Search results for: shared frailty survival models

Authors: Ani Bajrami

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The birth of traditional medicine is related to plant diversity in a region, and the knowledge regarding them has been used and culturally transmitted over generations by members of a certain society. In this context, Traditional Ecological Knowledge (TEK) concerning the use of plants for medicinal purposes had survival value and was adaptive for people living in different habitats around the world. Albanian flora has a high considerably number of medicinal plants, and they have been extensively used albeit expressed in folk medicinal knowledge and practices. Over the past decades, a number of ethnobotanical studies and extensive fieldwork has been conducted in Albania both by local and foreign scientists. In addition, ethnobotany is experiencing a theoretical and conceptual diversification. This article is a historical review of ethnobotanical studies conducted in Albania after the Second World War and provides theoretical considerations on how these studies should be conducted in the future.

Keywords: medicinal plants, traditional ecological knowledge, historical ethnobotany, theory, albania

Procedia PDF Downloads 172

Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Won Il Jung, Gene Lee, Luis Rabelo

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Since many weapon systems are getting more complex and diverse, various problems occur in terms of the acquisition cost, time, and performance limitation. As a matter of fact, the experiment execution in real world is costly, dangerous, and time-consuming to obtain Required Operational Characteristics (ROC) for a new weapon acquisition although enhancing the fidelity of experiment results. Also, until presently most of the research contained a large amount of assumptions so therefore a bias is present in the experiment results. At this moment, the new methodology is proposed to solve these problems without a variety of assumptions. ROC of the new weapon system is developed through the new methodology, which is a way to analyze big data generated by simulating various scenarios based on virtual and constructive models which are involving 6 Degrees of Freedom (6DoF). The new methodology enables us to identify unbiased ROC on new weapons by reducing assumptions and provide support in terms of the optimal weapon systems acquisition.

Keywords: big data, required operational characteristics (ROC), virtual and constructive models, weapon acquisition

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Authors: Carol C. Opara, Olukemi E. Adetuyi-Olu-Francis

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The teaching of French language, like every other language, has its numerous challenges. A multilingual community, however, is a linguistic environment housing diverse languages, each with its peculiarity, both pros, and cones. A foreign language will have to strive hard for survival in an environment where various indigenous languages, as well as an established official language, exist. This study examined the challenges and prospects of the teaching of French as a foreign language in a multilingual community. A 22-item questionnaire was used to elicit information from 40 Nigerian Secondary school teachers of French. One of the findings of this study showed that the teachers of the French language are not motivated. Also, the linguistic environment is not favourable for the teaching and learning of French language in Nigeria. One of the recommendations was that training and re-training of teachers of French should be of utmost importance to the Nigerian Federal Ministry of Education.

Keywords: challenges, french as foreign language, multilingual community, teaching

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Authors: Silvia Zanazzi

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Artificial intelligence (AI) has become an integral part of our world, transitioning from scientific exploration to practical applications that impact daily life. The emergence of generative AI is reshaping education, prompting new questions about the role of teachers, the nature of learning, and the overall purpose of schooling. While AI offers the potential for optimizing teaching and learning processes, concerns about discrimination and bias arising from training data and algorithmic decisions persist. There is a risk of a disconnect between the rapid development of AI and the goals of building inclusive educational environments. The prevailing discourse on AI in education often prioritizes efficiency and individual skill acquisition. This narrow focus can undermine the importance of collaborative learning and shared experiences. A growing body of research challenges this perspective, advocating for AI that enhances, rather than replaces, human interaction in education. This study aims to examine the relationship between AI and education critically. Reviewing existing research will identify both AI implementation’s potential benefits and risks. The goal is to develop a framework that supports the ethical and effective integration of AI into education, ensuring it serves the needs of all learners. The theoretical reflection will be developed based on a review of national and international scientific literature on artificial intelligence in education. The primary objective is to curate a selection of critical contributions from diverse disciplinary perspectives and/or an inter- and transdisciplinary viewpoint, providing a state-of-the-art overview and a critical analysis of potential future developments. Subsequently, the thematic analysis of these contributions will enable the creation of a framework for understanding and critically analyzing the role of artificial intelligence in schools and education, highlighting promising directions and potential pitfalls. The expected results are (1) a classification of the cognitive biases present in representations of AI in education and the associated risks and (2) a categorization of potentially beneficial interactions between AI applications and teaching and learning processes, including those already in use or under development. While not exhaustive, the proposed framework will serve as a guide for critically exploring the complexity of AI in education. It will help to reframe dystopian visions often associated with technology and facilitate discussions on fostering synergies that balance the ‘dream’ of quality education for all with the realities of AI implementation. The discourse on artificial intelligence in education, highlighting reductionist models rooted in fragmented and utilitarian views of knowledge, has the merit of stimulating the construction of alternative perspectives that can ‘return’ teaching and learning to education, human growth, and the well-being of individuals and communities.

Keywords: education, artificial intelligence, teaching, learning

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Authors: Muhammad Usman Ghani

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Religious tolerance is essential for the establishment of peace in the world. In the system created by Almighty Allah where a lot of diversity is found, still, this world holds unity itself. In today's world, human beings have been divided into clashes of civilizations or divided on the basis of religions or lingual differences. A religious scholar of Indo- Pak subcontinent describes four ethics, on the basis of which all religions of the world can unite. He says in his philosophy of religion that, there is a number of elements common in all religions but four are very common and they are: cleanliness, nobel deeds, relation to Almighty (existence of Almighty) and justice. He says that this universe also holds its integrity in itself. All humans are different in their attributes but to be a human being is common in them. Similarly, all species of the universe are different in their nature, but to be the creature of God is commonly shared by all of them.

Keywords: inter-religious relation, peace and harmony, unity, four common ethics/virtues

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Authors: Siham Soulaimi

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Europe has become a difficult destination due to strict migration policies and border controls, making Morocco an immigration country. Morocco is currently at the center of the international migration debate because it not only hosts regular migrants but also must deal with the problem of irregular migrants entering its territory. Sub-Saharan irregular migration is full of challenges that might cause a delay for the migrants, announcing a death sentence for many others. The journey's hurdles are likely to cause a crisis in expectations, resulting in serious consequences on the migrants' mental health. Our research study emphasizes that sub-Saharan migrants begin irregular journeys with high hopes, only to be disappointed by how unexpectedly cruel it turns out to be. We also pointed to specific physical and, more crucially, mental health problems that they end up with after survival, resulting in somatic disorders.

Keywords: irregular migration, Sub-Saharan migrants, challenges, experiences crisis, mental health, somatoform disorder

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Authors: Jing-Gang Xie

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The research, in this case, considers the integration of the Quantum Field Theory and the General Relativity Theory. As two successful models in explaining behaviors of particles, they are incompatible since they work at different masses and scales of energy, with the evidence that regards the description of black holes and universe formation. It is so considering previous efforts in merging the two theories, including the likes of the String Theory, Quantum Gravity models, and others. In a bid to prove an actionable experiment, the paper’s approach starts with the derivations of the existing theories at present. It goes on to test the derivations by applying the same initial assumptions, coupled with several deviations. The resulting equations get similar results to those of classical Newton model, quantum mechanics, and general relativity as long as conditions are normal. However, outcomes are different when conditions are extreme, specifically with no breakdowns even for less than Schwarzschild radius, or at Planck length cases. Even so, it proves the possibilities of integrating the two theories.

Keywords: general relativity theory, particles, photons, Quantum Gravity Model, gravitational frequency shift

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Authors: Y. Bhatt, N. Ghosh, N. Tiwari

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Acreage response function are modeled taking account of expected harvest prices, weather related variables and other non-price variables allowing for partial adjustment possibility. At the outset, based on the literature on price expectation formation, we explored suitable formulations for estimating the farmer’s expected prices. Assuming that farmers form expectations rationally, the prices of food and biofuel crops are modeled using time-series methods for possible ARCH/GARCH effects to account for volatility. The prices projected on the basis of the models are then inserted to proxy for the expected prices in the acreage response functions. Food crop acreages in different growing states are found sensitive to their prices relative to those of one or more of the biofuel crops considered. The required percentage improvement in food crop yields is worked to offset the acreage loss.

Keywords: acreage response function, biofuel, food security, sustainable development

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Authors: Zainab Zendana Shafii

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Lullabies have been broadly defined as songs that are sung to calm and soothe children. While this is true, this paper intends to show that lullabies exceed these functions. The paper, in exploring Nupe lullabies, examines the various functions that lullabies perform in terms of language development, cultural enrichment and also the retelling of history as it relates to the culture of the Nupe people of northern Nigeria. The theoretical framework used is the functionalist theory. This theory postulates that all cultural or social phenomena have a positive function and that all are indispensable. The functionalist theory is based on the premise that all aspects of a society—institutions, roles, norms, etc.—serve a purpose and that all are indispensable for the long-term survival of the society. To this end, this paper dissects the various lullabies in Nupeland with a view to exploring the meaning that these songs generate and why they are even sung at all. The qualitative research methodology has been used to gather materials.

Keywords: Nupe, lullabies, Nigeria, northern

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Authors: Meshal M. Abdullah, Rusty A. Feagin, Layla Musawi

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When humans mismanage arid landscapes, soil erosion can become a primary mechanism that leads to desertification. This study focuses on applying soil erosion models to a disturbed landscape in Umm Nigga, Kuwait, and identifying its predicted change under restoration plans, The northern portion of Umm Nigga, containing both coastal and desert ecosystems, falls within the boundaries of the Demilitarized Zone (DMZ) adjacent to Iraq, and has been fenced off to restrict public access since 1994. The central objective of this project was to utilize GIS and remote sensing to compare the MPSIAC (Modified Pacific South West Inter Agency Committee), EMP (Erosion Potential Method), and USLE (Universal Soil Loss Equation) soil erosion models and determine their applicability for arid regions such as Kuwait. Spatial analysis was used to develop the necessary datasets for factors such as soil characteristics, vegetation cover, runoff, climate, and topography. Results showed that the MPSIAC and EMP models produced a similar spatial distribution of erosion, though the MPSIAC had more variability. For the MPSIAC model, approximately 45% of the land surface ranged from moderate to high soil loss, while 35% ranged from moderate to high for the EMP model. The USLE model had contrasting results and a different spatial distribution of the soil loss, with 25% of area ranging from moderate to high erosion, and 75% ranging from low to very low. We concluded that MPSIAC and EMP were the most suitable models for arid regions in general, with the MPSIAC model best. We then applied the MPSIAC model to identify the amount of soil loss between coastal and desert areas, and fenced and unfenced sites. In the desert area, soil loss was different between fenced and unfenced sites. In these desert fenced sites, 88% of the surface was covered with vegetation and soil loss was very low, while at the desert unfenced sites it was 3% and correspondingly higher. In the coastal areas, the amount of soil loss was nearly similar between fenced and unfenced sites. These results implied that vegetation cover played an important role in reducing soil erosion, and that fencing is much more important in the desert ecosystems to protect against overgrazing. When applying the MPSIAC model predictively, we found that vegetation cover could be increased from 3% to 37% in unfenced areas, and soil erosion could then decrease by 39%. We conclude that the MPSIAC model is best to predict soil erosion for arid regions such as Kuwait.

Keywords: soil erosion, GIS, modified pacific South west inter agency committee model (MPSIAC), erosion potential method (EMP), Universal soil loss equation (USLE)

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Authors: Rakesh Namdeti

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The liquid waste-wastewater- is essentially the water supply of the community after it has been used in a variety of applications. In recent years, heavy metal concentrations, besides other pollutants, have increased to reach dangerous levels for the living environment in many regions. Among the heavy metals, Lead has the most damaging effects on human health. It can enter the human body through the uptake of food (65%), water (20%), and air (15%). In this background, certain low-cost and easily available biosorbent was used and reported in this study. The scope of the present study is to remove Lead from its aqueous solution using Olea EuropaeaResin as biosorbent. The results showed that the biosorption capacity of Olea EuropaeaResin biosorbent was more for Lead removal. The Langmuir, Freundlich, Tempkin, and Dubinin-Radushkevich (D-R) models were used to describe the biosorption equilibrium of Lead Olea EuropaeaResin biosorbent, and the biosorption followed the Langmuir isotherm. The kinetic models showed that the pseudo-second-order rate expression was found to represent well the biosorption data for the biosorbent.

Keywords: novel biosorbent, central composite design, Lead, isotherms, kinetics

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Authors: Elena Maria Scalisi

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Currently, photodegradation by nanoparticles (NPs) is a common solution for wastewater treatment. Nanoparticles are efficient for removing organic and inorganic pollutants, heavy metals from wastewater and killing microorganisms through environmentally friendly. In this context, the major representative of photocatalytic technology for industrial wastewater treatment are TiO₂ nanoparticles (TiO₂-NPs). TiO₂-NPs have a strong catalytic activity that depends to their physicochemical properties. Thanks to their small size (between 1-100 nm), nanoparticles occupy less volume, then their surface area increases. The increase in the surface-to-volume ratio results in the increase of the particle surface energy, which improve their reactivity potential. However, these unique properties represent risks to the ecosystems and organisms when unintentionally TiO₂-NPs are release into the environment and absorbed by living organisms. Several studies confirm that there is a high level of interest concerning the safety of TiO₂-NPs in the aquatic environment, furthermore, ecotoxicological tools are useful to correctly evaluate their toxicity. In the current study, we aimed to characterize potential toxic effects of TiO₂-NP suspension to zebrafish during embryo-larval stages to evaluate parameters such as survival rates, malformation, hatching, the overall length of the larvae heartbeat, and biochemical biomarkers that reflect the acute toxicity and sublethal effects of TiO₂-NPs. Zebrafish embryos were exposed to titanium dioxide nanoparticles (TiO₂-NPs at 1mg/L, 2mg/L, and 4mg/L) from fertilization to the free swimming stage (144hpf). Every day, we recorded the toxicological endpoints, moreover, immunohistochemical analysis has been performed at the end of the exposure. In particular, we have evaluate the expression of the following biomarkers: Heat Shock Protein 70 (HSP70), Poly ADP-Ribose Polymerase-1 (PARP-1), Metallothioneins (MTs). Our results have shown that hatch ability, survival, and malformation rate were not affected by TiO₂ NPs at these exposure levels. However, TiO₂-NPs caused an increase of heartbeat and reduction of body length; at the same time, TiO₂-NPs have inducted the production of ROS and the expression of oxidative stress biomarkers HSP70 and PARP-1. Hight positivity for PARP-1 at all concentration tested was observed. As regards MT, positivity was found in the expression of this biomarker in the whole body of the embryo, with the exception of the end of the tail. Metallothioneins (MT) are biomarkers widely used in environmental monitoring programs for aquatic creatures. At the light of our results i.e. no death until the end of the experiment (144hpf), no malformation and expression of the biomarkers mentioned, it is evident that zebrafish larvae with their natural detoxification pathways are able to resist the presence of toxic substances and then they can tolerate the presence of metal concentrations. However, an excessive oxidative state can compromise cell function, therefore the uncontrolled release of nanoparticles into the environment is severe and must be constantly monitored.

Keywords: nanoparticles, embryo zebrafish, HSP70, PARP-1

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Authors: Matevž Breška, Iztok Peruš, Vlado Stankovski

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Systematic overview of existing Ground Motion Prediction Equations (GMPEs) has been published by Douglas. The number of earthquake recordings that have been used for fitting these equations has increased in the past decades. The current PF-L database contains 3550 recordings. Since the GMPEs frequently model the peak ground acceleration (PGA) the goal of the present study was to refit a selection of 44 of the existing equation models for PGA in light of the latest data. The algorithm Levenberg-Marquardt was used for fitting the coefficients of the equations and the results are evaluated both quantitatively by presenting the root mean squared error (RMSE) and qualitatively by drawing graphs of the five best fitted equations. The RMSE was found to be as low as 0.08 for the best equation models. The newly estimated coefficients vary from the values published in the original works.

Keywords: Ground Motion Prediction Equations, Levenberg-Marquardt algorithm, refitting PF-L database, peak ground acceleration

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Authors: J. Bartus, J. Odrobinak

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The paper presents a nonlinear analysis 3D model of composite steel and concrete beams with web openings using the Finite Element Method (FEM). The core of the study is the introduction of basic modeling techniques comprehending the description of material behavior, appropriate elements selection, and recommendations for overcoming problems with convergence. Results from various finite element models are compared in the study. The main objective is to observe the concrete failure mechanism and its influence on the structural performance of numerical models of the beams at particular load stages. The bearing capacity of beams, corresponding deformations, stresses, strains, and fracture patterns were determined. The results show how load-bearing elements consisting of concrete parts can be analyzed using FEM software with various options to create the most suitable numerical model. The paper demonstrates the versatility of Ansys software usage for structural simulations.

Keywords: Ansys, concrete, modeling, steel

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Authors: Yu Lu

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Fixed point theory is a basic tool for the study of the existence of Nash equilibria in game theory. This paper presents a significant generalization of the Veinott-Zhou fixed point theorem for increasing correspondences, which serves as an essential framework for investigating the existence of Nash equilibria in supermodular and quasisupermodular games. To establish our proofs, we explore different conceptions of multivalued increasingness and provide comprehensive results concerning the existence of the largest/least fixed point. We provide two distinct approaches to the proof, each offering unique insights and advantages. These advancements not only extend the applicability of the Veinott-Zhou theorem to a broader range of economic scenarios but also enhance the theoretical framework for analyzing equilibrium behavior in complex game-theoretic models. Our findings pave the way for future research in the development of more sophisticated models of economic behavior and strategic interaction.

Keywords: fixed-point, Tarski’s fixed-point theorem, Nash equilibrium, supermodular game

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Authors: Jihad S. Daba, J. P. Dubois

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Understanding the statistics of non-isotropic scattering multipath channels that fade randomly with respect to time, frequency, and space in a mobile environment is very crucial for the accurate detection of received signals in wireless and cellular communication systems. In this paper, we derive stochastic models for the probability density function (PDF) of the shift in the carrier frequency caused by the Doppler Effect on the received illuminating signal in the presence of a dominant line of sight. Our derivation is based on a generalized Clarke’s and a two-wave partially developed scattering models, where the statistical distribution of the frequency shift is shown to be consistent with the power spectral density of the Doppler shifted signal.

Keywords: Doppler shift, filtered Poisson process, generalized Clark’s model, non-isotropic scattering, partially developed scattering, Rician distribution

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Dilay Merve Temur

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How knowledge is produced and how scientific knowledge progress are questions that science philosophers have investigated for centuries. When the scientific and technological developments reached the 20th century, Kuhn proposed a completely new view among all the approaches. In this article, firstly, Kuhn's theory is represented. Secondly, the criticisms of Kuhn's theory directed to him are examined, and Galison's proposal for the trade area term of the incommensurability thesis is shared. The interaction of Humoral Theory with nutrition has been illustrated extensively, and the transition to modern medicine has been described historically by including scientific and technological developments in the field of medicine. This paper will seek to see how the concept of nutrition is positioned as a trading zone within the medicine paradigm, which has experienced a revolution within the framework of the paradigm concept introduced by Kuhn.

Keywords: food studies, incommensurability, nutrition and dietetics, trading zone

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Authors: Analise Borg, Paul Micallef

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Over the past few years, the online multimedia collection has grown at a fast pace. Several companies showed interest to study the different ways to organize the amount of audio information without the need of human intervention to generate metadata. In the past few years, many applications have emerged on the market which are capable of identifying a piece of music in a short time. Different audio effects and degradation make it much harder to identify the unknown piece. In this paper, an audio fingerprinting system which makes use of a non-parametric based algorithm is presented. Parametric analysis is also performed using Gaussian Mixture Models (GMMs). The feature extraction methods employed are the Mel Spectrum Coefficients and the MPEG-7 basic descriptors. Bin numbers replaced the extracted feature coefficients during the non-parametric modelling. The results show that non-parametric analysis offer potential results as the ones mentioned in the literature.

Keywords: audio fingerprinting, mapping algorithm, Gaussian Mixture Models, MFCC, MPEG-7

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Authors: Ibrahim A. Elshaer, Alaa M. Shaker

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Several previous empirical studies have operationalized service quality as either a multidimensional or unidimensional construct. While few earlier studies investigated some practices of the assumed dimensional structure of service quality, no study has been found to have tested the construct’s dimensionality using confirmatory factor analysis (CFA). To gain a better insight into the dimensional structure of service quality construct, this paper tests its dimensionality using three CFA models (higher order factor model, oblique factor model, and one factor model) on a set of data collected from 390 British tourists visited Egypt. The results of the three tests models indicate that service quality construct is multidimensional. This result helps resolving the problems that might arise from the lack of clarity concerning the dimensional structure of service quality, as without testing the dimensional structure of a measure, researchers cannot assume that the significant correlation is a result of factors measuring the same construct.

Keywords: service quality, dimensionality, confirmatory factor analysis, Egypt

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Francesco Morgan Bono, Simone Cinquemani

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This research aims to enhance bridge monitoring by employing innovative techniques that incorporate exogenous factors into the modeling of sensor signals, thereby improving long-term predictability beyond traditional static methods. Using real datasets from two different bridges equipped with Linear Variable Displacement Transducer (LVDT) sensors, the study investigates the fundamental principles governing sensor behavior for more precise long-term forecasts. Additionally, the research evaluates performance on noisy and synthetically damaged data, proposing a residual-based alarm system to detect anomalies in the bridge. In summary, this novel approach combines advanced modeling, exogenous factors, and anomaly detection to extend prediction horizons and improve preemptive damage recognition, significantly advancing structural health monitoring practices.

Keywords: structural health monitoring, dynamic models, sindy, railway bridges

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Authors: Jude K. Safo

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Knowledge Graphs KG) and their relation to Graph Embeddings (GE) represent a unique data structure in the landscape of machine learning (relative to image, text and acoustic data). Unlike the latter, GEs are the only data structure sufficient for representing hierarchically dense, semantic information needed for use-cases like supply chain data and protein folding where the search space exceeds the limits traditional search methods (e.g. page-rank, Dijkstra, etc.). While GEs are effective for compressing low rank tensor data, at scale, they begin to introduce a new problem of ’data retreival’ which we observe in Large Language Models. Notable attempts by transE, TransR and other prominent industry standards have shown a peak performance just north of 57% on WN18 and FB15K benchmarks, insufficient practical industry applications. They’re also limited, in scope, to next node/link predictions. Traditional linear methods like Tucker, CP, PARAFAC and CANDECOMP quickly hit memory limits on tensors exceeding 6.4 million nodes. This paper outlines a topological framework for linear mapping between concepts in KG space and GE space that preserve cardinality. Most importantly we introduce a traceable framework for composing dense linguistic strcutures. We demonstrate performance on WN18 benchmark this model hits. This model does not rely on Large Langauge Models (LLM) though the applications are certainy relevant here as well.

Keywords: representation theory, large language models, graph embeddings, applied algebraic topology, applied knot theory, combinatorics

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Deniz Dincel, Sena Ardicli, Hale Samli, Mustafa Ogan, Faruk Balci

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The aim of the study was to determine the effects of some environmental and genetic factors on growth, fertility traits, milk yield and composition in Saanen goats. For this purpose, the total of 173 Saanen goats and kids were investigated for growth, fertility and milk traits in Marmara Region of Turkey. Fertility parameters (n=70) were evaluated during two years. Milk samples were collected during the lactation and the milk yield/components (n=59) of each goat were calculated. In terms of CSN3 and AGPAT6 gene; the genotypes were defined by PCR-RFLP. Saanen kids (n=86-112) were measured from birth to 6 months of life. The birth, weaning, 60ᵗʰ, 90ᵗʰ, 120ᵗʰ and 180tᵗʰ days of average live weights were calculated. The effects of maternal age on pregnancy rate (p < 0.05), birth rate (p < 0.05), infertility rate (p < 0.05), single born kidding (p < 0.001), twinning rate (p < 0.05), triplet rate (p < 0.05), survival rate of kids until weaning (p < 0.05), number of kids per parturition (p < 0.01) and number of kids per mating (p < 0.01) were found significant. The impacts of year on birth rate (p < 0.05), abortion rate (p < 0.001), single born kidding (p < 0.01), survival rate of kids until weaning (p < 0.01), number of kids per mating (p < 0.01) were found significant for fertility traits. The impacts of lactation length on all milk yield parameters (lactation milk, protein, fat, totally solid, solid not fat, casein and lactose yield) (p < 0.001) were found significant. The effects of age on all milk yield parameters (lactation milk, protein, fat, total solid, solid not fat, casein and lactose yield) (p < 0.001), protein rate (p < 0.05), fat rate (p < 0.05), total solid rate (p < 0.01), solid not fat rate (p < 0.05), casein rate (p < 0.05) and lactation length (p < 0.01), were found significant too. However, the effect of AGPAT6 gene on milk yield and composition was not found significant in Saanen goats. The herd was found monomorphic (FF) for CSN3 gene. The effects of sex on live weights until 90ᵗʰ days of life (birth, weaning and 60ᵗʰ day of average weight) were found significant statistically (p < 0.001). The maternal age affected only birth weight (p < 0,001). The effects month at birth on all of the investigated day [the birth, 120ᵗʰ, 180ᵗʰ days (p < 0.05); the weaning, 60ᵗʰ, 90ᵗʰ days (p < 0,001)] were found significant. The birth type was found significant on the birth (p < 0,001), weaning (p < 0,01), 60ᵗʰ (p < 0,01) and 90ᵗʰ (p < 0,01) days of average live weights. As a result, screening the other regions of CSN3, AGPAT6 gene and also investigation the phenotypic association of them should be useful to clarify the efficiency of target genes. Environmental factors such as maternal age, year, sex and birth type were found significant on some growth, fertility and milk traits in Saanen goats. So consideration of these factors could be used as selection criteria in dairy goat breeding.

Keywords: fertility, growth, milk yield, Saanen goats

Procedia PDF Downloads 166

Authors: Huimin Zhao, Xiaoquan Liu, Haochen Liu

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The major challenge facing Alzheimer's Disease (AD) drug development is how to effectively improve cognitive function in clinical practice. Growing evidence indicates that stimulating hippocampal neurogenesis is a strategy for restoring cognition in animal models of AD. The mitogen-activated protein kinase (MAPK) pathway is a crucial factor in neurogenesis, which is negatively regulated by Dual-specificity phosphatase 16 (DUSP16). Transcriptome analysis of post-mortem brain tissue revealed up-regulation of DUSP16 expression in AD patients. Additionally, DUSP16 was involved in regulating the proliferation and neural differentiation of neural progenitor cells (NPCs). Nevertheless, whether the effect of DUSP16 on ameliorating cognitive disorders by influencing NPCs differentiation in AD mice remains unclear. Our study demonstrates an association between DUSP16 SNPs and clinical progression in individuals with mild cognitive impairment (MCI). Besides, we found that increased DUSP16 expression in both 3×Tg and SAMP8 models of AD led to NPC differentiation impairments. By silencing DUSP16, cognitive benefits, the induction of AHN and synaptic plasticity, were observed in AD mice. Furthermore, we found that DUSP16 is involved in the process of NPC differentiation by regulating c-Jun N-terminal kinase (JNK) phosphorylation. Moreover, the increased DUSP16 may be regulated by the ETS transcription factor (ELK1), which binds to the promoter region of DUSP16. Loss of ELK1 resulted in decreased DUSP16 mRNA and protein levels. Our data uncover a potential regulatory role for DUSP16 in adult hippocampal neurogenesis and provide a possibility to find the target of AD intervention.

Keywords: alzheimer's disease, cognitive function, DUSP16, hippocampal neurogenesis

Procedia PDF Downloads 72

Authors: Surbhi Rani, Sunita Gakkhar

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The four-dimensional food web system consisting of two prey species for a generalist middle predator and a top predator is proposed and investigated. The middle predator is predating both the prey species with a modified Holling type-II functional response. The food web model is found to be well-posed, bounded, and dissipative. The proposed model's essential dynamical features are studied in terms of local stability. The four species' survival is explored, and persistence conditions are established. The numerical simulations reveal the persistence in the form of a chaotic attractor or stable focus. The conclusion is that providing additional food to the middle predator may help to control the food chain's chaos.

Keywords: predator-prey model, existence of equilibrium points, local stability, chaos, numerical simulations

Procedia PDF Downloads 109

Authors: Shidokht Rashiddadash, Mojtaba Sadighi, Soheil Dariushi

Abstract:

Since sandwich structures are used in many areas ranging from ships, trains, automobiles, aircrafts, bridge and building, connecting sandwich structures is necessary almost in all industries. So application of metallic joints between sandwich panels is increasing. Various joining methods are available such as mechanically fastened joints (riveting or bolting) or adhesively bonded joints and choosing one of them depends on the application. In this research, sandwich specimens were fabricated with two different types of metallic connections with dissimilar geometries. These specimens included beams and plates and were manufactured using glass-epoxy skins and aluminum honeycomb core. After construction of the specimens, bending and low velocity impact tests were executed on them and the behaviors of specimens were discussed. Numerical models were developed using LS-DYNA software and validated with test results. Finally, parametric studies were performed on the thicknesses and lengths of two connections by employing the numerical models.

Keywords: connection, honeycomb, low velocity impact, sandwich panel, static test

Procedia PDF Downloads 56

Authors: A. Fettahoglu

Abstract:

Theory of viscoelasticity is used by many researchers to represent the behavior of many materials such as pavements on roads or bridges. Several researches used analytical methods and rheology to predict the material behaviors of simple models. Today, more complex engineering structures are analyzed using Finite Element Method, in which material behavior is embedded by means of three dimensional viscoelastic material laws. As a result, structures of unordinary geometry and domain can be analyzed by means of Finite Element Method and three dimensional viscoelastic equations. In the scope of this study, rheological models embedded in ANSYS, namely, generalized Maxwell model and Prony series, which are two methods used by ANSYS to represent viscoelastic material behavior, are presented explicitly. Afterwards, a guide is illustrated to ease using of viscoelasticity tool in ANSYS.

Keywords: ANSYS, generalized Maxwell model, finite element method, Prony series, viscoelasticity, viscoelastic material curve fitting

Procedia PDF Downloads 604