Search results for: Bauesian networks

Authors: Leticia Cervantes, Edith Garcia, Oscar Castillo

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At today, there are several control problems where the main objective is to obtain the best control in the study to decrease the error in the application. Many techniques can use to control these problems such as Neural Networks, PID control, Fuzzy Logic, Optimization techniques and many more. In this case, fuzzy logic with fuzzy system and an optimization technique are used to control the case of study. In this case, Ant Lion Optimization is used to optimize a fuzzy system to control the velocity of a simple treadmill. The main objective is to achieve the control of the velocity in the control problem using the ALO optimization. First, a simple fuzzy system was used to control the velocity of the treadmill it has two inputs (error and error change) and one output (desired speed), then results were obtained but to decrease the error the ALO optimization was developed to optimize the fuzzy system of the treadmill. Having the optimization, the simulation was performed, and results can prove that using the ALO optimization the control of the velocity was better than a conventional fuzzy system. This paper describes some basic concepts to help to understand the idea in this work, the methodology of the investigation (control problem, fuzzy system design, optimization), the results are presented and the optimization is used for the fuzzy system. A comparison between the simple fuzzy system and the optimized fuzzy systems are presented where it can be proving the optimization improved the control with good results the major findings of the study is that ALO optimization is a good alternative to improve the control because it helped to decrease the error in control applications even using any control technique to optimized, As a final statement is important to mentioned that the selected methodology was good because the control of the treadmill was improve using the optimization technique.

Keywords: ant lion optimization, control problem, fuzzy control, fuzzy system

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Authors: Marwa T. Badawy, Alaa F. Bakr, Nesrine Hegazi, Mohamed A. Farag, Ahmed Abdellatif

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Diabetes Mellitus (DM) is a chronic disease affecting a large population worldwide. Africa is rich in native medicinal plants with myriad health benefits, though less explored towards the development of specific drug therapy as in diabetes. This study aims to determine the in vivo antidiabetic potential of the well-reported and traditionally used fruits of Baobab (Adansonia digitata L.) using STZ induced diabetic model. The in-vitro cytotoxic and antioxidant properties were examined using MTT assay on L-929 fibroblast cells and DPPH antioxidant assays, respectively. The extract showed minimal cytotoxicity with an IC50 value of 105.7 µg/mL. Histopathological and immunohistochemical investigations showed the hepatoprotective and the renoprotective effects of A. digitata fruits’ extract, implying its protective effects against diabetes complications. These findings were further supported by biochemical assays, which showed that i.p., injection of a low dose (150 mg/kg) of A. digitata twice a week lowered the fasting blood glucose levels, lipid profile, hepatic and renal markers. For a comprehensive overview of extract metabolites composition, ultrahigh performance (UHPLC) analysis coupled to high-resolution tandem mass spectrometry (HRMS/MS) in synchronization with molecular networks led to the annotation of 77 metabolites, among which 50% are reported for the first time in A. digitata fruits.

Keywords: adansonia digital, diabetes mellitus, metabolomics, streptozotocin, Sprague, dawley rats

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Authors: Saimir Heta, Kers Kapaj, Rialda Xhizdari, Ilma Robo

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Dealing with emerging cases of dental malpractice with justifications that stem from the clear rules of dental ethics is a phenomenon with an increasing trend in today's dental practice. Dentists should clearly understand how far the limit of malpractice goes, with or without minimal or major consequences, for the affected patient, which can be justified as a complication of dental treatment, in support of the rules of dental ethics in the dental office. Indeed, malpractice can occur in cases of lack of professionalism, but it can also come as a consequence of anatomical and physiological limitations in the implementation of the dental protocols, predetermined and indicated by the patient in the paragraph of the treatment plan in his personal card. This study is of the review type with the aim of the latest findings published in the literature about the problem of dealing with these phenomena. The combination of keywords is done in such a way with the aim to give the necessary space for collecting the right information in the networks of publications about this field, always first from the point of view of the dentist and not from that of the lawyer or jurist. From the findings included in this article, it was noticed the diversity of approaches towards the phenomenon depends on the different countries based on the legal basis that these countries have. There is a lack of or a small number of articles that touch on this topic, and these articles are presented with a limited number of data on the same topic. Conclusions: Dental malpractice should not be hidden under the guise of various dental complications that we justify with the strict rules of ethics for patients treated in the dental chair. The individual experience of dental malpractice must be published with the aim of serving as a source of experience for future generations of dentists.

Keywords: dental ethics, malpractice, professional protocol, random deviation

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Mojtaba Sadeghi, Farshid Baniassadi, Hamed Kashani

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One of the challenging issues faced by seismic retrofitting consultants and employers is quick decision-making on the demolition or retrofitting of a structure at the current time or in the future. For this reason, the existing models proposed by researchers have only covered one of the aspects of cost, execution method, and structural vulnerability. Given the effect of each factor on the final decision, it is crucial to devise a new comprehensive model capable of simultaneously covering all the factors. This study attempted to provide an integrated framework that can be utilized to select the most appropriate earthquake risk mitigation solution for buildings. This framework can overcome the limitations of current models by taking into account several factors such as cost, execution method, risk-taking and structural failure. In the newly proposed model, the database and essential information about retrofitting projects are developed based on the historical data on a retrofit project. In the next phase, an analysis is conducted in order to assess the vulnerability of the building under study. Then, artificial neural networks technique is employed to calculate the cost of retrofitting. While calculating the current price of the structure, an economic analysis is conducted to compare demolition versus retrofitting costs. At the next stage, the optimal method is identified. Finally, the implementation of the framework was demonstrated by collecting data concerning 155 previous projects.

Keywords: decision making, demolition, construction management, seismic retrofit

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Tal Remez, Or Litany, Alex Bronstein

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The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.

Keywords: binary pixels, maximum likelihood, neural networks, sparse coding

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Authors: Onur Kaya, Ilknur Tukenmez

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In this study, we consider vehicle routing problems on networks with alternative direct links between nodes, and we analyze a multi-objective problem considering the financial, environmental and social objectives in this context. In real life, there might exist several alternative direct roads between two nodes, and these roads might have differences in terms of their lengths and durations. For example, a road might be shorter than another but might require longer time due to traffic and speed limits. Similarly, some toll roads might be shorter or faster but require additional payment, leading to higher costs. We consider such alternative links in our problem and develop a mixed integer linear programming model that determines which alternative link to use between two nodes, in addition to determining the optimal routes for different vehicles, depending on the model objectives and constraints. We consider the minimum cost routing as the financial objective for the company, minimizing the CO2 emissions and gas usage as the environmental objectives, and optimizing the driver working conditions/working hours, and minimizing the risks of accidents as the social objectives. With these objective functions, we aim to determine which routes, and which alternative links should be used in addition to the speed choices on each link. We discuss the results of the developed vehicle routing models and compare their results depending on the system parameters.

Keywords: vehicle routing, alternative links between nodes, mixed integer linear programming, triple bottom line accounting

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Authors: Eugene Wong, Felix Chan, Linsey Chen, Joey Cheung

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The widespread use of technologies and cyber/digital means for complex maritime operations have led to a sharp rise in global cyber-attacks. They have generated an increasing number of liability disputes, insurance claims, and legal proceedings. An array of antiquated case law, regulations, international conventions, and obsolete contractual clauses drafted in the pre-technology era have become grossly inadequate in addressing the contemporary challenges. This paper offers a critique of the ambiguity of cybersecurity liabilities under the obligation of seaworthiness entailed in the Hague-Visby Rules, which apply either by law in a large number of jurisdictions or by express incorporation into the shipping documents. This paper also evaluates the legal and technological criteria for assessing whether a vessel is properly equipped with the latest offshore technologies for navigation and cargo delivery operations. Examples include computer applications, networks and servers, enterprise systems, global positioning systems, and data centers. A critical analysis of the carriers’ obligations to exercise due diligence in preventing or mitigating cyber-attacks is also conducted in this paper. It is hoped that the present study will offer original and crucial insights to policymakers, regulators, carriers, cargo interests, and insurance underwriters closely involved in dispute prevention and resolution arising from cybersecurity liabilities.

Keywords: seaworthiness, cybersecurity, liabilities, risks, maritime, transport

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Authors: Amber Brittain-Hale

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This research investigates the evolving role of social media in crisis situations by employing discourse analysis methodology and honing in on the Russo-Ukrainian War, particularly Ukraine's use of LinkedIn. The study posits that social media platforms, such as LinkedIn, play a crucial role in shaping communication, disseminating information, and influencing geopolitical strategies during conflicts. Focusing on Ukraine's official state account on LinkedIn and analyzing its posts and interactions, the research aims to unveil discourse dynamics in high-stakes scenarios and provide valuable insights for leaders navigating complex global challenges. A comprehensive analysis of the data will contribute to a deeper understanding of the tactics adopted by political leaders in managing communication, the bidirectional nature of discourse provided by online social networks, and the rapid advancement of technology that has led to the growing significance of social media platforms in crisis situations. Through this approach, the geopolitical factors that influenced the country's social media strategy during the Russo-Ukrainian War will be illuminated, offering a broader perspective on the role of social media in such challenging times. Ultimately, the study seeks to uncover lessons that can be drawn from Ukraine's LinkedIn approach, informing future strategies for utilizing social media during crises and advancing the understanding of how social media can be harnessed to address intricate global issues.

Keywords: russo-ukrainian war, social media, crisis, discourse analysis

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Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader

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The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.

Keywords: condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network

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Authors: Sara Chinnasamy, Michelle Gun, M. Adnan Hashim

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Federal Constitution guarantees Malaysians the right to free speech and expression; yet hatred speech can be commonly found on social media platforms such as Facebook, Twitter, and Instagram. In Malaysia social media sphere, most hatred speech involves religion, race and politics. Recent cases of racial attacks on social media have created social tensions among Malaysians. Many Malaysians always argue on their rights to freedom of speech. However, there are laws that limit their expression to the public and protecting social media users from being a victim of hate speech. This paper aims to explore the attitude and involvement of Malaysian netizens towards freedom of speech and hatred speech on social media. It also examines the relationship between involvement in hatred speech among Malaysian netizens and attitude towards freedom of speech. For most Malaysians, practicing total freedom of speech in the open is unthinkable. As a result, the best channel to articulate their feelings and opinions liberally is the internet. With the advent of the internet medium, more and more Malaysians are conveying their viewpoints using the various internet channels although sensitivity of the audience is seldom taken into account. Consequently, this situation has led to pockets of social disharmony among the citizens. Although this unhealthy activity is denounced by the authority, netizens are generally of the view that they have the right to write anything they want. Using the quantitative method, survey was conducted among Malaysians aged between 18 and 50 years who are active social media users. Results from the survey reveal that despite a weak relationship level between hatred speech involvement on social media and attitude towards freedom of speech, the association is still considerably significant. As such, it can be safely presumed that hatred speech on social media occurs due to the freedom of speech that exists by way of social media channels.

Keywords: freedom of speech, hatred speech, social media, Malaysia, netizens

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Authors: Ayman M. Othman, Hassan Y. Ahmed, Tallat A. Ali

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Inadequate maintenance programs and funds allocated for highway networks in the developed countries have led to fast deterioration of road side elements. Therefore, this research focuses on developing a performance model for road side elements periodic maintenance activities. Road side elements that receive periodic maintenance include; earthen shoulder, road signs and traffic markings. Using the level of service concept, the developed model can determine the optimal periodic maintenance intervals for those elements based on a selected level of service suitable with the available periodic maintenance budget. Data related to time periods for progressive deterioration stages for the chosen elements were collected. Ten maintenance experts in Aswan, Sohag and Assiut cities were interviewed for that purpose. Time in months related to 10%, 25%, 40%, 50%, 75%, 90% and 100% deterioration of each road side element was estimated based on the experts opinion. Least square regression analysis has shown that a power function represents the best fit for earthen shoulders edge drop-off and damage of road signs with time. It was also evident that, the progressive dirtiness of road signs could be represented by a quadratic function an a linear function could represent the paint degradation nature of both traffic markings and road signs. Actual measurements of earthen shoulder edge drop-off agree considerably with the developed model.

Keywords: deterioration, level of service, periodic maintenance, performance model, road side element

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Authors: Ande Nesmith

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There is an ongoing shortage of foster parents worldwide to take on a growing population of children in need of out-of-home care. Currently, resources are primarily aimed at recruitment rather than retention. Retention rates are extraordinarily low, especially in the first two years of fostering. Qualitative interviews with 19 foster parents averaging 20 years of service provided insight into the challenges they faced and how they overcame them. Thematic analysis of interview transcripts identified sources of stress and resiliency. Key stressors included lack of support and responsiveness from the children’s social workers, false maltreatment allegations, and secondary trauma from children’s destructive behaviors and emotional dysregulation. Resilient parents connected with other foster parents for support, engaged in creative problem-solving, recognized that positive feedback from children usually arrives years later, and through training, understood the neurobiological impact of trauma on child behavior. Recommendations include coordinating communication between the foster parent licensing agency social workers and the children’s social workers, creating foster parent support networks and mentoring, and continuous training on trauma including effective parenting strategies. Research is needed to determine whether these resilience indicators in fact lead to long-term retention. Policies should include a mechanism to develop a cohesive line of communication and connection between foster parents and the children’s social workers as well as their respective agencies.

Keywords: foster care stability, foster parent burnout, foster parent resiliency, foster parent retention, trauma-informed fostering

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Authors: L. Freeborn

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Recent years have seen an increasing number of neuroimaging studies related to language learning as imaging techniques such as fMRI and EEG have become more widely accessible to researchers. By using a variety of structural and functional neuroimaging techniques, these studies have already made considerable progress in terms of our understanding of neural networks and processing related to first and second language acquisition. However, the methodological designs employed in neuroimaging studies to test language learning have been questioned by applied linguists working within the field of second language acquisition (SLA). One of the major criticisms is that tasks designed to measure language learning gains rarely have a communicative function, and seldom assess learners’ ability to use the language in authentic situations. This brings the validity of many neuroimaging tasks into question. The fundamental reason why people learn a language is to communicate, and it is well-known that both first and second language proficiency are developed through meaningful social interaction. With this in mind, the SLA field is in agreement that second language acquisition and proficiency should be measured through learners’ ability to communicate in authentic real-life situations. Whilst authenticity is not always possible to achieve in a classroom environment, the importance of task authenticity should be reflected in the design of language assessments, teaching materials, and curricula. Tasks that bear little relation to how language is used in real-life situations can be considered to lack construct validity. This paper first describes the typical tasks used in neuroimaging studies to measure language gains and proficiency, then analyses to what extent these tasks can validly assess these constructs.

Keywords: neuroimaging studies, research design, second language acquisition, task validity

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Authors: Deepak Khare, Radha Krishan, Bhaskar Nikam

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The present study deals with the results of the Ortho-rectified Cartosat-1 PAN (2.5 m resolution) satellite data analysis for the extraction of canal networks under the Eastern Ganga Canal (EGC) command. Based on the information derived through the remote sensing data, in terms of the number of canals, their physical status and hydraulic connectivity from the source, irrigation potential (IP) created in the command was assessed by comparing with planned/design canal-wise irrigation potentials. All the geospatial information generated in the study is organized in a geodatabase. The EGC project irrigates the command through one main canal, five branch canals, 36 distributaries and 186 minors. The study was conducted with the main objectives of inventory and mapping of irrigation infrastructure using geographic information system (GIS), remote sensing and field data. Likewise, the assessment of irrigation potential created using the mapped infrastructure was performed as on March 2017. Results revealed that the canals were not only pending but were also having gap/s, and hydraulically disconnected in each branch canal and also in minors of EGC. A total of 16622.3 ha of commands were left untouched with canal water just due to the presence of gaps in the EGC project. The sum of all the gaps present in minor canals was 11.92 km, while in distributary, it was 2.63 km. This is a very good scenario that balances IP can be achieved by working on the gaps present in minor canals. Filling the gaps in minor canals can bring most of the area under irrigation, especially the tail reaches command.

Keywords: canal command, GIS, hydraulic connectivity, irrigation potential

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Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Olakanmi Oladayo Olufemi, Bamifewe Olusegun James, Badmus Yaya Opeyemi, Adegoke Kayode

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The wireless sensor network (WSN) has made a significant contribution to the emergence of various intelligent services or cloud-based applications. Most of the time, these data are stored on a cloud platform for efficient management and sharing among different services or users. However, the sensitivity of the data makes them prone to various confidentiality and performance-related attacks during and after harvesting. Various security schemes have been developed to ensure the integrity and confidentiality of the WSNs' data. However, their specificity towards particular attacks and the resource constraint and heterogeneity of WSNs make most of these schemes imperfect. In this paper, we propose a secure variance-aware routing and authentication scheme with two-tier verification to collect, share, and manage WSN data. The scheme is capable of classifying WSN into different subnets, detecting any attempt of wormhole and black hole attack during harvesting, and enforcing access control on the harvested data stored in the cloud. The results of the analysis showed that the proposed scheme has more security functionalities than other related schemes, solves most of the WSNs and cloud security issues, prevents wormhole and black hole attacks, identifies the attackers during data harvesting, and enforces access control on the harvested data stored in the cloud at low computational, storage, and communication overheads.

Keywords: data block, heterogeneous IoT network, data harvesting, wormhole attack, blackhole attack access control

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Elizelle Juaneé Cilliers

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The green hype is becoming stronger within various disciplines, modern practices and academic thinking, enforced by concepts such as eco-health, eco-tourism, eco-cities, and eco-engineering. There is currently also an expanded scientific understanding regarding the value and benefits relating to green infrastructure, for both communities and their host cities, linked to broader sustainability and resilience thinking. The integration and implementation of green infrastructure as part of spatial planning approaches and municipal planning, are, however, more complex, especially in South Africa, inflated by limitations of budgets and human resources, development pressures, inequities in terms of green space availability and political legacies of the past. The prevailing approach to spatial planning is further contributing to complexity, linked to misguided perceptions of the function and value of green infrastructure. As such, green spaces are often considered a luxury, and green infrastructure a costly alternative, resulting in green networks being susceptible to land-use changes and under-prioritized in local authority decision-making. Spatial planning, in this sense, may well be a valuable tool to realise the green agenda, encapsulating various initiatives of sustainability as provided by a range of disciplines. This paper aims to clarify the importance and value of green infrastructure planning as a component of spatial planning approaches, in order to inform and encourage local authorities to embed sustainability thinking into city planning and decision-making approaches. It reflects on the decisive role of land-use management to guide the green agenda and refers to some recent planning initiatives. Lastly, it calls for trans-disciplinary planning approaches to build a case towards green(er) cities.

Keywords: green infrastructure, spatial planning, transdisciplinary, integrative

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Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: Bahram Khan, Anderson Rocha Ramos, Rui R. Paulo, Fernando J. Velez

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With the growing demand for a new blend of applications, the users dependency on the internet is increasing day by day. Mobile internet users are giving more attention to their own experiences, especially in terms of communication reliability, high data rates and service stability on move. This increase in the demand is causing saturation of existing radio frequency bands. To address these challenges, researchers are investigating the best approaches, Carrier Aggregation (CA) is one of the newest innovations, which seems to fulfill the demands of the future spectrum, also CA is one the most important feature for Long Term Evolution - Advanced (LTE-Advanced). For this purpose to get the upcoming International Mobile Telecommunication Advanced (IMT-Advanced) mobile requirements (1 Gb/s peak data rate), the CA scheme is presented by 3GPP, which would sustain a high data rate using widespread frequency bandwidth up to 100 MHz. Technical issues such as aggregation structure, its implementations, deployment scenarios, control signal techniques, and challenges for CA technique in LTE-Advanced, with consideration of backward compatibility, are highlighted in this paper. Also, performance evaluation in macro-cellular scenarios through a simulation approach is presented, which shows the benefits of applying CA, low-complexity multi-band schedulers in service quality, system capacity enhancement and concluded that enhanced multi-band scheduler is less complex than the general multi-band scheduler, which performs better for a cell radius longer than 1800 m (and a PLR threshold of 2%).

Keywords: component carrier, carrier aggregation, LTE-advanced, scheduling

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Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

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Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: bridge management systems (BMS), cost optimization condition assessment, fund allocation, Markov chain

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Authors: Gaurav Kumar Sinha

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The oil and gas industry is a critical component of the global economy, relying heavily on industrial control systems (ICS) to manage and monitor operations. However, these systems are increasingly becoming targets for cyber-attacks, posing significant risks to operational continuity, safety, and environmental integrity. This paper explores comprehensive cybersecurity strategies for protecting oil and gas industrial control systems. It delves into the unique vulnerabilities of ICS in this sector, including outdated legacy systems, integration with IT networks, and the increased connectivity brought by the Industrial Internet of Things (IIoT). We propose a multi-layered defense approach that includes the implementation of robust network security protocols, regular system updates and patch management, advanced threat detection and response mechanisms, and stringent access control measures. We illustrate the effectiveness of these strategies in mitigating cyber risks and ensuring the resilient and secure operation of oil and gas industrial control systems. The findings underscore the necessity for a proactive and adaptive cybersecurity framework to safeguard critical infrastructure in the face of evolving cyber threats.

Keywords: cybersecurity, industrial control systems, oil and gas, cyber-attacks, network security, IoT, threat detection, system updates, patch management, access control, cybersecurity awareness, critical infrastructure, resilience, cyber threats, legacy systems, IT integration, multi-layered defense, operational continuity, safety, environmental integrity

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Authors: Fariba Ebrahimi, Mehdi Ghorbani

Abstract:

Comprehensive water management considers economic, environmental, technical and social sustainability of water resources for future generations. Integrated water management implies cooperative approach and involves all stakeholders and also introduces issues to managers and decision makers. Solving these issues needs integrated and system approach according to the recognition of actors or key persons in necessary to apply cooperative management of water resources. Therefore, social network analysis can be used to demonstrate the most effective actors for environmental base decisions. The linkage of diverse sets of actors and knowledge systems across management levels and institutional boundaries often poses one of the greatest challenges in adaptive water management. Bridging stakeholder can facilitate interactions among actors in management settings by lowering the transaction costs of collaboration. This research examines how network connections between group members affect in co- management. Cohesive network structures allow groups to more effectively achieve their goals and objectives Strong; centralized leadership is a better predictor of working group success in achieving goals and objectives. Finally, geometric position of each actor was illustrated in the network. The results of the research based on between centrality index have a key and bridging actor in recognition of cooperative management of water resources in Darbandsar village and also will help managers and planners of water in the case of recognition to organization and implementation of sustainable management of water resources and water security.

Keywords: co-management, water management, social network, bridging stakeholder, darbandsar village

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Authors: Leo Latasch, Mario Di Gennaro

Abstract:

Introduction: The goal of this project was to increase the resilience of the population as well as rescue organizations to make both quality and time-related improvements in handling crises. A helper network was created for this purpose. Methods: Social questions regarding the structure and purpose of helper networks were considered - specifically with regard to helper motivation, the level of commitment and collaboration between populations and agencies. The exchange of information, the coordinated use of volunteers, and the distribution of available resources will be ensured through defined communication and cooperation routines. Helper smartphones will also be used provide a picture of the situation on the ground. Results: The helper network was established and deployed based on the RESIBES information technology system. It consists of a service platform, a web portal and a smartphone app. The service platform is the central element for collaboration between the various rescue organizations, as well as for persons, associations, and companies from the population offering voluntary aid. The platform was used for: Registering helpers and resources and then requesting and assigning it in case of a disaster. These services allow the population's resources to be organized. The service platform also allows for a secure data exchange between services and external systems. Conclusions: The social and technical work priorities have allowed us to cover a full cycle of advance structural work, gaining an overview, damage management, evaluation, and feedback on experiences. This cycle allows experiences gained while handling the crisis to feed back into the cycle and improve preparations and management strategies.

Keywords: coordination, disaster, resilience, volunteers

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Authors: Hichem Sassi

Abstract:

Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Zahra Tork

Abstract:

Before the spread of the Internet and the use of mobile phones, women were generally far from society and many restrictions were applied to them, but after the spread of the Internet and the cultural and social changes brought about by it, women in society got a new one and many restrictions regarding them disappeared; As we can see today, many women, like men, have a high social base, they earn for themselves, they can travel alone, stay out late at night, take personal and family photos or videos on virtual pages. Publish themselves (while before this, showing or publishing photos of women was considered dishonorable or indecent). In this article, an attempt is made to examine the effect of the internet on mobile phones and virtual social networks in changing beliefs, norms and social values and their relationship with the social mobility of women and the effect of these factors on social and human development be paid. For this reason, social and human development is discussed first, and then the role of the media in development is explained, and finally, the social mobility of women is discussed. Since the purpose of this study is to better understand the social mobility of Iranian women through the development of the Internet in mobile phones, a qualitative study using focus groups has been adopted. The results of this research indicated that the Internet has caused changes in the value and cultural system of the Iranian people, and women have also redefined their roles and identity. In this new definition, many of the past restrictions have disappeared and women have gained the same freedoms as men. Finally, these factors (change in values and norms and redefinition of the role of women) joined hands and caused the social mobility of women in Iran.

Keywords: development of communication in Iran, development of mobile phones, development of the Internet, women's social group, social mobility

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Authors: Ali A. Ukasha, Majdi F. Elbireki, Mohammad F. Abdullah

Abstract:

Data security needed in data transmission, storage, and communication to ensure the security. This paper is divided into two parts. This work interests with the color image which is decomposed into red, green and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using the key image that has same original size and are generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours from color images recovery can be obtained with accepted level of distortion using single step parallel contour extraction (SSPCE) method. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Color images and completely reconstructed without any distortion. Also shown that the analyzed algorithm has extremely large security against some attacks like salt and pepper and Jpeg compression. Its proof that the color images can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression and salt and peppers attacks, bitplanes decomposition, Arnold transform, color image, wavelet transform, lossless image encryption

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

Abstract:

The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

Procedia PDF Downloads 173