Present Model Research Articles

Translating strain to stress: a single-layer Bi-LSTM approach to predicting stress-strain curves in alloys during hot deformation

This study introduces a novel deep learning network that integrates a single-layer bidirectional long short-term memory (Bi-LSTM) network with a coding layer to analyze the hot deformation behavior of various alloys. The single-layer Bi-LSTM model adeptly predicts experimental stress–strain curves obtained under different deformation temperatures and strain rates, demonstrating superior effectiveness and excellent performance in modeling hot deformation behaviors of the FGH98 nickel-based alloy and TiAl intermetallic alloy. The present model achieves the coefficient of determination of 0.9051 for FGH98 and 0.9307 for TiAl alloys, whereas the corresponding values of 0.8105 and 0.8356 are obtained by the conventional strain-compensated Sellars constitutive equation (SCS model). Additionally, the mean absolute percentage error of the single-layer Bi-LSTM model are 11.37% for FGH98 and 7.16% for TiAl alloys, while the SCS model gains the corresponding error of 15.29% and 17.01%. These results show that the present model has enhances the predictive accuracy exceeding 10% for both FGH98 and TiAl alloys over the SCS model. Consequently, the proposed single-layer Bi-LSTM model provides substantial potential for optimizing manufacturing processes and improving material properties.

Finite/fixed-time cluster synchronization for directed and multiplex coupled dynamic networks

This article deals with finite/fixed-time cluster synchronization (FnTCS/FdTCS) issue for directed and multiplex coupled dynamic networks (MCDNs). Finite/fixed-time synchronization of multiplex networks has become a popular research topic, which indicates that, state information is utilized for synchronization, and the settling time depends on original condition for a range of finite time, while the time is bounded with arbitrary original value for fixed-time one. Through constructing appropriate feedback control protocol, FnTCS and FdTCS matters for MCDNs are settled under re-arranging variables' order technique. The present model contains the case that outer matrices (OMs) can be directed, with competitive elements, and not even connected, while previous and related researches assumed that OMs were undirected, cooperative and connected, so existing results can be improved for more general and broader regulations. This strategy above is presented to solve directed CDNs with multiweights from angle of inner and outer matrices, and we prove that if the weighted group of added OMs for each dimension is strongly connected, then cluster synchronization rules are established in finite/fixed time. Moreover, we also earn finite/fixed-time synchronization criteria when the cluster is single. In addition, the issue of FnTCS/FdTCS is developed for directed and multiplex coupled reaction-diffusion networks (MCRDNs) as an application. The validity of these gained results is verified by simulated examples.

Activation of the PGE2-EP2 pathway as a potential drug target for treating eosinophilic rhinosinusitis.

Current treatments of eosinophilic chronic rhinosinusitis (ECRS) involve corticosteroids with various adverse effects and costly therapies such as dupilumab, highlighting the need for improved treatments. However, because of the lack of a proper mouse ECRS model that recapitulates human ECRS, molecular mechanisms underlying this disease are incompletely understood. ECRS is often associated with aspirin-induced asthma, suggesting that dysregulation of lipid mediators in the nasal mucosa may underlie ECRS pathology. We herein found that the expression of microsomal PGE synthase-1 (encoded by PTGES) was significantly lower in the nasal mucosa of ECRS patients than that of non-ECRS subjects. Histological, transcriptional, and lipidomics analyses of Ptges-deficient mice revealed that defective PGE2 biosynthesis facilitated eosinophil recruitment into the nasal mucosa, elevated expression of type-2 cytokines and chemokines, and increased pro-allergic and decreased anti-allergic lipid mediators following challenges with Aspergillus protease and ovalbumin. A nasal spray containing agonists for the PGE2 receptor EP2 or EP4, including omidenepag isopropyl that has been clinically used for treatment of glaucoma, markedly reduced intranasal eosinophil infiltration in Ptges-deficient mice. These results suggest that the present model using Ptges-deficient mice is more relevant to human ECRS than are previously reported models and that eosinophilic inflammation in the nasal mucosa can be efficiently blocked by activation of the PGE2-EP2 pathway. Furthermore, our findings suggest that drug repositioning of omidenepag isopropyl may be useful for treatment of patients with ECRS.

Effectiveness of SNAPPS Implementation in the Family Medicine Residency Program in Erbil: A Randomized Controlled Trial.

Background and objectives There is a crucial need to embrace modern methodologies for enhancing medical education in disciplines such as Family Medicine. The study introduces the SNAPPS (Summarize, Narrow, Analyze, Probe, Plan, and Select) model, a six-step mnemonic representing a learner-centered case presentation approach that streamlines fact reporting while encouraging clinical reasoning, aiming to evaluate the effectiveness of the SNAPPS method as compared to the traditional model of case presentation in Family Medicine outpatient clinics and to gain insights into how students and preceptors perceive it. Methods A randomized controlled trial was conducted in Family Medicine outpatient clinics in Erbil, Iraq, from March 15, 2023, to August 30, 2023. Using convenience sampling, all Family Medicine board residents (n=30) in Erbiland six preceptors from the Community and Family Medicine department/College of Medicine/Hawler Medical University were randomly assigned into two groups by using the RAND function in Microsoft Excel (Microsoft Corporation, Redmond, Washington, United States). The SNAPPS method was introduced to the SNAPPS group employing approved tools and methods, while no intervention was needed in the control group. Subsequently, 30 cases were presented in each group with a total of 60 case presentations; the case presentations served as the units for data analysis. Feedback and data were gathered after each presentation using validated data recording sheets. Results The study showed a significant advantage for the SNAPPS group over the control group in terms of time efficiency, number of basic clinical attributes covered, and justified diagnoses (P value < 0.001). In the SNAPPS group,90% of the students sought clarification and information, surpassing 30% in the control group (P-value < 0.001). Almost all SNAPPS group students (96.7%) discussed case-related topics, compared to 43.3% in the controls (P-value<0.001). The SNAPPS group received superior overall ratings from both preceptors and students. Conclusion The SNAPPS method enhances clinical diagnostic reasoning in Family Medicine outpatient clinics. It is time-efficient and encourages students to articulate uncertainties, pose questions, and identify case-related topics for self-study.

Crustal thicknesses in Uruguay from joint inversion of receiver functions and surface wave dispersion

The development of a seismic network in Uruguay in recent years has enabled studies of crustal structure in a region with few seismological studies of this type. In this work, we update the crustal thicknesses and Vp/Vs ratios calculated by H-k stack and present S-wave velocity models based on joint inversion of receiver functions and Rayleigh wave group velocity dispersion curves.Some interesting results are the presence of a lower crust with a high S-wave velocity of 4.1 km/s below one of the stations located on the Río de la Plata Craton and the existence of a transitional Moho in Uruguay’s northernmost station on the Paraná Basin, perhaps suggesting the presence of localized underplated material. A relatively thick crust, 41.8 km, compared to surrounding stations, was found beneath the Sierra Ballena Shear Zone in the Dom Feliciano Belt. We confirm the decrease in crustal thickness when approaching the oceanic coast, reaching a Moho depth of 36.3 km in SE Uruguay. Finally, the calculated Poisson's ratio allows inferring a crust of felsic to intermediate composition beneath most of Uruguay.

Cadence Paradox in Cycling-Part 2: Theory and Simulation of Maximal Lactate Steady State and Carbohydrate Utilization Dependent on Cycling Cadence.

To develop and evaluate a theory on the frequent observation that cyclists prefer cadences (RPMs) higher than those considered most economical at submaximal exercise intensities via modeling and simulation of its mathematical description. The theory combines the parabolic power-to-velocity (v) relationship, where v is defined by crank length, RPM-dependent ankle velocity, and gear ratio, RPM effects on the maximal lactate steady state (MLSS), and lactate-dependent carbohydrate oxidation (CHO). It was tested against recent experimental results of 12 healthy male recreational cyclists determining the v-dependent peak oxygen uptake (VO2PEAKv), MLSS (MLSSv), corresponding power output (PMLSSv), oxygen uptake at PMLSSv (VO2MLSSv), and CHOMLSSv-management at 100 versus 50per minute, respectively. Maximum RPM (RPMMAX) attained at minimized pedal torque was measured. RPM-specific maximum sprint power output (PMAXv) was estimated at RPMs of 100 and 50, respectively. Modeling identified that MLSSv and PMLSSv related to PMAXv (IPMLSSv) promote CHO and that VO2MLSSv related to VO2PEAKv inhibits CHO. It shows that cycling at higher RPM reduces IPMLSSv. It suggests that high cycling RPMs minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. The present theory-guided modeling approach is exclusively based on data routinely measured in high-performance testing. It implies a higher performance reserve above IPMLSSv at higher RPM. Cyclists may prefer high cycling RPMs because they appear to minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX.

Numerical modeling on wave–current flows and bed shear stresses over an algal reef

AbstractCurrents in coastal zones under multiple mechanisms in terms of tides, waves, wind, and high roughness are difficult to model; bed shear stresses under wave–current flows are particularly challenging yet not being well studied. Few studies reported the modeling and validation of the bed shear stress in reef environments. In this paper, we present the first direct assessment of numerical modeling on depth-averaged currents and bed shear stresses over an algal reef using a coupled wave–current model (Delft-3D). The modeled results were validated and compared to the field observed data. The model considers hydrodynamic forcing in terms of tides, waves, wind stresses, and bed friction. Results show that the model generally reproduces the depth-averaged currents and bed shear stresses when considering all the mechanisms. Two numerical cases with and without wind forcing were tested to examine the effects of the winds. We found that the tide is mostly the primary factor driving the current, even in shallow waters within a depth of 3 m; however, the currents are also significantly affected by wind speeds and wind directions during high-wind events. When the wind direction is in the same direction as the tidal current, the current speed increases, suggesting the importance of the wind stress on the coastal currents. In addition, two models were chosen to study the nonlinear enhancement of bed shear stress by waves. We found a significant difference between the two models in predicting the bed shear stresses compared to the observed data. Nonlinear contribution from wave enhances the magnitude of bed shear stresses, which reduces the model error. The results highlight the nonlinear interaction between waves and currents is meaningful in predicting the bed shear stresses during high-wave-orbital motions; improvement of the present wave-current nonlinear interaction model for predicting the bed shear stresses may be needed.

Hierarchical Spiking-Based Model for Efficient Image Classification With Enhanced Feature Extraction and Encoding.

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be great computation-efficient models. The spiking neurons encode beneficial temporal facts and possess excessive anti-noise properties. However, the high-quality encoding of spatio-temporal complexity and also its training optimization of SNNs are restricted by means of the contemporary problem, this article proposes a novel hierarchical event-driven visual device to explore how information transmits and signifies in the retina the usage of biologically manageable mechanisms. This cognitive model is an augmented spiking-based framework consisting of the function learning capacity of convolutional neural networks (CNNs) with the cognition capability of SNNs. Furthermore, this visual device is modeled in a biological realism way with unsupervised learning rules and advanced spike firing rate encoding methods. We train and test them on some image datasets (Modified National Institute of Standards and Technology (MNIST), Canadian Institute for Advanced Research (CIFAR)10, and its noisy versions) to show that our mannequin can process greater vital data than present cognitive models. This article also proposes a novel quantization approach to make the proposed spiking-based model more efficient for neuromorphic hardware implementation. The outcomes show this joint CNN-SNN model can reap excessive focus accuracy and get more effective generalization ability.

Interrelated involvement of the endocannabinoid/endovanilloid (TRPV1) systems and epigenetic processes in anxiety- and working memory impairment-related behavioural effects of nicotine as a stressor.

The addictive use of nicotine contained in tobacco is associated with stressor-like emotional and cognitive effects such as anxiety and working memory impairment, and the involvement of epigenetic mechanisms such as histone acetylation has recently been reported. Although the precise nature of behavioural plasticity remains unclear, both anxiogenic- and working memory impairment-like effects were observed in the present experimental model of mice treated with repeated subcutaneous nicotine and/or immobilization stress, and these effects were commonly attenuated by the histone deacetylase (HDAC) inhibitors that induce histone acetylation. Such HDAC inhibitor-induced resilience was mimicked by ligands for the endocannabinoid (ECB) system, a neurotransmitter system that is closely associated with nicotine-induced addiction-related behaviours: the anxiogenic-like effects were mitigated by the cannabinoid type 1 (CB1) agonist arachidonylcyclopropylamide (ACPA), whereas the working memory impairment-like effects were mitigated by the CB1 antagonist SR 141716A. Moreover, the effects of the HDAC inhibitors were also mimicked by ligands for the endovanilloid (transient receptor potential vanilloid 1 [TRPV1]) system, a system that shares common characteristics with the ECB system: the anxiogenic-like effects were mitigated by the TRPV1 antagonist capsazepine, whereas the working memory impairment-like effects were mitigated by the TRPV1 agonist olvanil. Notably, the HDAC inhibitor-induced anxiolytic-like effects were attenuated by SR 141716A, which were further counteracted by capsazepine, whereas the working memory improvement-like effects were attenuated by capsazepine, which were further counteracted by SR 141716A. These results suggest the contribution of interrelated control of the ECB/TRPV1 systems and epigenetic processes such as histone acetylation to novel therapeutic approaches.

Estimation of the threshold for heat stress and genetic features for milk yield in Mehsana buffaloes in India

Heat stress is one of the primary environmental factors that harm both the productivity and health of buffaloes. The current study was conducted to estimate the threshold of temperature humidity index (THI)1 and genetic features for milk yield of first-lactation Mehsana buffaloes using an univariate repeatability test-day model. The data included 130,475 first lactation test-day milk yield (FLTDMY) records of 13,887 Mehsana buffaloes and the daily temperature and humidity. The statistical model included herd test day as fixed effects, days-in-milk (DIM) classes, age of the animal, as well as random factors such as the additive genetic effect (AGE) of animal in general conditions (intercept), AGE of the buffaloes subjected to heat stress (slope), permanent environmental effect of animal in general conditions (intercept), permanent environmental effect of animal under heat stress conditions (slope) and random residual effect. It was expected that the general effects and the heat-tolerance effects would be correlated, represented by the present investigation's repeatability models. The variance components of FLTDMY in the present study were computed using the REML method. The threshold for THI was 78. At the THI below the threshold, the heritability estimated for the FLTDMY trait was 0.29, and the additive genetic variance (AGV) for heat stress conditions was 0. At THI of 83, AGV for heat stress conditions was highest for FLTDMY. The genetic correlation of general AGE to heat-tolerant AGE was −0.40. The results indicated that a consistent selection for milk production, avoiding the thermal tolerance, may diminish the thermal tolerance capacity of Mehsana buffaloes.

The effects of drag models and operating condition on the simulation of photocatalytic degradation of pesticides in a fluidized bed reactor.

Fluidized bed reactor can enhance mass transfer and increase reaction rate. Numerical simulation helps to optimize fluidized bed reactors. The present paper models the photocatalytic oxidation of 2,4-dichlorophenoxy acetic acid in a fluidized bed reactor using the Eulerian-Eulerian model. The drag models have influences on the distribution of catalysts particles. The bed expands under the fluid flow and reaches a quasi-steady height at approximately 3s. The asymmetric distribution of catalysts with respect to the axis plane is predicted. The Gidaspow model predicts the nearly same bed expansion with the experimental data, whereas the Syamlal-O Brien model overestimates it. The simulation results at two pH values are in accordance with the experimental data. The removal in the continuous stirred tank reactor decreases with the increase in flow rate.

The moderating effect of entrepreneurial motivation on the relationship between entrepreneurial intention and behaviour: An extension of the theory of planned behaviour on emerging economy.

The study investigates the influence of antecedents of the theory of planned behaviour (TPB) and personality traits on entrepreneurial intention and behaviour among engineering students in an emerging economy. It employs the extension of the TPB model by focusing on the intention-behaviour gap, an under-researched area in research. Furthermore, it investigates the moderating effect of entrepreneurial motivation on the relationship between intention and behaviour to improve conceptual model predictability. A structured questionnaire was used to gather data from 1,564 engineering students, and the data were analyzed using structural equation modelling (SEM) with Amos software. The results revealed that subjective norms were the strongest predictor of entrepreneurial intention and behaviour, followed by entrepreneurial alertness, perceived behavioural control, attitude towards entrepreneurship, need for achievement, and risk tolerance. Moreover, the moderation analysis showed that entrepreneurial motivation was crucial in moderating the relationship between intention and behaviour. The present conceptual model contributes to the existing TPB model by improving predictive power to understand the intention-behaviour relationship. The results of the study will assist policymakers, academicians of higher education institutions, and universities in developing policies, strategies, and curricula to engage more students in entrepreneurial activities.

A dynamic target tracking model for uavs based on the fusion of twin networks and deep learning

Abstract In this paper, a deep learning model incorporating twin networks is proposed to address the challenge of dynamic target tracking by UAVs in complex environments. By exploring the combination of twin networks and convolutional neural networks (CNNs) in detail, this study aims to develop an efficient algorithm capable of accurately tracking dynamic targets under various environmental conditions. The design of the model focuses on utilizing the strong feature-matching capability of twin networks and the advantages of CNNs in image processing in order to improve tracking accuracy and response speed. Experimental simulations show that the model in this paper exhibits better accuracy and response time than the existing popular algorithm YOLOv3 in dynamic target tracking tasks at different difficulty levels. In particular, the present model demonstrates higher robustness and adaptability under different conditions. In addition, the practical application potential of the model is discussed in this paper, pointing out its wide application prospects in the fields of unmanned aerial surveillance and environment sensing of self-driving vehicles.

Growth of Allopeas gracile under starvation: does it comply with von Bertalanffy growth equation?

The growth of the land snails is a primary indicator of the fitness at the individual and population levels. Considering starvation as a limiting factor of growth, the adaptation of the land snail Allopeas gracile (Hutton 1834) (Gastropoda: Subulinidae) was assessed using von Bertanlaffy growth equation. As a starvation treatment, the laboratory reared 0-day old snails were fed at a gap of 1 to 6 days (six levels of starvation treatment) or fed regularly (control). In all instances, the shell length and the biomass (indicators of growth) increased with the time interval complying with the von Bertalanffy growth equation, but the magnitude of maximum shell length, Lmaximum, and the asymptotic length, L∞, decreased, while the growth rate coefficient (k) increased, as the duration of starvation period extended. The k and L∞ were represented as functions of the starvation gap (T) and substituted in classical von Bertalanffy equation, representing a better fit model explaining the growth. The results indicate that the food availability is a crucial factor determining the pace of growth in the land snail A. gracile. As an extension, the present model can be used for the portrayal of the growth of the macroinvertebrates under varying level of starvation.

Культурный трансфер в семиотической модели перевода (на примере художественного текста Дж. Хеллера)

The goal of the work is to identify the influence of temporary discursive practices on specific translation decisions. From this goal follow specific tasks in this work: to present discursive and interpretive models of translation; correlate the provisions of these concepts with real translation practice; use factual material to show translation decisions determined by temporary discursive practice. The article also explores ways to convey culturally significant information, provides successful and controversial cases of solving translation tasks through linguistic means. Translation problems, including literary translation, in contemporary philological and cultural studies are interdisciplinary. Only at the intersection of semiotic concepts and literary approaches it is possible to determine the accuracy and completeness of translation decisions in the practice of literary translation. In turn, the study of the issue of cultural transfer in this case, based on various translations of J. Heller’s novel, allows us to overcome the limiting framework of purely philological approach in translation studies. Comparing the work of translators with respect to the same text - in our case, “Catch-22” fills the-abstract concept of the “translator’s figure” with real content, especially when considering the socio-cultural environment that influenced the specificity of translation decisions. The article represents an analysis of current issues in the theory and practice of literary translation. The work offers a review of modern translation models, primarily a discursive model of translation, interpretative concept and the perception of translation as an interpretation of the source text in the existing discursive practice. This research is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23485752).

Implementation of generative artificial intelligence technologies in creative activities: development of a structural model of design thinking

The subject of the study is systemic changes in the methodology of design thinking, taking place under the influence of the development and spread of generative artificial intelligence (AI) technologies in design and other creative industries. The purpose of the work is: analysis of modern research on the impact of generative AI technologies on creative industries, design and on design thinking; development of a structural model of design thinking to further explore the evolution of the methodology. The following tasks are set in the article: to analyze modern scientific publications regarding the essence, structure and content of design thinking; review research on the benefits and challenges of using generative AI in design processes; to develop a model that allows identifying and describing changes in key components of the design thinking methodology arising under the influence of widespread adoption of generative AI technologies. During the research, the following methods were used: analysis and synthesis of the content of technical, economic, philosophical, linguistic, historical scientific and methodical research on the problems of forming the conceptual apparatus of the design-thinking methodology and the use of generative AI in design processes; comparative-historical, retrospective methods; structural and logical analysis. The following results were achieved: the actualized need for a comprehensive research approach to analyze the multifaceted impact of AI technologies on design; the key advantages and challenges associated with the integration of AI into creative processes are identified; a structural model of presentation of the design-thinking methodology was developed in the form of four interconnected structural layers with subsequent decomposition of each of the layers into constituent elements. The conclusions highlight the depth and multifaceted nature of the changes taking place in design and other creative industries under the influence of generative AI and need further in-depth research. The developed structural model of the design-thinking methodology allows to decompose the complex creative process to a certain extent, laying the foundation for a comprehensive analysis of the evolution of the methodology and the systematic introduction of generative artificial intelligence technologies into design processes.

Bottom-up butterfly model with thorax-pitch control and wing-pitch flexibility

The diversity in butterfly morphology has attracted many people around the world since ancient times. Despite morphological diversity, the wing and body kinematics of butterflies have several common features. In the present study, we constructed a bottom-up butterfly model, whose morphology and kinematics are simplified while preserving the important features of butterflies. The present bottom-up butterfly model is composed of two trapezoidal wings and a rod-shaped body with a thorax and abdomen. Its wings are flapped downward in the downstroke and backward in the upstroke by changing the geometric angle of attack (AOA). The geometric AOA is determined by the thorax-pitch and wing-pitch angles. The thorax-pitch angle is actively controlled by abdominal undulation, and the wing-pitch angle is passively determined because of a rotary spring representing the basalar and subalar muscles connecting the wings and thorax. We investigated the effectiveness of abdominal undulation for thorax-pitch control and how wing-pitch flexibility affects aerodynamic-force generation and thorax-pitch control, through numerical simulations using the immersed boundary–lattice Boltzmann method. As a result, the thorax-pitch angle perfectly follows the desired angle through abdominal undulation. In addition, there is an optimal wing-pitch flexibility that maximizes the flying speed in both the forward and upward directions, but the effect of wing-pitch flexibility on thorax-pitch control is not significant. Finally, we compared the flight behavior of the present bottom-up butterfly model with that of an actual butterfly. It was found that the present model does not reproduce reasonable body kinematics but can provide reasonable aerodynamics in butterfly flights.

Non-linear sloshing characteristics of liquid inside a chamfered bottom tank with submerged internal object under near-and far-fault earthquakes

Cylindrical and rectangular-shaped tanks have a concentration of stagnant impulsive liquid at the bottom corners that do not participate in sloshing. While used as tuned liquid dampers (TLDs), this non-participating fluid adds extra liquid mass to the tank but does not have any role in the vibration control aspect. In the present study, to decrease the stagnant impulsive liquid, a chamfered bottom-shaped tank with a submerged internal object is demonstrated. Due to the high-energy velocity pulse present in near-fault earthquakes, there is a possibility of violent liquid sloshing which may cause unwanted structural damage to the tank. Hence, the present study focuses on the influence of near- and far-fault earthquakes on the non-linear seismic behavior of the tank with submerged internal object. The non-linear sloshing is modeled using the finite element method based on a combined Eulerian-Lagrangian approach. The accuracy of the present non-linear numerical model is checked by verifying the results with the previously published results. In addition to the absolute response, the dynamic convective and impulsive response components are quantified. The proposed non-linear finite element scheme can be utilized in the context of structure-mounted chamfer bottom TLDs in the future.

Artificial neural network model for wear characteristic analysis of WC-10Co4Cr and Stellite 6 thermal spray coatings

In this paper, wear characteristic analysis of the thermal spray powders namely WC-10Co4Cr and Stellite 6 high velocity oxy-fuel (HVOF) coated on SS 316 L steel were studied. Artificial neural network (ANN) model predicts the accuracy by the coefficient of R= 0.99 and R2 = 0.98. The R2 of present ANN model was far better as compared to regression models Enbt (0.85), Coarse Tree (0.04), linear regression i.e. LR (0.37), and Enbag (0.19). Results show that uncoated SS 316 L steel was severely eroded, and wear occurred on the bare SS 316 L was about 7.5 ± 2.5 times higher as compared to WC-10Co4Cr coating, and 3.5 ± 1.5 times than Stellite 6 coating.

Target Detection of Diamond Nanostructures Based on Improved YOLOv8 Modeling.

Boron-doped diamond thin films exhibit extensive applications in chemical sensing, in which the performance could be further enhanced by nano-structuring of the surfaces. In order to discover the relationship between diamond nanostructures and properties, this paper is dedicated to deep learning target detection methods. However, great challenges, such as noise, unclear target boundaries, and mutual occlusion between targets, are inevitable during the target detection of nanostructures. To tackle these challenges, DWS-YOLOv8 (DCN + WIoU + SA + YOLOv8n) is introduced to optimize the YOLOv8n model for the detection of diamond nanostructures. A deformable convolutional C2f (DCN_C2f) module is integrated into the backbone network, as is a shuffling attention (SA) mechanism, for adaptively tuning the perceptual field of the network and reducing the effect of noise. Finally, Wise-IoU (WIoU)v3 is utilized as a bounding box regression loss to enhance the model's ability to localize diamond nanostructures. Compared to YOLOv8n, a 9.4% higher detection accuracy is achieved for the present model with reduced computational complexity. Additionally, the enhancement of precision (P), recall (R), mAP@0.5, and mAP@0.5:0.95 is demonstrated, which validates the effectiveness of the present DWS-YOLOv8 method. These methods provide effective support for the subsequent understanding and customization of the properties of surface nanostructures.