaborate among them, in order to rejuvenate their product and become more competitive as a destination. Additionally, prior studies have considered an approach of different models of a public and private partnership, and collaborative and cooperative relations developed among the agents of a tourism destination. However, there are no studies that pay special attention to museums and the strategic alliances developed to obtain mutual benefits. Considering this background, the purpose of this study is to analyze in what extent museums of a given urban destination have established strategic links and relations among them, in order to improve their competitive position at both individual and destination level. In order to achieve the aim of this study, the city of Girona (Spain) and the museums located in this city are taken as a case study. Data collection was conducted using in-depth interviews, in order to collect all the qualitative data related to nature, strengthen and purpose of the relational ties established among the museums of the city or other relevant tourism agents of the city. To conduct data analysis, a Social Network Analysis (SNA) approach was taken using UCINET software. Position of the agents in the network and structure of the network was analyzed, and qualitative data from interviews were used to interpret SNA results. Finding reveals the existence of strong ties among some of the museums of the city, particularly to create and promote joint products. Nevertheless, there were detected outsiders who have an individual strategy, without collaboration and cooperation with other museums or agents of the city. Results also show that some relational ties have an institutional origin, while others are the result of a long process of cooperation with common projects. Conclusions put in evidence that collaboration and cooperation of museums had been positive to increase the attractiveness of the museum and the city as a cultural destination. Future research and managerial implications are also mentioned.

Keywords: cultural tourism, competitiveness, museums, Social Network analysis

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Authors: Aniket Ganvir, Ritarani Sahu, Suchismita Chinara

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The rapid digitization of various aspects of life is leading to the creation of smart IoT ecosystems, where interconnected devices generate significant amounts of valuable data. However, these IoT devices face constraints such as limited computational resources and bandwidth. Cloud computing emerges as a solution by offering ample resources for offloading tasks efficiently despite introducing latency issues, especially for time-sensitive applications like fog computing. Fog computing (FC) addresses latency concerns by bringing computation and storage closer to the network edge, minimizing data travel distance, and enhancing efficiency. Offloading tasks to fog nodes or the cloud can conserve energy and extend IoT device lifespan. The offloading process is intricate, with tasks categorized as full or partial, and its optimization presents an NP-hard problem. Traditional greedy search methods struggle to address the complexity of task offloading efficiently. To overcome this, the efficient crow search algorithm (ECSA) has been proposed as a meta-heuristic optimization algorithm. ECSA aims to effectively optimize computation offloading, providing solutions to this challenging problem.

Keywords: IoT, fog computing, task offloading, efficient crow search algorithm

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Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

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Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

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Authors: Niki K. Burns, James R. Pearson, Paul G. Stevenson, Xavier A. Conlan

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In Australian state Victoria, synthetic cannabinoids have recently been made illegal under an amendment to the drugs, poisons and controlled substances act 1981. Identification of synthetic cannabinoids in popular brands of ‘incense’ and ‘potpourri’ has been a difficult and challenging task due to the sample complexity and changes observed in the chemical composition of the cannabinoids of interest. This study has developed analytical methodology for the targeted extraction and determination of synthetic cannabinoids available pre-ban. A simple solvent extraction and solid phase extraction methodology was developed that selectively extracted the cannabinoid of interest. High performance liquid chromatography coupled with UV‐visible and chemiluminescence detection (acidic potassium permanganate and tris (2,2‐bipyridine) ruthenium(III)) were used to interrogate the synthetic cannabinoid products. Mass spectrometry and nuclear magnetic resonance spectroscopy were used for structural elucidation of the synthetic cannabinoids. The tris(2,2‐bipyridine)ruthenium(III) detection was found to offer better sensitivity than the permanganate based reagents. In twelve different brands of herbal incense, cannabinoids were extracted and identified including UR‐144, XLR 11, AM2201, 5‐F‐AKB48 and A796‐260.

Keywords: electrospray mass spectrometry, high performance liquid chromatography, solid phase extraction, synthetic cannabinoids

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Authors: Bhavana Gulaty, Arshia Chaudhri

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Great civic projects contribute integrally to a city, and every city undergoes a recurring cycle of urban transformations and regeneration by their insertion. The M.I.C.E. (Meetings, Incentives, Convention and Exhibitions) industry is the forbearer of one category of such catalytic civic projects. Through a specific focus on M.I.C.E. destinations, this paper illustrates the multifarious dimensions that urban catalysts impact the city on S.P.U.R. (Seed. Profile. Urbane. Reflections), the theoretical framework of this paper aims to unearth these dimensions in the realm of the COEX (Convention & Exhibition) biosphere. The ‘COEX Biosphere’ is the filter of such catalysts being ecosystems unto themselves. Like a ripple in water, the impact of these strategic interventions focusing on art, culture, trade, and promotion expands right from the trigger; the immediate context to the region and subsequently impacts the global scale. These ripples are known to bring about significant economic, social, and political and network changes. The COEX inventory in the Asian context has one such prominent addition; the proposed India International Convention and Exhibition Centre (IICC) at New Delhi. It is envisioned to be the largest facility in Asia currently and would position India on the global M.I.C.E map. With the first phase of the project scheduled to open for use in the end of 2019, this flagship project of the Government of India is projected to cater to a peak daily footfall of 3,20,000 visitors and estimated to generate 5,00,000 jobs. While the economic benefits are yet to manifest in real time and ‘Good design is good business’ holds true, for the urban transformation to be meaningful, the benefits have to go beyond just a balance sheet for the city’s exchequer. This aspect has been found relatively unexplored in research on these developments. The methodology for investigation will comprise of two steps. The first will be establishing an inventory of the global success stories and associated benefits of COEX projects over the past decade. The rationale for capping the timeframe is the significant paradigm shift that has been observed in their recent conceptualization; for instance ‘Innovation Districts’ conceptualised in the city of Albuquerque that converges into the global economy. The second step would entail a comparative benchmarking of the projected transformations by IICC through a toolkit of parameters. This is posited to yield a matrix that can form the test bed for mapping the catalytic trajectory for projects in the pipeline globally. As a ready reckoner, it purports to be a catalyst to substantiate decision making in the planning stage itself for future projects in similar contexts.

Keywords: catalysts, COEX, M.I.C.E., urban transformations

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: Muhammad Atif, Cang Yan

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The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on convolutional neural networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an autoencoder-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the PIX to PIX GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies.

Keywords: low light image enhancement, deep learning, convolutional neural network, image processing

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Authors: Hani Mekdash, Lina Jaber, Yehia Temsah

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Underground space is becoming a necessity nowadays, especially in highly congested urban areas. Retaining underground excavations using shoring systems is essential in order to protect adjoining structures from potential damage or collapse. Reinforced Concrete Piles (RCP) supported by multiple rows of tie-back anchors are commonly used type of shoring systems in deep excavations. However, executing anchors can sometimes be challenging because they might illegally trespass neighboring properties or get obstructed by infrastructure and other underground facilities. A technique is proposed in this paper, and it involves the addition of eccentric high-strength steel strands to the RCP section through ducts without providing the pile with lateral supports. The strands are then vertically stressed externally on the pile cap using a hydraulic jack, creating a compressive strengthening force in the concrete section. An experimental study about the behavior of the shoring wall by pre-stressed piles is presented during the execution of an open excavation in an urban area (Beirut city) followed by numerical analysis using finite element software. Based on the experimental results, this technique is proven to be cost-effective and provides flexible and sustainable construction of shoring works.

Keywords: deep excavation, prestressing, pre-stressed piles, shoring system

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Authors: Sultan Ayoub Meo

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Environmental pollution is a highly challenging global concern of the 21st century and is a major cause of various communicable and non-communicable diseases. We investigate the impact of air pollution on "lung function, fractional exhaled nitric oxide, and cognitive function"in a group of one hundred young students studying in a traffic-polluted school. The students wereselected based on their age, gender, height, weight, and ethnicity. After the clinical history, one hundred students were recruited from the schoolnear and away from the polluted areas. The lung and cognitive functions were recorded. The results revealed that lung and cognitive function parameters were reduced in groups of students studying in a school located in a traffic-polluted area compared to thosestudying in a schoolsituated away from the traffic-polluted area. Environmental pollution impairs students' lung and cognitive functions studying in schools located within traffic-polluted areas. The health officials and policymakers establish strategies to minimize environmental pollution and its allied health hazards. Prof. Sultan Ayoub Meo, MD, Ph.D Professor, Department of Physiology, College of Medicine, King Saud University, Saudi Arabia Email. [email protected] / [email protected]

Keywords: environmental pOllution, lung physiology, cognitive functions, air pollution

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Authors: Ivy Jessen Galvan

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Over the past decade, a phenomenon has emerged at the frontier of the digital economy: a wave of ‘disruptive’ technologies that offer digital solutions to variety of everyday problems, challenging the way traditional industries operate. Most of these disruptive technologies are applications ('apps') that rely on the Internet to connect people to people for sharing, selling, renting, or lending, creating a unique economic model wherein users provide for other users’ demand – called 'collaborative economy.' Although collaborative economy is spreading in every part of the world, there may be different ways in which this phenomenon is unfolding throughout the developing countries. In this study, the characteristics of collaborative economy in the Philippines are highlighted and compared from observations in the developed world. The paper looks at two leading collaborative economy ventures in the Philippines – Grab and Shopee – probing into how these smartphone-based platforms place technology into the 'micro-frictions' of the Philippine developing context. Using framing analysis on interviews conducted among Grab and Shopee users in Metro Manila, three frames have been identified: 1) metropolitan solution; 2) financial inclusion and; 3) formalization of labor. This research contextualizes the Fourth Industrial Revolution in ASEAN by analyzing the effect of a digital economy in everyday life.

Keywords: ASEAN Unicorns, collaborative economy, developing countries, fourth industrial revolution

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Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

Abstract:

In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

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Authors: Yathreb Sabsaby

Abstract:

Energy efficiency retrofitting of existing buildings was long ignored by public authorities who favored energy efficiency policies in new buildings, which are easier to implement. Indeed, retrofitting is more complex and difficult to organize because of the extreme diversity in existing buildings, administrative situations and occupation. Energy efficiency retrofitting of existing buildings has now become indispensable in all economies—even emerging countries—given the constraints imposed by energy security and climate change, and because it represents considerable potential energy savings. Addressing energy efficiency in the existing building stock has been acknowledged as one of the most critical yet challenging aspects of reducing our environmental footprint on the ecosystem. Tripoli, Lebanon chosen as case study area is a typical Mediterranean metropolis in the North Lebanon, where multifamily residential buildings are all around the city. This generally implies that the density of energy demand is extremely high, even the renewable energy facilities are involved, they can just play as a minor energy provider at the current technology level in the single family house. It seems only the low energy design for buildings can be made possible, not the zero energy certainly in developing country. This study reviews the latest research and experience and provides recommendations for deep energy retrofits that aim to save more than 50% of the energy used in a typical Tripoli apartment building.

Keywords: energy-efficiency, existing building, multifamily residential building, retrofit

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Authors: Mahmud Hafsat Hussaini, Eldah Ephraim Eldah, Bata Zoakah Amina

Abstract:

This research work was aimed at examining the challenges faced by passengers using Arik air for domestic and international travels. Passengers do complain of delay flights, theft and rude behavior by Arik staff while on transit or in the process of travelling using the aircraft. Being the national carrier in Nigeria these behaviors have tarnished the image of the airline and makes travel experience to be challenging. Hundred survey questionnaires were administered to travellers who have used the airline for domestic and international flights. Findings show that the staff of the airline do lack customer care skills and are sometimes rude to customers. The airline does have different agents that book for international flights who delays confirming bookings even after payment. The website of the airline is mostly down and makes bookings difficult. Other findings related to the study are a delay of domestic flights within Nigeria. Passengers are sometimes kept for 8 hours in the airport due to delay of flights. The study, therefore, recommends that flight schedule should be adhered to and staff should be trained to meet of with passengers demand. The security of guest luggage at the airport should be put in place to avoid theft. An effective booking platform should be accessible to passengers for easy booking.

Keywords: examining, challenges, domestic, international, travels

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Authors: Girish Choudhary

Abstract:

Education is in a state of flux. Not only one need to acquire skills in order to cope with a fast changing global world, an explosive growth in technology, on the other hand is providing a new wave of teaching tools - computer aided video instruction, hypermedia, multimedia, CD-ROMs, Internet connections, and collaborative software environments. The emerging technology incorporates the group qualities of interactive, classroom-based learning while providing individual students the flexibility to participate in an educational programme at their own time and place. The technology facilitating self learning also seems to provide a cost effective solution to the dilemma of delivering education to masses. Online education is a unique learning domain that provides for many to many communications as well. The computer conferencing software defines the boundaries of the virtual classroom. The changing paradigm provides access of instruction to a large proportion of society, promises a qualitative change in the quality of learning and echoes a new way of thinking in educational theory that promotes active learning and open new learning approaches. Putting it to practice is challenging and may fundamentally alter the nature of educational institutions. The subsequent part of paper addresses such questions viz. 'Do we need to radically re-engineer the curriculum and foster an alternate set of skills in students?' in the onward journey.

Keywords: on-line education, self learning, energy and power engineering, future education

Procedia PDF Downloads 325

Authors: S. Asplund, J. Åhlin, S. Åström, B. M. Lindgren

Abstract:

The aim of this qualitative study was to describe experiences of work-related stress among highly stressed municipal employees in rural northern Sweden. We interviewed 15 employees in the municipal sector in rural northern Sweden using a semi-structured guide and subjected the interviews to qualitative content analysis. Under the main theme of Suffering Though Endless Chaos, we summarized four themes: facing incompatible interests and high demands due to lack of time and resources; feeling powerless, trapped, and ignored due to lack of control; feeling insufficient, insecure, and guilty due to challenging relations and high expectations; and struggling with consequences such as health problems, spillover effects on family life, and difficulty coping. Findings from this study suggest the importance of acknowledging suffering among municipal employees in a stressful work environment. An imbalance between job demands and resources is affecting both the health and family lives of employees and also their ability to work. It seems important to improve the work environment through supportive leadership, job control, and reasonable job demands to prevent stress, reduce suffering, and create a healthy organization.

Keywords: manager, municipal sector, occupational health, qualitative content analysis

Procedia PDF Downloads 79