Physical Knowledge Research Articles

Experiment-based deep learning approach for power allocation with a programmable metasurface

Metasurfaces designed with deep learning approaches have emerged as efficient tools for manipulating electromagnetic waves to achieve beam steering and power allocation objectives. However, the effects of complex environmental factors like obstacle blocking and other unavoidable scattering need to be sufficiently considered for practical applications. In this work, we employ an experiment-based deep learning approach for programmable metasurface design to control powers delivered to specific locations generally with obstacle blocking. Without prior physical knowledge of the complex system, large sets of experimental data can be efficiently collected with a programmable metasurface to train a deep neural network (DNN). The experimental data can inherently incorporate complex factors that are difficult to include if only simulation data are used for training. Moreover, the DNN can be updated by collecting new experimental data on-site to adapt to changes in the environment. Our proposed experiment-based DNN demonstrates significant potential for intelligent wireless communication, imaging, sensing, and quiet-zone control for practical applications.

THE ISSUE OF CONTINUITY IN THE STUDY OF GENERAL PHYSICS USING ELEMENTS OF THEORETICAL PHYSICS

In this article pays special attention to the promotion of physical knowledge at the modern stage of scientific and technical development, and describes the issue of coherence among the elements of educational materials in the process of mastering.

The effect of experiential learning interventions on physical activity outcomes in children: A systematic review.

This systematic review examined the effectiveness of experiential learning interventions for improving children's physical activity knowledge, attitudes, and behaviours. It also aimed to identify intervention characteristics that resulted in the greatest impact. Four databases: Education Research Complete, Scopus, Web of Science and PsychINFO were searched from database inception to January 2023. Eligible studies: (1) included children 0-12 years; (2) assessed the effect of physical activity outcomes on children's physical activity knowledge, attitudes or behaviour and (3) were randomised controlled trials conducted in any setting. Study risk of bias was assessed by two independent reviewers using the Cochrane risk of bias tool. Intervention approaches were categorised, and effect sizes were compared across studies for each outcome. Twelve studies were included in the review: ten in school age and two in below five years. For behavioural outcomes, six of eight studies showed medium to large effects (effects size (ES) range: 0.3-0.9), two of the three studies that assessed attitudinal outcomes displayed medium effects (ES range: 0.4-0.5) and both studies that assessed knowledge outcomes displayed medium to large effects (ES range: 0.4-1.3). The two experiential learning interventions among children < 5 years demonstrated small to medium effects on behaviour change (ES range: 0.2-0.5). Effective interventions combined enjoyable practical activities (fitness activities, games and challenges), with behaviour change techniques (goal setting, and self-monitoring), were underpinned by a behaviour change theory, and were often of short duration (< 4 months) but intense (several sessions/week). Moderate to high statistical heterogeneity was observed for behaviour outcomes and risk of bias across studies was generally high. This review provides some evidence supporting the effectiveness of experiential learning interventions in improving physical activity outcomes in school-aged children. Additional evidence is needed in children <5 years old. Future experiential learning interventions need to strengthen the evidence with rigorous methodological quality and clear reporting of the experiential learning components.

Use of infection control measures in people with chronic lung disease: mixed methods study.

The introduction of community infection control measures during the COVID-19 pandemic was associated with a reduction in acute exacerbations of lung disease. We aimed to understand the acceptability of continued use of infection control measures among people with chronic lung disease and to understand the barriers and facilitators of use. Australian adults with chronic lung disease were invited to an online survey (last quarter of 2021) to specify infection control measures they would continue themselves post-pandemic and those they perceived should be adopted by the community. A subset of survey participants were interviewed (first quarter of 2022) with coded transcripts deductively mapped to the COM-B model and Theoretical Domains Framework. 193 people (COPD 84, bronchiectasis 41, interstitial lung disease 35, asthma 33) completed the survey. Physical distancing indoors (83%), handwashing (77%), and avoidance of busy places (71%) or unwell family and friends (77%) were measures most likely to be continued. Policies for the wider community that received most support were those during the influenza season including hand sanitiser being widely available (84%), wearing of face coverings by healthcare professionals (67%) and wearing of face coverings by the general population on public transport (66%). Barriers to use of infection control measures were related to physical skills, knowledge, environmental context and resources, social influences, emotion, beliefs about capabilities and beliefs about consequences. Adults with chronic lung diseases in Australia are supportive of physical distancing indoors, hand hygiene, and avoidance of busy places or unwell family and friends as long-term infection control measures.

Robust target detection of intelligent integrated optical camera and mmWave radar system

Target detection is pivotal for modern urban computing applications. While image-based techniques are widely adopted, they falter under challenging environmental conditions such as adverse weather, poor lighting, and occlusion. To improve the target detection performance under complex real-world scenarios, this paper proposes an intelligent integrated optical camera and millimeter-wave (mmWave) radar system. Utilizing both physical knowledge and data-driven methods, a long-term robust radar-camera fusion algorithm is proposed to solve the heterogeneous data fusion problem for detection improvement. For the occlusion scenarios, the proposed algorithm can effectively detect occluded targets with the help of memory through performing long-term detection. For dark scenarios with low-light conditions, the proposed algorithm can effectively mark the target in the dark picture as well as provide rough stickman imaging. The above two innovative functions of the hybrid optical camera and mmWave radar system are tested in real-world scenarios. The results demonstrate the robustness and significant enhancement in the target detection performance of our integrated system.

Physics-Informed Multi-Agent deep reinforcement learning enabled distributed voltage control for active distribution network using PV inverters

With the recent rapid uptake of photovoltaic (PV) resources, the overvoltage condition during low load periods or intermittent renewable generation is one of the most critical challenges for electricity distribution networks. Traditional model-based local or centralized control methods that rely on proper system parameters are difficult to mitigate rapid changes in power systems. Utilizing model-free/data-driven multiagent deep reinforcement learning (MADRL) has been recognized as an effective solution to active voltage control. However, existing MADRL-based control approaches trained solely on data are agnostic to the underlying real-world physics principles. Therefore, this paper aims to incorporate physical knowledge of regional distribution networks into MADRL’s decision-making. The main contributions of this paper are summarized as follows. First, a novel physics-informed MADRL -based distributed voltage control method is proposed, which is still under a centralized training and distributed execution framework and only requires local measurements. Second, graph neural networks are employed to help MADRL agents learn graph knowledge (node features and topological information). Further, transformer is introduced to extract discriminative representations and ensure agents’ cooperative control. Third, we adopt a physics-guided architecture of neural networks in the actor network to stabilize the training process and improve sample efficiency. Finally, simulation results based upon modified IEEE 33-bus and 141-bus networks validate the proposed method's effectiveness, robustness and computation efficiency.

A physics-informed long short-term memory (LSTM) model for estimating ammonia emissions from dairy manure during storage

Manure management on dairy farms impacts how farmers maximize its value as fertilizer, reduce operating costs, and minimize environmental pollution potential. A persistent challenge on many farms is minimizing ammonia losses through volatilization during storage to maintain manure nitrogen content. Knowing the quantities of emitted pollutants is at the core of designing and improving mitigation strategies for livestock operations. Although process-based models have improved the accuracy of estimating ammonia emissions, complex systems such as manure storage still need to be solved because some underlying science still needs work. This study presents a novel physics-informed long short-term memory (PI-LSTM) modeling approach combining traditional process-based with recurrent neural networks to estimate ammonia loss from dairy manure during storage. The method entails inverse modeling to optimize hyperparameters to improve the accuracy of estimating physicochemical properties pertinent to ammonia's transport and surface emissions. The study used open data sets from two on-farm studies on liquid dairy manure storage in Switzerland and Indiana, U.S.A. The root mean square errors were 1.51 g m−2 h−1 for the PI-LSTM model, 3.01 g m−2 h−1 for the base compartmental process-based (Base-CPBM) model, and 2.17 g m−2 h−1 for the hyperparameter-tuned compartmental process-based (HT-CPBM) model. In addition, the PI-LSTM model outperformed the Base-CPBM and the HT-CPBM models by 20 to 80 % during summer and spring, when most annual ammonia emissions occur. The study demonstrated that incorporating physical knowledge into machine learning models improves generalization accuracy. The outcomes of this study provide the scientific basis to improve policymaking decisions and the design of suitable on-farm strategies to minimize manure nutrient losses on dairy farms during storage periods.

Magnetic flux leakage defect size estimation method based on physics-informed neural network.

Magnetic flux leakage (MFL) is a magnetic method of non-destructive testing for in-pipe defect detection and sizing. Despite the fact that recent developments in machine learning have revolutionized disciplines like MFL defect size estimation, the most current methods for quantifying pipeline defects are primarily data-driven, which may violate the underlying physical knowledge. This paper proposes a physics-informed neural network-based method for MFL defect size estimation. The training process of neural network is guided by the MFL data and the physical constraints that is mathematically represented by the magnetic dipole model. We use synthetic MFL data produced by a virtual MFL testing of pipeline defects to validate the proposed method through a comparison to purely data-driven neural networks and support vector machines. The findings imply that the physics-informed strategy can both improve predictive accuracy and mitigate physical violations in MFL testing, providing us with a better knowledge of how neural networks perform in defect size estimation. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

On the functional brain networks involved in tool-related action understanding

Tool-use skills represent a significant cognitive leap in human evolution, playing a crucial role in the emergence of complex technologies. Yet, the neural mechanisms underlying such capabilities are still debated. Here we explore with fMRI the functional brain networks involved in tool-related action understanding. Participants viewed images depicting action-consistent (e.g., nail-hammer) and action-inconsistent (e.g., scarf-hammer) object-tool pairs, under three conditions: semantic (recognizing the tools previously seen in the pairs), mechanical (assessing the usability of the pairs), and control (looking at the pairs without explicit tasks). During the observation of the pairs, task-based left-brain functional connectivity differed within conditions. Compared to the control, both the semantic and mechanical conditions exhibited co-activations in dorsal (precuneus) and ventro-dorsal (inferior frontal gyrus) regions. However, the semantic condition recruited medial and posterior temporal areas, whereas the mechanical condition engaged inferior parietal and posterior temporal regions. Also, when distinguishing action-consistent from action-inconsistent pairs, an extensive frontotemporal neural circuit was activated. These findings support recent accounts that view tool-related action understanding as the combined product of semantic and mechanical knowledge. Furthermore, they emphasize how the left inferior parietal and anterior temporal lobes might be considered as hubs for the cross-modal integration of physical and conceptual knowledge, respectively.

Towards automatic feature extraction and sample generation of grain structure by variational autoencoder

Granularity is one of the most predominant structures for polycrystalline materials, from geological compounds to technical high-performance alloys. In particular, grain structures in metal alloys enable custom-tailored material properties if their formation can be modeled and simulated accurately to analyze, characterize, and generate granular materials. Despite significant advances in the field, established physics-based grain-growth models still need to improve to match real-life experimental data accurately. Instead of relying on physical knowledge, data-driven models provide a complementary way to study grain structures using only the information in the data. However, many machine learning methods require supervised learning on information that is challenging to measure or numerically expensive to compute. In this work, we propose a new strategy that uses unsupervised deep learning on synthetic physics-based data to generate realistic granular structures with customizable features and properties (grain size/number). A variational autoencoder learns to compress grain structures into a low-dimensional latent space, derive data-driven features that characterize grain structures, and use these features to generate new structures with representative grain size distributions in a computationally efficient way. These data-driven features can complement the physics-based features derived from classical materials research and help identify new characterizing properties. Thus, this work represents a prototype application of bridging machine learning methods to material characterization and generation.

Підготовка майбутнього вчителя фізики до формування дослідницької компетентності учнів із застосуванням інформаційно-комунікаційних технологій

The article discusses the topical issue of developing the research abilities of primary and high school students in the context of implementing a conceptual pedagogical model aimed at training physics teachers. The article analyzes the development of physical education and the possibilities of using information and communication technologies to achieve didactic goals, in particular the use of ICT tools, computer equipment, digital and cloud technologies, digital measuring complexes and systems, electronic resources, STEM technologies, and others. It is analyzed and found that «e-learning» is becoming an increasingly important component of the higher education system due to innovative technologies that expand its scope and significance. This leads to the introduction of a hybrid model of education, where the main emphasis is on e-learning, and traditional learning is gradually decreasing. In this regard, the article proposes a methodological system for the development of information and communication competencies in students studying physics in pedagogical institutions of higher education. The main purpose of this system is to develop methodological competencies of future physics teachers. Among the functions of the system are methodological, which promotes the use of professional concepts and theoretical knowledge of physics in the educational process; professionally oriented, which implements the content of physical knowledge in the practical side of learning; integrative, which promotes a systematic understanding of physical aspects; developmental, aimed at developing students' thinking, activity and creativity; prognostic, which focuses on the further development of the methodological system. The article proposes the content of the methodological system, which takes into account the content components of general physics, theoretical physics, methods of teaching physics and special courses. The possibility and feasibility of implementing the developed methodological system in the educational process, taking into account pedagogical, organizational and methodological conditions, are discussed.

Factors and mechanisms of mastering physical knowledge by technical university students

Introduction. Physics is one of the general professional disciplines at a technical university; its mastering is extremely important for future engineers. The process of formation of physical knowledge is accompanied by multiple difficulties, which actualises the problem of identifying the factors of assimilation thereof in students of a technical university as well as the mechanisms to influence these factors. The aim of the study is to identify and justify the factors of physical knowledge assimilation in students of a technical university as well as the mechanisms of influencing these factors. Materials and methods. The study involved 95 students of the National research university “MPEI” (Moscow Power Engineering Institute) (Russian Federation) majoring in the profiles: “Electric Power Engineering”, “Electrical Engineering”, “Biotechnical Systems”, “Electronics”. In order to assess the level of formation of students’ physical knowledge, a test comprising 15 questions was offered to them with a view to evaluate the students’ knowledge of the basic concepts and laws in the sections: “Mechanics”, “Molecular Physics” and “Electrodynamics”. The statistical analysis of test results was carried out by means of Mann-Whitney U test. Results. A number of factors influencing the formation of physical knowledge in students have been formulated, that were found to be in line with the objectives of studying physics at a technical university. The correctness of the revealed factors is confirmed by the statistics (Uemp. = 12 &lt; Ucr. = 16; p &lt; 0,01), i.e. the students who had poor performance at school or who failed the Unified State Examination are not motivated for studying physics at a university and have poor academic results in general physics. The success in test performance shown by students from weak Bachelor degree groups does not differ from the results of the same test performed by Master degree students (Uemp. = 47.5 &gt; Ucr. = 16). This result is explained by the influence of such factor as low relevance of physical knowledge for Master degree students at a given stage of study. A set of mechanisms directly aimed at students’ assimilation of basic physical concepts and laws was developed. They include: development of cognitive abilities due to the systemic nature of physical knowledge, students’ strengthened intrinsic motivation to study physics, discussion of mathematical and verbal formulation of physical concepts and laws, regular directed testing in physics at all stages of studying at the university. Conclusion. The practical significance of the study is determined by the possibility to use its results for improving the process of formation of physical knowledge in students of technical universities.

Studying turbulent flows with physics-informed neural networks and sparse data

Physics-informed neural networks (PINNs) have recently become a viable modelling method for the scientific machine-learning community. The appeal of this network architecture lies in the coupling of a deep neural network (DNN) with partial differential equations (PDEs): the DNN can be considered a universal function approximator, and the physical knowledge from the embedded PDEs regularises the network during training. This regularisation improves robustness of the network and means the network only requires sparse training data in the domain. We apply PINNs with embedded Reynolds-averaged Navier–Stokes (RANS) equations to a spatially developing adverse-pressure-gradient (APG) boundary layer 1000<Reθ<3000, and also to the periodic hill problem 5600≤Reb≤37,000. We do not use a turbulence model to close the RANS equations, rather the network infers the Reynolds-stress fields during training as a means to satisfy closure. As PINNs are able to produce robust predictions with sparse training data, this paper demonstrates how PINNs can be trained with data typical of experimental campaigns. For the APG boundary layer, we seek to quantify the amount and location of data required to model a time-averaged turbulent flow with PINNs, and show that PINNs can accurately model wall shear stress and wall pressure. We demonstrate how PINNs perform when trained without information of a critical feature of the flow (such as a separation bubble), and we highlight how the cost of training the network does not increase with Reynolds number. Finally, we undertake a series of benchmarks to demonstrate how the method scales with network size and the number of residual points in the problem. This work seeks to establish the quantity and type of data required to successfully apply PINNs to time-averaged flow without a closure model, and seeks to set expectations on the prediction quality of the network when trained on an available data set.

Creativity, Skill, Integrity, Intelligence and Community: A Conversation on the Nature of Practice in Hip Hop

Robert Hylton is a first-generation UK hip hop theatre practitioner, who is currently writing a PhD at the Centre for Dance Research Coventry University on the nature of transmission of knowledge in black British Popping. Jonathan Burrows is a choreographer and Associate Professor at C-DaRE Coventry, who has worked for many years with hip hop artist Jonzi-D, mentoring hip hop artists interested in theatre practice. Robert and Jonathan have shared a long and ongoing conversation over many years about the nature of hip hop and its uneasy relationship to contemporary dance. The following text reflects that conversation, focussing specifically on the nature of practice in hip hop dance including transmission of physical and philosophical knowledge, respect for lineage, innovation and the relationship or not to academic research and the space of theatre.

Physics guided neural network: Remaining useful life prediction of rolling bearings using long short-term memory network through dynamic weighting of degradation process

Bearing is a common rotating component, the health status of bearing affects the operation and maintenance of equipment. Thus, the prediction of bearing remaining useful life is of great significance. The remaining useful life prediction based on neural network has black box property, which makes the prediction result may be contrary to the actual physical law. In this paper, a physics guided long short-term memory (LSTM) network is proposed based on the change trend of the time-frequency domain feature indicators of bearings in the process of degradation. Specifically, indexes such as monotonicity are used to select feature indicators that are highly trendy in the process of bearings degradation. On this basis, a regularization term based on the consistent variation of the feature indicators and the remaining useful life (RUL) in the process of bearing degradation is constructed to make the result of the network more consistent with the actual physical law. Meanwhile, the variation of feature indicators is used as dynamic weight to enhance the potential physical consistency. The experimental comparison results show that the prediction results of the network are more accurate and consistent with the actual physical laws with the guidance of physical prior knowledge.

Localizing False Data Injection Attacks in Smart Grid: A Spectrum-Based Neural Network Approach

Smart grid is confronted with cyberattacks due to the increasing dependence on cyberspace. False data injection attacks (FDIAs) represent a major type of cyberattacks that cannot be detected by the traditional bad data detection (BDD). The majority of existing researches focus on how to detect FDIAs, while little attention has been paid to obtaining the exact attack locations, which are crucial for deploying countermeasures in time. In this paper, we propose a neural network-based approach for online localizing FDIAs in AC power systems. The approach transforms the multi-dimensional time series measurements to their time-frequency and spectral graph representations, to jointly capture the temporal and spatial correlations in the spectral domain. The spectral decomposition could better characterize the latent properties of measurements, such as auto-correlations of a bus and key dependency between buses, which would be poorly represented in the time domain and vertex domain. Specifically, the proposed approach first adopts the short-time Fourier transform (STFT) and a two-channel convolutional neural network (2C-CNN) to model the spectral-temporal correlations. Then the spectral-spatial relationships are modeled with a graph convolutional network (GCN), which combines the nodal admittance matrix and physical property of power systems. The prior physical knowledge further improves our approach’s interpretability and localization performance. We evaluate the effectiveness of our proposed approach with comprehensive case studies on various standard test systems. Numerical results indicate the superiority of our approach over baselines.

A Physics-Guided Deep Learning Approach for Functional Assessment of Cardiovascular Disease in IoT-Based Smart Health

The rapid development of the Internet of Things (IoT) widely supports the smart healthcare system. IoT-based smart health has significant importance for the diagnosis of cardiovascular disease in clinical practice. Combined with advanced artificial intelligence techniques, IoT-based smart health provides valuable and accurate diagnosis information remotely for cardiovascular disease. The functional assessment of cardiovascular disease is an essential task in clinical practice. It aims to determine the extent of myocardial ischemia through the measurement of the hemodynamic parameters of the coronary artery. However, the clinical adoption of the hemodynamic parameters is limited due to the potential risks and high health costs during measurements. Recent advances in artificial intelligence have enabled the computation of hemodynamic parameters based on the anatomical features of coronary arteries. However, the existing methods still lack explainability in the prediction. To address this issue, we present a physics-guided deep learning network for the functional assessment of cardiovascular disease in an IoT-based manner. We specifically design an attentive network to determine the effective features by considering the importance of coronary artery anatomy features and artery segments. To obtain the functional assessment with explainability, we incorporate physical knowledge related to the blood flow into the loss function. It can ensure that functional assessment follows the physical laws. Extensive experiments are performed on a synthetic dataset and a real-world clinical dataset. The results show that our approach can achieve accurate and physically consistent assessment. Moreover, our method promotes deeper adoption of IoT and deep learning in the field of smart health.

A Physics-Data Hybrid Framework to Develop Bridge Digital Twin Model in Structural Health Monitoring

Digital twin aims to create a virtual model for a physical structure by combining measurement data in structural health monitoring. The most important feature is to achieve the physical structure-monitoring data synchronization. For this purpose, a physics-data hybrid framework to develop the bridge digital twin model in structural health monitoring is proposed in the paper. The physical base is firstly formed by the finite element model of the digital representation for the physical bridge that can fully incorporate both structural geometry and structural state. The data base is then built by all measurement data of the monitored bridge. By defining the context that is common to both physical base and data base, the mirror relationship between physical base and data base for the specified context is formulated. To achieve the best matching of the mirror relationship by minimizing process, the digital twin model in terms of the specified context can be developed. In such a way, the proposed framework integrates physical knowledge and data intelligence into one model. A demonstration of a simulated simply supported beam is provided to show how the digital twin model is developed by using proposed physics-data hybrid framework. It is found that the generated digital twin model is consistent with the current structural state of the beam. The presented physics-data hybrid framework helps in clearer understanding of the realization of digital twin model in structural health monitoring, providing a new perspective for smart bridge solutions.

LOGICAL AND METHODOLOGICAL ANALYSIS OF THE CONTENT OF PHYSICAL CONCEPTS IN THE CONTEXT OF THE FORMATION OF A SCIENTIFIC PICTURE OF THE WORLD IN SPECIALIZED CLASSES WITH AN IN-DEPTH STUDY OF PHYSICS

The article analyzes the concepts of “mass”, “rest mass”, “relativistic mass”, “energy” and considers their content in accordance with modern scientific ideas. An analysis of the specified concepts in physics textbooks and their correspondence to modern scientific theories ‒ special relativity and quantum theory ‒ was carried out. Based on the analysis of the scientific literature, it is shown that according to Minkovskyʼs formalism, mass is a scalar quantity that depends on the 4-vector momentum, and therefore does not depend on the choice of reference system. The introduction of the concept of relativistic mass and the functional relationship between mass and speed is unjustified. In the teaching methodology of physics, it is necessary to take into account the specified features, since in textbooks there is an incorrect or inaccurate description of the concepts “rest mass”, “rest energy”, “total energy” and the connection of rest energy with the speed of light in a vacuum. These concepts play an important role in the formation of a scientific picture of the world in lyceum students.In most studies, despite their scientific value and practical significance for the methodology of teaching physics and natural sciences in general, insufficient attention is paid to the formation of fundamental concepts that are the basis of relevant physical theories. The analysis of modern programs and textbooks shows that the system of physical concepts in the school course consists mainly of fundamental concepts, the formation of which occurs throughout the course of physics taught in gymnasiums and lyceums. The level of formation of these concepts should correspond to the current level of their interpretation in science. However, the structuring of the educational material only in general terms repeats the structure of physical science, as it takes into account the didactic aspect of physical knowledge.&#x0D; Keywords: scientific picture of the world; special theory of relativity; quantum theory; mass; energy; momentum; rest mass; rest energy; relativistic mass.

IMPLEMENTATION OF THE CONCEPT OF FAMILY ENVIRONMENTAL EDUCATION FROM A HADITH PERSPECTIVE

The family is a living unit that provides a child's learning situation. The main task of the family for children's education is to lay the foundation for education. This means that families must also understand problems or matters related to how to educate children according to child development. The family's contribution to children's education is that children are trained to master how to take care of themselves, for example eating, talking, walking, praying. This is closely related to the child's personal development. This research method is literature, namely collecting important data or documents and looking at the nature of the research analyzed through qualitative research which is a descriptive research. ;1. Prioritizing religious and moral education for children. 2. Fulfillment of family income. Automatically Islamic education will become 5 parts 1) High moral formation due to Islamic education, without neglecting physical education, reason and practical knowledge; 2) Preparing and equipping children to look at life in this world and in the hereafter; 3) Grow and develop all aspects that can provide benefits; 4) Providing and cultivating scientific tips and enthusiasm by studying various sciences for the sake of science itself; and 5) Providing provision of professional skills for survival as capital for making a living