Search results for: artificial animal intelligence

Authors: Orit Wolf

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Classical music performance is undergoing a profound transformation, marked by a confluence of technological advancements and evolving cultural dynamics. This academic paper explores the multifaceted changes and challenges faced by classical music performance, considering the impact of artificial intelligence (AI) along with other vital factors shaping this evolution. In the contemporary era, classical music is experiencing shifts in performance practices. This paper delves into these changes, emphasizing the need for adaptability within the classical music world. From repertoire selection and concert formats to artistic expression, performers and institutions navigate a delicate balance between tradition and innovation. We explore how these changes impact the authenticity and vitality of classical music performances. Furthermore, the influence of AI in the classical music concert world cannot be underestimated. AI technologies are making inroads into various aspects, from composition assistance to rehearsal and live performances. This paper examines the transformative effects of AI, considering how it enhances precision, adaptability, and creative exploration for musicians. We explore the implications for composers, performers, and the overall concert experience while addressing ethical concerns and creative opportunities. In addition to AI, there is the importance of cross-genre interactions within the classical music sphere. Mash-ups and collaborations with artists from diverse musical backgrounds are redefining the boundaries of classical music and creating works that resonate with a wider and more diverse audience. The benefits of cross-pollination in classical music seem crucial, offering a fresh perspective to listeners. As an active concert artist, Orit Wolf will share how the expectations of classical music audiences are evolving. Modern concertgoers seek not only exceptional musical performances but also immersive experiences that may involve technology, multimedia, and interactive elements. This paper examines how classical musicians and institutions are adapting to these changing expectations, using technology and innovative concert formats to deliver a unique and enriched experience to their audiences. As these changes and challenges reshape the classical music world, the need for a harmonious coexistence of tradition, technology, and innovation becomes evident. Musicians, composers, and institutions are striving to find a balance that ensures classical music remains relevant in a rapidly changing cultural landscape while maintaining the value it brings to compositions and audiences. This paper, therefore, aims to explore the evolving trends in classical music performance. It considers the influence of AI as one element within the broader context of change, highlighting the necessity of adaptability, cross-genre interactions, and a response to evolving audience expectations. By doing so, the classical music world can navigate this transformative period while preserving its timeless traditions and adding value to both performers and listeners. Orit Wolf, an international concert pianist, fulfils her vision to bring this music in new ways to mass audiences and will share her personal and professional experience as an artist who goes on stage and makes disruptive concerts.

Keywords: cross culture collaboration, music performance and ai, classical music in the digital age, classical concerts, innovation and technology, performance innovation, audience engagement in classical concerts

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Authors: Catherine McWhorter

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Robotics is a fast-evolving field lacking comprehensive and harm-mitigating regulation; it also lacks critical data on how human-robot interaction (HRI) may affect human psychology. As most anthropomorphic robots are intended as substitutes for humans, this paper asserts that the commercial robotics industry should be preemptively regulated at the federal level such that robots capable of embodying a victim role in criminal scenarios (“vicbots”) are prohibited until clinical studies determine their effects on the user and society. The results of these studies should then inform more permanent legislation that strives to mitigate risks of harm without infringing upon fundamental rights or stifling innovation. This paper explores these concepts through the lens of the sex robot industry. The sexbot industry offers some of the most realistic, interactive, and customizable robots for sale today. From approximately 2010 until 2017, some sex robot producers, such as True Companion, actively promoted ‘vicbot’ culture with personalities like “Frigid Farrah” and “Young Yoko” but received significant public backlash for fetishizing rape and pedophilia. Today, “Frigid Farrah” and “Young Yoko” appear to have vanished. Sexbot producers have replaced preprogrammed vicbot personalities in favor of one generic, customizable personality. According to the manufacturer ainidoll.com, when asked, there is only one thing the user won’t be able to program the sexbot to do – “…give you drama”. The ability to customize vicbot personas is possible with today’s generic personality sexbots and may undermine the intent of some current legislative efforts. Current debate on the effects of vicbots indicates a lack of consensus. Some scholars suggest vicbots may reduce the rate of actual sex crimes, and some suggest that vicbots will, in fact, create sex criminals, while others cite their potential for rehabilitation. Vicbots may have value in some instances when prescribed by medical professionals, but the overall uncertainty and lack of data further underscore the need for preemptive regulation and clinical research. Existing literature on exposure to media violence and its effects on prosocial behavior, human aggression, and addiction may serve as launch points for specific studies into the hyperrealism of vicbots. Of course, the customization, anthropomorphism and artificial intelligence of sexbots, and therefore more mainstream robots, will continue to evolve. The existing sexbot industry offers an opportunity to preemptively regulate and to research answers to these and many more questions before this type of technology becomes even more advanced and mainstream. Robots pose complicated moral, ethical, and legal challenges, most of which are beyond the scope of this paper. By examining the possibility for custom vicbots via the sexbots industry, reviewing existing literature on regulation, media violence, and vicbot user effects, this paper strives to underscore the need for preemptive federal regulation prohibiting vicbot capabilities in robots while advocating for further research into the potential for the user and societal harm by the same.

Keywords: human-robot interaction effects, regulation, research, robots

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Yvonne Howard

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This project currently under development is centered around current learning processes, including a thorough literature review and ongoing practical experiences gained as a deputy head in a school. These daily experiences with students engaging in animal-assisted interventions and the school therapy dog form a strong base for this research. The primary objective of this research is to comprehensively explore the impact of dog-assisted well-being interventions on student motivation and affective engagement within primary and secondary school settings. The educational domain currently encounters a significant challenge due to the lack of substantial research in this area. Despite the perceived positive outcomes of such interventions being acknowledged and shared in various settings, the evidence supporting their effectiveness in an educational context remains limited. This study aims to bridge the gap in the research and shed light on the potential benefits of dog-assisted well-being interventions in promoting student motivation and affective engagement. The significance of this topic recognizes that education is not solely confined to academic achievement but encompasses the overall well-being and emotional development of students. Over recent years, there has been a growing interest in animal-assisted interventions, particularly in healthcare settings. This interest has extended to the educational context. While the effectiveness of these interventions in these areas has been explored in other fields, the educational sector lacks comprehensive research in this regard. Through a systematic and thorough research methodology, this study seeks to contribute valuable empirical data to the field, providing evidence to support informed decision-making regarding the implementation of dog-assisted well-being interventions in schools. This research will utilize a mixed-methods design, combining qualitative and quantitative measures to assess the research objectives. The quantitative phase will include surveys and standardized scales to measure student motivation and affective engagement, while the qualitative phase will involve interviews and observations to gain in-depth insights from students, teachers, and other stakeholders. The findings will contribute evidence-based insights, best practices, and practical guidelines for schools seeking to incorporate dog-assisted interventions, ultimately enhancing student well-being and improving educational outcomes.

Keywords: therapy dog, wellbeing, engagement, motivation, AAI, intervention, school

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Authors: Imèn Khanchouch, Malika Charrad, Mohamed Limam

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The interpretation of the quality of clusters and the determination of the optimal number of clusters is still a crucial problem in clustering. We focus in this paper on multi-SOM clustering method which overcomes the problem of extracting the number of clusters from the SOM map through the use of a clustering validity index. We then tested multi-SOM using real and artificial data sets with different evaluation criteria not used previously such as Davies Bouldin index, Dunn index and silhouette index. The developed multi-SOM algorithm is compared to k-means and Birch methods. Results show that it is more efficient than classical clustering methods.

Keywords: clustering, SOM, multi-SOM, DB index, Dunn index, silhouette index

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Authors: Coriolano Salvini, Ambra Giovannelli

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The use of renewable energy sources for electric power production leads to reduced CO2 emissions and contributes to improving the domestic energy security. On the other hand, the intermittency and unpredictability of their availability poses relevant problems in fulfilling safely and in a cost efficient way the load demand along the time. Significant benefits in terms of “grid system applications”, “end-use applications” and “renewable applications” can be achieved by introducing energy storage systems. Among the currently available solutions, CAES (Compressed Air Energy Storage) shows favorable features. Small-medium size plants equipped with artificial air reservoirs can constitute an interesting option to get efficient and cost-effective distributed energy storage systems. The present paper is addressed to the design and off-design analysis of the compression system of small size CAES plants suited to absorb electric power in the range of hundreds of kilowatt. The system of interest is constituted by an intercooled (in case aftercooled) multi-stage reciprocating compressor and a man-made reservoir obtained by connecting large diameter steel pipe sections. A specific methodology for the system preliminary sizing and off-design modeling has been developed. Since during the charging phase the electric power absorbed along the time has to change according to the peculiar CAES requirements and the pressure ratio increases continuously during the filling of the reservoir, the compressor has to work at variable mass flow rate. In order to ensure an appropriately wide range of operations, particular attention has been paid to the selection of the most suitable compressor capacity control device. Given the capacity regulation margin of the compressor and the actual level of charge of the reservoir, the proposed approach allows the instant-by-instant evaluation of minimum and maximum electric power absorbable from the grid. The developed tool gives useful information to appropriately size the compression system and to manage it in the most effective way. Various cases characterized by different system requirements are analysed. Results are given and widely discussed.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), compressor design, compression system management.

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Authors: Sylvie Michel, Emmanuelle Gagnou, Joanne Hamet

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Researchers in the fields of AI and Robotics frequently encounter ethical dilemmas throughout their research endeavors. Various ethical challenges have been pinpointed in the existing literature, including biases and discriminatory outcomes, diffusion of responsibility, and a deficit in transparency within AI operations. This research aims to pinpoint these ethical quandaries faced by researchers and shed light on the mechanisms behind ethical decision-making in the research process. By synthesizing insights from existing literature and acknowledging prevalent shortcomings, such as overlooking the heterogeneous nature of decision-making, non-accumulative results, and a lack of consensus on numerous factors due to limited empirical research, the objective is to conceptualize and validate a model. This model will incorporate influences from individual perspectives and situational contexts, considering potential moderating factors in the ethical decision-making process. Qualitative analyses were conducted based on direct observation of an AI/Robotics research team focusing on collaborative robotics for several months. Subsequently, semi-structured interviews with 16 team members were conducted. The entire process took place during the first semester of 2023. Observations were analyzed using an analysis grid, and the interviews underwent thematic analysis using Nvivo software. An initial finding involves identifying the ethical challenges that AI/robotics researchers confront, underlining a disparity between practical applications and theoretical considerations regarding ethical dilemmas in the realm of AI. Notably, researchers in AI prioritize the publication and recognition of their work, sparking the genesis of these ethical inquiries. Furthermore, this article illustrated that researchers tend to embrace a consequentialist ethical framework concerning safety (for humans engaging with robots/AI), worker autonomy in relation to robots, and the societal implications of labor (can robots displace jobs?). A second significant contribution entails proposing a model for ethical decision-making within the AI/Robotics research sphere. The model proposed adopts a process-oriented approach, delineating various research stages (topic proposal, hypothesis formulation, experimentation, conclusion, and valorization). Across these stages and the ethical queries, they entail, a comprehensive four-point comprehension of ethical decision-making is presented: recognition of the moral quandary; moral judgment, signifying the decision-maker's aptitude to discern the morally righteous course of action; moral intention, reflecting the ability to prioritize moral values above others; and moral behavior, denoting the application of moral intention to the situation. Variables such as political inclinations ((anti)-capitalism, environmentalism, veganism) seem to wield significant influence. Moreover, age emerges as a noteworthy moderating factor. AI and robotics researchers are continually confronted with ethical dilemmas during their research endeavors, necessitating thoughtful decision-making. The contribution involves introducing a contextually tailored model, derived from meticulous observations and insightful interviews, enabling the identification of factors that shape ethical decision-making at different stages of the research process.

Keywords: ethical decision making, artificial intelligence, robotics, research

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Authors: M. Sarikanat, E. Akar, I. Şen, Y. Seki, O. C. Yılmaz, B. O. Gürses, L. Cetin, O. Özdemir, K. Sever

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Chitosan is a natural, nontoxic, polyelectrolyte, cheap polymer. In this study, chitosan based electroactive polymer (CBEAP) was fabricated. Electroactive properties of this polymer were investigated at different voltages. It exhibited excellent tip displacement at low voltages (1, 3, 5, 7 V). Tip displacement was increased as the applied voltage increased. Best tip displacement was investigated as 28 mm at 5V. Characterization of CBEAP was investigated by scanning electron microscope, X-ray diffraction and tensile testing. CBEAP exhibited desired electroactive properties at low voltages. It is suitable for using in artificial muscle and various robotic applications.

Keywords: chitosan, electroactive polymer, electroactive properties

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Authors: Halim Djerroud, Arab Ali Cherif

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We assume that conscious functions are implemented automatically. In other words that consciousness as well as the non-consciousness aspect of human thought, planning, and perception, are produced by biologically adaptive algorithms. We propose that the mechanisms of consciousness can be produced using similar adaptive algorithms to those executed by the mechanism. In this paper, we propose a computational model of consciousness, the ”Global Abstraction Workspace” which is an internal environmental modelling perceived as a multi-agent system. This system is able to evolve and generate new data and processes as well as actions in the environment.

Keywords: artificial consciousness, cognitive architecture, global abstraction workspace, multi-agent system

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Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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Authors: Sylwia Laba, Robert Laba, Krystian Szczepanski, Mikolaj Niedek, Anna Kaminska-Dworznicka

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Meat and its products are considered food products, having the most unfavorable effect on the environment that requires rational management of these products and waste, originating throughout the whole chain of manufacture, processing, transport, and trade of meat. From the economic and environmental viewpoints, it is important to limit the losses and food wastage and the food waste in the whole meat sector. The link to basic production includes obtaining raw meat, i.e., animal breeding, management, and transport of animals to the slaughterhouse. Food is any substance or product, intended to be consumed by humans. It was determined (for the needs of the present studies) when the raw material is considered as a food. It is the moment when the animals are prepared to loading with the aim to be transported to a slaughterhouse and utilized for food purposes. The aim of the studies was to determine the reasons for loss generation in the basic production of the meat sector in Poland during the years 2017 – 2018. The studies on food losses and waste in the meat sector in basic production were carried out in two areas: red meat i.e., pork and beef and poultry meat. The studies of basic production were conducted in the period of March-May 2019 at the territory of the whole country on a representative trial of 278 farms, including 102 pork production, 55–beef production, and 121 poultry meat production. The surveys were carried out with the utilization of questionnaires by the PAPI (Paper & Pen Personal Interview) method; the pollsters conducted direct questionnaire interviews. Research results indicate that it is followed that any losses were not recorded during the preparation, loading, and transport of the animals to the slaughterhouse in 33% of the visited farms. In the farms where the losses were indicated, the crushing and suffocations, occurring during the production of pigs, beef cattle and poultry, were the main reasons for these losses. They constituted ca. 40% of the reported reasons. The stress generated by loading and transport caused 16 – 17% (depending on the season of the year) of the loss reasons. In the case of poultry production, in 2017, additionally, 10.7% of losses were caused by inappropriate conditions of loading and transportation, while in 2018 – 11.8%. The diseases were one of the reasons for the losses in pork and beef production (7% of the losses). The losses and waste, generated during livestock production and in meat processing and trade cannot be managed or recovered. They have to be disposed of. It is, therefore, important to prevent and minimize the losses throughout the whole production chain. It is possible to introduce the appropriate measures, connected mainly with the appropriate conditions and methods of animal loading and transport.

Keywords: food losses, food waste, livestock production, meat sector

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Authors: R. K. Purohit

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Ionizing radiation induces cellular damage through direct ionization of DNA and other cellular targets and indirectly via reactive oxygen species which may include effects from epigenetic changes. So there is a need of hour is to search for an ideal radioprotector which could minimize the deleterious and damaging effects caused by ionizing radiation. Radioprotectors are agents which reduce the radiation effects on cell when applied prior to exposure of radiation. The aim of this study was to access the efficacy of Emblica officinalis in reducing radiation and lead induced changes in mice liver. For the present experiment, healthy male Swiss albino mice (6-8 weeks) were selected and maintained under standard conditions of temperature and light. Fruit extract of Emblica was fed orally at the dose of 0.01 ml/animal/day. The animal were divided into seven groups according to the treatment i.e. lead acetate solution as drinking water (group-II) or exposed to 3.5 or 7.0 Gy gamma radiation (group-III) or combined treatment of radiation and lead acetate (group-IV). The animals of experimental groups were administered Emblica extract seven days prior to radiation or lead acetate treatment (group V, VI and VII) respectively. The animals from all the groups were sacrificed by cervical dislocation at each post-treatment intervals of 1, 2, 4, 7, 14 and 28 days. After sacrificing the animals pieces of liver were taken out and some of them were kept at -20°C for different biochemical parameters. The histopathological changes included cytoplasmic degranulation, vacuolation, hyperaemia, pycnotic and crenated nuclei. The changes observed in the control groups were compared with the respective experimental groups. An increase in the value of total proteins, glycogen, acid phosphtase, alkaline phosphatase activity and RNA was observed up to day-14 in the non drug treated group and day 7 in the Emblica treated groups, thereafter value declined up to day-28 without reaching to normal. The value of cholesterol and DNA showed a decreasing trend up to day -14 in non drug treated groups and day-7 in drug treated groups, thereafter value elevated up to day-28. The biochemical parameters were observed in the form of increase or decrease in the values. The changes were found dose dependent. After combined treatment of radiation and lead acetate synergistic effect were observed. The liver of Emblica treated animals exhibited less severe damage as compared to non-drug treated animals at all the corresponding intervals. An early and fast recovery was also noticed in Emblica pretreated animals. Thus, it appears that Emblica is potent enough to check lead and radiation induced heptic lesion in Swiss albino mice.

Keywords: radiation, lead , emblica, mice, liver

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Authors: Isma Benmazouz, Bálint Joszef Nagy, Bence Bálacs, Gábor Kardos, László Kővér

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Vancomycin-resistant enterococci (VRE) are among the major nosocomial threats, which have a potential for zoonotic transmission due to the ubiquity of enterococci in the environment and in animal microbiota, e.g., wild birds. . In order to assess the prevalence in an urbanized bird species, 221 fecal samples were collected from Hooded crows (Corvus cornix) in 2020. Fecal samples were screened using VRE agar plates. None of the samples yielded VRE. The absence of VRE isolates in sampled urban hooded crows indicates that crows residing in the city do not necessarily constitute a reservoir of VREs.

Keywords: resistance, crows, Enterococci, wild birds

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Authors: Prince Vivek, Vijay Kumar Bharti, Deepak Kumar, Rohit Kumar, Kapil Nehra, Dhananjay Singh, Om Prakash Chaurasia, Bhuvnesh Kumar

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High altitude native people still rely heavily on animal transport for logistic support at eastern and northern Himalayas regions. The prevalent mountainous terrains and rugged region are not suitable for the motorized vehicle to use in logistic transport. Therefore, people required high endurance pack animals for load carrying and riding. So far to the best of our knowledge, no studies have been taken to evaluate the effect of loads on the physiology of ponies in high altitude region. So, in this view, we evaluated variation in physiological, hematological, biochemical, and cytokines indices of Zanskar ponies during load carrying at high altitude. Total twelve (12) of Zanskar ponies, mare, age 4-6 years selected for this study, Feed was offered at 2% of body weight, and water ad libitum. Ponies were divided into three groups; group-A (without load), group-B (60 kg), and group-C (80 kg) of backpack loads. The track was very narrow and slippery with gravel, uneven with a rocky surface and has a steep gradient of 4 km uphill at altitude 3291 to 3500m. When we evaluate these parameters, it is understood that the heart rate, pulse rate, and respiration rate was significantly increased in 80 kg group among the three groups. The hematology parameters viz. hemoglobin significantly increased in 80 kg group on 1st day after load carrying among the three groups which was followed by control and 60 kg whereas, PCV, lymphocytes, monocytes percentage significantly increased however, ESR and eosinophil % significantly decreased in 80 kg group after load carrying on 7th day after load carrying among the three groups which were followed by control and 60 kg group. In biochemical parameters viz. LA, LDH, TP, hexokinase (HK), cortisol (CORT), T3, GPx, FRAP and IL-6 significantly increased in 80 kg group on the 7th day after load carrying among the three groups which were followed by control and 60 kg group. The ALT, ALB, GLB, UR, and UA significantly increased in 80 kg group on the 7th day before and after load carrying among the three groups which were followed by control and 60 kg group. The CRT, AST, and CK-MB were significantly increased in 80 kg group on the 1st and 7th day after load carrying among the three groups which were followed by control and 60 kg group. It has been concluded that, heart rate, respiration rate, hematological indices like PCV, lymphocytes, monocytes, Hb and ESR, biochemical indices like lactic acid, LDH, TP, HK, CORT, T3, ALT, AST and CRT, ALB, GLB, UR, UA, GPx, FRAP and IL-6 are important biomarkers to assess effect of load on animal physiology and endurance. Further, this result has revealed the strong correlation of change in biomarkers level with performance in ponies during load carry. Hence, these parameters might be used for the performance of endurance of Zanskar ponies in the high mountain region.

Keywords: biochemical, endurance, high altitude, load, ponies

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Authors: Amin Nazerzadeh, Afsaneh Nouri Houshyar , Azadeh Noori Hoshyar

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With the rapid growing of information technology, the industry and manufacturing systems are becoming more automated. Therefore, achieving the highly accurate automatic systems with reliable security is becoming more critical. Biometrics that refers to identifying individual based on physiological or behavioral traits are unique identifiers provide high reliability and security in different industrial systems. As biometric cannot easily be transferred between individuals or copied, it has been receiving extensive attention. Due to the importance of security applications, this paper provides an overview on biometrics and discuss about background, types and applications of biometric as an effective tool for the industrial applications.

Keywords: Industial and manufacturing applications, intelligence and security, information technology, recognition; security technology; biometrics

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Authors: Aidin Foroutan, David S. Wishart, Leluo L. Guan, Carolyn Fitzsimmons

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Maximizing efficiency and growth potential of beef cattle requires not only genetic selection (i.e. residual feed intake (RFI)) but also adequate nutrition throughout all stages of growth and development. Nutrient restriction during gestation has been shown to negatively affect post-natal growth and development as well as fertility of the offspring. This, when combined with RFI may affect progeny traits. This study aims to investigate the impact of selection for divergent genetic potential for RFI and maternal nutrition during early- to mid-gestation, on bull calf traits such as fertility and muscle development using multiple ‘omics’ approaches. Comparisons were made between High-diet vs. Low-diet and between High-RFI vs. Low-RFI animals. An epigenetics experiment on semen samples identified 891 biomarkers associated with growth and development. A gene expression study on Longissimus thoracis muscle, semimembranosus muscle, liver, and testis identified 4 genes associated with muscle development and immunity of which Myocyte enhancer factor 2A [MEF2A; induces myogenesis and control muscle differentiation] was the only differentially expressed gene identified in all four tissues. An initial metabolomics experiment on serum samples using nuclear magnetic resonance (NMR) identified 4 metabolite biomarkers related to energy and protein metabolism. Once all the biomarkers are identified, bioinformatics approaches will be used to create a database covering all the ‘omics’ data collected from this project. This database will be broadened by adding other information obtained from relevant literature reviews. Association analyses with these data sets will be performed to reveal key biological pathways affected by RFI and maternal nutrition. Through these association studies between the genome and metabolome, it is expected that candidate biomarker genes and metabolites for feed efficiency, fertility, and/or muscle development are identified. If these gene/metabolite biomarkers are validated in a larger animal population, they could potentially be used in breeding programs to select superior animals. It is also expected that this work will lead to the development of an online tool that could be used to predict future traits of interest in an animal given its measurable ‘omics’ traits.

Keywords: biomarker, maternal nutrition, omics, residual feed intake

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Authors: Farhan J. M. Al-Behadili, Vineeta Bilgi, Miyuki Taniguchi, Junxi Li, Wei Xu

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Mediterranean fruit fly (Ceratitis capitata) is one of the most destructive pests of fruits and vegetables. Medfly originated from Africa and spread in many countries, and is currently an endemic pest in Western Australia. Medfly has been recorded from over 300 plant species including fruits, vegetables, nuts and its main hosts include blueberries, citrus, stone fruit, pome fruits, peppers, tomatoes, and figs. Global trade of fruits and other farm fresh products are suffering from the damages of this pest, which prompted towards the need to develop more effective ways to control these pests. The available quarantine treatment technologies mainly include chemical treatment (e.g., fumigation) and non-chemical treatments (e.g., cold, heat and irradiation). In recent years, with the loss of several chemicals, it has become even more important to rely on non-chemical postharvest control technologies (i.e., heat, cold and irradiation) to control fruit flies. Cold treatment is one of the most potential trends of focus in postharvest treatment because it is free of chemical residues, mitigates or kills the pest population, increases the strength of the fruits, and prolongs storage time. It can also be applied to fruits after packing and ‘in transit’ during lengthy transport by sea during their exports. However, limited systematic study on cold treatment of Medfly stages in artificial diets was reported, which is critical to provide a scientific basis to compare with previous research in plant products and design an effective cold treatment suitable for exported plant products. The overall purpose of this study was to evaluate and understand Medfly responses to cold treatments. Medfly stages were tested. The long-term goal was to optimize current postharvest treatments and develop more environmentally-friendly, cost-effective, and efficient treatments for controlling Medfly. Cold treatment with different exposure times is studied to evaluate cold eradication treatment of Mediterranean fruit fly (Ceratitis capitata), that reared on carrot diet. Mortality is important aspect was studied in this study. On the other hand, study effects of exposure time on mortality means of medfly stages.

Keywords: cold treatment, fruit fly, Ceratitis capitata, carrot diet, temperature effects

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Visvaganthie Moodley

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Language use and aesthetic appreciation are integral to meaning-making in prose, as they are in poetry. However, in comparison to poetic analysis, a literary analysis of prose that focuses on linguistics and stylistics is somewhat scarce as it generally requires the study of lengthy texts. Nevertheless, the effect of linguistic and stylistic features in prose as conscious design by authors for creating specific effects and conveying preconceived messages is drawing increasing attention of linguists and literary experts. A close examination of language use in prose can, among a host of literary purposes, convey emotive and cognitive values and contribute to making interpretations about how fictional characters are represented to the imaginative reader. This paper provides a literary analysis of Yann Martel’s narrative of a 14-year-old Indian boy, Pi, who had survived the wreck of a Japanese cargo ship, by focusing on his 227-day journey of tribulations, along with a Bengal tiger, on a lifeboat. The study favours a pluralistic approach blending literary criticism, linguistic analysis and stylistic description. It adopts Leech and Short’s (2007) broad framework of linguistic and stylistic categories (lexical categories, grammatical categories, figures of speech etc. [sic] and context and cohesion) as well as a range of other relevant linguistic phenomena to show how the narrator, Pi, and the author influence the reader’s interpretations of Pi’s character. Such interpretations are made using the lens of Freud’s psychoanalytical theory (which focuses on the interplay of the instinctual id, the ego and the moralistic superego) and Blake’s philosophy of innocence and experience (the two contrary states of the human soul). The paper traces Pi’s transformation from animal-loving, God-fearing vegetarian to brutal animal slayer and cannibal in his journey of survival. By a close examination of the linguistic and stylistic features of the narrative, it argues that, despite evidence of butchery and cannibalism, Pi’s gruesome behaviour is motivated by extreme physiological and psychological duress and not intentional malice. Finally, the paper concludes that the voice of the narrator, Pi, and that of the author, Martel, act as powerful persuasive agents in influencing the reader to respond with a sincere flow of sympathy for Pi and judge him as having retained his innocence in his instinctual need for survival.

Keywords: foregrounding, innocence and experience, lexis, literary analysis, psychoanalytical lens, style

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%.

Keywords: ADALINE, neural network, gradient decent, process mining, resource behaviour, polynomial regression model

Procedia PDF Downloads 378

Authors: Hafid Nadia, Meziane Toufik

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The aim of this study was the characterization of the goat meat by the determination of quantity and the quality in Batna region. The first part was the evaluation of production and consumption. The investigations show that the goat meat third after mutton and beef, it’s especially consumed by the indigenous population located in the Mountain and steep area. The second part of this review treats nutritional quality of this meat by the quantification of the chemical composition, including fat profile, and establishes a link between animal age and the values of these parameters. Moisture, fat contents, and cholesterol levels varied with age. Because of the decreasing level of cholesterol in the Chevon meat, it is more recommended for consumption to prevent or reduce the incidence of coronary disease and heart attack.

Keywords: biochemical composition, cholesterol, goat meat, heart attack

Procedia PDF Downloads 663

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Vipin Kumar Verma, Neeta Sehgal, Om Prakash

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Cyprinus carpio has a wide spread occurrence in the lakes and rivers of Europe and Asia. Heavy losses in natural environment due to anthropogenic activities, including pollution as well as pathogenic diseases have landed this fish in IUCN red list of vulnerable species. The significance of a suitable diet in preserving the health status of fish is widely recognized. In present study, artificial feed supplemented with leaves of two weed plants, Eichhornia crassipes and Ricinus communis were evaluated for their role on the fish immune system. To achieve this objective fish were acclimatized to laboratory conditions (25 ± 1 °C; 12 L: 12D) for 10 days prior to start of experiment and divided into 4 groups: non-challenged (negative control= A), challenged [positive control (B) and experimental (C & D)]. Group A, B were fed with non-supplemented feed while group C & D were fed with feed supplemented with 5% Eichhornia crassipes and 5% Ricinus communis respectively. Supplemented feeds were evaluated for their effect on growth, health, immune system and disease resistance in fish when challenged with Vibrio harveyi. Fingerlings of C. carpio (weight, 2.0±0.5 g) were exposed with fresh overnight culture of V. harveyi through bath immunization (concentration 2 Χ 105) for 2 hours on 10 days interval for 40 days. The growth was monitored through increase in their relative weight. The rate of mortality due to bacterial infection as well as due to effect of feed was recorded accordingly. Immune response of fish was analyzed through differential leucocyte count, percentage phagocytosis and phagocytic index. The effect of V. harveyi on fish organs were examined through histo-pathological examination of internal organs like spleen, liver and kidney. The change in the immune response was also observed through gene expression analysis. The antioxidant potential of plant extracts was measured through DPPH and FRAP assay and amount of total phenols and flavonoids were calculates through biochemical analysis. The chemical composition of plant’s methanol extracts was determined by GC-MS analysis, which showed presence of various secondary metabolites and other compounds. Investigation revealed immuno-modulatory effect of plants, when supplemented with the artificial feed of fish.

Keywords: immuno-modulation, gc-ms, Cyprinus carpio, Eichhornia crassipes, Ricinus communis

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Authors: Diana Islam, Juan Fang, Vito Fanelli, Bing Han, Julie Khang, Jianfeng Wu, Arthur S. Slutsky, Haibo Zhang

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Introduction: MSC delivery in preclinical models of ARDS has demonstrated significant improvements in lung function and recovery from acute injury. However, the role of MSC delivery in ARDS associated pulmonary fibrosis is not well understood. Some animal studies using bleomycin, asbestos, and silica-induced pulmonary fibrosis show that MSC delivery can suppress fibrosis. While other animal studies using radiation induced pulmonary fibrosis, liver, and kidney fibrosis models show that MSC delivery can contribute to fibrosis. Hypothesis: The beneficial and deleterious effects of MSC in ARDS are modulated by the lung microenvironment at the time of MSC delivery. Methods: To induce ARDS a two-hit mouse model of Hydrochloric acid (HCl) aspiration (day 0) and mechanical ventilation (MV) (day 2) was used. HCl and injurious MV generated fibrosis within 14-28 days. 0.5x106 mouse MSCs were delivered (via both intratracheal and intravenous routes) either in the active inflammatory phase (day 2) or during the remodeling phase (day 14) of ARDS (mouse fibroblasts or PBS used as a control). Lung injury accessed using inflammation score and elastance measurement. Pulmonary fibrosis was accessed using histological score, tissue collagen level, and collagen expression. In addition alveolar epithelial (E) and mesenchymal (M) marker expression profile was also measured. All measurements were taken at day 2, 14, and 28. Results: MSC delivery 2 days after HCl exacerbated lung injury and fibrosis compared to HCl alone, while the day 14 delivery showed protective effects. However in the absence of HCl, MSC significantly reduced the injurious MV-induced fibrosis. HCl injury suppressed E markers and up-regulated M markers. MSC delivery 2 days after HCl further amplified M marker expression, indicating their role in myofibroblast proliferation/activation. While with 14-day delivery E marker up-regulation was observed indicating their role in epithelial restoration. Conclusions: Early MSC delivery can be protective of injurious MV. Late MSC delivery during repair phase may also aid in recovery. However, early MSC delivery during the exudative inflammatory phase of HCl-induced ARDS can result in pro-fibrotic profiles. It is critical to understand the interaction between MSC and the lung microenvironment before MSC-based therapies are utilized for ARDS.

Keywords: acute respiratory distress syndrome (ARDS), mesenchymal stem cells (MSC), hydrochloric acid (HCl), mechanical ventilation (MV)

Procedia PDF Downloads 662

Authors: Pandurang Balwant, Ashutosh Mishra, Jyothi V., Abhay Soni, Padmakar C., Rafat Quamar, Ramesh J.

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The sea water intrusion in the coastal aquifers has become one of the major environmental concerns. Although, it is a natural phenomenon but, it can be induced with anthropogenic activities like excessive exploitation of groundwater, seacoast mining, etc. The geological and hydrogeological conditions including groundwater heads and groundwater pumping pattern in the coastal areas also influence the magnitude of seawater intrusion. However, this problem can be remediated by taking some preventive measures like rainwater harvesting and artificial recharge. The present study is an attempt to identify suitable sites for rainwater harvesting in salt intrusion affected area near coastal aquifer of Jafrabad town, Amreli district, Gujrat, India. The physico-chemical water quality results show that out of 25 groundwater samples collected from the study area most of samples were found to contain high concentration of Total Dissolved Solids (TDS) with major fractions of Na and Cl ions. The Cl/HCO3 ratio was also found greater than 1 which indicates the salt water contamination in the study area. The geophysical survey was conducted at nine sites within the study area to explore the extent of contamination of sea water. From the inverted resistivity sections, low resistivity zone (<3 Ohm m) associated with seawater contamination were demarcated in North block pit and south block pit of NCJW mines, Mitiyala village Lotpur and Lunsapur village at the depth of 33 m, 12 m, 40 m, 37 m, 24 m respectively. Geospatial techniques in combination of Analytical Hierarchy Process (AHP) considering hydrogeological factors, geographical features, drainage pattern, water quality and geophysical results for the study area were exploited to identify potential zones for the Rainwater Harvesting. Rainwater harvesting suitability model was developed in ArcGIS 10.1 software and Rainwater harvesting suitability map for the study area was generated. AHP in combination of the weighted overlay analysis is an appropriate method to identify rainwater harvesting potential zones. The suitability map can be further utilized as a guidance map for the development of rainwater harvesting infrastructures in the study area for either artificial groundwater recharge facilities or for direct use of harvested rainwater.

Keywords: analytical hierarchy process, groundwater quality, rainwater harvesting, seawater intrusion

Procedia PDF Downloads 163

Authors: Beatriz González-rodrigo, Diego Hidalgo-leiva, Omar Flores, Claudia Germoso, Maribel Jiménez-martínez, Laura Navas-sánchez, Belén Orta, Nicola Tarque, Orlando Hernández- Rubio, Miguel Marchamalo, Juan Gregorio Rejas, Belén Benito-oterino

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Cities facing seismic hazard necessitate detailed risk assessments for effective urban planning and vulnerability identification, ensuring the safety and sustainability of urban infrastructure. Comprehensive studies involving seismic hazard, vulnerability, and exposure evaluations are pivotal for estimating potential losses and guiding proactive measures against seismic events. However, broad-scale traditional risk studies limit consideration of specific local threats and identify vulnerable housing within a structural typology. Achieving precise results at neighbourhood levels demands higher resolution seismic hazard exposure, and vulnerability studies. This research aims to bolster sustainability and safety against seismic disasters in three Central American and Caribbean capitals. It integrates geospatial techniques and artificial intelligence into seismic risk studies, proposing cost-effective methods for exposure data collection and damage prediction. The methodology relies on prior seismic threat studies in pilot zones, utilizing existing exposure and vulnerability data in the region. Emphasizing detailed building attributes enables the consideration of behaviour modifiers affecting seismic response. The approach aims to generate detailed risk scenarios, facilitating prioritization of preventive actions pre-, during, and post-seismic events, enhancing decision-making certainty. Detailed risk scenarios necessitate substantial investment in fieldwork, training, research, and methodology development. Regional cooperation becomes crucial given similar seismic threats, urban planning, and construction systems among involved countries. The outcomes hold significance for emergency planning and national and regional construction regulations. The success of this methodology depends on cooperation, investment, and innovative approaches, offering insights and lessons applicable to regions facing moderate seismic threats with vulnerable constructions. Thus, this framework aims to fortify resilience in seismic-prone areas and serves as a reference for global urban planning and disaster management strategies. In conclusion, this research proposes a comprehensive framework for seismic risk assessment in high-risk urban areas, emphasizing detailed studies at finer resolutions for precise vulnerability evaluations. The approach integrates regional cooperation, geospatial technologies, and adaptive fragility curve adjustments to enhance risk assessment accuracy, guiding effective mitigation strategies and emergency management plans.

Keywords: assessment, behaviour modifiers, emergency management, mitigation strategies, resilience, vulnerability

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Authors: Nurkhairul Bariyah Baharun, Afzan Adam, Reena Rahayu Md Zin

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Tumor microenvironment (TME) in breast cancer is mainly composed of cancer cells, immune cells, and stromal cells. The interaction between cancer cells and their microenvironment plays an important role in tumor development, progression, and treatment response. The TME in breast cancer includes tumor-infiltrating lymphocytes (TILs) that are implicated in killing tumor cells. TILs can be found in tumor stroma (sTILs) and within the tumor (iTILs). TILs in triple negative breast cancer (TNBC) have been demonstrated to have prognostic and potentially predictive value. The international Immune-Oncology Biomarker Working Group (TIL-WG) had developed a guideline focus on the assessment of sTILs using hematoxylin and eosin (H&E)-stained slides. According to the guideline, the pathologists use “eye balling” method on the H&E stained- slide for sTILs assessment. This method has low precision, poor interobserver reproducibility, and is time-consuming for a comprehensive evaluation, besides only counted sTILs in their assessment. The TIL-WG has therefore recommended that any algorithm for computational assessment of TILs utilizing the guidelines provided to overcome the limitations of manual assessment, thus providing highly accurate and reliable TILs detection and classification for reproducible and quantitative measurement. This study is carried out to develop a TNBC digital whole slide image (WSI) dataset from H&E-stained slides and IHC (CD4+ and CD8+) stained slides. TNBC cases were retrieved from the database of the Department of Pathology, Hospital Canselor Tuanku Muhriz (HCTM). TNBC cases diagnosed between the year 2010 and 2021 with no history of other cancer and available block tissue were included in the study (n=58). Tissue blocks were sectioned approximately 4 µm for H&E and IHC stain. The H&E staining was performed according to a well-established protocol. Indirect IHC stain was also performed on the tissue sections using protocol from Diagnostic BioSystems PolyVue™ Plus Kit, USA. The slides were stained with rabbit monoclonal, CD8 antibody (SP16) and Rabbit monoclonal, CD4 antibody (EP204). The selected and quality-checked slides were then scanned using a high-resolution whole slide scanner (Pannoramic DESK II DW- slide scanner) to digitalize the tissue image with a pixel resolution of 20x magnification. A manual TILs (sTILs and iTILs) assessment was then carried out by the appointed pathologist (2 pathologists) for manual TILs scoring from the digital WSIs following the guideline developed by TIL-WG 2014, and the result displayed as the percentage of sTILs and iTILs per mm² stromal and tumour area on the tissue. Following this, we aimed to develop an automated digital image scoring framework that incorporates key elements of manual guidelines (including both sTILs and iTILs) using manually annotated data for robust and objective quantification of TILs in TNBC. From the study, we have developed a digital dataset of TNBC H&E and IHC (CD4+ and CD8+) stained slides. We hope that an automated based scoring method can provide quantitative and interpretable TILs scoring, which correlates with the manual pathologist-derived sTILs and iTILs scoring and thus has potential prognostic implications.

Keywords: automated quantification, digital pathology, triple negative breast cancer, tumour infiltrating lymphocytes

Procedia PDF Downloads 106

Authors: Vimal Kumar Dewangan, T. S. Sampath Kumar, Mukesh Doble, Viju Daniel Varghese

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The apatite-based, i.e., calcium-deficient hydroxyapatite (CDHAp) bone cement is well-known potential bone graft/substitute in orthopaedics due to its similar chemical composition with natural bone minerals. Therefore, an easy approach was attempted to fabricate the apatite-based (CDHAp) bone cement with improved injectability, bioresorbability, and macroporosity. In this study, the desired bone cement was developed by mixing the solid phase (consisting of wet chemically synthesized nanocrystalline hydroxyapatite and commercially available (synthetic) tricalcium phosphate) and the liquid phase (consisting of cement binding accelerator with few biopolymers in a dilute acidic solution) along with a liquid porogen as polysorbate or a solid porogen as mannitol (for comparison) in an optimized liquid-to-powder ratio. The fabricated cement sets within clinically preferred setting time (≤20 minutes) are better injectable (>70%) and also stable at ~7.3-7.4 (physiological pH). The CDHAp phased bone cement was resulted by immersing the fabricated after-set cement in phosphate buffer solution and other similar artificial body fluids and incubated at physiological conditions for seven days, confirmed through the X-ray diffraction and Fourier transform-infrared spectroscopy analyses. The so-formed synthetic apatite-based bone cement holds the acceptable compressive strength (within the range of trabecular bone) with average interconnected pores size falls in a macropores range (~50-200μm) inside the cement, verified by scanning electron microscopy (SEM), mercury intrusion porosimetry and micro-CT analysis techniques. Also, it is biodegradable (degrades ~19-22% within 10-12 weeks) when incubated in artificial body fluids under physiological conditions. The biocompatibility study of the bone cement, when incubated with MG63 cells, shows a significant increase in the cell viability after 3rd day of incubation compared with the control, and the cells were well-attached and spread completely on the surface of the bone cement, confirmed through SEM and fluorescence microscopy analyses. With this all, we can conclude that the developed synthetic macroporous apatite-based bone cement may have the potential to become promising material used as a trabecular bone substitute.

Keywords: calcium deficient hydroxyapatite, synthetic apatite-based bone cement, injectability, macroporosity, trabecular bone substitute

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Authors: Jubaida Gulshan Ara, Zebun Nasreen Ahmed

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Urban flood and drought has been a major problem of Dhaka city, particularly in recent years. Continuous increase of the city built up area, and limiting rainwater infiltration zone, are thought to be the main causes of the problem. Proper rainwater management, even at the individual plot level, might bring significant improvement in this regard. As residential use pattern occupies a significant portion of the city surface, the scope of rainwater harvesting (RWH) in residential buildings can be investigated. This paper reports on a research which explored the scope of rainwater harvesting in residential plots, with multifamily apartment buildings, in Dhaka city. The research investigated the basics of RWH, contextual information, i.e., hydro-geological, meteorological data of Dhaka city and the rules and legislations for residential building construction. The study also explored contemporary rainwater harvesting practices in the local and international contexts. On the basis of theoretical understanding, 21 sample case-studies, in different phases of construction, were selected from seven different categories of plot sizes, in different residential areas of Dhaka city. Primary data from the 21 case-study buildings were collected from a physical survey, from design drawings, accompanied by a questionnaire survey. All necessary secondary data were gathered from published and other relevant sources. Collected primary and secondary data were used to calculate and analyze the RWH needs for each case study, based on the theoretical understanding. The main findings have been compiled and compared, to observe residential development trends with regards to building rainwater harvesting system. The study has found that, in ‘Multifamily Apartment Building’ of Dhaka city, storage, and recharge structure size for rainwater harvesting, increases along with occupants’ number, and with the increasing size of the plot. Hence, demand vs. supply ratio remains almost the same for different sizes of plots, and consequently, the size of the storage structure increases significantly, in large-scale plots. It has been found that rainwater can meet only 12%-30% of the total restricted water demand of these residential buildings of Dhaka city. Therefore, artificial groundwater recharge might be the more suitable option for RWH, than storage. The study came up with this conclusion that, in multifamily residential apartments of Dhaka city, artificial groundwater recharge might be the more suitable option for RWH, than storing the rainwater on site.

Keywords: Dhaka city, rainwater harvesting, residential plots, urban flood

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

Procedia PDF Downloads 139