Search results for: robust filtering

Authors: Okike Benjamin, Garba E J. D.

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Encryption is used to conceal information from prying eyes. Presently, information and data encryption are common due to the volume of data and information in transit across the globe on daily basis. Image encryption is yet to receive the attention of the researchers as deserved. In other words, video and multimedia documents are exposed to unauthorized accessors. The authors propose image encryption using matrix transpose. An algorithm that would allow image encryption is developed. In this proposed image encryption technique, the image to be encrypted is split into parts based on the image size. Each part is encrypted separately using matrix transpose. The actual encryption is on the picture elements (pixel) that make up the image. After encrypting each part of the image, the positions of the encrypted images are swapped before transmission of the image can take place. Swapping the positions of the images is carried out to make the encrypted image more robust for any cryptanalyst to decrypt.

Keywords: image encryption, matrices, pixel, matrix transpose

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Authors: Muhammed Rabiu, Moses S. Oluyomi, Joshua Olorundare

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Quarry activities are important to modern day life and the socio-economic development of local communities. Unfortunately, this industry is usually associated with air pollution. To assess the impact of quarry dust on plant biodiversity and agriculture, PM2.5, PM10 and some meteorological parameters were measured using Gas analyzer, handheld thermometer and Multifunction Anemometer (PCE-EM 888) as well as taking a social survey. High amount of particulate matters that exceeded the international standard were recorded at the study locations which include the Julius Berger Quarry and 1km away from the quarry site which serve as the base for the farmlands. The correlation coefficient between the particulate matters with the meteorological parameters of the locations all show a strong relationship with temperature recording a stronger value of 0.952 and 0.931 for PM2.5 and PM10 respectively. Similarly, the coefficient of determination 0.906 and 0.866 shows that temperature has the highest meteorological percentage variation on PM2.5 and PM10. Furthermore, a notable negative impact of quarrying on plant biodiversity and local farm crops are also revealed based on respondents’ results where wide range of local plants were affected with Maize and Azadiracta indica (Neem) been the most with respondent of 31.5% and 27.5%. According to the obtained results, it is highly recommended to develop green belt surrounding the quarrying using pollutant-tolerant trees (usually with broad leaves) in order to restrict spreading of quarrying dust via intercepting, filtering and absorbing pollutants.

Keywords: agriculture, air pollution, biodiversity, quarry

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Authors: Mostafa Vahdani, Mohsen Asadnia, Shuying Wu

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Huge amounts of e-waste are being produced by the rapidly expanding use of electronics; the majority of this material is either burned or dumped directly in landfills since recycling would either be impracticable or too expensive. Degradable and environmentally friendly materials are therefore seen as the answer to this urgent problem. Here, we create strain sensors that are biodegradable, robust, and incredibly flexible using thin films of sodium carboxymethyl cellulose (NaCMC), glycerol, and polyvinyl alcohol (PVA). Due to the creation of many inter- or intramolecular hydrogen bonds, the polymer blends (NaCMC/PVA/glycerol) exhibit a failure strain of up to 330% and negligible hysteresis when exposed to cyclic stretching-releasing. What's more intriguing is that the sensors can degrade completely in deionized water at a temperature of 95 °C in about 25 minutes. This project illustrates a novel method for developing wearable electronics that are environmentally beneficial.

Keywords: degradable, stretchable, strain sensors, wearable electronics.

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Authors: Kilari Nikhil, Ankur Tibrewal, Srinivas Kruthiventi S. S.

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Conventional speaker identification systems often fall short of capturing the diverse variations present in speech data due to fixed-scale architectures. In this research, we propose a CNN-based architecture, USENet, designed to overcome these limitations. Leveraging two key techniques, our approach achieves superior performance on the VoxCeleb 1 Dataset without any pre-training. Firstly, we adopt a U-net-inspired design to extract features at multiple scales, empowering our model to capture speech characteristics effectively. Secondly, we introduce the squeeze and excitation block to enhance spatial feature learning. The proposed architecture showcases significant advancements in speaker identification, outperforming existing methods, and holds promise for future research in this domain.

Keywords: multi-scale feature extraction, squeeze and excitation, VoxCeleb1 speaker identification, mel-spectrograms, USENet

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Authors: Waseem Tahir, Syed Hussain Imran Jaffery, Mohammad Azam

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In this research work, liquid nitrogen gas (LN2) is used as a cryogenic media to optimize the cutting parameters for evaluation of tool flank wear width of Tungsten Carbide Insert (CNMG 120404-WF 4215) while turning a high strength alloy steel. Robust design concept of Taguchi L9 (34) method is applied to determine the optimum conditions. The analysis is revealed that cryogenic impact is more significant in reduction of the tool flank wear. However, High Speed Machining is shown most significant as compare to cooling media on work piece surface roughness.

Keywords: turning, cryogenic cooling, liquid nitrogen, flank wear, surface finish

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Authors: Elena Krelja Kurelović, Jasminka Tomljanović, Vlatka Davidović

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The development of web technologies and mobile devices makes creating, accessing, using and sharing information or communicating with each other simpler every day. However, while the amount of information constantly increasing it is becoming harder to effectively organize and find quality information despite the availability of web search engines, filtering and indexing tools. Although digital technologies have overall positive impact on students’ lives, frequent use of these technologies and digital media enriched with dynamic hypertext and hypermedia content, as well as multitasking, distractions caused by notifications, calls or messages; can decrease the attention span, make thinking, memorizing and learning more difficult, which can lead to stress and mental exhaustion. This is referred to as “information overload”, “information glut” or “information anxiety”. Objective of this study is to determine whether students show signs of information overload and to identify the possible predictors. Research was conducted using a questionnaire developed for the purpose of this study. The results show that students frequently use technology (computers, gadgets and digital media), while they show moderate level of information literacy. They have sometimes experienced symptoms of information overload. According to the statistical analysis, higher frequency of technology use and lower level of information literacy are correlated with larger information overload. The multiple regression analysis has confirmed that the combination of these two independent variables has statistically significant predictive capacity for information overload. Therefore, the information science teachers should pay attention to improving the level of students’ information literacy and educate them about the risks of excessive technology use.

Keywords: information overload, computers, mobile devices, digital media, information literacy, students

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Authors: Paiboon Jeamponk, Tasanee Ponglaa, Patchapon Srisanguan

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The aim of this research paper was based on an examination of sources of water supply and water quality for local consumption, conducted at eco-tourism villages of Suan Luang Sub- District Municipality of Amphawa District, Samut Songkram Province. The study incorporated both questionnaire and field work of water testing as the research tool and method. The sample size of 288 households was based on the population of the district, whereas the selected sample water sources were from 60 households: 30 samples were ground water and another 30 were surface water. Degree of heavy metal contamination in the water including copper, iron, manganese, zinc, cadmium and lead was investigated utilizing the Atomic Absorption- Direct Aspiration method. The findings unveiled that 96.0 percent of household water consumption was based on water supply, while the rest on canal, river and rain water. The household behaviour of consumption revealed that 47.2 percent of people routinely consumed water without boiling or filtering prior to consumption. The investigation of water supply quality found that the degree of heavy metal contamination including metal, lead, iron, copper, manganese and cadmium met the standards of the Department of Health.

Keywords: sources of water supply, water quality, water supply, Thailand

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Authors: J. S. Dhillon, K. K. Dhaliwal

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In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: Sempiga Olivier, Dominika Latusek-Jurczak

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Climate change has allegedly been attributed to a high consumption of fossil fuels, leading to severe environmental problems. The energy sector has been among the most polluting sectors for many decades. Consequently, there is a lack of trust in several energy firms, especially those in fossil fuels and nuclear energy. A robust regulatory framework is needed, and more investment in renewable energy sources is paramount for a better environmental outcome. Given the significant environmental impact of energy production and consumption in the energy sector, sustainable marketing practices have become increasingly important. Although the latter has had the lion’s share in polluting the environment, much effort has been made recently to move away from fossil fuels and privilege renewable energy sources. How this shift would help rebuild trust in the energy industry is unclear. For the shift to have lasting effects, it may be essential that regulatory agencies examine how energy firms engage in sustainable investment. There is little empirical evidence on whether adopting regulating marketing practices and investment initiatives can help different organizations reduce their environmental impact and promote sustainable development. Little is known about how and whether the environmental value in firms goes beyond rhetoric, greenwashing and publicity to translate into economic gains and environmental performance. The study investigates how regulatory agencies can help energy firms invest sustainably and take sustainable initiatives even amid the energy crisis caused by the Russia-Ukraine conflict and how these sustainable practices relate to renewed consumer trust. Using data from Corporate Knights, the study, through time series, analyses the relationship between sustainable regulation, sustainable practices of energy firms from around the world and their relation to consumer trust and environmental performance over the past 20 years. It examines how their sustainable investment, energy, and carbon productivity relate to environmental sustainability and consumer trust. This longitudinal study provides empirical evidence of the interplay between regulation, trust and environmental performance. The research is grounded in institutional trust theory, which emphasizes the role of regulatory frameworks and organizational practices in shaping public perceptions of fairness, transparency, and legitimacy. Results show that organizations in the energy sector, supported by robust regulatory tools, can overcome the negative image of polluters and compete with other companies in the fight against climate change and global warming. However, to do so, energy firms should consider investing more in renewable energy sources and implementing sustainable strategies and practices that go beyond greenwashing to improve their environmental performance, thereby rebuilding consumer trust in the energy sector. Results allow regulatory regimes and organizations to learn why it is crucial for energy firms to invest in renewable energy sources and engage in various sustainable initiatives and practices to contribute to better environmental outcomes and higher levels of trust.

Keywords: consumer trust, energy, environmental performance, regulation, renewable energy sources, sustainable practices

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Authors: Abdelkrim Belhaoua, Jean-Pierre Radoux

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In France, the railway network is one of the main transport infrastructures and is the second largest European network. Therefore, railway inspection is an important task in railway maintenance to ensure safety for passengers using significant means in personal and technical facilities. Artificial vision has recently been applied to several railway applications due to its potential to improve the efficiency and accuracy when analyzing large databases of acquired images. In this paper, we present a vision system able to detect fastening elements based on artificial vision approach. This system acquires railway images using a CCD camera installed under a control carriage. These images are stitched together before having processed. Experimental results are presented to show that the proposed method is robust for detection fasteners in a complex environment.

Keywords: computer vision, image processing, railway inspection, image stitching, fastener recognition, neural network

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Authors: Naresh Radaliyagoda, Nigel M. Jennett, Rong Lan, David Parfitt

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A series of length scale engineered multilayer material with temperature robust mechanical properties has been suggested. A range of polycrystalline copper sub-layers with the thickness varying from 1 to 25μm and buried in between two nickel layers was produced using electrodeposition dual bath technique. The structure of the multilayers was characterized using Electron Backscatter Diffraction and Scanning Electron Microscope. The interface effect on the hardness and elastic modulus was tested using Nano-indentation. Results of the grain size and layer thickness measurements, and indentation hardness have been compared. It is found that there is a combined length scale effect that improves mechanical properties in Ni/Cu multilayer structures.

Keywords: nano-indentation, size effect, multilayers, electrodeposition

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Authors: Carlos G. Santos, Frederico A. Genezini, A. P. Dos Santos, H. Yorivaz, P. T. D. Siqueira

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The Prompt-Gamma Activation Analysis (PGAA) is a valuable technique for investigating the elemental composition of various samples. However, the installation of a PGAA system entails specific conditions such as filtering the neutron beam according to the target and providing adequate shielding for both users and detectors. These requirements incur substantial costs, exceeding $100,000, including manpower. Nevertheless, a cost-effective approach involves leveraging an existing neutron beam facility to create a hybrid system integrating PGAA and Neutron Tomography (NT). The IEA-R1 nuclear reactor at IPEN/USP possesses an NT facility with suitable conditions for adapting and implementing a PGAA device. The NT facility offers a thermal flux slightly colder and provides shielding for user protection. The key additional requirement involves designing detector shielding to mitigate high gamma ray background and safeguard the HPGe detector from neutron-induced damage. This study employs Monte Carlo simulations with the MCNP6 code to optimize the collimator plug for PGAA within the IEA-R1 NT facility. Three collimator models are proposed and simulated to assess their effectiveness in shielding gamma and neutron radiation from nucleon fission. The aim is to achieve a focused prompt-gamma signal while shielding ambient gamma radiation. The simulation results indicate that one of the proposed designs is particularly suitable for the PGAA-NT hybrid system.

Keywords: MCNP6.1, neutron, prompt-gamma ray, prompt-gamma activation analysis

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Authors: Muhammad Akhtaruzzaman

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The impact of corporate environmental social and governance (ESG) on financial performance is often difficult to quantify despite the ESG related theories predict that ESG performance improves financial performance of a company. This research examines the link between corporate ESG performance and the financial performance of the NZX (New Zealand Stock Exchange) listed companies. For this purpose, this research utilizes mixed methods approaches to examine and understand this link. While quantitative results found no robust evidence of such a link, however, the qualitative analysis of content data suggests a strong cooccurrence exists between ESG performance and financial performance. The findings of this research have important implications for policymakers to support higher ESG-performing companies and for management practitioners to develop ESG-related strategies.

Keywords: ESG, financial performance, New Zealand firms, thematic analysis, mixed methods

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Authors: Keunhong Chae, Seokho Yoon

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In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments.

Keywords: frequency offset estimation, maximum-likelihood, non-Gaussian noise environment, OFDM, training symbol

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Authors: Noha Badkook, Maryam Alsubaie, Samaher Dawood, Enas Khairullah

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Cloud computing has introduced transformative benefits in data storage and accessibility while posing unique forensic challenges. This paper explores cloud forensics, focusing on investigating and analyzing evidence from cloud environments to address issues such as unauthorized data access, manipulation, and breaches. The research highlights the practical use of open-source forensic tools like Autopsy and Bulk Extractor in real-world scenarios, including unauthorized data sharing via Google Drive and the misuse of personal cloud storage for sensitive information leaks. This work underscores the growing importance of robust forensic procedures and accessible tools in ensuring data security and accountability in cloud ecosystems.

Keywords: cloud forensic, tools, challenge, autopsy, bulk extractor

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Authors: Sozon, Pok Wei Fong, Sia Bee Chuan, Omar Hamdan Mohammad

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Owing to the widespread phenomenon of plagiarism and cheating in higher education institutions (HEIs), it is now difficult to ensure academic integrity and quality education. Moreover, the COVID-19 pandemic has intensified the issue by shifting educational institutions into virtual teaching and assessment mode. Thus, there is a need to carry out an extensive and holistic systematic review of the literature to highlight plagiarism and cheating in both prevalence and form among HEIs. This paper systematically reviews the literature concerning academic plagiarism and cheating in HEIs to determine the most common forms and suggest strategies for resolution and boosting the academic integrity of students. The review included 45 articles and publications for the period from February 12, 2018, to September 12, 2022, in the Scopus database aligned with the Systematic Review and Meta-Analysis (PRISMA) guidelines in the selection, filtering, and reporting of the papers for review from which a conclusion can be drawn. Based on the results, out of the studies reviewed, 48% of the quantitative results of students were plagiarized and obtained through cheating, with 84% coming from the fields of Humanities. Moreover, Psychology and Social Sciences studies accumulated 9% and 7% articles respectively. Based on the results, individual factors, institutional factors, and social and cultural factors have contributed to plagiarism and cheating cases in HEIs. The resolution of this issue can be the establishment of ethical and moral development initiatives and modern academic policies and guidelines supported by technological strategies of testing.

Keywords: plagiarism, cheating, systematic review, academic integrity

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Authors: Unal Atakan Kahraman, Yilmaz Uyaroğlu

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The chaotic signals generated by chaotic systems have some properties such as randomness, complexity and sensitive dependence on initial conditions, which make them particularly suitable for secure communications. Since the 1990s, the problem of secure communication, based on chaos synchronization, has been thoroughly investigated and many methods, for instance, robust and adaptive control approaches, have been proposed to realize the chaos synchronization. In this paper, an improved secure communication model is proposed based on control of supply chain management system. Control and masking communication simulation results are used to visualize the effectiveness of chaotic supply chain system also performed on the application of secure communication to the chaotic system. So, we discover the secure phenomenon of chaos-amplification in supply chain system

Keywords: chaotic analyze, control, secure communication, supply chain attractor

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Authors: S. Yaman, S. Rostami

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In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.

Keywords: function tuner method (FTM), fuzzy modeling, fuzzy PID controller, genetic algorithm (GA)

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Authors: Changho Han, Jaemin Lee, Li Hua, Seokkwon Jeong

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Design of membership function ranges in fuzzy logic control (FLC) is presented for robust control of a variable speed refrigeration system (VSRS). The criterion values of the membership function ranges can be carried out from the static experimental data, and two different values are offered to compare control performance. Some simulations and real experiments for the VSRS were conducted to verify the validity of the designed membership functions. The experimental results showed good agreement with the simulation results, and the error change rate and its sampling time strongly affected the control performance at transient state of the VSRS.

Keywords: variable speed refrigeration system, fuzzy logic control, membership function range, control performance

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Authors: Sello Mokwena, Monyepao Thabang

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In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques.

Keywords: camera network, convolutional neural network topology, person tracking, person re-identification, siamese

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Authors: Gianna Zou

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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

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Authors: S. A. Al-Qallaf, S. A. Al-Mawsawi, A. Haider

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In order to evaluate the performance of a unified power flow controller (UPFC), mathematical models for steady state and dynamic analysis are to be developed. The steady state model is mainly concerned with the incorporation of the UPFC in load flow studies. Several load flow models for UPFC have been introduced in literature, and one of the most reliable models is the decoupled UPFC model. In spite of UPFC decoupled load flow model simplicity, it is more robust compared to other UPFC load flow models and it contains unique capabilities. Some shortcoming such as additional set of nonlinear equations are to be solved separately after the load flow solution is obtained. The aim of this study is to investigate the different control strategies that can be realized in the decoupled load flow model (individual control and combined control), and the impact of the location of the UPFC in the network on its control parameters.

Keywords: UPFC, decoupled model, load flow, control parameters

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Authors: Montree Bunruangses, Sonath Bhattacharyya, Somchat Sonasang, Preecha Yupapin

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A two-level system is manipulated by a microstrip add-drop circuit configured as an atom like system for wave-particle behavior investigation when its traveling speed along the circuit perimeter is the speed of light. The entangled pair formed by the upper and lower sideband peaks is bound by the angular displacement, which is given by 0≤θ≤π/2. The control signals associated with 3-peak signal frequencies are applied by the external inputs via the microstrip add-drop multiplexer ports, where they are time functions without the space term involved. When a system satisfies the speed of light conditions, the mass term has been changed to energy based on the relativistic limit described by the Lorentz factor and Einstein equation. The different applied frequencies can be utilized to form the 3-phase torques that can be applied for quantum engines. The experiment will use the two-level system circuit and be conducted in the laboratory. The 3-phase torques will be recorded and investigated for quantum engine driving purpose. The obtained results will be compared to the simulation. The optimum amplification of torque can be obtained by the resonant successive filtering operation. Torque will be vanished when the system is balanced at the stopped position, where |Time|=0, which is required to be a system stability condition. It will be discussed for future applications. A larger device may be tested in the future for realistic use. A synchronous and asynchronous driven motor is also discussed for the warp drive use.

Keywords: quantum engine, relativistic motor, 3-phase torque, atomic engine

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Authors: Sendi Zohra, Belhadjsalah Hedi, Labergere Carl, Saanouni Khemais

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The modeling of mechanical problems including both material and geometric nonlinearities with Finite Element Method (FEM) remains challenging. Meshless methods offer special properties to get rid of well-known drawbacks of the FEM. The main objective of Meshless Methods is to eliminate the difficulty of meshing and remeshing the entire structure by simply insertion or deletion of nodes, and alleviate other problems associated with the FEM, such as element distortion, locking and others. In this study, a robust numerical implementation of an Element Free Galerkin Method for an elastoplastic coupled to damage problem is presented. Several results issued from the numerical simulations by a DynamicExplicit resolution scheme are analyzed and critically compared with Element Finite Method results. Finally, different numerical examples are carried out to demonstrate the efficiency of this method.

Keywords: damage, dynamic explicit, elastoplasticity, isotropic hardening, meshless

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Authors: Saurabh Ahluwalia, Sul Kassicieh

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The conventional wisdom has maintained that being in proximity to entrepreneurial ecosystems helps startups to raise financing, develop and grow. In this paper, we examine the effect of a major component of an entrepreneurial ecosystem- financial or venture capital clusters on the exit of a startup through mergers and acquisitions (M&A). We find that the presence of a venture capitalist in a venture capital (VC) cluster is a major success factor for M&A exits. The location of startups in the top VC clusters did not turn out to be significant for success. Our results are robust to different specifications of the model that use different time periods, types of success, the reputation of VC, industry and the quality of the startup company. Our results provide evidence for VCs, startups and policymakers who want to better understand the components of entrepreneurial ecosystems and their relation to the M&A exits of startups.

Keywords: financial institution, mergers and acquisitions, startup financing, venture capital

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Authors: Mamiza Haq

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This paper examines the relevance of bank capital and charter value on bank insolvency and liquidity risks. Using an unbalanced panel of 2,111 unique local banks across 22 countries over 1998-2012, we find that both bank capital and charter value lower insolvency and liquidity risks, but this effect varies among conventional, Islamic, and Islamic-window banks. The risk constraining effect of bank capital becomes more prominent in the post 2007-2008 global financial crisis. Moreover, the relationships vary when conditioned upon other key bank-specific characteristics. For instance, the effect of capital on risk-reduction diminishes in the presence of high charter value for conventional-G7 and Islamic-window banks, during-GFC and pre-GFC period; respectively. Our findings have important policy implications related to bank safety. The results are robust to a range of robustness tests.

Keywords: bank capital, charter value, risk, financial crisis

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Gonzalez Carlos, Martinez Fransisco

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The creation of a seismic prediction model that considers all the regional variations and perfectly adjusts its results to the response spectra is very complicated. To achieve statistically acceptable results, it is necessary to process a sufficiently robust data set, and even if high efficiencies are achieved, this model will only work properly in this region. However, when using it in other regions, differences are found due to different parameters that have not been calibrated to other regions, such as the site effect. The fact that impedance contrasts, as well as other factors belonging to the site, have a great influence on the local response is well known, which is why this work, using the residual method, is intended to establish a regional calibration of the corresponding parameters site effect for the Spain region in the global GMPM BSSA 14.

Keywords: GMPM, seismic prediction equations, residual method, response spectra, impedance contrast

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Authors: Astrid de Leeuw, Pierre Valois

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Environmental education is the key to enhancing or changing students’ ways of thinking and acting in order to create an environmentally robust future for all. The present study investigates the beliefs of 812 primary school students, which merit consideration when developing educational interventions. Results of multiple regression analyses reveal that educational interventions should focus on promoting students’ feelings of control over pro-environmental behaviors (PEB). For example, schools could provide recycling bins on the premises. Furthermore, it is critical to develop positive attitudes in students by stressing the various benefits of PEB for keeping our planet clean and protecting wildlife. Unfortunately, our results indicate that students believe that PEB is boring and annoying. Suggestions are offered for making PEB more interesting and relevant. Further research is needed to test the effectiveness of interventions based on the present results.

Keywords: pro-environmental behavior, primary school students, theory of planned behavior, beliefs, educational interventions

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Authors: Mohammad Abdullah-Al-Wadud

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In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.

Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty

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