Physical Formulas Research Articles

The Possible Factors that Influence the Stability of Binary Star System

Binary star systems are a crucial subject in astrophysics, as they provide insights into stellar formation, evolution, and dynamics. Despite extensive research, there remains a gap in understanding the factors influencing the longterm stability of such systems. This paper discusses the factors that may affect the stability of binary star systems by analyzing and exploring the physical laws and formulas of the operation of binary star systems and using the operation data of specific binary star systems as evidence. It can be concluded that the factors affecting the stability of a binary star system may be as follows: 1. Mass ratio of two stars, 2. Stellar spacing, 3. Orbital eccentricity, 4. Influence of stellar evolution stage, 5. The influence of external bodies. Future research should further explore the combined effects of these factors in more complex systems to better understand binary star stability in various astrophysical environments. Additionally, understanding how these factors interact in multi-body systems could provide deeper insights into the evolution of complex stellar systems.

An interpretable formula for lattice thermal conductivity of crystals

Lattice thermal conductivity (κL) is a crucial physical property of crystals with applications in thermal management, such as heat dissipation, insulation, and thermoelectric energy conversion. However, accurately and rapidly determining κL poses a considerable challenge. In this study, we introduce a formula that achieves high precision (mean relative error = 8.97 %) and provides fast predictions, taking less than 1 min, for κL across a wide range of inorganic binary and ternary materials. Our interpretable, dimensionally aligned and physical grounded formula forecasts κL values for 4601 binary and 6995 ternary materials in the Materials Project database. Notably, we predict undiscovered high κL values for AlBN2 (κL = 101 W m−1 K−1) and the undetected low κL Cs2Se (κL = 0.98 W m−1 K−1) at room temperature. This method for determining κL streamlines the traditionally time-consuming process associated with complex phonon physics. It provides insights into microscopic heat transport and facilitates the design and screening of materials with targeted and extreme κL values through the application of phonon engineering. Our findings offer opportunities for controlling and optimizing macroscopic transport properties of materials by engineering their bulk modulus, shear modulus, and Grüneisen parameter.

Pore pressure prediction based on rock physics theory and its application in seismic inversion

Formation overpressure seriously affects drilling and downhole operation. Accurate prediction on the formation pore pressure can not only reduce the probability of drilling accidents, but also quantitatively evaluate the original formation pressure of underground pore space, which provides an important reference for site selection of carbon sink projects using underground space resources such as CO2 geological storage. It is therefore necessary to set up a widely applicable method that is based on rock physics theory and conforms to the characteristics of rock mechanics and fluid mechanic. This method is suitable for both logging prediction and seismic inversion of pore pressure. The traditional method of predicting pore pressure based on P-wave velocity has multiple solutions, and the prediction based on S-wave velocity which is not sensitive to fluid has new significance. Based on the Hertz-Mindlin petrophysical model that considering pressure variation and the Gassmann fluid substitution equation that addresses the change in fluid saturation, this paper firstly derived rock physical formulas for predicting pore pressure in logging, and then derived the intrinsic power function relationship between the effective pressure (Pe) and S-wave velocity (Vs) as well as S-wave impedance (Is). Based on this, a set of geophysical methods integrating S-wave velocity prediction, pore pressure prediction in well and seismic inversion is finally established. The efficacy of this method has been well validated, with an average error of 2.35% in S-wave velocities prediction, 4.5% in single-well pore pressure prediction. The results of seismic inversion of pore pressure are consistent with the phenomenon of overpressure development in actual working area. This method can be further extended to other areas, providing invaluable reference for underground operation such as oil and gas exploration and CO2 geological storage.

AUTOMATIC SOLVING OF PHYSICS WORD PROBLEMS

We present a system that solves simple physics word problems (PWPs) stated in the English language. The main feature of the system is that it is deterministic and yields a correct solution based on real physics formulas. The program generates the solution in a tabular form, showing givens, unknowns, and solution steps. We performed a thorough analysis on the previous work in this field. Most of the research was accumulated in math word problem (MWP) solvers. We have found that these programs are not capable of solving problems from Ukrainian physics textbooks. We identified several types of physics problems: theoretical, value conversion, value comparison, unknowns finding, value change. We developed separate problem-solving strategies for each type. The program uses named entity recognition (NER), a technique in natural language processing (NLP), to identify key elements in the problem text. We created a set of rules for marking these entities. Then, problem type recognition is performed. Depending on the type, the list of entities is converted into the internal problem representation. Value conversion and comparison problems are easy to handle. We developed a recursive algorithm for solving unknowns-finding problems which turned out to be a simplified version of Stanford Research Institute Problem Solver (STRIPS) algorithm. However, developing a universal algorithm for solving value-change problems presents a significant challenge. We believe this problem type belongs to the NP-hard class, indicating inherent difficulty in finding optimal solutions. The interface of the program is a web-application. The user can type the problem text and see the solution on a web page. Additionally, the result of NER is presented. Constructing a general problem solver is challenging. While our program can solve basic physics problems, complex problems involving forces, energy, etc., remain unsolved. However, our solver has great potential for future development. We have thoroughly analyzed its capabilities and limitations and proposed ideas for future research.

Didactic conditions for improving demonstration experiments in physics in pedagogical universities

Relevance. The relevance of the research is conditioned upon the problems of improving the methodological foundations of teaching physics in the format of a visual experiment for the sustainable development of the educational process in a pedagogical higher educational institution. Purpose. The purpose of the study is to develop a model for conducting demonstration experiments on the subject of physics using computer technology in pedagogical universities. Methodology. The leading method to investigate this problem is testing developed by M.I. Lukyanova and N.V. Kalinina's "Methodology for studying the motivation of students' learning". Results. For more successful assimilation and understanding of practical material within the subject of physics, a model for organising practical experiments considering environmental orientation was developed. It includes a demonstration of a multi-level process with didactic conditions, such as starting from the final version in its practical application of a physical phenomenon or a set of mechanisms. The process gradually deepens knowledge, beginning from the mechanical work of the object made to a detailed examination of each chain of its operations. This is accompanied by explanations at the level of physical formulas and regularities. Conclusions. The study showed that the developed model enhances learning experiences in physics. It emphasizes practical experiments visible through computer simulations, fostering intellectual development and environmental conservation awareness. This model enhances both the educational process and practical applications in physics, benefiting teachers, physicists, and educators alike. Keywords: physics; experiment demonstration; methodology; visibility of the educational process; education

Reifying actions into artifacts: process–object duality from an embodied perspective on mathematics learning

Grasping mathematical objects as related to processes is often considered critical for mathematics understanding. Yet, the ontology of mathematical objects remains under debate. In this paper, we theoretically oppose internalist approaches that claim mental entities as the endpoints of process–object transitions and externalist approaches that stress mathematical artifacts—such as physical manipulatives and formulas—as constituting mathematical objects. We search for a view on process–object duality that overcomes the dualism of mind and body. One such approach is commognition that describes mathematical objects as discursive entities. This paper expands the nature of mathematical objects beyond discourse and highlights the role of learners’ interaction with the environment by adopting ecological onto-epistemology. We develop a functional dynamic systems perspective on process–object duality in mathematics learning emphasizing embodied actions and the re-invention of artifacts’ affordances. As a main result, we reconsider process–object duality as a reification of repetitive actions into a cultural artifact that consists of two steps: (1) forming a new sensory-motor coordination that brings new perception to the fore and (2) crystallizing a new artifact in a mathematical environment that captures this new perception. An empirical example from research on embodied action-based design for trigonometry illustrates our theoretical ideas.Graphical abstract

Use of the HEC RAS model for the analysis of exceptional floods in the Ouémé basin

Abstract. The Ouémé River basin extends over almost half of Benin's territory, entirely located in a humid tropical climate. This river system includes a deltaic zone (delta of the Ouémé) known for its high agricultural potential and thus subject to a socio-economic development agenda. The Ouémé delta is facing recurrent floods that maintain rural agricultural population into a retrograding crisis with significant damages such as losses of properties. The objective of this study is to improve decision-making in the Ouémé basin through the simulation of exceptional floods using the HEC-RAS model. The HEC RAS model is a conceptual model, which works through mathematical and physical formulas to implement environmental phenomena for forecasting, understanding and analysis purposes. The model inputs used are basin GIS data, hydro-meteorological data, characteristics of existing hydraulic structures, etc. The targeted outputs include 1D/2D/3D view plans with support of satellite images, tables, graphs and curves. It is worth mentioning that the model provides outputs compatible with other tools, such as civil engineering (Civil 3D, Revit, Infraworks, etc.) and GIS, that help to expand the valorization fields. The implementation of the model in the Ouémé basin has made it possible to note: (i) that the recurring effect of losses and damages is justified by the settlement of the population on the river banks; (ii) that there is an important agricultural production in areas of high flood risk; (iii) that depending on the occurrence of the phenomenon, the flooded extent and the height of submersion remains variable, and more important for extreme flooding; (iv) about 12.07 % occurrence of river flood against 13.24 % for flash flood at a return period of 30 years. Moreover, it is very relevant to note that most of flood waters converge to the western part of the basin (an area with a low risk of flooding, stretched over 63.68 km2) and to the eastern part around the Damè-Wogon depression (an area at high risk of flooding, stretched over 10.49 km2).

Bouldering & chronic traumatic encephalopathy – That is repeated mild traumatic brain injury from climbing

Mild traumatic brain injury (mTBI) is one of the most common neurological conditions resulting from linear accelerations in the range of 60 gravity. Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative syndrome. Its etiopathology is attributed to repeated blows to the head and the transmission of acceleration and deceleration forces to the brain. There are several types of climbing, the most popular are lead climbing, speed climbing and bouldering. Athletes involved in the latter sub-discipline (bouldering) appear to be most at risk of mTBI. Bouldering takes place on artificial walls or natural rock and involves climbing without a rope on routes up to several metres in height. We hypothesised that climbers in the bouldering sub-discipline are at risk of frequent mTBI and, as a consequence, may suffer from chronic traumatic encephalopathy. In the paper we presented physical formula to support our thesis, a verification of this formula and a survey of boulder climbers. The physical model was verified in a practical test. Differences in the range of ≈ 2g were demonstrated (MAE = 1.86, RMSE = 2.51). There is evidence in the form of a physiological model that boulderers are able to achieve accelerations that cause mTBI. The study population had a 12 % incidence of repeated falls and reported symptoms associated with mTBI during bouldering training sessions. Given the diagnosis of CTE, which can only be made after death, future research should focus on diagnosing and treating symptoms of mTBI and CTE (including depression, progressive memory loss, suicidal behaviour, cognitive impairment) in boulder climbers.

Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors.

Perancangan E-Kalfisika Dalam Pembelajaran Fisika Secara Digital

Physics is a science with many similarities in each subject. This research on physics calculations aims to create a simple physics calculator based on Python programming. The purpose of this research is to create an electronic physics calculator (e-KalFisika). E-KalFisika is an application used for digital or electronic physics learning. This application provides learning materials, experimental simulations, practice problems and other features that support the understanding of physics concepts. The e-KalFisika application is only in development to solve the problem of calculating physics formulas. The result is e-KalFisika with better calculation results than manually.

Assessment of the Hydroenergy Potential of the Gueeni Village Waterfall on the Kokoulo River in Pita, Guinea

Background and Aim : In Guinea, the supply of electrical energy still remains a major challenge, especially in rural areas. However, Guinea's hydroelectric energy potential is enormous, more than 6000 MW, with a very dense hydrographic network of 1165 rivers, which finds its origin mainly in the Fouta-Djallon. To date, only 5% of this potential has been developed and only benefits nearly 10% of the Guinean population. This study is part of this aspect, for the improvement of electricity supply in isolated areas of the country. Its objective is to evaluate the hydroenergetic potential of the waterfall in the Gueeni village on the Kokoulo river in Pita. Method : This study is based on field measurements, the use of physical formulas and sizing software to determine the energy characteristics of the site. Results: The main results obtained are: equipment flow (9.781m3/s), gross head (31 m), net head (28.264 mm), hydraulic power (3000 kW) and power net of falling water (2700 kW). Conclusion: The results obtained represent important basic data for the construction of the small power plant on the Gueeni site. Keywords: Hydroenergy potential, waterfall, Gueeni village, Electric power

Green Betel Leaves Extract and Red Betel Leaves Extract South Kalimantan And Central Kalimantan Indonesia are the Best Combination to Inhibit Escherichia Coli and Staphylococcus Aureus

Objective: Know “Herbal Medicine Formulation Emulgel Preparation Combination of Red Betel Leaf Extract and Green Betel Leaf Extract Rawa South Kalimantan and Central Kalimantan Indonesiaas an inhibitor of Escherichia coli and Staphylococcus aureus bacteria. Theoretical framework: Postpartum infections are the second leading cause of maternal death in Indonesia after bleeding. This research was within the theoretical framework of perineal wound care to assess the effectiveness of betel leaves in treating wounds (Damarini et al, 2012). Betel leaves have a distinctive aroma because they contain 1−4.2% essential oil, water, protein, fat, carbohydrates, calcium, phosphorus, vitamins A, B, C, iodine, sugar and starch. Of these various contents, in there essential oils are natural phenols which have antiseptic power 5 times stronger than ordinary phenols (bactericides and fungicides) but not sporacids. Essential oil from betel leaves contains 30% phenol and several derivatives. (Chakraborty, 2011). Essential oils act as anti-bacterials by interfering with the process of forming membranes or cell walls so that they do not form or form imperfectly (Ajizah, 2008). Method: Type of true experimental research. The ingredients used are red and green betel leaves originating from South Kalimantan and Central Kalimantan. The leaves are dried, then extracted using the maceration method, then the extract is dried. The extract was made in emulgel dosage form, then the physical properties of the emulgel were tested. Data was analyzed statistically. Results and conclusion: The formulation was able to produce an emulgel preparation from a combination of green and red swamp betel leaf extracts from South Kalimantan and Central Kalimantan.Test results of the physical properties of the emulgel product, a combination of green and red swamp betel leaf extracts in South Kalimantan and Central Kalimantan, with formula 1 results, namely homogeneous, viscosity 3173 Centipoise (cps), adhesive power 7.70 seconds, spread ability 8.62 cm2, and pH 6.23. while formula 2 is homogeneous, viscosity 3259 cps, adhesion power 7.80 seconds, spread ability 8.30 cm2, and pH 6.33. The results of statistical tests on the physical properties of the emulgel product, a combination of green and red swamp betel leaf extracts in South Kalimantan and Central Kalimantan as inhibitors of Escherichia coli and Staphylococcus aureus bacteria, show that formula 2 has the best ability when compared to placebo and formula 1. Emulgel formulation of Green Betel leaves extract and Red Betel leaves South Kalimantan and Central Kalimantan as inhibitions to Escherichia coli and Staphylococcus aureus which fulfill the physical requirements emulgel and formula 2 have the best ability. Implications of the research: this research has implications for continuing (Damarini et al, 2012) to focus on preventing perineal wound in postpartum mother against bacteria.

Kinetic Modeling of PROTAC-Induced Protein Degradation.

Kinetics of the PROTAC-induced protein degradation were modelled using the equilibrium approximation, accounting for the protein recovery rate with a time lag. The simulated kinetic curves resemble what is experimentally observed, and the physical formulas of the half-maximal degradation concentration (DC50 ) were derived from them. The equations reveal that DC50 is proportional to the dissociation constant of the ternary complex (Kd ) and inversely proportional to the expression level of the E3 ligase and the effective ubiquitylation rate (kub ). The predicted relationships were rigorously confirmed by experimental evidences from a matched molecular pair analysis using a set of published PROTACs.

A Triple-Double Convolutional Neural Network for Panchromatic Sharpening.

Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet.

Machine learning assisted intelligent design of meta structures: a review

In recent years, the rapid development of machine learning (ML) based on data-driven or environment interaction has injected new vitality into the field of meta-structure design. As a supplement to the traditional analysis methods based on physical formulas and rules, the involvement of ML has greatly accelerated the pace of performance exploration and optimization for meta-structures. In this review, we focus on the latest progress of ML in acoustic, elastic, and mechanical meta-structures from the aspects of band structures, wave propagation characteristics, and static characteristics. We finally summarize and envisage some potential research directions of ML in the field of meta-structures.

Needs Analysis of The Development STEM-PjBL Based LKPD To Train Students' Critical Thinking Skills

Critical thinking skills is very important in the science learning process. Teaching materials are needed to encourage critical thinking skills. This research was conducted to analyze the needs of teachers and students for the development of STEM-PjBL based LKPD to train students’ critical thinking skills. The research method used was descriptive to investigate the initial conditions. The research data was collected using an interview sheets and distributing needs analysis questionnaires through google form. The subjects of this study were physics teacher and 25 students of class XI IPA SMA in Tebing Tinggi. The datas were analysis using qualitative analysis techniques. The results showed that: (1) teachers had never used STEM-PjBL based LKPD before, (2) students were not familiar with learning activities that require critical thinking skills and they have difficulty in understanding the use of physics formulas, (3) students and teachers agree with the development of STEM-PjBL based LKPD to train critical thinking skills.

Numerical Simulation of Heat and Mass Transfer Behavior during Iron Ore Sintering: A Review

Accurate computational models of sintering behavior would assist to enhance sinter quality and are anticipated to play a role in yield prediction. Sintering is a vital process in the manufacturing of iron and steel. As a consequence, the primary objective of these models will be a thorough simulation of mass and heat transport during the sintering process. In this paper, based on the examination and integration of previous studies, the fundamental physical formula and chemical reactions of the numerical simulation of the sintering process are introduced in depth with mechanism analysis. Furthermore, in view of the current numerical simulation methods and sintering process technology innovation development, the studies on sintering numerical simulation are reviewed from different angles, of which the main methods and assumptions are discussed. Finally, the current state of sintering simulation including the numerical simulation of innovative algorithm and optimized sintering technology is discussed in detail, along with potential implications for model development.

A Novel In-Line Polymer Melt Viscosity Sensing System of Integrated Soft Sensor and Machine Learning

Real-time monitoring of melt viscosity is one of the many challenges faced in the polymer extrusion process. Viscosity is one of the important metric reflecting material properties during plastic process. However, viscosity is an indicator that can be evaluated by calculating the evaluation value through temperature, pressure, screw rotation speed, etc., but cannot be directly measured by physical sensors. Melt viscosity should be calculated at the exact melt temperature. Temperature sensors cannot accurately measure the temperature of the melt in the barrel. Soft sensing technique is the best solution for estimating material properties. It just needs some physical sensors and physical formulas. The proposed viscosity soft sensor consists of physical sensors, a temperature estimator, and a simulation analysis software for calculating material properties. Four physically temperature signals, one physically pressure signal, and the simulation properties data are used as the dataset for the temperature estimator. An ensemble machine learning model of temperature estimators consisting of Random Forests (RF) and Convolutional Neural Networks (CNN). Melt viscosity, shear stress and shear rate are calculated by physical formulas. Experimental results show that the proposed temperature estimator predicts that the temperature will reduce the MAE from 6.08 to 2.86. Compared with the current work, the prediction error rate of the soft sensor is also reduced from 4% to 1.1%. The proposed soft sensor can be used to better predict polymer melt temperature. Finally, according to the predicting results, the material property scatter chart of viscosity can be precisely plotted under the specific melt temperature. In the past, the melt viscosity could only be measured offline. The proposed method can be added as a plug-in to the existing process to achieve real-time viscosity monitoring.

Influence of Implant–Abutment Connection Biomechanics on Biological Response: A Literature Review on Interfaces between Implants and Abutments of Titanium and Zirconia

The interface between a dental implant and an abutment is stabilized by two mechanical characteristics: a preload of an abutment screw and the friction between the contact surfaces of the implant and the abutment. These mechanical properties are quantitatively analyzed by using physical and mechanical formulas. The important thing is that such mechanical properties cause various biological phenomena when medical devices are inserted into human bodies. Some mechanical complications in dental implant prostheses are closely associated with biological complications. This literature review explores the mechanical complications of the implant–abutment connection and their biological effects in a titanium dental implant system, which is the system most widely used in dental clinics. Understanding the biomechanics of the implant–abutment connection helps to predict the merits and limits of zirconia dental implants, which have been recently introduced and clinically applied.

Research on Physical Model and Criterion of Loose Strands in Tension Stringing Construction

In tension stringing construction, the conductor defects of loose strand often occur, which affects the construction quality and conductor life. There is no suitable method to express the physical state of such loose strand at present. Through the study of the loose strands section of the conductor, the physical model of the loose strands of the conductor is established, and the physical calculation formula of scattered strands and lantern included in loose strands defect is proposed, as well as the discrimination coefficient of the scattered strands k 1 and the lantern k 2 . 3D scanning technology of industrial CT was used to scan and model the loose strand samples of JL/G1A-630/45-45/7 and JL1/G2A-1250/100-84/19 ACSR obtained from the loose strand reproduction test of conductor. The CT scanning model is evenly intercepted to measure multiple sections, and the scattered strands coefficient and the lantern coefficient are calculated. The results show that: When k 1 and k 2 ≥ 1.2, the scattered strands and lantern defects of the conductor are more obvious, which can be used as a criterion.