Hasil untuk "Mechanics of engineering. Applied mechanics"

Dd Wang, Js Chen, 王东东

C. Duarte, J. Tinsley Oden

Tehseen Hussain, Fraz Ahmad, Dr. Zia Ur Rehman

The rapid integration of the Internet of Things (IoT) into healthcare ecosystems has revolutionized patient monitoring and data accessibility; however, it has simultaneously expanded the cyber-attack surface, leaving sensitive medical data vulnerable to sophisticated breaches. This systematic literature review (SLR) addresses the critical challenge of balancing high-level security with the severe resource constraints of medical sensors and edge devices. By synthesizing evidence from 80 high-impact studies including 18 primary research articles published between 2022 and 2025 this paper evaluates the quality and efficacy of emerging cryptographic frameworks. The methodology utilizes a rigorous quality assessment framework to categorize research into "Strong," "Moderate," and "Weak" tiers. Key findings reveal a significant paradigm shift toward lightweight symmetric ciphers, such as GIFT and PRESENT, and certificateless authentication protocols like ELWSCAS, which reduce communication overhead in narrow-band environments. The analysis further explores the role of blockchain-assisted decentralization and DNA-based encryption in mitigating Single Point of Failure risks and providing high entropy. While decentralized models significantly enhance data integrity, they frequently encounter a scalability wall regarding transaction latency. Furthermore, the review assesses quantum readiness, noting that while lattice-based standards are being ported to microcontrollers, memory footprints remain a barrier for simpler sensors. Ultimately, this SLR maps the current technical frontiers and provides a strategic roadmap for future research, emphasizing the transition toward lightweight, quantum-resistant architectures as the next essential step in securing the global healthcare IoT infrastructure. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Fabrication/Falsification Statement The author(s) declare that no data has been fabricated, falsified, or manipulated in this study. Participant Consent The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained. Copyright and Licensing For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).

Renjing Chen, Wenhai Liang, Yilin Xu et al.

Spatial intensity modulation in amplified laser beams, particularly hot spots, critically constrains attainable pulse peak power due to the damage threshold limitations of four-grating compressors. This study demonstrates that the double-smoothing grating compressor (DSGC) configuration effectively suppresses modulation through directional beam smoothing. Our systematic investigation validated the double-smoothing effect through numerical simulations and experimental measurements, with comprehensive spatiotemporal analysis revealing excellent agreement between numerical and practical pulse characteristics. Crucially, the DSGC enables a 1.74 times energy output boost compared to conventional compressors. These findings establish the DSGC as a pivotal advancement for next-generation ultrahigh-power laser systems, providing a viable pathway toward hundreds of PW output through optimized spatial energy redistribution.

Pablo Merino-Muñoz, Felipe Hermosilla-Palma, Nicolás Gómez-Álvarez et al.

<b>Background/Objectives</b>: Groin and hip injuries are common in sport, and muscle weakness has been identified as an intrinsic risk factor. So, analyzing the strength of the hip musculature becomes important. To date, there are no hip adductor isometric strength tests on force platforms. This study aims to analyze the intra-test reliability of a hip adductor strength test using force platforms. <b>Methods:</b> The study sample comprised 13 male professional soccer players with an average age of 22.3 ± 3 years, body mass of 75.8 ± 5.4 kg, and height of 1.8 ± 0.1 m. Assessments were conducted on a uniaxial force platform. The variables analyzed are peak force (PF), rate of force development (RFD), and impulse. Intra-test reliability was evaluated using the coefficient of variation (CV), intraclass correlation coefficient (ICC), and Bland–Altman plots. <b>Results:</b> Acceptable levels of reliability were identified solely for the variable of peak force, with CV values of D = 5.7% for the dominant profile and ND = 5.4% for the non-dominant profile. Furthermore, moderate and good relative reliability were observed in peak force for the dominant (ICC = 0.706) and non-dominant (ICC = 0.819) profiles, respectively. However, the remaining time-related variables, RFD and impulse, did not achieve acceptable levels of absolute reliability (CV > 10%) and displayed poor to moderate relative reliability. <b>Conclusions</b>: In summary, PF during the hip adductor isometric strength test demonstrated acceptable absolute and commendable relative reliability. Conversely, the time-related variables, specifically RFD and impulse, yielded unsatisfactory absolute and relative reliability levels.

Sagar Chokshi, Piyush Gohil, Vijay Parmar et al.

Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. In the present study, Artificial Neural Network (ANN), a computational tool is used to predict the elastic modulus of a composite of longitudinally placed fiber-reinforced polymeric composite. The novelty in carried work is that the property prediction is carried out considering interphase and its properties. For this, tensile properties data of Longitudinally Placed Bamboo Fiber Reinforced Polyester Composite (LUDBPC), Longitudinally Placed Flax Fiber Reinforced Polyester Composite (LUDFPC) and Longitudinally Placed Jute Fiber Reinforced Polyester Composite (LUDJPC) has been procured to generate ANN models. The Levenberg-Marquardt training algorithm is used to generate the ANN models as it gives more accurate results compared to other ANN algorithms based on interphase properties data. The validation of ANN models was also carried out based on fresh experimental results of BPC/FPC by doing the fabrication with hand layup technique and testing of composites with a Universal Testing Machine (UTM). The present work signifies that the developed ANN models give accurate results with experimental results for the prediction of elastic modulus of composite (Ecl) and it can be used for the prediction of longitudinally placed fiber-reinforced composite and Ecl of BPC at volume fraction of fiber (vf):22% is 2248.75 MPa and Ecl of FPC at vf:10% is 3210.50 MPa.

Florence Choong Chiao Mei, Bryan Ng Jan Hong

Agriculture is one of the largest sectors that contribute to the economic growth of countries, including Malaysia. However, plant diseases affect the quality of the harvest and impede farmers’ maximum yield output. Therefore, early detection of diseases in plants is vital to curb infection, reduce food waste, and reduce their carbon footprint. However, many detection methods are complex, require high computational power and time to perform the required analysis and focus only on a particular species or strain of the disease. These requirements would likely deter most users in remote areas or poorer economic states. This paper proposes a convolutional neural network to determine multi-class plant diseases that is memory efficient, has a small trainable parameter number, and is compact enough to work even on mobile devices. The plant images were pre-processed to ensure that they were validated accurately and to minimise overfitting. Then, the proposed convolutional neural network was trained using a publicly available dataset consisting of 54306 images, followed by validation and testing. Finally, the completed model is saved, and the data obtained is transferred to a cloud network using wireless sensor networks. The proposed method obtained 96.87% accuracy with 100 epoch training iterations, rivalling famous architectures such as VGG16 and MobileNetV2. The experimental results demonstrate the feasibility and robustness of the method for disease detection in multi-crop plants.

Mehran Ebrahimi, Masayuki Yano

We introduce a hyperreduced reduced basis element method for model reduction of parameterized, component-based systems in continuum mechanics governed by nonlinear partial differential equations. In the offline phase, the method constructs, through a component-wise empirical training, a library of archetype components defined by a component-wise reduced basis and hyperreduced quadrature rules with varying hyperreduction fidelities. In the online phase, the method applies an online adaptive scheme informed by the Brezzi-Rappaz-Raviart theorem to select an appropriate hyperreduction fidelity for each component to meet the user-prescribed error tolerance at the system level. The method accommodates the rapid construction of hyperreduced models for large-scale component-based nonlinear systems and enables model reduction of problems with many continuous and topology-varying parameters. The efficacy of the method is demonstrated on a two-dimensional nonlinear thermal fin system that comprises up to 225 components and 68 independent parameters.

Valentin Molotnikov, Antonina Molotnikova

Yuhai Xiang, Dohgyu Hwang, G. Wan et al.

Understanding peeling behavior in soft materials is integral in diverse applications, from tissue engineering, wound care, and drug delivery to electronics, automotive, and aerospace equipment. These applications often require either strong, permanent adhesion or moderate, temporary adhesion for ease of removal or transfer. Soft adhesives, especially when applied on soft substrates like elastomer-coated release liners, flexible packaging films, or human skin, present unique mechanical behaviors compared to adhesives applied on rigid substrates. This difference highlights the need to understand the influence of substrate rigidity on peeling mechanics. This review delves into both energy and stress-based analyses, where a thin tape with an adhesive layer is modeled as a flexible beam. The energy analysis encompasses components like the energy associated with tape deformation, kinetic energy, and energy lost due to interfacial slippage. The stress analysis, on the other hand, zeroes in on structures with thin, deformable substrates. Substrates are categorized into two types: those undergoing smaller deformations, typical of thin soft release liners, and thicker deformable substrates experiencing significant deformations. For substrates with small deformations, the linear Euler-Bernoulli beam theory is applied to the tape in the bonded region. Conversely, for substrates experiencing significant deformations, large deflection theory is utilized. These theoretical approaches are then linked to several practical, industrially-relevant applications. The discussion provides a strategic guide to selecting the appropriate peeling theory for a system, emphasizing its utility in comprehending peeling mechanisms and informing system design. The review concludes with prospective research avenues in this domain.

Erdal Öner, Ahmed Wasfi Hasan Al-Qado

V. Goodarzimehr, S. Talatahari, S. Shojaee et al.

Gabriel Zarzoso, Eduardo Roque, Francisco Montero-Chacón et al.

An FFT based method is proposed to simulate chemo-mechanical problems at the microscale including fracture, specially suited to predict crack formation during the intercalation process in batteries. The method involves three fields fully coupled, concentration, deformation gradient and damage. The mechanical problem is set in a finite strain framework and solved using Fourier Galerkin for non-linear problems in finite strains. The damage is modeled with Phase Field Fracture using a stress driving force. This problem is solved in Fourier space using conjugate gradient with an ad-hoc preconditioner. The chemical problem is modeled with the second Fick's law and physically based chemical potentials, is integrated using backward Euler and is solved by Newton-Raphson combined with a conjugate gradient solver. Buffer layers are introduced to break the periodicity and emulate Neumann boundary conditions for incoming mass flux. The framework is validated against Finite Elements the results of both methods are very close in all the cases. Finally, the framework is used to simulate the fracture of active particles of graphite during ion intercalation. The method is able to solve large problems at a reduced computational cost and reproduces the shape of the cracks observed in real particles.

Gang Hu, Yuxuan Guo, J. Zhong et al.

L. Mamedova, Alexander Rukovich, T. Likhouzova et al.

The World Health Organization announced the COVID-19 pandemic, which led to considerable disruption of the global education system and required an early adaptation of the educational process. In addition to the resumption of the educational process, it was necessary to preserve the academic performance of students of higher educational institutions, including engineering ones. This study aims to develop a curriculum for engineering students to increase their level of success. Igor Sikorsky Kyiv Polytechnic Institute (Ukraine) hosted the study. The sample consisted of 354 fourth-year students of the Engineering and Chemistry Faculty: 131 “Applied Mechanics”, 133 “Industrial Engineering”, and 151 “Automation and Computer-Integrated Technologies”. The sample included students of the Faculty of Computer Science and Computer Engineering: 121 “Software Engineering”, and 126 “Information Systems and Technologies” – 154 students from the 1st year and 60 students from the 2nd year. The study was conducted during 2019–2020. The data includes grades for in-line classes and final test scores. The result of the research has shown that modern digital tools such as Microsoft Teams, Google Classroom, Quizlet, YouTube, Skype, and Zoom, among others, have provided a highly effective educational process. The results of the educational process are as follows: 63 + 23 + 10 students received “Excellent/Perfect” (A) in 2019, 65 + 44 + 8 in 2020; 146 + 64 + 20 and 159 + 81 + 18 received “Good (B, C)”, respectively; 135 + 64 + 30 and 120 + 27 + 31 “Satisfactory” (D, E), respectively; – 10 + 3 + 0 and 10 + 2 + 3 “Unsatisfactory” (F), respectively. There was a tendency to increase the average score. The researchers found that the learning models were different before (offline) and during (online) the COVID-19 epidemic. However, the academic results of students were not different. The authors can conclude that e-learning (distance, online) is possible when training engineering students. The introduction of a new, jointly developed author’s course “Technology of mechanical engineering in Medicine and Pharmacy” will also allow future engineers to be more competitive in the labor market.

Tan Yan Lin, Pang Ying Han, Ooi Shih Yin et al.

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance. In this paper, a heterogeneous ensemble model is proposed for churn prediction in the telecom industry. The model involves exploratory data analysis, data pre-processing and data resampling to handle class imbalance. In this proposed model, multiple trained base classifiers with different characteristics are integrated through a stacking ensemble technique. Specifically, convolutional-based neural network, logistic regression, decision tree and Support Vector Machine (SVM) are considered as the base classifiers in this work. The proposed stacking ensemble model utilizes the unique strengths of each base classifier and leverages collective knowledge to improve prediction performance with a meta-learner. The efficacy of the proposed model is assessed on a real-world dataset, i.e., Cell2Cell. The empirical results demonstrate the superiority of the proposed model in churn prediction with 62.4% f1-score and 60.62% recall.

Muhammad Zubair Khan, Oleg E. Peil, Apoorva Sharma et al.

In the rapidly expanding field of two-dimensional materials, magnetic monolayers show great promise for the future applications in nanoelectronics, data storage, and sensing. The research in intrinsically magnetic two-dimensional materials mainly focuses on synthetic iodide and telluride based compounds, which inherently suffer from the lack of ambient stability. So far, naturally occurring layered magnetic materials have been vastly overlooked. These minerals offer a unique opportunity to explore air-stable complex layered systems with high concentration of local moment bearing ions. We demonstrate magnetic ordering in iron-rich two-dimensional phyllosilicates, focusing on mineral species of minnesotaite, annite, and biotite. These are naturally occurring van der Waals magnetic materials which integrate local moment baring ions of iron via magnesium/aluminium substitution in their octahedral sites. Due to self-inherent capping by silicate/aluminate tetrahedral groups, ultra-thin layers are air-stable. Chemical characterization, quantitative elemental analysis, and iron oxidation states were determined via Raman spectroscopy, wavelength disperse X-ray spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. Superconducting quantum interference device magnetometry measurements were performed to examine the magnetic ordering. These layered materials exhibit paramagnetic or superparamagnetic characteristics at room temperature. At low temperature ferrimagnetic or antiferromagnetic ordering occurs, with the critical ordering temperature of 38.7 K for minnesotaite, 36.1 K for annite, and 4.9 K for biotite. In-field magnetic force microscopy on iron bearing phyllosilicates confirmed the paramagnetic response at room temperature, present down to monolayers.

Hrvoje Nikolic

We develop a general formulation of quantum statistical mechanics in terms of probability currents that satisfy continuity equations in the multi-particle position space, for closed and open systems with a fixed number of particles. The continuity equation for any closed or open system suggests a natural Bohmian interpretation in terms of microscopic particle trajectories, that make the same measurable predictions as standard quantum theory. The microscopic trajectories are not directly observable, but provide a general, simple and intuitive microscopic interpretation of macroscopic phenomena in quantum statistical mechanics. In particular, we discuss how various notions of entropy, proper and improper mixtures, and thermodynamics are understood from the Bohmian perspective.

Shangen Wang, Jin-ru Ma, Xuesong Zhen et al.

Engineering Mechanics is an important subject for science and engineering students in higher vocational colleges. The teaching quality of this course has a direct impact on subsequent professional courses. However, there has been a concern about the poor teaching effect of Engineering Mechanics in higher vocational colleges for a long time. In order to solve the problem and improve the teaching quality, this paper expounds some problems existing in the teaching of Engineering Mechanics in higher vocational colleges and proposes corresponding measures for these problems. Educators need to pay more attention to diversified assessment methods and the application of new technologies. Diversified examination methods can improve students’ enthusiasm in learning, while new techniques, such as finite element simulation, generate digital materials, making it easier for students to understand abstract concepts. The suggested measures are worthy of reference and should be applied flexibly in the teaching of Engineering Mechanics.

Ірина Берник, Іван Назаренко, Олександр Луговський

В роботі проведено дослідження та визначення акустичних параметрів руху кавітаційної бульбашки в рідинному середовищі за дискретною та континуальною моделями. В основу виконання досліджень покладена гіпотеза, що визначення ефективних параметрів робочого процесу акустичної обробки реалізується шляхом застосування перехідної фізичної моделі від дискретного до континуального виду обробки технологічного середовища. Отримані аналітичні залежності дозволяють розрахувати амплітуду коливань та частоту власних коливань. За допомогою вказаних формул представляється можливим визначити зони посилення або ослаблення амплітуди коливань для різних частот коливань. Запропонована формула для  визначення частоти власних коливань, яка враховує зміни властивостей середовища від  однорідного на початковій стадії до появи кавітаційних бульбашок при визначені частоти власних коливань. Наведені числові значення інтенсивності, тиску, амплітуд коливань, швидкості, прискорення, в’язкості та максимального радіусу бульбашки. Отримані числові  значення можуть бути використанні в практичних розрахунках параметрів акустичної обробки різних за своєю природою та властивостям технологічних середовищ.