Knowledge Of Physics Research Articles

Изучение процессов горения твёрдых веществ и материалов: новые подходы в методике преподавания

Introduction. An integrative approach to teaching a number of special disciplines helps to realize interdisciplinary connections and can be considered as one of the pedagogical requirements that contribute to the formation of readiness of future specialists of EMERCOM of Russia to competently solve professional tasks. When studying the topic “Combustion of solid substances and materials” in the course "Physicochemical fundamentals of fire development and extinguishing", methodological difficulties arise due to the complexity of combining basic knowledge of physics and chemistry of solid aggregate state and understanding the essence of the combustion process of solids. At the same time, the combustion process of solid combustible materials should be associated with the classes of fires and the primary fire extinguishing agents used. Purposes and objectives. The aim of the study is to improve the methodological aspects of teaching the peculiarities of burning of solids and materials, taking into account an interdisciplinary approach, due to the fact that these processes are one of the threats to the stability of the technosphere. Methods. A comparative analysis of the teaching methods of integrated topics related to the peculiarities of burning substances and materials. To improve the quality of students' training, the author's methodology for studying the topic "Combustion of solid substances and materials" is proposed. The proposed methodology includes three types of classes: a lecture, practical work with individual tasks for calculating burning parameters and laboratory work. The latter contains both a demonstration experiment and self-performed experiments by students. The authors analyzed the effectiveness of the developed methodology by monitoring the knowledge of students. The analysis showed that such an integrated approach will allow students to achieve a stable understanding of the combustion of solid combustible materials as a physicochemical process. Results and discussion. The author's methodology for studying the topic "Combustion of solid substances and materials", which includes three components, has been developed: - original lecture material, including an active dialogue mode involving information and communication technologies and the formulation of problematic issues related to the threats of emergency situations in case of ignition of combustible solid organic and inorganic substances. - creation of methodological support: a) to the practical work "Calculation of the parameters of the combustion process of solids and materials" with the formation of a logical connection "composition of the substance" → "the burning temperature" and the further representation of the parameter "burning temperature" as a dynamic parameter of the development of the fire; b) to the laboratory work "Experimental determination of burning products of solid substances and materials", in which the ratio to heating of solid combustible materials of the 1st and 2nd kind is experimentally shown. Burning is visualized using the example of metals with different values of electrode potential. Conclusions. It is shown that the original methodology proposed by the authors allows students to achieve a stable understanding of the combustion process of solid materials as a physicochemical process that poses a threat to technosphere safety. Keywords: pedagogy of higher education; burning of solid substances and materials; teaching methods; physicochemical disciplines; training of personnel of EMERCOM of Russia.

Stephen Hawking's Warning on Contacting Aliens: A Physics Perspective on the Intelligence Trap.

The search for extraterrestrial intelligence is a fascinating and important endeavor, but it raises significant ethical and safety concerns. In the search for extraterrestrial intelligence, scientists use knowledge of physics to identify potential communication methods and signals that may be used by extraterrestrial civilizations. One of the most notable scientists to highlight these concerns was the late physicist Stephen Hawking, who cautioned that actively attempting to communicate with extraterrestrial civilizations could harm humanity. While it is true that we cannot predict the intentions of any potential extraterrestrial civilizations, some scientists argue that the potential advantages of seeking contact outweigh the potential risks. Deciding to initiate contact with extraterrestrial civilizations is a complex issue that requires balancing scientific curiosity with concerns for our own safety. The "Intelligence Trap" is a concept in psychology that suggests that highly intelligent people are more susceptible to cognitive biases and flawed thinking than less intelligent people. It can be argued that Hawking's warnings may be an example of the so-called intelligence trap, as some evidence from the field of physics suggests. Nonetheless, Hawking emphasized that it is crucial for scientists and policymakers to carefully weigh the potential risks and benefits of such efforts and proceed with caution.

Flexible multi-fidelity framework for load estimation of wind farms through graph neural networks and transfer learning

Abstract With global wind energy capacity ramping up, accurately predicting damage equivalent loads (DELs) and fatigue across wind turbine populations is critical, not only for ensuring the longevity of existing wind farms but also for the design of new farms. However, the estimation of such quantities of interests is hampered by the inherent complexity in modeling critical underlying processes, such as the aerodynamic wake interactions between turbines that increase mechanical stress and reduce useful lifetime. While high-fidelity computational fluid dynamics and aeroelastic models can capture these effects, their computational requirements limits real-world usage. Recently, fast machine learning-based surrogates which emulate more complex simulations have emerged as a promising solution. Yet, most surrogates are task-specific and lack flexibility for varying turbine layouts and types. This study explores the use of graph neural networks (GNNs) to create a robust, generalizable flow and DEL prediction platform. By conceptualizing wind turbine populations as graphs, GNNs effectively capture farm layout-dependent relational data, allowing extrapolation to novel configurations. We train a GNN surrogate on a large database of PyWake simulations of random wind farm layouts to learn basic wake physics, then fine-tune the model on limited data for a specific unseen layout simulated in HAWC2Farm for accurate adapted predictions. This transfer learning approach circumvents data scarcity limitations and leverages fundamental physics knowledge from the source low-resolution data. The proposed platform aims to match simulator accuracy, while enabling efficient adaptation to new higher-fidelity domains, providing a flexible blueprint for wake load forecasting across varying farm configurations.

Physics knowledge-based transfer learning between buildings for seismic response prediction

The recent advance in deep learning has attracted considerable interest for employing the state-of-the-art methods to solve engineering problems. However, the applicability of machine learning based models is hindered by the high cost of big data acquisition and task-specific difficulties. This paper presents a framework of physics knowledge-based transfer learning (Phy-KTL) neural networks that integrates the powerful learning capacity of physics-informed neural networks (PINNs) and the flexible transferability of model-based transfer learning technique to enhance structural seismic response prediction in the context of limited labelled datasets. The leverage of physics knowledge (represented by Runge-Kutta solver) allows the neural networks to better capture the structural nonlinear pattern. The use of model-based transfer learning improves the model generality by transferring features extracted from the source building to target buildings. The effectiveness of Phy-KTL in predicting seismic responses between target buildings is numerically validated as compared with Data-driven neural networks, PINNs, and Data-based transfer learning (Data-KTL). A practical application, which uses Phy-KTL to transfer features extracted from the numerical model to the physical building tested on the shaking table, validates that Phy-KTL is robust and effective to improve seismic response prediction of target buildings with limited labelled data.

PRACTICAL CLASSES AS A FORM OF COMPETITIVE PHARMACY SPECIALIST TRAINING

In the article, in line with the tasks of forming the organizational and managerial culture of masters of pharmacy based on the concept of the development of the pharmaceutical industry as an integral component of the country’s health care system, the goal is realized. It is to clarify the role of practical classes as the leading organizational form of the educational process of training highly qualified personnel who develop and implements a modern system of ensuring the quality of medicinal products, has not only fundamental knowledge of physics, chemistry, biology, scientific management, special disciplines, but also has an extensive system of practical skills and professional activity skills.
 On the basis of the general pedagogical principle of the unity of theory and practice, practical training is characterized as the leading organizational form of the educational process of professional training of pharmacy managers, which has its own objectives, intended for the development of students’ professional and practical skills necessary for specialists to perform their duties relationships and formation of professional and business qualities of graduates of the master’s level of education.
 The works of Ukrainian scientists in terms of types, structure, methods of conducting, specifics of training teachers and students for practical classes of various forms and types are adapted to the needs of a specific field of specialist training.
 It was concluded that the intensification of the educational process stimulates the use of advanced learning technologies accumulated by practice by teachers in practical classes. Practical classes are conducted using the elements of discussion, business game, "brainstorming", dyadic form of work of applicants (performance of tasks to consolidate the material in pairs, control of knowledge, evaluation of completed actions and tasks), etc. The summary of the results of a practical session with future pharmacists is often in the form of a colloquium, where the candidates announce their written results and briefly explain them. You can participate in the discussion of the proposed decision by expressing your opinion, motivating and arguing it.

A forward look at the alteration of the weather forecast by artificial intelligence

Most modern weather forecasts are accomplished by atmospheric simulations based on physics models. This method requires a lot of processing time due to the massive amount of computing work. Meanwhile, accuracy faces a limit due to the chaotic nature of the atmosphere. As machine learning gets better and becomes more involved in society, which has been an accelerated trend in recent years, some machine learning-based weather forecast models have been developed. This paper provides a forward-looking study of the potentials of machine learning in terms of theoretical abilities, shown advantages, and possible alterations in the field of weather forecasting based on existing literature and data. Explanations and discussions of two outstanding machine learning-based models, MetNet-2 and Pangu, are provided. Based on the results of tests of these two models, machine learning has good advantages that can be largely applied in the field of weather forecasting and other atmosphere-related work. Machine learning-based methods are likely going to replace physics models in most parts, while physics knowledge is still valuable in this field as it can help with the training of machine learning models and improve performance.

Physics-informed deep reinforcement learning for enhancement on tunnel boring machine's advance speed and stability

The traditional mode of Tunnel Boring Machine (TBM) operation is limited in their applicability and efficiency to meet the growing demand for underground spaces. Current methods to address this problem often lack physics interpretability and automatic adaptivity. In response to this issue, this paper describes an approach that integrates a physics-informed reinforcement learning (PIRL) algorithm into a TBM operation. The method combines a physics-informed machine learning (PIML) model and physics reward functions considering the working mechanism of earth pressure balance (EPB) TBMs, forming into a physics-informed Twin Delayed Deep Deterministic (pTD3) algorithm. The study reveals that the TBM performance can be improved by 69.3% using the pTD3 algorithm compared to manual operation. Integrating physics knowledge into reinforcement learning proves significantly effective in enhancing the TBM operations. The proposed method has the potential to revolutionize TBM operation and pave the way for more efficient, reliable, and automatic tunnel construction.

Fault diagnosis-based SDG transfer for zero-sample fault symptom

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. Therefore, studying the unseen cause-effect problem of fault symptoms is extremely challenging. As various faults often occur in a chemical plant, it is necessary to perform fault causal-effect diagnosis to find the root cause of the fault. However, only some fault causal-effect data are always available to construct a reliable causal-effect diagnosis model. Another worst thing is that measurement noise often contaminates the collected data. The above problems are very common in industrial operations. However, past-developed data-driven approaches rarely include causal-effect relationships between variables, particularly in the zero-shot of causal-effect relationships. This would cause incorrect inference of seen faults and make it impossible to predict unseen faults. This study effectively combines zero-shot learning, conditional variational autoencoders (CVAE), and the signed directed graph (SDG) to solve the above problems. Specifically, the learning approach that determines the cause-effect of all the faults using SDG with physics knowledge to obtain the fault description. SDG is used to determine the attributes of the seen and unseen faults. Instead of the seen fault label space, attributes can easily create an unseen fault space from a seen fault space. After having the corresponding attribute spaces of the failure cause, some failure causes are learned in advance by a CVAE model from the available fault data. The advantage of the CVAE is that process variables are mapped into the latent space for dimension reduction and measurement noise deduction; the latent data can more accurately represent the actual behavior of the process. Then, with the extended space spanned by unseen attributes, the migration capabilities can predict the unseen causes of failure and infer the causes of the unseen failures. Finally, the feasibility of the proposed method is verified by the data collected from chemical reaction processes.

Inverse lithography physics-informed deep neural level set for mask optimization.

As the feature size of integrated circuits continues to decrease, optical proximity correction (OPC) has emerged as a crucial resolution enhancement technology for ensuring high printability in the lithography process. Recently, level set-based inverse lithography technology (ILT) has drawn considerable attention as a promising OPC solution, showcasing its powerful pattern fidelity, especially in advanced processing. However, the massive computational time consumption of ILT limits its applicability to mainly correcting partial layers and hotspot regions. Deep learning (DL) methods have shown great potential in accelerating ILT. However, the lack of domain knowledge of inverse lithography limits the ability of DL-based algorithms in process window (PW) enhancement, etc. In this paper, we propose an inverse lithography physics-informed deep neural level set (ILDLS) approach for mask optimization. This approach utilizes level set-based ILT as a layer within the DL framework and iteratively conducts mask prediction and correction to significantly enhance printability and PW in comparison with results from pure DL and ILT. With this approach, the computational efficiency is significantly improved compared with ILT. By gearing up DL with the knowledge of inverse lithography physics, ILDLS provides a new and efficient mask optimization solution.

PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network

Fast and reliable performance monitoring of photovoltaic modules is essential for economic forecasting in large-scale installations. Deep Learning methods, such as convolutional neural networks, have the potential to predict module power directly from electroluminescence images in an automated workflow. However, neural networks must be trained using large numbers of electroluminescence images of defective modules. Due to budgetary or technical limitations, these training images will always be biased, limiting generalization. Here, we demonstrate a transparent method to discriminate the information learned by a convolutional neural network into generally valid physics and bias. Learning of physics is assessed by providing an artificial, unbiased feature list, which is converted into synthetic electroluminescence images. Using these images, we compare the predictions of the neural network trained on the biased dataset to those of a physics-based equivalent. Bias is assessed by a closer look at the deviations between the predictions from the equivalent circuit model and the trained neural network. The assessment of physics knowledge incorporated by a Deep Learning method gives insight into how the method achieves its predictive capacity.

Microphysical parameter variation in gamma-ray burst stratified afterglows and closure relations: from sub-GeV to TeV observations

ABSTRACT Gamma-ray bursts (GRBs) are one of the most exciting sources that offer valuable opportunities for investigating the evolution of energy fraction given to magnetic fields and particles through microphysical parameters during relativistic shocks. The delayed onset of GeV–TeV radiation from bursts detected by the Fermi Large Area Telescope and Cherenkov Telescopes provide crucial information in favour of the external-shock model. Derivation of the closure relations (CRs) and the light curves in external shocks requires knowledge of GRB afterglow physics. In this manuscript, we derive the CRs and light curves in a stratified medium with variations of microphysical parameters of the synchrotron and synchrotron self-Compton (SSC) afterglow model radiated by an electron distribution with a hard and soft spectral index. Using Markov chain Monte Carlo simulations, we apply the current model to investigate the evolution of the spectral and temporal indices of those GRBs reported in the Second Gamma-ray Burst Catalog (2FLGC), which comprises 29 bursts with photon energies above 10 GeV and of those bursts (GRB 180720B, 190114C, 190829A, and 221009A) with energetic photons above 100 GeV, which can hardly be modelled with the CRs of the standard synchrotron scenario. The analysis shows that (i) the most likely afterglow model using synchrotron and SSC emission on the 2FLGC corresponds to the constant-density scenario, and (ii) variations of spectral (temporal) index keeping the temporal (spectral) index constant could be associated with the evolution of microphysical parameters, as exhibited in GRB 190829A and GRB 221009A.

Prediction of acoustic impedance and density porosity using seismic inversion, and geostatistical methods on Krishna Godavari basin, India: A case study

Seismic inversion is a geophysical technique used to estimate subsurface acoustic impedance models from seismic reflection data. The basic concept behind seismic inversion is to establish mathematical assumptions that can relate seismic responses to geological formation properties. This study addresses two types of seismic inversion methods, namely Band-limited inversion (BLI) and Linear programming inversion (LPI) techniques for the delineation of gas reservoirs. The study presents the successful use of the inversion in a real example from a gas sand reservoir in KG-Basin, India. Inversion has led to the removal of ambiguity and revealing clear information about the target area. This work aims to use and compare the results of these two generic seismic post-stack inversion methods for characterizing the reservoir. The inversion reveals the presence of low acoustic impedance around 2320-2440 ms and is inferred as a sandstone layer with a potential hydrocarbon reservoir. One can improve the analysis of the elastic properties for reservoir characterization by using rock physics knowledge to understand better reservoir properties such as water saturation, porosity, and shale volume. This study also uses the Rock Physics Template (RPT) and plots it with elastic properties to aid the efficient interpretation. RPT helps to identify the lithology and fluid content in the area. Results from the seismic study suggest a comparatively better analysis done through the LPI technique over BLI. In the last, the Multi-attribute transform as a part of the Geostatistical method is used to predict porosity away from the borehole. The method shows a very high porosity section and clearly shows the distribution of porosity in and around the region. The interpretation shows that a very high porosity zone (15–25%) is present that is in cross-correlation with a low impedance zone (4000-8000 m/s*g/cc) and confirms the presence of a hydrocarbon zone in-between 2320 and 2440 ms two-way travel time. All seismic inversion and geostatistical methods can retrieve subsurface information with very high-resolution images but the analysis based on linear programming is found slightly better in each step and depicts good prediction results. The study finally gives a flowchart that combines seismic inversion and geostatistical methods to extract petrophysical parameters from seismic data and can help to interpret hydrocarbon-bearing formation in any virgin area.

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.

Preface

National Physics Seminar (SNF) 2023 Surabaya State University on the Integration of Free Learning in the Era of Society 5.0 Through Physics Innovation and Physics Education Towards Sustainable Development Goals (SDGs) 2030 We proudly present Seminar Nasional Fisika (SNF) 2023, Department of Physics, Universitas Negeri Surabaya (Unesa). This year, SNF, held on 26 August 2023 in Surabaya, is an essential event in our academic calendar. In our big theme, “Integration of Independent Learning in the Era of Society 5.0 Through Physics Innovation and Physics Education Towards Sustainable Development Goals (SDGs) 2030,” we try to explore the potential and challenges faced in achieving sustainable development through physics and physics education. The event was held via ZOOM meeting to facilitate SNF participants from within and outside the country. Participants are also unrestricted by time, accommodation, and other activity agendas.The success of SNF 2023 will only be possible with valuable contributions from various parties. We are very grateful to our three keynote speakers, namely Prof. Ir. Adi Susilo, Ph.D., Prof. Dr. Parmin, M.Pd, and Prof. Nadi Suprapto, Ph.D., who have provided in-depth insight into the development of physics and physics education in the Society 5.0 era. Thank you also to the Dean of the Faculty of Mathematics and Natural Sciences (FMNS) Unesa, Prof. Dr. Wasis, M.Si, who gave an inspiring opening speech.The event was held in two sessions: plenary and parallel session. The plenary session presented three keynote speakers. The parallel sessions were attended by invited speakers and authors presenting their papers and held in eight breakout room groups. As a scientific forum, SNF 2023 was attended by 443 participants consisting of students, teachers, lecturers, engineers, and practitioners who participated both as presenters and participants. The SNF 2023 aims to share ideas and knowledge and build collaboration. We received 81 papers selected for publication in the Journal of Physics: Conference Series with a scope of physics and related fields. In this volume, we accepted 52 papers for publication after fulfilling the scope, checking for originality, and conducting peer review. We hope this event can be a starting point for welcoming global developments. Besides, the publications packaged in this seminar will further strengthen the existence and reputation of the physics department as an educational institution that is superior in scientific publications at national and international levels. The research results must be disseminated widely to be scientifically valuable and provide practical value to society.One of the scientific fields that can significantly contribute to technological development is physics. Physics is a fundamental science used in future technological developments in creating various super-sophisticated technologies to support the development of more modern industries. As in physics education, it is hoped that graduates will not only have physics knowledge but also skills in life, such as creativity, critical thinking, communication, and collaboration through curriculum development and physics learning by utilizing the Internet of Things (IoT), Virtual reality (VR), Augmented reality (AR), and Artificial Intelligence (AI). Using various efforts such as research and technological development in Physics as well as curriculum development and physics learning to confront the era of society 5.0, we can increase awareness and improve the atmosphere for creating innovation that supports the achievement of Sustainable Development Goals (SDGs).The event organizing committee and editorial team hope you enjoy reading this volume of JPCS. In addition, we would like to express our deepest gratitude to the keynote speakers, invited speakers, and all presenters who have provided valuable contributions to this proceeding and the reviewers for their suggestions and constructive criticism in improving the articles presented. On behalf of the SNF 2023 committee, we thank all parties for supporting this publication. We want to invite the participants again to participate in SNF 2024, held by the Department of Physics FMNS Unesa.List of Seminar Nasional Fisika (SNF) 2023 Committee is available in this Pdf.

Physics-guided generative adversarial network for probabilistic structural system identification

Structural system identification (ID) is an important tool for many structural and infrastructure applications, such as structural health monitoring and structural model updating. A novel physics-guided generative adversarial network (PG-GAN) is proposed in this study for probabilistic structural parameter ID. The PG-GAN leverages the powerful distribution learning ability of GANs while also enabling physical awareness by incorporating additional generators for physical parameters and developing a physics-based loss function that links the data generator with the parameter generators through existing physics knowledge to guide the training of generative models. Two experiments are performed to validate the effectiveness of the PG-GAN for structural parameter ID and demonstrate its application in structural damage detection. The PG-GAN is shown to successfully capture the uncertainty inherent in structural parameter estimation and accurately locate and quantify structural damages in terms of probabilistic distributions of the damage extent. The main contributions of this study are threefold: (1) compared with traditional methods for structural parameter ID, the proposed PG-GAN provides an intelligent framework for probabilistic identification of structural parameters without the need for any assumption on the data distribution and does not require an initial guess of parameters; (2) unlike traditional data-driven GANs, the proposed PG-GAN provides a linkage between GAN and physics knowledge to enables physical awareness in generative model training; and (3) the proposed PG-GAN can serve as an intelligent diagnostic system to better inform the decision-making on structural health management as it can quantify the uncertainty in the structural damage extent.

Module division method of complex products for responding to user’s requirements

With the gradual diversification of personalized usage scenarios, user requirements play a direct role in product design decisions. Due to the problem of fuzzy demand caused by user cognitive bias, traditional design methods usually focus on realizing product functions and cannot effectively match user requirements. Therefore, this paper proposes a complex product module division method for user requirements. The method constitutes of three tasks, requirement analysis of module division, design mapping of module division and scheme implementation of module division. Firstly, based on the progressive architecture from initial requirements to precise requirements, the effective user requirements are obtained through similarity recommendation. Secondly, according to the four types of knowledge of function, geometry, physics and design, the design structure matrix is constructed to complete the Requirement-Function-Structure mapping. The improved Fuzzy C-means Algorithm is used to solve the mapping model, and finally a module division scheme that meets the user requirements is obtained. Taking the chip removal machine as an example, the rationality and effectiveness of the method are verified. The results show that the product modules divided by this method can effectively meet the multiple user requirements.

Physics informed neural networks for fault severity identification of axial piston pumps

Artificial intelligence (AI) has shown great potential in the maintenance stage of industrial manufacturing. However, the existing data-driven methods often lack integration with physics knowledge, leading to the implicit decision-making processes. To overcome this limitation, this study proposes a physics informed neural network for identifying fault severity in axial piston pumps. The method begins by establishing a discharge pressure model that incorporates physics information to describe instantaneous pressure changes. Subsequently, a neural network model is employed to estimate the temporal function of discharge pressure based on observed signals. Moreover, by allowing the learnability of fault-related physics parameters, the discharge pressure model is transformed into a physics informed loss term that guides the learning process. By incorporating the physics informed loss, the proposed method effectively combines observations and physics knowledge, providing explicit physical interpretation to the identified parameters. Additionally, the introduction of volume efficiency establishes a relationship between the physical degradation indicator and fault severity, offering a physical explanation of the identified results. Experimental results on an axial piston pump demonstrate the effectiveness and robustness of the proposed method, particularly in terms of interpretability for fault severity identification.

Certainty of Knowledge and Performance of Physics among Secondary School Students in Tharaka-Nithi County, Kenya

Certainty of Knowledge and Performance of Physics among Secondary School Students in Tharaka-Nithi County, Kenya

Do Microscopes Have Politics? Gendering the Electron Microscope in Laboratory Biological Research.

Objects like microscopes are gendered depending on their context. The introduction of the electron microscope at Leeds University in early 1940s Britain was under the control of high-status physicists, most of whom were men, who regulated its access over and against biologists. Moreover, the microscope required physical strength more associated with men than women, combined with a sound knowledge of physics. This article explores the challenges women encountered including access to scientific instruments when entering post-World War II electron microscopy through Irene Manton's career. It combines techno-political and gendered perspectives on the history of women in science. In particular, the study invites gendered understanding of early biological electron microscopy, at a university world-renowned on the subject, through the lens of one capital intensive microscope.

Practice and Challenges of Project-based Learning in Physics Education

Physics is a subject with strong practicality, and in the specific learning process, it is usually carried out in real situations, which further highlights the practical characteristics of physics knowledge in human life. Carrying out project-based learning in biology teaching is to enable students to independently construct project-based activities based on new situations around a certain concept, experiment, process, link, etc. In the activity, students are divided into project teams to break down disciplinary barriers and integrate their knowledge to complete specific sub project tasks. Although project-based learning emphasizes students' independent exploration, it does not mean that teachers can be separated from students' learning activities. Teachers need to develop and design project content based on teaching objectives. In the process of project-based learning, students discuss the driving issues and sub problems of the project in a collaborative learning manner. Students can practice by collecting and organizing data, designing exploration plans, or creating plans, thereby building their own understanding of knowledge and problems.